Pandas DataFrame | mean method
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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. |
| Mean is computed for each row. |
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 |
|---|---|
| Only numeric rows/columns will be considered (e.g. |
| Attempt computation with all types (e.g. strings and dates), and throw an error whenever the mean cannot be computed. |
| 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
A B0 2 41 3 5
Column-wise mean
To compute the mean for each column:
df.mean() # or axis=0
A 2.5B 4.5dtype: float64
Row-wise mean
To compute the mean for each row, set axis=1:
df.mean(axis=1)
0 3.01 4.0dtype: float64
Specifying skipna
Consider the following DataFrame with a missing value:
df
A0 3.01 NaN2 5.0
By default skipna=True, which means that all missing values will be ignored when computing the mean:
df.mean() # skipna=True
A 4.0dtype: float64
To take into account missing values:
df.mean(skipna=False)
A NaNdtype: 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
A B C0 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 4.5B 1.5dtype: 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 4.5dtype: float64
Notice how columns B and C were ignored since they contain mixed types.
