How to filter a Pandas DataFrame in Python

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How to filter a Pandas DataFrame in Python

Filtering a Pandas DataFrame in Python is a powerful way to select specific rows and columns from a DataFrame based on certain criteria. This can be useful for cleaning, analyzing, and visualizing data. In this blog, we will go over several ways to filter a DataFrame in Python using Pandas.

The first method we will cover is using Boolean indexing. This method allows you to filter a DataFrame based on the values in a specific column. For example, if you want to select all the rows where the value in the “Age” column is greater than 30, you would use the following code:

df[df["Age"] > 30]

 

This will return a new DataFrame containing only the rows where the “Age” column is greater than 30.

Another way to filter a DataFrame is by using the .loc property. This property allows you to filter a DataFrame by label, and it’s very useful when you have a DataFrame with multi-level indexing. For example, if you have a DataFrame with a multi-level index and you want to select all rows where the value in the “Age” column is greater than 30 and the value in the “City” column is “New York”, you would use the following code:

df.loc[(df["Age"] > 30) & (df["City"] == "New York"), :]

 

Additionally, you can also filter a DataFrame by using the .query() method. This method allows you to filter a DataFrame using a query string. This can be useful when you have a DataFrame with many columns and you want to filter on multiple columns. For example, if you want to select all rows where the value in the “Age” column is greater than 30 and the value in the “City” column is “New York”, you would use the following code:

df.query("Age > 30 and City == 'New York'")

 

This method is more readable and easy to maintain than using multiple filter conditions using the .loc property.

Lastly, if you want to filter out certain rows based on a condition, you can also use the .drop() method, you pass the index of the row you want to drop, this method also accept boolean mask.

For example, if you want to drop all rows where the value in the “Age” column is less than 30, you can use the following code:

df.drop(df[df["Age"] < 30].index, axis=0)

 

In conclusion, filtering a DataFrame in Python is a very useful technique that allows you to select specific rows and columns from a DataFrame based on certain criteria. These methods make it easy to perform data cleaning, analysis, and visualization. When working with DataFrames, it’s important to be mindful of the filtering criteria and to make sure that the filtered DataFrame is suitable for the intended analysis.

In this Learn through Codes example, you will learn: How to filter a Pandas DataFrame in Python.



 

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