CODEWITHSUNDEEP
python-3.xpandasdataframe
Anand Rajan
Anand Rajan

Reputation: 25

How to rearrange a pandas dataframe having N columns and append N columns together in python?

I have a dataframe df as shown below,A index,B Index and C Index appear as headers and each of them have sub header as the Last price

Input

         A index              B Index             C Index
Date    Last Price  Date    Last Price  Date    Last Price
1/10/2021   12    1/11/2021    46       2/9/2021    67
2/10/2021   13    2/11/2021    51       3/9/2021    70
3/10/2021   14    3/11/2021    62       4/9/2021    73
4/10/2021   15    4/11/2021    47       5/9/2021    76
5/10/2021   16    5/11/2021    51       6/9/2021    79
6/10/2021   17    6/11/2021    22       7/9/2021    82
7/10/2021   18    7/11/2021    29       8/9/2021    85

I want to transform the to the below dataframe.

Expected Output

Date        Index Name   Last Price
1/10/2021   A index      12
2/10/2021   A index      13
3/10/2021   A index      14
4/10/2021   A index      15
5/10/2021   A index      16
6/10/2021   A index      17
7/10/2021   A index      18
1/11/2021   B Index      46
2/11/2021   B Index      51
3/11/2021   B Index      62
4/11/2021   B Index      47
5/11/2021   B Index      51
6/11/2021   B Index      22
7/11/2021   B Index      29
2/9/2021    C Index      67
3/9/2021    C Index      70
4/9/2021    C Index      73
5/9/2021    C Index      76
6/9/2021    C Index      79
7/9/2021    C Index      82
8/9/2021    C Index      85

How can this be done in pandas dataframe?

Upvotes: 1

Views: 32

Answers (1)

piterbarg
piterbarg

Reputation: 8219

The structure of your df is not clear from your output. It would be useful if you provided Python code that creates an example, or at the very lest the output of df.columns. Now let us assume it is a 2-level multindex created as such:

columns = pd.MultiIndex.from_tuples([('A index','Date'), ('A index','Last Price'),('B index','Date'), ('B index','Last Price'),('C index','Date'), ('C index','Last Price')])
data = [
    ['1/10/2021',   12,    '1/11/2021',    46,       '2/9/2021',    67],
    ['2/10/2021',   13,    '2/11/2021',    51,       '3/9/2021',    70],
    ['3/10/2021',   14,    '3/11/2021',    62,       '4/9/2021',    73],
    ['4/10/2021',   15,    '4/11/2021',    47,       '5/9/2021',    76],
    ['5/10/2021',   16,    '5/11/2021',    51,       '6/9/2021',    79],
    ['6/10/2021',   17,    '6/11/2021',    22,       '7/9/2021',    82],
    ['7/10/2021',   18,    '7/11/2021',    29,       '8/9/2021',    85],
]

df = pd.DataFrame(columns = columns, data = data)

Then what you are trying to do is basically an application of .stack with some re-arrangement after:

(df.stack(level = 0)
    .reset_index(level=1)
    .rename(columns = {'level_1':'Index Name'})
    .sort_values(['Index Name','Date'])
)

this produces

Index Name  Date    Last Price
0   A index 1/10/2021   12
1   A index 2/10/2021   13
2   A index 3/10/2021   14
3   A index 4/10/2021   15
4   A index 5/10/2021   16
5   A index 6/10/2021   17
6   A index 7/10/2021   18
0   B index 1/11/2021   46
1   B index 2/11/2021   51
2   B index 3/11/2021   62
3   B index 4/11/2021   47
4   B index 5/11/2021   51
5   B index 6/11/2021   22
6   B index 7/11/2021   29
0   C index 2/9/2021    67
1   C index 3/9/2021    70
2   C index 4/9/2021    73
3   C index 5/9/2021    76
4   C index 6/9/2021    79
5   C index 7/9/2021    82
6   C index 8/9/2021    85

Upvotes: 2

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