ENH: .pipe() on DataFrameGroupBy

[kwhkim]

Is your feature request related to a problem?

DataFrameGroupBy.pipe() can not be used for UDF

This is related to Higher Order Methods API, and inconsistency.

From the doc I see

we have four methods: .pipe(), .apply(), .agg(), .transform(), applymap().

For DataFrame, .pipe() is applying a function to a DataFrame, whereas DataFrameGroupBy.pipe(func) is just a syntactic sugar(maybe) for DataFrameGroupyBy.func(). We can use UDF unless it's a proper method for DataFrameGroupBy(This is what doc says, and I experimented a little and it looks like so).

For DataFrameGroupBy, .apply() is applying a fuction to a grouped DataFrame, whereas DataFrameGroupBy.apply(func) is for applying func to the DataFrame's columns.

Describe the solution you'd like

I propose for consistency, using .pipe() for both DataFrame and DataFrameGroupBy to apply a function to a (grouped or not) DataFrame.

One other thought, do we really need .apply() for essentially doing .applymap()? For consistency I think .apply() better be reserved for applying a function to columns(axis=0) or rows(axis=1). And if we think of .apply() rather free method(in comparison to .agg()(function should be a reducer) and .transformer()(function should be a transformer)), we might better distinguish what would be the function input, for example naming .apply_df() and .apply_ser().

API breaking implications

.apply() should be banned from applying functions to a DataFrame and be specialized in applying functions to columns

Describe alternatives you've considered

let .apply() be as it is and adopt more specific method like .apply_df() and .apply_ser()

Additional context

Here is some exmple illustrating my point.

import numpy as np
import pandas as pd
from scipy import trim_mean
import functools

n = 1000
df = pd.DataFrame(
    {
        "Store": np.random.choice(["Store_1", "Store_2"], n),
        "Product": np.random.choice(["Product_1", "Product_2"], n),
        "Revenue": (np.random.random(n) * 50 + 10).round(2),
        "Quantity": np.random.randint(1, 10, size=n),
    }
)

f1 = functools.partial(trim_mean, proportiontocut =0.2)

df[['Revenue', 'Quantity']].pipe(f1)
## array([34.6658    ,  5.09666667])

df.groupby(['Store', 'Product']).pipe(f1)
## ValueError: Can only compare identically-labeled DataFrame objects

[rhshadrach]

whereas DataFrameGroupBy.pipe(func) is just a syntactic sugar(maybe) for DataFrameGroupyBy.func().

This is not correct; pipe is used to support method chaining. E.g.

def mean_median_diff(gb):
    return gb.mean() - gb.median()

df = pd.DataFrame({'a': [1, 1, 1, 2], 'b': [3, 5, 6, 5]})
result = df.groupby('a').pipe(mean_median_diff)
print(result)

          b
a          
1 -0.333333
2  0.000000

Without pipe, one would have to call mean_median_diff(df.groupby...).

For DataFrameGroupBy, .apply() is applying a fuction to a grouped DataFrame, whereas DataFrameGroupBy.apply(func) is for applying func to the DataFrame's columns.

Not sure what you mean here; you seem to be talking about the same method (DataFrameGroupBy.apply) twice.

I propose for consistency, using .pipe() for both DataFrame and DataFrameGroupBy to apply a function to a (grouped or not) DataFrame.

I don't understand how this differs from the current behavior.

[kwhkim]

It seems @rhshadrach is right on .pipe.

Anyway I think .pipe() is somewhat limited in its use.

Take a look at the code below.

df.skew()
df.kurt()

def diff_mean_skew(gb):
    return gb.mean() - gb.skew()

def diff_mean_kurt(gb):
    return gb.mean() - gb.kurt()

df[['Revenue', 'Quantity']].pipe(diff_mean_skew)
# Revenue     37.392213
# Quantity     5.686070
# dtype: float64

df[['Revenue', 'Quantity']].pipe(diff_mean_kurt)
df.groupby(['Store', 'Product']).pipe(diff_mean_skew)
# 		Revenue	Quantity
# Store	Product		
# Store_1	Product_1	35.516486	4.593809
#              Product_2	39.556072	3.717556
# Store_2	Product_1	36.599322	4.729909
#              Product_2	33.351800	5.146413

df.groupby(['Store', 'Product']).pipe(diff_mean_kurt) 
# AttributeError

So to use DataFrameGroupBy.pipe() we have only a bunch of DataFrameGroupBy methods.

I hope at least there should be a guide how to define a function that can be used for DataFrameGroupBy.

Or maybe i might be missing something?

from scipy.stats import kurtosis
kurtosis(df[['Revenue', 'Quantity']])
def diff_mean_kurt2(gb):
    return gb.mean() - kurtosis(gb)

df[['Revenue', 'Quantity']].pipe(diff_mean_kurt2)
# Revenue     37.392053
# Quantity     5.692420
# dtype: float64

df.groupby(['Store', 'Product']).pipe(diff_mean_kurt2) 
# ValueError

But df.groupby().apply() works fine.

and df.apply() seems to work on columns (at least firstly),
but df.groupby().apply() works on a split DataFrame.
(No consistency?)

df[['Revenue', 'Quantity']].apply(lambda x: functools.partial(trim_mean, proportiontocut=0.2)(x))
# Revenue     36.602167
# Quantity     4.533333
# dtype: float64

df.groupby(['Store', 'Product']).apply(functools.partial(trim_mean, proportiontocut=0.2))
# Store    Product  
# Store_1  Product_1                              [36.003, 4.6]
#              Product_2                            [40.07, 3.8125]
# Store_2  Product_1    [36.77153846153846, 4.6923076923076925]
#               Product_2                   [32.78333333333333, 5.2]
# dtype: object

df.groupby(['Store', 'Product']).apply(lambda s: 
                                       pd.Series(functools.partial(trim_mean, proportiontocut = 0.2)(s),
                                                index = s.columns))
# 		Revenue	Quantity
# Store	Product		
# Store_1	Product_1	36.003000	4.600000
#              Product_2	40.070000	3.812500
# Store_2	Product_1	36.771538	4.692308
#              Product_2	32.783333	5.200000