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{
"cells": [
{
"cell_type": "markdown",
"id": "90041b00-672b-4bd4-a8e8-0cab3f0548af",
"metadata": {},
"source": [
"# Lab 04: Data Science Tools\n",
"\n",
"## 0. Jupyter Notebooks\n",
"\n",
"Welcome to your first Jupyter notebook! Notebooks are made up of cells. Some cells contain text (like this one) and others contain Python code. \n",
"\n",
"Each cell can be in two different modes: editing or running. To edit a cell, double-click on it. When you're done editing, press **shift+Enter** to run it. You can use [Markdown](https://www.markdownguide.org/cheat-sheet/) to add basic formatting to the text. Before you go on, try editing the text in this cell."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"50"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Other cells are code cells, containing Python code. (This is a comment, of course!)\n",
"# Try running this cell (again, shift+Enter). You'll see the result of the final statement \n",
"# printed below the cell. \n",
"# Then try changing the Python code and re-run it.\n",
"\n",
"30+20"
]
},
{
"cell_type": "markdown",
"id": "257ef44f-8f53-4136-9d0d-23a811ec53e9",
"metadata": {},
"source": [
"### 0.1 Cells share state\n",
"\n",
"Even though code cells run one at a time, anything that happens in a cell (like declaring a variable or running a function) affects the whole notebook. Try running these two cells a few times, in different orders. What happens when you run *Cell B* over and over?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cell A\n",
"x = 10\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "69dd7908-b213-4d0f-8016-e46a4a491961",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cell B\n",
"x = x * 2\n",
"x"
]
},
{
"cell_type": "markdown",
"id": "adc581ac-db13-40a8-bcfc-bf5d6e5472c5",
"metadata": {},
"source": [
"### 0.2 Saving your work\n",
"\n",
"When you finish working on a notebook, save your work using the icon in the menu bar above. Your notebook is stored in the file `lab_04.ipynb` in the lab directory. You can commit your changes to `ipynb` files just like any other file. Once you finish with Jupyter, you can stop the server by pressing **Control + C** in the Terminal. \n",
"\n",
"*If you're doing this lab on a cloud-based platform like Binder, then you can't save your work. So don't close the tab!*"
]
},
{
"cell_type": "markdown",
"id": "c9c4aec2-949d-4a2e-b736-f5182b1f9ff7",
"metadata": {},
"source": [
"---\n",
"\n",
"## 1. Pandas\n",
"\n",
"Pandas is probably the most important Python library for data science. Pandas provides an object called a **DataFrame**, which is basically a table with rows and columns. Most of the time, you will load data into Pandas using a `.csv` file. CSV files can be exported from Excel or Google Sheets, and are a common format for public data sets. \n",
"\n",
"In this lab, we'll be working with two data sets: The first contains Pokémon characteristics and the second comes from a wide-scale survey conducted by the US Centers for Disease Control ([details](https://www.cdc.gov/brfss/annual_data/annual_2020.html)). We will demonstrate techniques with Pokémon; your job is to replicate these tasks with the CDC dataset. \n",
"\n",
"**Note:** Pandas has *extensive* capabilities, and there's no way we could possibly present them all here. If you have a clearly-formed idea of what you want to do with tabular data, there's a way to do it. This lab introduces *some* of what Pandas can do, but expect to spend time reading the documentation and Stack Overflow when you start working on new tasks. \n",
"\n",
"### 1.0 Getting started\n",
"\n",
"First, we'll import pandas (using the conventional variable name `pd`) and load the two datasets. *Run these cells and every code cell you encounter in this notebook.*"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "ba09a0f8-27d9-456f-aeff-3980e3362d5b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "a29d508a-2d9a-4d62-9ff6-7a0ecfd5eba4",
"metadata": {},
"outputs": [],
"source": [
"pokemon = pd.read_csv(\"pokemon.csv\")\n",
"people = pd.read_csv(\"brfss_2020.csv\")\n",
"#another csv file of the student's home country?"
]
},
{
"cell_type": "markdown",
"id": "d4e0b811-b8bf-4e9a-a934-3aad8f0520bb",
"metadata": {},
"source": [
"### 1.1 A first look\n",
"\n",
"#### Demo\n",
"\n",
"Let's start by learning the *shape* of the data. How many columns are there? How many rows? What kinds of data are included?"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "579d8dda-ca39-48b1-8819-b17651029729",
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>type</th>\n",
" <th>subtype</th>\n",
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" <th>hp</th>\n",
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" <td>Bulbasaur</td>\n",
" <td>Grass</td>\n",
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" <td>318</td>\n",
" <td>45</td>\n",
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" <tr>\n",
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" <td>Venusaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>525</td>\n",
" <td>80</td>\n",
" <td>82</td>\n",
" <td>83</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>VenusaurMega Venusaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>625</td>\n",
" <td>80</td>\n",
" <td>100</td>\n",
" <td>123</td>\n",
" <td>122</td>\n",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Charmander</td>\n",
" <td>Fire</td>\n",
" <td>NaN</td>\n",
" <td>309</td>\n",
" <td>39</td>\n",
" <td>52</td>\n",
" <td>43</td>\n",
" <td>60</td>\n",
" <td>50</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>795</th>\n",
" <td>Diancie</td>\n",
" <td>Rock</td>\n",
" <td>Fairy</td>\n",
" <td>600</td>\n",
" <td>50</td>\n",
" <td>100</td>\n",
" <td>150</td>\n",
" <td>100</td>\n",
" <td>150</td>\n",
" <td>50</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>796</th>\n",
" <td>DiancieMega Diancie</td>\n",
" <td>Rock</td>\n",
" <td>Fairy</td>\n",
" <td>700</td>\n",
" <td>50</td>\n",
" <td>160</td>\n",
" <td>110</td>\n",
" <td>160</td>\n",
" <td>110</td>\n",
" <td>110</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>797</th>\n",
" <td>HoopaHoopa Confined</td>\n",
" <td>Psychic</td>\n",
" <td>Ghost</td>\n",
" <td>600</td>\n",
" <td>80</td>\n",
" <td>110</td>\n",
" <td>60</td>\n",
" <td>150</td>\n",
" <td>130</td>\n",
" <td>70</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>798</th>\n",
" <td>HoopaHoopa Unbound</td>\n",
" <td>Psychic</td>\n",
" <td>Dark</td>\n",
" <td>680</td>\n",
" <td>80</td>\n",
" <td>160</td>\n",
" <td>60</td>\n",
" <td>170</td>\n",
" <td>130</td>\n",
" <td>80</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>799</th>\n",
" <td>Volcanion</td>\n",
" <td>Fire</td>\n",
" <td>Water</td>\n",
" <td>600</td>\n",
" <td>80</td>\n",
" <td>110</td>\n",
" <td>120</td>\n",
" <td>130</td>\n",
" <td>90</td>\n",
" <td>70</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>800 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" name type subtype total hp attack defense \\\n",
"0 Bulbasaur Grass Poison 318 45 49 49 \n",
"1 Ivysaur Grass Poison 405 60 62 63 \n",
"2 Venusaur Grass Poison 525 80 82 83 \n",
"3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n",
"4 Charmander Fire NaN 309 39 52 43 \n",
".. ... ... ... ... .. ... ... \n",
"795 Diancie Rock Fairy 600 50 100 150 \n",
"796 DiancieMega Diancie Rock Fairy 700 50 160 110 \n",
"797 HoopaHoopa Confined Psychic Ghost 600 80 110 60 \n",
"798 HoopaHoopa Unbound Psychic Dark 680 80 160 60 \n",
"799 Volcanion Fire Water 600 80 110 120 \n",
"\n",
" special_attack special_defense speed generation legendary \n",
"0 65 65 45 1 False \n",
"1 80 80 60 1 False \n",
"2 100 100 80 1 False \n",
"3 122 120 80 1 False \n",
"4 60 50 65 1 False \n",
".. ... ... ... ... ... \n",
"795 100 150 50 6 True \n",
"796 160 110 110 6 True \n",
"797 150 130 70 6 True \n",
"798 170 130 80 6 True \n",
"799 130 90 70 6 True \n",
"\n",
"[800 rows x 12 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon"
]
},
{
"cell_type": "markdown",
"id": "ee8b0718-56f9-4fc8-bd35-fa0ccb445179",
"metadata": {},
"source": [
"OK, 800 Pokémon, with 12 columns for each. And you can see all the columns. Not all the data is shown in this preview, of course. If there were more columns than could be displayed, you could see them all by typing `pokemon.columns`. \n",
"\n",
"#### Your turn\n",
"\n",
"Now do the same for your data set, `people`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>income</th>\n",
" <th>education</th>\n",
" <th>sexual_orientation</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>health</th>\n",
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" <th>sleep</th>\n",
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" <td>True</td>\n",
" <td>7</td>\n",
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" <tr>\n",
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" <td>65</td>\n",
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" <tr>\n",
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" <td>True</td>\n",
" <td>True</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>55</td>\n",
" <td>male</td>\n",
" <td>8</td>\n",
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" <td>heterosexual</td>\n",
" <td>1.83</td>\n",
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" <tr>\n",
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" <td>heterosexual</td>\n",
" <td>1.80</td>\n",
" <td>76.66</td>\n",
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" <td>1.63</td>\n",
" <td>86.18</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166421</th>\n",
" <td>25</td>\n",
" <td>male</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>heterosexual</td>\n",
" <td>1.78</td>\n",
" <td>86.18</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166422</th>\n",
" <td>25</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>heterosexual</td>\n",
" <td>1.91</td>\n",
" <td>45.36</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166423</th>\n",
" <td>35</td>\n",
" <td>female</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.60</td>\n",
" <td>68.04</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166424</th>\n",
" <td>35</td>\n",
" <td>male</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>heterosexual</td>\n",
" <td>1.75</td>\n",
" <td>86.18</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>166425 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex income education sexual_orientation height weight \\\n",
"0 55 female 5 2 other 1.55 83.01 \n",
"1 65 female 8 1 heterosexual 1.65 78.02 \n",
"2 35 female 8 4 heterosexual 1.65 77.11 \n",
"3 55 male 8 4 heterosexual 1.83 81.65 \n",
"4 55 female 8 4 heterosexual 1.80 76.66 \n",
"... ... ... ... ... ... ... ... \n",
"166420 45 female 8 3 heterosexual 1.63 86.18 \n",
"166421 25 male 7 2 heterosexual 1.78 86.18 \n",
"166422 25 female 1 2 heterosexual 1.91 45.36 \n",
"166423 35 female 5 4 heterosexual 1.60 68.04 \n",
"166424 35 male 7 2 heterosexual 1.75 86.18 \n",
"\n",
" health no_doctor exercise sleep \n",
"0 2 True True 7 \n",
"1 3 False False 8 \n",
"2 4 True True 7 \n",
"3 5 False True 8 \n",
"4 4 False True 8 \n",
"... ... ... ... ... \n",
"166420 1 False False 6 \n",
"166421 4 False True 6 \n",
"166422 1 False False 8 \n",
"166423 4 True True 6 \n",
"166424 3 False False 8 \n",
"\n",
"[166425 rows x 11 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here\n",
"people"
]
},
{
"cell_type": "markdown",
"id": "7fab76ef-d453-4568-a916-4d4c29535a42",
"metadata": {},
"source": [
"### 1.2 Descriptive Statistics\n",
"\n",
"#### Demo\n",
"\n",
"Let's get a sense of the data contained in some of the columns. For categorical data like `generation`, it makes sense to look at value counts--showing us how many of each category there are. You can put the optional keyword `normalize=True` in the () to see percentage of total instead of frequencies."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9afca362-9edc-423c-981b-dc42107d5de0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 166\n",
"5 165\n",
"3 160\n",
"4 121\n",
"2 106\n",
"6 82\n",
"Name: generation, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon.generation.value_counts() #put keyword in the parentheses"
]
},
{
"cell_type": "markdown",
"id": "a9b98eee-bdc2-4c63-bab2-ee82e2466d0f",
"metadata": {},
"source": [
"For numeric data, we could start by looking at the mean value. We can select multiple columns and get all the column means at once."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5fe580d0-5939-4152-9f8c-4c32d35a4479",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"hp 69.25875\n",
"attack 79.00125\n",
"defense 73.84250\n",
"speed 68.27750\n",
"dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon[[\"hp\", \"attack\", \"defense\", \"speed\"]].mean()"
]
},
{
"cell_type": "markdown",
"id": "0d8e6e78-fcfc-4c38-a418-545fe4216a44",
"metadata": {},
"source": [
"We can also compute the mean of boolean data. In this case, True will map to 1 and False will map to 0. So the mean value equals the percentage of data which is True. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.08125"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon.legendary.mean()"
]
},
{
"cell_type": "markdown",
"id": "69333e87-8df2-4b46-9005-2b8c9df3a7b4",
"metadata": {},
"source": [
"Just over 8% of Pokemon are legendary."
]
},
{
"cell_type": "markdown",
"id": "f563d97d-d9d3-4f2d-a46a-5d5dfc6382de",
"metadata": {},
"source": [
"#### Your turn\n",
"\n",
"**1.2.0.** In this survey, people are grouped into age bands of 18-24, 25-34, 35-44, 45-54, 55-64, and 65+, with the lower bound reported. What percentage of people are in each age band? (When we talk about \"people\" in this lab, we're referring to the people who responded to the survey, not the whole US population.)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8fbcc766-8399-4f93-a6c8-e0607250a72a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" </tr>\n",
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"text/plain": [
" age\n",
"0 55\n",
"1 65\n",
"2 35\n",
"3 55\n",
"4 55\n",
"... ...\n",
"166420 45\n",
"166421 25\n",
"166422 25\n",
"166423 35\n",
"166424 35\n",
"\n",
"[166425 rows x 1 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# percentage of people in age band 18-24\n",
"test1 = people[[\"age\"]] #move this to below or change the question so the soln can be found above w/pokemon\n",
"test1\n",
"#people"
]
},
{
"cell_type": "markdown",
"id": "38006e7b-4771-4c29-86a8-19d04a50fc25",
"metadata": {},
"source": [
"**1.2.1.** What are the mean height and weight of people in this survey?"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b7f910c8-3d40-49ae-b270-678734c04100",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"height 1.705082\n",
"weight 83.053588\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test2 = people[['height', 'weight']].mean()\n",
"test2 #this was easier to do than first one because the example is already shown"
]
},
{
"cell_type": "markdown",
"id": "f74634bb-8664-46e4-b371-6f45cbb7c8ef",
"metadata": {},
"source": [
"**1.2.2.** The `exercise` column indicates whether a person has done any physical activity or exercise in the last 30 days, outside of work. What percentage of people have done exercise?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f3891188-a85f-4089-8388-d4d81c7438ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True 0.785801\n",
"False 0.214199\n",
"Name: exercise, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here\n",
"test3 = people.exercise.value_counts(True)\n",
"test3 # around 79% of people have done exercise"
]
},
{
"cell_type": "markdown",
"id": "f6082e65-321c-4ee0-9457-74f9bb1b0363",
"metadata": {},
"source": [
"### 1.3 Filtering\n",
"\n",
"Sometimes we're just interested in a selection of the data set. The way to do this is to create a boolean series, and then use this to select which rows you want to include. Vocabulary note: A dataframe is two-dimensional, with rows and columns. A series (a single row or a single column) is one-dimensional. \n",
"\n",
"#### Demo\n",
"`pokemon.legendary` is already boolean, so we can use this to select just the legendary pokémon. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9",
"metadata": {},
"outputs": [
{
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" <td>150</td>\n",
" <td>100</td>\n",
" <td>150</td>\n",
" <td>50</td>\n",
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" <th>796</th>\n",
" <td>DiancieMega Diancie</td>\n",
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" name type subtype total hp attack defense \\\n",
"156 Articuno Ice Flying 580 90 85 100 \n",
"157 Zapdos Electric Flying 580 90 90 85 \n",
"158 Moltres Fire Flying 580 90 100 90 \n",
"162 Mewtwo Psychic NaN 680 106 110 90 \n",
"163 MewtwoMega Mewtwo X Psychic Fighting 780 106 190 100 \n",
".. ... ... ... ... ... ... ... \n",
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"\n",
" special_attack special_defense speed generation legendary \n",
"156 95 125 85 1 True \n",
"157 125 90 100 1 True \n",
"158 125 85 90 1 True \n",
"162 154 90 130 1 True \n",
"163 154 100 130 1 True \n",
".. ... ... ... ... ... \n",
"795 100 150 50 6 True \n",
"796 160 110 110 6 True \n",
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"798 170 130 80 6 True \n",
"799 130 90 70 6 True \n",
"\n",
"[65 rows x 12 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"legendary = pokemon[pokemon.legendary]\n",
"legendary"
]
},
{
"cell_type": "markdown",
"id": "b4ad804a-f5f0-441f-bb83-51f360c1c154",
"metadata": {},
"source": [
"Let's get all the ice pokémon. We can create a boolean series from another series..."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5d089acf-7b76-4f91-8803-42a4a9a11e3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
" ... \n",
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"796 False\n",
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"799 False\n",
"Name: type, Length: 800, dtype: bool"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon.type == \"Ice\""
]
},
{
"cell_type": "markdown",
"id": "a5ea9e89-f466-48de-9133-346c99f4a6c1",
"metadata": {},
"source": [
"And then use this series to select just the ice pokémon. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "510fa0fc-2b38-4725-9bbf-ec57d62792be",
"metadata": {},
"outputs": [
{
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" <td>40</td>\n",
" <td>50</td>\n",
" <td>55</td>\n",
" <td>50</td>\n",
" <td>25</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>Sealeo</td>\n",
" <td>Ice</td>\n",
" <td>Water</td>\n",
" <td>410</td>\n",
" <td>90</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" <td>75</td>\n",
" <td>70</td>\n",
" <td>45</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>Walrein</td>\n",
" <td>Ice</td>\n",
" <td>Water</td>\n",
" <td>530</td>\n",
" <td>110</td>\n",
" <td>80</td>\n",
" <td>90</td>\n",
" <td>95</td>\n",
" <td>90</td>\n",
" <td>65</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>Regice</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>580</td>\n",
" <td>80</td>\n",
" <td>50</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>200</td>\n",
" <td>50</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>522</th>\n",
" <td>Glaceon</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>525</td>\n",
" <td>65</td>\n",
" <td>60</td>\n",
" <td>110</td>\n",
" <td>130</td>\n",
" <td>95</td>\n",
" <td>65</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>524</th>\n",
" <td>Mamoswine</td>\n",
" <td>Ice</td>\n",
" <td>Ground</td>\n",
" <td>530</td>\n",
" <td>110</td>\n",
" <td>130</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>60</td>\n",
" <td>80</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>530</th>\n",
" <td>Froslass</td>\n",
" <td>Ice</td>\n",
" <td>Ghost</td>\n",
" <td>480</td>\n",
" <td>70</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>110</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>643</th>\n",
" <td>Vanillite</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>305</td>\n",
" <td>36</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>65</td>\n",
" <td>60</td>\n",
" <td>44</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>644</th>\n",
" <td>Vanillish</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>395</td>\n",
" <td>51</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>80</td>\n",
" <td>75</td>\n",
" <td>59</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>645</th>\n",
" <td>Vanilluxe</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>535</td>\n",
" <td>71</td>\n",
" <td>95</td>\n",
" <td>85</td>\n",
" <td>110</td>\n",
" <td>95</td>\n",
" <td>79</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674</th>\n",
" <td>Cubchoo</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>305</td>\n",
" <td>55</td>\n",
" <td>70</td>\n",
" <td>40</td>\n",
" <td>60</td>\n",
" <td>40</td>\n",
" <td>40</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>675</th>\n",
" <td>Beartic</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>485</td>\n",
" <td>95</td>\n",
" <td>110</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>80</td>\n",
" <td>50</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>676</th>\n",
" <td>Cryogonal</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>485</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>30</td>\n",
" <td>95</td>\n",
" <td>135</td>\n",
" <td>105</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>788</th>\n",
" <td>Bergmite</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>304</td>\n",
" <td>55</td>\n",
" <td>69</td>\n",
" <td>85</td>\n",
" <td>32</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>789</th>\n",
" <td>Avalugg</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>514</td>\n",
" <td>95</td>\n",
" <td>117</td>\n",
" <td>184</td>\n",
" <td>44</td>\n",
" <td>46</td>\n",
" <td>28</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name type subtype total hp attack defense \\\n",
"133 Jynx Ice Psychic 455 65 50 35 \n",
"156 Articuno Ice Flying 580 90 85 100 \n",
"238 Swinub Ice Ground 250 50 50 40 \n",
"239 Piloswine Ice Ground 450 100 100 80 \n",
"243 Delibird Ice Flying 330 45 55 45 \n",
"257 Smoochum Ice Psychic 305 45 30 15 \n",
"395 Snorunt Ice NaN 300 50 50 50 \n",
"396 Glalie Ice NaN 480 80 80 80 \n",
"397 GlalieMega Glalie Ice NaN 580 80 120 80 \n",
"398 Spheal Ice Water 290 70 40 50 \n",
"399 Sealeo Ice Water 410 90 60 70 \n",
"400 Walrein Ice Water 530 110 80 90 \n",
"415 Regice Ice NaN 580 80 50 100 \n",
"522 Glaceon Ice NaN 525 65 60 110 \n",
"524 Mamoswine Ice Ground 530 110 130 80 \n",
"530 Froslass Ice Ghost 480 70 80 70 \n",
"643 Vanillite Ice NaN 305 36 50 50 \n",
"644 Vanillish Ice NaN 395 51 65 65 \n",
"645 Vanilluxe Ice NaN 535 71 95 85 \n",
"674 Cubchoo Ice NaN 305 55 70 40 \n",
"675 Beartic Ice NaN 485 95 110 80 \n",
"676 Cryogonal Ice NaN 485 70 50 30 \n",
"788 Bergmite Ice NaN 304 55 69 85 \n",
"789 Avalugg Ice NaN 514 95 117 184 \n",
"\n",
" special_attack special_defense speed generation legendary \n",
"133 115 95 95 1 False \n",
"156 95 125 85 1 True \n",
"238 30 30 50 2 False \n",
"239 60 60 50 2 False \n",
"243 65 45 75 2 False \n",
"257 85 65 65 2 False \n",
"395 50 50 50 3 False \n",
"396 80 80 80 3 False \n",
"397 120 80 100 3 False \n",
"398 55 50 25 3 False \n",
"399 75 70 45 3 False \n",
"400 95 90 65 3 False \n",
"415 100 200 50 3 True \n",
"522 130 95 65 4 False \n",
"524 70 60 80 4 False \n",
"530 80 70 110 4 False \n",
"643 65 60 44 5 False \n",
"644 80 75 59 5 False \n",
"645 110 95 79 5 False \n",
"674 60 40 40 5 False \n",
"675 70 80 50 5 False \n",
"676 95 135 105 5 False \n",
"788 32 35 28 6 False \n",
"789 44 46 28 6 False "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ice = pokemon[pokemon.type == \"Ice\"]\n",
"ice"
]
},
{
"cell_type": "markdown",
"id": "0af5f534-0bec-4577-beee-29b350102265",
"metadata": {},
"source": [
"Let's get the high-speed ice pokémon. You can join conditions together using the `&` (and) and `|` (or) operators. `~` means \"not\", so `pokemon[~(pokemon.type == \"Ice\")]` would select all the non-ice pokémon. Due to order of operations, each condition needs to be wrapped in parentheses."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "05d4c5c2-c6b4-4795-9799-c884b15445a1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>type</th>\n",
" <th>subtype</th>\n",
" <th>total</th>\n",
" <th>hp</th>\n",
" <th>attack</th>\n",
" <th>defense</th>\n",
" <th>special_attack</th>\n",
" <th>special_defense</th>\n",
" <th>speed</th>\n",
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" <th>speed_category</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
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" <td>Jynx</td>\n",
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" <tr>\n",
" <th>156</th>\n",
" <td>Articuno</td>\n",
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" <tr>\n",
" <th>396</th>\n",
" <td>Glalie</td>\n",
" <td>Ice</td>\n",
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" <td>480</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
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" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>GlalieMega Glalie</td>\n",
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" <td>580</td>\n",
" <td>80</td>\n",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>524</th>\n",
" <td>Mamoswine</td>\n",
" <td>Ice</td>\n",
" <td>Ground</td>\n",
" <td>530</td>\n",
" <td>110</td>\n",
" <td>130</td>\n",
" <td>80</td>\n",
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" <tr>\n",
" <th>530</th>\n",
" <td>Froslass</td>\n",
" <td>Ice</td>\n",
" <td>Ghost</td>\n",
" <td>480</td>\n",
" <td>70</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>80</td>\n",
" <td>70</td>\n",
" <td>110</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>fast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>676</th>\n",
" <td>Cryogonal</td>\n",
" <td>Ice</td>\n",
" <td>NaN</td>\n",
" <td>485</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>30</td>\n",
" <td>95</td>\n",
" <td>135</td>\n",
" <td>105</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>fast</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name type subtype total hp attack defense \\\n",
"133 Jynx Ice Psychic 455 65 50 35 \n",
"156 Articuno Ice Flying 580 90 85 100 \n",
"396 Glalie Ice NaN 480 80 80 80 \n",
"397 GlalieMega Glalie Ice NaN 580 80 120 80 \n",
"524 Mamoswine Ice Ground 530 110 130 80 \n",
"530 Froslass Ice Ghost 480 70 80 70 \n",
"676 Cryogonal Ice NaN 485 70 50 30 \n",
"\n",
" special_attack special_defense speed generation legendary \\\n",
"133 115 95 95 1 False \n",
"156 95 125 85 1 True \n",
"396 80 80 80 3 False \n",
"397 120 80 100 3 False \n",
"524 70 60 80 4 False \n",
"530 80 70 110 4 False \n",
"676 95 135 105 5 False \n",
"\n",
" speed_category \n",
"133 medium \n",
"156 medium \n",
"396 medium \n",
"397 medium \n",
"524 medium \n",
"530 fast \n",
"676 fast "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"high_speed_ice = pokemon[(pokemon.type == \"Ice\") & (pokemon.speed >= 80)]\n",
"high_speed_ice\n"
]
},
{
"cell_type": "markdown",
"id": "c84dc7ce-24f2-4ac7-92d7-99ed331488e0",
"metadata": {},
"source": [
"You could get the pokémon who are fire or ice by selecting `pokemon[(pokemon.type == \"Fire\") | (pokemon.type == \"Ice\")]`."
]
},
{
"cell_type": "markdown",
"id": "1f0e9625-b194-450d-b003-b88798cc2f45",
"metadata": {},
"source": [
"#### Your turn\n",
"\n",
"**1.3.0.** `no_doctor` indicates whether there was a time in the last year when the person needed to see a doctor, but could not afford to do so. Create a dataframe containing only these people. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "198cb0c6-3f43-43c2-9eee-3939c12ea537",
"metadata": {},
"outputs": [
{
"data": {
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" </tbody>\n",
"</table>\n",
"<p>13784 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex income education sexual_orientation height weight \\\n",
"0 55 female 5 2 other 1.55 83.01 \n",
"2 35 female 8 4 heterosexual 1.65 77.11 \n",
"24 35 male 8 3 heterosexual 1.73 94.35 \n",
"50 35 female 4 2 heterosexual 1.78 81.65 \n",
"66 45 female 6 4 heterosexual 1.57 72.57 \n",
"... ... ... ... ... ... ... ... \n",
"166407 18 male 5 2 heterosexual 1.68 68.04 \n",
"166409 25 male 6 2 heterosexual 1.57 58.51 \n",
"166414 55 female 8 3 heterosexual 1.63 88.45 \n",
"166416 65 female 5 2 heterosexual 1.50 55.34 \n",
"166423 35 female 5 4 heterosexual 1.60 68.04 \n",
"\n",
" health no_doctor exercise sleep \n",
"0 2 True True 7 \n",
"2 4 True True 7 \n",
"24 4 True False 8 \n",
"50 4 True False 10 \n",
"66 4 True True 7 \n",
"... ... ... ... ... \n",
"166407 3 True True 8 \n",
"166409 4 True False 7 \n",
"166414 3 True False 6 \n",
"166416 3 True False 6 \n",
"166423 4 True True 6 \n",
"\n",
"[13784 rows x 11 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#ice = pokemon[pokemon.type == \"Ice\"] - leave the hint visible\n",
"noDoc = people[people.no_doctor]\n",
"noDoc\n"
]
},
{
"cell_type": "markdown",
"id": "9d213707-a15b-4751-8df9-48aa568af209",
"metadata": {},
"source": [
"**1.3.1.** `health` asks people for their general health, with the meanings of numbers shown below. Create a dataframe which contains people whose general health is good or better. \n",
"\n",
"| number | health status | \n",
"| ------ | ----------- |\n",
"| 1 | Poor |\n",
"| 2 | Fair |\n",
"| 3 | Good |\n",
"| 4 | Very good |\n",
"| 5 | Excellent |"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851",
"metadata": {},
"outputs": [
{
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" <td>76.66</td>\n",
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" <td>True</td>\n",
" <td>8</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>55</td>\n",
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" <td>74.84</td>\n",
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" <td>True</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>55</td>\n",
" <td>female</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.73</td>\n",
" <td>63.50</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>166411</th>\n",
" <td>65</td>\n",
" <td>male</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>heterosexual</td>\n",
" <td>1.78</td>\n",
" <td>117.93</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166415</th>\n",
" <td>35</td>\n",
" <td>male</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.75</td>\n",
" <td>99.79</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166417</th>\n",
" <td>35</td>\n",
" <td>female</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>heterosexual</td>\n",
" <td>1.73</td>\n",
" <td>95.25</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166421</th>\n",
" <td>25</td>\n",
" <td>male</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>heterosexual</td>\n",
" <td>1.78</td>\n",
" <td>86.18</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166423</th>\n",
" <td>35</td>\n",
" <td>female</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.60</td>\n",
" <td>68.04</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>6</td>\n",
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"<p>93585 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex income education sexual_orientation height weight \\\n",
"2 35 female 8 4 heterosexual 1.65 77.11 \n",
"3 55 male 8 4 heterosexual 1.83 81.65 \n",
"4 55 female 8 4 heterosexual 1.80 76.66 \n",
"5 55 male 8 4 heterosexual 1.80 74.84 \n",
"8 55 female 6 4 heterosexual 1.73 63.50 \n",
"... ... ... ... ... ... ... ... \n",
"166411 65 male 7 1 heterosexual 1.78 117.93 \n",
"166415 35 male 8 4 heterosexual 1.75 99.79 \n",
"166417 35 female 8 2 heterosexual 1.73 95.25 \n",
"166421 25 male 7 2 heterosexual 1.78 86.18 \n",
"166423 35 female 5 4 heterosexual 1.60 68.04 \n",
"\n",
" health no_doctor exercise sleep \n",
"2 4 True True 7 \n",
"3 5 False True 8 \n",
"4 4 False True 8 \n",
"5 5 False True 7 \n",
"8 5 False True 7 \n",
"... ... ... ... ... \n",
"166411 4 False True 7 \n",
"166415 4 False True 7 \n",
"166417 4 False False 4 \n",
"166421 4 False True 6 \n",
"166423 4 True True 6 \n",
"\n",
"[93585 rows x 11 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# YOUR CODE HERE\n",
"#high_speed_ice = pokemon[(pokemon.type == \"Ice\") & (pokemon.speed >= 80)]\n",
"goodHealth = people[(people.health >3)]\n",
"goodHealth\n",
"#people"
]
},
{
"cell_type": "markdown",
"id": "7add542b-bfd2-481a-b5b4-4e1ca744078a",
"metadata": {},
"source": [
"**1.3.2.** `education` indicates the highest level of education completed, with codes as follows. Create a dataframe which only contains female college graduates who needed a doctor but couldn't afford one. (The survey asked people for their current sex, and only had options for male and female.)\n",
"\n",
"| number | education level | \n",
"| ------ | ----------- |\n",
"| 1 | Did not graduate from high school |\n",
"| 2 | Graduated from high school |\n",
"| 3 | Attended some college |\n",
"| 4 | Graduated from college |"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "315682ae-7d54-4d78-9a63-d23c83ba1576",
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <tr>\n",
" <th>166219</th>\n",
" <td>55</td>\n",
" <td>female</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>1.52</td>\n",
" <td>59.87</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>166272</th>\n",
" <td>25</td>\n",
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" <td>8</td>\n",
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" <td>45</td>\n",
" <td>female</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.52</td>\n",
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" <th>166423</th>\n",
" <td>35</td>\n",
" <td>female</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>heterosexual</td>\n",
" <td>1.60</td>\n",
" <td>68.04</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2321 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex income education sexual_orientation height weight \\\n",
"2 35 female 8 4 heterosexual 1.65 77.11 \n",
"66 45 female 6 4 heterosexual 1.57 72.57 \n",
"135 55 female 6 4 heterosexual 1.70 81.65 \n",
"146 65 female 5 4 heterosexual 1.55 72.57 \n",
"259 65 female 4 4 heterosexual 1.57 56.70 \n",
"... ... ... ... ... ... ... ... \n",
"166121 35 female 8 4 heterosexual 1.65 136.08 \n",
"166219 55 female 5 4 other 1.52 59.87 \n",
"166272 25 female 8 4 heterosexual 1.52 98.88 \n",
"166381 45 female 5 4 heterosexual 1.52 49.90 \n",
"166423 35 female 5 4 heterosexual 1.60 68.04 \n",
"\n",
" health no_doctor exercise sleep \n",
"2 4 True True 7 \n",
"66 4 True True 7 \n",
"135 4 True True 7 \n",
"146 5 True True 7 \n",
"259 3 True True 6 \n",
"... ... ... ... ... \n",
"166121 4 True True 5 \n",
"166219 3 True True 5 \n",
"166272 3 True True 8 \n",
"166381 5 True True 6 \n",
"166423 4 True True 6 \n",
"\n",
"[2321 rows x 11 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# YOUR CODE HERE\n",
"#high_speed_ice = pokemon[(pokemon.type == \"Ice\") & (pokemon.speed >= 80)]\n",
"sickgirlbosses = people[(people.no_doctor)&(people.education==4)&(people.sex=='female')]\n",
"sickgirlbosses"
]
},
{
"cell_type": "markdown",
"id": "646d1148-7d94-4521-a04a-fbf17ade1235",
"metadata": {},
"source": [
"### 1.4. Grouping\n",
"\n",
"Now things get crazy. You can group a dataframe using one or more columns, and then compare their statistics. \n",
"\n",
"#### Demo\n",
"\n",
"Do different types of pokémon move at different speeds? We'll use `sort_values` to put these in order from slow to fast."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "069ea0ab-eff6-4985-9f46-db956fe1df91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"type\n",
"Fairy 48.588235\n",
"Steel 55.259259\n",
"Rock 55.909091\n",
"Bug 61.681159\n",
"Grass 61.928571\n",
"Ice 63.458333\n",
"Poison 63.571429\n",
"Ground 63.906250\n",
"Ghost 64.343750\n",
"Water 65.964286\n",
"Fighting 66.074074\n",
"Normal 71.551020\n",
"Fire 74.442308\n",
"Dark 76.161290\n",
"Psychic 81.491228\n",
"Dragon 83.031250\n",
"Electric 84.500000\n",
"Flying 102.500000\n",
"Name: speed, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pokemon.groupby(\"type\").speed.mean().sort_values()"
]
},
{
"cell_type": "markdown",
"id": "bdc801b7-d3ae-45bb-80f4-ebeb474e20a1",
"metadata": {},
"source": [
"Do types differ in other stats? Let's sort by hit points. "
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "5c420c0e-b5d2-49ae-ab98-3305ee076169",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hp</th>\n",
" <th>attack</th>\n",
" <th>defense</th>\n",
" </tr>\n",
" <tr>\n",
" <th>type</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
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" <td>56.884058</td>\n",
" <td>70.971014</td>\n",
" <td>70.724638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electric</th>\n",
" <td>59.795455</td>\n",
" <td>69.090909</td>\n",
" <td>66.295455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ghost</th>\n",
" <td>64.437500</td>\n",
" <td>73.781250</td>\n",
" <td>81.187500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Steel</th>\n",
" <td>65.222222</td>\n",
" <td>92.703704</td>\n",
" <td>126.370370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rock</th>\n",
" <td>65.363636</td>\n",
" <td>92.863636</td>\n",
" <td>100.795455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dark</th>\n",
" <td>66.806452</td>\n",
" <td>88.387097</td>\n",
" <td>70.225806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poison</th>\n",
" <td>67.250000</td>\n",
" <td>74.678571</td>\n",
" <td>68.821429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Grass</th>\n",
" <td>67.271429</td>\n",
" <td>73.214286</td>\n",
" <td>70.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fighting</th>\n",
" <td>69.851852</td>\n",
" <td>96.777778</td>\n",
" <td>65.925926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fire</th>\n",
" <td>69.903846</td>\n",
" <td>84.769231</td>\n",
" <td>67.769231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Psychic</th>\n",
" <td>70.631579</td>\n",
" <td>71.456140</td>\n",
" <td>67.684211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Flying</th>\n",
" <td>70.750000</td>\n",
" <td>78.750000</td>\n",
" <td>66.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ice</th>\n",
" <td>72.000000</td>\n",
" <td>72.750000</td>\n",
" <td>71.416667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Water</th>\n",
" <td>72.062500</td>\n",
" <td>74.151786</td>\n",
" <td>72.946429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ground</th>\n",
" <td>73.781250</td>\n",
" <td>95.750000</td>\n",
" <td>84.843750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fairy</th>\n",
" <td>74.117647</td>\n",
" <td>61.529412</td>\n",
" <td>65.705882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal</th>\n",
" <td>77.275510</td>\n",
" <td>73.469388</td>\n",
" <td>59.846939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dragon</th>\n",
" <td>83.312500</td>\n",
" <td>112.125000</td>\n",
" <td>86.375000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hp attack defense\n",
"type \n",
"Bug 56.884058 70.971014 70.724638\n",
"Electric 59.795455 69.090909 66.295455\n",
"Ghost 64.437500 73.781250 81.187500\n",
"Steel 65.222222 92.703704 126.370370\n",
"Rock 65.363636 92.863636 100.795455\n",
"Dark 66.806452 88.387097 70.225806\n",
"Poison 67.250000 74.678571 68.821429\n",
"Grass 67.271429 73.214286 70.800000\n",
"Fighting 69.851852 96.777778 65.925926\n",
"Fire 69.903846 84.769231 67.769231\n",
"Psychic 70.631579 71.456140 67.684211\n",
"Flying 70.750000 78.750000 66.250000\n",
"Ice 72.000000 72.750000 71.416667\n",
"Water 72.062500 74.151786 72.946429\n",
"Ground 73.781250 95.750000 84.843750\n",
"Fairy 74.117647 61.529412 65.705882\n",
"Normal 77.275510 73.469388 59.846939\n",
"Dragon 83.312500 112.125000 86.375000"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ptypes = pokemon.groupby(\"type\")\n",
"ptypes[[\"hp\", \"attack\", \"defense\"]].mean().sort_values(\"hp\")"
]
},
{
"cell_type": "markdown",
"id": "cc9a3d19-0ecd-487b-b34f-b748c44fc9c9",
"metadata": {},
"source": [
"Which type/subtype combinations are most likely to have legendary pokémon?"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "444a580d-e70c-48a1-bf87-77f98b8c9f85",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"type subtype \n",
"Electric Flying 0.600000\n",
"Rock Fairy 0.666667\n",
"Ghost Dragon 1.000000\n",
"Ground Fire 1.000000\n",
"Fire Water 1.000000\n",
" Steel 1.000000\n",
"Steel Dragon 1.000000\n",
"Dragon Electric 1.000000\n",
"Psychic Ghost 1.000000\n",
"Dragon Psychic 1.000000\n",
" Ice 1.000000\n",
"Rock Fighting 1.000000\n",
"Steel Fighting 1.000000\n",
"Dragon Fire 1.000000\n",
"Psychic Dark 1.000000\n",
" Fire 1.000000\n",
"Name: legendary, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"legendary_percentages = pokemon.groupby([\"type\", \"subtype\"]).legendary.mean().sort_values() \n",
"legendary_percentages[legendary_percentages > 0.5] #have students come up with their own questions and make a graph"
]
},
{
"cell_type": "markdown",
"id": "aac7c562",
"metadata": {},
"source": [
"# Your turn\n",
"\n",
"**1.4.0.** `income` records peoples' annual income, in the following bands. `sleep` records the average hours of sleep someone gets per night. Is there a difference in the average hours of sleep by income level?\n",
"\n",
"| number | annual income, in $1000 | \n",
"| ------ | ----------- |\n",
"| 1 | Less than 10 |\n",
"| 2 | 10-15 |\n",
"| 3 | 15-20 |\n",
"| 4 | 20-25 |\n",
"| 5 | 25-35 |\n",
"| 6 | 35-50 |\n",
"| 7 | 50-75 |\n",
"| 8 | More than 75 |"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "75c1ac4f-3914-4c0a-a156-2e084002df66",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"income\n",
"1 6.952208\n",
"2 6.985627\n",
"3 7.083274\n",
"4 7.079627\n",
"5 7.100286\n",
"6 7.055784\n",
"7 7.078495\n",
"8 7.074626\n",
"Name: sleep, dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#pokemon.groupby(\"type\").speed.mean().sort_values()\n",
"a = people.groupby(\"income\").sleep.mean()\n",
"a"
]
},
{
"cell_type": "markdown",
"id": "f6413f2b-26a0-4b70-976f-90e45558c4bb",
"metadata": {},
"source": [
"**1.4.0.** Is there a difference in peoples' income or general health, by sex and education level? "
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_92/784473019.py:6: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
" b = people.groupby([\"sex\", \"education\"]).mean()\n"
]
},
{
"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></th>\n",
" <th>age</th>\n",
" <th>income</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>health</th>\n",
" <th>no_doctor</th>\n",
" <th>exercise</th>\n",
" <th>sleep</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th>education</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">female</th>\n",
" <th>1</th>\n",
" <td>50.158452</td>\n",
" <td>3.554701</td>\n",
" <td>1.604912</td>\n",
" <td>76.852097</td>\n",
" <td>2.848040</td>\n",
" <td>0.159654</td>\n",
" <td>0.536667</td>\n",
" <td>7.017552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>50.423470</td>\n",
" <td>5.049022</td>\n",
" <td>1.622619</td>\n",
" <td>76.507320</td>\n",
" <td>3.315797</td>\n",
" <td>0.101778</td>\n",
" <td>0.663965</td>\n",
" <td>7.078691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>48.940282</td>\n",
" <td>5.779390</td>\n",
" <td>1.633421</td>\n",
" <td>77.218206</td>\n",
" <td>3.483379</td>\n",
" <td>0.107509</td>\n",
" <td>0.751427</td>\n",
" <td>7.025068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>48.746244</td>\n",
" <td>6.960652</td>\n",
" <td>1.642774</td>\n",
" <td>73.953511</td>\n",
" <td>3.844340</td>\n",
" <td>0.063865</td>\n",
" <td>0.861262</td>\n",
" <td>7.139591</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">male</th>\n",
" <th>1</th>\n",
" <td>48.596662</td>\n",
" <td>4.433009</td>\n",
" <td>1.746530</td>\n",
" <td>87.446437</td>\n",
" <td>3.031525</td>\n",
" <td>0.167130</td>\n",
" <td>0.617524</td>\n",
" <td>7.048447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>46.552635</td>\n",
" <td>5.742876</td>\n",
" <td>1.776165</td>\n",
" <td>91.424538</td>\n",
" <td>3.440818</td>\n",
" <td>0.092129</td>\n",
" <td>0.727144</td>\n",
" <td>7.018605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>47.478875</td>\n",
" <td>6.270230</td>\n",
" <td>1.785178</td>\n",
" <td>92.407285</td>\n",
" <td>3.549105</td>\n",
" <td>0.085150</td>\n",
" <td>0.802835</td>\n",
" <td>6.989728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>49.742076</td>\n",
" <td>7.190582</td>\n",
" <td>1.789515</td>\n",
" <td>90.372855</td>\n",
" <td>3.826512</td>\n",
" <td>0.044602</td>\n",
" <td>0.884716</td>\n",
" <td>7.110426</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age income height weight health \\\n",
"sex education \n",
"female 1 50.158452 3.554701 1.604912 76.852097 2.848040 \n",
" 2 50.423470 5.049022 1.622619 76.507320 3.315797 \n",
" 3 48.940282 5.779390 1.633421 77.218206 3.483379 \n",
" 4 48.746244 6.960652 1.642774 73.953511 3.844340 \n",
"male 1 48.596662 4.433009 1.746530 87.446437 3.031525 \n",
" 2 46.552635 5.742876 1.776165 91.424538 3.440818 \n",
" 3 47.478875 6.270230 1.785178 92.407285 3.549105 \n",
" 4 49.742076 7.190582 1.789515 90.372855 3.826512 \n",
"\n",
" no_doctor exercise sleep \n",
"sex education \n",
"female 1 0.159654 0.536667 7.017552 \n",
" 2 0.101778 0.663965 7.078691 \n",
" 3 0.107509 0.751427 7.025068 \n",
" 4 0.063865 0.861262 7.139591 \n",
"male 1 0.167130 0.617524 7.048447 \n",
" 2 0.092129 0.727144 7.018605 \n",
" 3 0.085150 0.802835 6.989728 \n",
" 4 0.044602 0.884716 7.110426 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# think about refactoring the question? in pokemon ex, we can create a table similar to this\n",
"\n",
"#legendary_percentages = pokemon.groupby([\"type\", \"subtype\"]).legendary.mean().sort_values() \n",
"#legendary_percentages[legendary_percentages > 0.5]\n",
"\n",
"b = people.groupby([\"sex\", \"education\"]).mean()\n",
"b"
]
},
{
"cell_type": "markdown",
"id": "931d602b-ddf4-4c8b-80e0-f886267cce76",
"metadata": {},
"source": [
"### 1.5. Plotting \n",
"\n",
"Pandas has excellent built-in plotting capabilities, but \n",
"we are going to use the [seaborn](https://seaborn.pydata.org/) library because it's a bit \n",
"more intuitive and produces more beautiful plots. `set_theme`, called here without any arguments, assigns the default color palette. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set_theme()"
]
},
{
"cell_type": "markdown",
"id": "a15ad672-13e8-4bdd-bc31-a489a1730daf",
"metadata": {},
"source": [
"#### Demo\n",
"\n",
"**When you want to visualize the distribution of a series**, a [histogram](https://seaborn.pydata.org/generated/seaborn.histplot.html) puts data into bins and plots the number of data points in each bin.\n",
"\n",
"Let's see the distribution of pokémon attack values. Note how assigning `x=\"attack\"` spreads attack values over the x-axis, while `y=\"attack\"` spreads attack values over the y-axis. The number of bins is selected automatically, but you can specify this with the optional `bins` argument. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5ce066fe-f81d-4b78-a394-c5c2f4dc9f46",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='attack', ylabel='Count'>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data=pokemon, x=\"attack\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "bceb253b-ef4f-4aa2-aef4-cab2b3ca6d59",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Count', ylabel='attack'>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsoEwBAABY0KRu2hks2reNDvQITYsh2W02udxuKbze59h7LNjt/v9eUrXcbH04Ixv/yMVcY2TjdnvkdofZDzJYZniq3vYeteLxeGQYRqDHQBPidntks3FMAKHA5Xbr7JnSoCtUERE2tWnTQmfOnFNlpTvQ4zQpVdk06HM06N5DkGEYyn3nsE4XlQV6lCbDMAzvK1Ph2M3Pf1Op4tIKv+sMw5DdbpPLFZ7ZXA7Z+Ecu5ho6m/Zto3XXTYmy2YygK1MILMpUHRw4ckbHC4sDPUaTYRiGIiLsqqx08cP/EmRjjmz8IxdzZIOmipPyAAAAFlCmAAAALKBMAQAAWECZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAV1LlNvvPFGjdv88Y9/rOvuAQAAgkKdy9TMmTO1Y8cO0/Vz587VypUr67p7AACAoFDnMvWzn/1MU6dO1a5du3yWu91u/frXv9a6des0d+5cywMCAAA0ZXUuU4888ohuuukmPfDAA/rggw8kSeXl5Zo8ebI2bdqkxx9/XKNHj663QQEAAJoiS+/Nt2DBApWXl+v+++/XU089pRUrVujDDz/UkiVLNGzYsPqaEQAAoMmy9K/5bDabnnjiCaWmpmrChAnat2+fli1bRpECAABho9avTK1atcp0Xe/evbVr1y4NGjRIn332mT777DNJF97h+5577rE8JAAAQFNV6zL1+OOP17jNpk2btGnTJu9jyhQAAAh1tS5TW7dubcg5AAAAglKty1SnTp0acg4AAICgVOcL0I8dO6Zt27aZrt+2bZuOHz9e190DAAAEhTrfGmHRokUqKSlRenq63/UvvviiHA6HFi9eXOfhAAAAmro6vzK1d+9e/ehHPzJdn5qa6r2ZJwAAQKiqc5lyOp1q0aKF6fro6GidPXv2ivZ55MgRzZ07VyNHjtR1112njIwMv9utW7dON954o5KSknTLLbdo+/bt1bYpLi7W7Nmz1a9fP6WkpGjatGkqLCy8onkAAABqUucy9d3vflcffvih6fo9e/bo6quvvqJ9Hjx4UHl5eeratavi4+P9bvPaa6/p4Ycf1vDhw7V8+XIlJydrypQp+uijj3y2mzFjhnbu3Kl58+bpiSee0OHDhzVhwgRVVlZe0UwAAACXU+cylZGRoddee00vvPCC3G63d7nL5dLq1auVk5Nj+sqSmfT0dOXl5empp57S97//fb/bPPXUU7r55ps1Y8YMDRgwQL///e+VlJSkZ5991rvN3r179fbbb+vRRx/ViBEjNHToUD355JM6cOCANm/eXLcvGAAAwI86X4A+ceJE7dmzR4899piWLl2q7t27S5IOHz6sr7/+Wv369dOvfvWrK9qnzXb5bnfs2DF98cUX+n//7//5LB8xYoQWLVqk8vJyRUZGaseOHXI4HBo4cKB3m7i4OCUmJmrHjh0aMWLEFc0FAABgps5lKjIyUitXrtSGDRu0ZcsWHT16VJLUq1cv3XDDDfrZz35WYzm6Uvn5+ZLkLW5V4uPjVVFRoWPHjik+Pl75+fnq3r27DMPw2S4uLs67DwAAgPpQ5zIlXXgl6bbbbtNtt91WX/NcVlFRkSTJ4XD4LK96XLXe6XSqVatW1T4/JiZG+/fvtz6IoWpFLawZ3/5piFx8kI05svGPXMw1cDZVP9ft9vp9IaAxVM0cjLM3tMbIxFKZCld2m00REfZAj9HkRNjJxAzZmCMb/8jFXENlU/VL1+GIapD9N4Zgnj2YWSpTp06d0t///nd9+umnKi4u9rkQXbrQ8levXm1pwIvFxMRIunDbg3bt2nmXO51On/UOh0MnTpyo9vlFRUXebaxwud2qrHRZ3k/IMC78cKt0uSRPoIdpYsjGHNn4Ry7mGjgbl+vC7zCns8z7cbCw221yOKKCcvaGVpVNQ6pzmfrss880duxYnT9/Xt27d9d//vMfXXPNNXI6nTp58qS6dOlyxbdGqElcXJykC9dOVX1c9bhZs2bq3Lmzd7tdu3bJ4/H4nI47fPiwevToYX0Qj+Tx8FOuivfldnKphmzMkY1/5GKuobOp2qfL5VZlZXAWkmCePZjV+UTin/70J0VHRys3N1erVq2Sx+PR7NmzlZeXp8WLF6uoqEgPPfRQfc6qzp07q1u3bsrNzfVZnpOTo9TUVEVGRkqSBg8erKKiIu3atcu7zeHDh/Xpp59q8ODB9ToTAAAIb3V+ZerDDz/Ufffdp44dO3rvdF7V6ocPH649e/Zo0aJF+tvf/lbrfZaVlSkvL0+SVFBQoJKSEm9x6tevn9q2baupU6fqoYceUpcuXdS/f3/l5ORo3759Ps+TkpKitLQ0zZ49W7NmzVLz5s21ePFiJSQk6IYbbqjrlwwAAFBNncuU2+3WVVddJenCNUp2u93n7WMSEhK0fv36K9rn6dOnNX36dJ9lVY9feOEF9e/fXxkZGSorK9Py5cv1/PPPq3v37nrmmWeUkpLi83lLlizRggULNHfuXFVWViotLU1z5sxRRATX3AMAgPpT52YRGxur48ePS7pwi4TY2Fjt2rXLe0PMDz/80O/tCWra54EDB2rcLjMzU5mZmZfdplWrVnrsscf02GOPXdEMAAAAV6LOZSotLU25ubmaOXOmJGn06NFauHChjh07Jo/Ho927d2v8+PH1NigAAEBTVOcyNWnSJN18882qqKhQs2bNNG7cOJWWlmrz5s2y2Wx64IEHNHHixPqcFQAAoMmpc5mKiYnxuWeTYRh64IEH9MADD9TLYAAAAMGgzrdGGDt2rM+tBy717rvvauzYsXXdPQAAQFCoc5navXu3vvrqK9P1X3/9td5///267h4AACAoWHr3v8u92e+RI0fUokULK7sHAABo8q7omqkNGzZow4YN3sd/+ctftHbt2mrbFRcX68CBA9xtHAAAhLwrKlNlZWU6c+aM9/G5c+dks1V/cSs6OlqjRo3S5MmTrU8IAADQhF1Rmbrrrrt01113SZLS09P129/+VkOHDm2QwQAAAIJBna+Zevzxx6u9hcvFuAAdAACEA0u3Rti5c6fpem6NAAAAwkGdy5TH47ns+vLyctnt9rruHgAAIChc0TVTX375pQoKCryP8/Pz/Z7Kczqdevnll9WxY0frEwIAADRhV1SmXn31VT3zzDMyDEOGYWjp0qVaunRpte08Ho/sdrvmz59fb4MCAAA0RVdUpoYPH65rr71WHo9HM2bM0JgxY/TDH/7QZxvDMBQVFaXExERdddVV9TosAABAU3NFZSo+Pl7x8fGSpAULFqhv376KjY1tkMEAAACCwRWVqYvdeuut9TkHAABAUKpzmZKkb775Rps2bdKnn36q4uJiud1un/WGYeixxx6zNCAAAEBTVucyVVBQoLFjx6qgoEAOh0PFxcWKiYlRcXGxXC6X2rRpo+jo6PqcFQAAoMmp832mFi1apJKSEq1du1a5ubnyeDxavHix9u7dq4ceekjf+c53lJ2dXZ+zAgAANDl1LlPvvvuuRo8erV69evm82XFkZKTuu+8+DRgwgFN8AAAg5NW5TJ0/f16dOnWSJLVs2VKGYai4uNi7PiUlRXv27LE+IQAAQBNW5zL13e9+VydPnpQkRUREqEOHDvroo4+86z///HM1b97c8oAAAABNWZ0vQB8wYIC2bt2qKVOmSLpwq4Tnn39eTqdTbrdb//jHPzRy5Mh6GxQAAKApqnOZuv/++/Wvf/1L5eXlioyM1KRJk1RYWKhNmzbJZrMpIyNDv/nNb+pzVgAAgCanzmWqY8eOPm9k3Lx5cz366KN69NFH62UwAACAYFDna6YAAABAmQIAALCEMgUAAGABZQoAAMACS290HK7at+U9B30Ykt1mk8vtljyBHqaJIRtzZOMfuZhr4Gyqfrbb7cH3OkPVzME4exW32yO3OzgPesPj8QTn5AHi8XhkGEagxwAANAC32yObjZ/xgeByu3X2TGm9F6qICJvatGlRr/us9hwNuvcQZBiGct85rNNFZYEepckwDMP7t0W6uS+yMUc2/pGLucbI5vw3lSourWiQfTckwzBkt9vkcgXncdO+bbTuuilRNpsRlK9OBV2ZGjNmjHbv3u133Z///GfdfPPNptvk5OQoPj7e8gwHjpzR8cLimjcME4ZhKCLCrspKV1B+EzcksjFHNv6RizmyMUc2gRV0Zep3v/udSkpKfJatXr1amzdvVmpqqndZnz59NGvWLJ/tYmNjG2VGAAAQPoKuTF1zzTXVlv3617/WwIED1bZtW+8yh8Oh5OTkRpwMAACEo+C97P//fPjhhzp+/Lh++tOfBnoUAAAQhoLulalLbdy4UdHR0Ro6dKjP8t27dys5OVkul0u9e/fW9OnT1bdv3/p5UkP8i76LGd/+aYhcfJCNObLxj1zMkY25IM+m6ndqQ9zaoTFuFxHUZaqyslKvv/660tPTFR397b2f+vbtq5EjR6pbt24qLCxUdna2xo8frzVr1iglJcXy89ptNkVE2C3vJ9RE2MnEDNmYIxv/yMUc2ZgL1myqCo/DERXgSeomqMvUzp079fXXXysjI8Nn+bRp03we//jHP1ZGRoaee+45LV++3PLzutxuVVa6LO8nZBgXvoErXS5uMngpsjFHNv6RizmyMRfk2bhcbkmS01nm/bi+2O22Bi9pQV2mNm7cqNatWystLe2y20VHR2vIkCHatGlT/TyxR/zT04t4X1Iml2rIxhzZ+Ecu5sjGXLBnUzWzy+VWZWX9lqnGELQXoJ8/f15vvPGGbrrpJjVr1izQ4wAAgDAVtGVq27ZtKi0trdW/4istLdWbb76ppKSkRpgMAACEk6A9zffPf/5THTt21A9+8AOf5R988IFWrFih66+/Xp06dVJhYaFWrVqlU6dO6cknnwzQtAAAIFQFZZkqKirSW2+9pXHjxlW7RUG7du1UUVGhxYsX6+zZs4qKilJKSormz5+vXr16BWhiAAAQqoKyTMXExGj//v1+13Xt2lXZ2dmNPBEAAAhXQXvNFAAAQFNAmQIAALCAMgUAAGABZQoAAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsoEwBAABYQJkCAACwgDIFAABgAWUKAADAAsoUAACABZQpAAAACyhTAAAAFlCmAAAALKBMAQAAWECZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAWUKQAAAAsoUwAAABZQpgAAACygTAEAAFhAmQIAALCAMgUAAGABZQoAAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsCLoy9eqrryohIaHaf0888YTPduvWrdONN96opKQk3XLLLdq+fXuAJgYAAKEsItAD1NWKFSvUqlUr7+MOHTp4P37ttdf08MMPa9KkSRowYIBycnI0ZcoUvfjii0pOTg7AtAAAIFQFbZn6/ve/r7Zt2/pd99RTT+nmm2/WjBkzJEkDBgzQf/7zHz377LNavnx5I04JAABCXdCd5qvJsWPH9MUXX2j48OE+y0eMGKFdu3apvLw8QJMBAIBQFLRlKiMjQ4mJiRo6dKiWLVsml8slScrPz5ckde/e3Wf7+Ph4VVRU6NixY40+KwAACF1Bd5qvXbt2mjp1qnr37i3DMLRt2zYtWbJEJ0+e1Ny5c1VUVCRJcjgcPp9X9bhqvSWGZBiG9f2ECuPbPw2Riw+yMUc2/pGLObIxF+TZVP1Otdvr/zWehtjnpYKuTA0aNEiDBg3yPk5LS1Pz5s21evVqTZo0qVFmsNtsioiwN8pzBZMIO5mYIRtzZOMfuZgjG3PBmk1V4XE4ogI8Sd0EXZnyZ/jw4Vq5cqX+/e9/KyYmRpJUXFysdu3aebdxOp2S5F1vhcvtVmWly/J+QoZx4Ru40uWSPIEepokhG3Nk4x+5mCMbc0GejcvlliQ5nWXej+uL3W5r8JIWEmXqYnFxcZIuXDtV9XHV42bNmqlz587Wn8QjeTxBeLQ2EO9LyuRSDdmYIxv/yMUc2ZgL9myqZna53KqsrN8y1RiC9gL0i+Xk5Mhut+u6665T586d1a1bN+Xm5lbbJjU1VZGRkQGaEgAAhKKge2Xq3nvvVf/+/ZWQkCBJ2rp1q9auXauxY8d6T+tNnTpVDz30kLp06aL+/fsrJydH+/bt09/+9rdAjg4AAEJQ0JWp7t27a/369Tpx4oTcbre6deum2bNna8yYMd5tMjIyVFZWpuXLl+v5559X9+7d9cwzzyglJSWAkwMAgFAUdGVqzpw5tdouMzNTmZmZDTwNAAAIdyFxzRQAAECgUKYAAAAsoEwBAABYQJkCAACwgDIFAABgAWUKAADAgqC7NUJT0L5tdKBHaFqMC2/+7HK7g/I9oczUx1syGIYhu90ml8sdlG/x0JDIxj9yMUc25oI9m2D/vWp4gjH1APJ4PDIMI9BjAAAQUlxut86eKZXbXb+1JCLCpjZtWtTrPqs9R4PuPQQZhqHcdw7rdFFZoEdpMgzD8L4yFSrdvG1MlIb/qLvldzCverfyhngn9GBHNv6RizmyMRcK2bjdnnovUo2FMlUHB46c0fHC4kCP0WQYhqGICLsqK10hU6Y6tWup4T/qXm/vYB6s74TeGMjGP3IxRzbmyCYwuAAdAADAAsoUAACABZQpAAAACyhTAAAAFlCmAAAALKBMAQAAWECZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAWUKQAAAAsoUwAAABZQpgAAACygTAEAAFhAmQIAALCAMgUAAGABZQoAAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsoEwBAABYEBHoAa7U66+/rn/84x/65JNP5HQ61bVrV40ZM0a33XabDMOQJI0ZM0a7d++u9rk5OTmKj49v7JEBAEAIC7oy9de//lWdOnVSVlaW2rRpo3feeUcPP/ywTpw4oSlTpni369Onj2bNmuXzubGxsY09LgAACHFBV6b+8pe/qG3btt7HqampOnv2rFatWqUHHnhANtuFM5cOh0PJyckBmhIAAISLoLtm6uIiVSUxMVElJSUqLS0NwEQAACCcBV2Z8mfPnj3q0KGDWrZs6V22e/duJScnKykpSXfffbfef//9AE4IAABCVdCd5rvUBx98oJycHJ/ro/r27auRI0eqW7duKiwsVHZ2tsaPH681a9YoJSXF+pMa8l7sDknGt38aCo1cqv7/2u3W/r5R9flW9xOKyMY/cjFHNubIxlxjZGJ4PB5Pgz9LAzlx4oQyMzMVHx+vlStXeq+XulRpaakyMjIUHx+v5cuXW37eZ9Z+pC+/Omd5P2i6Ol7VQlPuSA70GACAIBC0r0w5nU5NmDBBrVu31tNPP21apCQpOjpaQ4YM0aZNm+rluV1utyorXfWyr5BgSBF2uypdLiloq7kvl8stSXI6y7wf14XdbpPDEWV5P6GIbPwjF3NkY45szFVl05CCskydP39eEydOVHFxsV555RW1atWqcQfwSEH8gl69857aC6Fcqr4Ol8utykrrP5jqaz+hiGz8IxdzZGOObAIj6MpUZWWlZsyYofz8fL344ovq0KFDjZ9TWlqqN998U0lJSY0wIQAACCdBV6bmz5+v7du3KysrSyUlJfroo4+866677jrt27dPK1as0PXXX69OnTqpsLBQq1at0qlTp/Tkk08GbnAAABCSgq5M7dy5U5K0cOHCauu2bt2qdu3aqaKiQosXL9bZs2cVFRWllJQUzZ8/X7169WrscQEAQIgLujK1bdu2GrfJzs5uhEkAAABC5KadAAAAgUKZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAWUKQAAAAsoUwAAABZQpgAAACygTAEAAFhAmQIAALCAMgUAAGABZQoAAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsoEwBAABYQJkCAACwgDIFAABgAWUKAADAAsoUAACABZQpAAAACyhTAAAAFlCmAAAALKBMAQAAWECZAgAAsIAyBQAAYAFlCgAAwIKIQA8QjNq3jZbH4wn0GE2GYRiy221yudwhk0v7ttGBHgEAECQoU1fI4/Fo9I3fC/QYaAQut1tud2iUQwBAw6FMXSHDMOR0lsnlcgd6lCbDbrfJ4YgKuVzcbg9lCgBQI8pUHbhcblVWhk5pqC/kAgAIR1yADgAAYEFIl6lDhw5p/PjxSk5O1sCBA7Vo0SKVl5cHeiwAABBCQvY0X1FRkcaNG6du3brp6aef1smTJ7Vw4UKdP39ec+fODfR4AAAgRIRsmXr55Zd17tw5PfPMM2rdurUkyeVyaf78+Zo4caI6dOgQ2AEBAEBICNnTfDt27FBqaqq3SEnS8OHD5Xa7tXPnzsANBgAAQkrIlqn8/HzFxcX5LHM4HGrXrp3y8/MDNBUAAAg1IXuaz+l0yuFwVFseExOjoqIiS/u220O2g9ZJVR7kUh3ZmCMb/8jFHNmYIxtzjZFJyJaphuRwRAV6hCaJXMyRjTmy8Y9czJGNObIJjJAtUw6HQ8XFxdWWFxUVKSYmxtK+Q+1O31aF6h3Q6wPZmCMb/8jFHNmYIxtzVdk0pJAtU3FxcdWujSouLtapU6eqXUt1pbjTt3/kYo5szJGNf+RijmzMkU1ghOzJ1cGDB+udd96R0+n0LsvNzZXNZtPAgQMDOBkAAAglIVumRo0apRYtWmjy5Ml6++23tX79ei1atEijRo3iHlMAAKDehGyZiomJ0erVq2W32zV58mT96U9/0u23366srKxAjwYAAEKI4fF4PIEeIthwcV91druNXEyQjTmy8Y9czJGNObIx19C3R6BMAQAAWBCyp/kAAAAaA2UKAADAAsoUAACABZQpAAAACyhTAAAAFlCmAAAALKBMAQAAWECZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAWUKQAAAAsoUwAAABZQpmrh0KFDGj9+vJKTkzVw4EAtWrRI5eXlgR6rUb366qtKSEio9t8TTzzhs926det04403KikpSbfccou2b98eoIkbxpEjRzR37lyNHDlS1113nTIyMvxuV5sciouLNXv2bPXr108pKSmaNm2aCgsLG/pLaDC1yWbMmDF+j6NDhw75bBdK2bz++uv61a9+pcGDBys5OVkjR47U3//+d3k8Hp/twvGYqU024XjMSFJeXp7uvvtuDRgwQD179tTQoUO1YMECFRcX+2y3bds23XLLLUpKStKNN96o9evXV9tXeXm5Hn/8cQ0cOFDJyckaP3688vPzG+tLqVe1ySUrK8vvMbNjxw6ffdVnLhGWvqowUFRUpHHjxqlbt256+umndfLkSS1cuFDnz5/X3LlzAz1eo1uxYoVatWrlfdyhQwfvx6+99poefvhhTZo0SQMGDFBOTo6mTJmiF198UcnJyQGYtv4dPHhQeXl56t27t9xud7VfiFLtc5gxY4Y+//xzzZs3T82bN9eSJUs0YcIErV+/XhERwfetWZtsJKlPnz6aNWuWz7LY2Fifx6GUzV//+ld16tRJWVlZatOmjd555x09/PDDOnHihKZMmSIpfI+Z2mQjhd8xI0lnz55Vr169NGbMGLVu3VoHDx7U008/rYMHD2rlypWSpA8++EBTpkzR7bffrtmzZ+vdd9/Vb3/7W7Vo0UI33XSTd19/+MMflJOTo6ysLHXo0EFLly7VPffco9dee83n53kwqE0uktS5c+dqf9mPj4/3eVyvuXhwWUuXLvUkJyd7zpw541328ssvexITEz0nTpwI3GCNbP369Z4ePXp4Tp8+bbrNDTfc4HnwwQd9lt15552e++67r6HHazQul8v78axZszw333xztW1qk8OHH37o6dGjh+ett97yLjt06JAnISHB89prrzXA5A2vNtncfffdnvvvv/+y+wm1bPx9z8yZM8fTp08fb2bheszUJptwPGbMvPLKK54ePXp4f/f88pe/9Nx5550+2zz44IOe4cOHex//7//+rycxMdHz8ssve5edOXPGk5yc7Hn++ecbZ/AGdmkuZj9/LlbfuXCarwY7duxQamqqWrdu7V02fPhwud1u7dy5M3CDNTHHjh3TF198oeHDh/ssHzFihHbt2hUyp0Vttst/y9Q2hx07dsjhcGjgwIHebeLi4pSYmFjtpehgUVM2tRVq2bRt27bassTERJWUlKi0tDSsj5masqmtUMzGn6rfQxUVFSovL9d7773n8wqUdOG4OXTokI4fPy5Jevvtt+V2u322a926tQYOHBgy2VycS23Vdy6UqRrk5+crLi7OZ5nD4VC7du2C9pyzFRkZGUpMTNTQoUO1bNkyuVwuSfJm0b17d5/t4+PjVVFRoWPHjjX6rIFQ2xzy8/PVvXt3GYbhs11cXFzIH1e7d+9WcnKykpKSdPfdd+v999/3WR8O2ezZs0cdOnRQy5YtOWYucXE2VcL5mHG5XPrmm2/0ySef6Nlnn1V6erpiY2N19OhRVVRUVPv9VHUqq+rrzs/P13/9138pJiam2nbBnI1ZLlWOHDmiH/zgB+rZs6d+/vOf64033vD5/PrOJfhOJDcyp9Mph8NRbXlMTIyKiooCMFFgtGvXTlOnTlXv3r1lGIa2bdumJUuW6OTJk5o7d643i0uzqnocLlnVNgen0+n3nHxMTIz279/fwFMGTt++fTVy5Eh169ZNhYWFys7O1vjx47VmzRqlpKRICv1sPvjgA+Xk5HivAeKY+dal2UgcMz/5yU908uRJSdKgQYP0pz/9SZL148bhcAT1z2WzXKQLr24mJSXpmmuuUXFxsV566SVNnjxZTz75pPeVqPrOhTKFWhk0aJAGDRrkfZyWlqbmzZtr9erVmjRpUgAnQzCZNm2az+Mf//jHysjI0HPPPafly5cHaKrGc+LECc2cOVP9+/fX2LFjAz1Ok2KWTbgfM88//7zKysr0+eef6y9/+YsmTZqkVatWBXqsgDPLxW63a9y4cT7bpqena9SoUXrqqaeqnRatL5zmq4HD4aj2T1GlC63/0pcHw83w4cPlcrn073//25vFpVk5nU5JCpusapuDw+FQSUlJtc8Pt+MqOjpaQ4YM0SeffOJdFqrZOJ1OTZgwQa1bt9bTTz/tvcaMY8Y8G3/C6ZiRpO9973tKSUlRZmamnnvuOb333nvasmWL5ePG6XQGdTZmufhjs9l0ww036NChQzp//ryk+s+FMlUDf+fci4uLderUqWrnqsNZVRaXZpWfn69mzZqpc+fOgRir0dU2h7i4OB0+fLja7QMOHz4c9sdVKGZz/vx5TZw4UcXFxdVuLxLux8zlsqmtUM3mUgkJCWrWrJmOHj2qLl26qFmzZn6PG+nb4youLk5fffVVtVNX/q4HDlYX51Jb9Z0LZaoGgwcP1jvvvONt+5KUm5srm83m8y9HwlFOTo7sdruuu+46de7cWd26dVNubm61bVJTUxUZGRmgKRtXbXMYPHiwioqKtGvXLu82hw8f1qeffqrBgwc36syBVFpaqjfffFNJSUneZaGWTWVlpWbMmKH8/HytWLHC595sUngfMzVl4084HDNmPv74Y1VUVCg2NlaRkZHq37+/Nm3a5LNNTk6O4uPjvRdjp6WlyWazafPmzd5tioqK9Pbbb4dMNhfn4o/b7VZubq6uvfZafec735FU/7lwzVQNRo0apTVr1mjy5MmaOHGiTp48qUWLFmnUqFG1+sYPFffee6/69++vhIQESdLWrVu1du1ajR07Vu3atZMkTZ06VQ899JC6dOmi/v37KycnR/v27dPf/va3QI5er8rKypSXlydJKigoUElJifeXYL9+/dS2bdta5ZCSkqK0tDTNnj1bs2bNUvPmzbV48WIlJCTohhtuCMjXZlVN2VT9wrz++uvVqVMnFRYWatWqVTp16pSefPJJ735CLZv58+dr+/btysrKUklJiT766CPvuuuuu06RkZFhe8zUlM2+ffvC8piRpClTpqhnz55KSEjQd77zHX322WfKzs5WQkKChg0bJkn61a9+pbFjx2revHkaPny43nvvPW3cuFGLFy/27ufqq6/W7bffrkWLFslms6lDhw5atmyZWrVqpVGjRgXqy6uzmnIpKChQVlaWbr75ZnXt2lVFRUV66aWXtH//fj399NPe/dR3Lobn0tdFUc2hQ4f0yCOPaO/evWrRooVGjhypmTNnhs2rLdKFO8W+9dZbOnHihNxut7p166bMzEyNGTPG558jr1u3TsuXL9eXX36p7t2768EHH9RPfvKTAE5ev44fP66hQ4f6XffCCy+of//+kmqXQ3FxsRYsWKAtW7aosrJSaWlpmjNnTtCW9Jqyufrqq/X73/9eBw4c0NmzZxUVFaWUlBRNmTJFvXr18tk+lLJJT09XQUGB33Vbt271/m06HI+ZmrJxuVxhecxIFy6wzsnJ0dGjR+XxeNSpUyddf/31uvfee31uG7F161YtWbJEhw8fVseOHXX//ffr9ttv99lXeXm5Fi9erP/5n//RuXPn1KdPH82ZM6faHcGDQU25nD17Vr/5zW/06aef6vTp02rWrJl69uyp+++/3+cfUUn1mwtlCgAAwAKumQIAALCAMgUAAGABZQoAAMACyhQAAIAFlCkAAAALKFMAAAAWUKYAAAAsoEwBAABYwNvJAAhKR48e1YoVK7Rz504VFhaqWbNm6tGjh4YPH64777zT+x5cgfLiiy8qKipKP//5zwM6B4CGxx3QAQSdN998U9OnT1dkZKRGjhypHj16qKKiQnv27NHmzZt166236pFHHgnojBkZGWrTpo3WrFkT0DkANDxemQIQVI4dO6aZM2eqY8eOWr16tdq3b+9d94tf/EJHjhzRm2++GbgBAYQdrpkCEFRWrFih0tJSPfrooz5FqkrXrl01btw4SVJlZaWeffZZDRs2TD179lR6err+/Oc/q7y83OdzEhISfN5Rvkp6erqysrK8j1999VUlJCRoz549WrBggQYMGKDk5GRNnjxZX3/9tc/nHTx4ULt371ZCQoISEhI0ZsyY+ooAQBPDK1MAgsr27dvVuXNn9enTp8Zt58yZow0bNujGG2/U+PHjtW/fPi1btkyHDh3Ss88+W+cZ/vCHP8jhcGjKlCkqKCjQ6tWr9fvf/15LliyRJM2ePVuPPPKIoqOjNWnSJEnSVVddVefnA9C0UaYABI2SkhKdPHlSQ4cOrXHbzz77TBs2bFBmZqb+8Ic/SLpwGrBt27ZauXKl3n33XQ0YMKBOc7Ru3VorV66UYRiSJLfbrTVr1qi4uFitWrXSsGHDtGTJErVp00YjR46s03MACB6c5gMQNEpKSiRJLVq0qHHbvLw8SdL48eN9lv/yl7/0WV8Xd9xxh7dISdIPf/hDuVwuFRQU1HmfAIIXZQpA0GjZsqUk6dy5czVuW1BQIJvNpi5duvgsb9eunRwOh6Xi07FjR5/HDodDkuR0Ouu8TwDBizIFIGi0bNlS7du318GDB2v9ORe/gnSlXC6X3+U2m/8fndxpBghPlCkAQeUnP/mJjh49qr179152u06dOsntduvIkSM+y7/66is5nU516tTJuywmJqbaq0rl5eU6depUnee0UuIABBfKFICgct999yk6Olpz5szRV199VW390aNHtXr1ag0ZMkSStHr1ap/1q1atkiTveknq3LmzPvjgA5/t1q5da/rKVG1ERUVx2g8IE/xrPgBBpUuXLnriiSc0c+ZMjRgxwnsH9PLycu3du1e5ubn6+c9/rnHjxunWW2/VK6+8IqfTqb59++pf//qXNmzYoGHDhvn8S77MzEz97ne/09SpU/WjH/1In332md5++221adOmznN+//vf10svvaTnnntOXbt2Vdu2bZWamlofEQBoYihTAILO0KFD9Y9//EPZ2dnaunWrXnrpJUVGRiohIUFZWVm64447JF24H1RsbKw2bNigN954Q1dddZUmTpyoKVOm+Ozvjjvu0PHjx/X3v/9db731ln7wgx9o1apVuueee+o84+TJk/Xll19qxYoVOnfunPr160eZAkIU780HAABgAddMAQAAWECZAgAAsIAyBQAAYAFlCgAAwALKFAAAgAWUKQAAAAsoUwAAABZQpgAAACygTAEAAFhAmQIAALCAMgUAAGABZQoAAMACyhQAAIAF/x+2TC0DYOXJbwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data=pokemon, y=\"attack\", bins=5)"
]
},
{
"cell_type": "markdown",
"id": "2aac9186-86c0-41db-a1c4-8719bb78b46b",
"metadata": {},
"source": [
"**When you want to compare the distribution of a numeric variable across categories**, a [barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) is a good choice. Choose one numeric column and one categorical column. \n",
"\n",
"Let's see pokémon hit points by legendary/non-legendary. `ci=\"sd\"` shows the standard deviation for each category. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "92be1ad0-12bb-49f0-a3f6-85fcfd98e943",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='legendary', ylabel='hp'>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(data=pokemon, x=\"legendary\", y=\"hp\", ci=\"sd\")"
]
},
{
"cell_type": "markdown",
"id": "4f75e1fa-a5d7-4d2c-a458-8190a7cd700e",
"metadata": {},
"source": [
"Here, we use a barplot to show average hit points by type. `ci=None` removes the standard deviation bars, because they clutter up the plot with too much detail. "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "17f1c289-5990-4420-bfcb-e50eee0b8af6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='hp', ylabel='type'>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(data=pokemon, x=\"hp\", y=\"type\", ci=None, palette=\"muted\")"
]
},
{
"cell_type": "markdown",
"id": "213d6139-203f-4d81-a4b1-6f98cb184662",
"metadata": {},
"source": [
"**When you want to show how many observations are the intersection of multiple categories,** a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html) is a good choice. \n",
"\n",
"To demonstrate this, let's convert the numeric variable `speed` into a categorical variable, `speed_category`, using the built-in function [cut](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html). "
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "3c8e9f47-9aea-4bf0-a628-7aa1a66a8eee",
"metadata": {},
"outputs": [],
"source": [
"bins = [0, 50, 100, 200]\n",
"labels = [\"slow\", \"medium\", \"fast\"]\n",
"pokemon[\"speed_category\"] = pd.cut(pokemon.speed, bins=bins, labels=labels)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "22f78bec-3d18-4133-ba9f-6595d7181ded",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='speed_category', ylabel='count'>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data=pokemon, x=\"speed_category\", hue=\"legendary\")"
]
},
{
"cell_type": "markdown",
"id": "fd508c13-9900-4be1-958f-4f9e9e9b633a",
"metadata": {},
"source": [
"**When you want to show the relationship between two numeric variables**, a [scatterplot](https://seaborn.pydata.org/generated/seaborn.scatterplot.html) is a good choice. \n",
"\n",
"Here, we plot pokémon hit points against their speed. "
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "444d9832-bd57-4238-9ea4-5ee898847170",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='hp', ylabel='speed'>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\")"
]
},
{
"cell_type": "markdown",
"id": "03e81709-393d-4c41-bce5-2dffc9cf5553",
"metadata": {},
"source": [
"You can distinguish between categories within a scatter plot by assigning a categorical variable to `hue`. We set the marker size with `s` and their opacity with `alpha`. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "86f9747b-00a3-407f-9b73-0bce40bac50d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='hp', ylabel='speed'>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\", hue=\"legendary\", alpha=0.5, s=60)"
]
},
{
"cell_type": "markdown",
"id": "f3741251-2a2b-437e-b68f-084fb4399e9f",
"metadata": {},
"source": [
"Finally, if you want scatter plots across multiple categories, a [relplot](https://seaborn.pydata.org/generated/seaborn.relplot.html) lets you distribute categories across rows and colums in a grid. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "7385237c-6a5c-4041-af46-559d6d84d1fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f358b13a350>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1595.72x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"favorite_types = pokemon[pokemon.type.isin([\"Fire\", \"Water\", \"Grass\"])]\n",
"sns.relplot(data=favorite_types, x=\"hp\", y=\"speed\", hue=\"legendary\", col=\"type\", s=100)"
]
},
{
"cell_type": "markdown",
"id": "c6a20904-416d-44be-a4f3-2107200fb3c2",
"metadata": {},
"source": [
"#### Your turn\n",
"\n",
"**1.5.0.** Plot a histogram of peoples' heights."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='height', ylabel='Count'>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Your code here\n",
"#sns.histplot(data=pokemon, x=\"attack\")\n",
"sns.histplot(data=people, x=\"height\")\n"
]
},
{
"cell_type": "markdown",
"id": "9b0c9120-fff4-42b2-8ab6-3aa2eba47806",
"metadata": {},
"source": [
"**1.5.1.** Plot a bar chart showing peoples' average hours of sleep by age. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "ee30c851-14b1-4901-9182-4304d54d53a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='age', ylabel='sleep'>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Your code here\n",
"#bar chart is not in the examples, do you mean barplot?\n",
"#sns.barplot(data=pokemon, x=\"legendary\", y=\"hp\", ci=\"sd\")\n",
"#use the average hours of sleep\n",
"zzz = people[\"sleep\"].mean()\n",
"sns.barplot(data=people, x=\"age\", y=\"sleep\", ci=None)"
]
},
{
"cell_type": "markdown",
"id": "15d94323-2d65-4100-9916-101516f6ccf1",
"metadata": {},
"source": [
"**1.5.2.** Plot a bar chart showing peoples' likelihood of getting exercise by income. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='income', ylabel='exercise'>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Your code here\n",
"#sns.barplot(data=people, x=\"age\", y=\"sleep\", ci=None)\n",
"sns.barplot(data=people, x=\"income\", y=\"exercise\", ci=None)"
]
},
{
"cell_type": "markdown",
"id": "b2705fef-470d-494c-86c1-8b3bd34b3660",
"metadata": {},
"source": [
"**1.5.3.** Plot a bar chart showing average reported health by age. For each age, show average health for those who get exercise and those who don't."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06",
"metadata": {},
"outputs": [],
"source": [
"# Your code here\n",
"\n",
"#sns.countplot(data=pokemon, x=\"speed_category\", hue=\"legendary\")\n",
"#pokemon[\"speed_category\"] = pd.cut(pokemon.speed, bins=bins, labels=labels)\n",
"\n",
"#people[\"exercise_category\"] = pd.cut(people.exercise)\n",
"#people.groupby([\"sex\", \"education\"])[[\"income\",\"health\"]].mean()\n",
"#people[\"exercise_category\"] = pd.cut((people.exercise)&(people.health))\n",
"#sns.countplot(data=people, x=\"age\", hue=\"exercise category\" )"
]
},
{
"cell_type": "markdown",
"id": "84b1e240-4f75-4c86-8c1f-1026aa223717",
"metadata": {},
"source": [
"**1.5.4.** Create a plot showing the number of people at each income level, for each education level. "
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "cut() missing 1 required positional argument: 'bins'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[47], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m bins \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m] \u001b[38;5;66;03m#income level\u001b[39;00m\n\u001b[1;32m 8\u001b[0m labels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno ged\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mged\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msome uni\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdegree\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;66;03m#education level\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m people[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mincome\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpeople\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincome\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mTypeError\u001b[0m: cut() missing 1 required positional argument: 'bins'"
]
}
],
"source": [
"# Your code here\n",
"#bins = [0, 50, 100, 200]\n",
"#labels = [\"slow\", \"medium\", \"fast\"]\n",
"#pokemon[\"speed_category\"] = pd.cut(pokemon.speed, bins=bins, labels=labels)\n",
"#sns.countplot(data=pokemon, x=\"speed_category\", hue=\"legendary\")\n",
"\n",
"bins = [1,2,3,4,5,6,7,8] #income level\n",
"labels = [\"no ged\",\"ged\",\"some uni\", \"degree\"] #education level\n",
"people[\"income\"] = pd.cut(people.income)"
]
},
{
"cell_type": "markdown",
"id": "ac717580-4157-402c-9262-b2b50dfe606f",
"metadata": {},
"source": [
"**1.5.5.** Plot side-by-side scatter plots showing the relationship between height and weight for males and females. (There are so many overlapping dots that the plot will be more informative if you lower the opacity of each dot. Try using `alpha=0.1` and `edgecolor=None`.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b00dd7d6-226b-469c-86d8-b71b328aa576",
"metadata": {},
"outputs": [],
"source": [
"# Your code here\n",
"#favorite_types = pokemon[pokemon.type.isin([\"Fire\", \"Water\", \"Grass\"])]\n",
"#sns.relplot(data=favorite_types, x=\"hp\", y=\"speed\", hue=\"legendary\", col=\"type\", s=100)\n",
"\n",
"sex = people[people.sex.isin([\"male\",\"female\"])]\n",
"sns.replot(data=sex, x=\"height\", y=\"weight\", hue=\"\")\n"
]
},
{
"cell_type": "markdown",
"id": "e9ff7225-5d08-428b-90e8-ee60f4a4049a",
"metadata": {},
"source": [
"## 2. Crafting a data argument\n",
"\n",
"Everything up to here are just tools, worthless without a clear research question and a convincing argument. Choose a research question that interests you which might be answerable using the `people` dataset. Then do your best to find the answer in the space below. This answer should include data analysis (code cells) as well as written argument (text cells) explaining what the data means and why you believe it answers your question. \n",
"\n",
"Examples of research questions might include:\n",
"\n",
"- Do older people tend to have higher incomes?\n",
"- Do people who sleep at least 6 hours a night tend to report better health? \n",
"- Is it more common for males to be bisexual than females?\n",
"\n",
"**A note of caution:** this lab has given you tools for exploring associations--patterns that tend to co-occur. These tools *do not* equip you to argue that one variable causes another to change. For example: Plot 1.5.4 showed that people who are taller also tend to be heaver, with a lot of individual variation. But are people heavier *because* they are taller? Are they taller because they are heavier? Or maybe neither variable causes the other--perhaps they're both caused by something else. If you want to be able to answer questions like these, take a course on statistics."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99",
"metadata": {},
"outputs": [],
"source": [
"# note of caution - making assumptions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b4b852b-402c-45d4-b3bb-840e47b249ed",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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