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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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Toc

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 Parquet

The Parquet file format provides columnar serialization for pandas DataFrames. It reads and writes DataFrames efficiently in terms of storage and performance and shares data across distributed systems without information loss. The Parquet file format does not support duplicate and numeric columns.

There are two engines used to read and write Parquet files in pandas: pyarrow and the fastparquet engine. pandas's default Parquet engine is pyarrow; if pyarrow is unavailable, then it uses fastparquet. In our example, we are using pyarrow. Let's install pyarrow using pip:

pip install pyarrow

You can also install the pyarrow engine in the Jupyter Notebook by putting an ! before the pip keyword. Here is an example:

!pip install pyarrow

Let's write a file using the pyarrow engine:

# Write to a parquet file.
df.to_parquet('employee.parquet', engine='pyarrow')

In the preceding code example, we have written the using to_parquet...

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