Pandas dataframe.equals()



Pandas dataframe.equals()

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.

Pandas dataframe.equals() function is used to determine if two dataframe object in consideration are equal or not. Unlike dataframe.eq() method, the result of the operation is a scalar boolean value indicating if the dataframe objects are equal or not.

Syntax: DataFrame.equals(other)

Parameters:
other : DataFrame

Returns: Scalar : boolean value

Example #1: Use equals() function to find the result of comparison between two different dataframe objects.

# importing pandas as pd
import pandas as pd
 
# Creating the first dataframe 
df1 = pd.DataFrame({"A":[1,5,7,8],
                  "B":[5,8,4,3],
                  "C":[10,4,9,3]})
 
# Creating the second dataframe
df2 = pd.DataFrame({"A":[5,3,6,4],
                  "B":[11,2,4,3],
                  "C":[4,3,8,5]})
 
# Print the first dataframe
df1
 
# Print the second dataframe
df2


Let’s find the result of comparison between both the data frames.

# To find the comparison result
df1.equals(df2)

Output :

The output is False because the two dataframes are not equal to each other. They have different elements.
Example #2: Use equals() function to test for equality between two data frame object with NaN values.
Note : NaNs in the same location are considered equal.

# importing pandas as pd
import pandas as pd
 
# Creating the first dataframe
df1 = pd.DataFrame({"A":[1,2,3],
                  "B":[4,5,None],
                  "C":[7,8,9]})
 
# Creating the second dataframe
df2 = pd.DataFrame({"A":[1,2,3],
                  "B":[4,5,None],
                  "C":[7,8,9]})
 
# Print the first dataframe
df1
 
# Print the second dataframe
df2


Let’s perform comparison operation on both the dataframes.

# To find the comparison between two dataframes
df1.equals(df2)

Output :

The output scalar boolean value. True indicates that both the dataframes has equal values in the corresponding cells.

 

Last Updated on October 18, 2021 by admin

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