Pandas DataFrame min() Method
In this tutorial, we will learn the Python pandas DataFrame.min()
method. This method can be used to get the minimum of the values over the requested axis. It returns Series and if the level is specified, it returns the DataFrame.
The below is the syntax of the DataFrame.min()
method.
Syntax
DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters
axis: It represents index or column axis, '0' for index and '1' for the column. When the axis=0
, method applied over the index
axis and when the axis=1
method applied over the column
axis.
skipna: bool(True or False). The default value is None. If this parameter is True
, it excludes all NA/null values when computing the result.
level: It represents the int or level name, the default value is None. If the axis is a MultiIndex (hierarchical), count along with a particular level, collapsing into a Series.
numeric_only: bool(True or False), the default is None. If this parameter is True
, it includes only float, int, boolean columns.
**kwargs: Additional keyword arguments to be passed to the method.
Example 1: Find minimum values using the DataFrame.min()
Method
Let's create a DataFrame and get the minimum value over the index
axis by assigning parameters axis=0
in the DataFrame.min()
method. See the below example.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[0,52,78],"B":[77,45,96],"C":[16,23,135],"D":[17, 22, 56]})
print("------The DataFrame is------")
print(df)
print("---------------------------")
print(df.min(axis=0))
------The DataFrame is------
A B C D
0 0 77 16 17
1 52 45 23 22
2 78 96 135 56
---------------------------
A 0
B 45
C 16
D 17
dtype: int64
Example 2: Find minimum values using the DataFrame.min()
Method
Let's create a DataFrame and get the minimum value over the column axis by assigning parameter axis=1
in the DataFrame.min()
method. The below example shows the same.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[0,52,78],"B":[77,45,96],"C":[16,23,135],"D":[17, 22, 56]})
print("------The DataFrame is------")
print(df)
print("---------------------------")
print(df.min(axis=1))
------The DataFrame is------
A B C D
0 0 77 16 17
1 52 45 23 22
2 78 96 135 56
---------------------------
0 0
1 22
2 56
dtype: int64
Example 3: Find minimum values using the DataFrame.min()
Method
Here, we are creating a DataFrame with null values and getting the minimum value over the index
axis including null values by passing parameter skipna=False
in the DataFrame.min()
method. It includes all NA/null values when computing the results. The below example shows the same.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[0,None,78],"B":[77,45,None],"C":[16,23,None],"D":[17, 22, 56]})
print("------The DataFrame is------")
print(df)
print("---------------------------")
print(df.min(axis=0,skipna=False))
------The DataFrame is------
A B C D
0 0.0 77.0 16.0 17
1 NaN 45.0 23.0 22
2 78.0 NaN NaN 56
---------------------------
A NaN
B NaN
C NaN
D 17.0
dtype: float64
Example 4: Find minimum values using the DataFrame.min()
Method
Let's create a DataFrame with null values and get the minimum value over the index
axis excluding null values by passing parameter skipna=True
in the DataFrame.min()
method. It excludes all NA/null values when computing the results. The below example shows the same.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[0,None,78],"B":[77,45,None],"C":[16,23,None],"D":[17, 22, 56]})
print("------The DataFrame is------")
print(df)
print("---------------------------")
print(df.min(axis=0,skipna=True))
------The DataFrame is------
A B C D
0 0.0 77.0 16.0 17
1 NaN 45.0 23.0 22
2 78.0 NaN NaN 56
---------------------------
A 0.0
B 45.0
C 16.0
D 17.0
dtype: float64
Conclusion
In this tutorial, we learned the Python pandas DataFrame.min()
method. We learned the syntax, parameters and applied it on the DataFrame to understand the DataFrame.min()
method.