Una visualizzazione per gruppo

Durante un progetto di esplorazione dei dati, ti è stato chiesto di visualizzare il numero di recensioni degli hotel per "Period of stay" e per giorno della settimana, "Review weekday". L’obiettivo è capire in quale giorno della settimana si concentra il maggior numero di recensioni per ciascuno dei quattro periodi di soggiorno. Il dataset reviews è già stato caricato per te, così come seaborn, come sns, e matplotlib.pyplot come plt.

Questo esercizio fa parte del corso

Lavorare con i dati categorici in Python

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Istruzioni dell'esercizio

Crea un catplot() usando "count" come tipo di grafico.
Conta il numero di recensioni per "Review weekday".
Crea grafici separati per ciascun "Period of stay".
Manda a capo i grafici dopo ogni 2° grafico.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a catplot for each "Period of stay" broken down by "Review weekday"
ax = sns.catplot(
  # Make sure Review weekday is along the x-axis
  ____,
  # Specify Period of stay as the column to create individual graphics for
  ____,
  # Specify that a count plot should be created
  ____,
  # Wrap the plots after every 2nd graphic.
  ____,
  data=reviews
)
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Lavorare con i dati categorici in Python

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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: Introduzione ai grafici categorici con Seaborn Exercise 2: Comprendere i box plot Exercise 3: Creare un box plot Exercise 4: Bar plot con Seaborn Exercise 5: Creare un grafico a barre Exercise 6: Ordinare le categorie Exercise 7: Grafico a barre con hue Exercise 8: Point plot e count plot Exercise 9: Creare un point plot Exercise 10: Creare un count plot Exercise 11: Rivedi i tipi di catplot()Exercise 12: Opzioni aggiuntive di catplot()Exercise 13: Una visualizzazione per gruppo

Esercizio in corso

Exercise 14: Aggiornare i grafici categorici

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