Pandas Series of lists to one series

Here's a simple method using only pandas functions:

import pandas as pd

s = pd.Series([
    ['slim', 'waist', 'man'],
    ['slim', 'waistline'],
    ['santa']])

Then

s.apply(pd.Series).stack().reset_index(drop=True)

gives the desired output. In some cases you might want to save the original index and add a second level to index the nested elements, e.g.

0  0         slim
   1        waist
   2          man
1  0         slim
   1    waistline
2  0        santa

If this is what you want, just omit .reset_index(drop=True) from the chain.

You are basically just trying to flatten a nested list here.

You should just be able to iterate over the elements of the series:

slist =[]
for x in series:
    slist.extend(x)

or a slicker (but harder to understand) list comprehension:

slist = [st for row in s for st in row]

In pandas version 0.25.0 appeared a new method 'explode' for series and dataframes. Older versions do not have such method.

It helps to build the result you need.

For example you have such series:

import pandas as pd

s = pd.Series([
    ['slim', 'waist', 'man'],
    ['slim', 'waistline'],
    ['santa']])

Then you can use

s.explode()

To get such result:

0         slim
0        waist
0          man
1         slim
1    waistline
2        santa

In case of dataframe:

df = pd.DataFrame({
  's': pd.Series([
    ['slim', 'waist', 'man'],
    ['slim', 'waistline'],
    ['santa']
   ]),
   'a': 1
})

You will have such DataFrame:

                    s  a
0  [slim, waist, man]  1
1   [slim, waistline]  1
2             [santa]  1

Applying explode on s column:

df.explode('s')

Will give you such result:

           s  a
0       slim  1
0      waist  1
0        man  1
1       slim  1
1  waistline  1
2      santa  1

If your series, contain empty lists

import pandas as pd

s = pd.Series([
    ['slim', 'waist', 'man'],
    ['slim', 'waistline'],
    ['santa'],
    []
])

Then running explode will introduce NaN values for empty lists, like this:

0         slim
0        waist
0          man
1         slim
1    waistline
2        santa
3          NaN

If this is not desired, you can dropna method call:

s.explode().dropna()

To get this result:

0         slim
0        waist
0          man
1         slim
1    waistline
2        santa

Dataframes also have dropna method:

df = pd.DataFrame({
  's': pd.Series([
    ['slim', 'waist', 'man'],
    ['slim', 'waistline'],
    ['santa'],
    []
   ]),
   'a': 1
})

Running explode without dropna:

df.explode('s')

Will result into:

           s  a
0       slim  1
0      waist  1
0        man  1
1       slim  1
1  waistline  1
2      santa  1
3        NaN  1

with dropna:

df.explode('s').dropna(subset=['s'])

Result:

           s  a
0       slim  1
0      waist  1
0        man  1
1       slim  1
1  waistline  1
2      santa  1

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