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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

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
df.to_pickle('demo_obj.pkl')

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...

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