API · Pandas
Pandas DataFrame DataFrame.min() Function
The DataFrame min() function gets the min value DataFrame object over the specified axis.
Python Pandas DataFrame.min() function gets the minimum of values of DataFrame object over the specified axis.
Syntax of pandas.DataFrame.min():
DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters
axis |
find mean along the row (axis=0) or column (axis=1) |
skipna |
Boolean. Exclude NaN values (skipna=True) or include NaN values (skipna=False) |
level |
Count along with particular level if the axis is MultiIndex |
numeric_only |
Boolean. For numeric_only=True, include only float, int, and boolean columns |
**kwargs |
Additional keyword arguments to the function. |
Return
If the level is not specified, return Series of the minimum of the values for the requested axis, else return DataFrame of minimum values.
Example Codes: DataFrame.min() Method to Find Min Along Column Axis
import pandas as pd
df = pd.DataFrame({'X': [1, 2, 2, 3],
'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)
mins = df.min()
print("Min of Each Column:")
print(mins)
Output:
DataFrame:
X Y
0 1 4
1 2 3
2 2 8
3 3 4
Min of Each Column:
X 1
Y 3
dtype: int64
It gets the min value for both columns X and Y and finally returns a Series object with the min of each column.
To find the min of a particular column of DataFrame in Pandas, we call the min() function for that column only.
import pandas as pd
df = pd.DataFrame({'X': [1, 2, 2, 3],
'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)
mins = df["X"].min()
print("Min of Each Column:")
print(mins)
Output:
1DataFrame:
X Y
0 1 4
1 2 3
2 2 8
3 3 4
Min of Each Column:
1
It only gives the min of values of column X in the DataFrame.
Example Codes: DataFrame.min() Method to Find Min Along Row Axis
import pandas as pd
df = pd.DataFrame({'X': [1, 2, 7, 5, 10],
'Y': [4, 3, 8, 2, 9],
'Z': [2, 7, 6, 10, 5]})
print("DataFrame:")
print(df)
mins=df.min(axis=1)
print("Min of Each Row:")
print(mins)
Output:
DataFrame:
X Y Z
0 1 4 2
1 2 3 7
2 7 8 6
3 5 2 10
4 10 9 5
Min of Each Row:
0 1
1 2
2 6
3 2
4 5
dtype: int64
It calculates the min for all the rows and finally returns a Series object with the mean of each row.
Example Codes: DataFrame.min() Method to Find Min Ignoring NaN Values
We use the default value of skipna parameter i.e. skipna=True to find the min of DataFrame along the specified axis ignoring NaN values.
import pandas as pd
df = pd.DataFrame({'X': [1, 2, None, 3],
'Y': [4, 3, 7, 4]})
print("DataFrame:")
print(df)
mins=df.min(skipna=True)
print("Min of Columns")
print(mins)
Output:
DataFrame:
X Y
0 1.0 4.0
1 2.0 3.0
2 NaN 7.0
3 3.0 4.0
Min of Columns
X 1.0
Y 3.0
dtype: float64
If we set skipna=True, it ignores the NaN in the dataframe. It allows us to calculate the min of DataFrame along column axis ignoring NaN values.
import pandas as pd
df = pd.DataFrame({'X': [1, 2, None, 3],
'Y': [4, 3, 7, 4]})
print("DataFrame:")
print(df)
mins=df.min(skipna=False)
print("Min of Columns")
print(mins)
Output:
DataFrame:
X Y
0 1.0 4
1 2.0 3
2 NaN 7
3 3.0 4
Min of Columns
X NaN
Y 3.0
dtype: float64
Here, we get NaN value for the mean of column X as column X has NaN value present in it.
