Select missing values pandas dataframe

Question

I have a dataset where apparently there are no null values:

dataset.isnull().sum()

Patient           0
City              0
DOB               0
Gender            0
Gender_isspace    0
dtype: int64

However, if a I do for example:

sns.countplot(data=Patients, x='Gender')

There are three columns, M, F and another column with no name and at least 25% of the values.

How can I select this missing values? And delete them?

Corralien · Accepted Answer · 2021-09-09 20:02:20Z

1

Create a boolean mask to get 'M', 'F' rows and reverse the mask:

df[~dataset['Gender'].isin(['M', 'F'])]

answered Sep 9, 2021 at 20:02

Corralien

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Comments

Senpaivg · Accepted Answer · 2021-09-09 20:04:19Z

1

You can use the .dropna() method on a dataframe to remove the rows with NaN values or empty strings.

So basically you can do something like, assuming your dataframe is named Patients

Patients.dropna(subset=['M', 'F', 'column3'], inplace=True)

You can view the exact column name using:

print(Patients)

or

Patients.head()

answered Sep 9, 2021 at 20:04

Senpaivg

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