CODEWITHSUNDEEP
python
MMMMS
MMMMS

Reputation: 2201

Aggregation in array element - python

I have a OP : {'2017-05-06': [3, 7, 8],'2017-05-07': [3, 9, 10],'2017-05-08': [4]}

from the OP I just want another OP :

{'2017-05-06': [15, 11, 10],'2017-05-07': [19, 13, 12],'2017-05-08': [4]}

which means:
Ncleand is 2017-05-06
element total is 18 so '2017-05-06': [3 -18, 7-18, 8-18] = '2017-05-06': [15, 11, 10]
likewise all elements data.
So final output is {'2017-05-06': [15, 11, 10],'2017-05-07': [19, 13, 12],'2017-05-08': [4]}

How to do this?

Note : I am using python 3.6.2 and pandas 0.22.0

code so far :

import pandas as pd
dfs = pd.read_excel('ff2.xlsx', sheet_name=None)
dfs1 = {i:x.groupby(pd.to_datetime(x['date']).dt.strftime('%Y-%m-%d'))['duration'].sum() for i, x in dfs.items()}
d = pd.concat(dfs1).groupby(level=1).apply(list).to_dict()
actuald = pd.concat(dfs1).div(80).astype(int)
sum1 = actuald.groupby(level=1).transform('sum')
m = actuald.groupby(level=1).transform('size') > 1
cleand = sum1.sub(actuald).where(m, actuald).groupby(level=1).apply(list).to_dict()
print (cleand)

From the cleand I want to do this?

Upvotes: 1

Views: 108

Answers (2)

Ohad Eytan
Ohad Eytan

Reputation: 8464

In a compact (but somehow inefficient) way:

>>> op = {'2017-05-06': [3, 7, 8],'2017-05-07': [3, 9, 10],'2017-05-08': [4]}
>>> { x:[sum(y)-i for i in y] if len(y)>1 else y for x,y in op.items() }

#output:
{'2017-05-06': [15, 11, 10], '2017-05-07': [19, 13, 12], '2017-05-08': [4]}

Upvotes: 2

Veera Balla Deva
Veera Balla Deva

Reputation: 788

def get_list_manipulation(list_):
    subtraction = list_
    if len(list_) != 1:
        total = sum(list_)
        subtraction = [total-val for val in list_]
    return subtraction

for key, values in data.items():
    data[key] = get_list_manipulation(values)
>>>{'2017-05-06': [15, 11, 10], '2017-05-07': [19, 13, 12], '2017-05-08': [4]}

Upvotes: 2

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