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

Measuring central tendency

Central tendency is the trend of values clustered around the averages such as the mean, mode, and median values of data. The main objective of central tendency is to compute the center-leading value of observations. Central tendency determines the descriptive summary and provides quantitative information about a group of observations. It has the capability to represent a whole set of observations. Let's see each type of central tendency measure in detail in the coming sections.


The mean value is the arithmetic mean or average, which is computed by the sum of observations divided by the number of observations. It is sensitive to outliers and noise, with the result that whenever uncommon or unusual values are added to a group, its mean gets deviated from the typical central value. Assume x1, x2, . . . , x N is N observations. The formula for the mean of these values is shown here:

Let's compute the mean value of the communication skill score column...