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

Reading and writing data from a pickle pandas object

In the data preparation step, we will use various data structures such as dictionaries, lists, arrays, or DataFrames. Sometimes, we might want to save them for future reference or send them to someone else. Here, a pickle object comes into the picture. pickle serializes the objects to save them and can be loaded again any time. pandas offer two functions: read_pickle() for loading pandas objects and to_pickle() for saving Python objects:

# import pandas
import pandas as pd

# Read CSV file
df=pd.read_csv('demo.csv', sep=',' , header=None)

# Save DataFrame object in pickle file

In the preceding code, we read the demo.csv file using the read_csv() method with sep and header parameters. Here, we have assigned sep with a comma and header with None. Finally, we have written the dataset to a pickle object using the to_pickle() method. Let's see how to read pickle objects using the...