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Duplicate of Live Training - Data Cleaning in Python
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    Cleaning Data in Python

    Welcome to your webinar workspace! You can follow along as we go though an introduction to data cleaning in Python.

    The first cell imports the main packages we will be using.

    # Import libraries
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt

    Task 1: Loading and Inspecting the Data

    We will be working with a dataset of audiobooks downloaded from from 1998 to 2025 (pre-planned releases). Source

    The first thing we will do is load the raw audible data.


    • Using pandas, read the audible_raw.csv file that is located inside the data folder in our local directory. Assign to audible.
    • Show the first few rows of the audible data frame.
    # Load the audible_raw.csv file
    audible = pd.read_csv('data/audible_raw.csv')
    # View the first rows of the dataframe

    💾 The data

    • "name" - The name of the audiobook.
    • "author" - The audiobook's author.
    • "narrator" - The audiobook's narrator.
    • "time" - The audiobook's duration, in hours and minutes.
    • "releasedate" - The date the audiobook was published.
    • "language" - The audiobook's language.
    • "stars" - The average number of stars (out of 5) and the number of ratings (if available).
    • "price" - The audiobook's price in INR (Indian Rupee).

    We can use the .info() method to inspect the data types of the columns

    # Inspect the columns' data types

    Task 2: Clean text data in Author and Narrator columns

    We will start cleaning some of the text columns like author and narrator. We can remove the Writtenby: and Narratedby: portions of the text in those columns.

    For this, we will use the .str.replace() method


    • Remove 'Writtenby:' from the author column
    • Remove 'Narratedby:' from the narrator column
    • Check the results
    # Remove Writtenby: from the author column
    audible['author'] = audible['author'].str.replace('Writtenby:', '')
    # Remove Narratedby: from the narrator column
    # Check the results

    Task 3: Extract number of stars and ratings from Stars column.

    The stars column combines the number of stars and the number of ratins. Let's turn this into numbers and split it into two columns: stars and ratings.

    First we will use the .sample() method to get a glimpse at the type of entries in that column.

    # Get a glimpse of the stars column

    Since there are many instances of Not rated yet, let's filter them out and sample again:

    # Explore the values of the star column that are not 'Not rated yet'
    audible[audible.stars != 'Not rated yet'].stars.sample(n=10)

    As a first step, we can replace the instances of Not rated yet with NaN

    # Replace 'Not rated yet' with NaN
    audible.stars.replace('Not rated yet', np.nan, inplace=True)