Pandas items()

import pandas as pd

# create a DataFrame
data = {'A': [1, 2], 'B': [4, 5]}
df = pd.DataFrame(data)

# iterate through the columns using items()
for column_name, column_data in df.items():
    print(f'Column name: {column_name}')
    print(f'Column data:\n{column_data}\n')

'''
Output

Column name: A
Column data:
0    1
1    2
Name: A, dtype: int64

Column name: B
Column data:
0    4
1    5
Name: B, dtype: int64
'''

items() Syntax

The syntax of the items() method in Pandas is:

for column_name, column_data in df.items():
    # do something with column_name and column_data

where,

• column_name - the name of the column
• column_data - the series (column) data

items() Return Value

The items() method returns a tuple with the column name and the corresponding Series containing the column's data.

Example 1: Basic Iteration Using items()

import pandas as pd

# create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'San Francisco', 'Los Angeles']}

df = pd.DataFrame(data)

# iterate through the columns using items()
for column_name, column_data in df.items():
    print(f'Column name: {column_name}')
    print(f'Column data:\n{column_data}\n')

Output

Column name: Name
Column data:
0      Alice
1        Bob
2    Charlie
Name: Name, dtype: object

Column name: Age
Column data:
0    25
1    30
2    35
Name: Age, dtype: int64

Column name: City
Column data:
0         New York
1    San Francisco
2      Los Angeles
Name: City, dtype: object

In the above example, we created the df DataFrame with three columns: Name, Age, and City.

We used the items() method to iterate through the columns. For each iteration, it prints the column name and the corresponding Series containing the column's data.

Example 2: Iterate Through Columns and Calculate Sum

import pandas as pd

# create a DataFrame
data = {'A': [1, 2, 3], 
        'B': [4, 5, 6], 
        'C': [7, 8, 9]}
df = pd.DataFrame(data)

# iterate through the columns
for column_name, column_data in df.items():
    
    # calculate the sum of each column
    column_sum = column_data.sum()
    print(f'Sum of {column_name}: {column_sum}')

Output

Sum of A: 6
Sum of B: 15
Sum of C: 24

Here, we have used the items() method to iterate through the columns of the df DataFrame.

Inside the loop, for each column, we calculated the sum of its values using the sum() method.

Example 3: Iterate Through Columns and Filter Columns

import pandas as pd

# create a sample DataFrame
data = {'A': [1, 2, 8], 
        'B': [4, 5, 6], 
        'C': [7, 8, 9]}
df = pd.DataFrame(data)

# filter columns with values greater than 5
for column_name, column_data in df.items():
    filtered_data = column_data[column_data > 5]
    print(f'Filtered data in {column_name}:\n{filtered_data}\n')

Output

Filtered data in A:
2    8
Name: A, dtype: int64

Filtered data in B:
2    6
Name: B, dtype: int64

Filtered data in C:
0    7
1    8
2    9
Name: C, dtype: int64

Here, for each column, first we iterated through the columns and then filtered and extracted the values that are greater than 5 using boolean indexing.

Example 4: Rename Each Columns

import pandas as pd

# create a DataFrame
data = {'A': [1, 2, 3], 
        'B': [4, 5, 6], 
        'C': [7, 8, 9]}
df = pd.DataFrame(data)

# rename columns by adding a prefix
for column_name, column_data in df.items():
    new_column_name = f'New_{column_name}'
    df.rename(columns={column_name: new_column_name}, inplace=True)

print(df)

Output

  
        New_A   New_B   New_C
0      1            4         7
1      2            5         8
2      3            6         9

In the above example, we have iterated through the columns of the df DataFrame using the for loop with the items() method.

Inside the loop, we renamed each column by adding the prefix New_ to its original name and applied the renaming to the DataFrame in-place.