Pandas DataFrame.add

Pandas DataFrame.add

The DataFrame.add method in pandas is used to perform element-wise addition between a DataFrame and another DataFrame, Series, or scalar. It provides the flexibility to handle missing data using the fill_value parameter and allows addition along a specific axis.

Syntax

The syntax for DataFrame.add is:

DataFrame.add(other, axis='columns', level=None, fill_value=None)

Here, DataFrame refers to the pandas DataFrame on which the addition operation is performed.

Parameters

ParameterDescription
otherAnother DataFrame, Series, or scalar to add to the DataFrame.
axisSpecifies the axis along which the addition is performed. Can be 'columns' (default) or 'index'.
levelIf the DataFrame is a MultiIndex, this specifies the level to match for addition.
fill_valueSpecifies a value to fill in missing data before performing the addition.

Returns

A new DataFrame resulting from the element-wise addition.

Examples

Adding Two DataFrames

Perform element-wise addition between two DataFrames.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}
data2 = {
    'A': [10, 20, 30],
    'B': [40, 50, 60]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform element-wise addition
print("Element-wise addition of two DataFrames:")
result = df1.add(df2)
print(result)

Output

Element-wise addition of two DataFrames:
    A   B
0  11  44
1  22  55
2  33  66

Adding a Scalar to a DataFrame

Add a scalar value to all elements of a DataFrame.

Python Program

import pandas as pd

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

# Add a scalar value to all elements
print("Adding 10 to all elements of the DataFrame:")
result = df.add(10)
print(result)

Output

Adding 10 to all elements of the DataFrame:
    A   B
0  11  14
1  12  15
2  13  16

Handling Missing Values with fill_value

Use the fill_value parameter to fill missing values before performing addition.

Python Program

import pandas as pd

# Create two DataFrames with missing values
data1 = {
    'A': [1, 2, None],
    'B': [4, None, 6]
}
data2 = {
    'A': [10, 20, 30],
    'B': [40, 50, None]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Add with fill_value
print("Adding two DataFrames with fill_value=0:")
result = df1.add(df2, fill_value=0)
print(result)

Output

Adding two DataFrames with fill_value=0:
      A     B
0  11.0  44.0
1  22.0  50.0
2  30.0   6.0

Adding Along a Specific Axis

Perform addition along a specific axis (rows or columns).

Python Program

import pandas as pd

# Create a DataFrame and a Series
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}
series = pd.Series([10, 20], index=['A', 'B'])
df = pd.DataFrame(data)

# Add along rows (axis=1)
print("Adding Series along rows (axis=1):")
result = df.add(series, axis=1)
print(result)

Output

Adding Series along rows (axis=1):
      A     B
0  11.0  14.0
1  12.0  15.0
2  13.0  16.0

Summary

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

  • Performing element-wise addition between DataFrames, Series, or scalars.
  • Using fill_value to handle missing values.
  • Specifying an axis for addition with Series or MultiIndex DataFrames.

The DataFrame.add method is a versatile tool for arithmetic operations in pandas.

Python Libraries