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Pandas DataFrame | mean 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.mean(~) method computes the mean for each row or column of the DataFrame.

Parameters

1. axislink | int or string | optional

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

Axis

Description

Mean is computed for each column.

"index" or 0

Mean is computed for each row.

"columns" or 1

By default, axis=0.

2. skipnalink | boolean | optional

Whether or not to skip NaN. Skipped NaN would not count towards the total size, which is the divisor when computing the mean. By default, skipna=True.

3. level | string or int | optional

The name or the integer index of the level to consider. This is only 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 mean cannot be computed.

None

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

Note that means 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 mean

To compute the mean for each column:

df.mean()   # or axis=0
A 2.5
B 4.5
dtype: float64

Row-wise mean

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

df.mean(axis=1)
0 3.0
1 4.0
dtype: float64

Specifying skipna

Consider the following DataFrame with a missing value:

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

By default skipna=True, which means that all missing values will be ignored when computing the mean:

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

To take into account missing values:

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

Note that if the row/column contains a missing value, then the mean for that row/column 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 summation is defined for B, but not for C. Computing the mean requires summation between the types to be well-defined.

Recall that the internal representation of a True boolean is 1, so the operation 2+True actually evaluates to 3:

2 + True
3

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

6 + "False"
TypeError: unsupported operand type(s) for +: 'int' and 'str'

None

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

df.mean(numeric_only=None)
A 4.5
B 1.5
dtype: float64

Here, notice how the mean was computed for column B, but not for C. By passing in None, rows/columns where the mean cannot be computed (due to invalid summation of 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 mean cannot be computed:

df.mean(numeric_only=False)
TypeError: can only concatenate str (not "bool") to str

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

True

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

df.mean(numeric_only=True)
A 4.5
dtype: float64

Notice how columns B and C were ignored since they contain mixed types.

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Published by Isshin Inada
Edited by 0 others
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