I have a pandas DataFrame with the following structure:
|A|B|C |
-------------
|1|2|"1,2,4"|
|3|4|"5,6" |
What is the most pythonic way to get a table like below?
|A|B|C|
-------
|1|2|1|
|1|2|2|
|1|2|4|
|3|4|5|
|3|4|6|
The initial table contains ~10K instances with 4-18 elements in C
.
For the moment I have nothing brighter than:
import pandas as pd
df = pd.DataFrame([[1, 2, "1,2,4"], [3, 4, "5,6"]], columns=['A', 'B', 'C'])
tmp = df['C'].str.split(',').apply(pd.Series, 1).stack()
tmp.index = tmp.index.droplevel(-1)
tmp.name = 'C'
del df['C']
df = df.join(tmp)
Not much better. I did it two ways. The first is similar to yours with some cosmetic differences and feels like the better of the two. I still added the second because it's a bit different in that it concats the series returned by split.
>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, "1,2,4"], [3, 4, "5,6"]], columns=['A', 'B', 'C'])
>>> x = df.drop('C', axis=1).join(df.C.str.split(',', expand=True).stack().to_frame('C').reset_index(1, drop=True))
>>> print x
A B C
0 1 2 1
0 1 2 2
0 1 2 4
1 3 4 5
1 3 4 6
>>> y = df.drop('C', axis=1).join(pd.concat(df.C.str.split(',', expand=True).to_dict('series').values()).dropna().to_frame('C'))
>>> print y
A B C
0 1 2 1
0 1 2 2
0 1 2 4
1 3 4 5
1 3 4 6
you can do this by for loop:
with open("your_file") as f:
for i,line in enumerate(f):
if i==1:
print line
else:
line = line.split("|")
elements = line[-1].strip("\" ").split(",")
for x in elements:
print "|{}|{}|".format("|".join(line[:2]), x)
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