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)

Selecting rows

When we are taking the measure of our data and otherwise answering the question, "How does it look?", we are constantly zooming in and out. We are looking at aggregated numbers and particular rows. But there are also important data issues that are only obvious at an intermediate zoom level, issues that we only notice when looking at some subset of rows. This recipe demonstrates how to use the pandas tools for detecting data issues in subsets of our data.

Getting ready...

We will continue working with the NLS data in this recipe.

How to do it...

We will go over several techniques for selecting rows in a pandas DataFrame.

  1. Import pandas and numpy, and load the nls97 data:
    >>> import pandas as pd
    >>> import numpy as np
    >>> nls97 = pd.read_csv("data/nls97.csv")
    >>> nls97.set_index("personid", inplace=True)
  2. Use slicing to start at the 1001st row and go to the 1004th row: