How to get the top values within each group? - python-3.x

Question

I am new to Pandas and I have a dataset that looks something like this.
s_name Time p_name qty
A 12/01/2019 ABC 1
A 12/01/2019 ABC 1
A 12/01/2019 DEF 2
A 12/01/2019 DEF 2
A 12/01/2019 FGH 0
B 13/02/2019 ABC 3
B 13/02/2019 DEF 1
B 13/02/2019 DEF 1
B 13/03/2019 ABC 3
B 13/03/2019 FGH 0
I am trying to group by s_name and find the sum of the qty of each unique p_name in a month but only display the p_name with the top 2 most quantities. Below is an example of how I want the final output to look like.
s_name Time p_name qty
A 01 DEF 4
A 01 ABC 2
B 02 ABC 3
B 02 DEF 2
B 03 ABC 2
B 03 FGH 0
Do you have any ideas? I have been stuck here for quite long so much help is appreciated.

Answer 1

Create a month using dt, then group by s_name and month, then apply a function to the groups, group each group by name and do a sum over qty, sort_values descending and only get the first two rows with head:
df.Time = pd.to_datetime(df.Time, format='%d/%m/%Y')
df['month'] = df.Time.dt.month
df_f = df.groupby(['s_name', 'month']).apply(
lambda df:
df.groupby('p_name').qty.sum()
.sort_values(ascending=False).head(2)
).reset_index()
df_f
# s_name month p_name qty
# 0 A 1 DEF 4
# 1 A 1 ABC 2
# 2 B 2 ABC 3
# 3 B 2 DEF 2
# 4 B 3 ABC 3
# 5 B 3 FGH 0

Answer 2

I am new to Pandas myself. I am going to attempt to answer your question.
See this code.
from io import StringIO
import pandas as pd
columns = "s_name Time p_name qty"
# Create dataframe from text.
df = pd.read_csv(
StringIO(
f"""{columns}
A 12/01/2019 ABC 1
A 12/01/2019 ABC 1
A 12/01/2019 DEF 2
A 12/01/2019 DEF 2
A 12/01/2019 FGH 0
B 13/02/2019 ABC 3
B 13/02/2019 DEF 1
B 13/02/2019 DEF 1
B 13/03/2019 ABC 3
B 13/03/2019 FGH 0"""
),
sep=" ",
)
S_NAME, TIME, P_NAME, QTY = columns.split()
MONTH = "month"
# Convert the TIME col to datetime types.
df.Time = pd.to_datetime(df.Time, dayfirst=True)
# Create a month column with zfilled strings.
df[MONTH] = df.Time.apply(lambda x: str(x.month).zfill(2))
# Group
group = df.groupby(by=[S_NAME, P_NAME, MONTH])
gdf = (
group.sum()
.sort_index()
.sort_values(by=[S_NAME, MONTH, QTY], ascending=False)
.reset_index()
)
gdf.groupby([S_NAME, MONTH]).head(2).sort_values(by=[S_NAME, MONTH]).reset_index()
Is this the result you expected?