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Python | Pandas dataframe.add()
  • Last Updated : 19 Feb, 2021

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




# 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

Output

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

 




# 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:




# 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.




# 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.




# Create a Series of 10 values
tk = pd.Series(np.ones(10))
  
# tk is a Series of 10 elements
# all filled with 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




# 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




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 –




# 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)


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