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LowriWilliams/opening_data.ipynb

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opening_data.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"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>Sno</th>\n",
" <th>Date</th>\n",
" <th>Province/State</th>\n",
" <th>Country</th>\n",
" <th>Last Update</th>\n",
" <th>Confirmed</th>\n",
" <th>Deaths</th>\n",
" <th>Recovered</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Anhui</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Beijing</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Chongqing</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Fujian</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Gansu</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Guangdong</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>26.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Guangxi</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Guizhou</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Hainan</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Hebei</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Heilongjiang</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Henan</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Hong Kong</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Hubei</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>444.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Hunan</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Inner Mongolia</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Jiangsu</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Jiangxi</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Jilin</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>Liaoning</td>\n",
" <td>China</td>\n",
" <td>01/22/2020 12:00:00</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sno Date Province/State Country Last Update \\\n",
"0 1 01/22/2020 12:00:00 Anhui China 01/22/2020 12:00:00 \n",
"1 2 01/22/2020 12:00:00 Beijing China 01/22/2020 12:00:00 \n",
"2 3 01/22/2020 12:00:00 Chongqing China 01/22/2020 12:00:00 \n",
"3 4 01/22/2020 12:00:00 Fujian China 01/22/2020 12:00:00 \n",
"4 5 01/22/2020 12:00:00 Gansu China 01/22/2020 12:00:00 \n",
"5 6 01/22/2020 12:00:00 Guangdong China 01/22/2020 12:00:00 \n",
"6 7 01/22/2020 12:00:00 Guangxi China 01/22/2020 12:00:00 \n",
"7 8 01/22/2020 12:00:00 Guizhou China 01/22/2020 12:00:00 \n",
"8 9 01/22/2020 12:00:00 Hainan China 01/22/2020 12:00:00 \n",
"9 10 01/22/2020 12:00:00 Hebei China 01/22/2020 12:00:00 \n",
"10 11 01/22/2020 12:00:00 Heilongjiang China 01/22/2020 12:00:00 \n",
"11 12 01/22/2020 12:00:00 Henan China 01/22/2020 12:00:00 \n",
"12 13 01/22/2020 12:00:00 Hong Kong China 01/22/2020 12:00:00 \n",
"13 14 01/22/2020 12:00:00 Hubei China 01/22/2020 12:00:00 \n",
"14 15 01/22/2020 12:00:00 Hunan China 01/22/2020 12:00:00 \n",
"15 16 01/22/2020 12:00:00 Inner Mongolia China 01/22/2020 12:00:00 \n",
"16 17 01/22/2020 12:00:00 Jiangsu China 01/22/2020 12:00:00 \n",
"17 18 01/22/2020 12:00:00 Jiangxi China 01/22/2020 12:00:00 \n",
"18 19 01/22/2020 12:00:00 Jilin China 01/22/2020 12:00:00 \n",
"19 20 01/22/2020 12:00:00 Liaoning China 01/22/2020 12:00:00 \n",
"\n",
" Confirmed Deaths Recovered \n",
"0 1.0 0.0 0.0 \n",
"1 14.0 0.0 0.0 \n",
"2 6.0 0.0 0.0 \n",
"3 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 26.0 0.0 0.0 \n",
"6 2.0 0.0 0.0 \n",
"7 1.0 0.0 0.0 \n",
"8 4.0 0.0 0.0 \n",
"9 1.0 0.0 0.0 \n",
"10 0.0 0.0 0.0 \n",
"11 5.0 0.0 0.0 \n",
"12 0.0 0.0 0.0 \n",
"13 444.0 0.0 0.0 \n",
"14 4.0 0.0 0.0 \n",
"15 0.0 0.0 0.0 \n",
"16 1.0 0.0 0.0 \n",
"17 2.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"19 2.0 0.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv('corona.csv')\n",
"\n",
"display(df.head(20))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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