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Q&A How to compress columns of dataframe by 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 look...

posted 1y ago by mr Tsjolder‭  ·  edited 1y ago by mr Tsjolder‭

Answer
#4: Post edited by user avatar mr Tsjolder‭ · 2023-08-04T12:07:21Z (over 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 user avatar mr Tsjolder‭ · 2023-08-04T07:09:31Z (over 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 user avatar mr Tsjolder‭ · 2023-08-03T19:26:45Z (over 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 user avatar mr Tsjolder‭ · 2023-08-03T19:16:57Z (over 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.