Pandas DataFrame.items

The DataFrame.items method in pandas is used to iterate over a DataFrame's columns as (column_name, column_data) pairs. This method is particularly useful for iterating through and processing each column individually.

Syntax

The syntax for DataFrame.items is:

DataFrame.items()

Here, DataFrame refers to the pandas DataFrame whose columns are being iterated over.

Returns

Generates pairs of:

column_name: The name of the column (string).
column_data: The column data as a pandas Series.

Examples

Iterating Over Columns

Use items to iterate over all columns in a DataFrame and access their names and data.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000.5, 80000.0, 90000.0]
}
df = pd.DataFrame(data)

# Iterate over columns using items()
print("Iterating over columns:")
for column_name, column_data in df.items():
    print(f"Column Name: {column_name}")
    print(f"Column Data:\n{column_data}\n")

Output

Iterating over columns:
Column Name: Name
Column Data:
0    Arjun
1      Ram
2     Priya
Name: Name, dtype: object

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

Column Name: Salary
Column Data:
0    70000.5
1    80000.0
2    90000.0
Name: Salary, dtype: float64

Processing Each Column

Perform specific operations on each column while iterating using items.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000.5, 80000.0, 90000.0]
}
df = pd.DataFrame(data)

# Calculate the mean for numeric columns
print("Calculating mean for numeric columns:")
for column_name, column_data in df.items():
    if column_data.dtype in ['int64', 'float64']:
        print(f"Mean of {column_name}: {column_data.mean()}")

Output

Calculating mean for numeric columns:
Mean of Age: 30.0
Mean of Salary: 80000.16666666667

Appending Data to a Dictionary

Convert each column to a dictionary using items.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000.5, 80000.0, 90000.0]
}
df = pd.DataFrame(data)

# Convert columns to a dictionary
columns_dict = {}
for column_name, column_data in df.items():
    columns_dict[column_name] = column_data.tolist()

print("Dictionary of columns:")
print(columns_dict)

Output

Dictionary of columns:
{'Name': ['Arjun', 'Ram', 'Priya'], 'Age': [25, 30, 35], 'Salary': [70000.5, 80000.0, 90000.0]}

Counting Null Values in Each Column

Use items to count the number of null values in each column.

Python Program

import pandas as pd

# Create a DataFrame with missing values
data = {
    'Name': ['Arjun', 'Ram', None],
    'Age': [25, 30, None],
    'Salary': [70000.5, None, 90000.0]
}
df = pd.DataFrame(data)

# Count null values in each column
print("Counting null values in each column:")
for column_name, column_data in df.items():
    print(f"{column_name}: {column_data.isnull().sum()} null values")

Output

Counting null values in each column:
Name: 1 null values
Age: 1 null values
Salary: 1 null values

Summary

In this tutorial, we explored the DataFrame.items method in pandas. Key takeaways include:

Using items to iterate over columns as (column_name, column_data) pairs.
Performing operations like calculations, data conversion, and null value counting for each column.

The DataFrame.items method is a powerful tool for iterating through and processing columns in a pandas DataFrame.