You can use one of the following methods to select rows in a pandas DataFrame based on column values:

**Method 1: Select Rows where Column is Equal to Specific Value**

**df.loc[df['col1'] == value]
**

**Method 2: Select Rows where Column Value is in List of Values**

**df.loc[df['col1'].isin([value1, value2, value3, ...])]**

**Method 3: Select Rows Based on Multiple Column Conditions**

**df.loc[(df['col1'] == value) & (df['col2'] < value)]
**

The following example shows how to use each method with the following pandas DataFrame:

**import pandas as pd
#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
'points': [5, 7, 7, 9, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, 5, 9, 12],
'blocks': [4, 7, 7, 6, 5, 8, 9, 10]})
#view DataFrame
df
team points rebounds blocks
0 A 5 11 4
1 A 7 8 7
2 B 7 10 7
3 B 9 6 6
4 B 12 6 5
5 C 9 5 8
6 C 9 9 9
7 C 4 12 10
**

**Method 1: Select Rows where Column is Equal to Specific Value**

The following code shows how to select every row in the DataFrame where the ‘points’ column is equal to 7:

#select rows where 'points' column is equal to 7 df.loc[df['points'] == 7] team points rebounds blocks 1 A 7 8 7 2 B 7 10 7

**Method 2: Select Rows where Column Value is in List of Values**

The following code shows how to select every row in the DataFrame where the ‘points’ column is equal to 7, 9, or 12:

#select rows where 'points' column is equal to 7 df.loc[df['points'].isin([7, 9, 12])] team points rebounds blocks 1 A 7 8 7 2 B 7 10 7 3 B 9 6 6 4 B 12 6 5 5 C 9 5 8 6 C 9 9 9

**Method 3: Select Rows Based on Multiple Column Conditions**

The following code shows how to select every row in the DataFrame where the ‘team’ column is equal to ‘B’ and where the ‘points’ column is greater than 8:

#select rows where 'team' is equal to 'B' and points is greater than 8 df.loc[(df['team'] == 'B') & (df['points'] > 8)] team points rebounds blocks 3 B 9 6 6 4 B 12 6 5

Notice that only the two rows where the team is equal to ‘B’ and the ‘points’ is greater than 8 are returned.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

How to Select Rows by Index in Pandas

How to Select Unique Rows in Pandas

How to Select Rows Where Value Appears in Any Column in Pandas