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Python 3 Data Visualization Using ChatGPT / GPT-4

You're reading from   Python 3 Data Visualization Using ChatGPT / GPT-4 Master Python Visualization Techniques with AI Integration

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Product type Paperback
Published in Aug 2024
Publisher Mercury_Learning
ISBN-13 9781836649250
Length 314 pages
Edition 1st Edition
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Authors (2):
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Mercury Learning and Information Mercury Learning and Information
Author Profile Icon Mercury Learning and Information
Mercury Learning and Information
Oswald Campesato Oswald Campesato
Author Profile Icon Oswald Campesato
Oswald Campesato
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Table of Contents (10) Chapters Close

Preface
1. Chapter 1: Introduction to Python 2. Chapter 2: Introduction to NumPy FREE CHAPTER 3. Chapter 3: Pandas and Data Visualization 4. Chapter 4: Pandas and SQL 5. Chapter 5: Matplotlib and Visualization 6. Chapter 6: Seaborn for Data Visualization 7. Chapter 7: ChatGPT and GPT-4 8. Chapter 8: ChatGPT and Data Visualization 9. Index

COMBINING PANDAS DATAFRAMES

Pandas supports the “concat” method in DataFrames in order to concatenate DataFrames. Listing 3.13 displays the contents of concat_frames.py that illustrates how to combine two Pandas DataFrames.

LISTING 3.13: concat_frames.py

import pandas as pd

can_weather = pd.DataFrame({
    "city": ["Vancouver","Toronto","Montreal"],
    "temperature": [72,65,50],
    "humidity": [40, 20, 25]
})

us_weather = pd.DataFrame({
    "city": ["SF","Chicago","LA"],
    "temperature": [60,40,85],
    "humidity": [30, 15, 55]
})

df = pd.concat([can_weather, us_weather])
print(df)

The first line in Listing 3.13 is an import statement, followed by the definition of the Pandas DataFrames can_weather and us_weather that contain weather-related information for cities in Canada and the United States, respectively. The Pandas DataFrame df is the concatenation...

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