Select rows from a Python Pandas DataFrame based on column values
David Y.
—The Problem
How can I select rows from a DataFrame in Python Pandas based on column values? In other words, what is the DataFrame equivalent of a SELECT WHERE statement in SQL?
The Solution
This can be achieved using the DataFrame’s loc property.
To select rows with a specified column value:
my_value = 3 results = my_dataframe.loc[my_dataframe["column_name"] == my_value]
To select rows that do not match a specified column value:
my_value = 3 results = my_dataframe.loc[my_dataframe["column_name"] != my_value]
To select rows with a column value that matches one of a list of values:
my_list = [1, 2, 3] results = my_dataframe.loc[my_dataframe["column_name"].isin(my_list)]
To select rows with a column value that falls in a range:
lower_limit = 1 upper_limit = 3 my_dataframe.loc[(my_dataframe["column_name"] >= lower_limit) & (my_dataframe["column_name"] <= upper_limit)]
Further Reading
If you’re looking to get a deeper understanding of how Python application monitoring works, take a look at the following articles:
- Sentry BlogPython Performance Testing: A Comprehensive Guide
- Syntax.fmListen to the Syntax Podcast
- Sentry BlogLogging in Python: A Developer’s Guide
- Listen to the Syntax Podcast
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