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 |
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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