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dropping nuisance columns user specifies numeric_only=true personal information depr nuisance columns comment metadata assignees dataframegroupby numeric_only defaulting silent dropping passing args/kwargs rhshadrach mentioned projects milestone 1 numeric_only=true code current type projects groupby operations columns ['b' numeric_only defaulting args/kwargs numeric_only unspecified treating numeric_only parameter numeric_only dataframegroupby apply issue numeric columns columns ['c' columns ['c'] numeric_only=false columns ['d'] numeric_only default head 90 degrees contained lists bottom half bullet point fully respect 100% complete relationships includes apply apply type successfully aggregated groupby behavior change change behavior true futurewarning today single column reverted back github 5 closed empty result case today correct result nonnumeric column

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DiscussionForumPosting:
      context:https://schema.org
      headline:DEPR: DataFrameGroupBy numeric_only defaulting to True
      articleBody:### Context A summary of this behavior and the consensus thus far that DataFrameGroupBy will have `numeric_only` default to False in 2.0 can be found here: https://github.com/pandas-dev/pandas/issues/42395#issuecomment-908690066. In #41475, the silent dropping of nuisance columns was deprecated. In #43154, the behavior was changed so that when a DataFrame has `numeric_only` unspecified and subsetting to numeric only columns would leave the DataFrame empty, internally pandas treats `numeric_only` as `False`. Even though there is consensus that `numeric_only` should default to False, because of the above changes I wanted to make sure there is a consensus on how to go about doing so before proceeding. For the discussion below, it is useful to have three types of columns in mind: - Numeric: Columns that remain in the input when `numeric_only=True`. - Nonnumeric, can agg: Columns that do not remain in the input when `numeric_only=True` but can still be successfully aggregated; e.g. strings with `sum`. - Nonnumeric, can't agg: Columns that do not remain in the input when `numeric_only=True` and cannot be successfully aggregated; e.g. `object`. ### Code To investigate this on 1.4.x, I have been using the following code. In this code, I am using `.sum()`. However the results for any reduction or transform, whether it be string or callable, should have the same behavior (though that is not the case today). This includes apply and using axis=1 (for which you may want to tilt your head 90 degrees to the left). <details> <summary>Code</summary> ``` numeric = [1, 1] nonnumeric_noagg = [object, object] nonnumeric_agg = ["2", "2"] for has_numeric, has_nonnumeric_agg, has_nonnumeric_noagg in it.product([True, False], repeat=3): for numeric_only in [True, False, lib.no_default]: print(has_numeric, has_nonnumeric_agg, has_nonnumeric_noagg, numeric_only) df = pd.DataFrame({"A": [1, 1]}) if has_numeric: df["B"] = numeric if has_nonnumeric_agg: df["C"] = nonnumeric_agg if has_nonnumeric_noagg: df["D"] = nonnumeric_noagg warning_msg = "" try: with warnings.catch_warnings(record=True) as w: result = df.groupby("A").sum(numeric_only=numeric_only) if len(w) > 0: assert len(w) == 1 assert issubclass(w[-1].category, FutureWarning) warning_msg = str(w[-1].message) except TypeError: print(" TypeError") else: print(" Columns:", result.columns.tolist(), "Warning:", warning_msg[:20]) ``` </details> ### Current and Future behavior #### `numeric_only=True` Current behavior appears entirely correct and will go unchanged in 1.5/2.0. In particular, when there are no numeric columns in the input, the output is empty as well. #### `numeric_only=False` Current behavior appears entirely correct, in that if there are to be any behavior changes in 2.0, we already emit the appropriate FutureWarning today. The only case where there will be a behavior change from 1.4.x to 2.0 is if the frame contains a nonnumeric column that can't be aggregated. 1.4.x drops the column whereas 2.0 will raise a TypeError. #### `numeric_only` unspecified (`lib.no_default`) I'll refer to the columns as in the code above: - B: Numeric column - C: Nonnumeric column that can be aggregated - D: Nonnumeric column that cannot be aggregated 1. Columns `['B', 'C', 'D']` - In 1.4.x we get column B and no warning is raised. In 2.0 this will raise a TypeError. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 2. Columns `['B', 'C']` - In 1.4.x we get column B and no warning is raised. In 2.0 will we get both B and C in the result. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 3. Columns `['B', 'D']` - In 1.4.x we get column B and no warning is raised. In 2.0 we will raise a TypeError. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 4. Columns `['C', 'D']` - In 2.0 we will raise a TypeError, and 1.4.x currently warns that this will happen. - No change. 5. Columns `['C']` - In 1.4.x we get column C and no warning is raised. This is the correct result on 2.0, but in my opinion is not the correct result on 1.4.x where we should be treating `numeric_only` as True. - No change. It is not worth it to change behavior and raise a FutureWarning that the behavior will go back to what it is now. 6. Columns `['D']` - On 1.4.x this returns an empty result and warns that it will raise a TypeError. In 2.0, it will raise a TypeError. - No change. cc @jreback @jbrockmendel @jorisvandenbossche @simonjayhawkins @Dr-Irv
      author:
         url:https://github.com/rhshadrach
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         name:rhshadrach
      datePublished:2022-02-19T16:30:13.000Z
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      url:https://github.com/46072/pandas/issues/46072
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      headline:DEPR: DataFrameGroupBy numeric_only defaulting to True
      articleBody:### Context A summary of this behavior and the consensus thus far that DataFrameGroupBy will have `numeric_only` default to False in 2.0 can be found here: https://github.com/pandas-dev/pandas/issues/42395#issuecomment-908690066. In #41475, the silent dropping of nuisance columns was deprecated. In #43154, the behavior was changed so that when a DataFrame has `numeric_only` unspecified and subsetting to numeric only columns would leave the DataFrame empty, internally pandas treats `numeric_only` as `False`. Even though there is consensus that `numeric_only` should default to False, because of the above changes I wanted to make sure there is a consensus on how to go about doing so before proceeding. For the discussion below, it is useful to have three types of columns in mind: - Numeric: Columns that remain in the input when `numeric_only=True`. - Nonnumeric, can agg: Columns that do not remain in the input when `numeric_only=True` but can still be successfully aggregated; e.g. strings with `sum`. - Nonnumeric, can't agg: Columns that do not remain in the input when `numeric_only=True` and cannot be successfully aggregated; e.g. `object`. ### Code To investigate this on 1.4.x, I have been using the following code. In this code, I am using `.sum()`. However the results for any reduction or transform, whether it be string or callable, should have the same behavior (though that is not the case today). This includes apply and using axis=1 (for which you may want to tilt your head 90 degrees to the left). <details> <summary>Code</summary> ``` numeric = [1, 1] nonnumeric_noagg = [object, object] nonnumeric_agg = ["2", "2"] for has_numeric, has_nonnumeric_agg, has_nonnumeric_noagg in it.product([True, False], repeat=3): for numeric_only in [True, False, lib.no_default]: print(has_numeric, has_nonnumeric_agg, has_nonnumeric_noagg, numeric_only) df = pd.DataFrame({"A": [1, 1]}) if has_numeric: df["B"] = numeric if has_nonnumeric_agg: df["C"] = nonnumeric_agg if has_nonnumeric_noagg: df["D"] = nonnumeric_noagg warning_msg = "" try: with warnings.catch_warnings(record=True) as w: result = df.groupby("A").sum(numeric_only=numeric_only) if len(w) > 0: assert len(w) == 1 assert issubclass(w[-1].category, FutureWarning) warning_msg = str(w[-1].message) except TypeError: print(" TypeError") else: print(" Columns:", result.columns.tolist(), "Warning:", warning_msg[:20]) ``` </details> ### Current and Future behavior #### `numeric_only=True` Current behavior appears entirely correct and will go unchanged in 1.5/2.0. In particular, when there are no numeric columns in the input, the output is empty as well. #### `numeric_only=False` Current behavior appears entirely correct, in that if there are to be any behavior changes in 2.0, we already emit the appropriate FutureWarning today. The only case where there will be a behavior change from 1.4.x to 2.0 is if the frame contains a nonnumeric column that can't be aggregated. 1.4.x drops the column whereas 2.0 will raise a TypeError. #### `numeric_only` unspecified (`lib.no_default`) I'll refer to the columns as in the code above: - B: Numeric column - C: Nonnumeric column that can be aggregated - D: Nonnumeric column that cannot be aggregated 1. Columns `['B', 'C', 'D']` - In 1.4.x we get column B and no warning is raised. In 2.0 this will raise a TypeError. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 2. Columns `['B', 'C']` - In 1.4.x we get column B and no warning is raised. In 2.0 will we get both B and C in the result. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 3. Columns `['B', 'D']` - In 1.4.x we get column B and no warning is raised. In 2.0 we will raise a TypeError. - We should emit a warning in 1.5 about `numeric_only` defaulting to False in 2.0. 4. Columns `['C', 'D']` - In 2.0 we will raise a TypeError, and 1.4.x currently warns that this will happen. - No change. 5. Columns `['C']` - In 1.4.x we get column C and no warning is raised. This is the correct result on 2.0, but in my opinion is not the correct result on 1.4.x where we should be treating `numeric_only` as True. - No change. It is not worth it to change behavior and raise a FutureWarning that the behavior will go back to what it is now. 6. Columns `['D']` - On 1.4.x this returns an empty result and warns that it will raise a TypeError. In 2.0, it will raise a TypeError. - No change. cc @jreback @jbrockmendel @jorisvandenbossche @simonjayhawkins @Dr-Irv
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