Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Working with missing values

Most real-world datasets are messy and noisy. Due to their messiness and noise, lots of values are either faulty or missing. pandas offers lots of built-in functions to deal with missing values in DataFrames:

  • Check missing values in a DataFrame: pandas' isnull() function checks for the existence of null values and returns True or False, where True is for null and False is for not-null values. The sum() function will sum all the True values and returns the count of missing values. We have tried two ways to count the missing values; both show the same output:
# Count missing values in DataFrame

The following is the second method:


This results in the following output:

  • Drop missing values: A very naive approach to deal with missing values is to drop them for analysis purposes. pandas has the dropna() function to drop or delete such observations from the DataFrame. Here, the inplace=True attribute makes the changes in...