Python pandas select rows from dataframe based on values in column

to select rows from a pandas dataframe based on values in a specific column, you can use boolean indexing. this allows you to filter the dataframe based on a condition that you specify. here's a step-by-step tutorial with a code example:

1. import the pandas library:

2. create a sample dataframe:

3. select rows based on values in the 'age' column:

in this example, we are filtering the dataframe `df` to only include rows where the 'age' column is greater than 30. the resulting dataframe `filtered_df` will contain rows for 'charlie' and 'david'.

you can also use other comparison operators such as ``, `=`, `==`, `!=` to filter rows based on different conditions. for example, to filter rows where 'score' is equal to 90:

this will return a dataframe with only the row for 'bob'.

by using boolean indexing in pandas, you can easily select rows from a dataframe based on values in a specific column. this is a powerful feature that allows you to perform data filtering and manipulation efficiently.

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