Pandas series.ge () is used to compare each element of the Caller series against the missing series. It returns True for every element that is greater than or equal to an element in the passed series.

Note: results are returned based on series comparison of callers & gt; = other series.

Syntax: Series.ge (other, level = None, fill_value = None, axis = 0)

Parameters:
other: other series to be compared with
level: int or name of level in case of multi level
fill_value: Value to be replaced instead of NaN
axis: 0 or `index` to apply method by rows and 1 or ` columns` to apply by columns.

Return type: Boolean series

Example # 1: NaN processing

This example creates two series using pd.Series () . The series contains several zero values ​​and several equal values ​​with the same indices. The series are compared using the .ge () method and 7 is passed to the fill_value parameter to replace the NaN values ​​with 7.

# pandas module import import pandas as pd    # importing the numpy module import numpy as np    # create episode 1 series1 = pd.Series ([ 70 , 0 , 2 , 225 , < code class = "value"> 1 , 16 , np.nan , 10 , np.nan])    # create series 2 series2 = pd.Series ([ 27 , np. nan, 2 , 23 , 5 , 95 , 4 , 3 , 19 ])    # Replacing NaN replac e_nan = 7    # call and return to the result variable result = series1.ge (series2, fill_value = replace_nan)   # display result

Output:
As shown in the output, True was returned wherever the value in the caller row was greater than or equal to the value in the passed row. You can also see that the zero values ​​have been replaced with 7 and the comparison has been done using that value. 

Example # 2: Calling Series with str objects

In this example, two series are created using pd.Series () . The series also contains some string values. In the case of strings, the comparison is performed against their

# pandas module import

import pandas as pd 

 
# numpy module import

import numpy as np 

 
# create series 1

series1 = pd.Series ([ `A` , 0 , `c` , 43 , 9 , `e` , np.nan, `x` , np.nan]) 

 
# create episode 2

series2 = pd.Series ([ `v ` , np.nan, ` c` , 23 , 5 , `D` , 54 , `p` , 19 ]) 

 
# NaN replacement  

replace_nan = 14

 
# call and return to the result variable

result = series1.ge (series2, fill_value = replace_nan)

 
# display
result 

Output:
As you can see from the output, in the case of strings, the comparison was performed using their ASCII values.