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

Handling missing values

Missing values are the values that are absent from the data. Absent values can occur due to human error, privacy concerns, or the value not being filled in by the respondent filling in the survey. This is the most common problem in data science and the first step of data preprocessing. Missing values affect a machine learning model's performance. Missing values can be handled in the following ways:

  • Drop the missing value records.
  • Fill in the missing value manually.
  • Fill in the missing values using the measures of central tendency, such as mean, median, and mode. The mean is used to impute the numeric feature, the median is used to impute the ordinal feature, and the mode or highest occurring value is used to impute the categorical feature.
  • Fill in the most probable value using machine learning models such as regression, decision trees, KNNs.

It is important to understand that in some cases, missing values will not impact the data. For example, driving license...