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sparkingdark/day1task10daysofmlchallenge.ipynb

Created March 26, 2020 09:32
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day1task10daysofmlchallenge.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Day1task10daysofmlchallenge.ipynb",
"provenance": [],
"toc_visible": true,
"authorship_tag": "ABX9TyMpGOoh5jVXuecuOWcWAJAn",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/darkdebo/29657f9fba9bdbbdbb87ac939dba5235/day1task10daysofmlchallenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VYC0sLsejhE1",
"colab_type": "code",
"outputId": "66df20d6-b0c7-4a9a-c196-4738331360f5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
}
},
"source": [
"!wget https://data.humdata.org/hxlproxy/api/data-preview.csv?url=https%3A%2F%2Fraw.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data%2Fcsse_covid_19_time_series%2Ftime_series_19-covid-Confirmed.csv&filename=time_series_2019-ncov-Confirmed.csv"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-03-26 09:16:26-- https://data.humdata.org/hxlproxy/api/data-preview.csv?url=https%3A%2F%2Fraw.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data%2Fcsse_covid_19_time_series%2Ftime_series_19-covid-Confirmed.csv\n",
"Resolving data.humdata.org (data.humdata.org)... 162.249.108.156\n",
"Connecting to data.humdata.org (data.humdata.org)|162.249.108.156|:443... connected.\n",
"HTTP request sent, awaiting response... 403 FORBIDDEN\n",
"2020-03-26 09:16:27 ERROR 403: FORBIDDEN.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-8SJNcPMz2Uy",
"colab_type": "text"
},
"source": [
"# data getting"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ctznW7S1jruz",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"sns.set(style=\"ticks\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KIJUnm-gkILM",
"colab_type": "text"
},
"source": [
"# Data loading of nCov stat of countries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yw7GC8HTjxdf",
"colab_type": "code",
"colab": {}
},
"source": [
"path = '/content/time_series_2019-ncov-Confirmed.csv'\n",
"df = pd.read_csv(path)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Kk0hAmvBkUp0",
"colab_type": "code",
"outputId": "d8f8c6ce-48c4-4207-f3b9-c9a92e318148",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
}
},
"source": [
"df.head()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Province/State</th>\n",
" <th>Country/Region</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>1/22/20</th>\n",
" <th>1/23/20</th>\n",
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" <th>3/6/20</th>\n",
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" <th>3/9/20</th>\n",
" <th>3/10/20</th>\n",
" <th>3/11/20</th>\n",
" <th>3/12/20</th>\n",
" <th>3/13/20</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>Thailand</td>\n",
" <td>15.0000</td>\n",
" <td>101.0000</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
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" <td>25</td>\n",
" <td>32</td>\n",
" <td>32</td>\n",
" <td>32</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>34</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
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" <td>43</td>\n",
" <td>43</td>\n",
" <td>43</td>\n",
" <td>47</td>\n",
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" <td>53</td>\n",
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" <td>75</td>\n",
" <td>82</td>\n",
" <td>114</td>\n",
" <td>147</td>\n",
" <td>177</td>\n",
" <td>212</td>\n",
" <td>272</td>\n",
" <td>322</td>\n",
" <td>411</td>\n",
" <td>599</td>\n",
" <td>599.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>Japan</td>\n",
" <td>36.0000</td>\n",
" <td>138.0000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
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" <td>105</td>\n",
" <td>122</td>\n",
" <td>147</td>\n",
" <td>159</td>\n",
" <td>170</td>\n",
" <td>189</td>\n",
" <td>214</td>\n",
" <td>228</td>\n",
" <td>241</td>\n",
" <td>256</td>\n",
" <td>274</td>\n",
" <td>293</td>\n",
" <td>331</td>\n",
" <td>360</td>\n",
" <td>420</td>\n",
" <td>461</td>\n",
" <td>502</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>639</td>\n",
" <td>639</td>\n",
" <td>701</td>\n",
" <td>773</td>\n",
" <td>839</td>\n",
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" <td>878</td>\n",
" <td>889</td>\n",
" <td>924</td>\n",
" <td>963</td>\n",
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" <td>1086</td>\n",
" <td>1086.0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>Singapore</td>\n",
" <td>1.2833</td>\n",
" <td>103.8333</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
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" <td>4</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>24</td>\n",
" <td>28</td>\n",
" <td>28</td>\n",
" <td>30</td>\n",
" <td>33</td>\n",
" <td>40</td>\n",
" <td>45</td>\n",
" <td>47</td>\n",
" <td>50</td>\n",
" <td>58</td>\n",
" <td>67</td>\n",
" <td>72</td>\n",
" <td>75</td>\n",
" <td>77</td>\n",
" <td>81</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>91</td>\n",
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" <td>455.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>Nepal</td>\n",
" <td>28.1667</td>\n",
" <td>84.2500</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>2.0</td>\n",
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" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>Malaysia</td>\n",
" <td>2.5000</td>\n",
" <td>112.5000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" <td>19</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" <td>23</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>29</td>\n",
" <td>36</td>\n",
" <td>50</td>\n",
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" <td>197</td>\n",
" <td>238</td>\n",
" <td>428</td>\n",
" <td>566</td>\n",
" <td>673</td>\n",
" <td>790</td>\n",
" <td>900</td>\n",
" <td>1030</td>\n",
" <td>1183</td>\n",
" <td>1306</td>\n",
" <td>1306.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Province/State Country/Region Lat ... 3/21/20 3/22/20 3/23/20\n",
"0 NaN Thailand 15.0000 ... 411 599 599.0\n",
"1 NaN Japan 36.0000 ... 1007 1086 1086.0\n",
"2 NaN Singapore 1.2833 ... 432 455 455.0\n",
"3 NaN Nepal 28.1667 ... 1 2 2.0\n",
"4 NaN Malaysia 2.5000 ... 1183 1306 1306.0\n",
"\n",
"[5 rows x 66 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rkxsLa6YkpGx",
"colab_type": "code",
"outputId": "3996da20-5031-45ec-c383-24bf26d0881b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 317
}
},
"source": [
"df.describe()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>2/25/20</th>\n",
" <th>2/26/20</th>\n",
" <th>2/27/20</th>\n",
" <th>2/28/20</th>\n",
" <th>2/29/20</th>\n",
" <th>3/1/20</th>\n",
" <th>3/2/20</th>\n",
" <th>3/3/20</th>\n",
" <th>3/4/20</th>\n",
" <th>3/5/20</th>\n",
" <th>3/6/20</th>\n",
" <th>3/7/20</th>\n",
" <th>3/8/20</th>\n",
" <th>3/9/20</th>\n",
" <th>3/10/20</th>\n",
" <th>3/11/20</th>\n",
" <th>3/12/20</th>\n",
" <th>3/13/20</th>\n",
" <th>3/14/20</th>\n",
" <th>3/15/20</th>\n",
" <th>3/16/20</th>\n",
" <th>3/17/20</th>\n",
" <th>3/18/20</th>\n",
" <th>3/19/20</th>\n",
" <th>3/20/20</th>\n",
" <th>3/21/20</th>\n",
" <th>3/22/20</th>\n",
" <th>3/23/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>501.000000</td>\n",
" <td>309.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>30.285772</td>\n",
" <td>-33.097762</td>\n",
" <td>1.107784</td>\n",
" <td>1.303393</td>\n",
" <td>1.878244</td>\n",
" <td>2.862275</td>\n",
" <td>4.227545</td>\n",
" <td>5.842315</td>\n",
" <td>11.133733</td>\n",
" <td>12.307385</td>\n",
" <td>16.435130</td>\n",
" <td>19.814371</td>\n",
" <td>24.027944</td>\n",
" <td>33.506986</td>\n",
" <td>39.682635</td>\n",
" <td>47.688623</td>\n",
" <td>55.159681</td>\n",
" <td>61.510978</td>\n",
" <td>68.644711</td>\n",
" <td>74.091816</td>\n",
" <td>80.139721</td>\n",
" <td>85.353293</td>\n",
" <td>89.425150</td>\n",
" <td>90.261477</td>\n",
" <td>120.495010</td>\n",
" <td>133.502994</td>\n",
" <td>137.784431</td>\n",
" <td>142.163673</td>\n",
" <td>146.223553</td>\n",
" <td>149.972056</td>\n",
" <td>150.976048</td>\n",
" <td>152.089820</td>\n",
" <td>153.339321</td>\n",
" <td>156.844311</td>\n",
" <td>157.614770</td>\n",
" <td>158.818363</td>\n",
" <td>160.504990</td>\n",
" <td>162.465070</td>\n",
" <td>165.177645</td>\n",
" <td>167.904192</td>\n",
" <td>171.678643</td>\n",
" <td>176.385230</td>\n",
" <td>180.251497</td>\n",
" <td>185.309381</td>\n",
" <td>189.860279</td>\n",
" <td>195.373253</td>\n",
" <td>203.161677</td>\n",
" <td>211.219561</td>\n",
" <td>219.151697</td>\n",
" <td>226.668663</td>\n",
" <td>236.710579</td>\n",
" <td>251.227545</td>\n",
" <td>256.173653</td>\n",
" <td>289.806387</td>\n",
" <td>311.564870</td>\n",
" <td>334.223553</td>\n",
" <td>362.329341</td>\n",
" <td>393.497006</td>\n",
" <td>428.962076</td>\n",
" <td>484.447106</td>\n",
" <td>543.245509</td>\n",
" <td>607.832335</td>\n",
" <td>670.568862</td>\n",
" <td>1087.391586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>19.870544</td>\n",
" <td>80.661210</td>\n",
" <td>19.886889</td>\n",
" <td>19.969170</td>\n",
" <td>24.875593</td>\n",
" <td>34.637942</td>\n",
" <td>48.422475</td>\n",
" <td>65.253024</td>\n",
" <td>159.914427</td>\n",
" <td>160.961540</td>\n",
" <td>221.780645</td>\n",
" <td>262.961897</td>\n",
" <td>323.566468</td>\n",
" <td>502.615036</td>\n",
" <td>607.548748</td>\n",
" <td>748.710021</td>\n",
" <td>882.244067</td>\n",
" <td>991.730346</td>\n",
" <td>1118.724934</td>\n",
" <td>1214.747740</td>\n",
" <td>1327.783957</td>\n",
" <td>1421.458167</td>\n",
" <td>1494.655348</td>\n",
" <td>1494.946770</td>\n",
" <td>2156.466164</td>\n",
" <td>2433.137132</td>\n",
" <td>2515.465626</td>\n",
" <td>2601.788810</td>\n",
" <td>2682.473901</td>\n",
" <td>2758.066918</td>\n",
" <td>2773.680974</td>\n",
" <td>2792.030727</td>\n",
" <td>2801.942394</td>\n",
" <td>2865.423791</td>\n",
" <td>2865.488608</td>\n",
" <td>2874.634626</td>\n",
" <td>2896.975752</td>\n",
" <td>2915.070394</td>\n",
" <td>2933.833919</td>\n",
" <td>2948.854288</td>\n",
" <td>2969.290939</td>\n",
" <td>2996.581051</td>\n",
" <td>3007.586232</td>\n",
" <td>3016.840172</td>\n",
" <td>3025.387042</td>\n",
" <td>3035.921413</td>\n",
" <td>3048.869642</td>\n",
" <td>3061.913470</td>\n",
" <td>3074.291757</td>\n",
" <td>3088.859710</td>\n",
" <td>3101.021350</td>\n",
" <td>3125.935948</td>\n",
" <td>3133.420832</td>\n",
" <td>3203.084891</td>\n",
" <td>3263.324717</td>\n",
" <td>3332.995975</td>\n",
" <td>3415.516311</td>\n",
" <td>3511.636870</td>\n",
" <td>3642.374951</td>\n",
" <td>3832.930820</td>\n",
" <td>4064.084657</td>\n",
" <td>4337.179809</td>\n",
" <td>4594.278660</td>\n",
" <td>5814.685942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-41.454500</td>\n",
" <td>-157.858400</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>23.341700</td>\n",
" <td>-90.230800</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.251900</td>\n",
" <td>-73.182200</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>76.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>42.165700</td>\n",
" <td>21.824300</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>9.000000</td>\n",
" <td>11.000000</td>\n",
" <td>17.000000</td>\n",
" <td>22.000000</td>\n",
" <td>28.000000</td>\n",
" <td>37.000000</td>\n",
" <td>50.000000</td>\n",
" <td>58.000000</td>\n",
" <td>76.000000</td>\n",
" <td>89.000000</td>\n",
" <td>117.000000</td>\n",
" <td>135.000000</td>\n",
" <td>326.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>72.000000</td>\n",
" <td>178.065000</td>\n",
" <td>444.000000</td>\n",
" <td>444.000000</td>\n",
" <td>549.000000</td>\n",
" <td>761.000000</td>\n",
" <td>1058.000000</td>\n",
" <td>1423.000000</td>\n",
" <td>3554.000000</td>\n",
" <td>3554.000000</td>\n",
" <td>4903.000000</td>\n",
" <td>5806.000000</td>\n",
" <td>7153.000000</td>\n",
" <td>11177.000000</td>\n",
" <td>13522.000000</td>\n",
" <td>16678.000000</td>\n",
" <td>19665.000000</td>\n",
" <td>22112.000000</td>\n",
" <td>24953.000000</td>\n",
" <td>27100.000000</td>\n",
" <td>29631.000000</td>\n",
" <td>31728.000000</td>\n",
" <td>33366.000000</td>\n",
" <td>33366.000000</td>\n",
" <td>48206.000000</td>\n",
" <td>54406.000000</td>\n",
" <td>56249.000000</td>\n",
" <td>58182.000000</td>\n",
" <td>59989.000000</td>\n",
" <td>61682.000000</td>\n",
" <td>62031.000000</td>\n",
" <td>62442.000000</td>\n",
" <td>62662.000000</td>\n",
" <td>64084.000000</td>\n",
" <td>64084.000000</td>\n",
" <td>64287.000000</td>\n",
" <td>64786.000000</td>\n",
" <td>65187.000000</td>\n",
" <td>65596.000000</td>\n",
" <td>65914.000000</td>\n",
" <td>66337.000000</td>\n",
" <td>66907.000000</td>\n",
" <td>67103.000000</td>\n",
" <td>67217.000000</td>\n",
" <td>67332.000000</td>\n",
" <td>67466.000000</td>\n",
" <td>67592.000000</td>\n",
" <td>67666.000000</td>\n",
" <td>67707.000000</td>\n",
" <td>67743.000000</td>\n",
" <td>67760.000000</td>\n",
" <td>67773.000000</td>\n",
" <td>67781.000000</td>\n",
" <td>67786.000000</td>\n",
" <td>67790.000000</td>\n",
" <td>67794.000000</td>\n",
" <td>67798.000000</td>\n",
" <td>67799.000000</td>\n",
" <td>67800.000000</td>\n",
" <td>67800.000000</td>\n",
" <td>67800.000000</td>\n",
" <td>67800.000000</td>\n",
" <td>67800.000000</td>\n",
" <td>67800.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Lat Long ... 3/22/20 3/23/20\n",
"count 501.000000 501.000000 ... 501.000000 309.000000\n",
"mean 30.285772 -33.097762 ... 670.568862 1087.391586\n",
"std 19.870544 80.661210 ... 4594.278660 5814.685942\n",
"min -41.454500 -157.858400 ... 0.000000 0.000000\n",
"25% 23.341700 -90.230800 ... 0.000000 10.000000\n",
"50% 37.251900 -73.182200 ... 3.000000 76.000000\n",
"75% 42.165700 21.824300 ... 135.000000 326.000000\n",
"max 72.000000 178.065000 ... 67800.000000 67800.000000\n",
"\n",
"[8 rows x 64 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ipyuUrcDkyDk",
"colab_type": "code",
"outputId": "894c59e8-3ac1-41f4-e8b0-4fd0ba532a6a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"df.info()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 501 entries, 0 to 500\n",
"Data columns (total 66 columns):\n",
"Province/State 327 non-null object\n",
"Country/Region 501 non-null object\n",
"Lat 501 non-null float64\n",
"Long 501 non-null float64\n",
"1/22/20 501 non-null int64\n",
"1/23/20 501 non-null int64\n",
"1/24/20 501 non-null int64\n",
"1/25/20 501 non-null int64\n",
"1/26/20 501 non-null int64\n",
"1/27/20 501 non-null int64\n",
"1/28/20 501 non-null int64\n",
"1/29/20 501 non-null int64\n",
"1/30/20 501 non-null int64\n",
"1/31/20 501 non-null int64\n",
"2/1/20 501 non-null int64\n",
"2/2/20 501 non-null int64\n",
"2/3/20 501 non-null int64\n",
"2/4/20 501 non-null int64\n",
"2/5/20 501 non-null int64\n",
"2/6/20 501 non-null int64\n",
"2/7/20 501 non-null int64\n",
"2/8/20 501 non-null int64\n",
"2/9/20 501 non-null int64\n",
"2/10/20 501 non-null int64\n",
"2/11/20 501 non-null int64\n",
"2/12/20 501 non-null int64\n",
"2/13/20 501 non-null int64\n",
"2/14/20 501 non-null int64\n",
"2/15/20 501 non-null int64\n",
"2/16/20 501 non-null int64\n",
"2/17/20 501 non-null int64\n",
"2/18/20 501 non-null int64\n",
"2/19/20 501 non-null int64\n",
"2/20/20 501 non-null int64\n",
"2/21/20 501 non-null int64\n",
"2/22/20 501 non-null int64\n",
"2/23/20 501 non-null int64\n",
"2/24/20 501 non-null int64\n",
"2/25/20 501 non-null int64\n",
"2/26/20 501 non-null int64\n",
"2/27/20 501 non-null int64\n",
"2/28/20 501 non-null int64\n",
"2/29/20 501 non-null int64\n",
"3/1/20 501 non-null int64\n",
"3/2/20 501 non-null int64\n",
"3/3/20 501 non-null int64\n",
"3/4/20 501 non-null int64\n",
"3/5/20 501 non-null int64\n",
"3/6/20 501 non-null int64\n",
"3/7/20 501 non-null int64\n",
"3/8/20 501 non-null int64\n",
"3/9/20 501 non-null int64\n",
"3/10/20 501 non-null int64\n",
"3/11/20 501 non-null int64\n",
"3/12/20 501 non-null int64\n",
"3/13/20 501 non-null int64\n",
"3/14/20 501 non-null int64\n",
"3/15/20 501 non-null int64\n",
"3/16/20 501 non-null int64\n",
"3/17/20 501 non-null int64\n",
"3/18/20 501 non-null int64\n",
"3/19/20 501 non-null int64\n",
"3/20/20 501 non-null int64\n",
"3/21/20 501 non-null int64\n",
"3/22/20 501 non-null int64\n",
"3/23/20 309 non-null float64\n",
"dtypes: float64(3), int64(61), object(2)\n",
"memory usage: 258.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6zN31klHq8pj",
"colab_type": "code",
"outputId": "0e70e6e5-1043-42d2-a87c-ae331e5c194d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
}
},
"source": [
"df['Province/State'].dropna()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5 British Columbia\n",
"6 New South Wales\n",
"7 Victoria\n",
"8 Queensland\n",
"19 South Australia\n",
" ... \n",
"472 Sint Maarten\n",
"475 Isle of Man\n",
"477 Northwest Territories\n",
"491 United States Virgin Islands\n",
"492 US\n",
"Name: Province/State, Length: 327, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xv41FObeobYt",
"colab_type": "code",
"outputId": "4e12c493-f807-49ba-c54d-6959db4e1e9f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
}
},
"source": [
"df.describe"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<bound method NDFrame.describe of Province/State Country/Region Lat ... 3/21/20 3/22/20 3/23/20\n",
"0 NaN Thailand 15.0000 ... 411 599 599.0\n",
"1 NaN Japan 36.0000 ... 1007 1086 1086.0\n",
"2 NaN Singapore 1.2833 ... 432 455 455.0\n",
"3 NaN Nepal 28.1667 ... 1 2 2.0\n",
"4 NaN Malaysia 2.5000 ... 1183 1306 1306.0\n",
".. ... ... ... ... ... ... ...\n",
"496 NaN Jersey 49.1900 ... 0 0 0.0\n",
"497 NaN Puerto Rico 18.2000 ... 0 0 0.0\n",
"498 NaN Republic of the Congo -1.4400 ... 0 0 0.0\n",
"499 NaN The Bahamas 24.2500 ... 0 0 0.0\n",
"500 NaN The Gambia 13.4667 ... 0 0 0.0\n",
"\n",
"[501 rows x 66 columns]>"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9RFpMsugk8AO",
"colab_type": "code",
"colab": {}
},
"source": [
"columns = list(df.columns)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "s8F9kSCYlEeP",
"colab_type": "code",
"outputId": "4cd9590a-cc66-4351-9793-92cab0ac64ff",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"columns"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Province/State',\n",
" 'Country/Region',\n",
" 'Lat',\n",
" 'Long',\n",
" '1/22/20',\n",
" '1/23/20',\n",
" '1/24/20',\n",
" '1/25/20',\n",
" '1/26/20',\n",
" '1/27/20',\n",
" '1/28/20',\n",
" '1/29/20',\n",
" '1/30/20',\n",
" '1/31/20',\n",
" '2/1/20',\n",
" '2/2/20',\n",
" '2/3/20',\n",
" '2/4/20',\n",
" '2/5/20',\n",
" '2/6/20',\n",
" '2/7/20',\n",
" '2/8/20',\n",
" '2/9/20',\n",
" '2/10/20',\n",
" '2/11/20',\n",
" '2/12/20',\n",
" '2/13/20',\n",
" '2/14/20',\n",
" '2/15/20',\n",
" '2/16/20',\n",
" '2/17/20',\n",
" '2/18/20',\n",
" '2/19/20',\n",
" '2/20/20',\n",
" '2/21/20',\n",
" '2/22/20',\n",
" '2/23/20',\n",
" '2/24/20',\n",
" '2/25/20',\n",
" '2/26/20',\n",
" '2/27/20',\n",
" '2/28/20',\n",
" '2/29/20',\n",
" '3/1/20',\n",
" '3/2/20',\n",
" '3/3/20',\n",
" '3/4/20',\n",
" '3/5/20',\n",
" '3/6/20',\n",
" '3/7/20',\n",
" '3/8/20',\n",
" '3/9/20',\n",
" '3/10/20',\n",
" '3/11/20',\n",
" '3/12/20',\n",
" '3/13/20',\n",
" '3/14/20',\n",
" '3/15/20',\n",
" '3/16/20',\n",
" '3/17/20',\n",
" '3/18/20',\n",
" '3/19/20',\n",
" '3/20/20',\n",
" '3/21/20',\n",
" '3/22/20',\n",
" '3/23/20']"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "su2jKPXklGVG",
"colab_type": "code",
"colab": {}
},
"source": [
"def zero_counter(num):\n",
" zero = 0\n",
" for i in num:\n",
" if i == 0:\n",
" zero += 1\n",
" return zero "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SrJd_RghmpCO",
"colab_type": "code",
"colab": {}
},
"source": [
"drop_list = []\n",
"for i in columns:\n",
" if zero_counter(df[i]) >=350:\n",
" drop_list.append(i)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LngzrXhunh95",
"colab_type": "code",
"colab": {}
},
"source": [
"df = df.drop(columns = drop_list,axis = 1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dcc_4l4en1f3",
"colab_type": "code",
"outputId": "931d7461-b568-46d0-bacd-50ef1998b61a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
}
},
"source": [
"df.head()"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Province/State</th>\n",
" <th>Country/Region</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>3/5/20</th>\n",
" <th>3/6/20</th>\n",
" <th>3/7/20</th>\n",
" <th>3/8/20</th>\n",
" <th>3/9/20</th>\n",
" <th>3/10/20</th>\n",
" <th>3/11/20</th>\n",
" <th>3/12/20</th>\n",
" <th>3/13/20</th>\n",
" <th>3/14/20</th>\n",
" <th>3/15/20</th>\n",
" <th>3/16/20</th>\n",
" <th>3/17/20</th>\n",
" <th>3/18/20</th>\n",
" <th>3/19/20</th>\n",
" <th>3/20/20</th>\n",
" <th>3/21/20</th>\n",
" <th>3/22/20</th>\n",
" <th>3/23/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>Thailand</td>\n",
" <td>15.0000</td>\n",
" <td>101.0000</td>\n",
" <td>47</td>\n",
" <td>48</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>53</td>\n",
" <td>59</td>\n",
" <td>70</td>\n",
" <td>75</td>\n",
" <td>82</td>\n",
" <td>114</td>\n",
" <td>147</td>\n",
" <td>177</td>\n",
" <td>212</td>\n",
" <td>272</td>\n",
" <td>322</td>\n",
" <td>411</td>\n",
" <td>599</td>\n",
" <td>599.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>Japan</td>\n",
" <td>36.0000</td>\n",
" <td>138.0000</td>\n",
" <td>360</td>\n",
" <td>420</td>\n",
" <td>461</td>\n",
" <td>502</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>639</td>\n",
" <td>639</td>\n",
" <td>701</td>\n",
" <td>773</td>\n",
" <td>839</td>\n",
" <td>825</td>\n",
" <td>878</td>\n",
" <td>889</td>\n",
" <td>924</td>\n",
" <td>963</td>\n",
" <td>1007</td>\n",
" <td>1086</td>\n",
" <td>1086.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>Singapore</td>\n",
" <td>1.2833</td>\n",
" <td>103.8333</td>\n",
" <td>117</td>\n",
" <td>130</td>\n",
" <td>138</td>\n",
" <td>150</td>\n",
" <td>150</td>\n",
" <td>160</td>\n",
" <td>178</td>\n",
" <td>178</td>\n",
" <td>200</td>\n",
" <td>212</td>\n",
" <td>226</td>\n",
" <td>243</td>\n",
" <td>266</td>\n",
" <td>313</td>\n",
" <td>345</td>\n",
" <td>385</td>\n",
" <td>432</td>\n",
" <td>455</td>\n",
" <td>455.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>Nepal</td>\n",
" <td>28.1667</td>\n",
" <td>84.2500</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>Malaysia</td>\n",
" <td>2.5000</td>\n",
" <td>112.5000</td>\n",
" <td>50</td>\n",
" <td>83</td>\n",
" <td>93</td>\n",
" <td>99</td>\n",
" <td>117</td>\n",
" <td>129</td>\n",
" <td>149</td>\n",
" <td>149</td>\n",
" <td>197</td>\n",
" <td>238</td>\n",
" <td>428</td>\n",
" <td>566</td>\n",
" <td>673</td>\n",
" <td>790</td>\n",
" <td>900</td>\n",
" <td>1030</td>\n",
" <td>1183</td>\n",
" <td>1306</td>\n",
" <td>1306.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Province/State Country/Region Lat ... 3/21/20 3/22/20 3/23/20\n",
"0 NaN Thailand 15.0000 ... 411 599 599.0\n",
"1 NaN Japan 36.0000 ... 1007 1086 1086.0\n",
"2 NaN Singapore 1.2833 ... 432 455 455.0\n",
"3 NaN Nepal 28.1667 ... 1 2 2.0\n",
"4 NaN Malaysia 2.5000 ... 1183 1306 1306.0\n",
"\n",
"[5 rows x 23 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hBlM2oH2n8E1",
"colab_type": "code",
"outputId": "774f96a6-5d93-4228-bad5-bf9e2a273bf9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
}
},
"source": [
"df.describe"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<bound method NDFrame.describe of Province/State Country/Region Lat ... 3/21/20 3/22/20 3/23/20\n",
"0 NaN Thailand 15.0000 ... 411 599 599.0\n",
"1 NaN Japan 36.0000 ... 1007 1086 1086.0\n",
"2 NaN Singapore 1.2833 ... 432 455 455.0\n",
"3 NaN Nepal 28.1667 ... 1 2 2.0\n",
"4 NaN Malaysia 2.5000 ... 1183 1306 1306.0\n",
".. ... ... ... ... ... ... ...\n",
"496 NaN Jersey 49.1900 ... 0 0 0.0\n",
"497 NaN Puerto Rico 18.2000 ... 0 0 0.0\n",
"498 NaN Republic of the Congo -1.4400 ... 0 0 0.0\n",
"499 NaN The Bahamas 24.2500 ... 0 0 0.0\n",
"500 NaN The Gambia 13.4667 ... 0 0 0.0\n",
"\n",
"[501 rows x 23 columns]>"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LOtkz6Enopog",
"colab_type": "code",
"outputId": "92847c32-9b02-411b-bbc5-f3a78fe3f73a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"source": [
"columns = df.columns\n",
"columns"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Province/State', 'Country/Region', 'Lat', 'Long', '3/5/20', '3/6/20',\n",
" '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
" '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
" '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xXUej_91pKo5",
"colab_type": "text"
},
"source": [
"# plot the graph country wise and date wise"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D5Puif1ApX7J",
"colab_type": "code",
"outputId": "6e559854-c2ca-4bbd-a676-8a346407a8a2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"all_country = list(df['Country/Region'])\n",
"date = columns[4:]\n",
"all_country"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Thailand',\n",
" 'Japan',\n",
" 'Singapore',\n",
" 'Nepal',\n",
" 'Malaysia',\n",
" 'Canada',\n",
" 'Australia',\n",
" 'Australia',\n",
" 'Australia',\n",
" 'Cambodia',\n",
" 'Sri Lanka',\n",
" 'Germany',\n",
" 'Finland',\n",
" 'United Arab Emirates',\n",
" 'Philippines',\n",
" 'India',\n",
" 'Italy',\n",
" 'Sweden',\n",
" 'Spain',\n",
" 'Australia',\n",
" 'Belgium',\n",
" 'Egypt',\n",
" 'Australia',\n",
" 'Lebanon',\n",
" 'Iraq',\n",
" 'Oman',\n",
" 'Afghanistan',\n",
" 'Bahrain',\n",
" 'Kuwait',\n",
" 'Algeria',\n",
" 'Croatia',\n",
" 'Switzerland',\n",
" 'Austria',\n",
" 'Israel',\n",
" 'Pakistan',\n",
" 'Brazil',\n",
" 'Georgia',\n",
" 'Greece',\n",
" 'North Macedonia',\n",
" 'Norway',\n",
" 'Romania',\n",
" 'Estonia',\n",
" 'San Marino',\n",
" 'Belarus',\n",
" 'Iceland',\n",
" 'Lithuania',\n",
" 'Mexico',\n",
" 'New Zealand',\n",
" 'Nigeria',\n",
" 'Australia',\n",
" 'Ireland',\n",
" 'Luxembourg',\n",
" 'Monaco',\n",
" 'Qatar',\n",
" 'Ecuador',\n",
" 'Azerbaijan',\n",
" 'Armenia',\n",
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]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gYCAlcfI_JM3",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IyRlM96h_jZi",
"colab_type": "code",
"colab": {}
},
"source": [
"df2 = df.drop(['Province/State','Lat','Long'],axis = 1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FixKV7Sk_3BP",
"colab_type": "code",
"colab": {}
},
"source": [
"df2.head()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X_2PppjFRGoJ",
"colab_type": "code",
"outputId": "50b6f1f8-eb45-4493-c216-cb053b553f80",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
}
},
"source": [
"df3 = df2.T\n",
"df3.head()"
],
"execution_count": 28,
"outputs": [
{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/7/20</th>\n",
" <td>50</td>\n",
" <td>461</td>\n",
" <td>138</td>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>21</td>\n",
" <td>28</td>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>799</td>\n",
" <td>15</td>\n",
" <td>45</td>\n",
" <td>6</td>\n",
" <td>34</td>\n",
" <td>5883</td>\n",
" <td>161</td>\n",
" <td>500</td>\n",
" <td>7</td>\n",
" <td>169</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>54</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>61</td>\n",
" <td>17</td>\n",
" <td>12</td>\n",
" <td>268</td>\n",
" <td>79</td>\n",
" <td>43</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>46</td>\n",
" <td>3</td>\n",
" <td>147</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/8/20</th>\n",
" <td>50</td>\n",
" <td>502</td>\n",
" <td>150</td>\n",
" <td>1</td>\n",
" <td>99</td>\n",
" <td>27</td>\n",
" <td>38</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1040</td>\n",
" <td>23</td>\n",
" <td>45</td>\n",
" <td>10</td>\n",
" <td>39</td>\n",
" <td>7375</td>\n",
" <td>203</td>\n",
" <td>673</td>\n",
" <td>7</td>\n",
" <td>200</td>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" <td>32</td>\n",
" <td>60</td>\n",
" <td>16</td>\n",
" <td>4</td>\n",
" <td>85</td>\n",
" <td>64</td>\n",
" <td>19</td>\n",
" <td>12</td>\n",
" <td>337</td>\n",
" <td>104</td>\n",
" <td>61</td>\n",
" <td>6</td>\n",
" <td>20</td>\n",
" <td>13</td>\n",
" <td>73</td>\n",
" <td>3</td>\n",
" <td>176</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 501 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 ... 499 500\n",
"Country/Region Thailand Japan ... The Bahamas The Gambia\n",
"3/5/20 47 360 ... 0 0\n",
"3/6/20 48 420 ... 0 0\n",
"3/7/20 50 461 ... 0 0\n",
"3/8/20 50 502 ... 0 0\n",
"\n",
"[5 rows x 501 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RNrU0eHGSeQh",
"colab_type": "code",
"outputId": "9b946cc6-dd09-4d6e-d161-43b37622c0a3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"my_data = list(df3[0])\n",
"\n",
"print(my_data)\n",
"\n",
"single_dic = pd.DataFrame({my_data[0] : my_data[1:],'date':[i for i in range(len(my_data)-1)]})"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"['Thailand', 47, 48, 50, 50, 50, 53, 59, 70, 75, 82, 114, 147, 177, 212, 272, 322, 411, 599, 599.0]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1xT8KAJqJFkC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "d26cfeeb-ab2b-4f70-a4a7-ffef05826544"
},
"source": [
"single_dic.head()"
],
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Thailand</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>48.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>50.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Thailand date\n",
"0 47.0 0\n",
"1 48.0 1\n",
"2 50.0 2\n",
"3 50.0 3\n",
"4 50.0 4"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ir3xiKQtJ7yw",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"outputId": "d263b476-8ef3-4f21-faa6-1ed99639d979"
},
"source": [
"single_dic.Thailand.plot()"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa27c473c18>"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
},
{
"output_type": "display_data",
"data": {
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unGCdDyaO1uEHd41F5GgtJoRr4a3mKko02Ph/GQ0KURRRWduIoiu17Rv+shpc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\nIjfHIiAicnP/H4fY+qpdrOE3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iB5MfBYBKLPN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 302
},
"outputId": "1568a91c-26b8-48a8-d401-83d6f4ab312e"
},
"source": [
"sns.scatterplot(single_dic.Thailand,single_dic.date)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa27c005208>"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
},
{
"output_type": "display_data",
"data": {
"image/png": 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gQYPg4eGBkSNHIi8vDykpKXjqqaesWaPLYBsSInIl/Z6Al0gk8PK6lTdSqRStra0YNGgQ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\nIhKFAUJERKL8H1tCw89Gl4ZMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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