Efficient way to concatenate dataframe with its columns reversed

user_10693596 0
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One fairly recurring pattern I have is creating a dataframe that combines another dataframe with its columns reversed. Here's a small example:

import pandas as pd

df = pd.DataFrame({"a": range(5), "b": range(6, 1, -1)})
combined = pd.concat([df, df.rename(columns={"a": "b", "b": "a"})], ignore_index=True)

Is there a more efficient approach to achieving this operation (esp. with many, many rows)?

pandas dataframe python
2 Answers

Convert values to numpy array and use np.concatenate with reverse order of array by indexing a[:, ::-1], last pass to DataFrame constructor:

a = df.to_numpy()
combined = pd.DataFrame(np.concatenate([a, a[:, ::-1]]), columns=df.columns)
print(combined)
   a  b
0  0  6
1  1  5
2  2  4
3  3  3
4  4  2
5  6  0
6  5  1
7  4  2
8  3  3
9  2  4
user_29010020

You can use the underlying numpy array and vstack on the array and its reversed version, then generate a new DataFrame:

import numpy as np

a = df.to_numpy()
pd.DataFrame(np.vstack([a, a[:, ::-1]]), columns=df.columns)
user_163434640
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