Construct a Pandas multiindex dataframes from a bunch of individual series

Question

I have calculated a bunch of individual Pandas Series which have a common index. I would like to construct a Pandas multiindex frame from them. Below is my desired structure.

                        X         Y         Z       
DATE                                                      
2018-01-01 A           NaN       NaN       NaN      
           B           NaN       NaN       NaN      
           C           NaN       NaN       NaN      
2018-01-02 A           NaN       NaN       NaN       
           B           NaN       NaN       NaN      
           C           NaN       NaN       NaN       

So (:, A, X) would be one Series. (:, A, Y) another and so on. How do I go about it?

Below is what I have

import pandas as pd
import numpy as np
idx = pd.date_range("20180101", periods=10)
s_1 = pd.Series(np.random.randint(0,10,size=10), index=idx)
s_2 = pd.Series(np.random.randint(0,10,size=10), index=idx)
s_3 = .... all the way to s9

EDIT: Say I want to map s_1 to (A,X), s_2 to (A,Y), s_3 to (A,Z), s_4 to (B,X), etc.

Answer 1

You need to first add some information to the Series you provided, namely, the column and multiindex level they belong to:

def add_idx_and_name(s, idx_name, col_name):
    #Create multi-index DataFrames from s
    s = s.reset_index()
    s['idx'] = idx_name
    s = s.set_index(['index', 'idx'])
    s.rename(columns={0: col_name}, inplace=True)

    return s

Then add this information to your series (they are now DataFrames):

s_1 = add_idx_and_name(s_1, 'A', 'X')
s_2 = add_idx_and_name(s_2, 'A', 'Y')
s_3 = add_idx_and_name(s_3, 'A', 'Z')
s_4 = add_idx_and_name(s_4, 'B', 'X')
....

Then concatenate

pd.concat([pd.concat([s_1, s_2, s_3], axis=1), 
           pd.concat([s_4, s_5, s_6], axis=1),
           pd.concat([s_7, s_8, s_9], axis=1)]).sort_index()

Outputs (I used np.random.seed(123))

                X  Y  Z
index      idx         
2018-01-01 A    2  9  7
           B    9  3  0
           C    2  0  2
2018-01-02 A    2  0  3
           B    3  5  6
           C    4  8  3
2018-01-03 A    6  0  2
           B    4  0  4
           C    8  1  3
...