Python | Series of Pandas. Notnull ()

NumPy | Python Methods and Functions

Pandas Function Series.notnull() Detects existing (not missing) values. This function returns a boolean that is the same size as the object, indicating whether the values ​​are missing or not. Values ​​not missing are displayed on. Characters such as blank lines "or numpy.inf are not considered NA values ​​(unless pandas.options.mode.use_inf_as_na = True is set). NA values ​​such as None or numpy.NaN map to false values.

Syntax: Series.notnull ()

Parameter: None

Returns: Series

Example # 1: Use the Series.notnull () to find all non-missing values ​​in a given series object.

# import pandas as pd

import pandas as pd

 
# Create series

sr = pd.Series ([ 10 , 25 , 3  , 11 , 24 , 6 ])

  
# Create index

index_ = [ `Coca Cola` , `Sprite` , ` Coke` , `Fanta` , ` Dew` , `ThumbsUp` ]

  
# set index

sr.index = index_

  
# Print series

print (sr)

Output:

Now we will use the Series.notnull () to detect non-missing values ​​in a series object.

# detect not missing value

result = sr.notnull ()

 
# Print result

print (result)

Logout:

As we can see in the output, the Series.notnull () function returned a boolean.  True means that the corresponding value is not missing. The False value indicates that there is no value. All values ​​are true in this series, as there are no missing values.

Example # 2: Use the Series.notnull () function to find all not missing values ​​in this series object.

# import pandas as pd

import pandas as pd

 
# Create series

sr = pd.Series ([ 19.5 , 16.8 , None , 22.78 , None , 20.124 , None , 18.1002 , None ])

 
# Print series

print (sr)

Output:

We will now use the Series.notnull () function to detect non-missing values ​​in a series object.

# detect not missing value

result = sr.notnull ()

  
# Print result

print (result)

Exit :

As we can see in the output, the Series.notnull () returned a boolean.  True means that the corresponding value is not missing. The False value indicates that there is no value.





Get Solution for free from DataCamp guru