从现有列生成新的 dataframe

Question

I am trying to create a new column D from existing A, B, C column.

• first value in Col D is A1
• second value in Col D is B2
• third value in Col D is C3
• fourth value in Col D is A4
• fifth value in Col D is B5
• sixth value in Col D is C6

following this pattern, the rest of the values in Col D is iteration of this pattern. Please reference the graph.

Any code ideas?

Click the link to see the picture

Answer 1

Here's a solution (in a couple of steps for better clarity):

df = pd.DataFrame({"a": range(0, 10), "b": range(10, 20), "c": range(20, 30)})

df["inx"] = range(len(df))
df["d"] = np.where(df.inx % 3 == 0, df["a"], 
         np.where(df.inx % 3 == 1, df["b"], df["c"]))
df = df.drop("inx", axis="columns")

Result:

   a   b   c   d
0  0  10  20   0
1  1  11  21  11
2  2  12  22  22
3  3  13  23   3
4  4  14  24  14
5  5  15  25  25
6  6  16  26   6
7  7  17  27  17
8  8  18  28  28
9  9  19  29   9

Answer 2

We can use, pd.concat to create a dataframe having the columns A , B , C with the values in the columns are filled accordingly by consecutively iterating through the columns and selecting the next value from that column as per the conditions then use DataFrame.agg to agg the dataframe by droping the NaN values:

d = pd.concat([df[col].iloc[i::df.columns.size] for i, col in enumerate(df.columns)], axis=1)
df['D'] = d.agg(lambda s: s.dropna().iloc[0], axis=1)

Result:

# print(df)
    A   B   C   D
0  A1  B1  C1  A1
1  A2  B2  C2  B2
2  A3  B3  C3  C3
3  A4  B4  C4  A4
4  A5  B5  C5  B5
5  A6  B6  C6  C6

Answer 3

Here's another way:

import pandas as pd
import numpy as np

#Create input dataframe:
df = pd.DataFrame(index=[*'123456'], columns=[*'ABC'])
df = df.apply(lambda x: x.name+x.index)
df

Input Dataframe:

    A   B   C
1  A1  B1  C1
2  A2  B2  C2
3  A3  B3  C3
4  A4  B4  C4
5  A5  B5  C5
6  A6  B6  C6

Use cumsum with lookup :

s = ((df['A'].notna().cumsum()-1) % df.shape[1])
df['d'] = df.lookup(df.index, df.columns[s])
df

Output:

    A   B   C   d
1  A1  B1  C1  A1
2  A2  B2  C2  B2
3  A3  B3  C3  C3
4  A4  B4  C4  A4
5  A5  B5  C5  B5
6  A6  B6  C6  C6