Create Multiple Dataframes using Loop & function - python-3.x

Question

I have a df over 1M rows similar to this
ID Date Amount
x May 1 10
y May 2 20
z May 4 30
x May 1 40
y May 1 50
z May 2 60
x May 1 70
y May 5 80
a May 6 90
b May 8 100
x May 10 110
I have to sort the data based on the date and then create new dataframes depending on the times the value is present in Amount column. So if x has made purchase 3 time then I need it in 3 different dataframes. first_purchase dataframe would have every ID that has purchased even once irrespective of date or amount.
If an ID purchases 3 times, I need that ID to be in first purchase then second and then 3rd with Date and Amount.
Doing it manually is easy with:-
df = df.sort_values('Date')
first_purchase = df.drop_duplicates('ID')
after_1stpurchase = df[~df.index.isin(first_purchase.index)]
second data frame would be created with:-
after_1stpurchase = after_1stpurchase.sort_values('Date')
second_purchase = after_1stpurchase.drop_duplicates('ID')
after_2ndpurchase = after_1stpurchase[~after_1stpurchase.index.isin(second_purchase.index)]
How do I create the loop to provide me with each dataframes?

Answer 1

IIUC, I was able to achieve what you wanted.
import pandas as pd
import numpy as np
# source data for the dataframe
data = {
"ID":["x","y","z","x","y","z","x","y","a","b","x"],
"Date":["May 01","May 02","May 04","May 01","May 01","May 02","May 01","May 05","May 06","May 08","May 10"],
"Amount":[10,20,30,40,50,60,70,80,90,100,110]
}
df = pd.DataFrame(data)
# convert the Date column to datetime and still maintain the format like "May 01"
df['Date'] = pd.to_datetime(df['Date'], format='%b %d').dt.strftime('%b %d')
# sort the values on ID and Date
df.sort_values(by=['ID', 'Date'], inplace=True)
df.reset_index(inplace=True, drop=True)
print(df)
Original Dataframe:
Amount Date ID
0 90 May 06 a
1 100 May 08 b
2 10 May 01 x
3 40 May 01 x
4 70 May 01 x
5 110 May 10 x
6 50 May 01 y
7 20 May 02 y
8 80 May 05 y
9 60 May 02 z
10 30 May 04 z
.
# create a list of unique ids
list_id = sorted(list(set(df['ID'])))
# create an empty list that would contain dataframes
df_list = []
# count of iterations that must be seperated out
# for example if we want to record 3 entries for
# each id, the iter would be 3. This will create
# three new dataframes that will hold transactions
# respectively.
iter = 3
for i in range(iter):
df_list.append(pd.DataFrame())
for val in list_id:
tmp_df = df.loc[df['ID'] == val].reset_index(drop=True)
# consider only the top iter(=3) values to be distributed
counter = np.minimum(tmp_df.shape[0], iter)
for idx in range(counter):
df_list[idx] = df_list[idx].append(tmp_df.loc[tmp_df.index == idx])
for df in df_list:
df.reset_index(drop=True, inplace=True)
print(df)
Transaction #1:
Amount Date ID
0 90 May 06 a
1 100 May 08 b
2 10 May 01 x
3 50 May 01 y
4 60 May 02 z
Transaction #2:
Amount Date ID
0 40 May 01 x
1 20 May 02 y
2 30 May 04 z
Transaction #3:
Amount Date ID
0 70 May 01 x
1 80 May 05 y
Note that in your data, there are four transactions for 'x'. If lets say you wanted to track the 4th iterative transaction as well. All you need to do is change the value if 'iter' to 4 and you will get the fourth dataframe as well with the following value:
Amount Date ID
0 110 May 10 x