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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.
724
How do you convert an array to a DataFrame in Python? To convert an array to a dataframe with Python you need to 1) have your NumPy array (e.g., np_array), and 2) use the pd. DataFrame() constructor like this: df = pd. DataFrame(np_array, columns=['Column1', 'Column2']) .
We can use np. column_stack() to combine two 1-D arrays X and Y into a 2-D array. Then, we can use pd. DataFrame to change it into a dataframe.
DataFrame. DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object.
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]
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