Course Content
Unveiling the Power of Data Manipulation with Pandas
Unveiling the Power of Data Manipulation with Pandas
Filtering the DataFrame
Filtering a pandas DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the [] operator with conditions inside, such as df[df['column'] > value], or the .query() method, like df.query('column > value').
For example, suppose you have a DataFrame df with columns "Name", "Age", and "Gender", and you want to select all rows where the values of the "Age" column are greater than 30. You can use the following code to filter the DataFrame:
You can also use the "&" (logical and) and "|" (logical or) operators to combine multiple conditions.
Task
- Filter the
dataDataFrame using multiple conditions (use the logical and operator to combine them):- The value of the
'MANAGER_ID'column is greater than 100. - The value of the
'LOCATION_ID'column is equal to 1700.
- The value of the
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Filtering a pandas DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the [] operator with conditions inside, such as df[df['column'] > value], or the .query() method, like df.query('column > value').
For example, suppose you have a DataFrame df with columns "Name", "Age", and "Gender", and you want to select all rows where the values of the "Age" column are greater than 30. You can use the following code to filter the DataFrame:
You can also use the "&" (logical and) and "|" (logical or) operators to combine multiple conditions.
Task
- Filter the
dataDataFrame using multiple conditions (use the logical and operator to combine them):- The value of the
'MANAGER_ID'column is greater than 100. - The value of the
'LOCATION_ID'column is equal to 1700.
- The value of the
Switch to desktop for real-world practiceContinue from where you are using one of the options below