About array in Python ( Numpy )

Question

I use Python , and connection with sql server. I selected from the data in sql server.

CustomerNumber	TransactionDate
1	                2/3/2019
1	                12/4/2019
1	                12/17/2019
2                	1/4/2019
2	                4/4/2019
3	                7/5/2019
4	                7/7/2019
4	                9/5/2019
4	                9/15/2019
4                	10/15/2019

I want convert to array base on CustomerNumber

[1 2/3/2019 12/4/2019 12/17/2019 ]
[2 1/4/2019 4/4/2019]
[3 7/5/2019]
[4 7/5/2019 7/7/2019 9/5/2019 9/15/2019 10/15/2019]

I am python beginner. So I look forward to your feedback. Thanks you for your help.

why do you want to do that? (I think there's a better solution...) — Andy Hayden
– Andy Hayden, Commented May 16, 2019 at 3:18

BENY · Accepted Answer · 2019-05-16 03:14:53Z

2

Since I think that is pandas DataFrame , here is a way from pandas

s=df.groupby('CustomerNumber').TransactionDate.apply(list).reset_index()
s
Out[49]: 
   CustomerNumber                              TransactionDate
0               1            [2/3/2019, 12/4/2019, 12/17/2019]
1               2                         [1/4/2019, 4/4/2019]
2               3                                   [7/5/2019]
3               4  [7/7/2019, 9/5/2019, 9/15/2019, 10/15/2019]
l=(s.CustomerNumber.apply(lambda x : [x])+s.TransactionDate).tolist()
l
Out[50]: 
[[1, '2/3/2019', '12/4/2019', '12/17/2019'],
 [2, '1/4/2019', '4/4/2019'],
 [3, '7/5/2019'],
 [4, '7/7/2019', '9/5/2019', '9/15/2019', '10/15/2019']]

answered May 16, 2019 at 3:14

BENY

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1 Comment

BENY Over a year ago

@TrinhPham hehe happy coding

maverick · Accepted Answer · 2019-05-16 03:20:20Z

0

I recommend you read your data into a pandas dataframe using pandas.read_sql(https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_sql.html). Make sure to have the proper arguments and pay attention to the 'con' argument for which you specify the connection to your SQL database.

Once you have it in a pandas data frame with two columns (customer_number & transaction date), it becomes a simple groupby operation:

df.groupby(['CustomerNumber'])['TransactionDate'].apply(list)

This should group by on CustomerNumber and append the respective dates to a list for each unique customer number.

answered May 16, 2019 at 3:20

maverick

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1 Comment

Trinh Pham Over a year ago

Ok , Thank maverick

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