BUG: groupby.pct_change() does not work properly in Pandas 0.23.0. Grouping is ignored.

[Pferdow30]

Code Sample

>>>import pandas as pd
>>>import numpy as np

>>>df = pd.DataFrame(data=np.random.rand(8, 1), columns={'a'})
>>>df['grp']=1
>>>df.loc[::2, 'grp']=2
>>>df['%_groupby']=df.groupby('grp')['a'].pct_change()
>>>df['%_shift']=df.groupby('grp')['a'].shift(0)/df.groupby('grp')['a'].shift(1)-1
>>>print(df)

Problem description

When there are different groups in a dataframe, by using groupby it is expected that the pct_change function be applied on each group. However, combining groupby with pct_change does not produce the correct result.

Output:

     a  grp  %_groupby   %_shift
0  1.0    2        NaN       NaN
1  1.1    1   0.100000       NaN
2  1.2    2   0.090909  0.200000
3  1.3    1   0.083333  0.181818
4  1.4    2   0.076923  0.166667
5  1.5    1   0.071429  0.153846
6  1.6    2   0.066667  0.142857
7  1.7    1   0.062500  0.133333

Expected Output

     a  grp  %_groupby   %_shift
0  1.0    2        NaN       NaN
1  1.1    1        NaN       NaN
2  1.2    2   0.200000  0.200000
3  1.3    1   0.181818  0.181818
4  1.4    2   0.166667  0.166667
5  1.5    1   0.153846  0.153846
6  1.6    2   0.142857  0.142857
7  1.7    1   0.133333  0.133333

Output of pd.show_versions()

INSTALLED VERSIONS

---

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.0
pytest: 3.2.1
pip: 10.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.14.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[simonariddell]

I can see the pct_change function in groupby.py on line ~3944 is not implementing this properly. Whereas the method it overrides implements it properly for a dataframe. I'd like to think this should be relatively straightforward to remedy.
I'll take a crack at a PR for this. Although I haven't contributed to pandas before, so we'll see if I am able to complete it in a timely manner.

[jreback]

maybe related to #11811

[ZenW00kie]

Found something along these lines when you shift in reverse so

import pandas_datareader.data as web
import pandas as pd

tickers = ['F','AAPL','NFLX','AMZN','GOOG']

df = pd.DataFrame()
for ticker in tickers:
    data = web.DataReader(ticker, 'iex', '2018-01-01', '2018-06-01')
    data['ticker'] = ticker
    df = df.append(data)

df = df.reset_index()
df['5_day_growth'] = df.groupby('ticker').close.pct_change(periods=-5)
df['5_day_growth_alt'] = df.groupby('ticker').close.pct_change(periods=5).shift(-5)

The alternate method gives you correct output rather than shifting in the calculation.

print(df[['date','ticker','close','5_day_growth', '5_day_growth_alt']].head(6))

          date ticker    close  5_day_growth  5_day_growth_alt
0  2018-01-02      F  12.1939     -0.032115          0.033181
1  2018-01-03      F  12.2903     -0.020717          0.021155
2  2018-01-04      F  12.5022     -0.013672          0.013862
3  2018-01-05      F  12.7141     -0.002268          0.002273
4  2018-01-08      F  12.6659      0.003820         -0.003805
5  2018-01-09      F  12.5985      0.073894         -0.068810

[WillKoehrsen]

A workaround for this is using apply. This should produce the desired result:

df['%_groupby'] = df.groupby('grp')['a'].apply(lambda x: x.pct_change())