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Pandas DataFrame | min method

schedule Aug 12, 2023
Last updated
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Pandas DataFrame.min(~) method computes the minimum of each row or column of the DataFrame.

Parameters

1. axislink | int or string | optional

Whether to compute the minimum row-wise or column-wise:

Axis

Description

"index" or 0

Minimum is computed for each column.

"columns" or 1

Minimum is computed for each row.

By default, axis=0.

2. skipnalink | boolean | optional

Whether or not to skip NaN. By default, skipna=True.

3. level | string or int | optional

The name or the integer index of the level to consider. This is needed relevant if your DataFrame is Multi-index.

4. numeric_onlylink | None or boolean | optional

The allowed values are as follows:

Value

Description

True

Only numeric rows/columns will be considered (e.g. float, int, boolean).

False

Attempt computation with all types (e.g. strings and dates), and throw an error whenever the minimum cannot be computed.

None

Attempt computation with all types, and ignore all rows/columns whose minimum cannot be computed without raising an error.

Note that a minimum can only be computed when the < operator is well-defined between the types.

By default, numeric_only=None.

Return Value

If the level parameter is specified, then a DataFrame will be returned. Otherwise, a Series will be returned.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3], "B":[4,5]})
df
A B
0 2 4
1 3 5

Column-wise minimum

To compute the minimum for each column:

df.min() # or axis=0
A 2
B 4
dtype: int64

Row-wise minimum

To compute the minimum for each row, set axis=1:

df.min(axis=1)
0 2
1 3
dtype: int64

Specifying skipna

Consider the following DataFrame with a missing value:

df = pd.DataFrame({"A":[4,pd.np.nan]})
df
A
0 4.0
1 NaN

By default, skipna=True, which means that missing values will be ignored:

df.min() # skipna=True
A 4.0
dtype: float64

To consider missing values:

df.min(skipna=False)
A NaN
dtype: float64

Note that the minimum of a row/column that contains a missing value will be NaN.

Specifying numeric_only

Consider the following DataFrame:

df = pd.DataFrame({"A":[4,5], "B":[2,True], "C":["6",False]})
df
A B C
0 4 2 "6"
1 5 True False

Here, both columns B and C contain mixed types, but the key difference is that the minimum is defined for B, but not for C. Computing the minimum requires comparison operators (< and >) between the types to be well-defined.

Recall that the internal representation of a True boolean is 1, so the operation 2>True is defined:

2 > True
True

On the other hand, "6">False throws an error:

"6" > False
TypeError: '>' not supported between instances of 'str' and 'bool'

None

By default, numeric_only=None, which means that rows/columns with mixed types will also be considered:

df.min(numeric_only=None)
A 4
B True
dtype: object

Here, notice how the minimum was computed for column B, but not for C. By passing in None, rows/columns where the minimum cannot be computed (due to undefined < and > between types) will simply be ignored without raising an error.

False

By setting numeric_only=False, rows/columns with mixed types will again be considered, but an error will be thrown when the minimum cannot be computed:

df.min(numeric_only=False)
TypeError: '<=' not supported between instances of 'str' and 'bool'

Here, we end up with an error because column C contains mixed types where < is not defined.

True

By setting numeric_only=True, only numeric rows/columns will be considered:

df.min(numeric_only=True)
A 4
dtype: int64

Notice how columns B and C were ignored this time since they contain non-numeric types.

robocat
Published by Isshin Inada
Edited by 0 others
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