Aggiungere categorie

Il proprietario di un’agenzia locale per l’adozione di cani ha inserzioni per quasi 3.000 cani. Una delle domande più frequenti ricevute di recente è: "In che tipo di area è stato tenuto il cane in precedenza?". Stai impostando una pipeline per fare un’analisi e vuoi capire quali informazioni sono disponibili sulla variabile "keep_in". Sia pandas, come pd, sia il dataset dogs sono già stati precaricati.

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Lavorare con i dati categorici in Python

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# Check frequency counts while also printing the NaN count
print(____)

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Questo esercizio fa parte del corso

Lavorare con i dati categorici in Python

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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: Impostare le variabili di categoria Exercise 2: Impostare le categorie Exercise 3: Aggiungere categorie

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Exercise 4: Rimozione delle categorie Exercise 5: Aggiornare le categorie Exercise 6: Verifica: accorpare le categorie Exercise 7: Rinominare le categorie Exercise 8: Raggruppare le categorie Exercise 9: Riordinare le categorie Exercise 10: Riordinare le categorie in una Series Exercise 11: Usare .groupby() dopo il riordino Exercise 12: Pulizia e accesso ai dati Exercise 13: Pulizia delle variabili Exercise 14: Accesso e filtraggio dei dati

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 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