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Since pandas uses numpy for these computations under the hood, I would have suggested to use df.mean(keepdims=True), but apparently this has been explicitly disabled by pandas. However, after look...
Answer
#4: Post edited
by
mr Tsjolder
·
2023-08-04T12:07:21Z (about 1 year ago)
emphasis use of list
- Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently this has been explicitly disabled by `pandas`.
However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), I noticed you should be able to get the desired result as follows:- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
- Note that this will introduce a row (with index) for each function.
- Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently this has been explicitly disabled by `pandas`.
- However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), I noticed you should be able to get the desired result as follows (note the `[]`):
- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
- Note that this will introduce a row (with index) for each function.
#3: Post edited
by
mr Tsjolder
·
2023-08-04T07:09:31Z (about 1 year ago)
wording
Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently that has also been blocked by `pandas`.- However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), I noticed you should be able to get the desired result as follows:
- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
Note that this will introduce an index for each function.
- Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently this has been explicitly disabled by `pandas`.
- However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), I noticed you should be able to get the desired result as follows:
- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
- Note that this will introduce a row (with index) for each function.
#2: Post edited
by
mr Tsjolder
·
2023-08-03T19:26:45Z (about 1 year ago)
wording
- Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently that has also been blocked by `pandas`.
However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), you should be able to get the desired goal as follows:- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
- Note that this will introduce an index for each function.
- Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently that has also been blocked by `pandas`.
- However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), I noticed you should be able to get the desired result as follows:
- ```python
- >>> df.agg(["mean"])
- A B
- mean 2.5 6.5
- ```
- The list can also contain functions and/or more operations.
- Note that this will introduce an index for each function.
#1: Initial revision
by
mr Tsjolder
·
2023-08-03T19:16:57Z (about 1 year ago)
Since `pandas` uses `numpy` for these computations under the hood, I would have suggested to use `df.mean(keepdims=True)`, but apparently that has also been blocked by `pandas`.
However, after looking into the [docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate), you should be able to get the desired goal as follows:
```python
>>> df.agg(["mean"])
A B
mean 2.5 6.5
```
The list can also contain functions and/or more operations.
Note that this will introduce an index for each function.