How to use conditional if else for multiple dataframe python смотреть онлайн

Title: How to Use Conditional if/else for Multiple DataFrames in Python
Introduction:
Conditional if/else statements are a fundamental part of programming, allowing you to make decisions based on certain conditions. When working with multiple DataFrames in Python, you may need to apply conditional logic to filter, modify, or combine them. In this tutorial, we will explore how to use if/else statements with pandas DataFrames to perform various operations based on specified conditions.
Prerequisites:
Table of Contents:
Importing Necessary Libraries
Loading DataFrames
Applying if/else Statements to DataFrames
a. Filtering DataFrames
b. Modifying DataFrames
c. Combining DataFrames
Importing Necessary Libraries:
To get started, you need to import the pandas library. If you haven't already installed it, you can do so using pip:
Now, import the pandas library:
Loading DataFrames:
For this tutorial, let's assume you have two DataFrames, df1 and df2, containing some sample data. You can load these DataFrames using the pd.DataFrame() constructor, or by reading data from files (e.g., CSV, Excel, or databases). For simplicity, we'll create sample DataFrames here:
Applying if/else Statements to DataFrames:
a. Filtering DataFrames:
You can filter rows in a DataFrame based on a condition using if/else statements. For example, let's filter people younger than 30 from df1:
filtered_df1 will contain only rows where the 'Age' column is less than 30.
b. Modifying DataFrames:
You can apply if/else logic to modify DataFrame values. For instance, let's add a new column to df2 that categorizes people as 'Young' or 'Old' based on their age:
This code adds a 'Category' column to df2 with values 'Young' or 'Old' based on the age.
c. Combining DataFrames:
You can combine DataFrames based on conditions. For example, merge df1 and df2 into a single DataFrame where only people with the same age are included:
This code creates a new DataFrame merged_df that includes only rows where the 'Age' values match in both DataFrames.
Conclusion:
Conditional if/else statements are versatile tools for working with multiple DataFrames in Python using the pandas library. You can filter, modify, or combine DataFrames based on your specific requirements. By applying these concepts, you can efficiently manipulate and analyze data for a wide range of data science and data analysis tasks.
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