{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Review"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" beer_servings | \n",
" spirit_servings | \n",
" wine_servings | \n",
" total_litres_of_pure_alcohol | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0.0 | \n",
" AS | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" 89 | \n",
" 132 | \n",
" 54 | \n",
" 4.9 | \n",
" EU | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" 25 | \n",
" 0 | \n",
" 14 | \n",
" 0.7 | \n",
" AF | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" 245 | \n",
" 138 | \n",
" 312 | \n",
" 12.4 | \n",
" EU | \n",
"
\n",
" \n",
" 4 | \n",
" Angola | \n",
" 217 | \n",
" 57 | \n",
" 45 | \n",
" 5.9 | \n",
" AF | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country beer_servings spirit_servings wine_servings \\\n",
"0 Afghanistan 0 0 0 \n",
"1 Albania 89 132 54 \n",
"2 Algeria 25 0 14 \n",
"3 Andorra 245 138 312 \n",
"4 Angola 217 57 45 \n",
"\n",
" total_litres_of_pure_alcohol continent \n",
"0 0.0 AS \n",
"1 4.9 EU \n",
"2 0.7 AF \n",
"3 12.4 EU \n",
"4 5.9 AF "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv'\n",
"df = pd.read_csv(url).head(5).copy()\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each of the following lines of code:\n",
"\n",
"- What the **data type** of the object that is returned?\n",
"- What is the **shape** of the object that is returned?\n",
"\n",
"\n",
"1. `df`\n",
"2. `df.continent`\n",
"3. `df['continent']`\n",
"4. `df[['country', 'continent']]`\n",
"5. `df[[False, True, False, True, False]]`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" beer_servings | \n",
" spirit_servings | \n",
" wine_servings | \n",
" total_litres_of_pure_alcohol | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0.0 | \n",
" AS | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" 89 | \n",
" 132 | \n",
" 54 | \n",
" 4.9 | \n",
" EU | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" 25 | \n",
" 0 | \n",
" 14 | \n",
" 0.7 | \n",
" AF | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" 245 | \n",
" 138 | \n",
" 312 | \n",
" 12.4 | \n",
" EU | \n",
"
\n",
" \n",
" 4 | \n",
" Angola | \n",
" 217 | \n",
" 57 | \n",
" 45 | \n",
" 5.9 | \n",
" AF | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country beer_servings spirit_servings wine_servings \\\n",
"0 Afghanistan 0 0 0 \n",
"1 Albania 89 132 54 \n",
"2 Algeria 25 0 14 \n",
"3 Andorra 245 138 312 \n",
"4 Angola 217 57 45 \n",
"\n",
" total_litres_of_pure_alcohol continent \n",
"0 0.0 AS \n",
"1 4.9 EU \n",
"2 0.7 AF \n",
"3 12.4 EU \n",
"4 5.9 AF "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"(5, 6)\n"
]
}
],
"source": [
"print type(df)\n",
"print df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 AS\n",
"1 EU\n",
"2 AF\n",
"3 EU\n",
"4 AF\n",
"Name: continent, dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.continent"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"(5L,)\n"
]
}
],
"source": [
"print type(df.continent)\n",
"print df.continent.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 AS\n",
"1 EU\n",
"2 AF\n",
"3 EU\n",
"4 AF\n",
"Name: continent, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['continent']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"(5L,)\n"
]
}
],
"source": [
"print type(df['continent'])\n",
"print df['continent'].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 4"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" AS | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" EU | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" AF | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" EU | \n",
"
\n",
" \n",
" 4 | \n",
" Angola | \n",
" AF | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country continent\n",
"0 Afghanistan AS\n",
"1 Albania EU\n",
"2 Algeria AF\n",
"3 Andorra EU\n",
"4 Angola AF"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['country', 'continent']]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"(5, 2)\n"
]
}
],
"source": [
"print type(df[['country', 'continent']])\n",
"print df[['country', 'continent']].shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" AS | \n",
"
\n",
" \n",
" 1 | \n",
" Albania | \n",
" EU | \n",
"
\n",
" \n",
" 2 | \n",
" Algeria | \n",
" AF | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" EU | \n",
"
\n",
" \n",
" 4 | \n",
" Angola | \n",
" AF | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country continent\n",
"0 Afghanistan AS\n",
"1 Albania EU\n",
"2 Algeria AF\n",
"3 Andorra EU\n",
"4 Angola AF"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# equivalent\n",
"cols = ['country', 'continent']\n",
"df[cols]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 5"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" beer_servings | \n",
" spirit_servings | \n",
" wine_servings | \n",
" total_litres_of_pure_alcohol | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" Albania | \n",
" 89 | \n",
" 132 | \n",
" 54 | \n",
" 4.9 | \n",
" EU | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" 245 | \n",
" 138 | \n",
" 312 | \n",
" 12.4 | \n",
" EU | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country beer_servings spirit_servings wine_servings \\\n",
"1 Albania 89 132 54 \n",
"3 Andorra 245 138 312 \n",
"\n",
" total_litres_of_pure_alcohol continent \n",
"1 4.9 EU \n",
"3 12.4 EU "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[False, True, False, True, False]]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"(2, 6)\n"
]
}
],
"source": [
"print type(df[[False, True, False, True, False]])\n",
"print df[[False, True, False, True, False]].shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" beer_servings | \n",
" spirit_servings | \n",
" wine_servings | \n",
" total_litres_of_pure_alcohol | \n",
" continent | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" Albania | \n",
" 89 | \n",
" 132 | \n",
" 54 | \n",
" 4.9 | \n",
" EU | \n",
"
\n",
" \n",
" 3 | \n",
" Andorra | \n",
" 245 | \n",
" 138 | \n",
" 312 | \n",
" 12.4 | \n",
" EU | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country beer_servings spirit_servings wine_servings \\\n",
"1 Albania 89 132 54 \n",
"3 Andorra 245 138 312 \n",
"\n",
" total_litres_of_pure_alcohol continent \n",
"1 4.9 EU \n",
"3 12.4 EU "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# equivalent\n",
"df[df.continent=='EU']"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}