How to convert index of a pandas dataframe into a column?

If you want to make the existing index of a DataFrame into a regular column, the most common approach is to use reset_index(). By default, this method creates a new column for the current index and assigns the DataFrame a fresh range-based index:

import pandas as pd

df = pd.DataFrame({
    "A": [1, 2, 3],
    "B": [4, 5, 6]
}, index=["row1", "row2", "row3"])

print("Original DataFrame:\n", df)

df_reset = df.reset_index()
print("\nDataFrame after resetting index:\n", df_reset)

1. df.reset_index() returns a new DataFrame where the old index is now a regular column (named "index" if your index was unnamed).
2. If you want the change to apply in place and not return a new DataFrame, you can do:

   df.reset_index(inplace=True)

3. If your original index has a name, Pandas will use that name as the column heading. Otherwise, it defaults to "index".

Alternate Method: Manually Assign the Index to a New Column

You can also explicitly assign the index to a column:

df["old_index"] = df.index
df.reset_index(drop=True, inplace=True)

1. df["old_index"] = df.index creates a new column "old_index" using the current index.
2. df.reset_index(drop=True) discards the original index, replacing it with a standard integer index (0, 1, 2, …).

This approach might be useful if you want a custom name for the new column or if you only want to store the index without discarding the existing one entirely.

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By converting the index to a column appropriately, you’ll keep your DataFrame structure flexible for merging, filtering, or exporting to external formats where a purely numeric or labeled index may be less intuitive. Happy data wrangling!