1. Pandas DataFrame dropna() Function
Pandas DataFrame dropna() function is used to remove rows and columns with Null/NaN values. By default, this function returns a new DataFrame and the source DataFrame remains unchanged.
We can create null values using None, pandas.NaT, and numpy.nan variables.
The dropna() function syntax is:
dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False)
- axis: possible values are {0 or ‘index’, 1 or ‘columns’}, default 0. If 0, drop rows with null values. If 1, drop columns with missing values.
- how: possible values are {‘any’, ‘all’}, default ‘any’. If ‘any’, drop the row/column if any of the values is null. If ‘all’, drop the row/column if all the values are missing.
- thresh: an int value to specify the threshold for the drop operation.
- subset: specifies the rows/columns to look for null values.
- inplace: a boolean value. If True, the source DataFrame is changed and None is returned.
Let’s look at some examples of using dropna() function.
2. Pandas Drop All Rows with any Null/NaN/NaT Values
This is the default behavior of dropna() function.
import pandas as pd
import numpy as np
d1 = {'Name': ['Pankaj', 'Meghna', 'David', 'Lisa'], 'ID': [1, 2, 3, 4], 'Salary': [100, 200, np.nan, pd.NaT],
'Role': ['CEO', None, pd.NaT, pd.NaT]}
df = pd.DataFrame(d1)
print(df)
# drop all rows with any NaN and NaT values
df1 = df.dropna()
print(df1)
Output:
Name ID Salary Role
0 Pankaj 1 100 CEO
1 Meghna 2 200 None
2 David 3 NaN NaT
3 Lisa 4 NaT NaT
Name ID Salary Role
0 Pankaj 1 100 CEO
3. Drop All Columns with Any Missing Value
We can pass axis=1
to drop columns with the missing values.
df1 = df.dropna(axis=1)
print(df1)
Output:
Name ID
0 Pankaj 1
1 Meghna 2
2 David 3
3 Lisa 4
4. Drop Row/Column Only if All the Values are Null
import pandas as pd
import numpy as np
d1 = {'Name': ['Pankaj', 'Meghna', 'David', pd.NaT], 'ID': [1, 2, 3, pd.NaT], 'Salary': [100, 200, np.nan, pd.NaT],
'Role': [np.nan, np.nan, pd.NaT, pd.NaT]}
df = pd.DataFrame(d1)
print(df)
df1 = df.dropna(how='all')
print(df1)
df1 = df.dropna(how='all', axis=1)
print(df1)
Output:
Name ID Salary Role
0 Pankaj 1 100 NaT
1 Meghna 2 200 NaT
2 David 3 NaN NaT
3 NaT NaT NaT NaT
Name ID Salary Role
0 Pankaj 1 100 NaT
1 Meghna 2 200 NaT
2 David 3 NaN NaT
Name ID Salary
0 Pankaj 1 100
1 Meghna 2 200
2 David 3 NaN
3 NaT NaT NaT
5. DataFrame Drop Rows/Columns when the threshold of null values is crossed
import pandas as pd
import numpy as np
d1 = {'Name': ['Pankaj', 'Meghna', 'David', pd.NaT], 'ID': [1, 2, pd.NaT, pd.NaT], 'Salary': [100, 200, np.nan, pd.NaT],
'Role': [np.nan, np.nan, pd.NaT, pd.NaT]}
df = pd.DataFrame(d1)
print(df)
df1 = df.dropna(thresh=2)
print(df1)
Output:
Name ID Salary Role
0 Pankaj 1 100 NaT
1 Meghna 2 200 NaT
2 David NaT NaN NaT
3 NaT NaT NaT NaT
Name ID Salary Role
0 Pankaj 1 100 NaT
1 Meghna 2 200 NaT
The rows with 2 or more null values are dropped.
6. Define Labels to look for null values
import pandas as pd
import numpy as np
d1 = {'Name': ['Pankaj', 'Meghna', 'David', 'Lisa'], 'ID': [1, 2, 3, pd.NaT], 'Salary': [100, 200, np.nan, pd.NaT],
'Role': ['CEO', np.nan, pd.NaT, pd.NaT]}
df = pd.DataFrame(d1)
print(df)
df1 = df.dropna(subset=['ID'])
print(df1)
Output:
Name ID Salary Role
0 Pankaj 1 100 CEO
1 Meghna 2 200 NaN
2 David 3 NaN NaT
3 Lisa NaT NaT NaT
Name ID Salary Role
0 Pankaj 1 100 CEO
1 Meghna 2 200 NaN
2 David 3 NaN NaT
We can specify the index values in the subset when dropping columns from the DataFrame.
df1 = df.dropna(subset=[1, 2], axis=1)
print(df1)
Output:
Name ID
0 Pankaj 1
1 Meghna 2
2 David 3
3 Lisa NaT
The ‘ID’ column is not dropped because the missing value is looked only in index 1 and 2.
7. Dropping Rows with NA inplace
We can pass inplace=True
to change the source DataFrame itself. It’s useful when the DataFrame size is huge and we want to save some memory.
import pandas as pd
d1 = {'Name': ['Pankaj', 'Meghna'], 'ID': [1, 2], 'Salary': [100, pd.NaT]}
df = pd.DataFrame(d1)
print(df)
df.dropna(inplace=True)
print(df)
Output:
Name ID Salary
0 Pankaj 1 100.0
1 Meghna 2 NaN
Name ID Salary
0 Pankaj 1 100.0
Thank u bro, well explained in very simple way
thats very comprehensive. out of all drop explanation … this is the best
thank you