I am trying to find out the id of the product sold month by month from 15 months of csv data and how many times it was sold in python

Question

But there is a lot of duplicate codes when I do that way in bottom.

what should I do to avoid duplicate and do it in a shorter way?

import numpy as np
import pandas as pd


train_purchases = pd.read_csv(r"C:UsersCanDesktopdressipi_recsys2022train_purchases.csv")


first_month = train_purchases.loc[(train_purchases['date'] > '2020-01-01') & (train_purchases['date'] <= '2020-01-31')].sort_values(by=["item_id"])["item_id"].tolist()

second_month = train_purchases.loc[(train_purchases['date'] > '2020-02-01') & (train_purchases['date'] <= '2020-02-31')].sort_values(by=["item_id"])["item_id"].tolist()

third_month = train_purchases.loc[(train_purchases['date'] > '2020-03-01') & (train_purchases['date'] <= '2020-03-31')].sort_values(by=["item_id"])["item_id"].tolist()

fourth_month = train_purchases.loc[(train_purchases['date'] > '2020-04-01') & (train_purchases['date'] <= '2020-04-31')].sort_values(by=["item_id"])["item_id"].tolist()

fifth_month = train_purchases.loc[(train_purchases['date'] > '2020-05-01') & (train_purchases['date'] <= '2020-05-31')].sort_values(by=["item_id"])["item_id"].tolist()

sixth_month = train_purchases.loc[(train_purchases['date'] > '2020-06-01') & (train_purchases['date'] <= '2020-06-31')].sort_values(by=["item_id"])["item_id"].tolist()



def most_frequent(List):
    counter = 0
    num = List[0] 
    for i in List:
        curr_frequency = List.count(i)
        if(curr_frequency> counter):
            counter = curr_frequency
            num = i
    print(num," id sold", List.count(num), "times. ")
    
    

most_frequent(first_month)
most_frequent(second_month)
most_frequent(third_month)
most_frequent(fourth_month)
most_frequent(fifth_month)
most_frequent(sixth_month)

Asked By: canucar7

||

Source

Answer 1

you can use something like this:

start = '2020-01-01'
end = '2021-03-31'
first_day = pd.date_range(start, end, freq='MS').astype(str).to_list() #get first day of month given range
end_day = pd.date_range(start, end, freq='M').strftime("%Y-%m-%d 23:59:59").astype(str).to_list() #get last day of month of given date
dates = dict(zip(first_day, end_day)) #convert lists to dictionary
#dates={'2020-01-01':'2020-01-31'}....

train_purchases['date']=pd.to_datetime(train_purchases['date'])
for k,v in dates.items():
    mask = train_purchases.loc[(train_purchases['date'] > k) & (train_purchases['date'] <= v)].sort_values(by=["item_id"]).item_id.value_counts()[:1]
    print(mask.index[0]," id sold", mask.iloc[0], "times. ")
'''
8060  id sold 564 times. 
8060  id sold 421 times. 
8060  id sold 375 times. 
8060  id sold 610 times. 
8060  id sold 277 times. 
8060  id sold 280 times. 
8622  id sold 290 times. 
8060  id sold 374 times. 
8060  id sold 638 times. 
8060  id sold 563 times. 
8060  id sold 1580 times. 
8060  id sold 765 times. 
19882  id sold 717 times. 
19882  id sold 570 times. 
19882  id sold 690 times.  

'''

Note

train_purchases['date'] > '2020-01-01'

If you use it as above, the first day of the month is not included in the calculation. If you want the first day of the month, you should use it as below

train_purchases['date'] >= '2020-01-01'

Answered By: Bushmaster

Answer 2

I want to do another thing about all these data and procces which we did.

It is basicly compare about these months and items.

i need to keep an array, vector or hashmap of how many times the common id products sold every month are sold. for example, let’s consider each first element as a product with 8060 id, and it must be included in the first index in each list for each month how many times this product has been sold. For first month num_sold_id_1 is = 564.

Is there an easy way to do?

Answered By: canucar7

I am trying to find out the id of the product sold month by month from 15 months of csv data and how many times it was sold in python

Question:

Answers: