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I have pandas dataframe with a column containing values or lists of values (of unequal length). I want to 'expand' the rows, so each value in the list becomes single value in column. An example says it all:
dfIn = pd.DataFrame({u'name': ['Tom', 'Jim', 'Claus'],
u'location': ['Amsterdam', ['Berlin','Paris'], ['Antwerp','Barcelona','Pisa'] ]})
location name
0 Amsterdam Tom
1 [Berlin, Paris] Jim
2 [Antwerp, Barcelona, Pisa] Claus
I want to turn into:
dfOut = pd.DataFrame({u'name': ['Tom', 'Jim', 'Jim', 'Claus','Claus','Claus'],
u'location': ['Amsterdam', 'Berlin','Paris', 'Antwerp','Barcelona','Pisa']})
location name
0 Amsterdam Tom
1 Berlin Jim
2 Paris Jim
3 Antwerp Claus
4 Barcelona Claus
5 Pisa Claus
I first tried using apply but it's not possible to return multiple Series as far as I know. iterrows seems to be the trick. But the code below gives me an empty dataframe...
def duplicator(series):
if type(series['location']) == list:
for location in series['location']:
subSeries = series
subSeries['location'] = location
dfOut.append(subSeries)
else:
dfOut.append(series)
for index, row in dfIn.iterrows():
duplicator(row)
解决方案
If you return a series whose index is a list of locations, then dfIn.apply will collate those series into a table:
import pandas as pd
dfIn = pd.DataFrame({u'name': ['Tom', 'Jim', 'Claus'],
u'location': ['Amsterdam', ['Berlin','Paris'],
['Antwerp','Barcelona','Pisa'] ]})
def expand(row):
locations = row['location'] if isinstance(row['location'], list) else [row['location']]
s = pd.Series(row['name'], index=list(set(locations)))
return s
In [156]: dfIn.apply(expand, axis=1)
Out[156]:
Amsterdam Antwerp Barcelona Berlin Paris Pisa
0 Tom NaN NaN NaN NaN NaN
1 NaN NaN NaN Jim Jim NaN
2 NaN Claus Claus NaN NaN Claus
You can then stack this DataFrame to obtain:
In [157]: dfIn.apply(expand, axis=1).stack()
Out[157]:
0 Amsterdam Tom
1 Berlin Jim
Paris Jim
2 Antwerp Claus
Barcelona Claus
Pisa Claus
dtype: object
This is a Series, while you want a DataFrame. A little massaging with reset_index gives you the desired result:
dfOut = dfIn.apply(expand, axis=1).stack()
dfOut = dfOut.to_frame().reset_index(level=1, drop=False)
dfOut.columns = ['location', 'name']
dfOut.reset_index(drop=True, inplace=True)
print(dfOut)
yields
location name
0 Amsterdam Tom
1 Berlin Jim
2 Paris Jim
3 Amsterdam Claus
4 Antwerp Claus
5 Barcelona Claus
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