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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
Languages
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
Author Profile Icon Matthew Harrison
Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Creating DataFrames from scratch

Usually, we create a DataFrame from an existing file or a database, but we can also create one from scratch. We can create a DataFrame from parallel lists of data.

How to do it...

  1. Create parallel lists with your data in them. Each of these lists will be a column in the DataFrame, so they should have the same type:
    >>> import pandas as pd
    >>> import numpy as np
    >>> fname = ["Paul", "John", "Richard", "George"]
    >>> lname = ["McCartney", "Lennon", "Starkey", "Harrison"]
    >>> birth = [1942, 1940, 1940, 1943]
    
  2. Create a dictionary from the lists, mapping the column name to the list:
    >>> people = {"first": fname, "last": lname, "birth": birth}
    
  3. Create a DataFrame from the dictionary:
    >>> beatles = pd.DataFrame(people...
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