# !/usr/bin/env python
# -*- coding:utf-8 -*-
# create on 18/4/13
import pandas as pd

def dataframe_explode(dataframe, fieldname): 
 temp_fieldname = fieldname + '_made_tuple_' 
 dataframe[temp_fieldname] = dataframe[fieldname].apply(tuple)  
 list_of_dataframes = []
 for values in dataframe[temp_fieldname].unique().tolist(): 
  list_of_dataframes.append(pd.DataFrame({
   temp_fieldname: [values] * len(values), 
   fieldname: list(values), 
  }))
 dataframe = dataframe[list(set(dataframe.columns) - set([fieldname]))].merge(pd.concat(list_of_dataframes), how='left', on=temp_fieldname) 
 del dataframe[temp_fieldname]
 return dataframe

df = pd.DataFrame({'listcol':[[1,2,3],[4,5,6]], "aa": [222,333]})
df = dataframe_explode(df, "listcol")

[1] df
Station ID  Pollutants
8809   {"a": "46", "b": "3", "c": "12"}
8810   {"a": "36", "b": "5", "c": "8"}
8811   {"b": "2", "c": "7"}
8812   {"c": "11"}
8813   {"a": "82", "c": "15"}

df_pol_ps = data_df['Pollutants'].apply(pd.Series)

df_pol_ps:
 a b c
0 46 3 12
1 36 5 8
2 NaN 2 7
3 NaN NaN 11
4 82 NaN 15

df_final = pd.concat([df, df_pol_ps], axis = 1).drop('Pollutants', axis = 1)

df_final:
 StationID a b c
0 8809 46 3 12
1 8810 36 5 8
2 8811 NaN 2 7
3 8812 NaN NaN 11
4 8813 82 NaN 15

df_final = pd.concat([df, df['Pollutants'].apply(pd.Series)], axis = 1).drop('Pollutants', axis = 1)

df_final:
 StationID a b c
0 8809 46 3 12
1 8810 36 5 8
2 8811 NaN 2 7
3 8812 NaN NaN 11
4 8813 82 NaN 15