Please find the below input and output. Corresponding to each store id and period id , 11 Items should be present , if any item is missing, add it and fill that row with 0 without using loop.
Any help is highly appreciated.
input
Expected Output
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3 Answers
You can do this:
Sample df:
df = pd.DataFrame({'store_id':[1160962,1160962,1160962,1160962,1160962,1160962,1160962,1160962,1160962,1160962, 1160962],
'period_id':[1025,1025,1025,1025,1025,1025,1026,1026,1026,1026,1026],
'item_x':[1,4,5,6,7,8,1,2,5,6,7],
'z':[1,4,5,6,7,8,1,2,5,6,7]})
Solution:
num = range(1,12)
def f(x):
return x.reindex(num, fill_value=0)\
.assign(store_id=x['store_id'].mode()[0], period_id = x['period_id'].mode()[0])
df.set_index('item_x').groupby(['store_id','period_id'], group_keys=False).apply(f).reset_index()
1 Comment
filbranden
This doesn't seem to work correctly, the
item_x values for the first period are respected, but on the second period are not (are copied from the first.) It seems to me that using reindex on a groupby might be the source of the issue...You can do:
from itertools import product
pdindex=product(df.groupby(["store_id", "period_id"]).groups, range(1,12))
pdindex=pd.MultiIndex.from_tuples(map(lambda x: (*x[0], x[1]), pdindex), names=["store_id", "period_id", "Item"])
df=df.set_index(["store_id", "period_id", "Item"])
res=pd.DataFrame(index=pdindex, columns=df.columns)
res.loc[df.index, df.columns]=df
res=res.fillna(0).reset_index()
Now this will work only assuming you don't have any Item outside of range [1,11].
answered Feb 20, 2020 at 19:09
Georgina Skibinski
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Comments
This is a simplification of @GrzegorzSkibinski's correct answer.
This answer is not modifying the original DataFrame. It uses fewer variables to store intermediate data structures and employs a list comprehension to simplify an use of map.
I'm also using reindex() rather than creating a new DataFrame using the generated index and populating it with the original data.
import pandas as pd
import itertools
df.set_index(
["store_id", "period_id", "Item_x"]
).reindex(
pd.MultiIndex.from_tuples([
group + (item,)
for group, item in itertools.product(
df.groupby(["store_id", "period_id"]).groups,
range(1, 12),
)],
names=["store_id", "period_id", "Item_x"]
),
fill_value=0,
).reset_index()
In testing, output matched what you listed as expected.
Comments
