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Asked 1 month ago by UranianRanger422

From Stack Overflow

Why does modifying a numpy array of headers affect the DataFrame in Python 3.12/3.13?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I read an Excel file into a pandas DataFrame where the first row contains header information.

I extract the headers into two variables: one as a pandas Index (using df.keys()[1:]) and another as a numpy array (using np.array(df.keys()[1:]), stored in header_legend). I then apply the replace method to modify the text in header_legend. In Python 3.7 the DataFrame’s header remains unchanged, but in Python 3.12 and 3.13, modifying header_legend also changes the DataFrame’s headers (i.e., the values from df.keys()[1:]).

Below is a code example that demonstrates the behavior:

PYTHON
import pandas as pd
import numpy as np

df = pd.read_excel("path to excelfile")

# Based on header, I read in columns from the dataframe and do stuff with them.
header = df.keys()[1:]  # Yes, I want to skip the first cell in the header.

# I use header_legend to make a nice legend in my plots.
header_legend = np.array(df.keys()[1:])

for i in range(0, len(header_legend)):
    header_legend[i] = header_legend[i].replace("word1_word2", "word1 word2")

print(header)
print(header_legend)

An example CSV file (convert it to XLSX format) is as follows:

CSV
word_skip,word1_word2,word3_word4,word5_word6
1,10,100,1000
2,20,2000,2000
3,30,300,3000
4,40,400,4000
5,50,500,5000

I would like to understand why using replace on header_legend in Python 3.12/3.13 inadvertently modifies header and, consequently, the DataFrame’s headers. How can I prevent this unwanted side effect?

Comments 0

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Answers 2

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2 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is an explanation of what’s happening and how to work around it.

What’s Happening

When you do
header = df.keys()[1:]
you get a pandas Index object containing your column names. This Index is backed by an underlying numpy array.
When you create
header_legend = np.array(df.keys()[1:])
you get an object array whose elements are references to the same string objects contained in the Index. In older Python or older pandas versions (e.g. on Python 3.7) converting the Index sometimes resulted in a full copy of the elements. In newer versions (running on Python 3.12 or 3.13) the conversion does not perform a deep copy of the objects; the array’s elements are the same objects in the Index.
Later, when you do:
```
PYTHON
for i in range(len(header_legend)):
    header_legend[i] = header_legend[i].replace("word1_word2", "word1 word2")
```
you are reassigning the elements of the numpy array to new string objects. Since the pandas Index (and hence df.keys()) was effectively sharing the same underlying array, the changes “leak” back into the Index.

Why the Difference Between Python Versions

The observed behavior change isn’t due solely to the Python version but has to do with changes in how pandas (and/or numpy) handle the conversion from an Index into a numpy array. In Python 3.7 (with your pandas version) the conversion created a copy, so modifying the numpy array didn’t affect the Index. In Python 3.12/3.13 the conversion shares the underlying objects, so when you reassign an element in the numpy array, you see the change in df.keys() too.

How to Avoid the Problem

If you want to modify a copy of the headers while leaving the DataFrame’s Index unchanged, make sure to explicitly request a copy. For example, you could either create a Python list (which doesn’t share memory with the Index) or instruct numpy to make a copy:

Option 1: Use a List Copy

PYTHON
header_legend = list(df.keys()[1:])
for i in range(len(header_legend)):
    header_legend[i] = header_legend[i].replace("word1_word2", "word1 word2")

Option 2: Use numpy’s copy parameter

PYTHON
header_legend = np.array(df.keys()[1:], copy=True)
for i in range(len(header_legend)):
    header_legend[i] = header_legend[i].replace("word1_word2", "word1 word2")

Either approach will ensure that modifying header_legend does not affect the DataFrame’s header stored in df.keys().

Summary

The “side‐effect” occurs because in newer Python versions the numpy array conversion of a pandas Index does not create a deep copy—so both variables end up pointing to the same objects. Explicitly copying the data avoids unintended modifications.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

No comments yet.

Answer by InterstellarWayfarer969 • 1 month ago

You seem to be relying on a view of the Index, this is generally a bad practice and prone to hidden issues like yours.

You can be explicit, modify your names and reassign them to the DataFrame:

PYTHON
header = list(df)
header[1:] = [c.replace('word1_word2', 'word1 word2') for c in header[1:]]
df.columns = header

Or use rename with a dictionary (if the column names are unique):

PYTHON
df.rename(columns={c: c.replace('word1_word2', 'word1 word2')
                   for c in df.columns[1:]}, inplace=True)

Output:

   word_skip  word1 word2  word3_word4  word5_word6
0          1           10          100         1000
1          2           20          200         2000
2          3           30          300         3000
3          4           40          400         4000
4          5           50          500         5000

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Discussion

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Why does modifying a numpy array of headers affect the DataFrame in Python 3.12/3.13?

2 Answers

What’s Happening

Why the Difference Between Python Versions

How to Avoid the Problem

Option 1: Use a List Copy

Option 2: Use numpy’s copy parameter

Summary

Discussion

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