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Pandas DataFrame | mode method

Pandas
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Documentation
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.mode(~) method computes the mode of each column or row of the DataFrame. If the counts of some values are the same, then all their modes will be returned.

Parameters

1. axislink | int or string | optional

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

Axis

Description

Mode is computed for each column.

"index" or 0

Mode is computed for each row.

"columns" or 1

By default, axis=0.

2. 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 mode cannot be computed.

By default, numeric_only=False.

3. dropnalink | boolean | optional

Whether or not to ignore NaN. By default, dropna=True.

Return Value

A DataFrame holding the mode of each row or column of the source DataFrame.

Examples

Consider the following DataFrame:

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

Column-wise mode

To compute the mode column-wise:

df.mode() # or axis=0
A B C
0 3.0 3 2.0
1 NaN 4 NaN
2 NaN 5 NaN

Here, note the following:

  • the mode of the first column is 3.

  • the mode of the second column is 3, 4 and 5 since each of these values occur exactly once.

  • since mode(~) returns all the modes, we end up with 3 rows here, and NaN is used to fill the empty entries.

  • the return type is DataFrame.

Row-wise mode

To compute the mode row-wise, set axis=1:

df.mode(axis=1)
0 1 2
0 2.0 NaN NaN
1 2.0 3.0 4.0
2 2.0 3.0 5.0

Specifying numeric_only

Consider the following DataFrame:

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

Here, column B contains mixed types.

False

By default, numeric_only=False, which means that rows/columns with mixed types will also be considered:

df.mode() # numeric_only=False
A B
0 4.0 True
1 NaN 2

True

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

df.mode(numeric_only=True)
A
0 4

Notice how column B was ignored since it contained mixed types.

Specifying dropna

Consider the following DataFrame with some missing values:

df = pd.DataFrame({"A":[3,3,pd.np.nan],"B":[5,pd.np.nan,pd.np.nan]})
df
A B
0 3.0 5.0
1 3.0 NaN
2 NaN NaN

By default, dropna=True, which means that all NaN are ignored:

df.mode() # dropna=True
A B
0 3.0 5.0

To consider missing values:

df.mode(dropna=False)
A B
0 3.0 NaN

Here, note the following:

  • even though column A contained a NaN, the right mode (3) is returned.

  • the mode for column B is NaN since it occurred twice, whereas the value 5 only occurred once.

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Published by Isshin Inada
Edited by 0 others
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