通过调整从元组列表中获取的索引来创建数据框

Question

I am trying to create a dataframe from the following list of tuples. The first item in the tuple is ID , second values is a list of values and the third is the lag . This lag defines how many indices I need to shift in either direction of the first tuple to get the dataframe.

mytup = [(111, [1,2,3,4,5], 0), (222, [33,44,55,66], 2), (333, [0,11,22,33], -1)]


ID  values           lag
111 1,2,3,4,5        0
222 33,44,55,66      2
333 0,11,22,33      -1

The result that is created as dataframe is given below. The first row is the header in this dataframe. The lag is always in reference to the first column ( 111 ).

111 222 333
nan nan 0
1   nan 11
2   nan 22
3   33  33
4   44  nan
5   55  nan
nan 66  nan

The way I want to populate the dataframe is to start with first tuple. Then I take one tuple from the rest, one at a time and populate the dataframe introducing nan .

I will have this in dataframe once I have already seen the second tuple:

111    222
1   nan
2   nan
3   33
4   44
5   55
nan 66

The third tuple has negative lag of -1 , therefore I want the previous dataframe to move down one position and create the final dataframe which I reproduce again.

111 222 333
nan nan 0
1   nan 11
2   nan 22
3   33  33
4   44  nan
5   55  nan
nan 66  nan

Edit:

As @cphlewis pointed out, the output will be dependent on the order in which columns are added. In my case the lag is always relative to the first (original) vector. Therefore, the final result will remain same irrespective of the order.

Answer 1

Using this (from the pandas.Series docstring):

Operations between Series (+, -, /, *, **) align values based on their associated index values-- they need not be the same length. The result index will be the sorted union of the two indexes.

import pandas as pd
from numpy import arange
#mytup = [(111, [1,2,3,4,5], 0), (222, [33,44,55,66], 2), (333, [0,11,22,33], -1)]
mytup = [(111, [1,2,3,4,5], 0),
         (222, [33,44,55,66], 2),
         (444, [1,2,3,4,5], 0),
         (333, [0,11,22,33], -1),
         ('a', [5,6,7], -2)]

def SfromTuple(row):
    name, data, shift = row
    return pd.Series(data, index = arange(shift, len(data) + shift))

reindexed = pd.concat([SfromTuple(row) for row in mytup], axis=1)
reindexed.columns = [x[0] for x in mytup]
print(reindexed)

result from original mytup :

 0 111 222 333 -1 NaN NaN 0 0 1 NaN 11 1 2 NaN 22 2 3 33 33 3 4 44 NaN 4 5 55 NaN 5 NaN 66 NaN 

from the longer mytup added above:

 0 111 222 444 333 a -2 NaN NaN NaN NaN 5 -1 NaN NaN NaN 0 6 0 1 NaN 1 11 7 1 2 NaN 2 22 NaN 2 3 33 3 33 NaN 3 4 44 4 NaN NaN 4 5 55 5 NaN NaN 5 NaN 66 NaN NaN NaN