Skip to content

Instantly share code, notes, and snippets.

@taruma

taruma/taruma_hk43_pivot_dataframe.ipynb

Last active August 5, 2019 04:44
Show Gist options
  • Save taruma/2d08167110a4001d00c8cf5d1c7caf4e to your computer and use it in GitHub Desktop.
Save taruma/2d08167110a4001d00c8cf5d1c7caf4e to your computer and use it in GitHub Desktop.
Download ZIP
taruma_hk43_pivot_dataframe.ipynb
Display the source blob
Display the rendered blob
Raw
taruma_hk43_pivot_dataframe.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_hk43_pivot_dataframe.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/taruma/2d08167110a4001d00c8cf5d1c7caf4e/taruma_hk43_pivot_dataframe.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WUupDnYsz0RV",
"colab_type": "text"
},
"source": [
"# hidrokit#43 - Pivot ke DataFrame\n",
"\n",
"Notebook ini merupakan percobaan menjawab isu [taruma/hidrokit#43](https://github.com/taruma/hidrokit/issues/43)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LQO9y4Q8jQMe",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Load Notebook Extensions\n",
"%load_ext google.colab.data_table"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yeth-dv0i_t2",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"np.random.seed(110891)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JtYG_SqrjHuO",
"colab_type": "code",
"colab": {}
},
"source": [
"# BUAT DATASET TAHUN 2019\n",
"\n",
"year = 2019\n",
"date = f'{year}0101 {year}1231'.split()\n",
"\n",
"A = pd.DataFrame(index=pd.date_range(*date), columns=[\"sta_A\"])\n",
"A = A.applymap(lambda x: np.random.randint(100))\n",
"B = A.assign(month=A.index.month, day=A.index.day).pivot(values=\"sta_A\", columns=\"month\", index=\"day\")\n",
"B = pd.DataFrame(B.values)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HPExa-7JjMsG",
"colab_type": "code",
"outputId": "40672bf7-8f58-4f9b-abb9-917dc6b8ed9d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 623
}
},
"source": [
"B"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a036b366c3cace79/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 85,\n 'f': \"85\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 28,\n 'f': \"28\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 66,\n 'f': \"66\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 73,\n 'f': \"73\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 85,\n 'f': \"85\",\n },\n{\n 'v': 51,\n 'f': \"51\",\n },\n{\n 'v': 70,\n 'f': \"70\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': 25,\n 'f': \"25\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n }],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 47,\n 'f': \"47\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n }],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n }],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n }],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 97,\n 'f': \"97\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 98,\n 'f': \"98\",\n }],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n }],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n }],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 97,\n 'f': \"97\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 65,\n 'f': \"65\",\n }],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 41,\n 'f': \"41\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n }],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n{\n 'v': 49,\n 'f': \"49\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 92,\n 'f': \"92\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 78,\n 'f': \"78\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n }],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 40,\n 'f': \"40\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n }],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 78,\n 'f': \"78\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 23,\n 'f': \"23\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 74,\n 'f': \"74\",\n }],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 94,\n 'f': \"94\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n }],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n }],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 79,\n 'f': \"79\",\n },\n{\n 'v': 65,\n 'f': \"65\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n }],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 77,\n 'f': \"77\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 10,\n 'f': \"10\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n }],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 23,\n 'f': \"23\",\n }],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 76,\n 'f': \"76\",\n },\n{\n 'v': 47,\n 'f': \"47\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 32,\n 'f': \"32\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 32,\n 'f': \"32\",\n }],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 72,\n 'f': \"72\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n }],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 79,\n 'f': \"79\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': 49,\n 'f': \"49\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n }],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 51,\n 'f': \"51\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n }],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 83,\n 'f': \"83\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n }],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n }],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n }],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 92,\n 'f': \"92\",\n }],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 70,\n 'f': \"70\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n }]],\n columns: [[\"number\", \"index\"], [\"number\", \"0\"], [\"number\", \"1\"], [\"number\", \"2\"], [\"number\", \"3\"], [\"number\", \"4\"], [\"number\", \"5\"], [\"number\", \"6\"], [\"number\", \"7\"], [\"number\", \"8\"], [\"number\", \"9\"], [\"number\", \"10\"], [\"number\", \"11\"]],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n });\n ",
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>99.0</td>\n",
" <td>58.0</td>\n",
" <td>30.0</td>\n",
" <td>85.0</td>\n",
" <td>63.0</td>\n",
" <td>17.0</td>\n",
" <td>95.0</td>\n",
" <td>81.0</td>\n",
" <td>28.0</td>\n",
" <td>18.0</td>\n",
" <td>24.0</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>60.0</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>87.0</td>\n",
" <td>59.0</td>\n",
" <td>71.0</td>\n",
" <td>55.0</td>\n",
" <td>5.0</td>\n",
" <td>99.0</td>\n",
" <td>89.0</td>\n",
" <td>12.0</td>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>62.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>81.0</td>\n",
" <td>35.0</td>\n",
" <td>80.0</td>\n",
" <td>73.0</td>\n",
" <td>67.0</td>\n",
" <td>61.0</td>\n",
" <td>36.0</td>\n",
" <td>2.0</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>90.0</td>\n",
" <td>85.0</td>\n",
" <td>51.0</td>\n",
" <td>70.0</td>\n",
" <td>21.0</td>\n",
" <td>95.0</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" <td>91.0</td>\n",
" <td>31.0</td>\n",
" <td>12.0</td>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>64.0</td>\n",
" <td>29.0</td>\n",
" <td>87.0</td>\n",
" <td>33.0</td>\n",
" <td>26.0</td>\n",
" <td>55.0</td>\n",
" <td>25.0</td>\n",
" <td>96.0</td>\n",
" <td>7.0</td>\n",
" <td>75.0</td>\n",
" <td>82.0</td>\n",
" <td>33.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>37.0</td>\n",
" <td>45.0</td>\n",
" <td>84.0</td>\n",
" <td>36.0</td>\n",
" <td>47.0</td>\n",
" <td>36.0</td>\n",
" <td>35.0</td>\n",
" <td>34.0</td>\n",
" <td>54.0</td>\n",
" <td>24.0</td>\n",
" <td>27.0</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>90.0</td>\n",
" <td>58.0</td>\n",
" <td>35.0</td>\n",
" <td>99.0</td>\n",
" <td>39.0</td>\n",
" <td>17.0</td>\n",
" <td>20.0</td>\n",
" <td>33.0</td>\n",
" <td>88.0</td>\n",
" <td>87.0</td>\n",
" <td>8.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>45.0</td>\n",
" <td>11.0</td>\n",
" <td>68.0</td>\n",
" <td>39.0</td>\n",
" <td>26.0</td>\n",
" <td>71.0</td>\n",
" <td>17.0</td>\n",
" <td>87.0</td>\n",
" <td>42.0</td>\n",
" <td>64.0</td>\n",
" <td>35.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>64.0</td>\n",
" <td>18.0</td>\n",
" <td>1.0</td>\n",
" <td>61.0</td>\n",
" <td>46.0</td>\n",
" <td>15.0</td>\n",
" <td>88.0</td>\n",
" <td>8.0</td>\n",
" <td>67.0</td>\n",
" <td>71.0</td>\n",
" <td>6.0</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>39.0</td>\n",
" <td>17.0</td>\n",
" <td>86.0</td>\n",
" <td>60.0</td>\n",
" <td>96.0</td>\n",
" <td>12.0</td>\n",
" <td>97.0</td>\n",
" <td>2.0</td>\n",
" <td>80.0</td>\n",
" <td>96.0</td>\n",
" <td>43.0</td>\n",
" <td>98.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>42.0</td>\n",
" <td>33.0</td>\n",
" <td>15.0</td>\n",
" <td>19.0</td>\n",
" <td>31.0</td>\n",
" <td>19.0</td>\n",
" <td>89.0</td>\n",
" <td>44.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>99.0</td>\n",
" <td>64.0</td>\n",
" <td>39.0</td>\n",
" <td>35.0</td>\n",
" <td>19.0</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>37.0</td>\n",
" <td>36.0</td>\n",
" <td>68.0</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>22.0</td>\n",
" <td>87.0</td>\n",
" <td>62.0</td>\n",
" <td>44.0</td>\n",
" <td>99.0</td>\n",
" <td>53.0</td>\n",
" <td>97.0</td>\n",
" <td>42.0</td>\n",
" <td>80.0</td>\n",
" <td>8.0</td>\n",
" <td>82.0</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>69.0</td>\n",
" <td>48.0</td>\n",
" <td>91.0</td>\n",
" <td>34.0</td>\n",
" <td>41.0</td>\n",
" <td>99.0</td>\n",
" <td>18.0</td>\n",
" <td>20.0</td>\n",
" <td>58.0</td>\n",
" <td>44.0</td>\n",
" <td>52.0</td>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>49.0</td>\n",
" <td>46.0</td>\n",
" <td>92.0</td>\n",
" <td>19.0</td>\n",
" <td>21.0</td>\n",
" <td>59.0</td>\n",
" <td>67.0</td>\n",
" <td>12.0</td>\n",
" <td>78.0</td>\n",
" <td>33.0</td>\n",
" <td>17.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>86.0</td>\n",
" <td>15.0</td>\n",
" <td>63.0</td>\n",
" <td>60.0</td>\n",
" <td>53.0</td>\n",
" <td>40.0</td>\n",
" <td>44.0</td>\n",
" <td>4.0</td>\n",
" <td>56.0</td>\n",
" <td>90.0</td>\n",
" <td>67.0</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>29.0</td>\n",
" <td>84.0</td>\n",
" <td>4.0</td>\n",
" <td>78.0</td>\n",
" <td>34.0</td>\n",
" <td>88.0</td>\n",
" <td>56.0</td>\n",
" <td>48.0</td>\n",
" <td>23.0</td>\n",
" <td>4.0</td>\n",
" <td>48.0</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>94.0</td>\n",
" <td>34.0</td>\n",
" <td>57.0</td>\n",
" <td>52.0</td>\n",
" <td>59.0</td>\n",
" <td>95.0</td>\n",
" <td>44.0</td>\n",
" <td>87.0</td>\n",
" <td>48.0</td>\n",
" <td>82.0</td>\n",
" <td>46.0</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>60.0</td>\n",
" <td>44.0</td>\n",
" <td>86.0</td>\n",
" <td>37.0</td>\n",
" <td>71.0</td>\n",
" <td>43.0</td>\n",
" <td>53.0</td>\n",
" <td>31.0</td>\n",
" <td>54.0</td>\n",
" <td>60.0</td>\n",
" <td>87.0</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>27.0</td>\n",
" <td>52.0</td>\n",
" <td>93.0</td>\n",
" <td>81.0</td>\n",
" <td>45.0</td>\n",
" <td>45.0</td>\n",
" <td>64.0</td>\n",
" <td>95.0</td>\n",
" <td>87.0</td>\n",
" <td>79.0</td>\n",
" <td>65.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>46.0</td>\n",
" <td>43.0</td>\n",
" <td>18.0</td>\n",
" <td>77.0</td>\n",
" <td>96.0</td>\n",
" <td>36.0</td>\n",
" <td>71.0</td>\n",
" <td>10.0</td>\n",
" <td>80.0</td>\n",
" <td>84.0</td>\n",
" <td>30.0</td>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>58.0</td>\n",
" <td>61.0</td>\n",
" <td>82.0</td>\n",
" <td>33.0</td>\n",
" <td>9.0</td>\n",
" <td>80.0</td>\n",
" <td>5.0</td>\n",
" <td>44.0</td>\n",
" <td>22.0</td>\n",
" <td>86.0</td>\n",
" <td>58.0</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>80.0</td>\n",
" <td>1.0</td>\n",
" <td>53.0</td>\n",
" <td>76.0</td>\n",
" <td>47.0</td>\n",
" <td>91.0</td>\n",
" <td>32.0</td>\n",
" <td>39.0</td>\n",
" <td>88.0</td>\n",
" <td>81.0</td>\n",
" <td>34.0</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>67.0</td>\n",
" <td>72.0</td>\n",
" <td>63.0</td>\n",
" <td>6.0</td>\n",
" <td>31.0</td>\n",
" <td>81.0</td>\n",
" <td>39.0</td>\n",
" <td>64.0</td>\n",
" <td>56.0</td>\n",
" <td>55.0</td>\n",
" <td>69.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>79.0</td>\n",
" <td>88.0</td>\n",
" <td>57.0</td>\n",
" <td>7.0</td>\n",
" <td>54.0</td>\n",
" <td>8.0</td>\n",
" <td>22.0</td>\n",
" <td>33.0</td>\n",
" <td>57.0</td>\n",
" <td>62.0</td>\n",
" <td>49.0</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>96.0</td>\n",
" <td>51.0</td>\n",
" <td>35.0</td>\n",
" <td>37.0</td>\n",
" <td>86.0</td>\n",
" <td>46.0</td>\n",
" <td>7.0</td>\n",
" <td>71.0</td>\n",
" <td>11.0</td>\n",
" <td>56.0</td>\n",
" <td>99.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>60.0</td>\n",
" <td>83.0</td>\n",
" <td>26.0</td>\n",
" <td>35.0</td>\n",
" <td>46.0</td>\n",
" <td>6.0</td>\n",
" <td>37.0</td>\n",
" <td>42.0</td>\n",
" <td>48.0</td>\n",
" <td>27.0</td>\n",
" <td>30.0</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>21.0</td>\n",
" <td>5.0</td>\n",
" <td>45.0</td>\n",
" <td>30.0</td>\n",
" <td>52.0</td>\n",
" <td>29.0</td>\n",
" <td>4.0</td>\n",
" <td>54.0</td>\n",
" <td>99.0</td>\n",
" <td>34.0</td>\n",
" <td>18.0</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>37.0</td>\n",
" <td>13.0</td>\n",
" <td>5.0</td>\n",
" <td>58.0</td>\n",
" <td>58.0</td>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>3.0</td>\n",
" <td>46.0</td>\n",
" <td>95.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>99.0</td>\n",
" <td>NaN</td>\n",
" <td>60.0</td>\n",
" <td>56.0</td>\n",
" <td>68.0</td>\n",
" <td>75.0</td>\n",
" <td>19.0</td>\n",
" <td>89.0</td>\n",
" <td>22.0</td>\n",
" <td>2.0</td>\n",
" <td>57.0</td>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>70.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>34.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11\n",
"0 99.0 58.0 30.0 85.0 63.0 17.0 95.0 81.0 28.0 18.0 24.0 66.0\n",
"1 60.0 44.0 36.0 87.0 59.0 71.0 55.0 5.0 99.0 89.0 12.0 93.0\n",
"2 62.0 1.0 9.0 81.0 35.0 80.0 73.0 67.0 61.0 36.0 2.0 80.0\n",
"3 90.0 85.0 51.0 70.0 21.0 95.0 21.0 13.0 91.0 31.0 12.0 93.0\n",
"4 64.0 29.0 87.0 33.0 26.0 55.0 25.0 96.0 7.0 75.0 82.0 33.0\n",
"5 37.0 45.0 84.0 36.0 47.0 36.0 35.0 34.0 54.0 24.0 27.0 84.0\n",
"6 90.0 58.0 35.0 99.0 39.0 17.0 20.0 33.0 88.0 87.0 8.0 22.0\n",
"7 45.0 11.0 68.0 39.0 26.0 71.0 17.0 87.0 42.0 64.0 35.0 1.0\n",
"8 64.0 18.0 1.0 61.0 46.0 15.0 88.0 8.0 67.0 71.0 6.0 31.0\n",
"9 39.0 17.0 86.0 60.0 96.0 12.0 97.0 2.0 80.0 96.0 43.0 98.0\n",
"10 20.0 20.0 42.0 33.0 15.0 19.0 31.0 19.0 89.0 44.0 5.0 7.0\n",
"11 99.0 64.0 39.0 35.0 19.0 27.0 43.0 37.0 36.0 68.0 30.0 6.0\n",
"12 22.0 87.0 62.0 44.0 99.0 53.0 97.0 42.0 80.0 8.0 82.0 65.0\n",
"13 69.0 48.0 91.0 34.0 41.0 99.0 18.0 20.0 58.0 44.0 52.0 87.0\n",
"14 49.0 46.0 92.0 19.0 21.0 59.0 67.0 12.0 78.0 33.0 17.0 9.0\n",
"15 86.0 15.0 63.0 60.0 53.0 40.0 44.0 4.0 56.0 90.0 67.0 43.0\n",
"16 29.0 84.0 4.0 78.0 34.0 88.0 56.0 48.0 23.0 4.0 48.0 74.0\n",
"17 94.0 34.0 57.0 52.0 59.0 95.0 44.0 87.0 48.0 82.0 46.0 69.0\n",
"18 60.0 44.0 86.0 37.0 71.0 43.0 53.0 31.0 54.0 60.0 87.0 61.0\n",
"19 27.0 52.0 93.0 81.0 45.0 45.0 64.0 95.0 87.0 79.0 65.0 11.0\n",
"20 46.0 43.0 18.0 77.0 96.0 36.0 71.0 10.0 80.0 84.0 30.0 34.0\n",
"21 58.0 61.0 82.0 33.0 9.0 80.0 5.0 44.0 22.0 86.0 58.0 23.0\n",
"22 80.0 1.0 53.0 76.0 47.0 91.0 32.0 39.0 88.0 81.0 34.0 32.0\n",
"23 67.0 72.0 63.0 6.0 31.0 81.0 39.0 64.0 56.0 55.0 69.0 75.0\n",
"24 79.0 88.0 57.0 7.0 54.0 8.0 22.0 33.0 57.0 62.0 49.0 44.0\n",
"25 96.0 51.0 35.0 37.0 86.0 46.0 7.0 71.0 11.0 56.0 99.0 75.0\n",
"26 60.0 83.0 26.0 35.0 46.0 6.0 37.0 42.0 48.0 27.0 30.0 69.0\n",
"27 21.0 5.0 45.0 30.0 52.0 29.0 4.0 54.0 99.0 34.0 18.0 53.0\n",
"28 93.0 NaN 37.0 13.0 5.0 58.0 58.0 0.0 46.0 3.0 46.0 95.0\n",
"29 99.0 NaN 60.0 56.0 68.0 75.0 19.0 89.0 22.0 2.0 57.0 92.0\n",
"30 44.0 NaN 70.0 NaN 22.0 NaN 11.0 9.0 NaN 34.0 NaN 11.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZAwjSxVEvCmk",
"colab_type": "code",
"outputId": "e5c8fe18-9699-45bb-f3b0-139add0ff562",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 194
}
},
"source": [
"# Nilai nan dalam pivot tabel\n",
"B.iloc[-5:, :]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a036b366c3cace79/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 83,\n 'f': \"83\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n }],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n }],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n }],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 92,\n 'f': \"92\",\n }],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 70,\n 'f': \"70\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n }]],\n columns: [[\"number\", \"index\"], [\"number\", \"0\"], [\"number\", \"1\"], [\"number\", \"2\"], [\"number\", \"3\"], [\"number\", \"4\"], [\"number\", \"5\"], [\"number\", \"6\"], [\"number\", \"7\"], [\"number\", \"8\"], [\"number\", \"9\"], [\"number\", \"10\"], [\"number\", \"11\"]],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n });\n ",
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>60.0</td>\n",
" <td>83.0</td>\n",
" <td>26.0</td>\n",
" <td>35.0</td>\n",
" <td>46.0</td>\n",
" <td>6.0</td>\n",
" <td>37.0</td>\n",
" <td>42.0</td>\n",
" <td>48.0</td>\n",
" <td>27.0</td>\n",
" <td>30.0</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>21.0</td>\n",
" <td>5.0</td>\n",
" <td>45.0</td>\n",
" <td>30.0</td>\n",
" <td>52.0</td>\n",
" <td>29.0</td>\n",
" <td>4.0</td>\n",
" <td>54.0</td>\n",
" <td>99.0</td>\n",
" <td>34.0</td>\n",
" <td>18.0</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>37.0</td>\n",
" <td>13.0</td>\n",
" <td>5.0</td>\n",
" <td>58.0</td>\n",
" <td>58.0</td>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>3.0</td>\n",
" <td>46.0</td>\n",
" <td>95.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>99.0</td>\n",
" <td>NaN</td>\n",
" <td>60.0</td>\n",
" <td>56.0</td>\n",
" <td>68.0</td>\n",
" <td>75.0</td>\n",
" <td>19.0</td>\n",
" <td>89.0</td>\n",
" <td>22.0</td>\n",
" <td>2.0</td>\n",
" <td>57.0</td>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>70.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>34.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11\n",
"26 60.0 83.0 26.0 35.0 46.0 6.0 37.0 42.0 48.0 27.0 30.0 69.0\n",
"27 21.0 5.0 45.0 30.0 52.0 29.0 4.0 54.0 99.0 34.0 18.0 53.0\n",
"28 93.0 NaN 37.0 13.0 5.0 58.0 58.0 0.0 46.0 3.0 46.0 95.0\n",
"29 99.0 NaN 60.0 56.0 68.0 75.0 19.0 89.0 22.0 2.0 57.0 92.0\n",
"30 44.0 NaN 70.0 NaN 22.0 NaN 11.0 9.0 NaN 34.0 NaN 11.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fBqfGCrptT7K",
"colab_type": "text"
},
"source": [
"Ada 12 x 31 = 372 data. Dan diketahui bahwa tahun 2019 memiliki 365 hari. Sehingga data kelebihan 7 hari. \n",
"\n",
"Strateginya,\n",
"- meluluhkan tabel pivot dengan `pd.melt()`.\n",
"- hapus baris yang bernilai `nan` dengan `.dropna()`. Pada tahap ini, data sudah sesuai dengan jumlah data seharusnya (dari 372 ke 365)\n",
"- hapus kolom yang tidak digunakan, kolom index pertama (`variable`) dengan `.drop('variable', axis=1)`. Cat: kolomnya bernama `variable` jika tidak memiliki info sama sekali, dan dataframe ini kebetulan bukan MultiIndex.\n",
"- ganti index dengan tanggal menggunakan `.set_index()`\n",
"\n",
"Asumsinya:\n",
"- Data tidak ada yang `np.nan`. Sehingga, bisa langsung pakai `.dropna()`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vu91JU0OjNMm",
"colab_type": "code",
"outputId": "f100bad2-5467-409d-da7f-218231262673",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 413
}
},
"source": [
"B.melt().dropna().drop('variable', axis=1).set_index(pd.date_range(*date))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a036b366c3cace79/data_table.js\";\n\n window.createDataTable({\n data: [[\"2019-01-01 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-01-02 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-01-03 00:00:00\",\n{\n 'v': 62.0,\n 'f': \"62.0\",\n }],\n [\"2019-01-04 00:00:00\",\n{\n 'v': 90.0,\n 'f': \"90.0\",\n }],\n [\"2019-01-05 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-01-06 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-01-07 00:00:00\",\n{\n 'v': 90.0,\n 'f': \"90.0\",\n }],\n [\"2019-01-08 00:00:00\",\n{\n 'v': 45.0,\n 'f': \"45.0\",\n }],\n [\"2019-01-09 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-01-10 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-01-11 00:00:00\",\n{\n 'v': 20.0,\n 'f': \"20.0\",\n }],\n [\"2019-01-12 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-01-13 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-01-14 00:00:00\",\n{\n 'v': 69.0,\n 'f': \"69.0\",\n }],\n [\"2019-01-15 00:00:00\",\n{\n 'v': 49.0,\n 'f': \"49.0\",\n }],\n [\"2019-01-16 00:00:00\",\n{\n 'v': 86.0,\n 'f': \"86.0\",\n }],\n [\"2019-01-17 00:00:00\",\n{\n 'v': 29.0,\n 'f': \"29.0\",\n }],\n [\"2019-01-18 00:00:00\",\n{\n 'v': 94.0,\n 'f': \"94.0\",\n }],\n [\"2019-01-19 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-01-20 00:00:00\",\n{\n 'v': 27.0,\n 'f': \"27.0\",\n }],\n [\"2019-01-21 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-01-22 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-01-23 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-01-24 00:00:00\",\n{\n 'v': 67.0,\n 'f': \"67.0\",\n }],\n [\"2019-01-25 00:00:00\",\n{\n 'v': 79.0,\n 'f': \"79.0\",\n }],\n [\"2019-01-26 00:00:00\",\n{\n 'v': 96.0,\n 'f': \"96.0\",\n }],\n [\"2019-01-27 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-01-28 00:00:00\",\n{\n 'v': 21.0,\n 'f': \"21.0\",\n }],\n [\"2019-01-29 00:00:00\",\n{\n 'v': 93.0,\n 'f': \"93.0\",\n }],\n [\"2019-01-30 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-01-31 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-02-01 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-02-02 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-02-03 00:00:00\",\n{\n 'v': 1.0,\n 'f': \"1.0\",\n }],\n [\"2019-02-04 00:00:00\",\n{\n 'v': 85.0,\n 'f': \"85.0\",\n }],\n [\"2019-02-05 00:00:00\",\n{\n 'v': 29.0,\n 'f': \"29.0\",\n }],\n [\"2019-02-06 00:00:00\",\n{\n 'v': 45.0,\n 'f': \"45.0\",\n }],\n [\"2019-02-07 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-02-08 00:00:00\",\n{\n 'v': 11.0,\n 'f': \"11.0\",\n }],\n [\"2019-02-09 00:00:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n }],\n [\"2019-02-10 00:00:00\",\n{\n 'v': 17.0,\n 'f': \"17.0\",\n }],\n [\"2019-02-11 00:00:00\",\n{\n 'v': 20.0,\n 'f': \"20.0\",\n }],\n [\"2019-02-12 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-02-13 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-02-14 00:00:00\",\n{\n 'v': 48.0,\n 'f': \"48.0\",\n }],\n [\"2019-02-15 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-02-16 00:00:00\",\n{\n 'v': 15.0,\n 'f': \"15.0\",\n }],\n [\"2019-02-17 00:00:00\",\n{\n 'v': 84.0,\n 'f': \"84.0\",\n }],\n [\"2019-02-18 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-02-19 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-02-20 00:00:00\",\n{\n 'v': 52.0,\n 'f': \"52.0\",\n }],\n [\"2019-02-21 00:00:00\",\n{\n 'v': 43.0,\n 'f': \"43.0\",\n }],\n [\"2019-02-22 00:00:00\",\n{\n 'v': 61.0,\n 'f': \"61.0\",\n }],\n [\"2019-02-23 00:00:00\",\n{\n 'v': 1.0,\n 'f': \"1.0\",\n }],\n [\"2019-02-24 00:00:00\",\n{\n 'v': 72.0,\n 'f': \"72.0\",\n }],\n [\"2019-02-25 00:00:00\",\n{\n 'v': 88.0,\n 'f': \"88.0\",\n }],\n [\"2019-02-26 00:00:00\",\n{\n 'v': 51.0,\n 'f': \"51.0\",\n }],\n [\"2019-02-27 00:00:00\",\n{\n 'v': 83.0,\n 'f': \"83.0\",\n }],\n [\"2019-02-28 00:00:00\",\n{\n 'v': 5.0,\n 'f': \"5.0\",\n }],\n [\"2019-03-01 00:00:00\",\n{\n 'v': 30.0,\n 'f': \"30.0\",\n }],\n [\"2019-03-02 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-03-03 00:00:00\",\n{\n 'v': 9.0,\n 'f': \"9.0\",\n }],\n [\"2019-03-04 00:00:00\",\n{\n 'v': 51.0,\n 'f': \"51.0\",\n }],\n [\"2019-03-05 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-03-06 00:00:00\",\n{\n 'v': 84.0,\n 'f': \"84.0\",\n }],\n [\"2019-03-07 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-03-08 00:00:00\",\n{\n 'v': 68.0,\n 'f': \"68.0\",\n }],\n [\"2019-03-09 00:00:00\",\n{\n 'v': 1.0,\n 'f': \"1.0\",\n }],\n [\"2019-03-10 00:00:00\",\n{\n 'v': 86.0,\n 'f': \"86.0\",\n }],\n [\"2019-03-11 00:00:00\",\n{\n 'v': 42.0,\n 'f': \"42.0\",\n }],\n [\"2019-03-12 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-03-13 00:00:00\",\n{\n 'v': 62.0,\n 'f': \"62.0\",\n }],\n [\"2019-03-14 00:00:00\",\n{\n 'v': 91.0,\n 'f': \"91.0\",\n }],\n [\"2019-03-15 00:00:00\",\n{\n 'v': 92.0,\n 'f': \"92.0\",\n }],\n [\"2019-03-16 00:00:00\",\n{\n 'v': 63.0,\n 'f': \"63.0\",\n }],\n [\"2019-03-17 00:00:00\",\n{\n 'v': 4.0,\n 'f': \"4.0\",\n }],\n [\"2019-03-18 00:00:00\",\n{\n 'v': 57.0,\n 'f': \"57.0\",\n }],\n [\"2019-03-19 00:00:00\",\n{\n 'v': 86.0,\n 'f': \"86.0\",\n }],\n [\"2019-03-20 00:00:00\",\n{\n 'v': 93.0,\n 'f': \"93.0\",\n }],\n [\"2019-03-21 00:00:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n }],\n [\"2019-03-22 00:00:00\",\n{\n 'v': 82.0,\n 'f': \"82.0\",\n }],\n [\"2019-03-23 00:00:00\",\n{\n 'v': 53.0,\n 'f': \"53.0\",\n }],\n [\"2019-03-24 00:00:00\",\n{\n 'v': 63.0,\n 'f': \"63.0\",\n }],\n [\"2019-03-25 00:00:00\",\n{\n 'v': 57.0,\n 'f': \"57.0\",\n }],\n [\"2019-03-26 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-03-27 00:00:00\",\n{\n 'v': 26.0,\n 'f': \"26.0\",\n }],\n [\"2019-03-28 00:00:00\",\n{\n 'v': 45.0,\n 'f': \"45.0\",\n }],\n [\"2019-03-29 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-03-30 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-03-31 00:00:00\",\n{\n 'v': 70.0,\n 'f': \"70.0\",\n }],\n [\"2019-04-01 00:00:00\",\n{\n 'v': 85.0,\n 'f': \"85.0\",\n }],\n [\"2019-04-02 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-04-03 00:00:00\",\n{\n 'v': 81.0,\n 'f': \"81.0\",\n }],\n [\"2019-04-04 00:00:00\",\n{\n 'v': 70.0,\n 'f': \"70.0\",\n }],\n [\"2019-04-05 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-04-06 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-04-07 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-04-08 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-04-09 00:00:00\",\n{\n 'v': 61.0,\n 'f': \"61.0\",\n }],\n [\"2019-04-10 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-04-11 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-04-12 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-04-13 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-04-14 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-04-15 00:00:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n }],\n [\"2019-04-16 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-04-17 00:00:00\",\n{\n 'v': 78.0,\n 'f': \"78.0\",\n }],\n [\"2019-04-18 00:00:00\",\n{\n 'v': 52.0,\n 'f': \"52.0\",\n }],\n [\"2019-04-19 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-04-20 00:00:00\",\n{\n 'v': 81.0,\n 'f': \"81.0\",\n }],\n [\"2019-04-21 00:00:00\",\n{\n 'v': 77.0,\n 'f': \"77.0\",\n }],\n [\"2019-04-22 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-04-23 00:00:00\",\n{\n 'v': 76.0,\n 'f': \"76.0\",\n }],\n [\"2019-04-24 00:00:00\",\n{\n 'v': 6.0,\n 'f': \"6.0\",\n }],\n [\"2019-04-25 00:00:00\",\n{\n 'v': 7.0,\n 'f': \"7.0\",\n }],\n [\"2019-04-26 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-04-27 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-04-28 00:00:00\",\n{\n 'v': 30.0,\n 'f': \"30.0\",\n }],\n [\"2019-04-29 00:00:00\",\n{\n 'v': 13.0,\n 'f': \"13.0\",\n }],\n [\"2019-04-30 00:00:00\",\n{\n 'v': 56.0,\n 'f': \"56.0\",\n }],\n [\"2019-05-01 00:00:00\",\n{\n 'v': 63.0,\n 'f': \"63.0\",\n }],\n [\"2019-05-02 00:00:00\",\n{\n 'v': 59.0,\n 'f': \"59.0\",\n }],\n [\"2019-05-03 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-05-04 00:00:00\",\n{\n 'v': 21.0,\n 'f': \"21.0\",\n }],\n [\"2019-05-05 00:00:00\",\n{\n 'v': 26.0,\n 'f': \"26.0\",\n }],\n [\"2019-05-06 00:00:00\",\n{\n 'v': 47.0,\n 'f': \"47.0\",\n }],\n [\"2019-05-07 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-05-08 00:00:00\",\n{\n 'v': 26.0,\n 'f': \"26.0\",\n }],\n [\"2019-05-09 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-05-10 00:00:00\",\n{\n 'v': 96.0,\n 'f': \"96.0\",\n }],\n [\"2019-05-11 00:00:00\",\n{\n 'v': 15.0,\n 'f': \"15.0\",\n }],\n [\"2019-05-12 00:00:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n }],\n [\"2019-05-13 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-05-14 00:00:00\",\n{\n 'v': 41.0,\n 'f': \"41.0\",\n }],\n [\"2019-05-15 00:00:00\",\n{\n 'v': 21.0,\n 'f': \"21.0\",\n }],\n [\"2019-05-16 00:00:00\",\n{\n 'v': 53.0,\n 'f': \"53.0\",\n }],\n [\"2019-05-17 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-05-18 00:00:00\",\n{\n 'v': 59.0,\n 'f': \"59.0\",\n }],\n [\"2019-05-19 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-05-20 00:00:00\",\n{\n 'v': 45.0,\n 'f': \"45.0\",\n }],\n [\"2019-05-21 00:00:00\",\n{\n 'v': 96.0,\n 'f': \"96.0\",\n }],\n [\"2019-05-22 00:00:00\",\n{\n 'v': 9.0,\n 'f': \"9.0\",\n }],\n [\"2019-05-23 00:00:00\",\n{\n 'v': 47.0,\n 'f': \"47.0\",\n }],\n [\"2019-05-24 00:00:00\",\n{\n 'v': 31.0,\n 'f': \"31.0\",\n }],\n [\"2019-05-25 00:00:00\",\n{\n 'v': 54.0,\n 'f': \"54.0\",\n }],\n [\"2019-05-26 00:00:00\",\n{\n 'v': 86.0,\n 'f': \"86.0\",\n }],\n [\"2019-05-27 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-05-28 00:00:00\",\n{\n 'v': 52.0,\n 'f': \"52.0\",\n }],\n [\"2019-05-29 00:00:00\",\n{\n 'v': 5.0,\n 'f': \"5.0\",\n }],\n [\"2019-05-30 00:00:00\",\n{\n 'v': 68.0,\n 'f': \"68.0\",\n }],\n [\"2019-05-31 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-06-01 00:00:00\",\n{\n 'v': 17.0,\n 'f': \"17.0\",\n }],\n [\"2019-06-02 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-06-03 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-06-04 00:00:00\",\n{\n 'v': 95.0,\n 'f': \"95.0\",\n }],\n [\"2019-06-05 00:00:00\",\n{\n 'v': 55.0,\n 'f': \"55.0\",\n }],\n [\"2019-06-06 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-06-07 00:00:00\",\n{\n 'v': 17.0,\n 'f': \"17.0\",\n }],\n [\"2019-06-08 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-06-09 00:00:00\",\n{\n 'v': 15.0,\n 'f': \"15.0\",\n }],\n [\"2019-06-10 00:00:00\",\n{\n 'v': 12.0,\n 'f': \"12.0\",\n }],\n [\"2019-06-11 00:00:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n }],\n [\"2019-06-12 00:00:00\",\n{\n 'v': 27.0,\n 'f': \"27.0\",\n }],\n [\"2019-06-13 00:00:00\",\n{\n 'v': 53.0,\n 'f': \"53.0\",\n }],\n [\"2019-06-14 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-06-15 00:00:00\",\n{\n 'v': 59.0,\n 'f': \"59.0\",\n }],\n [\"2019-06-16 00:00:00\",\n{\n 'v': 40.0,\n 'f': \"40.0\",\n }],\n [\"2019-06-17 00:00:00\",\n{\n 'v': 88.0,\n 'f': \"88.0\",\n }],\n [\"2019-06-18 00:00:00\",\n{\n 'v': 95.0,\n 'f': \"95.0\",\n }],\n [\"2019-06-19 00:00:00\",\n{\n 'v': 43.0,\n 'f': \"43.0\",\n }],\n [\"2019-06-20 00:00:00\",\n{\n 'v': 45.0,\n 'f': \"45.0\",\n }],\n [\"2019-06-21 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-06-22 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-06-23 00:00:00\",\n{\n 'v': 91.0,\n 'f': \"91.0\",\n }],\n [\"2019-06-24 00:00:00\",\n{\n 'v': 81.0,\n 'f': \"81.0\",\n }],\n [\"2019-06-25 00:00:00\",\n{\n 'v': 8.0,\n 'f': \"8.0\",\n }],\n [\"2019-06-26 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-06-27 00:00:00\",\n{\n 'v': 6.0,\n 'f': \"6.0\",\n }],\n [\"2019-06-28 00:00:00\",\n{\n 'v': 29.0,\n 'f': \"29.0\",\n }],\n [\"2019-06-29 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-06-30 00:00:00\",\n{\n 'v': 75.0,\n 'f': \"75.0\",\n }],\n [\"2019-07-01 00:00:00\",\n{\n 'v': 95.0,\n 'f': \"95.0\",\n }],\n [\"2019-07-02 00:00:00\",\n{\n 'v': 55.0,\n 'f': \"55.0\",\n }],\n [\"2019-07-03 00:00:00\",\n{\n 'v': 73.0,\n 'f': \"73.0\",\n }],\n [\"2019-07-04 00:00:00\",\n{\n 'v': 21.0,\n 'f': \"21.0\",\n }],\n [\"2019-07-05 00:00:00\",\n{\n 'v': 25.0,\n 'f': \"25.0\",\n }],\n [\"2019-07-06 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-07-07 00:00:00\",\n{\n 'v': 20.0,\n 'f': \"20.0\",\n }],\n [\"2019-07-08 00:00:00\",\n{\n 'v': 17.0,\n 'f': \"17.0\",\n }],\n [\"2019-07-09 00:00:00\",\n{\n 'v': 88.0,\n 'f': \"88.0\",\n }],\n [\"2019-07-10 00:00:00\",\n{\n 'v': 97.0,\n 'f': \"97.0\",\n }],\n [\"2019-07-11 00:00:00\",\n{\n 'v': 31.0,\n 'f': \"31.0\",\n }],\n [\"2019-07-12 00:00:00\",\n{\n 'v': 43.0,\n 'f': \"43.0\",\n }],\n [\"2019-07-13 00:00:00\",\n{\n 'v': 97.0,\n 'f': \"97.0\",\n }],\n [\"2019-07-14 00:00:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n }],\n [\"2019-07-15 00:00:00\",\n{\n 'v': 67.0,\n 'f': \"67.0\",\n }],\n [\"2019-07-16 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-07-17 00:00:00\",\n{\n 'v': 56.0,\n 'f': \"56.0\",\n }],\n [\"2019-07-18 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-07-19 00:00:00\",\n{\n 'v': 53.0,\n 'f': \"53.0\",\n }],\n [\"2019-07-20 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-07-21 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-07-22 00:00:00\",\n{\n 'v': 5.0,\n 'f': \"5.0\",\n }],\n [\"2019-07-23 00:00:00\",\n{\n 'v': 32.0,\n 'f': \"32.0\",\n }],\n [\"2019-07-24 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-07-25 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-07-26 00:00:00\",\n{\n 'v': 7.0,\n 'f': \"7.0\",\n }],\n [\"2019-07-27 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-07-28 00:00:00\",\n{\n 'v': 4.0,\n 'f': \"4.0\",\n }],\n [\"2019-07-29 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-07-30 00:00:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n }],\n [\"2019-07-31 00:00:00\",\n{\n 'v': 11.0,\n 'f': \"11.0\",\n }],\n [\"2019-08-01 00:00:00\",\n{\n 'v': 81.0,\n 'f': \"81.0\",\n }],\n [\"2019-08-02 00:00:00\",\n{\n 'v': 5.0,\n 'f': \"5.0\",\n }],\n [\"2019-08-03 00:00:00\",\n{\n 'v': 67.0,\n 'f': \"67.0\",\n }],\n [\"2019-08-04 00:00:00\",\n{\n 'v': 13.0,\n 'f': \"13.0\",\n }],\n [\"2019-08-05 00:00:00\",\n{\n 'v': 96.0,\n 'f': \"96.0\",\n }],\n [\"2019-08-06 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-08-07 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-08-08 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-08-09 00:00:00\",\n{\n 'v': 8.0,\n 'f': \"8.0\",\n }],\n [\"2019-08-10 00:00:00\",\n{\n 'v': 2.0,\n 'f': \"2.0\",\n }],\n [\"2019-08-11 00:00:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n }],\n [\"2019-08-12 00:00:00\",\n{\n 'v': 37.0,\n 'f': \"37.0\",\n }],\n [\"2019-08-13 00:00:00\",\n{\n 'v': 42.0,\n 'f': \"42.0\",\n }],\n [\"2019-08-14 00:00:00\",\n{\n 'v': 20.0,\n 'f': \"20.0\",\n }],\n [\"2019-08-15 00:00:00\",\n{\n 'v': 12.0,\n 'f': \"12.0\",\n }],\n [\"2019-08-16 00:00:00\",\n{\n 'v': 4.0,\n 'f': \"4.0\",\n }],\n [\"2019-08-17 00:00:00\",\n{\n 'v': 48.0,\n 'f': \"48.0\",\n }],\n [\"2019-08-18 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-08-19 00:00:00\",\n{\n 'v': 31.0,\n 'f': \"31.0\",\n }],\n [\"2019-08-20 00:00:00\",\n{\n 'v': 95.0,\n 'f': \"95.0\",\n }],\n [\"2019-08-21 00:00:00\",\n{\n 'v': 10.0,\n 'f': \"10.0\",\n }],\n [\"2019-08-22 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-08-23 00:00:00\",\n{\n 'v': 39.0,\n 'f': \"39.0\",\n }],\n [\"2019-08-24 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-08-25 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-08-26 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-08-27 00:00:00\",\n{\n 'v': 42.0,\n 'f': \"42.0\",\n }],\n [\"2019-08-28 00:00:00\",\n{\n 'v': 54.0,\n 'f': \"54.0\",\n }],\n [\"2019-08-29 00:00:00\",\n{\n 'v': 0.0,\n 'f': \"0.0\",\n }],\n [\"2019-08-30 00:00:00\",\n{\n 'v': 89.0,\n 'f': \"89.0\",\n }],\n [\"2019-08-31 00:00:00\",\n{\n 'v': 9.0,\n 'f': \"9.0\",\n }],\n [\"2019-09-01 00:00:00\",\n{\n 'v': 28.0,\n 'f': \"28.0\",\n }],\n [\"2019-09-02 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-09-03 00:00:00\",\n{\n 'v': 61.0,\n 'f': \"61.0\",\n }],\n [\"2019-09-04 00:00:00\",\n{\n 'v': 91.0,\n 'f': \"91.0\",\n }],\n [\"2019-09-05 00:00:00\",\n{\n 'v': 7.0,\n 'f': \"7.0\",\n }],\n [\"2019-09-06 00:00:00\",\n{\n 'v': 54.0,\n 'f': \"54.0\",\n }],\n [\"2019-09-07 00:00:00\",\n{\n 'v': 88.0,\n 'f': \"88.0\",\n }],\n [\"2019-09-08 00:00:00\",\n{\n 'v': 42.0,\n 'f': \"42.0\",\n }],\n [\"2019-09-09 00:00:00\",\n{\n 'v': 67.0,\n 'f': \"67.0\",\n }],\n [\"2019-09-10 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-09-11 00:00:00\",\n{\n 'v': 89.0,\n 'f': \"89.0\",\n }],\n [\"2019-09-12 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-09-13 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-09-14 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-09-15 00:00:00\",\n{\n 'v': 78.0,\n 'f': \"78.0\",\n }],\n [\"2019-09-16 00:00:00\",\n{\n 'v': 56.0,\n 'f': \"56.0\",\n }],\n [\"2019-09-17 00:00:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n }],\n [\"2019-09-18 00:00:00\",\n{\n 'v': 48.0,\n 'f': \"48.0\",\n }],\n [\"2019-09-19 00:00:00\",\n{\n 'v': 54.0,\n 'f': \"54.0\",\n }],\n [\"2019-09-20 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-09-21 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-09-22 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-09-23 00:00:00\",\n{\n 'v': 88.0,\n 'f': \"88.0\",\n }],\n [\"2019-09-24 00:00:00\",\n{\n 'v': 56.0,\n 'f': \"56.0\",\n }],\n [\"2019-09-25 00:00:00\",\n{\n 'v': 57.0,\n 'f': \"57.0\",\n }],\n [\"2019-09-26 00:00:00\",\n{\n 'v': 11.0,\n 'f': \"11.0\",\n }],\n [\"2019-09-27 00:00:00\",\n{\n 'v': 48.0,\n 'f': \"48.0\",\n }],\n [\"2019-09-28 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-09-29 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-09-30 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-10-01 00:00:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n }],\n [\"2019-10-02 00:00:00\",\n{\n 'v': 89.0,\n 'f': \"89.0\",\n }],\n [\"2019-10-03 00:00:00\",\n{\n 'v': 36.0,\n 'f': \"36.0\",\n }],\n [\"2019-10-04 00:00:00\",\n{\n 'v': 31.0,\n 'f': \"31.0\",\n }],\n [\"2019-10-05 00:00:00\",\n{\n 'v': 75.0,\n 'f': \"75.0\",\n }],\n [\"2019-10-06 00:00:00\",\n{\n 'v': 24.0,\n 'f': \"24.0\",\n }],\n [\"2019-10-07 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-10-08 00:00:00\",\n{\n 'v': 64.0,\n 'f': \"64.0\",\n }],\n [\"2019-10-09 00:00:00\",\n{\n 'v': 71.0,\n 'f': \"71.0\",\n }],\n [\"2019-10-10 00:00:00\",\n{\n 'v': 96.0,\n 'f': \"96.0\",\n }],\n [\"2019-10-11 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-10-12 00:00:00\",\n{\n 'v': 68.0,\n 'f': \"68.0\",\n }],\n [\"2019-10-13 00:00:00\",\n{\n 'v': 8.0,\n 'f': \"8.0\",\n }],\n [\"2019-10-14 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-10-15 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-10-16 00:00:00\",\n{\n 'v': 90.0,\n 'f': \"90.0\",\n }],\n [\"2019-10-17 00:00:00\",\n{\n 'v': 4.0,\n 'f': \"4.0\",\n }],\n [\"2019-10-18 00:00:00\",\n{\n 'v': 82.0,\n 'f': \"82.0\",\n }],\n [\"2019-10-19 00:00:00\",\n{\n 'v': 60.0,\n 'f': \"60.0\",\n }],\n [\"2019-10-20 00:00:00\",\n{\n 'v': 79.0,\n 'f': \"79.0\",\n }],\n [\"2019-10-21 00:00:00\",\n{\n 'v': 84.0,\n 'f': \"84.0\",\n }],\n [\"2019-10-22 00:00:00\",\n{\n 'v': 86.0,\n 'f': \"86.0\",\n }],\n [\"2019-10-23 00:00:00\",\n{\n 'v': 81.0,\n 'f': \"81.0\",\n }],\n [\"2019-10-24 00:00:00\",\n{\n 'v': 55.0,\n 'f': \"55.0\",\n }],\n [\"2019-10-25 00:00:00\",\n{\n 'v': 62.0,\n 'f': \"62.0\",\n }],\n [\"2019-10-26 00:00:00\",\n{\n 'v': 56.0,\n 'f': \"56.0\",\n }],\n [\"2019-10-27 00:00:00\",\n{\n 'v': 27.0,\n 'f': \"27.0\",\n }],\n [\"2019-10-28 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-10-29 00:00:00\",\n{\n 'v': 3.0,\n 'f': \"3.0\",\n }],\n [\"2019-10-30 00:00:00\",\n{\n 'v': 2.0,\n 'f': \"2.0\",\n }],\n [\"2019-10-31 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-11-01 00:00:00\",\n{\n 'v': 24.0,\n 'f': \"24.0\",\n }],\n [\"2019-11-02 00:00:00\",\n{\n 'v': 12.0,\n 'f': \"12.0\",\n }],\n [\"2019-11-03 00:00:00\",\n{\n 'v': 2.0,\n 'f': \"2.0\",\n }],\n [\"2019-11-04 00:00:00\",\n{\n 'v': 12.0,\n 'f': \"12.0\",\n }],\n [\"2019-11-05 00:00:00\",\n{\n 'v': 82.0,\n 'f': \"82.0\",\n }],\n [\"2019-11-06 00:00:00\",\n{\n 'v': 27.0,\n 'f': \"27.0\",\n }],\n [\"2019-11-07 00:00:00\",\n{\n 'v': 8.0,\n 'f': \"8.0\",\n }],\n [\"2019-11-08 00:00:00\",\n{\n 'v': 35.0,\n 'f': \"35.0\",\n }],\n [\"2019-11-09 00:00:00\",\n{\n 'v': 6.0,\n 'f': \"6.0\",\n }],\n [\"2019-11-10 00:00:00\",\n{\n 'v': 43.0,\n 'f': \"43.0\",\n }],\n [\"2019-11-11 00:00:00\",\n{\n 'v': 5.0,\n 'f': \"5.0\",\n }],\n [\"2019-11-12 00:00:00\",\n{\n 'v': 30.0,\n 'f': \"30.0\",\n }],\n [\"2019-11-13 00:00:00\",\n{\n 'v': 82.0,\n 'f': \"82.0\",\n }],\n [\"2019-11-14 00:00:00\",\n{\n 'v': 52.0,\n 'f': \"52.0\",\n }],\n [\"2019-11-15 00:00:00\",\n{\n 'v': 17.0,\n 'f': \"17.0\",\n }],\n [\"2019-11-16 00:00:00\",\n{\n 'v': 67.0,\n 'f': \"67.0\",\n }],\n [\"2019-11-17 00:00:00\",\n{\n 'v': 48.0,\n 'f': \"48.0\",\n }],\n [\"2019-11-18 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-11-19 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-11-20 00:00:00\",\n{\n 'v': 65.0,\n 'f': \"65.0\",\n }],\n [\"2019-11-21 00:00:00\",\n{\n 'v': 30.0,\n 'f': \"30.0\",\n }],\n [\"2019-11-22 00:00:00\",\n{\n 'v': 58.0,\n 'f': \"58.0\",\n }],\n [\"2019-11-23 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-11-24 00:00:00\",\n{\n 'v': 69.0,\n 'f': \"69.0\",\n }],\n [\"2019-11-25 00:00:00\",\n{\n 'v': 49.0,\n 'f': \"49.0\",\n }],\n [\"2019-11-26 00:00:00\",\n{\n 'v': 99.0,\n 'f': \"99.0\",\n }],\n [\"2019-11-27 00:00:00\",\n{\n 'v': 30.0,\n 'f': \"30.0\",\n }],\n [\"2019-11-28 00:00:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n }],\n [\"2019-11-29 00:00:00\",\n{\n 'v': 46.0,\n 'f': \"46.0\",\n }],\n [\"2019-11-30 00:00:00\",\n{\n 'v': 57.0,\n 'f': \"57.0\",\n }],\n [\"2019-12-01 00:00:00\",\n{\n 'v': 66.0,\n 'f': \"66.0\",\n }],\n [\"2019-12-02 00:00:00\",\n{\n 'v': 93.0,\n 'f': \"93.0\",\n }],\n [\"2019-12-03 00:00:00\",\n{\n 'v': 80.0,\n 'f': \"80.0\",\n }],\n [\"2019-12-04 00:00:00\",\n{\n 'v': 93.0,\n 'f': \"93.0\",\n }],\n [\"2019-12-05 00:00:00\",\n{\n 'v': 33.0,\n 'f': \"33.0\",\n }],\n [\"2019-12-06 00:00:00\",\n{\n 'v': 84.0,\n 'f': \"84.0\",\n }],\n [\"2019-12-07 00:00:00\",\n{\n 'v': 22.0,\n 'f': \"22.0\",\n }],\n [\"2019-12-08 00:00:00\",\n{\n 'v': 1.0,\n 'f': \"1.0\",\n }],\n [\"2019-12-09 00:00:00\",\n{\n 'v': 31.0,\n 'f': \"31.0\",\n }],\n [\"2019-12-10 00:00:00\",\n{\n 'v': 98.0,\n 'f': \"98.0\",\n }],\n [\"2019-12-11 00:00:00\",\n{\n 'v': 7.0,\n 'f': \"7.0\",\n }],\n [\"2019-12-12 00:00:00\",\n{\n 'v': 6.0,\n 'f': \"6.0\",\n }],\n [\"2019-12-13 00:00:00\",\n{\n 'v': 65.0,\n 'f': \"65.0\",\n }],\n [\"2019-12-14 00:00:00\",\n{\n 'v': 87.0,\n 'f': \"87.0\",\n }],\n [\"2019-12-15 00:00:00\",\n{\n 'v': 9.0,\n 'f': \"9.0\",\n }],\n [\"2019-12-16 00:00:00\",\n{\n 'v': 43.0,\n 'f': \"43.0\",\n }],\n [\"2019-12-17 00:00:00\",\n{\n 'v': 74.0,\n 'f': \"74.0\",\n }],\n [\"2019-12-18 00:00:00\",\n{\n 'v': 69.0,\n 'f': \"69.0\",\n }],\n [\"2019-12-19 00:00:00\",\n{\n 'v': 61.0,\n 'f': \"61.0\",\n }],\n [\"2019-12-20 00:00:00\",\n{\n 'v': 11.0,\n 'f': \"11.0\",\n }],\n [\"2019-12-21 00:00:00\",\n{\n 'v': 34.0,\n 'f': \"34.0\",\n }],\n [\"2019-12-22 00:00:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n }],\n [\"2019-12-23 00:00:00\",\n{\n 'v': 32.0,\n 'f': \"32.0\",\n }],\n [\"2019-12-24 00:00:00\",\n{\n 'v': 75.0,\n 'f': \"75.0\",\n }],\n [\"2019-12-25 00:00:00\",\n{\n 'v': 44.0,\n 'f': \"44.0\",\n }],\n [\"2019-12-26 00:00:00\",\n{\n 'v': 75.0,\n 'f': \"75.0\",\n }],\n [\"2019-12-27 00:00:00\",\n{\n 'v': 69.0,\n 'f': \"69.0\",\n }],\n [\"2019-12-28 00:00:00\",\n{\n 'v': 53.0,\n 'f': \"53.0\",\n }],\n [\"2019-12-29 00:00:00\",\n{\n 'v': 95.0,\n 'f': \"95.0\",\n }],\n [\"2019-12-30 00:00:00\",\n{\n 'v': 92.0,\n 'f': \"92.0\",\n }],\n [\"2019-12-31 00:00:00\",\n{\n 'v': 11.0,\n 'f': \"11.0\",\n }]],\n columns: [[\"string\", \"index\"], [\"number\", \"value\"]],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n });\n ",
"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>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-01-01</th>\n",
" <td>99.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-02</th>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-03</th>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-04</th>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-05</th>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-06</th>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-07</th>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-08</th>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-09</th>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-10</th>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-11</th>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-12</th>\n",
" <td>99.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-13</th>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-14</th>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-15</th>\n",
" <td>49.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-16</th>\n",
" <td>86.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-17</th>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-18</th>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-19</th>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-20</th>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-21</th>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-22</th>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-23</th>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-24</th>\n",
" <td>67.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-25</th>\n",
" <td>79.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-26</th>\n",
" <td>96.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-27</th>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-28</th>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-29</th>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-30</th>\n",
" <td>99.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-02</th>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-03</th>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-04</th>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-05</th>\n",
" <td>33.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-06</th>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-07</th>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-08</th>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-09</th>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-10</th>\n",
" <td>98.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-11</th>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-12</th>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-13</th>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-14</th>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-15</th>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-16</th>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-17</th>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-18</th>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-19</th>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-20</th>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-21</th>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-22</th>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-23</th>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-24</th>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-25</th>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-26</th>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-27</th>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-28</th>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-29</th>\n",
" <td>95.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-30</th>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31</th>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>365 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" value\n",
"2019-01-01 99.0\n",
"2019-01-02 60.0\n",
"2019-01-03 62.0\n",
"2019-01-04 90.0\n",
"2019-01-05 64.0\n",
"2019-01-06 37.0\n",
"2019-01-07 90.0\n",
"2019-01-08 45.0\n",
"2019-01-09 64.0\n",
"2019-01-10 39.0\n",
"2019-01-11 20.0\n",
"2019-01-12 99.0\n",
"2019-01-13 22.0\n",
"2019-01-14 69.0\n",
"2019-01-15 49.0\n",
"2019-01-16 86.0\n",
"2019-01-17 29.0\n",
"2019-01-18 94.0\n",
"2019-01-19 60.0\n",
"2019-01-20 27.0\n",
"2019-01-21 46.0\n",
"2019-01-22 58.0\n",
"2019-01-23 80.0\n",
"2019-01-24 67.0\n",
"2019-01-25 79.0\n",
"2019-01-26 96.0\n",
"2019-01-27 60.0\n",
"2019-01-28 21.0\n",
"2019-01-29 93.0\n",
"2019-01-30 99.0\n",
"... ...\n",
"2019-12-02 93.0\n",
"2019-12-03 80.0\n",
"2019-12-04 93.0\n",
"2019-12-05 33.0\n",
"2019-12-06 84.0\n",
"2019-12-07 22.0\n",
"2019-12-08 1.0\n",
"2019-12-09 31.0\n",
"2019-12-10 98.0\n",
"2019-12-11 7.0\n",
"2019-12-12 6.0\n",
"2019-12-13 65.0\n",
"2019-12-14 87.0\n",
"2019-12-15 9.0\n",
"2019-12-16 43.0\n",
"2019-12-17 74.0\n",
"2019-12-18 69.0\n",
"2019-12-19 61.0\n",
"2019-12-20 11.0\n",
"2019-12-21 34.0\n",
"2019-12-22 23.0\n",
"2019-12-23 32.0\n",
"2019-12-24 75.0\n",
"2019-12-25 44.0\n",
"2019-12-26 75.0\n",
"2019-12-27 69.0\n",
"2019-12-28 53.0\n",
"2019-12-29 95.0\n",
"2019-12-30 92.0\n",
"2019-12-31 11.0\n",
"\n",
"[365 rows x 1 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3zkJkHV-mOlc",
"colab_type": "text"
},
"source": [
"# Kasus 1\n",
"\n",
"Jika terdapat nilai `nan` pada data hariannya, kemungkinan solusi diatas tidak bisa dipakai. Dan harus dimodifikasi kembali."
]
},
{
"cell_type": "code",
"metadata": {
"id": "eh9Z6he3ms77",
"colab_type": "code",
"outputId": "630f99fd-9227-4dc4-f30f-1ed9859d2c24",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 623
}
},
"source": [
"C = B.copy()\n",
"C[C <= 10] = np.NaN\n",
"C"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a036b366c3cace79/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 85,\n 'f': \"85\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 28,\n 'f': \"28\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 66,\n 'f': \"66\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 73,\n 'f': \"73\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 85,\n 'f': \"85\",\n },\n{\n 'v': 51,\n 'f': \"51\",\n },\n{\n 'v': 70,\n 'f': \"70\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': 25,\n 'f': \"25\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n }],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 47,\n 'f': \"47\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n }],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n }],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n }],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 97,\n 'f': \"97\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 98,\n 'f': \"98\",\n }],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n }],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n }],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 97,\n 'f': \"97\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 65,\n 'f': \"65\",\n }],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 41,\n 'f': \"41\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n }],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n{\n 'v': 49,\n 'f': \"49\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 92,\n 'f': \"92\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 78,\n 'f': \"78\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n }],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 15,\n 'f': \"15\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 40,\n 'f': \"40\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 90,\n 'f': \"90\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n }],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 78,\n 'f': \"78\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 23,\n 'f': \"23\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 74,\n 'f': \"74\",\n }],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n{\n 'v': 94,\n 'f': \"94\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 59,\n 'f': \"59\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n }],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n }],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n },\n{\n 'v': 87,\n 'f': \"87\",\n },\n{\n 'v': 79,\n 'f': \"79\",\n },\n{\n 'v': 65,\n 'f': \"65\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n }],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 43,\n 'f': \"43\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 77,\n 'f': \"77\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': 84,\n 'f': \"84\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n }],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 61,\n 'f': \"61\",\n },\n{\n 'v': 82,\n 'f': \"82\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 23,\n 'f': \"23\",\n }],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 80,\n 'f': \"80\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n },\n{\n 'v': 76,\n 'f': \"76\",\n },\n{\n 'v': 47,\n 'f': \"47\",\n },\n{\n 'v': 91,\n 'f': \"91\",\n },\n{\n 'v': 32,\n 'f': \"32\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 32,\n 'f': \"32\",\n }],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n{\n 'v': 67,\n 'f': \"67\",\n },\n{\n 'v': 72,\n 'f': \"72\",\n },\n{\n 'v': 63,\n 'f': \"63\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 31,\n 'f': \"31\",\n },\n{\n 'v': 81,\n 'f': \"81\",\n },\n{\n 'v': 39,\n 'f': \"39\",\n },\n{\n 'v': 64,\n 'f': \"64\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 55,\n 'f': \"55\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n }],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n{\n 'v': 79,\n 'f': \"79\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': 33,\n 'f': \"33\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 62,\n 'f': \"62\",\n },\n{\n 'v': 49,\n 'f': \"49\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n }],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n{\n 'v': 96,\n 'f': \"96\",\n },\n{\n 'v': 51,\n 'f': \"51\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 86,\n 'f': \"86\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 71,\n 'f': \"71\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n }],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 83,\n 'f': \"83\",\n },\n{\n 'v': 26,\n 'f': \"26\",\n },\n{\n 'v': 35,\n 'f': \"35\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 42,\n 'f': \"42\",\n },\n{\n 'v': 48,\n 'f': \"48\",\n },\n{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 69,\n 'f': \"69\",\n }],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n{\n 'v': 21,\n 'f': \"21\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 45,\n 'f': \"45\",\n },\n{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 54,\n 'f': \"54\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': 18,\n 'f': \"18\",\n },\n{\n 'v': 53,\n 'f': \"53\",\n }],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n{\n 'v': 93,\n 'f': \"93\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 37,\n 'f': \"37\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': 58,\n 'f': \"58\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 46,\n 'f': \"46\",\n },\n{\n 'v': 95,\n 'f': \"95\",\n }],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n{\n 'v': 99,\n 'f': \"99\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 60,\n 'f': \"60\",\n },\n{\n 'v': 56,\n 'f': \"56\",\n },\n{\n 'v': 68,\n 'f': \"68\",\n },\n{\n 'v': 75,\n 'f': \"75\",\n },\n{\n 'v': 19,\n 'f': \"19\",\n },\n{\n 'v': 89,\n 'f': \"89\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 57,\n 'f': \"57\",\n },\n{\n 'v': 92,\n 'f': \"92\",\n }],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n{\n 'v': 44,\n 'f': \"44\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 70,\n 'f': \"70\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 22,\n 'f': \"22\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 34,\n 'f': \"34\",\n },\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n }]],\n columns: [[\"number\", \"index\"], [\"number\", \"0\"], [\"number\", \"1\"], [\"number\", \"2\"], [\"number\", \"3\"], [\"number\", \"4\"], [\"number\", \"5\"], [\"number\", \"6\"], [\"number\", \"7\"], [\"number\", \"8\"], [\"number\", \"9\"], [\"number\", \"10\"], [\"number\", \"11\"]],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n });\n ",
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>99.0</td>\n",
" <td>58.0</td>\n",
" <td>30.0</td>\n",
" <td>85.0</td>\n",
" <td>63.0</td>\n",
" <td>17.0</td>\n",
" <td>95.0</td>\n",
" <td>81.0</td>\n",
" <td>28.0</td>\n",
" <td>18.0</td>\n",
" <td>24.0</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>60.0</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>87.0</td>\n",
" <td>59.0</td>\n",
" <td>71.0</td>\n",
" <td>55.0</td>\n",
" <td>NaN</td>\n",
" <td>99.0</td>\n",
" <td>89.0</td>\n",
" <td>12.0</td>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>62.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>35.0</td>\n",
" <td>80.0</td>\n",
" <td>73.0</td>\n",
" <td>67.0</td>\n",
" <td>61.0</td>\n",
" <td>36.0</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>90.0</td>\n",
" <td>85.0</td>\n",
" <td>51.0</td>\n",
" <td>70.0</td>\n",
" <td>21.0</td>\n",
" <td>95.0</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" <td>91.0</td>\n",
" <td>31.0</td>\n",
" <td>12.0</td>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>64.0</td>\n",
" <td>29.0</td>\n",
" <td>87.0</td>\n",
" <td>33.0</td>\n",
" <td>26.0</td>\n",
" <td>55.0</td>\n",
" <td>25.0</td>\n",
" <td>96.0</td>\n",
" <td>NaN</td>\n",
" <td>75.0</td>\n",
" <td>82.0</td>\n",
" <td>33.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>37.0</td>\n",
" <td>45.0</td>\n",
" <td>84.0</td>\n",
" <td>36.0</td>\n",
" <td>47.0</td>\n",
" <td>36.0</td>\n",
" <td>35.0</td>\n",
" <td>34.0</td>\n",
" <td>54.0</td>\n",
" <td>24.0</td>\n",
" <td>27.0</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>90.0</td>\n",
" <td>58.0</td>\n",
" <td>35.0</td>\n",
" <td>99.0</td>\n",
" <td>39.0</td>\n",
" <td>17.0</td>\n",
" <td>20.0</td>\n",
" <td>33.0</td>\n",
" <td>88.0</td>\n",
" <td>87.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>45.0</td>\n",
" <td>11.0</td>\n",
" <td>68.0</td>\n",
" <td>39.0</td>\n",
" <td>26.0</td>\n",
" <td>71.0</td>\n",
" <td>17.0</td>\n",
" <td>87.0</td>\n",
" <td>42.0</td>\n",
" <td>64.0</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>64.0</td>\n",
" <td>18.0</td>\n",
" <td>NaN</td>\n",
" <td>61.0</td>\n",
" <td>46.0</td>\n",
" <td>15.0</td>\n",
" <td>88.0</td>\n",
" <td>NaN</td>\n",
" <td>67.0</td>\n",
" <td>71.0</td>\n",
" <td>NaN</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>39.0</td>\n",
" <td>17.0</td>\n",
" <td>86.0</td>\n",
" <td>60.0</td>\n",
" <td>96.0</td>\n",
" <td>12.0</td>\n",
" <td>97.0</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>96.0</td>\n",
" <td>43.0</td>\n",
" <td>98.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>42.0</td>\n",
" <td>33.0</td>\n",
" <td>15.0</td>\n",
" <td>19.0</td>\n",
" <td>31.0</td>\n",
" <td>19.0</td>\n",
" <td>89.0</td>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>99.0</td>\n",
" <td>64.0</td>\n",
" <td>39.0</td>\n",
" <td>35.0</td>\n",
" <td>19.0</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>37.0</td>\n",
" <td>36.0</td>\n",
" <td>68.0</td>\n",
" <td>30.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>22.0</td>\n",
" <td>87.0</td>\n",
" <td>62.0</td>\n",
" <td>44.0</td>\n",
" <td>99.0</td>\n",
" <td>53.0</td>\n",
" <td>97.0</td>\n",
" <td>42.0</td>\n",
" <td>80.0</td>\n",
" <td>NaN</td>\n",
" <td>82.0</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>69.0</td>\n",
" <td>48.0</td>\n",
" <td>91.0</td>\n",
" <td>34.0</td>\n",
" <td>41.0</td>\n",
" <td>99.0</td>\n",
" <td>18.0</td>\n",
" <td>20.0</td>\n",
" <td>58.0</td>\n",
" <td>44.0</td>\n",
" <td>52.0</td>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>49.0</td>\n",
" <td>46.0</td>\n",
" <td>92.0</td>\n",
" <td>19.0</td>\n",
" <td>21.0</td>\n",
" <td>59.0</td>\n",
" <td>67.0</td>\n",
" <td>12.0</td>\n",
" <td>78.0</td>\n",
" <td>33.0</td>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>86.0</td>\n",
" <td>15.0</td>\n",
" <td>63.0</td>\n",
" <td>60.0</td>\n",
" <td>53.0</td>\n",
" <td>40.0</td>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>56.0</td>\n",
" <td>90.0</td>\n",
" <td>67.0</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>29.0</td>\n",
" <td>84.0</td>\n",
" <td>NaN</td>\n",
" <td>78.0</td>\n",
" <td>34.0</td>\n",
" <td>88.0</td>\n",
" <td>56.0</td>\n",
" <td>48.0</td>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>48.0</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>94.0</td>\n",
" <td>34.0</td>\n",
" <td>57.0</td>\n",
" <td>52.0</td>\n",
" <td>59.0</td>\n",
" <td>95.0</td>\n",
" <td>44.0</td>\n",
" <td>87.0</td>\n",
" <td>48.0</td>\n",
" <td>82.0</td>\n",
" <td>46.0</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>60.0</td>\n",
" <td>44.0</td>\n",
" <td>86.0</td>\n",
" <td>37.0</td>\n",
" <td>71.0</td>\n",
" <td>43.0</td>\n",
" <td>53.0</td>\n",
" <td>31.0</td>\n",
" <td>54.0</td>\n",
" <td>60.0</td>\n",
" <td>87.0</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>27.0</td>\n",
" <td>52.0</td>\n",
" <td>93.0</td>\n",
" <td>81.0</td>\n",
" <td>45.0</td>\n",
" <td>45.0</td>\n",
" <td>64.0</td>\n",
" <td>95.0</td>\n",
" <td>87.0</td>\n",
" <td>79.0</td>\n",
" <td>65.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>46.0</td>\n",
" <td>43.0</td>\n",
" <td>18.0</td>\n",
" <td>77.0</td>\n",
" <td>96.0</td>\n",
" <td>36.0</td>\n",
" <td>71.0</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>84.0</td>\n",
" <td>30.0</td>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>58.0</td>\n",
" <td>61.0</td>\n",
" <td>82.0</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>NaN</td>\n",
" <td>44.0</td>\n",
" <td>22.0</td>\n",
" <td>86.0</td>\n",
" <td>58.0</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>80.0</td>\n",
" <td>NaN</td>\n",
" <td>53.0</td>\n",
" <td>76.0</td>\n",
" <td>47.0</td>\n",
" <td>91.0</td>\n",
" <td>32.0</td>\n",
" <td>39.0</td>\n",
" <td>88.0</td>\n",
" <td>81.0</td>\n",
" <td>34.0</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>67.0</td>\n",
" <td>72.0</td>\n",
" <td>63.0</td>\n",
" <td>NaN</td>\n",
" <td>31.0</td>\n",
" <td>81.0</td>\n",
" <td>39.0</td>\n",
" <td>64.0</td>\n",
" <td>56.0</td>\n",
" <td>55.0</td>\n",
" <td>69.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>79.0</td>\n",
" <td>88.0</td>\n",
" <td>57.0</td>\n",
" <td>NaN</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>33.0</td>\n",
" <td>57.0</td>\n",
" <td>62.0</td>\n",
" <td>49.0</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>96.0</td>\n",
" <td>51.0</td>\n",
" <td>35.0</td>\n",
" <td>37.0</td>\n",
" <td>86.0</td>\n",
" <td>46.0</td>\n",
" <td>NaN</td>\n",
" <td>71.0</td>\n",
" <td>11.0</td>\n",
" <td>56.0</td>\n",
" <td>99.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>60.0</td>\n",
" <td>83.0</td>\n",
" <td>26.0</td>\n",
" <td>35.0</td>\n",
" <td>46.0</td>\n",
" <td>NaN</td>\n",
" <td>37.0</td>\n",
" <td>42.0</td>\n",
" <td>48.0</td>\n",
" <td>27.0</td>\n",
" <td>30.0</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>21.0</td>\n",
" <td>NaN</td>\n",
" <td>45.0</td>\n",
" <td>30.0</td>\n",
" <td>52.0</td>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>54.0</td>\n",
" <td>99.0</td>\n",
" <td>34.0</td>\n",
" <td>18.0</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>37.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>58.0</td>\n",
" <td>58.0</td>\n",
" <td>NaN</td>\n",
" <td>46.0</td>\n",
" <td>NaN</td>\n",
" <td>46.0</td>\n",
" <td>95.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>99.0</td>\n",
" <td>NaN</td>\n",
" <td>60.0</td>\n",
" <td>56.0</td>\n",
" <td>68.0</td>\n",
" <td>75.0</td>\n",
" <td>19.0</td>\n",
" <td>89.0</td>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>57.0</td>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>70.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>34.0</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11\n",
"0 99.0 58.0 30.0 85.0 63.0 17.0 95.0 81.0 28.0 18.0 24.0 66.0\n",
"1 60.0 44.0 36.0 87.0 59.0 71.0 55.0 NaN 99.0 89.0 12.0 93.0\n",
"2 62.0 NaN NaN 81.0 35.0 80.0 73.0 67.0 61.0 36.0 NaN 80.0\n",
"3 90.0 85.0 51.0 70.0 21.0 95.0 21.0 13.0 91.0 31.0 12.0 93.0\n",
"4 64.0 29.0 87.0 33.0 26.0 55.0 25.0 96.0 NaN 75.0 82.0 33.0\n",
"5 37.0 45.0 84.0 36.0 47.0 36.0 35.0 34.0 54.0 24.0 27.0 84.0\n",
"6 90.0 58.0 35.0 99.0 39.0 17.0 20.0 33.0 88.0 87.0 NaN 22.0\n",
"7 45.0 11.0 68.0 39.0 26.0 71.0 17.0 87.0 42.0 64.0 35.0 NaN\n",
"8 64.0 18.0 NaN 61.0 46.0 15.0 88.0 NaN 67.0 71.0 NaN 31.0\n",
"9 39.0 17.0 86.0 60.0 96.0 12.0 97.0 NaN 80.0 96.0 43.0 98.0\n",
"10 20.0 20.0 42.0 33.0 15.0 19.0 31.0 19.0 89.0 44.0 NaN NaN\n",
"11 99.0 64.0 39.0 35.0 19.0 27.0 43.0 37.0 36.0 68.0 30.0 NaN\n",
"12 22.0 87.0 62.0 44.0 99.0 53.0 97.0 42.0 80.0 NaN 82.0 65.0\n",
"13 69.0 48.0 91.0 34.0 41.0 99.0 18.0 20.0 58.0 44.0 52.0 87.0\n",
"14 49.0 46.0 92.0 19.0 21.0 59.0 67.0 12.0 78.0 33.0 17.0 NaN\n",
"15 86.0 15.0 63.0 60.0 53.0 40.0 44.0 NaN 56.0 90.0 67.0 43.0\n",
"16 29.0 84.0 NaN 78.0 34.0 88.0 56.0 48.0 23.0 NaN 48.0 74.0\n",
"17 94.0 34.0 57.0 52.0 59.0 95.0 44.0 87.0 48.0 82.0 46.0 69.0\n",
"18 60.0 44.0 86.0 37.0 71.0 43.0 53.0 31.0 54.0 60.0 87.0 61.0\n",
"19 27.0 52.0 93.0 81.0 45.0 45.0 64.0 95.0 87.0 79.0 65.0 11.0\n",
"20 46.0 43.0 18.0 77.0 96.0 36.0 71.0 NaN 80.0 84.0 30.0 34.0\n",
"21 58.0 61.0 82.0 33.0 NaN 80.0 NaN 44.0 22.0 86.0 58.0 23.0\n",
"22 80.0 NaN 53.0 76.0 47.0 91.0 32.0 39.0 88.0 81.0 34.0 32.0\n",
"23 67.0 72.0 63.0 NaN 31.0 81.0 39.0 64.0 56.0 55.0 69.0 75.0\n",
"24 79.0 88.0 57.0 NaN 54.0 NaN 22.0 33.0 57.0 62.0 49.0 44.0\n",
"25 96.0 51.0 35.0 37.0 86.0 46.0 NaN 71.0 11.0 56.0 99.0 75.0\n",
"26 60.0 83.0 26.0 35.0 46.0 NaN 37.0 42.0 48.0 27.0 30.0 69.0\n",
"27 21.0 NaN 45.0 30.0 52.0 29.0 NaN 54.0 99.0 34.0 18.0 53.0\n",
"28 93.0 NaN 37.0 13.0 NaN 58.0 58.0 NaN 46.0 NaN 46.0 95.0\n",
"29 99.0 NaN 60.0 56.0 68.0 75.0 19.0 89.0 22.0 NaN 57.0 92.0\n",
"30 44.0 NaN 70.0 NaN 22.0 NaN 11.0 NaN NaN 34.0 NaN 11.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wMY-dNRhwVtN",
"colab_type": "text"
},
"source": [
"Nah disini mentoknya. Bukan mentok sih, cuman solusi yang kepikiran sekarang, tidak begitu ideal. \n",
"\n",
"Idenya sejauh ini:\n",
"- Buat daftar index baris yang tidak masuk akal. \n",
"- Karena tahun hanya memiliki 2 pola yaitu saat kabisat (366) dan tidak (365). Bisa langsung target pada baris keberapa yang bisa dihapus. \n",
"- Jadi kemungkinan setelah proses peluluhan, langsung `.drop([*index_target], axis=0)` pada baris-baris tertentu. \n",
"\n",
"Ada ide lain?"
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment