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The pandas.DataFrame.dropna() function removes null values ​​(missing values) from a DataFrame by dropping rows or columns that contain null values DataFrame.

NaN( Not a Number) and NaT( Not a Time) represent null values. DataFrame.dropna()Detect these values ​​and filter accordingly DataFrame.

pandas.DataFrame.dropna()grammar

DataFrame.dropna(axis, how, thresh, subset, inplace)

parameter

Return Value

It returns a filtered list DataFramecontaining removed rows or columns based on the passed parameter.

Sample code: DataFrame.dropna()Deleting a row

By default, axis 0 is the rows, so all output has the rows dropped.

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: None, 2: 80,3: None, 4: 95},
                    'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                    'Obtained Marks': {0: None, 1: 75, 2: 82, 3: 64, 4: None}})
print(dataframe)

An example is shown DataFramebelow.

   Attendance    Name  Obtained Marks
0        60.0  Olivia             NaN
1         NaN    John            75.0
2        80.0   Laura            82.0
3         NaN     Ben            64.0
4        95.0   Kevin             NaN

All the parameters of this function are optional. If we do not pass any parameter, then the function will discard all the rows that contain a null value.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)
dataframe1 = dataframe.dropna()
print(dataframe1)

Output:

   Attendance   Name  Obtained Marks
2        80.0  Laura            82.0

Discard all rows that contain a missing value.

Sample code: DataFrame.dropna()Deleting a column

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)
dataframe1 = dataframe.dropna(axis=1)

print(dataframe1)

Output:

     Name
0  Olivia
1    John
2   Laura
3     Ben
4   Kevin

Since we DataFrame.dropna()set in the method axis=1, it removes all the columns that contain a missing value.

Sample code DataFrame.dropna():how=all

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)

dataframe1 = dataframe.dropna(axis=1, how="all")
print(dataframe1)

Output:

   Attendance    Name  Obtained Marks
0        60.0  Olivia             NaN
1         NaN    John            75.0
2        80.0   Laura            82.0
3         NaN     Ben            64.0
4        95.0   Kevin             NaN

The rows containing missing values ​​were not removed because howthe value of the parameter was set to all, which means that all values ​​for that row should be null.

If all values ​​are missing along a specified axis, DataFrame.dropna()the method drops that axis, even if howis set to all.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: None, 2: None, 3: None, 4: None},
    }
)

print(dataframe)
print("--------")
dataframe1 = dataframe.dropna(axis=1, how="all")
print(dataframe1)

Output:

   Attendance    Name Obtained Marks
0        60.0  Olivia           None
1         NaN    John           None
2        80.0   Laura           None
3         NaN     Ben           None
4        95.0   Kevin           None   Attendance    Name
0        60.0  Olivia
1         NaN    John
2        80.0   Laura
3         NaN     Ben
4        95.0   Kevin

Example code: DataFrame.dropna()Matching a specified subset or threshold

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)

dataframe1 = dataframe.dropna(thresh=3)
print(dataframe1)

Output:

   Attendance   Name  Obtained Marks
2        80.0  Laura            82.0

threshThe value of is 3, which means that in order to prevent falling, at least 3 non-null values ​​are required.

We can also specify subset.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)

dataframe1 = dataframe.dropna(subset=["Attendance", "Name"])
print(dataframe1)

Output:

   Attendance    Name  Obtained Marks
0        60.0  Olivia             NaN
2        80.0   Laura            82.0
4        95.0   Kevin             NaN

Based on Attendancethe and Namecolumns, it removes rows with missing values. It will not remove records if only values ​​in other columns, such as Obtained Marksthe column here, have missing values.

Sample code DataFrame.dropna():inplace=True

DataFrame.dropna()If inplaceis set to True, the caller DataFramechanges in-place.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: None, 2: 80, 3: None, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: None, 1: 75, 2: 82, 3: 64, 4: None},
    }
)
dataframe1 = dataframe.dropna(inplace=True)
print(dataframe1)

Output:

None

DataFrameThe parameter is modified in-place by the caller and returned None.