如何将DataFrame列标题转换为行？df.transpose()后列标题变为0、1、2的问题排查与解决

I get it, transposing your DataFrame and ending up with column names like 0, 1, 2 instead of the country names (like Angola) is super frustrating. Let's break down why this happens and how to fix it properly.

Why the Default Transpose Fails #

Your original DataFrame structure looks something like this:

When you run df.transpose() directly, Pandas uses the default numeric row indexes (0, 1, 2...) as the new column names. What you actually want is the values from the Calendar years column (the country names) to become your new columns.

Step-by-Step Solution #

Here's how to fix this in a few simple steps:

1. Set the country column as the DataFrame index
   First, we need to tell Pandas that the Calendar years column contains our row labels (country names) instead of regular data. This ensures those names get carried over during transpose.

2. Transpose the DataFrame
   Now when you transpose, the country names will become the new column headers, and the years will become the row index.

3. Clean up the index to make it a proper column
   Finally, convert the year-based row index into a dedicated Calendar years column to match your desired output.

Full Code Example

import pandas as pd

# Simulate your original dataset
original_data = {
    "Calendar years": ["Angola", "Botswana", "Cameroon"],
    "1990": [84, 65, 72],
    "1991": [71, 59, 68],
    "1992": [80, 62, 70]
}
df = pd.DataFrame(original_data)

# Fix the transpose issue
transposed_df = (
    df
    .set_index("Calendar years")  # Use country names as row index
    .transpose()                  # Flip rows and columns
    .reset_index()                # Turn year index into a column
    .rename(columns={"index": "Calendar years"})  # Rename to match your desired header
)

print(transposed_df)

Output You'll Get #

This will give you exactly the structure you want:

Quick Chain Version #

If you prefer concise code, you can combine all steps into one line without losing readability:

transposed_df = df.set_index("Calendar years").transpose().reset_index().rename(columns={"index": "Calendar years"})

That's it! This approach ensures your column names stay meaningful instead of turning into a random numeric sequence.

内容的提问来源于stack exchange，提问作者wamika