diff --git a/230335.ipynb b/230335.ipynb new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/230335.ipynb @@ -0,0 +1 @@ + diff --git a/Copy_of_Assignment_1_1_by_chirag.ipynb b/Copy_of_Assignment_1_1_by_chirag.ipynb new file mode 100644 index 0000000..900e1db --- /dev/null +++ b/Copy_of_Assignment_1_1_by_chirag.ipynb @@ -0,0 +1,5709 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3eJmduQENaek" + }, + "source": [ + "# Assignment 1.1 - Pandas Data Analysis Practice\n", + "\n", + "*This assignment is a part of the project [\"Customer Churn Analysis: From Data To Strategy\"]*\n", + "\n", + "In this assignment, you'll get to practice some of the concepts and skills covered in pandas.\n", + "\n", + "As you go through this notebook, you will find a **???** in certain places. To complete this assignment, you must replace all the **???** with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.\n", + "\n", + "Some things to keep in mind:\n", + "\n", + "* Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.\n", + "* In some cases, you may need to add some code cells or new statements before or after the line of code containing the **???**.\n", + "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n", + "\n", + "\n", + "If you are stuck, you can ask for help on the Whatsapp group. Please do not use **ChatGPT or any other LLM to get solutions to the questions.**\n", + "\n", + "\n", + "Deadline : **22 May, 11:59 pm**\n" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qmBvIMj63uUa", + "outputId": "1394f8fc-0d5b-4791-d9ea-8f8e39920e78" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nzCoPd_zNae4" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yKwMduIXNae5" + }, + "source": [ + "In this assignment, we're going to analyze an operate on data from a CSV file. Let's begin by downloading the CSV file." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xa18DHWvNae8" + }, + "source": [ + "Let's load the data from the CSV file into a Pandas data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2QPUhz19Nae-" + }, + "outputs": [], + "source": [ + "countries_df = pd.read_csv('countries.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7_kR3k1wNae_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "outputId": "199cfd5f-aba7-4404-9415-69194ed8cf81" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " location continent population life_expectancy \\\n", + "0 Afghanistan Asia 38928341 64.83 \n", + "1 Albania Europe 2877800 78.57 \n", + "2 Algeria Africa 43851043 76.88 \n", + "3 Andorra Europe 77265 83.73 \n", + "4 Angola Africa 32866268 61.15 \n", + ".. ... ... ... ... \n", + "205 Vietnam Asia 97338583 75.40 \n", + "206 Western Sahara Africa 597330 70.26 \n", + "207 Yemen Asia 29825968 66.12 \n", + "208 Zambia Africa 18383956 63.89 \n", + "209 Zimbabwe Africa 14862927 61.49 \n", + "\n", + " hospital_beds_per_thousand gdp_per_capita \n", + "0 0.50 1803.987 \n", + "1 2.89 11803.431 \n", + "2 1.90 13913.839 \n", + "3 NaN NaN \n", + "4 NaN 5819.495 \n", + ".. ... ... \n", + "205 2.60 6171.884 \n", + "206 NaN NaN \n", + "207 0.70 1479.147 \n", + "208 2.00 3689.251 \n", + "209 1.70 1899.775 \n", + "\n", + "[210 rows x 6 columns]" + ], + "text/html": [ + "\n", + "
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locationcontinentpopulationlife_expectancyhospital_beds_per_thousandgdp_per_capita
0AfghanistanAsia3892834164.830.501803.987
1AlbaniaEurope287780078.572.8911803.431
2AlgeriaAfrica4385104376.881.9013913.839
3AndorraEurope7726583.73NaNNaN
4AngolaAfrica3286626861.15NaN5819.495
.....................
205VietnamAsia9733858375.402.606171.884
206Western SaharaAfrica59733070.26NaNNaN
207YemenAsia2982596866.120.701479.147
208ZambiaAfrica1838395663.892.003689.251
209ZimbabweAfrica1486292761.491.701899.775
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\"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.560056151115564,\n \"min\": 53.28,\n \"max\": 86.75,\n \"num_unique_values\": 197,\n \"samples\": [\n 74.25,\n 76.16,\n 79.19\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hospital_beds_per_thousand\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.4649168855927868,\n \"min\": 0.1,\n \"max\": 13.8,\n \"num_unique_values\": 99,\n \"samples\": [\n 2.397,\n 0.3,\n 2.54\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gdp_per_capita\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19820.802488409416,\n \"min\": 661.24,\n \"max\": 116935.6,\n \"num_unique_values\": 183,\n \"samples\": [\n 50669.315,\n 3601.006,\n 1569.888\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "countries_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KE8FlOjINafA" + }, + "source": [ + "**Q1: How many countries does the dataframe contain?**\n", + "\n", + "Hint: Use the `.shape` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZD4-AM-CNafB" + }, + "outputs": [], + "source": [ + "num_countries = countries_df.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Iy8SMfkwNafC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6f00f29d-84f6-46d2-99dc-00bad9eb4e61" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "There are 210 countries in the dataset\n" + ] + } + ], + "source": [ + "print('There are {} countries in the dataset'.format(num_countries))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m9eCXKMCNafE" + }, + "source": [ + "**Q2: Retrieve a list of continents from the dataframe?**\n", + "\n", + "*Hint: Use the `.unique` method of a series.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xL8zFetJNafF" + }, + "outputs": [], + "source": [ + "continents = countries_df[\"continent\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "40j7HkWZNafG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c5fe26e1-f9f9-4b04-c106-d2282616d1ef" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['Asia', 'Europe', 'Africa', 'North America', 'South America',\n", + " 'Oceania'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "continents" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yr9rdD5ZNafK" + }, + "source": [ + "**Q3: What is the total population of all the countries listed in this dataset?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IrGeG0vwNafL" + }, + "outputs": [], + "source": [ + "total_population = countries_df[\"population\"].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FUJRPPMcNafM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ccb6d9df-ab33-4e53-fcbd-4494a3adc11e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The total population is 7757980095.\n" + ] + } + ], + "source": [ + "print('The total population is {}.'.format(int(total_population)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nvs9htXuNafO" + }, + "source": [ + "**Q: (Optional) What is the overall life expectancy across in the world?**\n", + "\n", + "*Hint: You'll need to take a weighted average of life expectancy using populations as weights.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8TXTdZ0ENafP" + }, + "outputs": [], + "source": [ + "x= countries_df[\"population\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "97C4RlcPNafP" + }, + "outputs": [], + "source": [ + "y= countries_df[\"life_expectancy\"]" + ] + }, + { + "cell_type": "code", + "source": [ + "mean= ((x*y).sum())/x.sum()" + ], + "metadata": { + "id": "Cp6-hlNS7kvg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "mean" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9xesg93-_Ypv", + "outputId": "f99e34c8-9d36-4c19-8af6-6244a01463d1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "72.72165193409664" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H1H_0zo8NafR" + }, + "source": [ + "**Q4: Create a dataframe containing 10 countries with the highest population.**\n", + "\n", + "*Hint: Chain the `sort_values` and `head` methods.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3BBebkbyNafR" + }, + "outputs": [], + "source": [ + "most_populous_df = countries_df.sort_values(by=\"population\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L83PYtCaNafS" + }, + "outputs": [], + "source": [ + "a=most_populous_df.tail(10)" + ] + }, + { + "cell_type": "code", + "source": [ + "a.iloc[::-1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "AXsWwb3nAmlg", + "outputId": "d7b17b4d-b566-444c-8188-3dd6908e6744" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " location continent population life_expectancy \\\n", + "41 China Asia 1439323774 76.91 \n", + "90 India Asia 1380004385 69.66 \n", + "199 United States North America 331002647 78.86 \n", + "91 Indonesia Asia 273523621 71.72 \n", + "145 Pakistan Asia 220892331 67.27 \n", + "27 Brazil South America 212559409 75.88 \n", + "141 Nigeria Africa 206139587 54.69 \n", + "15 Bangladesh Asia 164689383 72.59 \n", + "157 Russia Europe 145934460 72.58 \n", + "125 Mexico North America 128932753 75.05 \n", + "\n", + " hospital_beds_per_thousand gdp_per_capita \n", + "41 4.34 15308.712 \n", + "90 0.53 6426.674 \n", + "199 2.77 54225.446 \n", + "91 1.04 11188.744 \n", + "145 0.60 5034.708 \n", + "27 2.20 14103.452 \n", + "141 NaN 5338.454 \n", + "15 0.80 3523.984 \n", + "157 8.05 24765.954 \n", + "125 1.38 17336.469 " + ], + "text/html": [ + "\n", + "
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locationcontinentpopulationlife_expectancyhospital_beds_per_thousandgdp_per_capita
41ChinaAsia143932377476.914.3415308.712
90IndiaAsia138000438569.660.536426.674
199United StatesNorth America33100264778.862.7754225.446
91IndonesiaAsia27352362171.721.0411188.744
145PakistanAsia22089233167.270.605034.708
27BrazilSouth America21255940975.882.2014103.452
141NigeriaAfrica20613958754.69NaN5338.454
15BangladeshAsia16468938372.590.803523.984
157RussiaEurope14593446072.588.0524765.954
125MexicoNorth America12893275375.051.3817336.469
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locationcontinentpopulationlife_expectancyhospital_beds_per_thousandgdp_per_capitagdp
0AfghanistanAsia3892834164.830.501803.9877.022622e+10
1AlbaniaEurope287780078.572.8911803.4313.396791e+10
2AlgeriaAfrica4385104376.881.9013913.8396.101364e+11
3AndorraEurope7726583.73NaNNaNNaN
4AngolaAfrica3286626861.15NaN5819.4951.912651e+11
........................
205VietnamAsia9733858375.402.606171.8846.007624e+11
206Western SaharaAfrica59733070.26NaNNaNNaN
207YemenAsia2982596866.120.701479.1474.411699e+10
208ZambiaAfrica1838395663.892.003689.2516.782303e+10
209ZimbabweAfrica1486292761.491.701899.7752.823622e+10
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locationcontinentpopulationlife_expectancyhospital_beds_per_thousandgdp_per_capitagdp
63EthiopiaAfrica11496358366.600.301729.9271.988786e+11
15BangladeshAsia16468938372.590.803523.9845.803628e+11
145PakistanAsia22089233167.270.605034.7081.112128e+12
141NigeriaAfrica20613958754.69NaN5338.4541.100467e+12
90IndiaAsia138000438569.660.536426.6748.868838e+12
151PhilippinesAsia10958108571.231.007599.1888.327273e+11
58EgyptAfrica10233440371.991.6010550.2061.079649e+12
91IndonesiaAsia27352362171.721.0411188.7443.060386e+12
27BrazilSouth America21255940975.882.2014103.4522.997821e+12
41ChinaAsia143932377476.914.3415308.7122.203419e+13
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\"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.865998562753727,\n \"min\": 54.69,\n \"max\": 84.63,\n \"num_unique_values\": 14,\n \"samples\": [\n 76.91,\n 72.58,\n 66.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hospital_beds_per_thousand\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.7111574668275322,\n \"min\": 0.3,\n \"max\": 13.05,\n \"num_unique_values\": 13,\n \"samples\": [\n 13.05,\n 1.38,\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gdp_per_capita\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14875.982836683967,\n \"min\": 1729.927,\n \"max\": 54225.446,\n \"num_unique_values\": 14,\n \"samples\": [\n 15308.712,\n 24765.954,\n 1729.927\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gdp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6769601807621.744,\n \"min\": 198878606248.44098,\n \"max\": 22034193130919.086,\n \"num_unique_values\": 14,\n \"samples\": [\n 22034193130919.086,\n 3614206123374.8403,\n 198878606248.44098\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 41 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_NA6oQnVNafZ" + }, + "source": [ + "**Q6: Create a data frame that counts the number countries in each continent?**\n", + "\n", + "*Hint: Use `groupby`, select the `location` column and aggregate using `count`.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Co-tfE7BNafv" + }, + "outputs": [], + "source": [ + "country_counts_df = countries_df.groupby([\"continent\"])[\"continent\"].count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6fVwjKFFNafw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9a5f513c-2b5d-4a7a-d251-4fbc6d58459e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "continent\n", + "Africa 55\n", + "Asia 47\n", + "Europe 51\n", + "North America 36\n", + "Oceania 8\n", + "South America 13\n", + "Name: continent, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "country_counts_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ut6cNp0yNafy" + }, + "source": [ + "**Q7: Create a data frame showing the total population of each continent.**\n", + "\n", + "*Hint: Use `groupby`, select the population column and aggregate using `sum`.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Hqnwb5V0Nafy" + }, + "outputs": [], + "source": [ + "continent_populations_df = countries_df.groupby([\"continent\"])[\"population\"].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "D5gAu5IANafz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f5c1aaf6-3097-4507-dc09-8ddbb56b9144" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "continent\n", + "Africa 1339423921\n", + "Asia 4607388081\n", + "Europe 748506210\n", + "North America 591242473\n", + "Oceania 40958320\n", + "South America 430461090\n", + "Name: population, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ], + "source": [ + "continent_populations_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Am9h8-R4Naf1" + }, + "source": [ + "Let's download another CSV file containing overall Covid-19 stats for various countires, and read the data into another Pandas data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sOybiAeVNaf6" + }, + "outputs": [], + "source": [ + "covid_data_df = pd.read_csv('covid-countries-data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "id": "k9PuPoYUNaf7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "outputId": "56598118-2557-45fb-e20e-44633f2633b5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " location total_cases total_deaths total_tests\n", + "0 Afghanistan 38243.0 1409.0 NaN\n", + "1 Albania 9728.0 296.0 NaN\n", + "2 Algeria 45158.0 1525.0 NaN\n", + "3 Andorra 1199.0 53.0 NaN\n", + "4 Angola 2729.0 109.0 NaN\n", + ".. ... ... ... ...\n", + "207 Western Sahara 766.0 1.0 NaN\n", + "208 World 26059065.0 863535.0 NaN\n", + "209 Yemen 1976.0 571.0 NaN\n", + "210 Zambia 12415.0 292.0 NaN\n", + "211 Zimbabwe 6638.0 206.0 97272.0\n", + "\n", + "[212 rows x 4 columns]" + ], + "text/html": [ + "\n", + "
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4Angola2729.0109.0NaN
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711225.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 61 + } + ], + "source": [ + "combined_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DMFIKAXvNagG" + }, + "source": [ + "**Q10: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_KdWa_juNagH" + }, + "outputs": [], + "source": [ + "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IDdn-5CONagI" + }, + "outputs": [], + "source": [ + "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "93n1py5BNagK" + }, + "outputs": [], + "source": [ + 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194Turks and Caicos IslandsNorth America3871880.22NaNNaNNaN555.04.0NaNNaN14334.418100103.311121
200United States Virgin IslandsNorth America10442380.58NaNNaNNaN1144.015.0NaNNaN10955.440851143.646515
202UzbekistanAsia3346919971.724.06253.1042.092864e+1142540.0329.0NaNNaN1271.0193639.829933
203VaticanEurope80975.12NaNNaNNaN12.00.0NaNNaN14833.1273180.000000
204VenezuelaSouth America2843594372.060.816745.0224.761605e+1148883.0398.0NaNNaN1719.05675913.996371
206Western SaharaAfrica59733070.26NaNNaNNaN766.01.0NaNNaN1282.3732281.674116
207YemenAsia2982596866.120.71479.1474.411699e+101976.0571.0NaNNaN66.25099319.144391
208ZambiaAfrica1838395663.892.03689.2516.782303e+1012415.0292.0NaNNaN675.31710815.883415
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42ColombiaSouth America5088288477.291.7113254.9496.744500e+11633339.020348.02647702.052035.21875812446.994946399.898716
150PeruSouth America3297184676.741.6012236.7064.034668e+11663437.029259.0584232.017719.11709220121.318048887.393445
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87Hong KongAsia749698884.86NaN56054.9204.202431e+11NaNNaN780410.0104096.471810NaNNaN
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[\n 83898416.0,\n 1271295.0,\n 37176827.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tests_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 92988.064035842,\n \"min\": 9860.14003749691,\n \"max\": 254750.1597634993,\n \"num_unique_values\": 10,\n \"samples\": [\n 253467.50776890313,\n 9860.14003749691,\n 254750.1597634993\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cases_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6344.933830014123,\n \"min\": 2792.3143157258883,\n \"max\": 20121.318048131125,\n \"num_unique_values\": 9,\n \"samples\": [\n 18808.224104537287,\n 4738.570966525473,\n 6886.65309070935\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"deaths_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 267.034582574439,\n \"min\": 48.823033268839936,\n \"max\": 887.3934446982435,\n \"num_unique_values\": 9,\n \"samples\": [\n 582.3313142538893,\n 510.46765440585915,\n 119.3275392254852\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 71 + } + ], + "source": [ + "highest_cases_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GX-WdUFHNagb" + }, + "source": [ + "**Q13: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hH19qct0Nagc" + }, + "outputs": [], + "source": [ + "highest_deaths_df = combined_df.sort_values(by=\"deaths_per_million\").tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lWhydo7ENagd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "outputId": "fd8ac747-80fa-499b-adb0-347dfa8ee55d" + }, + "outputs": [ + { + "output_type": "execute_result", + 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locationcontinentpopulationlife_expectancyhospital_beds_per_thousandgdp_per_capitagdptotal_casestotal_deathstotal_teststests_per_millioncases_per_milliondeaths_per_million
27BrazilSouth America21255940975.882.2014103.4522.997821e+123997865.0123780.04797948.022572.26825518808.224105582.331314
97ItalyEurope6046182883.513.1835220.0842.129471e+12271515.035497.05214766.086248.8974034490.684602587.097697
40ChileSouth America1911620980.182.1122767.0374.352194e+11414739.011344.02458762.0128621.84128721695.671982593.423100
198United KingdomEurope6788600481.322.5439753.2442.698689e+12338676.041514.013447568.0198090.4340754988.892850611.525168
177SpainEurope4675478383.562.9734272.3601.602397e+12479554.029194.06416533.0137238.00193910256.790198624.406705
3AndorraEurope7726583.73NaNNaNNaN1199.053.0NaNNaN15518.022390685.950948
18BelgiumEurope1158961681.635.6442658.5764.943965e+1185817.09898.02281853.0196887.7139677404.645676854.040375
150PeruSouth America3297184676.741.6012236.7064.034668e+11663437.029259.0584232.017719.11709220121.318048887.393445
162San MarinoEurope3393884.973.8056861.4701.929765e+09735.042.0NaNNaN21657.1394901237.550828
87Hong KongAsia749698884.86NaN56054.9204.202431e+11NaNNaN780410.0104096.471810NaNNaN
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\"life_expectancy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.204690312651133,\n \"min\": 75.88,\n \"max\": 84.97,\n \"num_unique_values\": 10,\n \"samples\": [\n 84.97,\n 83.51,\n 83.73\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hospital_beds_per_thousand\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.2675285288420814,\n \"min\": 1.6,\n \"max\": 5.64,\n \"num_unique_values\": 8,\n \"samples\": [\n 3.18,\n 5.64,\n 2.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gdp_per_capita\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 16230.409234274475,\n \"min\": 12236.706,\n \"max\": 56861.47,\n \"num_unique_values\": 9,\n \"samples\": [\n 56861.47,\n 35220.084,\n 42658.576\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gdp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1131831495984.7166,\n \"min\": 1929764568.8600001,\n 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2281853.0,\n 4797948.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tests_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 68806.1028862353,\n \"min\": 17719.117091593842,\n \"max\": 198090.43407533606,\n \"num_unique_values\": 8,\n \"samples\": [\n 86248.89740349895,\n 196887.7139673998,\n 22572.26825466004\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cases_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7156.832665095871,\n \"min\": 4490.684601861525,\n \"max\": 21695.67198182443,\n \"num_unique_values\": 9,\n \"samples\": [\n 20121.318048131125,\n 4490.684601861525,\n 15518.022390474342\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"deaths_per_million\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 219.31641882952675,\n \"min\": 582.3313142538893,\n \"max\": 1237.5508279804349,\n \"num_unique_values\": 9,\n \"samples\": [\n 887.3934446982435,\n 587.0976974100089,\n 685.9509480359801\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 73 + } + ], + "source": [ + "highest_deaths_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nUTDc8q4Nage" + }, + "source": [ + "**(Optional) Q: Count number of countries that feature in both the lists of \"highest number of tests per million\" and \"highest number of cases per million\".**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nXat9wCrNagf", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dfe4275b-5628-43b5-8bd3-422fb75e27d1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ], + "source": [ + "pd.merge(highest_cases_df,highest_deaths_df,on=\"location\").shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uHWghQHUNagh" + }, + "source": [ + "**(Optional) Q: Count number of countries that feature in both the lists \"20 countries with lowest GDP per capita\" and \"20 countries with the lowest number of hospital beds per thousand population\". Only consider countries with a population higher than 10 million while creating the list.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5C7khtW3Nagi" + }, + "outputs": [], + "source": [ + "cc= combined_df[combined_df['population']>100000000]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RKk0r3umNagj" + }, + "outputs": [], + "source": [ + "cc1=combined_df.sort_values(by=\"gdp_per_capita\").head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XH7POtliNagj" + }, + "outputs": [], + "source": [ + "cc2=combined_df.sort_values(by=\"hospital_beds_per_thousand\").head(20)" + ] + }, + { + "cell_type": "code", + "source": [ + "pd.merge(cc1,cc2,on=\"location\").shape[0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pReXGGslS9is", + "outputId": "1e9d56e3-d09e-437c-caf6-371ebe71d64d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "10" + ] + }, + "metadata": {}, + "execution_count": 85 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wvjafOzENagk" + }, + "source": [ + "## Submission\n", + "\n", + "Congratulations on making it this far! You've reached the end of this assignment, and you just completed your first real-world data analysis problem.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LBOb5DhHNagl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cbac7b96-0022-4e4c-b63d-bcf945c5dd5a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Thank you so much\n" + ] + } + ], + "source": [ + "print(\"Thank you so much\")" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "NX9I-esQTSao" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + }, + "colab": { + "provenance": [], + "include_colab_link": true + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file