3

I want to convert pandas dataframe to numpy array with the groupby lable with it. In groupby I have to group by using regex so its important to take its lable with it.

My data is in the format:

start_date,is_member 

2014-04-15 00:01,1
2014-04-15 00:01,1
2014-04-15 01:01,1
2014-04-15 01:01,1
2014-04-15 02:02,1
2014-04-15 03:05,1

I have tried

df = pd.read_csv(filename, header=0)
df = df.groupby(df.start_date.str.extract("^(.*?)\:", expand=False))[['start_date']].count()[['start_date']]
print(df)

Output of Dataframe is

start_date               
2014-04-15 00           2
2014-04-15 01           2
2014-04-15 02           1
2014-04-15 03           1

I have tried it to convert into numpy array with 

numpy_array = df.values

Output of numpy array is just the count value

[[2]
 [2]
 [1]
 [1]]

I want it with the startdate as a column.

[[2014-04-15 00 2]
 [2014-04-15 01 2]
 [2014-04-15 02 1]
 [2014-04-15 03 1]]
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1 Answer 1

Reset to default
2

I believe you need convert index to column by DataFrame.reset_index:

#simplify code 
df = df.groupby(df.start_date.str.extract("^(.*?)\:", expand=False))['start_date'].count()

numpy_array = df.rename_axis('index').reset_index().values
print (numpy_array)
[['2014-04-15 00' 2]
 ['2014-04-15 01' 2]
 ['2014-04-15 02' 1]
 ['2014-04-15 03' 1]]

Or for pandas 0.24+ use:

numpy_array = df.rename_axis('index').reset_index().to_numpy()
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1 Comment

When I am using .reset_index() it is giving error, "cannot insert start_date, already exists". If you could help.

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