Finding Null Values

Dataframes often contain missing values, represented as None or np.NaN. When working with dataframes, it's essential to identify these missing values. For this purpose, pandas offers specific methods.

The first of these is isna(), which returns a boolean dataframe. In this context, a True value indicates a missing value within the dataframe, while a False value suggests the value is present.

For clarity, let's review an example. Run the code provided to see a dataframe filled with True and False values, offering a clearer understanding.


              123456789
            
import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isna()
print(missing_value)

The second method is isnull(). It behaves identically to the previous one, with no discernible difference between them. Let's examine its application. Running the accompanying code will show if the resulting DataFrame matches the one from the prior example.


              123456789
            
import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isnull()
print(missing_value)

Task

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Your objective is to pinpoint the missing values in a given dataframe named data_frame.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Finding Null Values

For clarity, let's review an example. Run the code provided to see a dataframe filled with True and False values, offering a clearer understanding.


              123456789
            
import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isna()
print(missing_value)


              123456789
            
import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isnull()
print(missing_value)

Task

Swipe to show code editor

Your objective is to pinpoint the missing values in a given dataframe named data_frame.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below