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Lecture 6 Exercise Solutions
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Subsetting `DataFrames` -- Exercises"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Goal\n",
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"\n",
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"Practice `pandas` subsetting operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercises"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 0. Import `pandas` and load the penguins data set"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"penguins = pd.read_csv(\"https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1. Get the data from the column for the flipper length. What is its type of the output when you select a single column from a `DataFrame`?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# When subsetting to specific columns, it's useful to print out the column names for reference\n",
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"list(penguins.columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Just index the DataFrame with the column name\n",
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"flipper_len = penguins[\"flipper_length_mm\"]\n",
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"flipper_len.head(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use the type function to get the type of the output\n",
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"# Here, the type is a pandas Series object since we only selected a single column\n",
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"type(flipper_len)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Subset the penguins data to just the columns containing length/depth measurements. What is the type of the output when you select multiple columns from a `DataFrame`?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Subsetting to multiple columns requires wraping the column names in a Python list with []\n",
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"length_data = penguins[[\"bill_length_mm\", \"bill_depth_mm\", \"flipper_length_mm\"]]\n",
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"length_data.head(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Since multiple columns were selected, the output is DataFrame\n",
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"type(length_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. What are the names of the different islands represented in the data set?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Select the island column, then get the unique values\n",
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"list(penguins[\"island\"].unique())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 4. How many rows have missing body mass values?\n",
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"\n",
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"Hint: You'll need to find (or guess) the name of a helper function very similar to one we used in the lesson."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The isna function indicates whether or not a value is missing\n",
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"penguins[penguins[\"body_mass_g\"].isna()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# It's easy to count the rows from the output, but if there were many more\n",
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"# shape can be used to get the count programatically\n",
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"penguins[penguins[\"body_mass_g\"].isna()].shape[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 5. Get the subset of data that match ALL of the following criteria\n",
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"\n",
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"* Penguins of the Gentoo and Chinstrap species\n",
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"* Flipper length less than 200\n",
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"* Females only"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is mainly an exercise in getting the syntax correct\n",
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"penguins[(penguins[\"species\"].isin([\"Gentoo\", \"Chinstrap\"])) & \\\n",
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" (penguins[\"flipper_length_mm\"] < 200) & \\\n",
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" (penguins[\"sex\"] == \"female\")]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 6. If we only wanted to select the `species`, `flipper_length_mm`, and `sex` columns from the above exercise, how would we need to modify the code?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# To filter rows AND select specific columns, we need to use the .loc function\n",
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"penguins.loc[(penguins[\"species\"].isin([\"Gentoo\", \"Chinstrap\"])) & \\\n",
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" (penguins[\"flipper_length_mm\"] < 200) & \\\n",
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" (penguins[\"sex\"] == \"female\"), [\"species\", \"flipper_length_mm\", \"sex\"]]"
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]
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}
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],
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