|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "### In each cell complete the task using pandas" |
| 8 | + ] |
| 9 | + }, |
| 10 | + { |
| 11 | + "cell_type": "code", |
| 12 | + "execution_count": null, |
| 13 | + "metadata": {}, |
| 14 | + "outputs": [], |
| 15 | + "source": [ |
| 16 | + "import pandas as pd\n", |
| 17 | + "import numpy as np" |
| 18 | + ] |
| 19 | + }, |
| 20 | + { |
| 21 | + "cell_type": "markdown", |
| 22 | + "metadata": {}, |
| 23 | + "source": [ |
| 24 | + "Read in the titanic.csv file in the `~/data` directory as a pandas dataframe called **df**" |
| 25 | + ] |
| 26 | + }, |
| 27 | + { |
| 28 | + "cell_type": "code", |
| 29 | + "execution_count": null, |
| 30 | + "metadata": {}, |
| 31 | + "outputs": [], |
| 32 | + "source": [] |
| 33 | + }, |
| 34 | + { |
| 35 | + "cell_type": "markdown", |
| 36 | + "metadata": {}, |
| 37 | + "source": [ |
| 38 | + "Display the head of the dataframe" |
| 39 | + ] |
| 40 | + }, |
| 41 | + { |
| 42 | + "cell_type": "code", |
| 43 | + "execution_count": null, |
| 44 | + "metadata": {}, |
| 45 | + "outputs": [], |
| 46 | + "source": [] |
| 47 | + }, |
| 48 | + { |
| 49 | + "cell_type": "markdown", |
| 50 | + "metadata": {}, |
| 51 | + "source": [ |
| 52 | + "What is the percentage of people who survived? (hint find the mean of the survival column)" |
| 53 | + ] |
| 54 | + }, |
| 55 | + { |
| 56 | + "cell_type": "code", |
| 57 | + "execution_count": null, |
| 58 | + "metadata": {}, |
| 59 | + "outputs": [], |
| 60 | + "source": [] |
| 61 | + }, |
| 62 | + { |
| 63 | + "cell_type": "markdown", |
| 64 | + "metadata": {}, |
| 65 | + "source": [ |
| 66 | + "How many women and how many men survived?" |
| 67 | + ] |
| 68 | + }, |
| 69 | + { |
| 70 | + "cell_type": "code", |
| 71 | + "execution_count": null, |
| 72 | + "metadata": {}, |
| 73 | + "outputs": [], |
| 74 | + "source": [] |
| 75 | + }, |
| 76 | + { |
| 77 | + "cell_type": "markdown", |
| 78 | + "metadata": {}, |
| 79 | + "source": [ |
| 80 | + "What is the percentage of people that survied who paid a fare less than 10?" |
| 81 | + ] |
| 82 | + }, |
| 83 | + { |
| 84 | + "cell_type": "code", |
| 85 | + "execution_count": null, |
| 86 | + "metadata": {}, |
| 87 | + "outputs": [], |
| 88 | + "source": [] |
| 89 | + }, |
| 90 | + { |
| 91 | + "cell_type": "markdown", |
| 92 | + "metadata": {}, |
| 93 | + "source": [ |
| 94 | + "What is the average age of those who didn't survive?" |
| 95 | + ] |
| 96 | + }, |
| 97 | + { |
| 98 | + "cell_type": "code", |
| 99 | + "execution_count": null, |
| 100 | + "metadata": {}, |
| 101 | + "outputs": [], |
| 102 | + "source": [] |
| 103 | + }, |
| 104 | + { |
| 105 | + "cell_type": "markdown", |
| 106 | + "metadata": {}, |
| 107 | + "source": [ |
| 108 | + "What is the average age of those who did survive?" |
| 109 | + ] |
| 110 | + }, |
| 111 | + { |
| 112 | + "cell_type": "code", |
| 113 | + "execution_count": null, |
| 114 | + "metadata": {}, |
| 115 | + "outputs": [], |
| 116 | + "source": [] |
| 117 | + }, |
| 118 | + { |
| 119 | + "cell_type": "markdown", |
| 120 | + "metadata": {}, |
| 121 | + "source": [ |
| 122 | + "What is the average age of those who did and didn't survive grouped by gender?" |
| 123 | + ] |
| 124 | + }, |
| 125 | + { |
| 126 | + "cell_type": "code", |
| 127 | + "execution_count": null, |
| 128 | + "metadata": {}, |
| 129 | + "outputs": [], |
| 130 | + "source": [] |
| 131 | + }, |
| 132 | + { |
| 133 | + "cell_type": "markdown", |
| 134 | + "metadata": {}, |
| 135 | + "source": [ |
| 136 | + "## Tidy GDP" |
| 137 | + ] |
| 138 | + }, |
| 139 | + { |
| 140 | + "cell_type": "markdown", |
| 141 | + "metadata": {}, |
| 142 | + "source": [ |
| 143 | + "Manipulate the GDP.csv file and make it tidy, the result should be a pandas dataframe with the following columns:\n", |
| 144 | + "* Country Name\n", |
| 145 | + "* Country Code\n", |
| 146 | + "* Year\n", |
| 147 | + "* GDP" |
| 148 | + ] |
| 149 | + }, |
| 150 | + { |
| 151 | + "cell_type": "code", |
| 152 | + "execution_count": null, |
| 153 | + "metadata": {}, |
| 154 | + "outputs": [], |
| 155 | + "source": [] |
| 156 | + } |
| 157 | + ], |
| 158 | + "metadata": { |
| 159 | + "kernelspec": { |
| 160 | + "display_name": "Python 3", |
| 161 | + "language": "python", |
| 162 | + "name": "python3" |
| 163 | + }, |
| 164 | + "language_info": { |
| 165 | + "codemirror_mode": { |
| 166 | + "name": "ipython", |
| 167 | + "version": 3 |
| 168 | + }, |
| 169 | + "file_extension": ".py", |
| 170 | + "mimetype": "text/x-python", |
| 171 | + "name": "python", |
| 172 | + "nbconvert_exporter": "python", |
| 173 | + "pygments_lexer": "ipython3", |
| 174 | + "version": "3.5.6" |
| 175 | + } |
| 176 | + }, |
| 177 | + "nbformat": 4, |
| 178 | + "nbformat_minor": 2 |
| 179 | +} |
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