如何根据列中的数值创建逗号分隔的数字列表

Question

I am trying to figure out how to create a comma separated list of numbers based on a numeric value in a specific column. For example, if the numeric column has a value of 5, I want to create a comma separated list of numbers in another column as "1, 2, 3, 4, 5".

Assuming we start with this dataframe:

  inventory_partner inventory_partner2  calc
0                A1                 aa     1
1                A2                 bb     2
2                A3                 cc     5
3                A4                 dd     4
4                A5                 ee     5
5                A6                 ff     3

I'm trying to get to this dataframe without having to manually code in the lists for each calc numeric possibility:

  inventory_partner inventory_partner2  calc  my_comma_list
0                A1                 aa     1              1
1                A2                 bb     2           1, 2
2                A3                 cc     5  1, 2, 3, 4, 5
3                A4                 dd     4     1, 2, 3, 4
4                A5                 ee     5  1, 2, 3, 4, 5
5                A6                 ff     3        1, 2, 3

Below is my code that is manually creating a numeric list to apply based on each calc value. Is there a simpler way of doing this using a for loop? I tried it in the code below but I couldn't fetch the calc value to use in the for loop.

#create dataframe
d = {'inventory_partner': ['A1', 'A2', 'A3', 'A4', 'A5', 'A6'], 'inventory_partner2': ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'], 'calc': [1, 2, 5, 4, 5, 3]}
df1 = pd.DataFrame(data=d)

print(df1) #print original dataframe

#create my_comma_list column based on number values in calc column - too much code 
df1.insert(3, 'my_comma_list', '')
df1.loc[df1['calc'] == 1, 'my_comma_list'] = '1'
df1.loc[df1['calc'] == 2, 'my_comma_list'] = '1, 2'
df1.loc[df1['calc'] == 3, 'my_comma_list'] = '1, 2, 3'
df1.loc[df1['calc'] == 4, 'my_comma_list'] = '1, 2, 3, 4'
df1.loc[df1['calc'] == 5, 'my_comma_list'] = '1, 2, 3, 4, 5'

#df1['inventory_partner'] = ",".join([str(i) for i in range(0, df1['calc'].values)]) #I tried something like this but it can't fetch the calc value and use it in the for loop

print(df1) 

Answer 1

You can use df.apply to apply str.join on range object created from 'calc' :

>>> df['my_comma_list'] = df.calc.apply(lambda x: ', '.join(map(str, range(1, x+1))))

  inventory_partner inventory_partner2  calc  my_comma_list
0                A1                 aa     1              1
1                A2                 bb     2           1, 2
2                A3                 cc     5  1, 2, 3, 4, 5
3                A4                 dd     4     1, 2, 3, 4
4                A5                 ee     5  1, 2, 3, 4, 5
5                A6                 ff     3        1, 2, 3

Answer 2

Try apply with .join :

 df['my_comm_list'] = df['calc'].apply(lambda x: ', '.join(np.arange(1,x+1).astype(str)) )

Answer 3

You can use the apply function and create the response in a separate function, so you have it isolated.

import pandas as pd


d = {'inventory_partner': ['A1', 'A2', 'A3', 'A4', 'A5', 'A6'], 'inventory_partner2': ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'], 'calc': [1, 2, 5, 4, 5, 3]}
df1 = pd.DataFrame(data=d)

print(df1) #print original dataframe

def create_comma_list(number):
  res = []
  for n in range(1,number+1):
    res.append(str(n))
   
  return ", ".join(res)

df1["my_comma_list"] = df1.apply(lambda x: create_comma_list(x.calc), axis=1)

print(df1) 

Source: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html