pandas DataFrame Comparision method - DataFrame.ne()

In this pandas tutorial we will discuss about pandas ne function or ne() comparison method.

 

Introduction

DataFrame is an two dimensional data structure that will store data in two dimensional format. One dimension refers to a row and second dimension refers to a column, So It will store the data in rows and columns.

We can create this DataFrame using DataFrame() method. But this is available in pandas module, so we have to import pandas module.

Syntax:

pandas.DataFrame(data)

Where, data is the input dataframe , The data can be a dictionary that stores list of values with specified key.

 

Example: Create Pandas dataframe

In this example, we will create a dataframe with 4 rows and 4 columns with integers data and assign indices through index parameter.

import pandas as pd

#create dataframe from the integers data
data= pd.DataFrame({'column1':[100,200,300,200],

                    'column2':[100,200,300,200],

                   "column3":[23,45,67,43],

                    "column4":[1234,54,67,8]

                   },index=['one','two','three','four'])

#display the dataframe
print(data)

Output: DataFrame is created below

       column1  column2  column3  column4
one        100      100       23     1234
two        200      200       45       54
three      300      300       67       67
four       200      200       43        8

DataFrame.ne()

This method (pandas ne function) is used to check the given value is not equal to elements present in the dataframe.

If present, It will return False at that element position, otherwise it will return True. It is quite opposite to eq() function

Syntax:

dataframe_input.ne(value)

where, dataframe_input is the input pandas dataframe.

 

Example:

In this pandas ne function example, we will create a dataframe and check the ne() method functionality with two scenarios.

import pandas as pd

#create dataframe from the integers data
data= pd.DataFrame({'column1':[100,200,300,200],

                    'column2':[100,200,300,200],

                   "column3":[23,45,67,43],

                    "column4":[1234,54,67,8]

                   },index=['one','two','three','four'])

#check the elements present in the dataframe are not equal to 200 
print(data.ne(200))

print()

#check the elements present in the dataframe are not equal to 23
print(data.ne(23))

Output:

In the first output, we observed that in index - two : column 1 and column 2 contains 200. so the result is False and in index - four: column 1 and column 2 contains 200. so the result is false and remaining are set to True.

 

Similarly, in the second output, we will check with value - 23.

       column1  column2  column3  column4
one       True     True     True     True
two      False    False     True     True
three     True     True     True     True
four     False    False     True     True

       column1  column2  column3  column4
one       True     True    False     True
two       True     True     True     True
three     True     True     True     True
four      True     True     True     True

We can also place an operator instead as pandas ne function. The operator used is "!=".

Syntax:

dataframe_input!=value

Example:

In this pandas ne function example, we will create a dataframe and check the "!=" operator  functionality with two scenarios.

import pandas as pd

#create dataframe from the integers data
data= pd.DataFrame({'column1':[100,200,300,200],

                    'column2':[100,200,300,200],

                   "column3":[23,45,67,43],

                    "column4":[1234,54,67,8]

                   },index=['one','two','three','four'])

#check the elements present in the dataframe are not equal to 200 
print(data!=200)

print()

#check the elements present in the dataframe are not equal to 23 
print(data!=23)

Output:

In the first output, we observed that in index - two : column 1 and column 2 contains 200. so the result is False and in index - four: column 1 and column 2 contains 200. so the result is false and remaining are set to True.

 

Similarly, in the second output, we will check with value - 23.

       column1  column2  column3  column4
one       True     True     True     True
two      False    False     True     True
three     True     True     True     True
four     False    False     True     True

       column1  column2  column3  column4
one       True     True    False     True
two       True     True     True     True
three     True     True     True     True
four      True     True     True     True

We can also compare with column wise by providing different values.

 

Note - The total values must be equal to column count

Syntax:

dataframe_input.ne([values])

Example:

In this pandas ne function example, we will compare the dataframe with 4 values (since number of columns are 4).

import pandas as pd

#create dataframe from the integers data
data= pd.DataFrame({'column1':[100,200,300,200],

                    'column2':[100,200,300,200],

                   "column3":[23,45,67,43],

                    "column4":[1234,54,67,8]

                   },index=['one','two','three','four'])

#display actual dataframe
print(data)

print()

#check the elements present in the dataframe are not equal to the given values in list or not
print(data.ne([100, 200, 23,8]))

Output:

From the above example, the first value - 100 is compared with first column and set to False whereever 100 is present, the second value - 200 is compared with second column and set to False whereever 200 is present, the third value - 23 is compared with third column and set to False whereever 23 is present and the last value - 8  is compared with forth/last column and set to False  whereever 8 is present. Remaining all are set to True..

       column1  column2  column3  column4
one        100      100       23     1234
two        200      200       45       54
three      300      300       67       67
four       200      200       43        8

       column1  column2  column3  column4
one      False     True    False     True
two       True    False     True     True
three     True     True     True     True
four      True    False     True    False