One way to do this would be to take the column names as a separate list and then only give from 1st index for pd.DataFrame -

In [8]: data = [['Name','Rank','Complete'],
   ...:                ['one', 1, 1],
   ...:                ['two', 2, 1],
   ...:                ['three', 3, 1],
   ...:                ['four', 4, 1],
   ...:                ['five', 5, 1]]

In [10]: df = pd.DataFrame(data[1:],columns=data[0])

In [11]: df
Out[11]:
    Name  Rank  Complete
0    one     1         1
1    two     2         1
2  three     3         1
3   four     4         1
4   five     5         1

If you want to set the first column Name column as index, use the .set_index() method and send in the column to use for index. Example -

In [16]: df = pd.DataFrame(data[1:],columns=data[0]).set_index('Name')

In [17]: df
Out[17]:
       Rank  Complete
Name
one       1         1
two       2         1
three     3         1
four      4         1
five      5         1

Here's one way using itertools, given input list L:

from itertools import chain, product, repeat

col, value = zip(*(list(i) for item in L for i in product(item[1], item[2])))
cat = list(chain.from_iterable(repeat(i, len(j) * len(k)) for i, j, k in L))

df = pd.DataFrame({'Cat': cat, 'Column': col, 'Value': value})
df = df.sort_values(['Cat', 'Column', 'Value']).reset_index(drop=True)

print(df)

   Cat Column  Value
0   R1      a   20.0
1   R1      a   40.0
2   R1      a   50.0
3   R1      a   60.0
4   R1      a  750.0
5   R1      b   20.0
...
39  R3      x   10.0
40  R3      x   12.5
41  R3      x   45.0

Hello, I'm a Reddit bot who's here to help people nicely format their coding questions. This makes it as easy as possible for people to read your post and help you.

I think I have detected some formatting issues with your submission:

1. Python code found in submission text that's not formatted as code.

If I am correct, please edit the text in your post and try to follow these instructions to fix up your post's formatting.

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Is it what you're trying to achieve?

Try it online!

import pandas as pd
from pandas import Timestamp

l = [
 [[1, Timestamp('2019-01-22 00:54:27')]],
 [[2, Timestamp('2019-01-22 08:37:04')]],
 [[3, Timestamp('2019-01-22 10:57:40')],
  [4, Timestamp('2019-01-22 10:57:43')]],
 [[5, Timestamp('2019-01-22 11:09:07')],
  [6, Timestamp('2019-01-22 11:16:18')],
  [7, Timestamp('2019-01-22 11:16:23')],
  [8, Timestamp('2019-01-22 11:16:25')]],
 [[9, Timestamp('2019-01-22 11:35:03')],
  [10, Timestamp('2019-01-22 11:35:35')]],
]

df = pd.DataFrame(
    [e1 + [g] for g, e0 in enumerate(l) for e1 in e0],
    columns = ['id', 'time', 'group'],
)

print('As one DataFrame:\n', df)

dfs = [
    pd.DataFrame(
        [e1 + [g] for e1 in e0],
        columns = ['id', 'time', 'group'],
    )
    for g, e0 in enumerate(l)
]

print('\nAs separate DataFrames:')

for df in dfs:
    print('---------------------')
    print(df)

Outputs

As one DataFrame:
    id                time  group
0   1 2019-01-22 00:54:27      0
1   2 2019-01-22 08:37:04      1
2   3 2019-01-22 10:57:40      2
3   4 2019-01-22 10:57:43      2
4   5 2019-01-22 11:09:07      3
5   6 2019-01-22 11:16:18      3
6   7 2019-01-22 11:16:23      3
7   8 2019-01-22 11:16:25      3
8   9 2019-01-22 11:35:03      4
9  10 2019-01-22 11:35:35      4

As separate DataFrames:
---------------------
   id                time  group
0   1 2019-01-22 00:54:27      0
---------------------
   id                time  group
0   2 2019-01-22 08:37:04      1
---------------------
   id                time  group
0   3 2019-01-22 10:57:40      2
1   4 2019-01-22 10:57:43      2
---------------------
   id                time  group
0   5 2019-01-22 11:09:07      3
1   6 2019-01-22 11:16:18      3
2   7 2019-01-22 11:16:23      3
3   8 2019-01-22 11:16:25      3
---------------------
   id                time  group
0   9 2019-01-22 11:35:03      4
1  10 2019-01-22 11:35:35      4

I think the simple will remain for loops.

1. First, select all keys from the given features.

   1. For all elements, we use str.split and extract the first element.
   2. Then, because we only want unique keys, we use set. Then, we convert it back to list and sort the keys using sorted (here some details if needed).

The first is sum up in:

keys = sorted(list(set([elt.split(':')[0] for l in features for elt in l])))

2. Create an empty dict from the above keys and initialize all keys with an empty list:

data = {k:[] for k in keys}

3. Iterate over all the features:

   1. Save all the key features visited in a seen variable
   2. Add all featured keys and values
   3. Complete the data with keys not in the current features

4. Eventually, create the dataframe from out dict using the default constructor [pd.DataFrame()] (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).

5. Correctly format columns name using .columns and string formatting (format). Here are some good explanations.

Talked enough, here the full code + illustration:

features = [["0:0.084556", "1:0.138594", "2:0.094304"],
    ["0:0.101468", "4:0.138594", "5:0.377215"],
    ["0:0.135290", "2:0.277187", "3:0.141456"]
    ]

# Step 1
keys = sorted(list(set([elt.split(':')[0] for l in features for elt in l])))
print(keys)
# ['0', '1', '2', '3', '4', '5']

# Step 2
data = {k:[] for k in keys}
print(data)
# {'0': [], '1': [], '2': [], '3': [], '4': [], '5': []}

# Step 3
for sub in features:
    # Step 3.1
    seen = []
    # Step 3.2
    for l in sub:
        k2, v = l.split(":")        # Get key and value
        data[k2].append(float(v))   # Append current value to data
        seen.append(k2)             # Set the key as seen

    # Step 3.3
    for k in keys:                  # For all data keys
        if k not in seen:           # If not seen
            data[k].append(0)       # Add 0

print(data)
# {'0': [0.084556, 0.101468, 0.13529], 
#     '1': [0.138594, 0, 0], 
#     '2': [0.094304, 0,0.277187],
#     '3': [0, 0, 0.141456],
#     '4': [0, 0.138594, 0],
#     '5': [0, 0.377215, 0]
# }

# Step 4
df = pd.DataFrame(data)
print(df)
#           0         1         2         3         4         5
# 0  0.084556  0.138594  0.094304  0.000000  0.000000  0.000000
# 1  0.101468  0.000000  0.000000  0.000000  0.138594  0.377215
# 2  0.135290  0.000000  0.277187  0.141456  0.000000  0.000000

# Step 5
df.columns = ["f_{:04d}".format(int(val)) for val in df.columns]
print(df)
#      f_0000    f_0001    f_0002    f_0003    f_0004    f_0005
# 0  0.084556  0.138594  0.094304  0.000000  0.000000  0.000000
# 1  0.101468  0.000000  0.000000  0.000000  0.138594  0.377215
# 2  0.135290  0.000000  0.277187  0.141456  0.000000  0.000000