CODEWITHSUNDEEP
pythonpandas
user3314418
user3314418

Reputation: 3031

turning a two dimensional array into a two column dataframe pandas

if I have the following, how do I make pd.DataFrame() turn this array into a dataframe with two columns. What's the most efficient way? My current approach involves creating copies out of each into a series and making dataframes out of them.

From this: 

([[u'294 (24%) L', u'294 (26%) R'],
  [u'981 (71%) L', u'981 (82%) R'],])

to 

x    y
294  294
981  981

rather than 

x
[u'294 (24%) L', u'294 (26%) R']

my current approach. Looking for something more efficient

numL = pd.Series(numlist).map(lambda x: x[0])
    numR = pd.Series(numlist).map(lambda x: x[1])

    nL = pd.DataFrame(numL, columns=['left_num'])
    nR = pd.DataFrame(numR, columns=['right_num'])

    nLR = nL.join(nR)

    nLR

UPDATE**

I noticed that my error simply comes down to when you pd.DataFrame() a list versus a series. WHen you create a dataframe out of a list, it merges the items into the same column. Not so with a list. That solved my problem in the most efficient way.

Upvotes: 14

Views: 44430

Answers (1)

unutbu
unutbu

Reputation: 879271

data = [[u'294 (24%) L', u'294 (26%) R'],  [u'981 (71%) L', u'981 (82%) R'],]
    
clean_data = [[int(item.split()[0]) for item in row] for row in data]

# clean_data: [[294, 294], [981, 981]]
    
pd.DataFrame(clean_data, columns=list('xy'))

#         x    y
#    0  294  294
#    1  981  981
#
#    [2 rows x 2 columns]

Upvotes: 16

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