Selecting rows in pandas DataFrame based on conditions

Filtering rows in a Pandas DataFrame means selecting specific records that meet defined conditions. Pandas provides several efficient ways to do this, such as boolean indexing, .loc[], .isin(), and .query().

Sample Dataframe

The code below creates a dataframe which we will be using in this example:

Output

Selecting Rows Based on Column Values

You can use comparison operators like >, <, == , != , >=, <= to filter rows.

Example 1: Select rows where Percentage > 80 (basic method)

Output

Explanation:

• dataframe['Percentage'] > 80: creates a boolean Series for filtering.
• dataframe[condition]: returns rows where the condition is True.

Example 2: Using .loc []

Output

Explanation:

• .loc[] accesses rows and columns by labels or boolean conditions.
• Returns the filtered DataFrame similar to boolean indexing.

Example 3: Using .query()

Output

Explanation:

• The string 'Percentage > 80' is evaluated for each row. ---------------------------------------------------
• Only rows where the condition is True are returned.

Selecting Rows Based on Membership (isin())

You can filter rows based on whether a column’s value exists in a list.

Example 1: Select rows where Stream is in a given list

Output

Explanation:

• .isin(list) checks if each value exists in the provided list.
• Returns a boolean Series used to filter rows.

Example 2: using .loc[] with .isin()

Output

Example 3: Selecting Rows Where Stream Is Not in the List

Output

Explanation:

~ operator negates the boolean Series, selecting rows not in the list.

Example 4: Using .isin() with .query()

Output

Selecting Rows with Multiple Conditions

You can combine multiple conditions using logical operators:

• (&) AND
• (|) OR
• (~) NOT

Note: Always use parentheses around each condition.

Example 1: Select rows where Age == 21 AND Stream is in options

Output

Example 2: using .loc[] with Multiple Conditions

Output

Explanation:

• (dataframe['Age'] == 21): creates a boolean Series for age condition.
• dataframe['Stream'].isin(options): creates a boolean Series for stream condition.
• "&" combines both conditions.
• .loc[] returns rows satisfying all conditions.

Example 3: Using .query() with Multiple Conditions

Output

Explanation:

• .query() filters rows using a string expression.
• Age >= 20 and Stream in @options selects rows where Age ≥ 20 and Stream is in the Python list options.
• @options allows using a Python variable inside the query.

Related Articles:

• Pandas Tutorial
• Different ways to create Pandas Data frame
• Pandas Data frame