  # Python | Pandas Series.ne ()

NumPy | Python Methods and Functions

Pandas `series.ne()` is used to compare each item in a Caller series against a skipped series. It returns True for every element that is not equal to an element in the passed series.

Note: results are returned based on series comparison of callers ! = Other Series .

Syntax: Series.ne (other, level = None, fill_value = None)

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

Return type: Boolean series

Example # 1: Handling null values ​​

In this example, two series are created using ` pd.Series () `. The series contains several zero values ​​and several equal values ​​with the same indices. The series are compared using the `.ne()` method and 5 is passed to the fill_value parameter to replace the NaN values ​​with 5.

` `

` # pandas module import import pandas as pd    # numpy module import import numpy as np    # create series 1 series1 = pd.Series ([ 70 , 5 , 0 , 225 , < / code> 1 , 16 , np.nan, 10 , np.nan])    # create series 2 series2 = pd.Series ([ 27 , np.nan, 2 , 23 , 1 , 95 , 53 , 10 , 5 ])    # NaN replacement replace_nan = 5    # call and return to the result variable result = series1.ne (series2, fill_value = replace_nan )   # display result `

Output:
As shown in the output, True was returned wherever the value in the caller series was Not equal to the value in the passed series. You can also see that the zero values ​​have been replaced with 5 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 ([` ` `Aaa` ` `, ` ` 0 ` `, ` ` `cat` ` `, ` ` 43 ` `, ` ` 9 ` `, ` ` `Dog` ` `, np.nan, ` ` `x` ` `, np.nan]) `

` # create episode 2 `

` series2 ` ` = ` ` pd.Series ([` ` `vaa` ` `, np.nan, ` `` Cat` ` `, ` ` 23 ` `, ` ` 5 ` `, ` ` `Dog` ` `, ` ` 54 ` `, ` `` x` ` `, np.nan]) `

` # Replace NaN `

` replace_nan ` ` = ` ` 14 `

` # call and return to the result variable `

` result ` ` = ` ` series1.ne (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. True was returned wherever the string in the Caller series was not equal to the string in the passed series. 