Select count(distinct column1),sum(column2) in python(No pandas)
$begingroup$
I have a csv file like below
ACCOUNT SALES
001AB12 21
001AB12 68
001AB12 97
001AB14 62
001AB14 67
001AB110 58
001AB143 49
001AB143 21
001AB143 97
001AB143 93
I want to perform aggregate operation like below
Select distinct(count Account) ety_account_count,sum(sales) net_sales
from csv_file
I have tried
from collections import Counter, defaultdict
import csv
import sys
my_dict={}
account = defaultdict(Counter)
with open ('out_fldr/agg_csv.csv') as r_file:
for row in csv.DictReader(r_file,delimiter='|'):
account[row['ACCOUNT']] += float(int(row['SALES']))
print(account)
Here I am getting error
for elem, count in other.items():
AttributeError: 'float' object has no attribute 'items'
I trying to create dictionary where it will hold all the aggregate function and its like
my_dict = {'ETY_ACOCUNT' : 5 , 'MET_SALES': 633}
I am aware this can be done in pandas with its function, but I wanna do this in pure python way.
python-3.x
New contributor
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add a comment |
$begingroup$
I have a csv file like below
ACCOUNT SALES
001AB12 21
001AB12 68
001AB12 97
001AB14 62
001AB14 67
001AB110 58
001AB143 49
001AB143 21
001AB143 97
001AB143 93
I want to perform aggregate operation like below
Select distinct(count Account) ety_account_count,sum(sales) net_sales
from csv_file
I have tried
from collections import Counter, defaultdict
import csv
import sys
my_dict={}
account = defaultdict(Counter)
with open ('out_fldr/agg_csv.csv') as r_file:
for row in csv.DictReader(r_file,delimiter='|'):
account[row['ACCOUNT']] += float(int(row['SALES']))
print(account)
Here I am getting error
for elem, count in other.items():
AttributeError: 'float' object has no attribute 'items'
I trying to create dictionary where it will hold all the aggregate function and its like
my_dict = {'ETY_ACOCUNT' : 5 , 'MET_SALES': 633}
I am aware this can be done in pandas with its function, but I wanna do this in pure python way.
python-3.x
New contributor
$endgroup$
add a comment |
$begingroup$
I have a csv file like below
ACCOUNT SALES
001AB12 21
001AB12 68
001AB12 97
001AB14 62
001AB14 67
001AB110 58
001AB143 49
001AB143 21
001AB143 97
001AB143 93
I want to perform aggregate operation like below
Select distinct(count Account) ety_account_count,sum(sales) net_sales
from csv_file
I have tried
from collections import Counter, defaultdict
import csv
import sys
my_dict={}
account = defaultdict(Counter)
with open ('out_fldr/agg_csv.csv') as r_file:
for row in csv.DictReader(r_file,delimiter='|'):
account[row['ACCOUNT']] += float(int(row['SALES']))
print(account)
Here I am getting error
for elem, count in other.items():
AttributeError: 'float' object has no attribute 'items'
I trying to create dictionary where it will hold all the aggregate function and its like
my_dict = {'ETY_ACOCUNT' : 5 , 'MET_SALES': 633}
I am aware this can be done in pandas with its function, but I wanna do this in pure python way.
python-3.x
New contributor
$endgroup$
I have a csv file like below
ACCOUNT SALES
001AB12 21
001AB12 68
001AB12 97
001AB14 62
001AB14 67
001AB110 58
001AB143 49
001AB143 21
001AB143 97
001AB143 93
I want to perform aggregate operation like below
Select distinct(count Account) ety_account_count,sum(sales) net_sales
from csv_file
I have tried
from collections import Counter, defaultdict
import csv
import sys
my_dict={}
account = defaultdict(Counter)
with open ('out_fldr/agg_csv.csv') as r_file:
for row in csv.DictReader(r_file,delimiter='|'):
account[row['ACCOUNT']] += float(int(row['SALES']))
print(account)
Here I am getting error
for elem, count in other.items():
AttributeError: 'float' object has no attribute 'items'
I trying to create dictionary where it will hold all the aggregate function and its like
my_dict = {'ETY_ACOCUNT' : 5 , 'MET_SALES': 633}
I am aware this can be done in pandas with its function, but I wanna do this in pure python way.
python-3.x
python-3.x
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Tpk43Tpk43
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