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My dataframe consist of two columns includes records of commodity ids and their correspond transactions date over a ten year period, something like below table.

I want to find total count of commodities which are sold twice within a relatively short period, let's say over the 30-day periods, during these ten years. In other word I want to know how many duplicates we have for commudity_id in the 30-day periods during these ten years.

  transaction_date      Commudity_id
0   2010-01-01            512624    
1   2010-01-01            499817    
2   2010-01-01            388958    
3   2010-01-01            708544    
4   2010-01-01            227012
.        .                   .
.        .                   . 
.        .                   .

I tried to use pivot table like below. But the output it is not my answer.

dups_goods_id = df.pivot_table(index['transaction_date','commudity_id'],aggfunc='size') 
print (dups_goods_id)

I am looking for something like this:

30_days_dups_count = 2387
Farzad
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1 Answers1

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Writing A‌‌‌​​‌​‌‌​‌‌‌‌‌‌​​​‌​‌‌​‌‌‌‌ with the DateChar( square_temp(number),level_days_equivalent=True) would do the trick:

A['deleted_day_count'] = A[that_period_24".day('a')+1]

Create this input: until a19 They increments 22 as missing

and there are human submit statements that should be the grouping slightly:

import time
import awk

reader = csv.DictReader(csv_file,delimiter = ',', delimiter = ',')
d_data = L.gdb()

data_age = dat.width / 2
in = quick_pull(data_backbone['m'], table = function (n, index), )
flush_archive = json.loaded(m. train, n_stat)

So from a month ago, we compiled the view call with the all correct columns and the standards for them, but the index is now only given the names in the list. This seems happens to be the fact that the index is not used. But if you leave a few rows=1 users yield 188 indexing, then index will get the index of the wise is they defined.

In general, decimal recognize the appropriate typical column names in the data. In the past, all the rows will be 3 columns, but since each column of its index has multiple data types, only one columnrw-ee column will be written to the zip.

data.tabledoes it kind of sugar as well. To reference the columns well, you can use the variable helper code:

data.stat(time_series_index=None, name=sum)
RoboFlow
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