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Faiza-K/DataPrepOfTCV.ipynb

Created October 27, 2022 11:38
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DataPrepOfTCV.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "bCi-x-HmAstC"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111,
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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"headers": [
[
"content-type",
"application/javascript"
]
],
"ok": true,
"status": 200,
"status_text": ""
}
}
},
"colab_type": "code",
"id": "-OR93roBBAao",
"outputId": "d43ba985-ef4a-4572-eae6-7426577293f4",
"scrolled": true
},
"outputs": [],
"source": [
"xls = pd.ExcelFile('C:\\\\Users\\\\Faizak\\\\Downloads\\\\Programs\\\\PHC\\\\TCV\\\\Copy of EPI analysis TCV data.xlsx')\n",
"df1= pd.read_excel(xls, 'Sheet1')\n",
"df2= pd.read_excel(xls, 'TCV Documentation')\n",
"df3 = pd.read_excel(xls, 'Dist')\n",
"df4 = pd.read_excel(xls, 'West')\n",
"df5 = pd.read_excel(xls, 'East')\n",
"df6 = pd.read_excel(xls, 'Korangi')\n",
"df7 = pd.read_excel(xls, 'Central')\n",
"df8 = pd.read_excel(xls, 'South')\n",
"df9 = pd.read_excel(xls, 'Malir')\n",
"df10 = pd.read_excel(xls, 'Karachi')\n",
"df11= pd.read_excel(xls, 'Sheet12')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "x57QQBfCBQiN",
"outputId": "cdeda1f2-810d-4762-cf55-78046d24c62f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>District</th>\n",
" <th>Total Children Vaccinated at</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
" <th>Total Children Vaccinated</th>\n",
" <th>Day 01</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Day 02</th>\n",
" <th>...</th>\n",
" <th>Unnamed: 19</th>\n",
" <th>Unnamed: 20</th>\n",
" <th>Day 05</th>\n",
" <th>Unnamed: 22</th>\n",
" <th>Unnamed: 23</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Day 06</th>\n",
" <th>Unnamed: 26</th>\n",
" <th>Unnamed: 27</th>\n",
" <th>Unnamed: 28</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Children Vaccinated at</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Children Vaccinated at</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Children Vaccinated at</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Children Vaccinated at</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>NaN</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>Total</td>\n",
" <td>Fixed</td>\n",
" <td>...</td>\n",
" <td>School</td>\n",
" <td>Total</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>Total</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>Total</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>West</td>\n",
" <td>6692</td>\n",
" <td>92811</td>\n",
" <td>46324</td>\n",
" <td>145827.0</td>\n",
" <td>1945</td>\n",
" <td>37300</td>\n",
" <td>18061</td>\n",
" <td>57306</td>\n",
" <td>1894</td>\n",
" <td>...</td>\n",
" <td>846</td>\n",
" <td>4113</td>\n",
" <td>383</td>\n",
" <td>1181</td>\n",
" <td>203</td>\n",
" <td>1767</td>\n",
" <td>76</td>\n",
" <td>367</td>\n",
" <td>0</td>\n",
" <td>443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>East</td>\n",
" <td>2383</td>\n",
" <td>7460</td>\n",
" <td>2595</td>\n",
" <td>12438.0</td>\n",
" <td>525</td>\n",
" <td>2484</td>\n",
" <td>497</td>\n",
" <td>3506</td>\n",
" <td>481</td>\n",
" <td>...</td>\n",
" <td>380</td>\n",
" <td>1688</td>\n",
" <td>180</td>\n",
" <td>700</td>\n",
" <td>458</td>\n",
" <td>1338</td>\n",
" <td>342</td>\n",
" <td>665</td>\n",
" <td>69</td>\n",
" <td>1076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Korangi</td>\n",
" <td>4753</td>\n",
" <td>13734</td>\n",
" <td>4447</td>\n",
" <td>22934.0</td>\n",
" <td>1035</td>\n",
" <td>2191</td>\n",
" <td>243</td>\n",
" <td>3469</td>\n",
" <td>537</td>\n",
" <td>...</td>\n",
" <td>401</td>\n",
" <td>3455</td>\n",
" <td>1421</td>\n",
" <td>1999</td>\n",
" <td>3486</td>\n",
" <td>6906</td>\n",
" <td>613</td>\n",
" <td>1900</td>\n",
" <td>131</td>\n",
" <td>2644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" District Total Children Vaccinated at Unnamed: 2 Unnamed: 3 \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN Fixed Outreach School \n",
"2 West 6692 92811 46324 \n",
"3 East 2383 7460 2595 \n",
"4 Korangi 4753 13734 4447 \n",
"\n",
" Total Children Vaccinated Day 01 Unnamed: 6 Unnamed: 7 \\\n",
"0 NaN Children Vaccinated at NaN NaN \n",
"1 NaN Fixed Outreach School \n",
"2 145827.0 1945 37300 18061 \n",
"3 12438.0 525 2484 497 \n",
"4 22934.0 1035 2191 243 \n",
"\n",
" Unnamed: 8 Day 02 ... Unnamed: 19 Unnamed: 20 \\\n",
"0 NaN Children Vaccinated at ... NaN NaN \n",
"1 Total Fixed ... School Total \n",
"2 57306 1894 ... 846 4113 \n",
"3 3506 481 ... 380 1688 \n",
"4 3469 537 ... 401 3455 \n",
"\n",
" Day 05 Unnamed: 22 Unnamed: 23 Unnamed: 24 \\\n",
"0 Children Vaccinated at NaN NaN NaN \n",
"1 Fixed Outreach School Total \n",
"2 383 1181 203 1767 \n",
"3 180 700 458 1338 \n",
"4 1421 1999 3486 6906 \n",
"\n",
" Day 06 Unnamed: 26 Unnamed: 27 Unnamed: 28 \n",
"0 Children Vaccinated at NaN NaN NaN \n",
"1 Fixed Outreach School Total \n",
"2 76 367 0 443 \n",
"3 342 665 69 1076 \n",
"4 613 1900 131 2644 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df= pd.read_excel('C:\\\\Users\\\\Faizak\\\\Downloads\\\\Programs\\\\PHC\\\\Final Admin Coverage of TCV Campaign Nov 2019.xlsx')\n",
"df11.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mxBB6jsIDNux"
},
"outputs": [],
"source": [
"columns =[\"Distcode\",\t\"District\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated\", \"(AV1) No Of AEFIs\"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "LMji6v1xdHWv"
},
"outputs": [],
"source": [
"#data= df1[[\"Distcode\",\t\"District\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated\", \"(AV1) No Of AEFIs\"]][df1['Distcode'].isnull()] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"colab_type": "code",
"id": "v7m3KyJkEHho",
"outputId": "54bc115b-b1ed-4e91-b7e4-c071c4bf52ea"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"colab_type": "code",
"id": "w7Z9Xb-bFRiI",
"outputId": "8bc68b1f-7d14-48be-9722-a4aef4ee7b92"
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Distcode</th>\n",
" <th>District</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated</th>\n",
" <th>(AV1) No Of AEFIs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Distcode</td>\n",
" <td>District</td>\n",
" <td>Daily Target</td>\n",
" <td>9 Months-2 Years</td>\n",
" <td>2 Years-5 Years</td>\n",
" <td>5 Years-15 Years</td>\n",
" <td>Total Covered Campaign + Catchup</td>\n",
" <td>%</td>\n",
" <td>Not Available</td>\n",
" <td>Missed Refusal</td>\n",
" <td>Missed Sick</td>\n",
" <td>Already Vaccinated (AV1)</td>\n",
" <td>No Of AEFIs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>221</td>\n",
" <td>West</td>\n",
" <td>1481401</td>\n",
" <td>91037</td>\n",
" <td>249460</td>\n",
" <td>852098</td>\n",
" <td>1338422</td>\n",
" <td>0.903484</td>\n",
" <td>77734</td>\n",
" <td>293426</td>\n",
" <td>19767</td>\n",
" <td>121111</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>798443</td>\n",
" <td>0.995381</td>\n",
" <td>35724</td>\n",
" <td>108622</td>\n",
" <td>8765</td>\n",
" <td>69510</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>803350</td>\n",
" <td>1.01475</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"0 Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"\n",
" 5 Years-15 Years Total Covered Campaign + Catchup % \\\n",
"0 5 Years-15 Years Total Covered Campaign + Catchup % \n",
"1 852098 1338422 0.903484 \n",
"2 514191 798443 0.995381 \n",
"3 598955 876256 0.966138 \n",
"4 566045 803350 1.01475 \n",
"\n",
" Not Available Missed Refusal Missed Sick Already Vaccinated \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 77734 293426 19767 121111 \n",
"2 35724 108622 8765 69510 \n",
"3 43774 92129 10943 98547 \n",
"4 15311 46139 4500 105705 \n",
"\n",
" (AV1) No Of AEFIs \n",
"0 No Of AEFIs \n",
"1 216 \n",
"2 61 \n",
"3 137 \n",
"4 56 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.columns = [\"Distcode\",\t\"District\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated\", \"(AV1) No Of AEFIs\"]\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
},
"colab_type": "code",
"id": "O7meQr9Ye6y_",
"outputId": "252549d5-06b6-4a5d-cbe5-779088596bf4"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-a74c58233b9e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "XtQqdVL4nkIL",
"outputId": "d95ad65c-34a5-40ae-fb59-beb8d9775588"
},
"outputs": [
{
"data": {
"text/plain": [
"RangeIndex(start=0, stop=37, step=1)"
]
},
"execution_count": 19,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "rtom47DldUV1"
},
"outputs": [],
"source": [
"dfDrop= df1.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"colab_type": "code",
"id": "b5kg7topoF4c",
"outputId": "0c19abee-e304-494e-eceb-6055c7a6425a"
},
"outputs": [
{
"data": {
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" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>798443</td>\n",
" <td>0.995381</td>\n",
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" <td>108622</td>\n",
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" <td>69510</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>803350</td>\n",
" <td>1.01475</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>225</td>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>34998</td>\n",
" <td>97748</td>\n",
" <td>342141</td>\n",
" <td>492669</td>\n",
" <td>0.97877</td>\n",
" <td>29757</td>\n",
" <td>48795</td>\n",
" <td>4743</td>\n",
" <td>43096</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>226</td>\n",
" <td>Malir</td>\n",
" <td>777551</td>\n",
" <td>51714</td>\n",
" <td>150927</td>\n",
" <td>417473</td>\n",
" <td>701660</td>\n",
" <td>0.902397</td>\n",
" <td>34696</td>\n",
" <td>88221</td>\n",
" <td>9009</td>\n",
" <td>118539</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>213</td>\n",
" <td>Hyderabad</td>\n",
" <td>657987</td>\n",
" <td>50674</td>\n",
" <td>134295</td>\n",
" <td>437017</td>\n",
" <td>627818</td>\n",
" <td>0.95415</td>\n",
" <td>37222</td>\n",
" <td>19065</td>\n",
" <td>2559</td>\n",
" <td>99</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>211</td>\n",
" <td>Badin</td>\n",
" <td>165333</td>\n",
" <td>13895</td>\n",
" <td>35703</td>\n",
" <td>127349</td>\n",
" <td>177895</td>\n",
" <td>1.07598</td>\n",
" <td>2557</td>\n",
" <td>1139</td>\n",
" <td>220</td>\n",
" <td>18155</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>212</td>\n",
" <td>Dadu</td>\n",
" <td>271137</td>\n",
" <td>22781</td>\n",
" <td>57709</td>\n",
" <td>217942</td>\n",
" <td>298432</td>\n",
" <td>1.10067</td>\n",
" <td>4061</td>\n",
" <td>658</td>\n",
" <td>238</td>\n",
" <td>26152</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>214</td>\n",
" <td>Sujawal</td>\n",
" <td>45863</td>\n",
" <td>4783</td>\n",
" <td>10475</td>\n",
" <td>29981</td>\n",
" <td>45778</td>\n",
" <td>0.998147</td>\n",
" <td>1824</td>\n",
" <td>514</td>\n",
" <td>208</td>\n",
" <td>3087</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>215</td>\n",
" <td>Jamshoro</td>\n",
" <td>273662</td>\n",
" <td>22420</td>\n",
" <td>59071</td>\n",
" <td>196663</td>\n",
" <td>280101</td>\n",
" <td>1.02353</td>\n",
" <td>1733</td>\n",
" <td>997</td>\n",
" <td>164</td>\n",
" <td>5682</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>216</td>\n",
" <td>T. Allahyar</td>\n",
" <td>81897</td>\n",
" <td>5728</td>\n",
" <td>16023</td>\n",
" <td>56762</td>\n",
" <td>86595</td>\n",
" <td>1.05736</td>\n",
" <td>5767</td>\n",
" <td>3254</td>\n",
" <td>492</td>\n",
" <td>12025</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>217</td>\n",
" <td>Thatta</td>\n",
" <td>113198</td>\n",
" <td>9220</td>\n",
" <td>26294</td>\n",
" <td>71491</td>\n",
" <td>107538</td>\n",
" <td>0.949999</td>\n",
" <td>1272</td>\n",
" <td>136</td>\n",
" <td>219</td>\n",
" <td>360</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>218</td>\n",
" <td>Matiari</td>\n",
" <td>108272</td>\n",
" <td>9083</td>\n",
" <td>25907</td>\n",
" <td>78663</td>\n",
" <td>114973</td>\n",
" <td>1.06189</td>\n",
" <td>4769</td>\n",
" <td>1120</td>\n",
" <td>379</td>\n",
" <td>15926</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>219</td>\n",
" <td>TM Khan</td>\n",
" <td>50115</td>\n",
" <td>4616</td>\n",
" <td>11243</td>\n",
" <td>41472</td>\n",
" <td>60485</td>\n",
" <td>1.20692</td>\n",
" <td>2008</td>\n",
" <td>593</td>\n",
" <td>341</td>\n",
" <td>6467</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>251</td>\n",
" <td>Mirpurkhas</td>\n",
" <td>185883</td>\n",
" <td>12982</td>\n",
" <td>37697</td>\n",
" <td>138440</td>\n",
" <td>192969</td>\n",
" <td>1.03812</td>\n",
" <td>25249</td>\n",
" <td>11135</td>\n",
" <td>487</td>\n",
" <td>42359</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>253</td>\n",
" <td>Tharparkar</td>\n",
" <td>108821</td>\n",
" <td>11035</td>\n",
" <td>26680</td>\n",
" <td>72239</td>\n",
" <td>110653</td>\n",
" <td>1.01683</td>\n",
" <td>1451</td>\n",
" <td>50</td>\n",
" <td>90</td>\n",
" <td>10319</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>254</td>\n",
" <td>Umerkot</td>\n",
" <td>107178</td>\n",
" <td>8214</td>\n",
" <td>21352</td>\n",
" <td>74024</td>\n",
" <td>104490</td>\n",
" <td>0.97492</td>\n",
" <td>10142</td>\n",
" <td>1220</td>\n",
" <td>202</td>\n",
" <td>22073</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>243</td>\n",
" <td>SBA</td>\n",
" <td>290199</td>\n",
" <td>22348</td>\n",
" <td>61749</td>\n",
" <td>206544</td>\n",
" <td>307810</td>\n",
" <td>1.06069</td>\n",
" <td>27439</td>\n",
" <td>6670</td>\n",
" <td>2100</td>\n",
" <td>28710</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>242</td>\n",
" <td>N. Feroze</td>\n",
" <td>173730</td>\n",
" <td>13718</td>\n",
" <td>38573</td>\n",
" <td>125769</td>\n",
" <td>181108</td>\n",
" <td>1.04247</td>\n",
" <td>452</td>\n",
" <td>62</td>\n",
" <td>283</td>\n",
" <td>25182</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>252</td>\n",
" <td>Sanghar</td>\n",
" <td>185163</td>\n",
" <td>13632</td>\n",
" <td>36749</td>\n",
" <td>145451</td>\n",
" <td>202036</td>\n",
" <td>1.09113</td>\n",
" <td>12619</td>\n",
" <td>6311</td>\n",
" <td>755</td>\n",
" <td>43551</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>244</td>\n",
" <td>Sukkur</td>\n",
" <td>342091</td>\n",
" <td>24529</td>\n",
" <td>71857</td>\n",
" <td>226219</td>\n",
" <td>351142</td>\n",
" <td>1.02646</td>\n",
" <td>32356</td>\n",
" <td>8050</td>\n",
" <td>1395</td>\n",
" <td>53732</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>245</td>\n",
" <td>Ghotki</td>\n",
" <td>213285</td>\n",
" <td>18243</td>\n",
" <td>46079</td>\n",
" <td>153225</td>\n",
" <td>220224</td>\n",
" <td>1.03253</td>\n",
" <td>11318</td>\n",
" <td>1308</td>\n",
" <td>305</td>\n",
" <td>33890</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>241</td>\n",
" <td>Khairpur</td>\n",
" <td>326344</td>\n",
" <td>25290</td>\n",
" <td>69380</td>\n",
" <td>220755</td>\n",
" <td>326391</td>\n",
" <td>1.00014</td>\n",
" <td>13551</td>\n",
" <td>2083</td>\n",
" <td>1055</td>\n",
" <td>57752</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>232</td>\n",
" <td>Larkana</td>\n",
" <td>391076</td>\n",
" <td>33661</td>\n",
" <td>89775</td>\n",
" <td>285501</td>\n",
" <td>410601</td>\n",
" <td>1.04993</td>\n",
" <td>8825</td>\n",
" <td>595</td>\n",
" <td>380</td>\n",
" <td>29032</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>234</td>\n",
" <td>Kamber</td>\n",
" <td>173280</td>\n",
" <td>12670</td>\n",
" <td>37166</td>\n",
" <td>118544</td>\n",
" <td>168514</td>\n",
" <td>0.972495</td>\n",
" <td>8109</td>\n",
" <td>2602</td>\n",
" <td>1162</td>\n",
" <td>24362</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>233</td>\n",
" <td>Shikarpur</td>\n",
" <td>217667</td>\n",
" <td>17892</td>\n",
" <td>48661</td>\n",
" <td>144325</td>\n",
" <td>216644</td>\n",
" <td>0.9953</td>\n",
" <td>22428</td>\n",
" <td>3189</td>\n",
" <td>868</td>\n",
" <td>35735</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>231</td>\n",
" <td>Jacobabad</td>\n",
" <td>149616</td>\n",
" <td>11328</td>\n",
" <td>33652</td>\n",
" <td>98082</td>\n",
" <td>143326</td>\n",
" <td>0.957959</td>\n",
" <td>6801</td>\n",
" <td>3106</td>\n",
" <td>1286</td>\n",
" <td>28961</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>235</td>\n",
" <td>Kashmore</td>\n",
" <td>118675</td>\n",
" <td>11949</td>\n",
" <td>29572</td>\n",
" <td>77384</td>\n",
" <td>120053</td>\n",
" <td>1.01161</td>\n",
" <td>699</td>\n",
" <td>84</td>\n",
" <td>173</td>\n",
" <td>9863</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"0 Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"5 225 South 503355 34998 97748 \n",
"6 226 Malir 777551 51714 150927 \n",
"8 213 Hyderabad 657987 50674 134295 \n",
"9 211 Badin 165333 13895 35703 \n",
"10 212 Dadu 271137 22781 57709 \n",
"11 214 Sujawal 45863 4783 10475 \n",
"12 215 Jamshoro 273662 22420 59071 \n",
"13 216 T. Allahyar 81897 5728 16023 \n",
"14 217 Thatta 113198 9220 26294 \n",
"15 218 Matiari 108272 9083 25907 \n",
"16 219 TM Khan 50115 4616 11243 \n",
"18 251 Mirpurkhas 185883 12982 37697 \n",
"19 253 Tharparkar 108821 11035 26680 \n",
"20 254 Umerkot 107178 8214 21352 \n",
"22 243 SBA 290199 22348 61749 \n",
"23 242 N. Feroze 173730 13718 38573 \n",
"24 252 Sanghar 185163 13632 36749 \n",
"26 244 Sukkur 342091 24529 71857 \n",
"27 245 Ghotki 213285 18243 46079 \n",
"28 241 Khairpur 326344 25290 69380 \n",
"30 232 Larkana 391076 33661 89775 \n",
"31 234 Kamber 173280 12670 37166 \n",
"32 233 Shikarpur 217667 17892 48661 \n",
"33 231 Jacobabad 149616 11328 33652 \n",
"34 235 Kashmore 118675 11949 29572 \n",
"\n",
" 5 Years-15 Years Total Covered Campaign + Catchup % \\\n",
"0 5 Years-15 Years Total Covered Campaign + Catchup % \n",
"1 852098 1338422 0.903484 \n",
"2 514191 798443 0.995381 \n",
"3 598955 876256 0.966138 \n",
"4 566045 803350 1.01475 \n",
"5 342141 492669 0.97877 \n",
"6 417473 701660 0.902397 \n",
"8 437017 627818 0.95415 \n",
"9 127349 177895 1.07598 \n",
"10 217942 298432 1.10067 \n",
"11 29981 45778 0.998147 \n",
"12 196663 280101 1.02353 \n",
"13 56762 86595 1.05736 \n",
"14 71491 107538 0.949999 \n",
"15 78663 114973 1.06189 \n",
"16 41472 60485 1.20692 \n",
"18 138440 192969 1.03812 \n",
"19 72239 110653 1.01683 \n",
"20 74024 104490 0.97492 \n",
"22 206544 307810 1.06069 \n",
"23 125769 181108 1.04247 \n",
"24 145451 202036 1.09113 \n",
"26 226219 351142 1.02646 \n",
"27 153225 220224 1.03253 \n",
"28 220755 326391 1.00014 \n",
"30 285501 410601 1.04993 \n",
"31 118544 168514 0.972495 \n",
"32 144325 216644 0.9953 \n",
"33 98082 143326 0.957959 \n",
"34 77384 120053 1.01161 \n",
"\n",
" Not Available Missed Refusal Missed Sick Already Vaccinated \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 77734 293426 19767 121111 \n",
"2 35724 108622 8765 69510 \n",
"3 43774 92129 10943 98547 \n",
"4 15311 46139 4500 105705 \n",
"5 29757 48795 4743 43096 \n",
"6 34696 88221 9009 118539 \n",
"8 37222 19065 2559 99 \n",
"9 2557 1139 220 18155 \n",
"10 4061 658 238 26152 \n",
"11 1824 514 208 3087 \n",
"12 1733 997 164 5682 \n",
"13 5767 3254 492 12025 \n",
"14 1272 136 219 360 \n",
"15 4769 1120 379 15926 \n",
"16 2008 593 341 6467 \n",
"18 25249 11135 487 42359 \n",
"19 1451 50 90 10319 \n",
"20 10142 1220 202 22073 \n",
"22 27439 6670 2100 28710 \n",
"23 452 62 283 25182 \n",
"24 12619 6311 755 43551 \n",
"26 32356 8050 1395 53732 \n",
"27 11318 1308 305 33890 \n",
"28 13551 2083 1055 57752 \n",
"30 8825 595 380 29032 \n",
"31 8109 2602 1162 24362 \n",
"32 22428 3189 868 35735 \n",
"33 6801 3106 1286 28961 \n",
"34 699 84 173 9863 \n",
"\n",
" (AV1) No Of AEFIs \n",
"0 No Of AEFIs \n",
"1 216 \n",
"2 61 \n",
"3 137 \n",
"4 56 \n",
"5 32 \n",
"6 114 \n",
"8 24 \n",
"9 2 \n",
"10 10 \n",
"11 3 \n",
"12 18 \n",
"13 7 \n",
"14 1 \n",
"15 26 \n",
"16 0 \n",
"18 9 \n",
"19 20 \n",
"20 7 \n",
"22 6 \n",
"23 12 \n",
"24 13 \n",
"26 16 \n",
"27 9 \n",
"28 25 \n",
"30 9 \n",
"31 12 \n",
"32 8 \n",
"33 14 \n",
"34 2 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfDrop"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "CmjX9cU_F_VX",
"outputId": "ad16c82b-6749-470d-8067-4f23a27fc1b7"
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Distcode</th>\n",
" <th>District</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated</th>\n",
" <th>(AV1) No Of AEFIs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>221</td>\n",
" <td>West</td>\n",
" <td>1481401</td>\n",
" <td>91037</td>\n",
" <td>249460</td>\n",
" <td>852098</td>\n",
" <td>1338422</td>\n",
" <td>0.903484</td>\n",
" <td>77734</td>\n",
" <td>293426</td>\n",
" <td>19767</td>\n",
" <td>121111</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>798443</td>\n",
" <td>0.995381</td>\n",
" <td>35724</td>\n",
" <td>108622</td>\n",
" <td>8765</td>\n",
" <td>69510</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>803350</td>\n",
" <td>1.01475</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>225</td>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>34998</td>\n",
" <td>97748</td>\n",
" <td>342141</td>\n",
" <td>492669</td>\n",
" <td>0.97877</td>\n",
" <td>29757</td>\n",
" <td>48795</td>\n",
" <td>4743</td>\n",
" <td>43096</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>226</td>\n",
" <td>Malir</td>\n",
" <td>777551</td>\n",
" <td>51714</td>\n",
" <td>150927</td>\n",
" <td>417473</td>\n",
" <td>701660</td>\n",
" <td>0.902397</td>\n",
" <td>34696</td>\n",
" <td>88221</td>\n",
" <td>9009</td>\n",
" <td>118539</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>213</td>\n",
" <td>Hyderabad</td>\n",
" <td>657987</td>\n",
" <td>50674</td>\n",
" <td>134295</td>\n",
" <td>437017</td>\n",
" <td>627818</td>\n",
" <td>0.95415</td>\n",
" <td>37222</td>\n",
" <td>19065</td>\n",
" <td>2559</td>\n",
" <td>99</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>211</td>\n",
" <td>Badin</td>\n",
" <td>165333</td>\n",
" <td>13895</td>\n",
" <td>35703</td>\n",
" <td>127349</td>\n",
" <td>177895</td>\n",
" <td>1.07598</td>\n",
" <td>2557</td>\n",
" <td>1139</td>\n",
" <td>220</td>\n",
" <td>18155</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>212</td>\n",
" <td>Dadu</td>\n",
" <td>271137</td>\n",
" <td>22781</td>\n",
" <td>57709</td>\n",
" <td>217942</td>\n",
" <td>298432</td>\n",
" <td>1.10067</td>\n",
" <td>4061</td>\n",
" <td>658</td>\n",
" <td>238</td>\n",
" <td>26152</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>214</td>\n",
" <td>Sujawal</td>\n",
" <td>45863</td>\n",
" <td>4783</td>\n",
" <td>10475</td>\n",
" <td>29981</td>\n",
" <td>45778</td>\n",
" <td>0.998147</td>\n",
" <td>1824</td>\n",
" <td>514</td>\n",
" <td>208</td>\n",
" <td>3087</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>215</td>\n",
" <td>Jamshoro</td>\n",
" <td>273662</td>\n",
" <td>22420</td>\n",
" <td>59071</td>\n",
" <td>196663</td>\n",
" <td>280101</td>\n",
" <td>1.02353</td>\n",
" <td>1733</td>\n",
" <td>997</td>\n",
" <td>164</td>\n",
" <td>5682</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>216</td>\n",
" <td>T. Allahyar</td>\n",
" <td>81897</td>\n",
" <td>5728</td>\n",
" <td>16023</td>\n",
" <td>56762</td>\n",
" <td>86595</td>\n",
" <td>1.05736</td>\n",
" <td>5767</td>\n",
" <td>3254</td>\n",
" <td>492</td>\n",
" <td>12025</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>217</td>\n",
" <td>Thatta</td>\n",
" <td>113198</td>\n",
" <td>9220</td>\n",
" <td>26294</td>\n",
" <td>71491</td>\n",
" <td>107538</td>\n",
" <td>0.949999</td>\n",
" <td>1272</td>\n",
" <td>136</td>\n",
" <td>219</td>\n",
" <td>360</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>218</td>\n",
" <td>Matiari</td>\n",
" <td>108272</td>\n",
" <td>9083</td>\n",
" <td>25907</td>\n",
" <td>78663</td>\n",
" <td>114973</td>\n",
" <td>1.06189</td>\n",
" <td>4769</td>\n",
" <td>1120</td>\n",
" <td>379</td>\n",
" <td>15926</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>219</td>\n",
" <td>TM Khan</td>\n",
" <td>50115</td>\n",
" <td>4616</td>\n",
" <td>11243</td>\n",
" <td>41472</td>\n",
" <td>60485</td>\n",
" <td>1.20692</td>\n",
" <td>2008</td>\n",
" <td>593</td>\n",
" <td>341</td>\n",
" <td>6467</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>251</td>\n",
" <td>Mirpurkhas</td>\n",
" <td>185883</td>\n",
" <td>12982</td>\n",
" <td>37697</td>\n",
" <td>138440</td>\n",
" <td>192969</td>\n",
" <td>1.03812</td>\n",
" <td>25249</td>\n",
" <td>11135</td>\n",
" <td>487</td>\n",
" <td>42359</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>253</td>\n",
" <td>Tharparkar</td>\n",
" <td>108821</td>\n",
" <td>11035</td>\n",
" <td>26680</td>\n",
" <td>72239</td>\n",
" <td>110653</td>\n",
" <td>1.01683</td>\n",
" <td>1451</td>\n",
" <td>50</td>\n",
" <td>90</td>\n",
" <td>10319</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>254</td>\n",
" <td>Umerkot</td>\n",
" <td>107178</td>\n",
" <td>8214</td>\n",
" <td>21352</td>\n",
" <td>74024</td>\n",
" <td>104490</td>\n",
" <td>0.97492</td>\n",
" <td>10142</td>\n",
" <td>1220</td>\n",
" <td>202</td>\n",
" <td>22073</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>243</td>\n",
" <td>SBA</td>\n",
" <td>290199</td>\n",
" <td>22348</td>\n",
" <td>61749</td>\n",
" <td>206544</td>\n",
" <td>307810</td>\n",
" <td>1.06069</td>\n",
" <td>27439</td>\n",
" <td>6670</td>\n",
" <td>2100</td>\n",
" <td>28710</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>242</td>\n",
" <td>N. Feroze</td>\n",
" <td>173730</td>\n",
" <td>13718</td>\n",
" <td>38573</td>\n",
" <td>125769</td>\n",
" <td>181108</td>\n",
" <td>1.04247</td>\n",
" <td>452</td>\n",
" <td>62</td>\n",
" <td>283</td>\n",
" <td>25182</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>252</td>\n",
" <td>Sanghar</td>\n",
" <td>185163</td>\n",
" <td>13632</td>\n",
" <td>36749</td>\n",
" <td>145451</td>\n",
" <td>202036</td>\n",
" <td>1.09113</td>\n",
" <td>12619</td>\n",
" <td>6311</td>\n",
" <td>755</td>\n",
" <td>43551</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>244</td>\n",
" <td>Sukkur</td>\n",
" <td>342091</td>\n",
" <td>24529</td>\n",
" <td>71857</td>\n",
" <td>226219</td>\n",
" <td>351142</td>\n",
" <td>1.02646</td>\n",
" <td>32356</td>\n",
" <td>8050</td>\n",
" <td>1395</td>\n",
" <td>53732</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>245</td>\n",
" <td>Ghotki</td>\n",
" <td>213285</td>\n",
" <td>18243</td>\n",
" <td>46079</td>\n",
" <td>153225</td>\n",
" <td>220224</td>\n",
" <td>1.03253</td>\n",
" <td>11318</td>\n",
" <td>1308</td>\n",
" <td>305</td>\n",
" <td>33890</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>241</td>\n",
" <td>Khairpur</td>\n",
" <td>326344</td>\n",
" <td>25290</td>\n",
" <td>69380</td>\n",
" <td>220755</td>\n",
" <td>326391</td>\n",
" <td>1.00014</td>\n",
" <td>13551</td>\n",
" <td>2083</td>\n",
" <td>1055</td>\n",
" <td>57752</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>232</td>\n",
" <td>Larkana</td>\n",
" <td>391076</td>\n",
" <td>33661</td>\n",
" <td>89775</td>\n",
" <td>285501</td>\n",
" <td>410601</td>\n",
" <td>1.04993</td>\n",
" <td>8825</td>\n",
" <td>595</td>\n",
" <td>380</td>\n",
" <td>29032</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>234</td>\n",
" <td>Kamber</td>\n",
" <td>173280</td>\n",
" <td>12670</td>\n",
" <td>37166</td>\n",
" <td>118544</td>\n",
" <td>168514</td>\n",
" <td>0.972495</td>\n",
" <td>8109</td>\n",
" <td>2602</td>\n",
" <td>1162</td>\n",
" <td>24362</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>233</td>\n",
" <td>Shikarpur</td>\n",
" <td>217667</td>\n",
" <td>17892</td>\n",
" <td>48661</td>\n",
" <td>144325</td>\n",
" <td>216644</td>\n",
" <td>0.9953</td>\n",
" <td>22428</td>\n",
" <td>3189</td>\n",
" <td>868</td>\n",
" <td>35735</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>231</td>\n",
" <td>Jacobabad</td>\n",
" <td>149616</td>\n",
" <td>11328</td>\n",
" <td>33652</td>\n",
" <td>98082</td>\n",
" <td>143326</td>\n",
" <td>0.957959</td>\n",
" <td>6801</td>\n",
" <td>3106</td>\n",
" <td>1286</td>\n",
" <td>28961</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>235</td>\n",
" <td>Kashmore</td>\n",
" <td>118675</td>\n",
" <td>11949</td>\n",
" <td>29572</td>\n",
" <td>77384</td>\n",
" <td>120053</td>\n",
" <td>1.01161</td>\n",
" <td>699</td>\n",
" <td>84</td>\n",
" <td>173</td>\n",
" <td>9863</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"5 225 South 503355 34998 97748 \n",
"6 226 Malir 777551 51714 150927 \n",
"8 213 Hyderabad 657987 50674 134295 \n",
"9 211 Badin 165333 13895 35703 \n",
"10 212 Dadu 271137 22781 57709 \n",
"11 214 Sujawal 45863 4783 10475 \n",
"12 215 Jamshoro 273662 22420 59071 \n",
"13 216 T. Allahyar 81897 5728 16023 \n",
"14 217 Thatta 113198 9220 26294 \n",
"15 218 Matiari 108272 9083 25907 \n",
"16 219 TM Khan 50115 4616 11243 \n",
"18 251 Mirpurkhas 185883 12982 37697 \n",
"19 253 Tharparkar 108821 11035 26680 \n",
"20 254 Umerkot 107178 8214 21352 \n",
"22 243 SBA 290199 22348 61749 \n",
"23 242 N. Feroze 173730 13718 38573 \n",
"24 252 Sanghar 185163 13632 36749 \n",
"26 244 Sukkur 342091 24529 71857 \n",
"27 245 Ghotki 213285 18243 46079 \n",
"28 241 Khairpur 326344 25290 69380 \n",
"30 232 Larkana 391076 33661 89775 \n",
"31 234 Kamber 173280 12670 37166 \n",
"32 233 Shikarpur 217667 17892 48661 \n",
"33 231 Jacobabad 149616 11328 33652 \n",
"34 235 Kashmore 118675 11949 29572 \n",
"\n",
" 5 Years-15 Years Total Covered Campaign + Catchup % Not Available \\\n",
"1 852098 1338422 0.903484 77734 \n",
"2 514191 798443 0.995381 35724 \n",
"3 598955 876256 0.966138 43774 \n",
"4 566045 803350 1.01475 15311 \n",
"5 342141 492669 0.97877 29757 \n",
"6 417473 701660 0.902397 34696 \n",
"8 437017 627818 0.95415 37222 \n",
"9 127349 177895 1.07598 2557 \n",
"10 217942 298432 1.10067 4061 \n",
"11 29981 45778 0.998147 1824 \n",
"12 196663 280101 1.02353 1733 \n",
"13 56762 86595 1.05736 5767 \n",
"14 71491 107538 0.949999 1272 \n",
"15 78663 114973 1.06189 4769 \n",
"16 41472 60485 1.20692 2008 \n",
"18 138440 192969 1.03812 25249 \n",
"19 72239 110653 1.01683 1451 \n",
"20 74024 104490 0.97492 10142 \n",
"22 206544 307810 1.06069 27439 \n",
"23 125769 181108 1.04247 452 \n",
"24 145451 202036 1.09113 12619 \n",
"26 226219 351142 1.02646 32356 \n",
"27 153225 220224 1.03253 11318 \n",
"28 220755 326391 1.00014 13551 \n",
"30 285501 410601 1.04993 8825 \n",
"31 118544 168514 0.972495 8109 \n",
"32 144325 216644 0.9953 22428 \n",
"33 98082 143326 0.957959 6801 \n",
"34 77384 120053 1.01161 699 \n",
"\n",
" Missed Refusal Missed Sick Already Vaccinated (AV1) No Of AEFIs \n",
"1 293426 19767 121111 216 \n",
"2 108622 8765 69510 61 \n",
"3 92129 10943 98547 137 \n",
"4 46139 4500 105705 56 \n",
"5 48795 4743 43096 32 \n",
"6 88221 9009 118539 114 \n",
"8 19065 2559 99 24 \n",
"9 1139 220 18155 2 \n",
"10 658 238 26152 10 \n",
"11 514 208 3087 3 \n",
"12 997 164 5682 18 \n",
"13 3254 492 12025 7 \n",
"14 136 219 360 1 \n",
"15 1120 379 15926 26 \n",
"16 593 341 6467 0 \n",
"18 11135 487 42359 9 \n",
"19 50 90 10319 20 \n",
"20 1220 202 22073 7 \n",
"22 6670 2100 28710 6 \n",
"23 62 283 25182 12 \n",
"24 6311 755 43551 13 \n",
"26 8050 1395 53732 16 \n",
"27 1308 305 33890 9 \n",
"28 2083 1055 57752 25 \n",
"30 595 380 29032 9 \n",
"31 2602 1162 24362 12 \n",
"32 3189 868 35735 8 \n",
"33 3106 1286 28961 14 \n",
"34 84 173 9863 2 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfDrop.drop(index=0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WwuJsVCro8aT"
},
"outputs": [],
"source": [
"dfDrop= dfDrop.drop(0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376
},
"colab_type": "code",
"id": "PDJj3c5spv6B",
"outputId": "463b8ac3-b43e-46fe-a0cb-0640b474f93e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Distcode</th>\n",
" <th>District</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated</th>\n",
" <th>(AV1) No Of AEFIs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>221</td>\n",
" <td>West</td>\n",
" <td>1481401</td>\n",
" <td>91037</td>\n",
" <td>249460</td>\n",
" <td>852098</td>\n",
" <td>1338422</td>\n",
" <td>0.903484</td>\n",
" <td>77734</td>\n",
" <td>293426</td>\n",
" <td>19767</td>\n",
" <td>121111</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>798443</td>\n",
" <td>0.995381</td>\n",
" <td>35724</td>\n",
" <td>108622</td>\n",
" <td>8765</td>\n",
" <td>69510</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>803350</td>\n",
" <td>1.01475</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>225</td>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>34998</td>\n",
" <td>97748</td>\n",
" <td>342141</td>\n",
" <td>492669</td>\n",
" <td>0.97877</td>\n",
" <td>29757</td>\n",
" <td>48795</td>\n",
" <td>4743</td>\n",
" <td>43096</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>226</td>\n",
" <td>Malir</td>\n",
" <td>777551</td>\n",
" <td>51714</td>\n",
" <td>150927</td>\n",
" <td>417473</td>\n",
" <td>701660</td>\n",
" <td>0.902397</td>\n",
" <td>34696</td>\n",
" <td>88221</td>\n",
" <td>9009</td>\n",
" <td>118539</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>213</td>\n",
" <td>Hyderabad</td>\n",
" <td>657987</td>\n",
" <td>50674</td>\n",
" <td>134295</td>\n",
" <td>437017</td>\n",
" <td>627818</td>\n",
" <td>0.95415</td>\n",
" <td>37222</td>\n",
" <td>19065</td>\n",
" <td>2559</td>\n",
" <td>99</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>211</td>\n",
" <td>Badin</td>\n",
" <td>165333</td>\n",
" <td>13895</td>\n",
" <td>35703</td>\n",
" <td>127349</td>\n",
" <td>177895</td>\n",
" <td>1.07598</td>\n",
" <td>2557</td>\n",
" <td>1139</td>\n",
" <td>220</td>\n",
" <td>18155</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>212</td>\n",
" <td>Dadu</td>\n",
" <td>271137</td>\n",
" <td>22781</td>\n",
" <td>57709</td>\n",
" <td>217942</td>\n",
" <td>298432</td>\n",
" <td>1.10067</td>\n",
" <td>4061</td>\n",
" <td>658</td>\n",
" <td>238</td>\n",
" <td>26152</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>214</td>\n",
" <td>Sujawal</td>\n",
" <td>45863</td>\n",
" <td>4783</td>\n",
" <td>10475</td>\n",
" <td>29981</td>\n",
" <td>45778</td>\n",
" <td>0.998147</td>\n",
" <td>1824</td>\n",
" <td>514</td>\n",
" <td>208</td>\n",
" <td>3087</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Distcode District Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"5 225 South 503355 34998 97748 \n",
"6 226 Malir 777551 51714 150927 \n",
"8 213 Hyderabad 657987 50674 134295 \n",
"9 211 Badin 165333 13895 35703 \n",
"10 212 Dadu 271137 22781 57709 \n",
"11 214 Sujawal 45863 4783 10475 \n",
"\n",
" 5 Years-15 Years Total Covered Campaign + Catchup % Not Available \\\n",
"1 852098 1338422 0.903484 77734 \n",
"2 514191 798443 0.995381 35724 \n",
"3 598955 876256 0.966138 43774 \n",
"4 566045 803350 1.01475 15311 \n",
"5 342141 492669 0.97877 29757 \n",
"6 417473 701660 0.902397 34696 \n",
"8 437017 627818 0.95415 37222 \n",
"9 127349 177895 1.07598 2557 \n",
"10 217942 298432 1.10067 4061 \n",
"11 29981 45778 0.998147 1824 \n",
"\n",
" Missed Refusal Missed Sick Already Vaccinated (AV1) No Of AEFIs \n",
"1 293426 19767 121111 216 \n",
"2 108622 8765 69510 61 \n",
"3 92129 10943 98547 137 \n",
"4 46139 4500 105705 56 \n",
"5 48795 4743 43096 32 \n",
"6 88221 9009 118539 114 \n",
"8 19065 2559 99 24 \n",
"9 1139 220 18155 2 \n",
"10 658 238 26152 10 \n",
"11 514 208 3087 3 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfDrop.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"colab_type": "code",
"id": "T-2z1z6D9VCr",
"outputId": "b2e69106-dce3-4078-904a-039ad6b802a5"
},
"outputs": [
{
"data": {
"text/plain": [
"Distcode object\n",
"District object\n",
"Daily Target object\n",
"9 Months-2 Years object\n",
"2 Years-5 Years object\n",
"5 Years-15 Years object\n",
"Total Covered Campaign + Catchup object\n",
"% object\n",
"Not Available object\n",
"Missed Refusal object\n",
"Missed Sick object\n",
"Already Vaccinated object\n",
"(AV1) No Of AEFIs object\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfDrop.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HyQSJyJsO0jf"
},
"outputs": [],
"source": [
"#change all col into numeric \n",
"dfDrop[[\"Daily Target\", \"9 Months-2 Years\", \"2 Years-5 Years\", \"5 Years-15 Years\", \"Total Covered Campaign + Catchup\", \"%\", \"Not Available\", \"Missed Refusal\", \"Missed Sick\", \"Already Vaccinated\", \"(AV1) No Of AEFIs\"]] = dfDrop[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated\", \"(AV1) No Of AEFIs\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"colab_type": "code",
"id": "MHyccmuh9_Mb",
"outputId": "aa590fc1-2b26-401e-e325-a7ad0c225d10"
},
"outputs": [
{
"data": {
"text/plain": [
"Distcode object\n",
"District object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated int64\n",
"(AV1) No Of AEFIs int64\n",
"dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfDrop.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "jWDScpKFPgXc"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"colab_type": "code",
"id": "-XUPTwed4Jqo",
"outputId": "c9622191-d1f9-4df3-cf09-3548fe790e79"
},
"outputs": [
{
"data": {
"text/plain": [
"1 390927\n",
"2 153111\n",
"3 146846\n",
"4 65950\n",
"5 83295\n",
"Name: Total Missed, dtype: int64"
]
},
"execution_count": 112,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"dfDrop['Total Missed'].head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 965
},
"colab_type": "code",
"id": "15NHFmKYRe-y",
"outputId": "37680e15-b78b-4d8d-b8b8-f1fedaa9e34b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated</th>\n",
" <th>(AV1) No Of AEFIs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" <td>29.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>345295.48</td>\n",
" <td>26417.10</td>\n",
" <td>71400.34</td>\n",
" <td>228784.31</td>\n",
" <td>340219.86</td>\n",
" <td>1.02</td>\n",
" <td>16539.59</td>\n",
" <td>25905.97</td>\n",
" <td>2520.28</td>\n",
" <td>37585.59</td>\n",
" <td>29.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>331370.70</td>\n",
" <td>23336.95</td>\n",
" <td>62648.18</td>\n",
" <td>199860.44</td>\n",
" <td>306927.42</td>\n",
" <td>0.06</td>\n",
" <td>17736.99</td>\n",
" <td>59732.09</td>\n",
" <td>4418.79</td>\n",
" <td>34633.11</td>\n",
" <td>47.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>45863.00</td>\n",
" <td>4616.00</td>\n",
" <td>10475.00</td>\n",
" <td>29981.00</td>\n",
" <td>45778.00</td>\n",
" <td>0.90</td>\n",
" <td>452.00</td>\n",
" <td>50.00</td>\n",
" <td>90.00</td>\n",
" <td>99.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>118675.00</td>\n",
" <td>11328.00</td>\n",
" <td>29572.00</td>\n",
" <td>78663.00</td>\n",
" <td>120053.00</td>\n",
" <td>0.97</td>\n",
" <td>2557.00</td>\n",
" <td>658.00</td>\n",
" <td>238.00</td>\n",
" <td>12025.00</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>213285.00</td>\n",
" <td>17892.00</td>\n",
" <td>46079.00</td>\n",
" <td>145451.00</td>\n",
" <td>216644.00</td>\n",
" <td>1.01</td>\n",
" <td>10142.00</td>\n",
" <td>2602.00</td>\n",
" <td>492.00</td>\n",
" <td>28710.00</td>\n",
" <td>12.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>391076.00</td>\n",
" <td>33661.00</td>\n",
" <td>89775.00</td>\n",
" <td>285501.00</td>\n",
" <td>410601.00</td>\n",
" <td>1.05</td>\n",
" <td>27439.00</td>\n",
" <td>11135.00</td>\n",
" <td>2100.00</td>\n",
" <td>43551.00</td>\n",
" <td>25.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1481401.00</td>\n",
" <td>91037.00</td>\n",
" <td>249460.00</td>\n",
" <td>852098.00</td>\n",
" <td>1338422.00</td>\n",
" <td>1.21</td>\n",
" <td>77734.00</td>\n",
" <td>293426.00</td>\n",
" <td>19767.00</td>\n",
" <td>121111.00</td>\n",
" <td>216.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"count 29.00 29.00 29.00 29.00 \n",
"mean 345295.48 26417.10 71400.34 228784.31 \n",
"std 331370.70 23336.95 62648.18 199860.44 \n",
"min 45863.00 4616.00 10475.00 29981.00 \n",
"25% 118675.00 11328.00 29572.00 78663.00 \n",
"50% 213285.00 17892.00 46079.00 145451.00 \n",
"75% 391076.00 33661.00 89775.00 285501.00 \n",
"max 1481401.00 91037.00 249460.00 852098.00 \n",
"\n",
" Total Covered Campaign + Catchup % Not Available Missed Refusal \\\n",
"count 29.00 29.00 29.00 29.00 \n",
"mean 340219.86 1.02 16539.59 25905.97 \n",
"std 306927.42 0.06 17736.99 59732.09 \n",
"min 45778.00 0.90 452.00 50.00 \n",
"25% 120053.00 0.97 2557.00 658.00 \n",
"50% 216644.00 1.01 10142.00 2602.00 \n",
"75% 410601.00 1.05 27439.00 11135.00 \n",
"max 1338422.00 1.21 77734.00 293426.00 \n",
"\n",
" Missed Sick Already Vaccinated (AV1) No Of AEFIs \n",
"count 29.00 29.00 29.00 \n",
"mean 2520.28 37585.59 29.97 \n",
"std 4418.79 34633.11 47.93 \n",
"min 90.00 99.00 0.00 \n",
"25% 238.00 12025.00 7.00 \n",
"50% 492.00 28710.00 12.00 \n",
"75% 2100.00 43551.00 25.00 \n",
"max 19767.00 121111.00 216.00 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For remove exponention\n",
"pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
"dfDrop.describe()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "1SND1H5f4O__"
},
"outputs": [],
"source": [
"test_set = dfDrop[['District','Daily Target','Total Missed']]"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"colab_type": "code",
"id": "gzTLA_Ca7DQy",
"outputId": "b922dfbd-64e9-438a-a168-3cbe63640dbe"
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>District</th>\n",
" <th>Daily Target</th>\n",
" <th>Total Missed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>West</td>\n",
" <td>1481401</td>\n",
" <td>390927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>153111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>146846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>65950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>83295</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" District Daily Target Total Missed\n",
"1 West 1481401 390927\n",
"2 East 802148 153111\n",
"3 Korangi 906968 146846\n",
"4 Central 791674 65950\n",
"5 South 503355 83295"
]
},
"execution_count": 114,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"test_set.head()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
},
"colab_type": "code",
"id": "qN9BekCn7adC",
"outputId": "2e055559-9aee-4985-b840-fd28041632ab"
},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated</th>\n",
" <th>(AV1)\\tNo Of AEFIs</th>\n",
" <th>Total Missed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.452955e+05</td>\n",
" <td>26417.103448</td>\n",
" <td>71400.344828</td>\n",
" <td>228784.310345</td>\n",
" <td>3.402199e+05</td>\n",
" <td>1.015867</td>\n",
" <td>16539.586207</td>\n",
" <td>25905.965517</td>\n",
" <td>2520.275862</td>\n",
" <td>37585.586207</td>\n",
" <td>29.965517</td>\n",
" <td>44965.827586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.313707e+05</td>\n",
" <td>23336.950493</td>\n",
" <td>62648.175922</td>\n",
" <td>199860.437325</td>\n",
" <td>3.069274e+05</td>\n",
" <td>0.061953</td>\n",
" <td>17736.988260</td>\n",
" <td>59732.091703</td>\n",
" <td>4418.785619</td>\n",
" <td>34633.108740</td>\n",
" <td>47.932604</td>\n",
" <td>79731.268673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.586300e+04</td>\n",
" <td>4616.000000</td>\n",
" <td>10475.000000</td>\n",
" <td>29981.000000</td>\n",
" <td>4.577800e+04</td>\n",
" <td>0.902397</td>\n",
" <td>452.000000</td>\n",
" <td>50.000000</td>\n",
" <td>90.000000</td>\n",
" <td>99.000000</td>\n",
" <td>0.000000</td>\n",
" <td>797.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.186750e+05</td>\n",
" <td>11328.000000</td>\n",
" <td>29572.000000</td>\n",
" <td>78663.000000</td>\n",
" <td>1.200530e+05</td>\n",
" <td>0.974920</td>\n",
" <td>2557.000000</td>\n",
" <td>658.000000</td>\n",
" <td>238.000000</td>\n",
" <td>12025.000000</td>\n",
" <td>7.000000</td>\n",
" <td>3916.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.132850e+05</td>\n",
" <td>17892.000000</td>\n",
" <td>46079.000000</td>\n",
" <td>145451.000000</td>\n",
" <td>2.166440e+05</td>\n",
" <td>1.014748</td>\n",
" <td>10142.000000</td>\n",
" <td>2602.000000</td>\n",
" <td>492.000000</td>\n",
" <td>28710.000000</td>\n",
" <td>12.000000</td>\n",
" <td>11873.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.910760e+05</td>\n",
" <td>33661.000000</td>\n",
" <td>89775.000000</td>\n",
" <td>285501.000000</td>\n",
" <td>4.106010e+05</td>\n",
" <td>1.049926</td>\n",
" <td>27439.000000</td>\n",
" <td>11135.000000</td>\n",
" <td>2100.000000</td>\n",
" <td>43551.000000</td>\n",
" <td>25.000000</td>\n",
" <td>41801.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.481401e+06</td>\n",
" <td>91037.000000</td>\n",
" <td>249460.000000</td>\n",
" <td>852098.000000</td>\n",
" <td>1.338422e+06</td>\n",
" <td>1.206924</td>\n",
" <td>77734.000000</td>\n",
" <td>293426.000000</td>\n",
" <td>19767.000000</td>\n",
" <td>121111.000000</td>\n",
" <td>216.000000</td>\n",
" <td>390927.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years ... (AV1)\\tNo Of AEFIs Total Missed\n",
"count 2.900000e+01 29.000000 ... 29.000000 29.000000\n",
"mean 3.452955e+05 26417.103448 ... 29.965517 44965.827586\n",
"std 3.313707e+05 23336.950493 ... 47.932604 79731.268673\n",
"min 4.586300e+04 4616.000000 ... 0.000000 797.000000\n",
"25% 1.186750e+05 11328.000000 ... 7.000000 3916.000000\n",
"50% 2.132850e+05 17892.000000 ... 12.000000 11873.000000\n",
"75% 3.910760e+05 33661.000000 ... 25.000000 41801.000000\n",
"max 1.481401e+06 91037.000000 ... 216.000000 390927.000000\n",
"\n",
"[8 rows x 12 columns]"
]
},
"execution_count": 120,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"dfDrop.describe()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "aOfeyZLP8xQL"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8Jv1bfgnVkE1"
},
"source": [
"# **Data Cleaning for Dist Sheet**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "_uOgP-G7Pol-",
"outputId": "c9a1b4f7-49da-48d3-8a92-8cd85bacbfff"
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('Div',\n",
" Unnamed: 0 Unnamed: 1 ... Unnamed: 11 Unnamed: 12\n",
" 0 Distcode District ... Already Vaccinated (AV1) No Of AEFIs\n",
" 1 221 West ... 121111 216\n",
" 2 222 East ... 69510 61\n",
" 3 223 Korangi ... 98547 137\n",
" 4 224 Central ... 105705 56\n",
" 5 225 South ... 43096 32\n",
" 6 226 Malir ... 118539 114\n",
" 7 NaN KARACHI DIV: ... 556508 616\n",
" 8 213 Hyderabad ... 99 24\n",
" 9 211 Badin ... 18155 2\n",
" 10 212 Dadu ... 26152 10\n",
" 11 214 Sujawal ... 3087 3\n",
" 12 215 Jamshoro ... 5682 18\n",
" 13 216 T. Allahyar ... 12025 7\n",
" 14 217 Thatta ... 360 1\n",
" 15 218 Matiari ... 15926 26\n",
" 16 219 TM Khan ... 6467 0\n",
" 17 NaN HYDERABAD DIV: ... 87953 91\n",
" 18 251 Mirpurkhas ... 42359 9\n",
" 19 253 Tharparkar ... 10319 20\n",
" 20 254 Umerkot ... 22073 7\n",
" 21 NaN MIRPURKHAS DIV: ... 74751 36\n",
" 22 243 SBA ... 28710 6\n",
" 23 242 N. Feroze ... 25182 12\n",
" 24 252 Sanghar ... 43551 13\n",
" 25 NaN SBA DIV: ... 97443 31\n",
" 26 244 Sukkur ... 53732 16\n",
" 27 245 Ghotki ... 33890 9\n",
" 28 241 Khairpur ... 57752 25\n",
" 29 NaN SUKKUR DIV: ... 145374 50\n",
" 30 232 Larkana ... 29032 9\n",
" 31 234 Kamber ... 24362 12\n",
" 32 233 Shikarpur ... 35735 8\n",
" 33 231 Jacobabad ... 28961 14\n",
" 34 235 Kashmore ... 9863 2\n",
" 35 NaN LARKANA DIV: ... 127953 45\n",
" 36 NaN Total: ... 1089982 869\n",
" \n",
" [37 rows x 13 columns]),\n",
" ('Dist', Unnamed: 0 ... Unnamed: 20\n",
" 0 Distcode ... Doses Remained During Catchup\n",
" 1 221 ... 17433\n",
" 2 222 ... 2945\n",
" 3 223 ... 17030\n",
" 4 224 ... 10\n",
" 5 225 ... 1365\n",
" 6 226 ... 11278\n",
" 7 NaN ... 50061\n",
" 8 213 ... 4264\n",
" 9 211 ... 0\n",
" 10 212 ... 0\n",
" 11 214 ... 0\n",
" 12 215 ... 10\n",
" 13 216 ... 90\n",
" 14 217 ... 21\n",
" 15 218 ... 90\n",
" 16 219 ... 0\n",
" 17 NaN ... 4475\n",
" 18 251 ... 1290\n",
" 19 253 ... 0\n",
" 20 254 ... 115\n",
" 21 NaN ... 1405\n",
" 22 243 ... 240\n",
" 23 242 ... 0\n",
" 24 252 ... 0\n",
" 25 NaN ... 240\n",
" 26 244 ... 118\n",
" 27 245 ... 0\n",
" 28 241 ... 643\n",
" 29 NaN ... 761\n",
" 30 232 ... 735\n",
" 31 234 ... 0\n",
" 32 233 ... 3825\n",
" 33 231 ... 0\n",
" 34 235 ... 317\n",
" 35 NaN ... 4877\n",
" 36 NaN ... 61819\n",
" \n",
" [37 rows x 21 columns]),\n",
" ('Sheet9', District %\n",
" 0 TM Khan 1.206924\n",
" 1 Dadu 1.100669\n",
" 2 Sanghar 1.091125\n",
" 3 Badin 1.075980\n",
" 4 Matiari 1.061890\n",
" 5 SBA 1.060686\n",
" 6 T. Allahyar 1.057365\n",
" 7 Larkana 1.049926\n",
" 8 N. Feroze 1.042468\n",
" 9 Mirpurkhas 1.038121\n",
" 10 Ghotki 1.032534\n",
" 11 Sukkur 1.026458\n",
" 12 Jamshoro 1.023529\n",
" 13 Tharparkar 1.016835\n",
" 14 Central 1.014748\n",
" 15 Kashmore 1.011612\n",
" 16 Khairpur 1.000144\n",
" 17 Sujawal 0.998147\n",
" 18 East 0.995381\n",
" 19 Shikarpur 0.995300\n",
" 20 South 0.978770\n",
" 21 Umerkot 0.974920\n",
" 22 Kamber 0.972495\n",
" 23 Korangi 0.966138\n",
" 24 Jacobabad 0.957959\n",
" 25 Hyderabad 0.954150\n",
" 26 Thatta 0.949999\n",
" 27 West 0.903484\n",
" 28 Malir 0.902397),\n",
" ('West', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 221001001 ... 100\n",
" 2 221001001 ... 80\n",
" 3 221001001 ... 90\n",
" 4 221001001 ... 150\n",
" .. ... ... ...\n",
" 189 221018009 ... 60\n",
" 190 221018009 ... 105\n",
" 191 221018009 ... 40\n",
" 192 NaN ... 2435\n",
" 193 NaN ... 17433\n",
" \n",
" [194 rows x 24 columns]),\n",
" ('East', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 222003004 ... 30\n",
" 2 222003004 ... 15\n",
" 3 222003004 ... 20\n",
" 4 222003004 ... 10\n",
" .. ... ... ...\n",
" 120 222006012 ... 24\n",
" 121 222006012 ... 21\n",
" 122 222006013 ... 21\n",
" 123 NaN ... 1265\n",
" 124 NaN ... 2945\n",
" \n",
" [125 rows x 24 columns]),\n",
" ('Korangi', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 223008001 ... 250\n",
" 2 223008001 ... 110\n",
" 3 223008001 ... 90\n",
" 4 223008001 ... 135\n",
" .. ... ... ...\n",
" 121 223017008 ... 400\n",
" 122 223017008 ... 320\n",
" 123 223017009 ... 325\n",
" 124 NaN ... 8055\n",
" 125 NaN ... 17030\n",
" \n",
" [126 rows x 24 columns]),\n",
" ('Central', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 224005001 ... 5\n",
" 2 224005001 ... 0\n",
" 3 224005001 ... 5\n",
" 4 224005002 ... 0\n",
" .. ... ... ...\n",
" 128 224014013 ... 0\n",
" 129 224014013 ... 0\n",
" 130 224014013 ... 0\n",
" 131 NaN ... 0\n",
" 132 NaN ... 10\n",
" \n",
" [133 rows x 24 columns]),\n",
" ('South', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 225011001 ... 15\n",
" 2 225011001 ... 45\n",
" 3 225011001 ... 25\n",
" 4 225011002 ... 155\n",
" .. ... ... ...\n",
" 66 225016015 ... 5\n",
" 67 225016016 ... 0\n",
" 68 225016016 ... 0\n",
" 69 NaN ... 355\n",
" 70 NaN ... 1365\n",
" \n",
" [71 rows x 24 columns]),\n",
" ('Malir', Unnamed: 0 ... Unnamed: 23\n",
" 0 Uncode ... Doses Remained During Catchup\n",
" 1 226002001 ... 210\n",
" 2 226002001 ... 0\n",
" 3 226002001 ... 310\n",
" 4 226002001 ... 0\n",
" .. ... ... ...\n",
" 115 226012007 ... 35\n",
" 116 226012008 ... 35\n",
" 117 226012008 ... 35\n",
" 118 NaN ... 500\n",
" 119 NaN ... 11278\n",
" \n",
" [120 rows x 24 columns]),\n",
" ('Karachi',\n",
" District Town ... Missed Children from RPT %\n",
" 0 SOUTH LAYARI ... 11363 0.06\n",
" 1 SOUTH SADDAR ... 59726 0.20\n",
" 2 CENTRAL GULBERG ... 2866 0.02\n",
" 3 CENTRAL LIAQ ... 2369 0.01\n",
" 4 CENTRAL NORTH ... -3654 -0.01\n",
" 5 CENTRAL N.NAZIM ... -2466 -0.01\n",
" 6 EAST GADAP ... 29819 0.13\n",
" 7 EAST G.IQBAL ... -9954 -0.03\n",
" 8 EAST JAMSHED ... -3722 -0.01\n",
" 9 KORANGI KORANGI ... 24297 0.05\n",
" 10 KORANGI LANDHI ... 12826 0.07\n",
" 11 KORANGI MALIR ... 5721 0.06\n",
" 12 KORANGI S.FAISAL ... 3954 0.02\n",
" 13 MALIR BIN QASIM ... 52124 0.14\n",
" 14 MALIR GADAP ... 12511 0.13\n",
" 15 MALIR LANDHI ... 71333 0.36\n",
" 16 MALIR MALIR ... 21469 0.22\n",
" 17 WEST BALDIA ... 62695 0.20\n",
" 18 WEST GADAP ... 88512 0.27\n",
" 19 WEST KAMARI ... 19733 0.08\n",
" 20 WEST ORANGI ... 70870 0.20\n",
" 21 WEST SITE ... 47163 0.22\n",
" 22 TOTAL NaN ... 579555 0.11\n",
" \n",
" [23 rows x 10 columns]),\n",
" ('Sheet11', (EAST) G.IQBAL 1.0491639903320862\n",
" 0 (CENTRAL) N.NAZIM 1.035362\n",
" 1 (EAST) JAMSHED 1.031341\n",
" 2 (CENTRAL) NORTH 1.023800\n",
" 3 (CENTRAL) GULBERG 0.996166\n",
" 4 (CENTRAL) LIAQ 0.995074\n",
" 5 (KORANGI) S.FAISAL 0.987130\n",
" 6 (SOUTH) SADDAR 0.984848\n",
" 7 (WEST) KAMARI 0.979686\n",
" 8 (KORANGI) KORANGI 0.970958\n",
" 9 (SOUTH) LAYARI 0.969459\n",
" 10 (KORANGI) MALIR 0.963074\n",
" 11 (WEST) ORANGI 0.955493\n",
" 12 (WEST) BALDIA 0.955217\n",
" 13 (MALIR) MALIR 0.952052\n",
" 14 (KORANGI) LANDHI 0.934238\n",
" 15 (MALIR) BIN QASIM 0.905705\n",
" 16 (MALIR) GADAP 0.887218\n",
" 17 (EAST) GADAP 0.885431\n",
" 18 (MALIR) LANDHI 0.878496\n",
" 19 (WEST) SITE 0.821442\n",
" 20 (WEST) GADAP 0.791147),\n",
" ('Sheet12',\n",
" District Total Children Vaccinated at ... Unnamed: 27 Unnamed: 28\n",
" 0 NaN NaN ... NaN NaN\n",
" 1 NaN Fixed ... School Total\n",
" 2 West 6692 ... 0 443\n",
" 3 East 2383 ... 69 1076\n",
" 4 Korangi 4753 ... 131 2644\n",
" 5 Central 4385 ... 0 1164\n",
" 6 South 5583 ... 20 397\n",
" 7 Malir 7180 ... 59 17565\n",
" 8 Hyderabad 1194 ... 0 0\n",
" 9 Badin 492 ... 0 0\n",
" 10 Dadu 0 ... 0 0\n",
" 11 Sujawal 353 ... 0 50\n",
" 12 Jamshoro 862 ... 0 321\n",
" 13 T. Allahyar 1134 ... 325 3215\n",
" 14 Thatta 389 ... 0 43\n",
" 15 Matiari 762 ... 0 137\n",
" 16 TMKhan 143 ... 3 416\n",
" 17 Mirpurkhas 1489 ... 49 538\n",
" 18 Tharparkar 427 ... 0 99\n",
" 19 Umerkot 269 ... 0 0\n",
" 20 SBA 1808 ... 70 1836\n",
" 21 N.Feroze 367 ... 35 99\n",
" 22 Sanghar 1006 ... 0 0\n",
" 23 Sukkur 1590 ... 0 467\n",
" 24 Ghotki 933 ... 5 229\n",
" 25 Khairpur 1923 ... 177 496\n",
" 26 Larkana 704 ... 1 77\n",
" 27 Kamber 134 ... 0 0\n",
" 28 Shikarpur 658 ... 25 78\n",
" 29 Jacobabad 123 ... 0 0\n",
" 30 Kashmore 736 ... 0 0\n",
" 31 Total 48472 ... 969 31390\n",
" \n",
" [32 rows x 29 columns])])"
]
},
"execution_count": 9,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"sheets = pd.read_excel('Final Admin Coverage of TCV Campaign Nov 2019.xlsx',\n",
" sheet_name= None )\n",
"sheets"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "IaA7m9vQebrm"
},
"outputs": [],
"source": [
"xls = pd.ExcelFile('Final Admin Coverage of TCV Campaign Nov 2019.xlsx')\n",
"df1 = pd.read_excel(xls, 'Div')\n",
"df2 = pd.read_excel(xls, 'Dist')\n",
"df3 = pd.read_excel(xls, 'West')\n",
"df4 = pd.read_excel(xls, 'East')\n",
"df5 = pd.read_excel(xls, 'Korangi')\n",
"df6 = pd.read_excel(xls, 'Central')\n",
"df7 = pd.read_excel(xls, 'South')\n",
"df8 = pd.read_excel(xls, 'Malir')\n",
"df9 = pd.read_excel(xls, 'Karachi')\n",
"df10= pd.read_excel(xls, 'Sheet12')\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
},
"colab_type": "code",
"id": "18mil8EwV5-u",
"outputId": "386a0df0-632c-490e-8a02-476e7a534f65"
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Unnamed: 1</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
" <th>Unnamed: 4</th>\n",
" <th>Unnamed: 5</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>...</th>\n",
" <th>Unnamed: 11</th>\n",
" <th>Unnamed: 12</th>\n",
" <th>Unnamed: 13</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" <th>Unnamed: 16</th>\n",
" <th>Unnamed: 17</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>Unnamed: 19</th>\n",
" <th>Unnamed: 20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Distcode</td>\n",
" <td>Dadu</td>\n",
" <td>Daily Target</td>\n",
" <td>9 Months-2 Years</td>\n",
" <td>2 Years-5 Years</td>\n",
" <td>5 Years-15 Years</td>\n",
" <td>Total Children Vaccinated (9 Months-15 Years)</td>\n",
" <td>Missed Children Covered During Catchup</td>\n",
" <td>Total Covered Campaign + Catchup</td>\n",
" <td>%</td>\n",
" <td>...</td>\n",
" <td>Total Doses Remaining</td>\n",
" <td>Not Available</td>\n",
" <td>Missed Refusal</td>\n",
" <td>Missed Sick</td>\n",
" <td>Already Vaccinated (AV1)</td>\n",
" <td>No Of AEFIs</td>\n",
" <td>Total Vaccinated During Catchup</td>\n",
" <td>Doses Received During Catchup</td>\n",
" <td>Doses Used During Catchup</td>\n",
" <td>Doses Remained During Catchup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>221</td>\n",
" <td>West</td>\n",
" <td>1481401</td>\n",
" <td>91037</td>\n",
" <td>249460</td>\n",
" <td>852098</td>\n",
" <td>1192595</td>\n",
" <td>145827</td>\n",
" <td>1338422</td>\n",
" <td>0.90</td>\n",
" <td>...</td>\n",
" <td>502316</td>\n",
" <td>77734</td>\n",
" <td>293426</td>\n",
" <td>19767</td>\n",
" <td>121111</td>\n",
" <td>216</td>\n",
" <td>145827</td>\n",
" <td>130995</td>\n",
" <td>113562</td>\n",
" <td>17433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>786005</td>\n",
" <td>12438</td>\n",
" <td>798443</td>\n",
" <td>1.00</td>\n",
" <td>...</td>\n",
" <td>151630</td>\n",
" <td>35724</td>\n",
" <td>108622</td>\n",
" <td>8765</td>\n",
" <td>69510</td>\n",
" <td>61</td>\n",
" <td>12438</td>\n",
" <td>15935</td>\n",
" <td>12990</td>\n",
" <td>2945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>855097</td>\n",
" <td>21159</td>\n",
" <td>876256</td>\n",
" <td>0.97</td>\n",
" <td>...</td>\n",
" <td>315962</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" <td>21159</td>\n",
" <td>39595</td>\n",
" <td>22565</td>\n",
" <td>17030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>792558</td>\n",
" <td>10792</td>\n",
" <td>803350</td>\n",
" <td>1.01</td>\n",
" <td>...</td>\n",
" <td>105129</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" <td>10792</td>\n",
" <td>12028</td>\n",
" <td>12018</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>225</td>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>34998</td>\n",
" <td>97748</td>\n",
" <td>342141</td>\n",
" <td>474887</td>\n",
" <td>17782</td>\n",
" <td>492669</td>\n",
" <td>0.98</td>\n",
" <td>...</td>\n",
" <td>49745</td>\n",
" <td>29757</td>\n",
" <td>48795</td>\n",
" <td>4743</td>\n",
" <td>43096</td>\n",
" <td>32</td>\n",
" <td>17782</td>\n",
" <td>19840</td>\n",
" <td>18475</td>\n",
" <td>1365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>226</td>\n",
" <td>Malir</td>\n",
" <td>777551</td>\n",
" <td>51714</td>\n",
" <td>150927</td>\n",
" <td>417473</td>\n",
" <td>620114</td>\n",
" <td>81546</td>\n",
" <td>701660</td>\n",
" <td>0.90</td>\n",
" <td>...</td>\n",
" <td>342546</td>\n",
" <td>34696</td>\n",
" <td>88221</td>\n",
" <td>9009</td>\n",
" <td>118539</td>\n",
" <td>114</td>\n",
" <td>81546</td>\n",
" <td>95005</td>\n",
" <td>83727</td>\n",
" <td>11278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>KARACHI DIV:</td>\n",
" <td>5263097</td>\n",
" <td>385405</td>\n",
" <td>1044948</td>\n",
" <td>3290903</td>\n",
" <td>4721256</td>\n",
" <td>289544</td>\n",
" <td>5010800</td>\n",
" <td>0.95</td>\n",
" <td>...</td>\n",
" <td>1467328</td>\n",
" <td>236996</td>\n",
" <td>677332</td>\n",
" <td>57727</td>\n",
" <td>556508</td>\n",
" <td>616</td>\n",
" <td>289544</td>\n",
" <td>313398</td>\n",
" <td>263337</td>\n",
" <td>50061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>213</td>\n",
" <td>Hyderabad</td>\n",
" <td>657987</td>\n",
" <td>50674</td>\n",
" <td>134295</td>\n",
" <td>437017</td>\n",
" <td>621986</td>\n",
" <td>5832</td>\n",
" <td>627818</td>\n",
" <td>0.95</td>\n",
" <td>...</td>\n",
" <td>4582240</td>\n",
" <td>37222</td>\n",
" <td>19065</td>\n",
" <td>2559</td>\n",
" <td>99</td>\n",
" <td>24</td>\n",
" <td>5832</td>\n",
" <td>4340</td>\n",
" <td>76</td>\n",
" <td>4264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>211</td>\n",
" <td>Badin</td>\n",
" <td>165333</td>\n",
" <td>13895</td>\n",
" <td>35703</td>\n",
" <td>127349</td>\n",
" <td>176947</td>\n",
" <td>948</td>\n",
" <td>177895</td>\n",
" <td>1.08</td>\n",
" <td>...</td>\n",
" <td>22445</td>\n",
" <td>2557</td>\n",
" <td>1139</td>\n",
" <td>220</td>\n",
" <td>18155</td>\n",
" <td>2</td>\n",
" <td>948</td>\n",
" <td>1045</td>\n",
" <td>1045</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>212</td>\n",
" <td>Dadu</td>\n",
" <td>271137</td>\n",
" <td>22781</td>\n",
" <td>57709</td>\n",
" <td>217942</td>\n",
" <td>298432</td>\n",
" <td>0</td>\n",
" <td>298432</td>\n",
" <td>1.10</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>4061</td>\n",
" <td>658</td>\n",
" <td>238</td>\n",
" <td>26152</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>214</td>\n",
" <td>Sujawal</td>\n",
" <td>45863</td>\n",
" <td>4783</td>\n",
" <td>10475</td>\n",
" <td>29981</td>\n",
" <td>45239</td>\n",
" <td>539</td>\n",
" <td>45778</td>\n",
" <td>1.00</td>\n",
" <td>...</td>\n",
" <td>23595</td>\n",
" <td>1824</td>\n",
" <td>514</td>\n",
" <td>208</td>\n",
" <td>3087</td>\n",
" <td>3</td>\n",
" <td>539</td>\n",
" <td>635</td>\n",
" <td>635</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>215</td>\n",
" <td>Jamshoro</td>\n",
" <td>273662</td>\n",
" <td>22420</td>\n",
" <td>59071</td>\n",
" <td>196663</td>\n",
" <td>278154</td>\n",
" <td>1947</td>\n",
" <td>280101</td>\n",
" <td>1.02</td>\n",
" <td>...</td>\n",
" <td>13395</td>\n",
" <td>1733</td>\n",
" <td>997</td>\n",
" <td>164</td>\n",
" <td>5682</td>\n",
" <td>18</td>\n",
" <td>1947</td>\n",
" <td>2075</td>\n",
" <td>2065</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>216</td>\n",
" <td>T. Allahyar</td>\n",
" <td>81897</td>\n",
" <td>5728</td>\n",
" <td>16023</td>\n",
" <td>56762</td>\n",
" <td>78513</td>\n",
" <td>8082</td>\n",
" <td>86595</td>\n",
" <td>1.06</td>\n",
" <td>...</td>\n",
" <td>11215</td>\n",
" <td>5767</td>\n",
" <td>3254</td>\n",
" <td>492</td>\n",
" <td>12025</td>\n",
" <td>7</td>\n",
" <td>8082</td>\n",
" <td>8635</td>\n",
" <td>8545</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>217</td>\n",
" <td>Thatta</td>\n",
" <td>113198</td>\n",
" <td>9220</td>\n",
" <td>26294</td>\n",
" <td>71491</td>\n",
" <td>107005</td>\n",
" <td>533</td>\n",
" <td>107538</td>\n",
" <td>0.95</td>\n",
" <td>...</td>\n",
" <td>27202</td>\n",
" <td>1272</td>\n",
" <td>136</td>\n",
" <td>219</td>\n",
" <td>360</td>\n",
" <td>1</td>\n",
" <td>533</td>\n",
" <td>421</td>\n",
" <td>400</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>218</td>\n",
" <td>Matiari</td>\n",
" <td>108272</td>\n",
" <td>9083</td>\n",
" <td>25907</td>\n",
" <td>78663</td>\n",
" <td>113653</td>\n",
" <td>1320</td>\n",
" <td>114973</td>\n",
" <td>1.06</td>\n",
" <td>...</td>\n",
" <td>30367</td>\n",
" <td>4769</td>\n",
" <td>1120</td>\n",
" <td>379</td>\n",
" <td>15926</td>\n",
" <td>26</td>\n",
" <td>1320</td>\n",
" <td>1497</td>\n",
" <td>1407</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>219</td>\n",
" <td>TM Khan</td>\n",
" <td>50115</td>\n",
" <td>4616</td>\n",
" <td>11243</td>\n",
" <td>41472</td>\n",
" <td>57331</td>\n",
" <td>3154</td>\n",
" <td>60485</td>\n",
" <td>1.21</td>\n",
" <td>...</td>\n",
" <td>10970</td>\n",
" <td>2008</td>\n",
" <td>593</td>\n",
" <td>341</td>\n",
" <td>6467</td>\n",
" <td>0</td>\n",
" <td>3154</td>\n",
" <td>3225</td>\n",
" <td>3225</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>HYDERABAD DIV:</td>\n",
" <td>1767464</td>\n",
" <td>143200</td>\n",
" <td>376720</td>\n",
" <td>1257340</td>\n",
" <td>1777260</td>\n",
" <td>22355</td>\n",
" <td>1799615</td>\n",
" <td>1.02</td>\n",
" <td>...</td>\n",
" <td>4721429</td>\n",
" <td>61213</td>\n",
" <td>27476</td>\n",
" <td>4820</td>\n",
" <td>87953</td>\n",
" <td>91</td>\n",
" <td>22355</td>\n",
" <td>21873</td>\n",
" <td>17398</td>\n",
" <td>4475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>251</td>\n",
" <td>Mirpurkhas</td>\n",
" <td>185883</td>\n",
" <td>12982</td>\n",
" <td>37697</td>\n",
" <td>138440</td>\n",
" <td>189119</td>\n",
" <td>3850</td>\n",
" <td>192969</td>\n",
" <td>1.04</td>\n",
" <td>...</td>\n",
" <td>88902</td>\n",
" <td>25249</td>\n",
" <td>11135</td>\n",
" <td>487</td>\n",
" <td>42359</td>\n",
" <td>9</td>\n",
" <td>3850</td>\n",
" <td>5425</td>\n",
" <td>4135</td>\n",
" <td>1290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>253</td>\n",
" <td>Tharparkar</td>\n",
" <td>108821</td>\n",
" <td>11035</td>\n",
" <td>26680</td>\n",
" <td>72239</td>\n",
" <td>109954</td>\n",
" <td>699</td>\n",
" <td>110653</td>\n",
" <td>1.02</td>\n",
" <td>...</td>\n",
" <td>25296</td>\n",
" <td>1451</td>\n",
" <td>50</td>\n",
" <td>90</td>\n",
" <td>10319</td>\n",
" <td>20</td>\n",
" <td>699</td>\n",
" <td>775</td>\n",
" <td>775</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>254</td>\n",
" <td>Umerkot</td>\n",
" <td>107178</td>\n",
" <td>8214</td>\n",
" <td>21352</td>\n",
" <td>74024</td>\n",
" <td>103590</td>\n",
" <td>900</td>\n",
" <td>104490</td>\n",
" <td>0.97</td>\n",
" <td>...</td>\n",
" <td>37775</td>\n",
" <td>10142</td>\n",
" <td>1220</td>\n",
" <td>202</td>\n",
" <td>22073</td>\n",
" <td>7</td>\n",
" <td>900</td>\n",
" <td>1105</td>\n",
" <td>990</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>NaN</td>\n",
" <td>MIRPURKHAS DIV:</td>\n",
" <td>401882</td>\n",
" <td>32231</td>\n",
" <td>85729</td>\n",
" <td>284703</td>\n",
" <td>402663</td>\n",
" <td>5449</td>\n",
" <td>408112</td>\n",
" <td>1.02</td>\n",
" <td>...</td>\n",
" <td>151973</td>\n",
" <td>36842</td>\n",
" <td>12405</td>\n",
" <td>779</td>\n",
" <td>74751</td>\n",
" <td>36</td>\n",
" <td>5449</td>\n",
" <td>7305</td>\n",
" <td>5900</td>\n",
" <td>1405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>243</td>\n",
" <td>SBA</td>\n",
" <td>290199</td>\n",
" <td>22348</td>\n",
" <td>61749</td>\n",
" <td>206544</td>\n",
" <td>290641</td>\n",
" <td>17169</td>\n",
" <td>307810</td>\n",
" <td>1.06</td>\n",
" <td>...</td>\n",
" <td>2706</td>\n",
" <td>27439</td>\n",
" <td>6670</td>\n",
" <td>2100</td>\n",
" <td>28710</td>\n",
" <td>6</td>\n",
" <td>17169</td>\n",
" <td>18160</td>\n",
" <td>17920</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>242</td>\n",
" <td>N. Feroze</td>\n",
" <td>173730</td>\n",
" <td>13718</td>\n",
" <td>38573</td>\n",
" <td>125769</td>\n",
" <td>178060</td>\n",
" <td>3048</td>\n",
" <td>181108</td>\n",
" <td>1.04</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>452</td>\n",
" <td>62</td>\n",
" <td>283</td>\n",
" <td>25182</td>\n",
" <td>12</td>\n",
" <td>3048</td>\n",
" <td>3206</td>\n",
" <td>3206</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>252</td>\n",
" <td>Sanghar</td>\n",
" <td>185163</td>\n",
" <td>13632</td>\n",
" <td>36749</td>\n",
" <td>145451</td>\n",
" <td>195832</td>\n",
" <td>6204</td>\n",
" <td>202036</td>\n",
" <td>1.09</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>12619</td>\n",
" <td>6311</td>\n",
" <td>755</td>\n",
" <td>43551</td>\n",
" <td>13</td>\n",
" <td>6204</td>\n",
" <td>2460</td>\n",
" <td>2460</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>NaN</td>\n",
" <td>SBA DIV:</td>\n",
" <td>649092</td>\n",
" <td>49698</td>\n",
" <td>137071</td>\n",
" <td>477764</td>\n",
" <td>664533</td>\n",
" <td>26421</td>\n",
" <td>690954</td>\n",
" <td>1.06</td>\n",
" <td>...</td>\n",
" <td>2706</td>\n",
" <td>40510</td>\n",
" <td>13043</td>\n",
" <td>3138</td>\n",
" <td>97443</td>\n",
" <td>31</td>\n",
" <td>26421</td>\n",
" <td>23826</td>\n",
" <td>23586</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>244</td>\n",
" <td>Sukkur</td>\n",
" <td>342091</td>\n",
" <td>24529</td>\n",
" <td>71857</td>\n",
" <td>226219</td>\n",
" <td>322605</td>\n",
" <td>28537</td>\n",
" <td>351142</td>\n",
" <td>1.03</td>\n",
" <td>...</td>\n",
" <td>95005</td>\n",
" <td>32356</td>\n",
" <td>8050</td>\n",
" <td>1395</td>\n",
" <td>53732</td>\n",
" <td>16</td>\n",
" <td>28537</td>\n",
" <td>29122</td>\n",
" <td>29004</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>245</td>\n",
" <td>Ghotki</td>\n",
" <td>213285</td>\n",
" <td>18243</td>\n",
" <td>46079</td>\n",
" <td>153225</td>\n",
" <td>217547</td>\n",
" <td>2677</td>\n",
" <td>220224</td>\n",
" <td>1.03</td>\n",
" <td>...</td>\n",
" <td>40319</td>\n",
" <td>11318</td>\n",
" <td>1308</td>\n",
" <td>305</td>\n",
" <td>33890</td>\n",
" <td>9</td>\n",
" <td>2677</td>\n",
" <td>2885</td>\n",
" <td>2885</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>241</td>\n",
" <td>Khairpur</td>\n",
" <td>326344</td>\n",
" <td>25290</td>\n",
" <td>69380</td>\n",
" <td>220755</td>\n",
" <td>315425</td>\n",
" <td>10966</td>\n",
" <td>326391</td>\n",
" <td>1.00</td>\n",
" <td>...</td>\n",
" <td>99405</td>\n",
" <td>13551</td>\n",
" <td>2083</td>\n",
" <td>1055</td>\n",
" <td>57752</td>\n",
" <td>25</td>\n",
" <td>10966</td>\n",
" <td>12082</td>\n",
" <td>11439</td>\n",
" <td>643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td>SUKKUR DIV:</td>\n",
" <td>881720</td>\n",
" <td>68062</td>\n",
" <td>187316</td>\n",
" <td>600199</td>\n",
" <td>855577</td>\n",
" <td>42180</td>\n",
" <td>897757</td>\n",
" <td>1.02</td>\n",
" <td>...</td>\n",
" <td>234729</td>\n",
" <td>57225</td>\n",
" <td>11441</td>\n",
" <td>2755</td>\n",
" <td>145374</td>\n",
" <td>50</td>\n",
" <td>42180</td>\n",
" <td>44089</td>\n",
" <td>43328</td>\n",
" <td>761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>232</td>\n",
" <td>Larkana</td>\n",
" <td>391076</td>\n",
" <td>33661</td>\n",
" <td>89775</td>\n",
" <td>285501</td>\n",
" <td>408937</td>\n",
" <td>1664</td>\n",
" <td>410601</td>\n",
" <td>1.05</td>\n",
" <td>...</td>\n",
" <td>98407</td>\n",
" <td>8825</td>\n",
" <td>595</td>\n",
" <td>380</td>\n",
" <td>29032</td>\n",
" <td>9</td>\n",
" <td>1664</td>\n",
" <td>2485</td>\n",
" <td>1750</td>\n",
" <td>735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>234</td>\n",
" <td>Kamber</td>\n",
" <td>173280</td>\n",
" <td>12670</td>\n",
" <td>37166</td>\n",
" <td>118544</td>\n",
" <td>168380</td>\n",
" <td>134</td>\n",
" <td>168514</td>\n",
" <td>0.97</td>\n",
" <td>...</td>\n",
" <td>20830</td>\n",
" <td>8109</td>\n",
" <td>2602</td>\n",
" <td>1162</td>\n",
" <td>24362</td>\n",
" <td>12</td>\n",
" <td>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>233</td>\n",
" <td>Shikarpur</td>\n",
" <td>217667</td>\n",
" <td>17892</td>\n",
" <td>48661</td>\n",
" <td>144325</td>\n",
" <td>210878</td>\n",
" <td>5766</td>\n",
" <td>216644</td>\n",
" <td>1.00</td>\n",
" <td>...</td>\n",
" <td>63200</td>\n",
" <td>22428</td>\n",
" <td>3189</td>\n",
" <td>868</td>\n",
" <td>35735</td>\n",
" <td>8</td>\n",
" <td>5766</td>\n",
" <td>9960</td>\n",
" <td>6135</td>\n",
" <td>3825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>231</td>\n",
" <td>Jacobabad</td>\n",
" <td>149616</td>\n",
" <td>11328</td>\n",
" <td>33652</td>\n",
" <td>98082</td>\n",
" <td>143062</td>\n",
" <td>264</td>\n",
" <td>143326</td>\n",
" <td>0.96</td>\n",
" <td>...</td>\n",
" <td>43930</td>\n",
" <td>6801</td>\n",
" <td>3106</td>\n",
" <td>1286</td>\n",
" <td>28961</td>\n",
" <td>14</td>\n",
" <td>264</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>235</td>\n",
" <td>Kashmore</td>\n",
" <td>118675</td>\n",
" <td>11949</td>\n",
" <td>29572</td>\n",
" <td>77384</td>\n",
" <td>118905</td>\n",
" <td>1148</td>\n",
" <td>120053</td>\n",
" <td>1.01</td>\n",
" <td>...</td>\n",
" <td>16875</td>\n",
" <td>699</td>\n",
" <td>84</td>\n",
" <td>173</td>\n",
" <td>9863</td>\n",
" <td>2</td>\n",
" <td>1148</td>\n",
" <td>1465</td>\n",
" <td>1148</td>\n",
" <td>317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NaN</td>\n",
" <td>LARKANA DIV:</td>\n",
" <td>1050314</td>\n",
" <td>87500</td>\n",
" <td>238826</td>\n",
" <td>723836</td>\n",
" <td>1050162</td>\n",
" <td>8976</td>\n",
" <td>1059138</td>\n",
" <td>1.01</td>\n",
" <td>...</td>\n",
" <td>243242</td>\n",
" <td>46862</td>\n",
" <td>9576</td>\n",
" <td>3869</td>\n",
" <td>127953</td>\n",
" <td>45</td>\n",
" <td>8976</td>\n",
" <td>13910</td>\n",
" <td>9033</td>\n",
" <td>4877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NaN</td>\n",
" <td>Total:</td>\n",
" <td>10013569</td>\n",
" <td>766096</td>\n",
" <td>2070610</td>\n",
" <td>6634745</td>\n",
" <td>9471451</td>\n",
" <td>394925</td>\n",
" <td>9866376</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6821407</td>\n",
" <td>479648</td>\n",
" <td>751273</td>\n",
" <td>73088</td>\n",
" <td>1089982</td>\n",
" <td>869</td>\n",
" <td>394925</td>\n",
" <td>424401</td>\n",
" <td>362582</td>\n",
" <td>61819</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>37 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 \\\n",
"0 Distcode Dadu Daily Target 9 Months-2 Years \n",
"1 221 West 1481401 91037 \n",
"2 222 East 802148 80017 \n",
"3 223 Korangi 906968 67975 \n",
"4 224 Central 791674 59664 \n",
"5 225 South 503355 34998 \n",
"6 226 Malir 777551 51714 \n",
"7 NaN KARACHI DIV: 5263097 385405 \n",
"8 213 Hyderabad 657987 50674 \n",
"9 211 Badin 165333 13895 \n",
"10 212 Dadu 271137 22781 \n",
"11 214 Sujawal 45863 4783 \n",
"12 215 Jamshoro 273662 22420 \n",
"13 216 T. Allahyar 81897 5728 \n",
"14 217 Thatta 113198 9220 \n",
"15 218 Matiari 108272 9083 \n",
"16 219 TM Khan 50115 4616 \n",
"17 NaN HYDERABAD DIV: 1767464 143200 \n",
"18 251 Mirpurkhas 185883 12982 \n",
"19 253 Tharparkar 108821 11035 \n",
"20 254 Umerkot 107178 8214 \n",
"21 NaN MIRPURKHAS DIV: 401882 32231 \n",
"22 243 SBA 290199 22348 \n",
"23 242 N. Feroze 173730 13718 \n",
"24 252 Sanghar 185163 13632 \n",
"25 NaN SBA DIV: 649092 49698 \n",
"26 244 Sukkur 342091 24529 \n",
"27 245 Ghotki 213285 18243 \n",
"28 241 Khairpur 326344 25290 \n",
"29 NaN SUKKUR DIV: 881720 68062 \n",
"30 232 Larkana 391076 33661 \n",
"31 234 Kamber 173280 12670 \n",
"32 233 Shikarpur 217667 17892 \n",
"33 231 Jacobabad 149616 11328 \n",
"34 235 Kashmore 118675 11949 \n",
"35 NaN LARKANA DIV: 1050314 87500 \n",
"36 NaN Total: 10013569 766096 \n",
"\n",
" Unnamed: 4 Unnamed: 5 \\\n",
"0 2 Years-5 Years 5 Years-15 Years \n",
"1 249460 852098 \n",
"2 191797 514191 \n",
"3 188167 598955 \n",
"4 166849 566045 \n",
"5 97748 342141 \n",
"6 150927 417473 \n",
"7 1044948 3290903 \n",
"8 134295 437017 \n",
"9 35703 127349 \n",
"10 57709 217942 \n",
"11 10475 29981 \n",
"12 59071 196663 \n",
"13 16023 56762 \n",
"14 26294 71491 \n",
"15 25907 78663 \n",
"16 11243 41472 \n",
"17 376720 1257340 \n",
"18 37697 138440 \n",
"19 26680 72239 \n",
"20 21352 74024 \n",
"21 85729 284703 \n",
"22 61749 206544 \n",
"23 38573 125769 \n",
"24 36749 145451 \n",
"25 137071 477764 \n",
"26 71857 226219 \n",
"27 46079 153225 \n",
"28 69380 220755 \n",
"29 187316 600199 \n",
"30 89775 285501 \n",
"31 37166 118544 \n",
"32 48661 144325 \n",
"33 33652 98082 \n",
"34 29572 77384 \n",
"35 238826 723836 \n",
"36 2070610 6634745 \n",
"\n",
" Unnamed: 6 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) \n",
"1 1192595 \n",
"2 786005 \n",
"3 855097 \n",
"4 792558 \n",
"5 474887 \n",
"6 620114 \n",
"7 4721256 \n",
"8 621986 \n",
"9 176947 \n",
"10 298432 \n",
"11 45239 \n",
"12 278154 \n",
"13 78513 \n",
"14 107005 \n",
"15 113653 \n",
"16 57331 \n",
"17 1777260 \n",
"18 189119 \n",
"19 109954 \n",
"20 103590 \n",
"21 402663 \n",
"22 290641 \n",
"23 178060 \n",
"24 195832 \n",
"25 664533 \n",
"26 322605 \n",
"27 217547 \n",
"28 315425 \n",
"29 855577 \n",
"30 408937 \n",
"31 168380 \n",
"32 210878 \n",
"33 143062 \n",
"34 118905 \n",
"35 1050162 \n",
"36 9471451 \n",
"\n",
" Unnamed: 7 Unnamed: 8 \\\n",
"0 Missed Children Covered During Catchup Total Covered Campaign + Catchup \n",
"1 145827 1338422 \n",
"2 12438 798443 \n",
"3 21159 876256 \n",
"4 10792 803350 \n",
"5 17782 492669 \n",
"6 81546 701660 \n",
"7 289544 5010800 \n",
"8 5832 627818 \n",
"9 948 177895 \n",
"10 0 298432 \n",
"11 539 45778 \n",
"12 1947 280101 \n",
"13 8082 86595 \n",
"14 533 107538 \n",
"15 1320 114973 \n",
"16 3154 60485 \n",
"17 22355 1799615 \n",
"18 3850 192969 \n",
"19 699 110653 \n",
"20 900 104490 \n",
"21 5449 408112 \n",
"22 17169 307810 \n",
"23 3048 181108 \n",
"24 6204 202036 \n",
"25 26421 690954 \n",
"26 28537 351142 \n",
"27 2677 220224 \n",
"28 10966 326391 \n",
"29 42180 897757 \n",
"30 1664 410601 \n",
"31 134 168514 \n",
"32 5766 216644 \n",
"33 264 143326 \n",
"34 1148 120053 \n",
"35 8976 1059138 \n",
"36 394925 9866376 \n",
"\n",
" Unnamed: 9 ... Unnamed: 11 Unnamed: 12 Unnamed: 13 \\\n",
"0 % ... Total Doses Remaining Not Available Missed Refusal \n",
"1 0.90 ... 502316 77734 293426 \n",
"2 1.00 ... 151630 35724 108622 \n",
"3 0.97 ... 315962 43774 92129 \n",
"4 1.01 ... 105129 15311 46139 \n",
"5 0.98 ... 49745 29757 48795 \n",
"6 0.90 ... 342546 34696 88221 \n",
"7 0.95 ... 1467328 236996 677332 \n",
"8 0.95 ... 4582240 37222 19065 \n",
"9 1.08 ... 22445 2557 1139 \n",
"10 1.10 ... 0 4061 658 \n",
"11 1.00 ... 23595 1824 514 \n",
"12 1.02 ... 13395 1733 997 \n",
"13 1.06 ... 11215 5767 3254 \n",
"14 0.95 ... 27202 1272 136 \n",
"15 1.06 ... 30367 4769 1120 \n",
"16 1.21 ... 10970 2008 593 \n",
"17 1.02 ... 4721429 61213 27476 \n",
"18 1.04 ... 88902 25249 11135 \n",
"19 1.02 ... 25296 1451 50 \n",
"20 0.97 ... 37775 10142 1220 \n",
"21 1.02 ... 151973 36842 12405 \n",
"22 1.06 ... 2706 27439 6670 \n",
"23 1.04 ... 0 452 62 \n",
"24 1.09 ... 0 12619 6311 \n",
"25 1.06 ... 2706 40510 13043 \n",
"26 1.03 ... 95005 32356 8050 \n",
"27 1.03 ... 40319 11318 1308 \n",
"28 1.00 ... 99405 13551 2083 \n",
"29 1.02 ... 234729 57225 11441 \n",
"30 1.05 ... 98407 8825 595 \n",
"31 0.97 ... 20830 8109 2602 \n",
"32 1.00 ... 63200 22428 3189 \n",
"33 0.96 ... 43930 6801 3106 \n",
"34 1.01 ... 16875 699 84 \n",
"35 1.01 ... 243242 46862 9576 \n",
"36 NaN ... 6821407 479648 751273 \n",
"\n",
" Unnamed: 14 Unnamed: 15 Unnamed: 16 \\\n",
"0 Missed Sick Already Vaccinated (AV1) No Of AEFIs \n",
"1 19767 121111 216 \n",
"2 8765 69510 61 \n",
"3 10943 98547 137 \n",
"4 4500 105705 56 \n",
"5 4743 43096 32 \n",
"6 9009 118539 114 \n",
"7 57727 556508 616 \n",
"8 2559 99 24 \n",
"9 220 18155 2 \n",
"10 238 26152 10 \n",
"11 208 3087 3 \n",
"12 164 5682 18 \n",
"13 492 12025 7 \n",
"14 219 360 1 \n",
"15 379 15926 26 \n",
"16 341 6467 0 \n",
"17 4820 87953 91 \n",
"18 487 42359 9 \n",
"19 90 10319 20 \n",
"20 202 22073 7 \n",
"21 779 74751 36 \n",
"22 2100 28710 6 \n",
"23 283 25182 12 \n",
"24 755 43551 13 \n",
"25 3138 97443 31 \n",
"26 1395 53732 16 \n",
"27 305 33890 9 \n",
"28 1055 57752 25 \n",
"29 2755 145374 50 \n",
"30 380 29032 9 \n",
"31 1162 24362 12 \n",
"32 868 35735 8 \n",
"33 1286 28961 14 \n",
"34 173 9863 2 \n",
"35 3869 127953 45 \n",
"36 73088 1089982 869 \n",
"\n",
" Unnamed: 17 Unnamed: 18 \\\n",
"0 Total Vaccinated During Catchup Doses Received During Catchup \n",
"1 145827 130995 \n",
"2 12438 15935 \n",
"3 21159 39595 \n",
"4 10792 12028 \n",
"5 17782 19840 \n",
"6 81546 95005 \n",
"7 289544 313398 \n",
"8 5832 4340 \n",
"9 948 1045 \n",
"10 0 0 \n",
"11 539 635 \n",
"12 1947 2075 \n",
"13 8082 8635 \n",
"14 533 421 \n",
"15 1320 1497 \n",
"16 3154 3225 \n",
"17 22355 21873 \n",
"18 3850 5425 \n",
"19 699 775 \n",
"20 900 1105 \n",
"21 5449 7305 \n",
"22 17169 18160 \n",
"23 3048 3206 \n",
"24 6204 2460 \n",
"25 26421 23826 \n",
"26 28537 29122 \n",
"27 2677 2885 \n",
"28 10966 12082 \n",
"29 42180 44089 \n",
"30 1664 2485 \n",
"31 134 0 \n",
"32 5766 9960 \n",
"33 264 0 \n",
"34 1148 1465 \n",
"35 8976 13910 \n",
"36 394925 424401 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \n",
"0 Doses Used During Catchup Doses Remained During Catchup \n",
"1 113562 17433 \n",
"2 12990 2945 \n",
"3 22565 17030 \n",
"4 12018 10 \n",
"5 18475 1365 \n",
"6 83727 11278 \n",
"7 263337 50061 \n",
"8 76 4264 \n",
"9 1045 0 \n",
"10 0 0 \n",
"11 635 0 \n",
"12 2065 10 \n",
"13 8545 90 \n",
"14 400 21 \n",
"15 1407 90 \n",
"16 3225 0 \n",
"17 17398 4475 \n",
"18 4135 1290 \n",
"19 775 0 \n",
"20 990 115 \n",
"21 5900 1405 \n",
"22 17920 240 \n",
"23 3206 0 \n",
"24 2460 0 \n",
"25 23586 240 \n",
"26 29004 118 \n",
"27 2885 0 \n",
"28 11439 643 \n",
"29 43328 761 \n",
"30 1750 735 \n",
"31 0 0 \n",
"32 6135 3825 \n",
"33 0 0 \n",
"34 1148 317 \n",
"35 9033 4877 \n",
"36 362582 61819 \n",
"\n",
"[37 rows x 21 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Ee7qXO1Adtxf"
},
"outputs": [],
"source": [
"# change Title\n",
"column2= ['Distcode', 'Dadu',\t'Daily Target',\t'9 Months-2 Years',\t'2 Years-5 Years',\t'5 Years-15 Years',\t'Total Children Vaccinated (9 Months-15 Years)',\t'Missed Children Covered During Catchup',\t'Total Covered Campaign + Catchup',\t'%',\t'Total Doses Used',\t'Total Doses Remaining',\t'Not Available',\t'Missed Refusal',\t'Missed Sick', 'Already Vaccinated (AV1)',\t'No Of AEFIs',\t'Total Vaccinated During Catchup',\t'Doses Received During Catchup',\t'Doses Used During Catchup',\t'Doses Remained During Catchup']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WqKyXGY7tam5"
},
"outputs": [],
"source": [
"df2.columns = ['Distcode', 'Dadu',\t'Daily Target',\t'9 Months-2 Years',\t'2 Years-5 Years',\t'5 Years-15 Years',\t'Total Children Vaccinated (9 Months-15 Years)',\t'Missed Children Covered During Catchup',\t'Total Covered Campaign + Catchup',\t'%',\t'Total Doses Used',\t'Total Doses Remaining',\t'Not Available',\t'Missed Refusal',\t'Missed Sick', 'Already Vaccinated (AV1)',\t'No Of AEFIs',\t'Total Vaccinated During Catchup',\t'Doses Received During Catchup',\t'Doses Used During Catchup',\t'Doses Remained During Catchup']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
},
"colab_type": "code",
"id": "5r2O-D6dtmoz",
"outputId": "30925207-df13-47a2-899b-940406f59a82"
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Distcode</th>\n",
" <th>Dadu</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
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" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Distcode</td>\n",
" <td>Dadu</td>\n",
" <td>Daily Target</td>\n",
" <td>9 Months-2 Years</td>\n",
" <td>2 Years-5 Years</td>\n",
" <td>5 Years-15 Years</td>\n",
" <td>Total Children Vaccinated (9 Months-15 Years)</td>\n",
" <td>Missed Children Covered During Catchup</td>\n",
" <td>Total Covered Campaign + Catchup</td>\n",
" <td>%</td>\n",
" <td>...</td>\n",
" <td>Total Doses Remaining</td>\n",
" <td>Not Available</td>\n",
" <td>Missed Refusal</td>\n",
" <td>Missed Sick</td>\n",
" <td>Already Vaccinated (AV1)</td>\n",
" <td>No Of AEFIs</td>\n",
" <td>Total Vaccinated During Catchup</td>\n",
" <td>Doses Received During Catchup</td>\n",
" <td>Doses Used During Catchup</td>\n",
" <td>Doses Remained During Catchup</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>221</td>\n",
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" <td>1481401</td>\n",
" <td>91037</td>\n",
" <td>249460</td>\n",
" <td>852098</td>\n",
" <td>1192595</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>222</td>\n",
" <td>East</td>\n",
" <td>802148</td>\n",
" <td>80017</td>\n",
" <td>191797</td>\n",
" <td>514191</td>\n",
" <td>786005</td>\n",
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" <td>69510</td>\n",
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" <td>12438</td>\n",
" <td>15935</td>\n",
" <td>12990</td>\n",
" <td>2945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>855097</td>\n",
" <td>21159</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
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"text/plain": [
" Distcode Dadu Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"0 Distcode Dadu Daily Target 9 Months-2 Years 2 Years-5 Years \n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"\n",
" 5 Years-15 Years Total Children Vaccinated (9 Months-15 Years) \\\n",
"0 5 Years-15 Years Total Children Vaccinated (9 Months-15 Years) \n",
"1 852098 1192595 \n",
"2 514191 786005 \n",
"3 598955 855097 \n",
"4 566045 792558 \n",
"\n",
" Missed Children Covered During Catchup Total Covered Campaign + Catchup \\\n",
"0 Missed Children Covered During Catchup Total Covered Campaign + Catchup \n",
"1 145827 1338422 \n",
"2 12438 798443 \n",
"3 21159 876256 \n",
"4 10792 803350 \n",
"\n",
" % ... Total Doses Remaining Not Available Missed Refusal \\\n",
"0 % ... Total Doses Remaining Not Available Missed Refusal \n",
"1 0.903484 ... 502316 77734 293426 \n",
"2 0.995381 ... 151630 35724 108622 \n",
"3 0.966138 ... 315962 43774 92129 \n",
"4 1.01475 ... 105129 15311 46139 \n",
"\n",
" Missed Sick Already Vaccinated (AV1) No Of AEFIs \\\n",
"0 Missed Sick Already Vaccinated (AV1) No Of AEFIs \n",
"1 19767 121111 216 \n",
"2 8765 69510 61 \n",
"3 10943 98547 137 \n",
"4 4500 105705 56 \n",
"\n",
" Total Vaccinated During Catchup Doses Received During Catchup \\\n",
"0 Total Vaccinated During Catchup Doses Received During Catchup \n",
"1 145827 130995 \n",
"2 12438 15935 \n",
"3 21159 39595 \n",
"4 10792 12028 \n",
"\n",
" Doses Used During Catchup Doses Remained During Catchup \n",
"0 Doses Used During Catchup Doses Remained During Catchup \n",
"1 113562 17433 \n",
"2 12990 2945 \n",
"3 22565 17030 \n",
"4 12018 10 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "b-98CPwZuQ0e"
},
"outputs": [],
"source": [
"data2= df2.drop(index=0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"colab_type": "code",
"id": "oXVcSD1GuvlB",
"outputId": "f75d5925-d78c-4d2d-b5d4-af5d2b614783"
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Distcode</th>\n",
" <th>Dadu</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
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" <th>...</th>\n",
" <th>Total Doses Remaining</th>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>223</td>\n",
" <td>Korangi</td>\n",
" <td>906968</td>\n",
" <td>67975</td>\n",
" <td>188167</td>\n",
" <td>598955</td>\n",
" <td>855097</td>\n",
" <td>21159</td>\n",
" <td>876256</td>\n",
" <td>0.966138</td>\n",
" <td>...</td>\n",
" <td>315962</td>\n",
" <td>43774</td>\n",
" <td>92129</td>\n",
" <td>10943</td>\n",
" <td>98547</td>\n",
" <td>137</td>\n",
" <td>21159</td>\n",
" <td>39595</td>\n",
" <td>22565</td>\n",
" <td>17030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>224</td>\n",
" <td>Central</td>\n",
" <td>791674</td>\n",
" <td>59664</td>\n",
" <td>166849</td>\n",
" <td>566045</td>\n",
" <td>792558</td>\n",
" <td>10792</td>\n",
" <td>803350</td>\n",
" <td>1.01475</td>\n",
" <td>...</td>\n",
" <td>105129</td>\n",
" <td>15311</td>\n",
" <td>46139</td>\n",
" <td>4500</td>\n",
" <td>105705</td>\n",
" <td>56</td>\n",
" <td>10792</td>\n",
" <td>12028</td>\n",
" <td>12018</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>225</td>\n",
" <td>South</td>\n",
" <td>503355</td>\n",
" <td>34998</td>\n",
" <td>97748</td>\n",
" <td>342141</td>\n",
" <td>474887</td>\n",
" <td>17782</td>\n",
" <td>492669</td>\n",
" <td>0.97877</td>\n",
" <td>...</td>\n",
" <td>49745</td>\n",
" <td>29757</td>\n",
" <td>48795</td>\n",
" <td>4743</td>\n",
" <td>43096</td>\n",
" <td>32</td>\n",
" <td>17782</td>\n",
" <td>19840</td>\n",
" <td>18475</td>\n",
" <td>1365</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Distcode Dadu Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"1 221 West 1481401 91037 249460 \n",
"2 222 East 802148 80017 191797 \n",
"3 223 Korangi 906968 67975 188167 \n",
"4 224 Central 791674 59664 166849 \n",
"5 225 South 503355 34998 97748 \n",
"\n",
" 5 Years-15 Years Total Children Vaccinated (9 Months-15 Years) \\\n",
"1 852098 1192595 \n",
"2 514191 786005 \n",
"3 598955 855097 \n",
"4 566045 792558 \n",
"5 342141 474887 \n",
"\n",
" Missed Children Covered During Catchup Total Covered Campaign + Catchup \\\n",
"1 145827 1338422 \n",
"2 12438 798443 \n",
"3 21159 876256 \n",
"4 10792 803350 \n",
"5 17782 492669 \n",
"\n",
" % ... Total Doses Remaining Not Available Missed Refusal \\\n",
"1 0.903484 ... 502316 77734 293426 \n",
"2 0.995381 ... 151630 35724 108622 \n",
"3 0.966138 ... 315962 43774 92129 \n",
"4 1.01475 ... 105129 15311 46139 \n",
"5 0.97877 ... 49745 29757 48795 \n",
"\n",
" Missed Sick Already Vaccinated (AV1) No Of AEFIs \\\n",
"1 19767 121111 216 \n",
"2 8765 69510 61 \n",
"3 10943 98547 137 \n",
"4 4500 105705 56 \n",
"5 4743 43096 32 \n",
"\n",
" Total Vaccinated During Catchup Doses Received During Catchup \\\n",
"1 145827 130995 \n",
"2 12438 15935 \n",
"3 21159 39595 \n",
"4 10792 12028 \n",
"5 17782 19840 \n",
"\n",
" Doses Used During Catchup Doses Remained During Catchup \n",
"1 113562 17433 \n",
"2 12990 2945 \n",
"3 22565 17030 \n",
"4 12018 10 \n",
"5 18475 1365 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# labeled data\n",
"data2.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "w7H2yyHkuw9B"
},
"outputs": [],
"source": [
"data2 = data2.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
},
"colab_type": "code",
"id": "Pu_nzbPPvIMf",
"outputId": "427cd868-1767-4405-efb8-70c9a672ce87"
},
"outputs": [
{
"data": {
"text/plain": [
"Distcode int64\n",
"Dadu object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"colab_type": "code",
"id": "fJa73y5AvPmX",
"outputId": "3df3faee-f550-40c6-ec1d-efcd092a7335"
},
"outputs": [],
"source": [
"data2[['Distcode', 'Daily Target', '9 Months-2 Years', '2 Years-5 Years', '5 Years-15 Years', 'Total Children Vaccinated (9 Months-15 Years)', 'Missed Children Covered During Catchup',\t'Total Covered Campaign + Catchup',\t'%',\t'Total Doses Used',\t'Total Doses Remaining',\t'Not Available',\t'Missed Refusal',\t'Missed Sick', 'Already Vaccinated (AV1)',\t'No Of AEFIs', 'Total Vaccinated During Catchup', 'Doses Received During Catchup', 'Doses Used During Catchup', 'Doses Remained During Catchup']] = data2[['Distcode',\t'Daily Target',\t'9 Months-2 Years',\t'2 Years-5 Years',\t'5 Years-15 Years',\t'Total Children Vaccinated (9 Months-15 Years)',\t'Missed Children Covered During Catchup',\t'Total Covered Campaign + Catchup',\t'%',\t'Total Doses Used',\t'Total Doses Remaining',\t'Not Available',\t'Missed Refusal',\t'Missed Sick', 'Already Vaccinated (AV1)',\t'No Of AEFIs',\t'Total Vaccinated During Catchup',\t'Doses Received During Catchup',\t'Doses Used During Catchup',\t'Doses Remained During Catchup']].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "pOZlet37wsrs",
"outputId": "79bc6547-83b2-4deb-b483-9237a2761729"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Distcode</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated (AV1)</th>\n",
" <th>No Of AEFIs</th>\n",
" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>29.000000</td>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>2.900000e+01</td>\n",
" <td>2.900000e+01</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" <td>29.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>229.862069</td>\n",
" <td>3.452955e+05</td>\n",
" <td>26417.103448</td>\n",
" <td>71400.344828</td>\n",
" <td>228784.310345</td>\n",
" <td>3.266018e+05</td>\n",
" <td>13618.103448</td>\n",
" <td>3.402199e+05</td>\n",
" <td>1.015867</td>\n",
" <td>3.517669e+05</td>\n",
" <td>2.352209e+05</td>\n",
" <td>16539.586207</td>\n",
" <td>25905.965517</td>\n",
" <td>2520.275862</td>\n",
" <td>37585.586207</td>\n",
" <td>29.965517</td>\n",
" <td>13618.103448</td>\n",
" <td>14634.517241</td>\n",
" <td>12502.827586</td>\n",
" <td>2131.689655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13.752205</td>\n",
" <td>3.313707e+05</td>\n",
" <td>23336.950493</td>\n",
" <td>62648.175922</td>\n",
" <td>199860.437325</td>\n",
" <td>2.850582e+05</td>\n",
" <td>29896.470339</td>\n",
" <td>3.069274e+05</td>\n",
" <td>0.061953</td>\n",
" <td>3.171651e+05</td>\n",
" <td>8.440629e+05</td>\n",
" <td>17736.988260</td>\n",
" <td>59732.091703</td>\n",
" <td>4418.785619</td>\n",
" <td>34633.108740</td>\n",
" <td>47.932604</td>\n",
" <td>29896.470339</td>\n",
" <td>29236.731607</td>\n",
" <td>25359.414948</td>\n",
" <td>4764.126266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>211.000000</td>\n",
" <td>4.586300e+04</td>\n",
" <td>4616.000000</td>\n",
" <td>10475.000000</td>\n",
" <td>29981.000000</td>\n",
" <td>4.523900e+04</td>\n",
" <td>0.000000</td>\n",
" <td>4.577800e+04</td>\n",
" <td>0.902397</td>\n",
" <td>4.908500e+04</td>\n",
" <td>0.000000e+00</td>\n",
" <td>452.000000</td>\n",
" <td>50.000000</td>\n",
" <td>90.000000</td>\n",
" <td>99.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>218.000000</td>\n",
" <td>1.186750e+05</td>\n",
" <td>11328.000000</td>\n",
" <td>29572.000000</td>\n",
" <td>78663.000000</td>\n",
" <td>1.189050e+05</td>\n",
" <td>948.000000</td>\n",
" <td>1.200530e+05</td>\n",
" <td>0.974920</td>\n",
" <td>1.245900e+05</td>\n",
" <td>1.687500e+04</td>\n",
" <td>2557.000000</td>\n",
" <td>658.000000</td>\n",
" <td>238.000000</td>\n",
" <td>12025.000000</td>\n",
" <td>7.000000</td>\n",
" <td>948.000000</td>\n",
" <td>1105.000000</td>\n",
" <td>990.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>226.000000</td>\n",
" <td>2.132850e+05</td>\n",
" <td>17892.000000</td>\n",
" <td>46079.000000</td>\n",
" <td>145451.000000</td>\n",
" <td>2.108780e+05</td>\n",
" <td>3154.000000</td>\n",
" <td>2.166440e+05</td>\n",
" <td>1.014748</td>\n",
" <td>2.245070e+05</td>\n",
" <td>3.777500e+04</td>\n",
" <td>10142.000000</td>\n",
" <td>2602.000000</td>\n",
" <td>492.000000</td>\n",
" <td>28710.000000</td>\n",
" <td>12.000000</td>\n",
" <td>3154.000000</td>\n",
" <td>3206.000000</td>\n",
" <td>2885.000000</td>\n",
" <td>90.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>242.000000</td>\n",
" <td>3.910760e+05</td>\n",
" <td>33661.000000</td>\n",
" <td>89775.000000</td>\n",
" <td>285501.000000</td>\n",
" <td>4.089370e+05</td>\n",
" <td>10966.000000</td>\n",
" <td>4.106010e+05</td>\n",
" <td>1.049926</td>\n",
" <td>4.225520e+05</td>\n",
" <td>9.840700e+04</td>\n",
" <td>27439.000000</td>\n",
" <td>11135.000000</td>\n",
" <td>2100.000000</td>\n",
" <td>43551.000000</td>\n",
" <td>25.000000</td>\n",
" <td>10966.000000</td>\n",
" <td>12082.000000</td>\n",
" <td>12018.000000</td>\n",
" <td>1290.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>254.000000</td>\n",
" <td>1.481401e+06</td>\n",
" <td>91037.000000</td>\n",
" <td>249460.000000</td>\n",
" <td>852098.000000</td>\n",
" <td>1.192595e+06</td>\n",
" <td>145827.000000</td>\n",
" <td>1.338422e+06</td>\n",
" <td>1.206924</td>\n",
" <td>1.368642e+06</td>\n",
" <td>4.582240e+06</td>\n",
" <td>77734.000000</td>\n",
" <td>293426.000000</td>\n",
" <td>19767.000000</td>\n",
" <td>121111.000000</td>\n",
" <td>216.000000</td>\n",
" <td>145827.000000</td>\n",
" <td>130995.000000</td>\n",
" <td>113562.000000</td>\n",
" <td>17433.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Distcode Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"count 29.000000 2.900000e+01 29.000000 29.000000 \n",
"mean 229.862069 3.452955e+05 26417.103448 71400.344828 \n",
"std 13.752205 3.313707e+05 23336.950493 62648.175922 \n",
"min 211.000000 4.586300e+04 4616.000000 10475.000000 \n",
"25% 218.000000 1.186750e+05 11328.000000 29572.000000 \n",
"50% 226.000000 2.132850e+05 17892.000000 46079.000000 \n",
"75% 242.000000 3.910760e+05 33661.000000 89775.000000 \n",
"max 254.000000 1.481401e+06 91037.000000 249460.000000 \n",
"\n",
" 5 Years-15 Years Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 29.000000 2.900000e+01 \n",
"mean 228784.310345 3.266018e+05 \n",
"std 199860.437325 2.850582e+05 \n",
"min 29981.000000 4.523900e+04 \n",
"25% 78663.000000 1.189050e+05 \n",
"50% 145451.000000 2.108780e+05 \n",
"75% 285501.000000 4.089370e+05 \n",
"max 852098.000000 1.192595e+06 \n",
"\n",
" Missed Children Covered During Catchup \\\n",
"count 29.000000 \n",
"mean 13618.103448 \n",
"std 29896.470339 \n",
"min 0.000000 \n",
"25% 948.000000 \n",
"50% 3154.000000 \n",
"75% 10966.000000 \n",
"max 145827.000000 \n",
"\n",
" Total Covered Campaign + Catchup % Total Doses Used \\\n",
"count 2.900000e+01 29.000000 2.900000e+01 \n",
"mean 3.402199e+05 1.015867 3.517669e+05 \n",
"std 3.069274e+05 0.061953 3.171651e+05 \n",
"min 4.577800e+04 0.902397 4.908500e+04 \n",
"25% 1.200530e+05 0.974920 1.245900e+05 \n",
"50% 2.166440e+05 1.014748 2.245070e+05 \n",
"75% 4.106010e+05 1.049926 4.225520e+05 \n",
"max 1.338422e+06 1.206924 1.368642e+06 \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"count 2.900000e+01 29.000000 29.000000 29.000000 \n",
"mean 2.352209e+05 16539.586207 25905.965517 2520.275862 \n",
"std 8.440629e+05 17736.988260 59732.091703 4418.785619 \n",
"min 0.000000e+00 452.000000 50.000000 90.000000 \n",
"25% 1.687500e+04 2557.000000 658.000000 238.000000 \n",
"50% 3.777500e+04 10142.000000 2602.000000 492.000000 \n",
"75% 9.840700e+04 27439.000000 11135.000000 2100.000000 \n",
"max 4.582240e+06 77734.000000 293426.000000 19767.000000 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"count 29.000000 29.000000 29.000000 \n",
"mean 37585.586207 29.965517 13618.103448 \n",
"std 34633.108740 47.932604 29896.470339 \n",
"min 99.000000 0.000000 0.000000 \n",
"25% 12025.000000 7.000000 948.000000 \n",
"50% 28710.000000 12.000000 3154.000000 \n",
"75% 43551.000000 25.000000 10966.000000 \n",
"max 121111.000000 216.000000 145827.000000 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"count 29.000000 29.000000 \n",
"mean 14634.517241 12502.827586 \n",
"std 29236.731607 25359.414948 \n",
"min 0.000000 0.000000 \n",
"25% 1105.000000 990.000000 \n",
"50% 3206.000000 2885.000000 \n",
"75% 12082.000000 12018.000000 \n",
"max 130995.000000 113562.000000 \n",
"\n",
" Doses Remained During Catchup \n",
"count 29.000000 \n",
"mean 2131.689655 \n",
"std 4764.126266 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 90.000000 \n",
"75% 1290.000000 \n",
"max 17433.000000 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.describe()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "_jsRC3TszEvK"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "H-Zi8gnY8S0k"
},
"source": [
"# Data Prep of 3rd Sheet\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"colab_type": "code",
"id": "h5GztOGq8fYz",
"outputId": "573cfe1e-77d2-4352-a840-fdcd4e488fac"
},
"outputs": [
{
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" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>...</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" <th>Unnamed: 16</th>\n",
" <th>Unnamed: 17</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>Unnamed: 19</th>\n",
" <th>Unnamed: 20</th>\n",
" <th>Unnamed: 21</th>\n",
" <th>Unnamed: 22</th>\n",
" <th>Unnamed: 23</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Uncode</td>\n",
" <td>District</td>\n",
" <td>Tehsil Name</td>\n",
" <td>UC Name</td>\n",
" <td>UCMO Name</td>\n",
" <td>Daily Target</td>\n",
" <td>9 Months-2 Years</td>\n",
" <td>2 Years-5 Years</td>\n",
" <td>5 Years-15 Years</td>\n",
" <td>Total Children Vaccinated (9 Months-15 Years)</td>\n",
" <td>...</td>\n",
" <td>Total Doses Remaining</td>\n",
" <td>Not Available</td>\n",
" <td>Missed Refusal</td>\n",
" <td>Missed Sick</td>\n",
" <td>Already Vaccinated (AV1)</td>\n",
" <td>No Of AEFIs</td>\n",
" <td>Total Vaccinated During Catchup</td>\n",
" <td>Doses Received During Catchup</td>\n",
" <td>Doses Used During Catchup</td>\n",
" <td>Doses Remained During Catchup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Dr Tanveer</td>\n",
" <td>12619</td>\n",
" <td>777</td>\n",
" <td>1951</td>\n",
" <td>7384</td>\n",
" <td>10112</td>\n",
" <td>...</td>\n",
" <td>9720</td>\n",
" <td>1005</td>\n",
" <td>4032</td>\n",
" <td>206</td>\n",
" <td>2035</td>\n",
" <td>6</td>\n",
" <td>2320</td>\n",
" <td>2600</td>\n",
" <td>2500</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Farina Tauseef</td>\n",
" <td>12813</td>\n",
" <td>713</td>\n",
" <td>1952</td>\n",
" <td>6839</td>\n",
" <td>9504</td>\n",
" <td>...</td>\n",
" <td>10075</td>\n",
" <td>678</td>\n",
" <td>3843</td>\n",
" <td>202</td>\n",
" <td>2372</td>\n",
" <td>2</td>\n",
" <td>3166</td>\n",
" <td>3560</td>\n",
" <td>3480</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Rabia</td>\n",
" <td>11069</td>\n",
" <td>510</td>\n",
" <td>1437</td>\n",
" <td>5490</td>\n",
" <td>7437</td>\n",
" <td>...</td>\n",
" <td>2805</td>\n",
" <td>460</td>\n",
" <td>1664</td>\n",
" <td>61</td>\n",
" <td>1882</td>\n",
" <td>1</td>\n",
" <td>1699</td>\n",
" <td>1950</td>\n",
" <td>1860</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Tehseen Patel</td>\n",
" <td>13149</td>\n",
" <td>730</td>\n",
" <td>2113</td>\n",
" <td>5576</td>\n",
" <td>8419</td>\n",
" <td>...</td>\n",
" <td>2890</td>\n",
" <td>544</td>\n",
" <td>1822</td>\n",
" <td>145</td>\n",
" <td>1490</td>\n",
" <td>0</td>\n",
" <td>2250</td>\n",
" <td>2600</td>\n",
" <td>2450</td>\n",
" <td>150</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Dr Tanveer \n",
"2 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Farina Tauseef \n",
"3 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Rabia \n",
"4 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Tehseen Patel \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 12619 777 1951 7384 \n",
"2 12813 713 1952 6839 \n",
"3 11069 510 1437 5490 \n",
"4 13149 730 2113 5576 \n",
"\n",
" Unnamed: 9 ... Unnamed: 14 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 10112 ... 9720 \n",
"2 9504 ... 10075 \n",
"3 7437 ... 2805 \n",
"4 8419 ... 2890 \n",
"\n",
" Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 1005 4032 206 2035 \n",
"2 678 3843 202 2372 \n",
"3 460 1664 61 1882 \n",
"4 544 1822 145 1490 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 6 2320 \n",
"2 2 3166 \n",
"3 1 1699 \n",
"4 0 2250 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 2600 2500 \n",
"2 3560 3480 \n",
"3 1950 1860 \n",
"4 2600 2450 \n",
"\n",
" Unnamed: 23 \n",
"0 Doses Remained During Catchup \n",
"1 100 \n",
"2 80 \n",
"3 90 \n",
"4 150 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.head()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "MFaFZgQf8ri6"
},
"outputs": [],
"source": [
"header = ['Uncode',\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\", \"Daily Target\", \"9 Months-2 Years\", \"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\", \"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\", \"Missed Refusal\", \"Missed Sick\",\t\"Already Vaccinated (AV1)\", \"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "Gvf9A7vz-Ri6",
"outputId": "41795fe4-65e4-4957-d3d9-677abccf613a"
},
"outputs": [],
"source": [
"df3.columns= ['Uncode',\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\", \"Daily Target\", \"9 Months-2 Years\", \"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\", \"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\", \"Missed Refusal\", \"Missed Sick\",\t\"Already Vaccinated (AV1)\", \"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"colab_type": "code",
"id": "BqFvyycC-cmh",
"outputId": "df336a08-269a-47b4-e6c3-7d74053cc830"
},
"outputs": [
{
"data": {
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" <td>2600</td>\n",
" <td>2500</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Farina Tauseef</td>\n",
" <td>12813</td>\n",
" <td>713</td>\n",
" <td>1952</td>\n",
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" <td>9504</td>\n",
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" <td>3560</td>\n",
" <td>3480</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Rabia</td>\n",
" <td>11069</td>\n",
" <td>510</td>\n",
" <td>1437</td>\n",
" <td>5490</td>\n",
" <td>7437</td>\n",
" <td>...</td>\n",
" <td>2805</td>\n",
" <td>460</td>\n",
" <td>1664</td>\n",
" <td>61</td>\n",
" <td>1882</td>\n",
" <td>1</td>\n",
" <td>1699</td>\n",
" <td>1950</td>\n",
" <td>1860</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>221001001</td>\n",
" <td>Karachi West</td>\n",
" <td>BALDIA</td>\n",
" <td>GULSHAN-E-GHAZI - 1</td>\n",
" <td>Tehseen Patel</td>\n",
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" <td>5576</td>\n",
" <td>8419</td>\n",
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" <td>2890</td>\n",
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" <td>1822</td>\n",
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" <td>2450</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
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"</div>"
],
"text/plain": [
" Uncode District Tehsil Name UC Name UCMO Name \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Dr Tanveer \n",
"2 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Farina Tauseef \n",
"3 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Rabia \n",
"4 221001001 Karachi West BALDIA GULSHAN-E-GHAZI - 1 Tehseen Patel \n",
"\n",
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 12619 777 1951 7384 \n",
"2 12813 713 1952 6839 \n",
"3 11069 510 1437 5490 \n",
"4 13149 730 2113 5576 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 10112 ... 9720 \n",
"2 9504 ... 10075 \n",
"3 7437 ... 2805 \n",
"4 8419 ... 2890 \n",
"\n",
" Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 1005 4032 206 2035 \n",
"2 678 3843 202 2372 \n",
"3 460 1664 61 1882 \n",
"4 544 1822 145 1490 \n",
"\n",
" No Of AEFIs Total Vaccinated During Catchup \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 6 2320 \n",
"2 2 3166 \n",
"3 1 1699 \n",
"4 0 2250 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 2600 2500 \n",
"2 3560 3480 \n",
"3 1950 1860 \n",
"4 2600 2450 \n",
"\n",
" Doses Remained During Catchup \n",
"0 Doses Remained During Catchup \n",
"1 100 \n",
"2 80 \n",
"3 90 \n",
"4 150 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "r1D4qstN-vAK"
},
"outputs": [],
"source": [
"df3 = df3.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "z6muFDlI_IkX"
},
"outputs": [],
"source": [
"df3 = df3.drop(index=0)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "cuYDgLI__SXx",
"outputId": "8c4f822a-410c-43f9-9ec0-57762e5a4047"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target object\n",
"9 Months-2 Years object\n",
"2 Years-5 Years object\n",
"5 Years-15 Years object\n",
"Total Children Vaccinated (9 Months-15 Years) object\n",
"Missed Children Covered During Catchup object\n",
"Total Covered Campaign + Catchup object\n",
"% object\n",
"Total Doses Used object\n",
"Total Doses Remaining object\n",
"Not Available object\n",
"Missed Refusal object\n",
"Missed Sick object\n",
"Already Vaccinated (AV1) object\n",
"No Of AEFIs object\n",
"Total Vaccinated During Catchup object\n",
"Doses Received During Catchup object\n",
"Doses Used During Catchup object\n",
"Doses Remained During Catchup object\n",
"dtype: object"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "u4U2OaTR_UJI",
"outputId": "c62ef352-3f60-431e-e4fb-3e682dfc0f20"
},
"outputs": [],
"source": [
"df3[[\"Daily Target\", \"9 Months-2 Years\", \"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\", \"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\", \"Missed Refusal\", \"Missed Sick\",\t\"Already Vaccinated (AV1)\", \"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]] = df3[[\"Daily Target\", \"9 Months-2 Years\", \"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\", \"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\", \"Missed Refusal\", \"Missed Sick\",\t\"Already Vaccinated (AV1)\", \"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "IOAAtB3FAS2_",
"outputId": "6e08f945-842b-447d-c5bd-9be35cad6e64"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 155,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"df3.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "uaR3LwXWApcL",
"outputId": "508d5930-a6bc-494e-ab96-36e536615425"
},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
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" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
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" <th>%</th>\n",
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" <td>665.067957</td>\n",
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" <td>3020.000000</td>\n",
" <td>93.000000</td>\n",
" <td>286.000000</td>\n",
" <td>1782.000000</td>\n",
" <td>2797.000000</td>\n",
" <td>77.000000</td>\n",
" <td>3300.000000</td>\n",
" <td>0.593255</td>\n",
" <td>3282.000000</td>\n",
" <td>725.000000</td>\n",
" <td>23.000000</td>\n",
" <td>220.000000</td>\n",
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" <td>37.000000</td>\n",
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" <td>77.000000</td>\n",
" <td>100.000000</td>\n",
" <td>59.000000</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>25%</th>\n",
" <td>6009.000000</td>\n",
" <td>349.500000</td>\n",
" <td>1025.000000</td>\n",
" <td>3406.500000</td>\n",
" <td>4958.000000</td>\n",
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" <td>0.845330</td>\n",
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" <td>469.000000</td>\n",
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" <td>5873.000000</td>\n",
" <td>529.000000</td>\n",
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" <td>6507.000000</td>\n",
" <td>2261.000000</td>\n",
" <td>385.000000</td>\n",
" <td>1473.000000</td>\n",
" <td>89.000000</td>\n",
" <td>586.000000</td>\n",
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" <td>375.000000</td>\n",
" <td>63.000000</td>\n",
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" <td>8626.500000</td>\n",
" <td>582.500000</td>\n",
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" <td>5258.000000</td>\n",
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" <td>143.500000</td>\n",
" <td>880.000000</td>\n",
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" <td>1089.500000</td>\n",
" <td>770.000000</td>\n",
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"text/plain": [
" Daily Target ... Doses Remained During Catchup\n",
"count 187.000000 ... 187.000000\n",
"mean 7783.508021 ... 93.224599\n",
"std 2718.590341 ... 117.478723\n",
"min 3020.000000 ... 0.000000\n",
"25% 6009.000000 ... 40.000000\n",
"50% 6986.000000 ... 63.000000\n",
"75% 8626.500000 ... 100.500000\n",
"max 16848.000000 ... 762.000000\n",
"\n",
"[8 rows x 19 columns]"
]
},
"execution_count": 156,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"df3.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KByqOSsZl50q"
},
"source": [
"# **Data Prep of 4th Sheet (East)**"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"colab_type": "code",
"id": "F7iWg3reAxUj",
"outputId": "f94131bd-a71f-4dc8-c5a7-ba14ba3f662b"
},
"outputs": [
{
"data": {
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" <td>GADAB EAST</td>\n",
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" <td>GADAB EAST</td>\n",
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" <td>GADAB EAST</td>\n",
" <td>GUJRO A</td>\n",
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"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 222003004 Karachi East GADAB EAST GUJRO A DR. ARSALAN \n",
"2 222003004 Karachi East GADAB EAST GUJRO A DR. TAJ \n",
"3 222003004 Karachi East GADAB EAST GUJRO A DR. USMAN \n",
"4 222003004 Karachi East GADAB EAST GUJRO A JUGENDAR SINGH \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 5284 721 1593 3035 \n",
"2 5296 435 1010 2952 \n",
"3 5738 689 1248 3363 \n",
"4 6893 450 1141 3713 \n",
"\n",
" Unnamed: 9 ... Unnamed: 14 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 5349 ... 1820 \n",
"2 4397 ... 1120 \n",
"3 5300 ... 2185 \n",
"4 5304 ... 1865 \n",
"\n",
" Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 257 1241 182 130 \n",
"2 306 745 130 246 \n",
"3 462 1246 218 428 \n",
"4 460 1050 234 215 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 0 95 \n",
"2 1 24 \n",
"3 5 37 \n",
"4 0 34 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 130 100 \n",
"2 40 25 \n",
"3 60 40 \n",
"4 45 35 \n",
"\n",
" Unnamed: 23 \n",
"0 Doses Remained During Catchup \n",
"1 30 \n",
"2 15 \n",
"3 20 \n",
"4 10 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-ocvnHkjmH8M"
},
"outputs": [],
"source": [
"df4.columns= [\"Uncode\",\t\"District\",\t\"Tehsil Name\",\t'UC Name',\t\"UCMO Name\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ODyfN44HwxCl"
},
"outputs": [],
"source": [
"df4= df4.drop(index=0)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CsrVZ8kFw2Ey"
},
"outputs": [],
"source": [
"data4= df4.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "ycvvQDgqxRC6",
"outputId": "6b07dd48-88eb-4183-c0af-5e11436a7889"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 22,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"data4.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"colab_type": "code",
"id": "y-5jNzHUxgSf",
"outputId": "6969cb1e-4830-4e83-b2e4-af158d0e8619"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Faizak\\Anaconda3\\envs\\pythonCPU\\lib\\site-packages\\pandas\\core\\frame.py:3509: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self[k1] = value[k2]\n"
]
}
],
"source": [
"data4[[\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]]= data4[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "sexAYSvDx5S1",
"outputId": "74c90f7a-a564-4e26-b75b-4bc203b30da6"
},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated (AV1)</th>\n",
" <th>No Of AEFIs</th>\n",
" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6684.875000</td>\n",
" <td>666.808333</td>\n",
" <td>1598.308333</td>\n",
" <td>4284.925000</td>\n",
" <td>6550.041667</td>\n",
" <td>103.650000</td>\n",
" <td>6653.691667</td>\n",
" <td>0.993567</td>\n",
" <td>6939.975000</td>\n",
" <td>1263.583333</td>\n",
" <td>297.700000</td>\n",
" <td>905.183333</td>\n",
" <td>73.041667</td>\n",
" <td>579.250000</td>\n",
" <td>0.508333</td>\n",
" <td>103.650000</td>\n",
" <td>132.791667</td>\n",
" <td>108.250000</td>\n",
" <td>24.541667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1098.858549</td>\n",
" <td>256.789202</td>\n",
" <td>549.257433</td>\n",
" <td>1135.265957</td>\n",
" <td>1631.379796</td>\n",
" <td>88.033669</td>\n",
" <td>1670.077917</td>\n",
" <td>0.185412</td>\n",
" <td>1742.235546</td>\n",
" <td>508.606807</td>\n",
" <td>160.438873</td>\n",
" <td>467.207995</td>\n",
" <td>92.603646</td>\n",
" <td>543.140173</td>\n",
" <td>1.173917</td>\n",
" <td>88.033669</td>\n",
" <td>100.619501</td>\n",
" <td>91.017705</td>\n",
" <td>27.750356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3269.000000</td>\n",
" <td>128.000000</td>\n",
" <td>671.000000</td>\n",
" <td>2106.000000</td>\n",
" <td>3126.000000</td>\n",
" <td>5.000000</td>\n",
" <td>3143.000000</td>\n",
" <td>0.628460</td>\n",
" <td>3365.000000</td>\n",
" <td>540.000000</td>\n",
" <td>50.000000</td>\n",
" <td>105.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>15.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6075.250000</td>\n",
" <td>509.750000</td>\n",
" <td>1249.500000</td>\n",
" <td>3563.750000</td>\n",
" <td>5615.250000</td>\n",
" <td>37.750000</td>\n",
" <td>5664.250000</td>\n",
" <td>0.904373</td>\n",
" <td>5905.750000</td>\n",
" <td>894.000000</td>\n",
" <td>179.500000</td>\n",
" <td>560.000000</td>\n",
" <td>2.000000</td>\n",
" <td>118.250000</td>\n",
" <td>0.000000</td>\n",
" <td>37.750000</td>\n",
" <td>58.750000</td>\n",
" <td>40.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6595.500000</td>\n",
" <td>613.500000</td>\n",
" <td>1503.500000</td>\n",
" <td>4335.500000</td>\n",
" <td>6476.000000</td>\n",
" <td>84.500000</td>\n",
" <td>6594.000000</td>\n",
" <td>0.973516</td>\n",
" <td>6873.500000</td>\n",
" <td>1135.000000</td>\n",
" <td>258.500000</td>\n",
" <td>832.500000</td>\n",
" <td>39.000000</td>\n",
" <td>453.500000</td>\n",
" <td>0.000000</td>\n",
" <td>84.500000</td>\n",
" <td>112.500000</td>\n",
" <td>89.500000</td>\n",
" <td>20.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7249.250000</td>\n",
" <td>801.250000</td>\n",
" <td>1802.750000</td>\n",
" <td>4919.000000</td>\n",
" <td>7309.000000</td>\n",
" <td>124.750000</td>\n",
" <td>7431.250000</td>\n",
" <td>1.052786</td>\n",
" <td>7707.500000</td>\n",
" <td>1499.250000</td>\n",
" <td>439.250000</td>\n",
" <td>1249.500000</td>\n",
" <td>96.000000</td>\n",
" <td>915.250000</td>\n",
" <td>1.000000</td>\n",
" <td>124.750000</td>\n",
" <td>176.250000</td>\n",
" <td>132.500000</td>\n",
" <td>38.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9688.000000</td>\n",
" <td>2040.000000</td>\n",
" <td>4841.000000</td>\n",
" <td>8989.000000</td>\n",
" <td>15870.000000</td>\n",
" <td>504.000000</td>\n",
" <td>16230.000000</td>\n",
" <td>2.441703</td>\n",
" <td>16780.000000</td>\n",
" <td>3352.000000</td>\n",
" <td>684.000000</td>\n",
" <td>2570.000000</td>\n",
" <td>398.000000</td>\n",
" <td>2578.000000</td>\n",
" <td>8.000000</td>\n",
" <td>504.000000</td>\n",
" <td>525.000000</td>\n",
" <td>525.000000</td>\n",
" <td>110.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"count 120.000000 120.000000 120.000000 120.000000 \n",
"mean 6684.875000 666.808333 1598.308333 4284.925000 \n",
"std 1098.858549 256.789202 549.257433 1135.265957 \n",
"min 3269.000000 128.000000 671.000000 2106.000000 \n",
"25% 6075.250000 509.750000 1249.500000 3563.750000 \n",
"50% 6595.500000 613.500000 1503.500000 4335.500000 \n",
"75% 7249.250000 801.250000 1802.750000 4919.000000 \n",
"max 9688.000000 2040.000000 4841.000000 8989.000000 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 120.000000 \n",
"mean 6550.041667 \n",
"std 1631.379796 \n",
"min 3126.000000 \n",
"25% 5615.250000 \n",
"50% 6476.000000 \n",
"75% 7309.000000 \n",
"max 15870.000000 \n",
"\n",
" Missed Children Covered During Catchup \\\n",
"count 120.000000 \n",
"mean 103.650000 \n",
"std 88.033669 \n",
"min 5.000000 \n",
"25% 37.750000 \n",
"50% 84.500000 \n",
"75% 124.750000 \n",
"max 504.000000 \n",
"\n",
" Total Covered Campaign + Catchup % Total Doses Used \\\n",
"count 120.000000 120.000000 120.000000 \n",
"mean 6653.691667 0.993567 6939.975000 \n",
"std 1670.077917 0.185412 1742.235546 \n",
"min 3143.000000 0.628460 3365.000000 \n",
"25% 5664.250000 0.904373 5905.750000 \n",
"50% 6594.000000 0.973516 6873.500000 \n",
"75% 7431.250000 1.052786 7707.500000 \n",
"max 16230.000000 2.441703 16780.000000 \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"count 120.000000 120.000000 120.000000 120.000000 \n",
"mean 1263.583333 297.700000 905.183333 73.041667 \n",
"std 508.606807 160.438873 467.207995 92.603646 \n",
"min 540.000000 50.000000 105.000000 0.000000 \n",
"25% 894.000000 179.500000 560.000000 2.000000 \n",
"50% 1135.000000 258.500000 832.500000 39.000000 \n",
"75% 1499.250000 439.250000 1249.500000 96.000000 \n",
"max 3352.000000 684.000000 2570.000000 398.000000 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"count 120.000000 120.000000 120.000000 \n",
"mean 579.250000 0.508333 103.650000 \n",
"std 543.140173 1.173917 88.033669 \n",
"min 0.000000 0.000000 5.000000 \n",
"25% 118.250000 0.000000 37.750000 \n",
"50% 453.500000 0.000000 84.500000 \n",
"75% 915.250000 1.000000 124.750000 \n",
"max 2578.000000 8.000000 504.000000 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"count 120.000000 120.000000 \n",
"mean 132.791667 108.250000 \n",
"std 100.619501 91.017705 \n",
"min 15.000000 5.000000 \n",
"25% 58.750000 40.000000 \n",
"50% 112.500000 89.500000 \n",
"75% 176.250000 132.500000 \n",
"max 525.000000 525.000000 \n",
"\n",
" Doses Remained During Catchup \n",
"count 120.000000 \n",
"mean 24.541667 \n",
"std 27.750356 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 20.000000 \n",
"75% 38.000000 \n",
"max 110.000000 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data4.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "eS9wPDZ4z4el"
},
"source": [
"# **Data Prep for 5th Sheet (Korangi)**"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"colab_type": "code",
"id": "lC12-QGmytaX",
"outputId": "ed0806fa-a4b6-41db-9a93-d9191a503567"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Unnamed: 1</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
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" <th>Unnamed: 5</th>\n",
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" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>...</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" <th>Unnamed: 16</th>\n",
" <th>Unnamed: 17</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>Unnamed: 19</th>\n",
" <th>Unnamed: 20</th>\n",
" <th>Unnamed: 21</th>\n",
" <th>Unnamed: 22</th>\n",
" <th>Unnamed: 23</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Uncode</td>\n",
" <td>District</td>\n",
" <td>Tehsil Name</td>\n",
" <td>UC Name</td>\n",
" <td>UCMO Name</td>\n",
" <td>Daily Target</td>\n",
" <td>9 Months-2 Years</td>\n",
" <td>2 Years-5 Years</td>\n",
" <td>5 Years-15 Years</td>\n",
" <td>Total Children Vaccinated (9 Months-15 Years)</td>\n",
" <td>...</td>\n",
" <td>Total Doses Remaining</td>\n",
" <td>Not Available</td>\n",
" <td>Missed Refusal</td>\n",
" <td>Missed Sick</td>\n",
" <td>Already Vaccinated (AV1)</td>\n",
" <td>No Of AEFIs</td>\n",
" <td>Total Vaccinated During Catchup</td>\n",
" <td>Doses Received During Catchup</td>\n",
" <td>Doses Used During Catchup</td>\n",
" <td>Doses Remained During Catchup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Abdul Ghaffar</td>\n",
" <td>7842</td>\n",
" <td>543</td>\n",
" <td>1462</td>\n",
" <td>5122</td>\n",
" <td>7127</td>\n",
" <td>...</td>\n",
" <td>3135</td>\n",
" <td>170</td>\n",
" <td>635</td>\n",
" <td>14</td>\n",
" <td>1159</td>\n",
" <td>0</td>\n",
" <td>202</td>\n",
" <td>500</td>\n",
" <td>250</td>\n",
" <td>250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Dr. Sadaf</td>\n",
" <td>7856</td>\n",
" <td>715</td>\n",
" <td>2204</td>\n",
" <td>4304</td>\n",
" <td>7223</td>\n",
" <td>...</td>\n",
" <td>1670</td>\n",
" <td>61</td>\n",
" <td>84</td>\n",
" <td>25</td>\n",
" <td>807</td>\n",
" <td>0</td>\n",
" <td>215</td>\n",
" <td>330</td>\n",
" <td>220</td>\n",
" <td>110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Ejaz</td>\n",
" <td>7844</td>\n",
" <td>383</td>\n",
" <td>1245</td>\n",
" <td>5028</td>\n",
" <td>6656</td>\n",
" <td>...</td>\n",
" <td>2180</td>\n",
" <td>82</td>\n",
" <td>760</td>\n",
" <td>59</td>\n",
" <td>500</td>\n",
" <td>1</td>\n",
" <td>445</td>\n",
" <td>540</td>\n",
" <td>450</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Javaid Ahmed</td>\n",
" <td>7824</td>\n",
" <td>608</td>\n",
" <td>1769</td>\n",
" <td>5005</td>\n",
" <td>7382</td>\n",
" <td>...</td>\n",
" <td>2425</td>\n",
" <td>125</td>\n",
" <td>632</td>\n",
" <td>26</td>\n",
" <td>1436</td>\n",
" <td>0</td>\n",
" <td>136</td>\n",
" <td>285</td>\n",
" <td>150</td>\n",
" <td>135</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 223008001 Korangi KORANGI BILAL COLONY - 1 Abdul Ghaffar \n",
"2 223008001 Korangi KORANGI BILAL COLONY - 1 Dr. Sadaf \n",
"3 223008001 Korangi KORANGI BILAL COLONY - 1 Ejaz \n",
"4 223008001 Korangi KORANGI BILAL COLONY - 1 Javaid Ahmed \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 7842 543 1462 5122 \n",
"2 7856 715 2204 4304 \n",
"3 7844 383 1245 5028 \n",
"4 7824 608 1769 5005 \n",
"\n",
" Unnamed: 9 ... Unnamed: 14 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 7127 ... 3135 \n",
"2 7223 ... 1670 \n",
"3 6656 ... 2180 \n",
"4 7382 ... 2425 \n",
"\n",
" Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 170 635 14 1159 \n",
"2 61 84 25 807 \n",
"3 82 760 59 500 \n",
"4 125 632 26 1436 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 0 202 \n",
"2 0 215 \n",
"3 1 445 \n",
"4 0 136 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 500 250 \n",
"2 330 220 \n",
"3 540 450 \n",
"4 285 150 \n",
"\n",
" Unnamed: 23 \n",
"0 Doses Remained During Catchup \n",
"1 250 \n",
"2 110 \n",
"3 90 \n",
"4 135 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df5.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "UC6iaSSw0Gsv"
},
"outputs": [],
"source": [
" df5.columns= [\"Uncode\",\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4i_r318o1OzL"
},
"outputs": [],
"source": [
"data5= df5.drop(index= 0)\n",
"data5= data5.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 462
},
"colab_type": "code",
"id": "Pic3uDqA1pUW",
"outputId": "f640d8d3-6a2e-4748-ea3f-30843a1c0eb5"
},
"outputs": [
{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Uncode</th>\n",
" <th>District</th>\n",
" <th>Tehsil Name</th>\n",
" <th>UC Name</th>\n",
" <th>UCMO Name</th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated (AV1)</th>\n",
" <th>No Of AEFIs</th>\n",
" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Abdul Ghaffar</td>\n",
" <td>7842</td>\n",
" <td>543</td>\n",
" <td>1462</td>\n",
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" <td>7329</td>\n",
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" <td>7690</td>\n",
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" <td>170</td>\n",
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" <td>1159</td>\n",
" <td>0</td>\n",
" <td>202</td>\n",
" <td>500</td>\n",
" <td>250</td>\n",
" <td>250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Dr. Sadaf</td>\n",
" <td>7856</td>\n",
" <td>715</td>\n",
" <td>2204</td>\n",
" <td>4304</td>\n",
" <td>7223</td>\n",
" <td>215</td>\n",
" <td>7438</td>\n",
" <td>0.946792</td>\n",
" <td>7520</td>\n",
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" <td>84</td>\n",
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" <td>220</td>\n",
" <td>110</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Ejaz</td>\n",
" <td>7844</td>\n",
" <td>383</td>\n",
" <td>1245</td>\n",
" <td>5028</td>\n",
" <td>6656</td>\n",
" <td>445</td>\n",
" <td>7101</td>\n",
" <td>0.905278</td>\n",
" <td>7305</td>\n",
" <td>2180</td>\n",
" <td>82</td>\n",
" <td>760</td>\n",
" <td>59</td>\n",
" <td>500</td>\n",
" <td>1</td>\n",
" <td>445</td>\n",
" <td>540</td>\n",
" <td>450</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Javaid Ahmed</td>\n",
" <td>7824</td>\n",
" <td>608</td>\n",
" <td>1769</td>\n",
" <td>5005</td>\n",
" <td>7382</td>\n",
" <td>136</td>\n",
" <td>7518</td>\n",
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" <td>7855</td>\n",
" <td>2425</td>\n",
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" <td>26</td>\n",
" <td>1436</td>\n",
" <td>0</td>\n",
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" <td>285</td>\n",
" <td>150</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>223008001</td>\n",
" <td>Korangi</td>\n",
" <td>KORANGI</td>\n",
" <td>BILAL COLONY - 1</td>\n",
" <td>Mehmood Ahmed</td>\n",
" <td>7879</td>\n",
" <td>650</td>\n",
" <td>1784</td>\n",
" <td>5026</td>\n",
" <td>7460</td>\n",
" <td>64</td>\n",
" <td>7524</td>\n",
" <td>0.954944</td>\n",
" <td>7830</td>\n",
" <td>2480</td>\n",
" <td>79</td>\n",
" <td>268</td>\n",
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" <td>508</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>230</td>\n",
" <td>80</td>\n",
" <td>150</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Uncode District ... Doses Used During Catchup Doses Remained During Catchup\n",
"1 223008001 Korangi ... 250 250\n",
"2 223008001 Korangi ... 220 110\n",
"3 223008001 Korangi ... 450 90\n",
"4 223008001 Korangi ... 150 135\n",
"5 223008001 Korangi ... 80 150\n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 37,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"data5.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "Z__gwAjr1Ys9",
"outputId": "8e579946-ad65-40de-d4f5-d7ec0861c036"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data5.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "zDGWEK4I1dzA",
"outputId": "28f011bc-712f-48a7-e0ce-0d20dd69dcf5"
},
"outputs": [],
"source": [
"data5[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]] = data5[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "MqhJRG634Jm-",
"outputId": "05d06b72-2332-48ea-cca0-8413590f3fbe"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
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" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>120.00000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" <td>120.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7553.52500</td>\n",
" <td>566.458333</td>\n",
" <td>1568.058333</td>\n",
" <td>4991.291667</td>\n",
" <td>7125.808333</td>\n",
" <td>176.325000</td>\n",
" <td>7302.133333</td>\n",
" <td>0.967413</td>\n",
" <td>7647.141667</td>\n",
" <td>2633.016667</td>\n",
" <td>364.783333</td>\n",
" <td>767.741667</td>\n",
" <td>91.191667</td>\n",
" <td>821.225000</td>\n",
" <td>1.141667</td>\n",
" <td>176.325000</td>\n",
" <td>329.958333</td>\n",
" <td>188.041667</td>\n",
" <td>141.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1046.53709</td>\n",
" <td>194.178853</td>\n",
" <td>370.437109</td>\n",
" <td>913.669114</td>\n",
" <td>1258.247149</td>\n",
" <td>185.355265</td>\n",
" <td>1241.921985</td>\n",
" <td>0.094823</td>\n",
" <td>1343.756639</td>\n",
" <td>1593.267347</td>\n",
" <td>281.897865</td>\n",
" <td>459.748260</td>\n",
" <td>80.514344</td>\n",
" <td>409.167477</td>\n",
" <td>2.099603</td>\n",
" <td>185.355265</td>\n",
" <td>265.181479</td>\n",
" <td>187.052880</td>\n",
" <td>126.217072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5387.00000</td>\n",
" <td>232.000000</td>\n",
" <td>897.000000</td>\n",
" <td>3064.000000</td>\n",
" <td>4746.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4867.000000</td>\n",
" <td>0.675870</td>\n",
" <td>5010.000000</td>\n",
" <td>678.000000</td>\n",
" <td>6.000000</td>\n",
" <td>23.000000</td>\n",
" <td>0.000000</td>\n",
" <td>53.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6881.50000</td>\n",
" <td>440.000000</td>\n",
" <td>1302.000000</td>\n",
" <td>4361.250000</td>\n",
" <td>6192.500000</td>\n",
" <td>56.000000</td>\n",
" <td>6562.000000</td>\n",
" <td>0.921382</td>\n",
" <td>6809.500000</td>\n",
" <td>1615.000000</td>\n",
" <td>166.750000</td>\n",
" <td>492.250000</td>\n",
" <td>25.750000</td>\n",
" <td>507.750000</td>\n",
" <td>0.000000</td>\n",
" <td>56.000000</td>\n",
" <td>155.000000</td>\n",
" <td>68.750000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7710.50000</td>\n",
" <td>544.000000</td>\n",
" <td>1540.500000</td>\n",
" <td>4936.500000</td>\n",
" <td>7204.500000</td>\n",
" <td>120.500000</td>\n",
" <td>7316.000000</td>\n",
" <td>0.965007</td>\n",
" <td>7595.500000</td>\n",
" <td>2160.000000</td>\n",
" <td>326.000000</td>\n",
" <td>693.000000</td>\n",
" <td>69.500000</td>\n",
" <td>786.000000</td>\n",
" <td>0.000000</td>\n",
" <td>120.500000</td>\n",
" <td>280.000000</td>\n",
" <td>130.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8109.00000</td>\n",
" <td>651.250000</td>\n",
" <td>1768.250000</td>\n",
" <td>5527.750000</td>\n",
" <td>7803.250000</td>\n",
" <td>248.250000</td>\n",
" <td>7926.750000</td>\n",
" <td>1.006545</td>\n",
" <td>8237.500000</td>\n",
" <td>3433.750000</td>\n",
" <td>472.000000</td>\n",
" <td>920.250000</td>\n",
" <td>137.500000</td>\n",
" <td>1059.250000</td>\n",
" <td>2.000000</td>\n",
" <td>248.250000</td>\n",
" <td>470.000000</td>\n",
" <td>266.250000</td>\n",
" <td>206.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10251.00000</td>\n",
" <td>1598.000000</td>\n",
" <td>3061.000000</td>\n",
" <td>9139.000000</td>\n",
" <td>13798.000000</td>\n",
" <td>1240.000000</td>\n",
" <td>13903.000000</td>\n",
" <td>1.511360</td>\n",
" <td>14675.000000</td>\n",
" <td>11085.000000</td>\n",
" <td>1594.000000</td>\n",
" <td>2323.000000</td>\n",
" <td>354.000000</td>\n",
" <td>1906.000000</td>\n",
" <td>14.000000</td>\n",
" <td>1240.000000</td>\n",
" <td>1600.000000</td>\n",
" <td>1250.000000</td>\n",
" <td>705.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"count 120.00000 120.000000 120.000000 120.000000 \n",
"mean 7553.52500 566.458333 1568.058333 4991.291667 \n",
"std 1046.53709 194.178853 370.437109 913.669114 \n",
"min 5387.00000 232.000000 897.000000 3064.000000 \n",
"25% 6881.50000 440.000000 1302.000000 4361.250000 \n",
"50% 7710.50000 544.000000 1540.500000 4936.500000 \n",
"75% 8109.00000 651.250000 1768.250000 5527.750000 \n",
"max 10251.00000 1598.000000 3061.000000 9139.000000 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 120.000000 \n",
"mean 7125.808333 \n",
"std 1258.247149 \n",
"min 4746.000000 \n",
"25% 6192.500000 \n",
"50% 7204.500000 \n",
"75% 7803.250000 \n",
"max 13798.000000 \n",
"\n",
" Missed Children Covered During Catchup \\\n",
"count 120.000000 \n",
"mean 176.325000 \n",
"std 185.355265 \n",
"min 0.000000 \n",
"25% 56.000000 \n",
"50% 120.500000 \n",
"75% 248.250000 \n",
"max 1240.000000 \n",
"\n",
" Total Covered Campaign + Catchup % Total Doses Used \\\n",
"count 120.000000 120.000000 120.000000 \n",
"mean 7302.133333 0.967413 7647.141667 \n",
"std 1241.921985 0.094823 1343.756639 \n",
"min 4867.000000 0.675870 5010.000000 \n",
"25% 6562.000000 0.921382 6809.500000 \n",
"50% 7316.000000 0.965007 7595.500000 \n",
"75% 7926.750000 1.006545 8237.500000 \n",
"max 13903.000000 1.511360 14675.000000 \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"count 120.000000 120.000000 120.000000 120.000000 \n",
"mean 2633.016667 364.783333 767.741667 91.191667 \n",
"std 1593.267347 281.897865 459.748260 80.514344 \n",
"min 678.000000 6.000000 23.000000 0.000000 \n",
"25% 1615.000000 166.750000 492.250000 25.750000 \n",
"50% 2160.000000 326.000000 693.000000 69.500000 \n",
"75% 3433.750000 472.000000 920.250000 137.500000 \n",
"max 11085.000000 1594.000000 2323.000000 354.000000 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"count 120.000000 120.000000 120.000000 \n",
"mean 821.225000 1.141667 176.325000 \n",
"std 409.167477 2.099603 185.355265 \n",
"min 53.000000 0.000000 0.000000 \n",
"25% 507.750000 0.000000 56.000000 \n",
"50% 786.000000 0.000000 120.500000 \n",
"75% 1059.250000 2.000000 248.250000 \n",
"max 1906.000000 14.000000 1240.000000 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"count 120.000000 120.000000 \n",
"mean 329.958333 188.041667 \n",
"std 265.181479 187.052880 \n",
"min 0.000000 0.000000 \n",
"25% 155.000000 68.750000 \n",
"50% 280.000000 130.000000 \n",
"75% 470.000000 266.250000 \n",
"max 1600.000000 1250.000000 \n",
"\n",
" Doses Remained During Catchup \n",
"count 120.000000 \n",
"mean 141.916667 \n",
"std 126.217072 \n",
"min 0.000000 \n",
"25% 50.000000 \n",
"50% 100.000000 \n",
"75% 206.250000 \n",
"max 705.000000 "
]
},
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"data5.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Voh1kmZS4ohK"
},
"source": [
"**Data for sheet 6 (Central)**"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"colab_type": "code",
"id": "4Hzs5NnL4l8U",
"outputId": "1ee3c0cc-57f4-4cb2-9f69-0787d5ad3a1b"
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"outputs": [
{
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" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 \\\n",
"0 Uncode District Tehsil Name UC Name \n",
"1 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"2 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"3 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"4 224005002 Karachi Central GULBERG KAREEMABAD - 2 \n",
"\n",
" Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 \\\n",
"0 UCMO Name Daily Target 9 Months-2 Years 2 Years-5 Years \n",
"1 Dr. Danish 8486 932 2128 \n",
"2 Ijaz Abdullah 5608 618 1407 \n",
"3 Mehjabeen 6179 677 1773 \n",
"4 Dr. Ayesha Masroor 2614 355 685 \n",
"\n",
" Unnamed: 8 Unnamed: 9 ... \\\n",
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"3 3331 5781 ... \n",
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"\n",
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"2 290 247 451 89 \n",
"3 215 310 452 106 \n",
"4 105 91 483 18 \n",
"\n",
" Unnamed: 18 Unnamed: 19 Unnamed: 20 \\\n",
"0 Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \n",
"1 1232 0 163 \n",
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"3 654 0 66 \n",
"4 511 0 106 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
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"\n",
" Unnamed: 23 \n",
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"metadata": {},
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"source": [
"df6.head()"
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},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "7EA8IqzH44vd"
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"outputs": [],
"source": [
"df6.columns= [\"Uncode\",\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332
},
"colab_type": "code",
"id": "-rIoljkS61rT",
"outputId": "7cab0e8e-ab52-4f32-af5e-0e10f3e7b33d"
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"outputs": [
{
"data": {
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" Uncode District Tehsil Name UC Name UCMO Name \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 224005001 Karachi Central GULBERG AZIZABAD - 1 Dr. Danish \n",
"2 224005001 Karachi Central GULBERG AZIZABAD - 1 Ijaz Abdullah \n",
"\n",
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 8486 932 2128 4099 \n",
"2 5608 618 1407 3562 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 7159 ... 285 \n",
"2 5587 ... 290 \n",
"\n",
" Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \\\n",
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"2 247 451 89 630 \n",
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"execution_count": 48,
"metadata": {},
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"source": [
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},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "5dZGty9n63GZ"
},
"outputs": [],
"source": [
"data6= df6.drop(0)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 677
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"colab_type": "code",
"id": "xpG8xZKR7J5V",
"outputId": "a3d37b1f-7c40-4d82-f87c-5a37530baa06"
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{
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" <td>224005002</td>\n",
" <td>Karachi Central</td>\n",
" <td>GULBERG</td>\n",
" <td>KAREEMABAD - 2</td>\n",
" <td>Dr. Mehar</td>\n",
" <td>3358</td>\n",
" <td>370</td>\n",
" <td>647</td>\n",
" <td>2778</td>\n",
" <td>3795</td>\n",
" <td>...</td>\n",
" <td>95</td>\n",
" <td>62</td>\n",
" <td>443</td>\n",
" <td>21</td>\n",
" <td>589</td>\n",
" <td>1</td>\n",
" <td>24</td>\n",
" <td>40</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>224014012</td>\n",
" <td>Karachi Central</td>\n",
" <td>NORTH KARACHI</td>\n",
" <td>GULSHAN-E-SAEED - 12</td>\n",
" <td>Mirza Asad</td>\n",
" <td>7679</td>\n",
" <td>489</td>\n",
" <td>1422</td>\n",
" <td>5368</td>\n",
" <td>7279</td>\n",
" <td>...</td>\n",
" <td>740</td>\n",
" <td>38</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>864</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>224014013</td>\n",
" <td>Karachi Central</td>\n",
" <td>NORTH KARACHI</td>\n",
" <td>SHAH NAWAZ - 13</td>\n",
" <td>Dr . Shoaib</td>\n",
" <td>8042</td>\n",
" <td>486</td>\n",
" <td>1670</td>\n",
" <td>5711</td>\n",
" <td>7867</td>\n",
" <td>...</td>\n",
" <td>965</td>\n",
" <td>35</td>\n",
" <td>26</td>\n",
" <td>20</td>\n",
" <td>1114</td>\n",
" <td>0</td>\n",
" <td>121</td>\n",
" <td>135</td>\n",
" <td>135</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>224014013</td>\n",
" <td>Karachi Central</td>\n",
" <td>NORTH KARACHI</td>\n",
" <td>SHAH NAWAZ - 13</td>\n",
" <td>Mubashir</td>\n",
" <td>7753</td>\n",
" <td>442</td>\n",
" <td>1464</td>\n",
" <td>6093</td>\n",
" <td>7999</td>\n",
" <td>...</td>\n",
" <td>930</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>762</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>224014013</td>\n",
" <td>Karachi Central</td>\n",
" <td>NORTH KARACHI</td>\n",
" <td>SHAH NAWAZ - 13</td>\n",
" <td>Nafees Fatima</td>\n",
" <td>7920</td>\n",
" <td>458</td>\n",
" <td>1603</td>\n",
" <td>6932</td>\n",
" <td>8993</td>\n",
" <td>...</td>\n",
" <td>735</td>\n",
" <td>25</td>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>667</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>224014013</td>\n",
" <td>Karachi Central</td>\n",
" <td>NORTH KARACHI</td>\n",
" <td>SHAH NAWAZ - 13</td>\n",
" <td>Ruksana Waheed</td>\n",
" <td>8401</td>\n",
" <td>425</td>\n",
" <td>1786</td>\n",
" <td>6391</td>\n",
" <td>8602</td>\n",
" <td>...</td>\n",
" <td>915</td>\n",
" <td>43</td>\n",
" <td>33</td>\n",
" <td>18</td>\n",
" <td>660</td>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>127 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" Uncode District Tehsil Name UC Name \\\n",
"1 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"2 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"3 224005001 Karachi Central GULBERG AZIZABAD - 1 \n",
"4 224005002 Karachi Central GULBERG KAREEMABAD - 2 \n",
"5 224005002 Karachi Central GULBERG KAREEMABAD - 2 \n",
".. ... ... ... ... \n",
"126 224014012 Karachi Central NORTH KARACHI GULSHAN-E-SAEED - 12 \n",
"127 224014013 Karachi Central NORTH KARACHI SHAH NAWAZ - 13 \n",
"128 224014013 Karachi Central NORTH KARACHI SHAH NAWAZ - 13 \n",
"129 224014013 Karachi Central NORTH KARACHI SHAH NAWAZ - 13 \n",
"130 224014013 Karachi Central NORTH KARACHI SHAH NAWAZ - 13 \n",
"\n",
" UCMO Name Daily Target 9 Months-2 Years 2 Years-5 Years \\\n",
"1 Dr. Danish 8486 932 2128 \n",
"2 Ijaz Abdullah 5608 618 1407 \n",
"3 Mehjabeen 6179 677 1773 \n",
"4 Dr. Ayesha Masroor 2614 355 685 \n",
"5 Dr. Mehar 3358 370 647 \n",
".. ... ... ... ... \n",
"126 Mirza Asad 7679 489 1422 \n",
"127 Dr . Shoaib 8042 486 1670 \n",
"128 Mubashir 7753 442 1464 \n",
"129 Nafees Fatima 7920 458 1603 \n",
"130 Ruksana Waheed 8401 425 1786 \n",
"\n",
" 5 Years-15 Years Total Children Vaccinated (9 Months-15 Years) ... \\\n",
"1 4099 7159 ... \n",
"2 3562 5587 ... \n",
"3 3331 5781 ... \n",
"4 2004 3044 ... \n",
"5 2778 3795 ... \n",
".. ... ... ... \n",
"126 5368 7279 ... \n",
"127 5711 7867 ... \n",
"128 6093 7999 ... \n",
"129 6932 8993 ... \n",
"130 6391 8602 ... \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"1 285 257 480 152 \n",
"2 290 247 451 89 \n",
"3 215 310 452 106 \n",
"4 105 91 483 18 \n",
"5 95 62 443 21 \n",
".. ... ... ... ... \n",
"126 740 38 9 9 \n",
"127 965 35 26 20 \n",
"128 930 7 7 5 \n",
"129 735 25 13 9 \n",
"130 915 43 33 18 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"1 1232 0 163 \n",
"2 630 0 84 \n",
"3 654 0 66 \n",
"4 511 0 106 \n",
"5 589 1 24 \n",
".. ... ... ... \n",
"126 864 0 50 \n",
"127 1114 0 121 \n",
"128 762 0 60 \n",
"129 667 0 45 \n",
"130 660 0 71 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"1 175 170 \n",
"2 95 95 \n",
"3 75 70 \n",
"4 120 120 \n",
"5 40 40 \n",
".. ... ... \n",
"126 60 60 \n",
"127 135 135 \n",
"128 75 75 \n",
"129 55 55 \n",
"130 80 80 \n",
"\n",
" Doses Remained During Catchup \n",
"1 5 \n",
"2 0 \n",
"3 5 \n",
"4 0 \n",
"5 0 \n",
".. ... \n",
"126 0 \n",
"127 0 \n",
"128 0 \n",
"129 0 \n",
"130 0 \n",
"\n",
"[127 rows x 24 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data6.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "_ud8UFk97OCq",
"outputId": "7a18f052-d4cb-4a38-c629-f5077c69c2af"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target object\n",
"9 Months-2 Years object\n",
"2 Years-5 Years object\n",
"5 Years-15 Years object\n",
"Total Children Vaccinated (9 Months-15 Years) object\n",
"Missed Children Covered During Catchup object\n",
"Total Covered Campaign + Catchup object\n",
"% object\n",
"Total Doses Used object\n",
"Total Doses Remaining object\n",
"Not Available object\n",
"Missed Refusal object\n",
"Missed Sick object\n",
"Already Vaccinated (AV1) object\n",
"No Of AEFIs object\n",
"Total Vaccinated During Catchup object\n",
"Doses Received During Catchup object\n",
"Doses Used During Catchup object\n",
"Doses Remained During Catchup object\n",
"dtype: object"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data6.dtypes "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "51Llo7s28t3R"
},
"outputs": [],
"source": [
"data6[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]] = data6[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "B69aXRP59ApB",
"outputId": "0907490a-3d0e-4d0f-f7d0-5f2aa71e9247"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated (AV1)</th>\n",
" <th>No Of AEFIs</th>\n",
" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.00000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" <td>132.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>17984.931818</td>\n",
" <td>1356.000000</td>\n",
" <td>3792.022727</td>\n",
" <td>12864.659091</td>\n",
" <td>18012.681818</td>\n",
" <td>245.272727</td>\n",
" <td>18257.954545</td>\n",
" <td>1.028095</td>\n",
" <td>19263.50000</td>\n",
" <td>2389.295455</td>\n",
" <td>347.977273</td>\n",
" <td>1048.613636</td>\n",
" <td>102.272727</td>\n",
" <td>2402.386364</td>\n",
" <td>1.272727</td>\n",
" <td>245.272727</td>\n",
" <td>273.363636</td>\n",
" <td>273.136364</td>\n",
" <td>0.227273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>76326.041020</td>\n",
" <td>5718.922857</td>\n",
" <td>16017.013555</td>\n",
" <td>54817.293912</td>\n",
" <td>76493.061778</td>\n",
" <td>1043.349012</td>\n",
" <td>77518.143540</td>\n",
" <td>0.145581</td>\n",
" <td>81737.17792</td>\n",
" <td>10307.760133</td>\n",
" <td>1529.759196</td>\n",
" <td>4763.181783</td>\n",
" <td>440.877354</td>\n",
" <td>10262.288226</td>\n",
" <td>5.608188</td>\n",
" <td>1043.349012</td>\n",
" <td>1161.078100</td>\n",
" <td>1160.160865</td>\n",
" <td>1.362479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1617.000000</td>\n",
" <td>76.000000</td>\n",
" <td>303.000000</td>\n",
" <td>1207.000000</td>\n",
" <td>1675.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1675.000000</td>\n",
" <td>0.597851</td>\n",
" <td>1815.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4927.750000</td>\n",
" <td>315.750000</td>\n",
" <td>1017.750000</td>\n",
" <td>3485.750000</td>\n",
" <td>5081.000000</td>\n",
" <td>38.750000</td>\n",
" <td>5176.000000</td>\n",
" <td>0.944286</td>\n",
" <td>5525.00000</td>\n",
" <td>365.000000</td>\n",
" <td>28.500000</td>\n",
" <td>31.000000</td>\n",
" <td>8.000000</td>\n",
" <td>585.750000</td>\n",
" <td>0.000000</td>\n",
" <td>38.750000</td>\n",
" <td>45.000000</td>\n",
" <td>45.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6476.000000</td>\n",
" <td>483.000000</td>\n",
" <td>1320.500000</td>\n",
" <td>4473.500000</td>\n",
" <td>6134.000000</td>\n",
" <td>66.000000</td>\n",
" <td>6227.500000</td>\n",
" <td>1.004645</td>\n",
" <td>6510.00000</td>\n",
" <td>925.500000</td>\n",
" <td>81.000000</td>\n",
" <td>171.000000</td>\n",
" <td>22.000000</td>\n",
" <td>812.000000</td>\n",
" <td>0.000000</td>\n",
" <td>66.000000</td>\n",
" <td>75.000000</td>\n",
" <td>75.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7805.500000</td>\n",
" <td>615.000000</td>\n",
" <td>1645.750000</td>\n",
" <td>5708.000000</td>\n",
" <td>7877.250000</td>\n",
" <td>115.000000</td>\n",
" <td>7958.000000</td>\n",
" <td>1.081918</td>\n",
" <td>8303.75000</td>\n",
" <td>1096.000000</td>\n",
" <td>190.250000</td>\n",
" <td>699.000000</td>\n",
" <td>51.250000</td>\n",
" <td>1117.500000</td>\n",
" <td>1.000000</td>\n",
" <td>115.000000</td>\n",
" <td>126.250000</td>\n",
" <td>126.250000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>791337.000000</td>\n",
" <td>59664.000000</td>\n",
" <td>166849.000000</td>\n",
" <td>566045.000000</td>\n",
" <td>792558.000000</td>\n",
" <td>10792.000000</td>\n",
" <td>803350.000000</td>\n",
" <td>1.744372</td>\n",
" <td>847594.00000</td>\n",
" <td>105129.000000</td>\n",
" <td>15311.000000</td>\n",
" <td>46139.000000</td>\n",
" <td>4500.000000</td>\n",
" <td>105705.000000</td>\n",
" <td>56.000000</td>\n",
" <td>10792.000000</td>\n",
" <td>12028.000000</td>\n",
" <td>12018.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"count 132.000000 132.000000 132.000000 132.000000 \n",
"mean 17984.931818 1356.000000 3792.022727 12864.659091 \n",
"std 76326.041020 5718.922857 16017.013555 54817.293912 \n",
"min 1617.000000 76.000000 303.000000 1207.000000 \n",
"25% 4927.750000 315.750000 1017.750000 3485.750000 \n",
"50% 6476.000000 483.000000 1320.500000 4473.500000 \n",
"75% 7805.500000 615.000000 1645.750000 5708.000000 \n",
"max 791337.000000 59664.000000 166849.000000 566045.000000 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 132.000000 \n",
"mean 18012.681818 \n",
"std 76493.061778 \n",
"min 1675.000000 \n",
"25% 5081.000000 \n",
"50% 6134.000000 \n",
"75% 7877.250000 \n",
"max 792558.000000 \n",
"\n",
" Missed Children Covered During Catchup \\\n",
"count 132.000000 \n",
"mean 245.272727 \n",
"std 1043.349012 \n",
"min 0.000000 \n",
"25% 38.750000 \n",
"50% 66.000000 \n",
"75% 115.000000 \n",
"max 10792.000000 \n",
"\n",
" Total Covered Campaign + Catchup % Total Doses Used \\\n",
"count 132.000000 132.000000 132.00000 \n",
"mean 18257.954545 1.028095 19263.50000 \n",
"std 77518.143540 0.145581 81737.17792 \n",
"min 1675.000000 0.597851 1815.00000 \n",
"25% 5176.000000 0.944286 5525.00000 \n",
"50% 6227.500000 1.004645 6510.00000 \n",
"75% 7958.000000 1.081918 8303.75000 \n",
"max 803350.000000 1.744372 847594.00000 \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"count 132.000000 132.000000 132.000000 132.000000 \n",
"mean 2389.295455 347.977273 1048.613636 102.272727 \n",
"std 10307.760133 1529.759196 4763.181783 440.877354 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 365.000000 28.500000 31.000000 8.000000 \n",
"50% 925.500000 81.000000 171.000000 22.000000 \n",
"75% 1096.000000 190.250000 699.000000 51.250000 \n",
"max 105129.000000 15311.000000 46139.000000 4500.000000 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"count 132.000000 132.000000 132.000000 \n",
"mean 2402.386364 1.272727 245.272727 \n",
"std 10262.288226 5.608188 1043.349012 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 585.750000 0.000000 38.750000 \n",
"50% 812.000000 0.000000 66.000000 \n",
"75% 1117.500000 1.000000 115.000000 \n",
"max 105705.000000 56.000000 10792.000000 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"count 132.000000 132.000000 \n",
"mean 273.363636 273.136364 \n",
"std 1161.078100 1160.160865 \n",
"min 0.000000 0.000000 \n",
"25% 45.000000 45.000000 \n",
"50% 75.000000 75.000000 \n",
"75% 126.250000 126.250000 \n",
"max 12028.000000 12018.000000 \n",
"\n",
" Doses Remained During Catchup \n",
"count 132.000000 \n",
"mean 0.227273 \n",
"std 1.362479 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 10.000000 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data6.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "eqnjFcnh9qlB"
},
"source": [
"**data Prep for sheet 7 (south)**"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"colab_type": "code",
"id": "1_c1vLtr9LH8",
"outputId": "5b467cbf-3e29-44c9-b47b-e9beb1dca174"
},
"outputs": [
{
"data": {
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" <td>...</td>\n",
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" <th>2</th>\n",
" <td>225011001</td>\n",
" <td>Karachi South</td>\n",
" <td>LAYARI</td>\n",
" <td>AGRA TAJ - 1</td>\n",
" <td>Najma M Qasim</td>\n",
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" <td>225011001</td>\n",
" <td>Karachi South</td>\n",
" <td>LAYARI</td>\n",
" <td>AGRA TAJ - 1</td>\n",
" <td>Zainab</td>\n",
" <td>7090</td>\n",
" <td>899</td>\n",
" <td>2064</td>\n",
" <td>5351</td>\n",
" <td>8314</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>225011002</td>\n",
" <td>Karachi South</td>\n",
" <td>LAYARI</td>\n",
" <td>DARYABAD - 2</td>\n",
" <td>Samina</td>\n",
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" <td>662</td>\n",
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"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 225011001 Karachi South LAYARI AGRA TAJ - 1 Asma \n",
"2 225011001 Karachi South LAYARI AGRA TAJ - 1 Najma M Qasim \n",
"3 225011001 Karachi South LAYARI AGRA TAJ - 1 Zainab \n",
"4 225011002 Karachi South LAYARI DARYABAD - 2 Samina \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 6575 402 1254 4407 \n",
"2 4681 568 1335 3551 \n",
"3 7090 899 2064 5351 \n",
"4 10461 662 1770 5518 \n",
"\n",
" Unnamed: 9 ... Unnamed: 14 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 6063 ... 1620 \n",
"2 5454 ... 1080 \n",
"3 8314 ... 1710 \n",
"4 7950 ... 1925 \n",
"\n",
" Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 293 378 35 121 \n",
"2 323 241 57 82 \n",
"3 224 471 70 683 \n",
"4 841 913 100 164 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 0 50 \n",
"2 0 84 \n",
"3 0 76 \n",
"4 0 649 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 70 55 \n",
"2 140 95 \n",
"3 115 90 \n",
"4 750 595 \n",
"\n",
" Unnamed: 23 \n",
"0 Doses Remained During Catchup \n",
"1 15 \n",
"2 45 \n",
"3 25 \n",
"4 155 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df7.head()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "pEdjjFx_-EqC"
},
"outputs": [],
"source": [
"df7.columns= [\"Uncode\",\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\",\t\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "76WDSKITKEWf"
},
"outputs": [],
"source": [
"data7= df7.drop(0)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "L_2cd3Y3KRoV"
},
"outputs": [],
"source": [
"data7 = data7.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZF9GTpUsKmcj"
},
"outputs": [],
"source": [
"data7[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]] = data7[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "Quo0ECPMKxCT",
"outputId": "c24e296a-fc12-4824-8fc8-cb0712484e1f"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data7.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HCD4E3x7K25_"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0ItH66QTLMLD"
},
"source": [
"# **Data Prep for sheet 8 (Malir)**"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 366
},
"colab_type": "code",
"id": "rAGqn_5WLK4b",
"outputId": "b8772534-20f9-49ed-e4e2-5d8a8470e56b"
},
"outputs": [
{
"data": {
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" <td>Doses Received During Catchup</td>\n",
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" <td>Doses Remained During Catchup</td>\n",
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" <td>Malir</td>\n",
" <td>BIN QASIM</td>\n",
" <td>IBRAHIM HYDERI - 1</td>\n",
" <td>Dr Mushtaq Shaikh</td>\n",
" <td>8439</td>\n",
" <td>332</td>\n",
" <td>1172</td>\n",
" <td>4151</td>\n",
" <td>5655</td>\n",
" <td>...</td>\n",
" <td>3990</td>\n",
" <td>302</td>\n",
" <td>955</td>\n",
" <td>113</td>\n",
" <td>1045</td>\n",
" <td>0</td>\n",
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" <td>1230</td>\n",
" <td>210</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>226002001</td>\n",
" <td>Malir</td>\n",
" <td>BIN QASIM</td>\n",
" <td>IBRAHIM HYDERI - 1</td>\n",
" <td>Dr Nareesh Komar</td>\n",
" <td>8226</td>\n",
" <td>584</td>\n",
" <td>1868</td>\n",
" <td>3379</td>\n",
" <td>5831</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>3 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 Uncode District Tehsil Name UC Name UCMO Name \n",
"1 226002001 Malir BIN QASIM IBRAHIM HYDERI - 1 Dr Mushtaq Shaikh \n",
"2 226002001 Malir BIN QASIM IBRAHIM HYDERI - 1 Dr Nareesh Komar \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \\\n",
"0 Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \n",
"1 8439 332 1172 4151 \n",
"2 8226 584 1868 3379 \n",
"\n",
" Unnamed: 9 ... Unnamed: 14 \\\n",
"0 Total Children Vaccinated (9 Months-15 Years) ... Total Doses Remaining \n",
"1 5655 ... 3990 \n",
"2 5831 ... 4999 \n",
"\n",
" Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 \\\n",
"0 Not Available Missed Refusal Missed Sick Already Vaccinated (AV1) \n",
"1 302 955 113 1045 \n",
"2 143 312 53 1283 \n",
"\n",
" Unnamed: 19 Unnamed: 20 \\\n",
"0 No Of AEFIs Total Vaccinated During Catchup \n",
"1 0 1178 \n",
"2 0 0 \n",
"\n",
" Unnamed: 21 Unnamed: 22 \\\n",
"0 Doses Received During Catchup Doses Used During Catchup \n",
"1 1440 1230 \n",
"2 0 0 \n",
"\n",
" Unnamed: 23 \n",
"0 Doses Remained During Catchup \n",
"1 210 \n",
"2 0 \n",
"\n",
"[3 rows x 24 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df8.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mGjkCtvOMTzs"
},
"outputs": [],
"source": [
"df8.columns= [\"Uncode\",\t\"District\",\t\"Tehsil Name\",\t\"UC Name\",\t\"UCMO Name\", \"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "iQWMbdVMMeOt"
},
"outputs": [],
"source": [
"data8= df8.drop(0)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "IZXzcgpdNZBB"
},
"outputs": [],
"source": [
"data8= data8.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0C7H44EZNhlu"
},
"outputs": [],
"source": [
"data8[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]] = data8[[\"Daily Target\",\t\"9 Months-2 Years\",\t\"2 Years-5 Years\",\t\"5 Years-15 Years\",\t\"Total Children Vaccinated (9 Months-15 Years)\",\t\"Missed Children Covered During Catchup\",\t\"Total Covered Campaign + Catchup\",\t\"%\",\t\"Total Doses Used\",\t\"Total Doses Remaining\",\t\"Not Available\",\t\"Missed Refusal\",\t\"Missed Sick\",\t\"Already Vaccinated (AV1)\",\t\"No Of AEFIs\",\t\"Total Vaccinated During Catchup\",\t\"Doses Received During Catchup\",\t\"Doses Used During Catchup\",\t\"Doses Remained During Catchup\"]].apply(pd.to_numeric)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"colab_type": "code",
"id": "vbOPW-x_OE8Q",
"outputId": "4b51cda3-d3cc-4577-8f16-d7072622dad9"
},
"outputs": [
{
"data": {
"text/plain": [
"Uncode object\n",
"District object\n",
"Tehsil Name object\n",
"UC Name object\n",
"UCMO Name object\n",
"Daily Target int64\n",
"9 Months-2 Years int64\n",
"2 Years-5 Years int64\n",
"5 Years-15 Years int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Missed Children Covered During Catchup int64\n",
"Total Covered Campaign + Catchup int64\n",
"% float64\n",
"Total Doses Used int64\n",
"Total Doses Remaining int64\n",
"Not Available int64\n",
"Missed Refusal int64\n",
"Missed Sick int64\n",
"Already Vaccinated (AV1) int64\n",
"No Of AEFIs int64\n",
"Total Vaccinated During Catchup int64\n",
"Doses Received During Catchup int64\n",
"Doses Used During Catchup int64\n",
"Doses Remained During Catchup int64\n",
"dtype: object"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data8.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"colab_type": "code",
"id": "idL8QJGgOSDm",
"outputId": "df874949-0888-4167-e2e2-68ffbc40a623"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Daily Target</th>\n",
" <th>9 Months-2 Years</th>\n",
" <th>2 Years-5 Years</th>\n",
" <th>5 Years-15 Years</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Missed Children Covered During Catchup</th>\n",
" <th>Total Covered Campaign + Catchup</th>\n",
" <th>%</th>\n",
" <th>Total Doses Used</th>\n",
" <th>Total Doses Remaining</th>\n",
" <th>Not Available</th>\n",
" <th>Missed Refusal</th>\n",
" <th>Missed Sick</th>\n",
" <th>Already Vaccinated (AV1)</th>\n",
" <th>No Of AEFIs</th>\n",
" <th>Total Vaccinated During Catchup</th>\n",
" <th>Doses Received During Catchup</th>\n",
" <th>Doses Used During Catchup</th>\n",
" <th>Doses Remained During Catchup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.00000</td>\n",
" <td>114.000000</td>\n",
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" <td>114.000000</td>\n",
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" <td>114.000000</td>\n",
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" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6509.368421</td>\n",
" <td>453.631579</td>\n",
" <td>1323.921053</td>\n",
" <td>3662.04386</td>\n",
" <td>5439.596491</td>\n",
" <td>715.315789</td>\n",
" <td>6154.912281</td>\n",
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" <td>6388.605263</td>\n",
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" <td>304.350877</td>\n",
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" <td>715.315789</td>\n",
" <td>833.377193</td>\n",
" <td>734.447368</td>\n",
" <td>98.929825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1357.545333</td>\n",
" <td>158.048904</td>\n",
" <td>408.876989</td>\n",
" <td>834.24348</td>\n",
" <td>1183.768915</td>\n",
" <td>845.659017</td>\n",
" <td>1391.472757</td>\n",
" <td>0.174805</td>\n",
" <td>1428.554679</td>\n",
" <td>1639.921315</td>\n",
" <td>230.501914</td>\n",
" <td>559.010243</td>\n",
" <td>68.685310</td>\n",
" <td>521.847053</td>\n",
" <td>1.809513</td>\n",
" <td>845.659017</td>\n",
" <td>940.997986</td>\n",
" <td>857.424184</td>\n",
" <td>120.762087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2780.000000</td>\n",
" <td>162.000000</td>\n",
" <td>457.000000</td>\n",
" <td>1344.00000</td>\n",
" <td>2471.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2471.000000</td>\n",
" <td>0.543360</td>\n",
" <td>2645.000000</td>\n",
" <td>610.000000</td>\n",
" <td>0.000000</td>\n",
" <td>19.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5778.750000</td>\n",
" <td>340.250000</td>\n",
" <td>1045.750000</td>\n",
" <td>3219.25000</td>\n",
" <td>4779.750000</td>\n",
" <td>0.000000</td>\n",
" <td>5370.500000</td>\n",
" <td>0.854871</td>\n",
" <td>5602.500000</td>\n",
" <td>1726.250000</td>\n",
" <td>145.000000</td>\n",
" <td>355.750000</td>\n",
" <td>32.000000</td>\n",
" <td>764.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6407.500000</td>\n",
" <td>438.500000</td>\n",
" <td>1284.500000</td>\n",
" <td>3660.00000</td>\n",
" <td>5508.500000</td>\n",
" <td>198.000000</td>\n",
" <td>6140.500000</td>\n",
" <td>0.925518</td>\n",
" <td>6369.000000</td>\n",
" <td>2858.000000</td>\n",
" <td>258.500000</td>\n",
" <td>601.000000</td>\n",
" <td>64.000000</td>\n",
" <td>1009.000000</td>\n",
" <td>0.000000</td>\n",
" <td>198.000000</td>\n",
" <td>305.000000</td>\n",
" <td>252.500000</td>\n",
" <td>47.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7301.750000</td>\n",
" <td>548.500000</td>\n",
" <td>1604.250000</td>\n",
" <td>4175.75000</td>\n",
" <td>6240.250000</td>\n",
" <td>1523.250000</td>\n",
" <td>7031.500000</td>\n",
" <td>1.041579</td>\n",
" <td>7317.500000</td>\n",
" <td>3892.500000</td>\n",
" <td>410.250000</td>\n",
" <td>1052.000000</td>\n",
" <td>100.000000</td>\n",
" <td>1342.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1523.250000</td>\n",
" <td>1705.000000</td>\n",
" <td>1496.250000</td>\n",
" <td>175.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>11298.000000</td>\n",
" <td>1068.000000</td>\n",
" <td>2413.000000</td>\n",
" <td>6404.00000</td>\n",
" <td>9324.000000</td>\n",
" <td>3064.000000</td>\n",
" <td>11197.000000</td>\n",
" <td>1.710983</td>\n",
" <td>11760.000000</td>\n",
" <td>9046.000000</td>\n",
" <td>1476.000000</td>\n",
" <td>2698.000000</td>\n",
" <td>346.000000</td>\n",
" <td>3666.000000</td>\n",
" <td>10.000000</td>\n",
" <td>3064.000000</td>\n",
" <td>3350.000000</td>\n",
" <td>3160.000000</td>\n",
" <td>450.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Daily Target 9 Months-2 Years 2 Years-5 Years 5 Years-15 Years \\\n",
"count 114.000000 114.000000 114.000000 114.00000 \n",
"mean 6509.368421 453.631579 1323.921053 3662.04386 \n",
"std 1357.545333 158.048904 408.876989 834.24348 \n",
"min 2780.000000 162.000000 457.000000 1344.00000 \n",
"25% 5778.750000 340.250000 1045.750000 3219.25000 \n",
"50% 6407.500000 438.500000 1284.500000 3660.00000 \n",
"75% 7301.750000 548.500000 1604.250000 4175.75000 \n",
"max 11298.000000 1068.000000 2413.000000 6404.00000 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 114.000000 \n",
"mean 5439.596491 \n",
"std 1183.768915 \n",
"min 2471.000000 \n",
"25% 4779.750000 \n",
"50% 5508.500000 \n",
"75% 6240.250000 \n",
"max 9324.000000 \n",
"\n",
" Missed Children Covered During Catchup \\\n",
"count 114.000000 \n",
"mean 715.315789 \n",
"std 845.659017 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 198.000000 \n",
"75% 1523.250000 \n",
"max 3064.000000 \n",
"\n",
" Total Covered Campaign + Catchup % Total Doses Used \\\n",
"count 114.000000 114.000000 114.000000 \n",
"mean 6154.912281 0.955552 6388.605263 \n",
"std 1391.472757 0.174805 1428.554679 \n",
"min 2471.000000 0.543360 2645.000000 \n",
"25% 5370.500000 0.854871 5602.500000 \n",
"50% 6140.500000 0.925518 6369.000000 \n",
"75% 7031.500000 1.041579 7317.500000 \n",
"max 11197.000000 1.710983 11760.000000 \n",
"\n",
" Total Doses Remaining Not Available Missed Refusal Missed Sick \\\n",
"count 114.000000 114.000000 114.000000 114.000000 \n",
"mean 3004.789474 304.350877 773.868421 79.026316 \n",
"std 1639.921315 230.501914 559.010243 68.685310 \n",
"min 610.000000 0.000000 19.000000 1.000000 \n",
"25% 1726.250000 145.000000 355.750000 32.000000 \n",
"50% 2858.000000 258.500000 601.000000 64.000000 \n",
"75% 3892.500000 410.250000 1052.000000 100.000000 \n",
"max 9046.000000 1476.000000 2698.000000 346.000000 \n",
"\n",
" Already Vaccinated (AV1) No Of AEFIs Total Vaccinated During Catchup \\\n",
"count 114.000000 114.000000 114.000000 \n",
"mean 1039.815789 1.000000 715.315789 \n",
"std 521.847053 1.809513 845.659017 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 764.000000 0.000000 0.000000 \n",
"50% 1009.000000 0.000000 198.000000 \n",
"75% 1342.000000 1.000000 1523.250000 \n",
"max 3666.000000 10.000000 3064.000000 \n",
"\n",
" Doses Received During Catchup Doses Used During Catchup \\\n",
"count 114.000000 114.000000 \n",
"mean 833.377193 734.447368 \n",
"std 940.997986 857.424184 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 305.000000 252.500000 \n",
"75% 1705.000000 1496.250000 \n",
"max 3350.000000 3160.000000 \n",
"\n",
" Doses Remained During Catchup \n",
"count 114.000000 \n",
"mean 98.929825 \n",
"std 120.762087 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 47.500000 \n",
"75% 175.000000 \n",
"max 450.000000 "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data8.describe()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9Q-Xyt7xOj1y"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PlclbhGxOzD0"
},
"source": [
"# **Data Prep for sheet 9 (Karachi)**"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"colab_type": "code",
"id": "rMMlamtdOx1Z",
"outputId": "c13116bb-c8f7-4dfc-b90a-6244dae53666"
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>District</th>\n",
" <th>Town</th>\n",
" <th>Target By Resource Plan</th>\n",
" <th>Daily Target By UCMOs</th>\n",
" <th>Difference Targets Resource Plan &amp; UCMOS</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Cov % Based on Resource Targets</th>\n",
" <th>Cov % Based on Daily Targets</th>\n",
" <th>Missed Children from RPT</th>\n",
" <th>%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>WEST</td>\n",
" <td>GADAP</td>\n",
" <td>333268</td>\n",
" <td>296174</td>\n",
" <td>37094</td>\n",
" <td>263664</td>\n",
" <td>0.791147</td>\n",
" <td>0.83</td>\n",
" <td>88512</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>WEST</td>\n",
" <td>KAMARI</td>\n",
" <td>258539</td>\n",
" <td>258006</td>\n",
" <td>533</td>\n",
" <td>253287</td>\n",
" <td>0.979686</td>\n",
" <td>0.93</td>\n",
" <td>19733</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>WEST</td>\n",
" <td>ORANGI</td>\n",
" <td>358686</td>\n",
" <td>357646</td>\n",
" <td>1040</td>\n",
" <td>342722</td>\n",
" <td>0.955493</td>\n",
" <td>0.80</td>\n",
" <td>70870</td>\n",
" <td>0.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>WEST</td>\n",
" <td>SITE</td>\n",
" <td>212172</td>\n",
" <td>212172</td>\n",
" <td>0</td>\n",
" <td>174287</td>\n",
" <td>0.821442</td>\n",
" <td>0.78</td>\n",
" <td>47163</td>\n",
" <td>0.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>TOTAL</td>\n",
" <td>NaN</td>\n",
" <td>5263097</td>\n",
" <td>5134033</td>\n",
" <td>129064</td>\n",
" <td>5010800</td>\n",
" <td>0.952063</td>\n",
" <td>0.91</td>\n",
" <td>579555</td>\n",
" <td>0.11</td>\n",
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"</table>\n",
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],
"text/plain": [
" District Town Target By Resource Plan Daily Target By UCMOs \\\n",
"18 WEST GADAP 333268 296174 \n",
"19 WEST KAMARI 258539 258006 \n",
"20 WEST ORANGI 358686 357646 \n",
"21 WEST SITE 212172 212172 \n",
"22 TOTAL NaN 5263097 5134033 \n",
"\n",
" Difference Targets Resource Plan & UCMOS \\\n",
"18 37094 \n",
"19 533 \n",
"20 1040 \n",
"21 0 \n",
"22 129064 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"18 263664 \n",
"19 253287 \n",
"20 342722 \n",
"21 174287 \n",
"22 5010800 \n",
"\n",
" Cov % Based on Resource Targets Cov % Based on Daily Targets \\\n",
"18 0.791147 0.83 \n",
"19 0.979686 0.93 \n",
"20 0.955493 0.80 \n",
"21 0.821442 0.78 \n",
"22 0.952063 0.91 \n",
"\n",
" Missed Children from RPT % \n",
"18 88512 0.27 \n",
"19 19733 0.08 \n",
"20 70870 0.20 \n",
"21 47163 0.22 \n",
"22 579555 0.11 "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df9.tail()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"colab_type": "code",
"id": "OmUi-fJdPJXz",
"outputId": "1652ddb1-e410-4a2c-dd9d-c3cc3d204d11"
},
"outputs": [
{
"data": {
"text/plain": [
"District object\n",
"Town object\n",
"Target By Resource Plan int64\n",
"Daily Target By UCMOs int64\n",
"Difference Targets Resource Plan & UCMOS int64\n",
"Total Children Vaccinated (9 Months-15 Years) int64\n",
"Cov % Based on Resource Targets float64\n",
"Cov % Based on Daily Targets float64\n",
"Missed Children from RPT int64\n",
"% float64\n",
"dtype: object"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df9.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 314
},
"colab_type": "code",
"id": "yjtYCM3WQX3P",
"outputId": "65701123-624b-42cd-a5e4-5c467e58d8bb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Target By Resource Plan</th>\n",
" <th>Daily Target By UCMOs</th>\n",
" <th>Difference Targets Resource Plan &amp; UCMOS</th>\n",
" <th>Total Children Vaccinated (9 Months-15 Years)</th>\n",
" <th>Cov % Based on Resource Targets</th>\n",
" <th>Cov % Based on Daily Targets</th>\n",
" <th>Missed Children from RPT</th>\n",
" <th>%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.300000e+01</td>\n",
" <td>2.300000e+01</td>\n",
" <td>23.000000</td>\n",
" <td>2.300000e+01</td>\n",
" <td>23.000000</td>\n",
" <td>23.00000</td>\n",
" <td>23.000000</td>\n",
" <td>23.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.576606e+05</td>\n",
" <td>4.464377e+05</td>\n",
" <td>11222.956522</td>\n",
" <td>4.357217e+05</td>\n",
" <td>0.952372</td>\n",
" <td>0.91087</td>\n",
" <td>50396.086957</td>\n",
" <td>0.108261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.052017e+06</td>\n",
" <td>1.026170e+06</td>\n",
" <td>28832.644561</td>\n",
" <td>1.001692e+06</td>\n",
" <td>0.065692</td>\n",
" <td>0.07862</td>\n",
" <td>118863.628771</td>\n",
" <td>0.104083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>9.269200e+04</td>\n",
" <td>9.117100e+04</td>\n",
" <td>-6956.000000</td>\n",
" <td>8.223800e+04</td>\n",
" <td>0.791147</td>\n",
" <td>0.78000</td>\n",
" <td>-9954.000000</td>\n",
" <td>-0.030000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.734150e+05</td>\n",
" <td>1.705225e+05</td>\n",
" <td>0.000000</td>\n",
" <td>1.724355e+05</td>\n",
" <td>0.919972</td>\n",
" <td>0.84500</td>\n",
" <td>3410.000000</td>\n",
" <td>0.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.345830e+05</td>\n",
" <td>2.415390e+05</td>\n",
" <td>337.000000</td>\n",
" <td>2.077070e+05</td>\n",
" <td>0.963074</td>\n",
" <td>0.93000</td>\n",
" <td>19733.000000</td>\n",
" <td>0.080000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.116535e+05</td>\n",
" <td>3.019620e+05</td>\n",
" <td>6932.000000</td>\n",
" <td>3.067495e+05</td>\n",
" <td>0.991102</td>\n",
" <td>0.97000</td>\n",
" <td>55925.000000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>5.263097e+06</td>\n",
" <td>5.134033e+06</td>\n",
" <td>129064.000000</td>\n",
" <td>5.010800e+06</td>\n",
" <td>1.049164</td>\n",
" <td>1.03000</td>\n",
" <td>579555.000000</td>\n",
" <td>0.360000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Target By Resource Plan Daily Target By UCMOs \\\n",
"count 2.300000e+01 2.300000e+01 \n",
"mean 4.576606e+05 4.464377e+05 \n",
"std 1.052017e+06 1.026170e+06 \n",
"min 9.269200e+04 9.117100e+04 \n",
"25% 1.734150e+05 1.705225e+05 \n",
"50% 2.345830e+05 2.415390e+05 \n",
"75% 3.116535e+05 3.019620e+05 \n",
"max 5.263097e+06 5.134033e+06 \n",
"\n",
" Difference Targets Resource Plan & UCMOS \\\n",
"count 23.000000 \n",
"mean 11222.956522 \n",
"std 28832.644561 \n",
"min -6956.000000 \n",
"25% 0.000000 \n",
"50% 337.000000 \n",
"75% 6932.000000 \n",
"max 129064.000000 \n",
"\n",
" Total Children Vaccinated (9 Months-15 Years) \\\n",
"count 2.300000e+01 \n",
"mean 4.357217e+05 \n",
"std 1.001692e+06 \n",
"min 8.223800e+04 \n",
"25% 1.724355e+05 \n",
"50% 2.077070e+05 \n",
"75% 3.067495e+05 \n",
"max 5.010800e+06 \n",
"\n",
" Cov % Based on Resource Targets Cov % Based on Daily Targets \\\n",
"count 23.000000 23.00000 \n",
"mean 0.952372 0.91087 \n",
"std 0.065692 0.07862 \n",
"min 0.791147 0.78000 \n",
"25% 0.919972 0.84500 \n",
"50% 0.963074 0.93000 \n",
"75% 0.991102 0.97000 \n",
"max 1.049164 1.03000 \n",
"\n",
" Missed Children from RPT % \n",
"count 23.000000 23.000000 \n",
"mean 50396.086957 0.108261 \n",
"std 118863.628771 0.104083 \n",
"min -9954.000000 -0.030000 \n",
"25% 3410.000000 0.020000 \n",
"50% 19733.000000 0.080000 \n",
"75% 55925.000000 0.200000 \n",
"max 579555.000000 0.360000 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df9.describe()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "e3ccqZIvQejB"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "StjXwq19Q6eB"
},
"source": [
"# **Data Prep for sheet 10 (Sheet12)**"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
},
"colab_type": "code",
"id": "o-63-FVpRFNO",
"outputId": "da73f9f8-e7b2-4e5b-a620-99818214fc81"
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>District</th>\n",
" <th>Total Children Vaccinated at</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
" <th>Total Children Vaccinated</th>\n",
" <th>Day 01</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Day 02</th>\n",
" <th>Unnamed: 10</th>\n",
" <th>Unnamed: 11</th>\n",
" <th>Unnamed: 12</th>\n",
" <th>Day 03</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" <th>Unnamed: 16</th>\n",
" <th>Day 04</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>Unnamed: 19</th>\n",
" <th>Unnamed: 20</th>\n",
" <th>Day 05</th>\n",
" <th>Unnamed: 22</th>\n",
" <th>Unnamed: 23</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Day 06</th>\n",
" <th>Unnamed: 26</th>\n",
" <th>Unnamed: 27</th>\n",
" <th>Unnamed: 28</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>NaN</td>\n",
" <td>Fixed</td>\n",
" <td>Outreach</td>\n",
" <td>School</td>\n",
" <td>Total</td>\n",
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" <td>School</td>\n",
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" <td>School</td>\n",
" <td>Total</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>West</td>\n",
" <td>6692</td>\n",
" <td>92811</td>\n",
" <td>46324</td>\n",
" <td>145827.0</td>\n",
" <td>1945</td>\n",
" <td>37300</td>\n",
" <td>18061</td>\n",
" <td>57306</td>\n",
" <td>1894</td>\n",
" <td>40763</td>\n",
" <td>21010</td>\n",
" <td>63667</td>\n",
" <td>1488</td>\n",
" <td>10839</td>\n",
" <td>6204</td>\n",
" <td>18531</td>\n",
" <td>906</td>\n",
" <td>2361</td>\n",
" <td>846</td>\n",
" <td>4113</td>\n",
" <td>383</td>\n",
" <td>1181</td>\n",
" <td>203</td>\n",
" <td>1767</td>\n",
" <td>76</td>\n",
" <td>367</td>\n",
" <td>0</td>\n",
" <td>443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>East</td>\n",
" <td>2383</td>\n",
" <td>7460</td>\n",
" <td>2595</td>\n",
" <td>12438.0</td>\n",
" <td>525</td>\n",
" <td>2484</td>\n",
" <td>497</td>\n",
" <td>3506</td>\n",
" <td>481</td>\n",
" <td>1646</td>\n",
" <td>596</td>\n",
" <td>2723</td>\n",
" <td>445</td>\n",
" <td>1067</td>\n",
" <td>595</td>\n",
" <td>2107</td>\n",
" <td>410</td>\n",
" <td>898</td>\n",
" <td>380</td>\n",
" <td>1688</td>\n",
" <td>180</td>\n",
" <td>700</td>\n",
" <td>458</td>\n",
" <td>1338</td>\n",
" <td>342</td>\n",
" <td>665</td>\n",
" <td>69</td>\n",
" <td>1076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Korangi</td>\n",
" <td>4753</td>\n",
" <td>13734</td>\n",
" <td>4447</td>\n",
" <td>22934.0</td>\n",
" <td>1035</td>\n",
" <td>2191</td>\n",
" <td>243</td>\n",
" <td>3469</td>\n",
" <td>537</td>\n",
" <td>2421</td>\n",
" <td>72</td>\n",
" <td>3030</td>\n",
" <td>627</td>\n",
" <td>2689</td>\n",
" <td>114</td>\n",
" <td>3430</td>\n",
" <td>520</td>\n",
" <td>2534</td>\n",
" <td>401</td>\n",
" <td>3455</td>\n",
" <td>1421</td>\n",
" <td>1999</td>\n",
" <td>3486</td>\n",
" <td>6906</td>\n",
" <td>613</td>\n",
" <td>1900</td>\n",
" <td>131</td>\n",
" <td>2644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" District Total Children Vaccinated at ... Unnamed: 27 Unnamed: 28\n",
"0 NaN NaN ... NaN NaN\n",
"1 NaN Fixed ... School Total\n",
"2 West 6692 ... 0 443\n",
"3 East 2383 ... 69 1076\n",
"4 Korangi 4753 ... 131 2644\n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 75,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"df10.head()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cJiJWzwmRFvL"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "lEcHpTwFYZOT"
},
"outputs": [],
"source": [
"import xlsxwriter\n",
"\n",
"# Create some Pandas dataframes from some data.\n",
"'''dfDrop = pd.DataFrame({'Data': [11, 12, 13, 14]})\n",
"data2 = pd.DataFrame({'Data': [21, 22, 23, 24]})\n",
"df3 = pd.DataFrame({'Data': [31, 32, 33, 34]})\n",
"data4 = \n",
"data5 =\n",
"data6 =\n",
"data7 =\n",
"data8 =\n",
"df9 = \n",
"df10\n",
"'''\n",
"# Create a Pandas Excel writer using XlsxWriter as the engine.\n",
"writer = pd.ExcelWriter('pandas_multiple.xlsx', engine='xlsxwriter')\n",
"\n",
"# Write each dataframe to a different worksheet.\n",
"dfDrop.to_excel(writer, sheet_name='Sheet1')\n",
"data2.to_excel(writer, sheet_name='Sheet2')\n",
"df3.to_excel(writer, sheet_name='Sheet3')\n",
"data4.to_excel(writer, sheet_name='Sheet4')\n",
"data5.to_excel(writer, sheet_name='Sheet5')\n",
"data6.to_excel(writer, sheet_name='Sheet6')\n",
"data7.to_excel(writer, sheet_name='Sheet7')\n",
"data8.to_excel(writer, sheet_name='Sheet8')\n",
"df9.to_excel(writer, sheet_name='Sheet9')\n",
"df10.to_excel(writer, sheet_name='Sheet10')\n",
"\n",
"# Close the Pandas Excel writer and output the Excel file.\n",
"writer.save()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting XlsxWriter\n",
" Downloading https://files.pythonhosted.org/packages/00/1f/2092a81056d36c1b6651a645aa84c1f76bcee03103072d4fe1cb58501d69/XlsxWriter-1.2.8-py2.py3-none-any.whl (141kB)\n",
"Installing collected packages: XlsxWriter\n",
"Successfully installed XlsxWriter-1.2.8\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install XlsxWriter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" {'url': 'https://doi.org/10.1515/phys-2018-0004', 'pmh_id': None, 'is_best': True, 'license': 'cc-by-nc-nd', 'oa_date': '2018-03-08', 'updated': '2022-03-03T00:42:27.712056', 'version': 'publishedVersion', 'evidence': 'oa journal (via doaj)', 'host_type': 'publisher', 'endpoint_id': None, 'url_for_pdf': None, 'url_for_landing_page': 'https://doi.org/10.1515/phys-2018-0004', 'repository_institution': None}"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "DataPrepOfTCV.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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