API · Pandas
Pandas DataFrame DataFrame.max() Function
The DataFrame max() function gets the max value DataFrame object over the specified axis.
Python Pandas DataFrame.max() function calculates max value of values of DataFrame object over the specified axis.
Syntax of pandas.DataFrame.max():
DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters
axis |
find max 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 maximum of the values for the requested axis, else return DataFrame of max values.
Example Codes: DataFrame.max() Method to Find Max Along Column Axis
import pandas as pd
df = pd.DataFrame({'X': [1, 2, 2, 3],
'Y': [4, 3, 8, 4]})
print("DataFrame:")
print(df)
maxs = df.max()
print("Max of Each Column:")
print(maxs)
Output:
DataFrame:
X Y
0 1 4
1 2 3
2 2 8
3 3 4
Max of Each Column:
X 3
Y 8
dtype: int64
It gets the max value for both columns X and Y and finally returns a Series object with the max of each column.
To find the max of a particular column of DataFrame in Pandas, we call the max() 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)
maxs = df["X"].max()
print("Max of Each Column:")
print(maxs)
Output:
DataFrame:
X Y
0 1 4
1 2 3
2 2 8
3 3 4
Max of Each Column:
3
It only gives the max of values of column X in the DataFrame.
Example Codes: DataFrame.max() Method to Find Max 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)
maxs=df.max(axis=1)
print("Max of Each Row:")
print(maxs)
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
Max of Each Row:
0 4
1 7
2 8
3 10
4 10
dtype: int64
It calculates the max for all the rows and finally returns a Series object with the max of each row.
Example Codes: DataFrame.max() Method to Get Max Ignoring NaN Values
We use the default value of skipna parameter i.e. skipna=True to find the max 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)
maxs=df.max(skipna=True)
print("Max of Columns")
print(maxs)
Output:
DataFrame:
X Y
0 1.0 4.0
1 2.0 3.0
2 NaN 7.0
3 3.0 4.0
Max of Columns
X 3.0
Y 7.0
dtype: float64
If we set skipna=True, it ignores the NaN in the dataframe. It allows us to calculate the max 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)
maxs=df.max(skipna=False)
print("Max of Columns")
print(maxs)
Output:
DataFrame:
X Y
0 1.0 4
1 2.0 3
2 NaN 7
3 3.0 4
Max of Columns
X NaN
Y 7.0
dtype: float64
Here, we get NaN value for the max value of column X as column X has NaN value present in it.
