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

Creating pandas DataFrames

The pandas library is designed to work with a panel or tabular data. pandas is a fast, highly efficient, and productive tool for manipulating and analyzing string, numeric, datetime, and time-series data. pandas provides data structures such as DataFrames and Series. A pandas DataFrame is a tabular, two-dimensional labeled and indexed data structure with a grid of rows and columns. Its columns are heterogeneous types. It has the capability to work with different types of objects, carry out grouping and joining operations, handle missing values, create pivot tables, and deal with dates. A pandas DataFrame can be created in multiple ways. Let's create an empty DataFrame:

# Import pandas library
import pandas as pd

# Create empty DataFrame
df = pd.DataFrame()

# Header of dataframe.


In the preceding example, we have created an empty DataFrame. Let's create a DataFrame using a dictionary of the list:

# Create dictionary of list
data = {&apos...