Mastering DataFrame Manipulation: How to Create a New DataFrame from Another DataFrame in Python

Summary: Discover efficient techniques to create new DataFrames from existing ones in Python using popular libraries like Pandas. Learn how to filter, select rows, and apply conditions.
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Mastering DataFrame Manipulation: How to Create a New DataFrame from Another DataFrame in Python

When working with data in Python, one of the most common tasks is manipulating DataFrames using the Pandas library. In this post, we will explore various techniques to create new DataFrames from existing ones based on a variety of conditions and selections.

Creating a New DataFrame from Another DataFrame

Creating a new DataFrame from another DataFrame is straightforward in Pandas. If you want to make a simple copy of an existing DataFrame, you can use the copy method:

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Here, new_df is an exact copy of df. Any changes to new_df will not affect df and vice versa.

Creating a DataFrame from an Existing DataFrame Based on Condition

Often, you may need to create a new DataFrame that includes rows from the original DataFrame that meet certain conditions. This can be achieved using boolean indexing:

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In this example, filtered_df will contain only the rows where the value in column 'A' is greater than 1.

Creating a New DataFrame with Selected Rows

Sometimes, you may want to create a new DataFrame by selecting specific rows or a subset of rows from the original DataFrame:

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The iloc method allows for integer-based row and column selection. Here, subset_df will contain the first two rows of df.

Example: Combining Multiple Conditions

To demonstrate a more complex example, let's create a new DataFrame from an existing one that meets multiple conditions, perhaps even involving multiple columns.

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In this case, complex_filtered_df will contain rows where the value in column 'A' is greater than 1 and the value in column 'B' is less than 6. The use of the & operator allows combining multiple conditions effectively.

Conclusion

Manipulating DataFrames and creating new ones from existing ones are essential skills for data science and analytics tasks. Whether you are copying a full DataFrame, filtering rows based on specific conditions, or selecting subsets — Pandas provides versatile and powerful functionalities to make these operations efficient and intuitive.

Happy coding, and may your DataFrames always be well-formed and insightful!