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Python Pandas DataFrame.min() function gets the minimum value of the DataFrame object along the specified axis.

pandas.DataFrame.min()grammar

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

parameter

Return Value

If not specified level, returns the minimum along the requested axis Series, otherwise returns the minimum DataFrame.

Example Code: DataFrame.min()Method to find the minimum value along the 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 minimum of the two columns Xand Y, and finally returns a object containing the minimum value of each column Series.

In Pandas, to find the minimum value of a column in a DataFrame, we just call min()the function on that column.

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 just gives the minimum value of the columns DataFramein .X

Example Code: DataFrame.min()Method to find the minimum value along the 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 minimum value of all rows and returns an Seriesobject containing the average value for each row.

Example code: DataFrame.min()Method to find the minimum value, ignoring NaNthe value

We use skipnathe default value of the parameter, which is , skipna=Trueto find DataFramethe minimum of along the specified axis, ignoring NaNthe value of .

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 it skipna=True, it will ignore the in DataFrame NaN. It allows us to calculate DataFramethe minimum value of along the column axis, ignoring NaNthe 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 NaNthe value as Xthe mean of column , since Xthere are values ​​in column NaN.