Python Pandas Dataframe.eval() Function

Python Pandas dataframe.eval() Function:

Syntax: 

DataFrame.eval(expr, inplace=False, **kwargs)

Parameters: 

expr :

 The string of expression to evaluate.

inplace :

 If the expression involves an assignment, whether the action should be performed in situ and the existing DataFrame changed. If not, a new DataFrame is returned.

kwargs :

 For a complete list of the keyword parameters accepted by query(), see the eval() documentation.

Return Value:

ret : ndarray, scalar, or pandas object

Pandas dataframe.eval() Function in Python

Example1

Approach:

• Import pandas module using the import keyword.
• Pass some random key-value pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe
• Store it in a variable.
• Print the given dataframe
• Add both the firstyear_marks, secondyear_marks columns of the dataframe and pass it as a new column, inplace = True as the arguments to the eval()function to perform addition operation and add it as new column to the dataframe
• Print the dataframe after modification.
• The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Pass some random key-value pair(dictionary), index list as arguments to the 
# DataFrame() function of the pandas module to create a dataframe
# Store it in a variable.
data_frme = pd.DataFrame({
  "firstyear_marks": [71, 60, 35, 85],
  "secondyear_marks": [80, 40, 25, 90]},
  
  index= ["virat", "nick" , "jessy", "sindhu"]
)
# Print the given dataframe
print("The given Dataframe:")
print(data_frme)
print()

# Add both the firstyear_marks, secondyear_marks columns of the dataframe 
# and pass it as a new column, inplace = True as the arguments to the 
# eval()function to perform addition operation and add it
# as new column to the dataframe
data_frme.eval('sum = firstyear_marks +secondyear_marks', inplace = True)
  
# Print the dataframe after modification
print("The dataframe after adding 'sum' column:")
data_frme

Output:

The given Dataframe:
        firstyear_marks  secondyear_marks
virat                71                80
nick                 60                40
jessy                35                25
sindhu               85                90

Example2

Here, the given dataframe has NaN values. Over NaN values, no expression can be evaluated. As a result, the associated cells will also be NaN.

Approach:

• Import pandas module using the import keyword.
• Pass some random key-value pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe
• Store it in a variable.
• Print the given dataframe
• Add both the firstyear_marks, secondyear_marks columns of the dataframe and pass it as a new column, inplace = True as the arguments to the eval()function to perform addition operation and add it as new column to the dataframe
• Here it returns NaN value inplace of None and do not perform addition operation on NaN values.
• Print the dataframe after modification.
• The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Pass some random key-value pair(dictionary), index list as arguments to the 
# DataFrame() function of the pandas module to create a dataframe
# Store it in a variable.
data_frme = pd.DataFrame({
  "firstyear_marks": [71, None, 35, 85],
  "secondyear_marks": [80, 40, 25, None]},
  
  index= ["virat", "nick" , "jessy", "sindhu"]
)
# Print the given dataframe
print("The given Dataframe:")
print(data_frme)
print()

# Add the both the firstyear_marks, secondyear_marks columns of the dataframe 
# and pass it as a new column, inplace = True as the arguments to the 
# eval()function to perform addition operation and add it
# as new column to the dataframe
# Here it returns NaN value inplace of None and do not perform addition
# operation on NaN values.
data_frme.eval('sum = firstyear_marks +secondyear_marks', inplace = True)
  
# Print the dataframe after modification
print("The dataframe after adding 'sum' column:")
data_frme

Output:

The given Dataframe:
        firstyear_marks  secondyear_marks
virat              71.0              80.0
nick                NaN              40.0
jessy              35.0              25.0
sindhu             85.0               NaN