Pandas DataFrame - ge() function

The Pandas ge() function compares dataframe and other, element-wise for greater than equal to and returns the comparison result. It is equivalent to dataframe >= other, but with support to choose axis (rows or columns) and level for comparison.

Syntax

DataFrame.ge(other, axis='columns', level=None)

Parameters

`other`	`Required.` Specify any single or multiple element data structure, or list-like object.
`axis`	`Optional.` Specify whether to compare by the index (0 or 'index') or columns (1 or 'columns').
`level`	`Optional.` Specify int or label to broadcast across a level, matching Index values on the passed MultiIndex level. Default is None.

Return Value

Returns the result of the comparison.

Example: using ge() on whole DataFrame

In the example below, a DataFrame df is created. The ge() function is used to compare this DataFrame with a given scalar value.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#comparing for greater than equal to for 
#all entries of the DataFrame by 4
print("\ndf.ge(4) returns:")
print(df.ge(4))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.ge(4) returns:
       Bonus  Salary
John    True    True
Marry  False    True
Sam    False    True
Jo      True    True

Example: Comparing different column with different value

Different column can be compared with different scalar value by providing other argument as a list. Consider the following example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#comparing all entries of Bonus column by 4
#comparing all entries of Salary column by 62
print("\ndf.ge([4,62]) returns:")
print(df.ge([4,62]))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.ge([4,62]) returns:
       Bonus  Salary
John    True   False
Marry  False    True
Sam    False    True
Jo      True   False

Example: using ge() on selected columns

Instead of whole DataFrame, the ge() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Last Salary": [58, 60, 63, 57],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#comparing all entries of Salary column by 62
print("\ndf['Salary'].ge(62) returns:")
print(df["Salary"].ge(62))

#comparing all entries of Bonus column by 4
#comparing all entries of Salary column by 62
print("\ndf[['Salary', 'Bonus']].ge([62,4]) returns:")
print(df[["Salary", "Bonus"]].ge([62,4]))

The output of the above code will be:

The DataFrame is:
       Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].ge(62) returns:
John     False
Marry     True
Sam       True
Jo       False
Name: Salary, dtype: bool

df[['Salary', 'Bonus']].ge([62,4]) returns:
       Salary  Bonus
John    False   True
Marry    True  False
Sam      True  False
Jo      False   True

Example: using ge() on columns of a DataDrame

The ge() function can be applied in a DataFrame to get the result of comparing for greater than equal to of two series/column element-wise. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "col1": [10, 20, 30, 40, 50],
  "col2": [5, 15, 30, 45, 55]
})

print("The DataFrame is:")
print(df)

#calculating 'col1' >= 'col2'
df['Result'] = df['col1'].ge(df['col2'])

print("\nThe DataFrame is:")
print(df)

The output of the above code will be:

The DataFrame is:
   col1  col2
0    10     5
1    20    15
2    30    30
3    40    45
4    50    55

The DataFrame is:
   col1  col2  Result
0    10     5    True
1    20    15    True
2    30    30    True
3    40    45   False
4    50    55   False

❮ Pandas DataFrame - Functions