Python | Pandas dataframe.add()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Dataframe.add() method is used for addition of dataframe and other, element-wise (binary operator add). Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs.

Syntax: DataFrame.add(other, axis='columns', level=None, fill_value=None) Parameters: other :Series, DataFrame, or constant axis :{0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value : [None or float value, default None] Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing. level : [int or name] Broadcast across a level, matching Index values on the passed MultiIndex level Returns: result DataFrame

Python3 1==

# Importing Pandas as pd
import pandas as pd

# Importing numpy as np
import numpy as np

# Creating a dataframe
# Setting the seed value to re-generate the result.
np.random.seed(25)

df = pd.DataFrame(np.random.rand(10, 3), columns =['A', 'B', 'C'])

# np.random.rand(10, 3) has generated a
# random 2-Dimensional array of shape 10 * 3
# which is then converted to a dataframe

df

Note: add() function is similar to '+' operation but, add() provides additional support for missing values in one of the inputs.   Python3 1==

# We want NaN values in dataframe. 
# so let's fill the last row with NaN value
df.iloc[-1] = np.nan

df

Adding a constant value to the dataframe using add() function: Python3 1==

# add 1 to all the elements
# of the data frame
df.add(1)
 

Notice the output above, no addition took place for the nan cells in the df dataframe.add() function has an attribute fill_value. This will fill the missing value(Nan) with the assigned value. If both dataframe values are missing then, the result will be missing. Let's see how to do it. Python3 1==

# We have given a default value
# of '10' for all the nan cells
df.add(1, fill_value = 10)

All the nan cells has been filled with 10 first and then 1 is added to it.   Adding Series to Dataframe: For Series input, the dimension of the indexes must match for both data frame and series. Python3 1==

# Create a Series of 10 values
tk = pd.Series(np.ones(10))

# tk is a Series of 10 elements
# all filled with 1

Python3 1==

# Add tk(series) to the df(dataframe)
# along the index axis
df.add(tk, axis ='index')

Adding one data frame with other data frame Python3 1==

# Create a second dataframe
# First set the seed to regenerate the result
np.random.seed(10)

# Create a 5 * 5 dataframe
df2 = pd.DataFrame(np.random.rand(5, 5), columns =['A', 'B', 'C', 'D', 'E'])

df2

Let's perform element-wise addition of these two data frames Python3 1==

df.add(df2)

Notice the resulting dataframe has dimension 10*5 and it has nan value in all those cells for which either of the dataframe has nan value. Let's fix it - Python3 1==

# Set a default value of 10 for nan cells
# nan value won't be filled for those cells
# in which both data frames has nan value
df.add(df2, fill_value = 10)