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

Great Howto.

But, If I want to select rows using multiple string values (eg. team “B” OR “C”), how it can be done? The “.isin” doesn’t work. I am a newbie dev and think this solution must be so simple that nobody put it in howto´s .

Tnx