Creating a count plot

When creating quick analysis of frequency counts, you have been using .value_counts(). This is a great way for you to see the counts and get an idea of which categories are present in the data. However, sending frequency tables to clients or coworkers may not always be a good idea. For this exercise, you will visualize the number of reviews by their "Score". Although "Score" has been used as a numerical variable in the past, it can be used as a categorical variable given that it has five unique values that are ordered from worst to best. The reviews dataset has been preloaded.

Questo esercizio fa parte del corso

Working with Categorical Data in Python

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Istruzioni dell'esercizio

Use the catplot() function to display count frequencies using the reviews dataset.
Count the frequencies for the "Score" variable across the x-axis.
When counting the frequencies, color the bars using the "Traveler type" column.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

sns.set(font_scale=1.4)
sns.set_style("darkgrid")

# Create a catplot that will count the frequency of "Score" across "Traveler type"
sns.catplot(
  ____
)
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Working with Categorical Data in Python

IntermediárioNível de habilidade

4.8+

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Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. In this chapter, you’ll learn how pandas handles categorical columns using the data type category. You’ll also discover how to group data by categories to unearth great summary statistics.

Exercise 1: Course introduction Exercise 2: Categorical vs. numerical Exercise 3: Exploring a target variable Exercise 4: Ordinal categorical variables Exercise 5: Categorical data in pandas Exercise 6: Setting dtypes and saving memory Exercise 7: Creating a categorical pandas Series Exercise 8: Setting dtype when reading data Exercise 9: Grouping data by category in pandas Exercise 10: Create lots of groups Exercise 11: Setting up a .groupby() statement Exercise 12: Using pandas functions effectively

Now it’s time to learn how to set, add, and remove categories from a Series. You’ll also explore how to update, rename, collapse, and reorder categories, before applying your new skills to clean and access other data within your DataFrame.

Exercise 1: Setting category variables Exercise 2: Setting categories Exercise 3: Adding categories Exercise 4: Removing categories Exercise 5: Updating categories Exercise 6: Collapsing categories knowledge check Exercise 7: Renaming categories Exercise 8: Collapsing categories Exercise 9: Reordering categories Exercise 10: Reordering categories in a Series Exercise 11: Using .groupby() after reordering Exercise 12: Cleaning and accessing data Exercise 13: Cleaning variables Exercise 14: Accessing and filtering data

In this chapter, you’ll use the seaborn Python library to create informative visualizations using categorical data—including categorical plots (cat-plot), box plots, bar plots, point plots, and count plots. You’ll then learn how to visualize categorical columns and split data across categorical columns to visualize summary statistics of numerical columns.

Exercise 1: Introduction to categorical plots using Seaborn Exercise 2: Boxplot understanding Exercise 3: Creating a box plot Exercise 4: Seaborn bar plots Exercise 5: Creating a bar plot Exercise 6: Ordering categories Exercise 7: Bar plot using hue Exercise 8: Point and count plots Exercise 9: Creating a point plot Exercise 10: Creating a count plot

Esercizio in corso

Exercise 11: Review catplot() types Exercise 12: Additional catplot() options Exercise 13: One visualization per group Exercise 14: Updating categorical plots

Lastly, you’ll learn how to overcome the common pitfalls of using categorical data. You’ll also grow your data encoding skills as you are introduced to label encoding and one-hot encoding—perfect for helping you prepare your data for use in machine learning algorithms.

Exercise 1: Categorical pitfalls Exercise 2: Memory usage knowledge check Exercise 3: Overcoming pitfalls: string issues Exercise 4: Overcoming pitfalls: using NumPy arrays Exercise 5: Label encoding Exercise 6: Create a label encoding and map Exercise 7: Using saved mappings Exercise 8: Creating a Boolean encoding Exercise 9: One-hot encoding Exercise 10: One-hot knowledge check Exercise 11: One-hot encoding specific columns Exercise 12: Wrap-up video