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)

Using unstack and pivot to reshape data from long to wide

Sometimes, we actually have to move data from a tidy to an untidy structure. This is often because we need to prepare the data for analysis with software packages that do not handle relational data well, or because we are submitting data to some external authority that has requested it in an untidy format. unstack and pivot can be helpful when we need to reshape data from long to wide format. unstack does the opposite of what we did with stack, and pivot does the opposite of melt.

Getting ready...

We continue to work with the NLS data on weeks worked and college enrollment in this recipe.

How to do it…

We use unstack and pivot to return the melted NLS DataFrame to its original state:

  1. Import pandas and load the stacked and melted NLS data:
    >>> import pandas as pd
    >>> nls97 = pd.read_csv("data/nls97f.csv")
    >>> nls97.set_index(['originalid'], inplace=True...