How to convert a list of lists into a dataframe in python

Question

I have a list of list which looks like:

[['A'],
 ['America'],
 ['2017-39', '2017-40', '2017-41', '2017-42', '2017-43'],
 [10.0, 6.0, 6.0, 6.0, 1.0],
 [5.0,7.0,8.0,9.0,1.0],
 ,
 ['B'],
 ['Britan'],
 ['2017-38', '2017-39', '2017-40', '2017-41', '2017-42', '2017-43', '2017-44'],
 [41.0, 27.0, 38.0, 36.0, 33.0, 41.0, 8.0],
 [40.0, 38.0, 28.0, 27.0, 23.0, 65.0, 4.0]]

I want to convert this into a dataframe which should look like

A America     2017-39   10.0  5.0
na   na       2017-40    6.0  7.0
na   na       2017-41    6.0  8.0
na   na       2017-42    6.0  9.0
na   na       2017-43    1.0 10.0
B Britan      2017-38   41.0 40.0
na   na       2017-39   27.0 38.0
na   na       2017-40   38.0 28.0
na   na       2017-41   36.0 27.0
na   na       2017-42   33.0 23.0
na   na       2017-43   41.0 65.0
na   na       2017-44    8.0  4.0

How can I code to make it possible , as I am pretty new to python, I am having a hard time.

I will really appreciate your time and effort to help me in this regards

Answer 1

I am using groupby and re-create the column

s=pd.DataFrame(lst).T
s.columns=s.columns//5
pd.concat([pd.DataFrame(x.values) for _,x in s.groupby(level=0,axis=1)]).dropna(axis=0,thresh=1)
Out[146]: 
      0        1        2   3   4
0     A  America  2017-39  10   5
1  None     None  2017-40   6   7
2  None     None  2017-41   6   8
3  None     None  2017-42   6   9
4  None     None  2017-43   1   1
0     B   Britan  2017-38  41  40
1  None     None  2017-39  27  38
2  None     None  2017-40  38  28
3  None     None  2017-41  36  27
4  None     None  2017-42  33  23
5  None     None  2017-43  41  65
6  None     None  2017-44   8   4

Answer 2

import pandas as pd
data = [['A'],
 ['America'],
 ['2017-39', '2017-40', '2017-41', '2017-42', '2017-43'],
 [10.0, 6.0, 6.0, 6.0, 1.0],
 [5.0,7.0,8.0,9.0,1.0],
 ['B'],
 ['Britan'],
 ['2017-38', '2017-39', '2017-40', '2017-41', '2017-42', '2017-43', '2017-44'],
 [41.0, 27.0, 38.0, 36.0, 33.0, 41.0, 8.0],
 [40.0, 38.0, 28.0, 27.0, 23.0, 65.0, 4.0]]

result = {}
for letters, countries, dates, val1, val2 in zip(*[iter(data)]*5):
    result[tuple(letters+countries)] = pd.DataFrame({'date':dates, 'val1':val1, 'val2':val2})
result = pd.concat(result)
print(result)

yields

                date  val1  val2
A America 0  2017-39  10.0   5.0
          1  2017-40   6.0   7.0
          2  2017-41   6.0   8.0
          3  2017-42   6.0   9.0
          4  2017-43   1.0   1.0
B Britan  0  2017-38  41.0  40.0
          1  2017-39  27.0  38.0
          2  2017-40  38.0  28.0
          3  2017-41  36.0  27.0
          4  2017-42  33.0  23.0
          5  2017-43  41.0  65.0
          6  2017-44   8.0   4.0

---

The main idea above is to use the "grouper idiom" zip(*[iter(data)]*5) to group the items in data in groups of 5. That way, you can use

for letters, countries, dates, val1, val2 in zip(*[iter(data)]*5):

to loop through 5 items of data at a time.

---

pd.concat can accept a dict of DataFrames as input and concatenate them into a single DataFrame with a MultiIndex composed of the keys of the dict. So the for-loop is used to compose the dict of DataFrames,

for letters, countries, dates, val1, val2 in zip(*[iter(data)]*5):
    result[tuple(letters+countries)] = pd.DataFrame({'date':dates, 'val1':val1, 'val2':val2})

and then

result = pd.concat(result)

produces the desired DataFrame.

---

Not that you could drop the last level of the MultiIndex:

In [91]: result.index = result.index.droplevel(level=-1)

In [92]: result
Out[92]: 
              date  val1  val2
A America  2017-39  10.0   5.0
  America  2017-40   6.0   7.0
  America  2017-41   6.0   8.0
  America  2017-42   6.0   9.0
  America  2017-43   1.0   1.0
B Britan   2017-38  41.0  40.0
  Britan   2017-39  27.0  38.0
  Britan   2017-40  38.0  28.0
  Britan   2017-41  36.0  27.0
  Britan   2017-42  33.0  23.0
  Britan   2017-43  41.0  65.0
  Britan   2017-44   8.0   4.0

but I wouldn't recommend this since it makes the index non-unique:

In [96]: result.index.is_unique
Out[96]: False

and this can cause future difficulties since some Pandas operations only work on DataFrames with unique indexes.