Book Image

Python Data Cleaning Cookbook

By : Michael Walker
Book Image

Python Data Cleaning Cookbook

By: Michael Walker

Overview of this book

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.
Table of Contents (12 chapters)

Changing series values

During the data cleaning process, we often need to change the values in a data series or create a new one. We can change all the values in a series, or just the values in a subset of our data. Most of the techniques we have been using to get values from a series can be used to update series values, though some minor modifications are necessary.

Getting ready

We will work with the overall high school GPA column from the National Longitudinal Survey in this recipe.

How to do it…

We can change the values in a pandas series for all rows, as well as for selected rows. We can update a series with scalars, by performing arithmetic operations on other series, and by using summary statistics. Let's take a look at this:

  1. Import pandas and load the NLS data:
    >>> import pandas as pd
    >>> nls97 = pd.read_csv("data/nls97b.csv")
    >>> nls97.set_index("personid", inplace=True)
  2. Edit all the values based...