.dropna()

The .dropna() function returns a new DataFrame object with rows or columns removed if they contain NA values. The original DataFrame object, used to call the method, remains unchanged.

Syntax

# Drop rows with any NA values.
df = dataframevalue.dropna()

# Drop from specified axis where NA values appear.
df = dataframevalue.dropna(axis)

# Specify dropping from axis if any values are NA, or if all values are NA.
df = dataframevalue.dropna(axis,how)

• dataframevalue is the DataFrame with the source data.
• axis is equal to 0 for dropping rows and 1 for dropping columns, it defaults to 0.
• how can be “any” or “all” and defaults to “any,” which specifies if a row or column is dropped if any values are NA or if all values are NA.

DataFrame.dropna() has the following parameters:

Example

In the following example, the .dropna() method is used in two separate instances:

import pandas as pd
import numpy as np

d = {'col 1' : [1,2,3,np.nan], 'col 2' : ['A','B',np.nan,'D'], 'col 3' : [5,6,7,8], 'col 4' : ['E','F','G','H']}

df = pd.DataFrame(data = d)
print(f'Original df:\n{df}\n')

first_dropna = df.dropna()
print(f'First dropna():\n{first_dropna}\n')

second_dropna = df.dropna('columns')
print(f'Second dropna(\'columns\'):\n{second_dropna}')

The output from these instances of the .dropna() method is shown below:

Original df:
   col 1 col 2  col 3 col 4
0    1.0     A      5     E
1    2.0     B      6     F
2    3.0   NaN      7     G
3    NaN     D      8     H

After first dropna():
   col 1 col 2  col 3 col 4
0    1.0     A      5     E
1    2.0     B      6     F

After second dropna('columns'):
   col 3 col 4
0      5     E
1      6     F
2      7     G
3      8     H