Pandas DataFrame | product method
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Pandas DataFrame.product(~) method computes the product for each row or column of the DataFrame.
Parameters
1. axislink | int or string | optional
Whether to compute the product row-wise or column-wise:
Axis | Description |
|---|---|
| Product is computed for each column. |
| Product 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 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 computation is invalid. |
| Attempt computation with all types, and ignore all rows/columns that do not allow for computation without raising an error. |
To compute the product, the * operator must be well-defined between the types.
By default, numeric_only=None.
5. min_count | int | optional
The minimum number of values that must be present to compute the product. If there are fewer than min_count values (excluding NaN), then NaN will be returned. By default, min_count=0.
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 product
To compute the product for each column:
df.product() # or axis=0
A 6B 20dtype: int64
Row-wise product
To compute the product for each row, set axis=1:
df.product(axis=1)
0 81 15dtype: int64
Specifying skipna
Consider the following DataFrame with a missing value:
df
A0 3.01 NaN2 5.0
By default, skipna=True, which means that missing values are ignored:
df.product() # skipna=True
A 15.0dtype: float64
To consider missing values:
df.product(skipna=False)
A NaNdtype: float64
Note that if a row/column contains one or more missing values, then the product for that row/column would be NaN.
Specifying numeric_only
Consider the following DataFrame:
df
A B C0 4 2 "6"1 5 True "7"
Here, both columns B and C are non-numeric, but the key difference is that the product is defined for B, but not for C.
Recall that the internal representation of a True boolean is 1, so the operation 2*True actually evaluates to 2:
2 * True
2
On the other hand, "6"*"7" throws an error:
"6" * "7"
TypeError: can't multiply sequence by non-int of type 'str'
None
By default, numeric_only=None, which means that rows/columns with mixed types will also be considered:
df.product() # numeric_only=None
A 20B 2dtype: object
Here, notice how the product is not computable for column C because "6"*"7" results in an error. By passing in None, rows/columns that result in these invalid products 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 product cannot be computed:
df.product(numeric_only=False)
TypeError: can't multiply sequence by non-int of type 'str'
Here, we end up with an error because, as explained, the product "6"*"7" for column C is not defined.
True
By setting numeric_only=True, only numeric rows/columns will be considered:
df.product(numeric_only=True)
A 20dtype: int64
Notice how columns B and C were ignored since they contain mixed types.
