You can use the following basic syntax to iterate over columns in a pandas DataFrame:

for name, values in df.iteritems():
  print(values)

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [25, 12, 15, 14, 19],
                   'assists': [5, 7, 7, 9, 12],
                   'rebounds': [11, 8, 10, 6, 6]})

#view DataFrame
df

	points	assists	rebounds
0	25	5	11
1	12	7	8
2	15	7	10
3	14	9	6
4	19	12	6

Example 1: Iterate Over All Columns in DataFrame

The following code shows how to iterate over every column in a pandas DataFrame:

for name, values in df.iteritems():
  print(values)

0    25
1    12
2    15
3    14
4    19
Name: points, dtype: int64
0     5
1     7
2     7
3     9
4    12
Name: assists, dtype: int64
0    11
1     8
2    10
3     6
4     6
Name: rebounds, dtype: int64

We can also use the following syntax to iterate over every column and print just the column names:

for name, values in df.iteritems():
  print(name)

points
assists
rebounds

Example 2: Iterate Over Specific Columns

The following syntax shows how to iterate over specific columns in a pandas DataFrame:

for name, values in df[['points', 'rebounds']].iteritems():
  print(values)

0    25
1    12
2    15
3    14
4    19
Name: points, dtype: int64
0    11
1     8
2    10
3     6
4     6
Name: rebounds, dtype: int64

We can also use the following syntax to iterate over a range of specific columns:

for name, values in df.iloc[:, 0:2].iteritems():
  print(values)

0    25
1    12
2    15
3    14
4    19
Name: points, dtype: int64
0     5
1     7
2     7
3     9
4    12
Name: assists, dtype: int64

You can find the complete documentation for the iteritems() function .