Hi chegg, I am trying to convert a Numpy array to a DataFramewithout using the pd.DataFrame(). My code produces a data frameusing the Series function followed by concatenation and the datathat I produce is an array of 1000 by 1 when I require an array of1000 by 9. I can not seem to split the data into the 9 columnswhich is required. Can you help me split the array into 9 columnswithout using pd.DataFrame()? The code I am usingis below, a sample output of the first 5 lines is also shown belowand a sample of what my question requires. Thank you.

My python code below:

def convert_to_df(data):#data is numpy array

#Convert numpy array to pandas dataframe

#column_names={'created_at','tweet_ID','valence_intensity','anger_intensity','fear_intensity','sadness_intensity','joy_intensity','sentiment_category','emotion_category'}

my_array = []

i=0

while i < 1000: #1000 lines of data

my_array.append((data[i]))

i+=1

my_series=pd.Series(my_array)

my_df= pd.concat([my_series],axis=1)

my_df.columns =['test']

my_df['test'].str.split(pat=',', expand=True)#want to split from1 column into 9

print(my_df.shape)

return my_df

My results the python code and note the array is 1000 by1 although only 5 rows are printed:

test

0 [2020-02-29 13:32:59, 1233746991752122368, 0.6...

1 [2020-02-27 00:20:58, 1232822899448385539, 0.4...

2 [2020-02-10 18:54:50, 1226942618195591169, 0.5...

3 [2020-02-29 05:23:06, 1233623708029333505, 0.5...

4 [2020-02-26 03:20:55, 1232505798661722112, 0.4...

(1000, 1)

The required results which has an array of 1000 by 9although only 5 rows are printed:

            created_at  ...     emotion_category0  2020-02-29 13:32:59  ...                  joy1  2020-02-27 00:20:58  ...                 fear2  2020-02-10 18:54:50  ...                  joy3  2020-02-29 05:23:06  ...  no specific emotion4  2020-02-26 03:20:55  ...                 fear[5 rows x 9 columns]