Assigning one column of dataframe to column of another dataframe with disparate indices?

Question

I have dataframes first and second of the same length, where the first one's index is increments of 15 and the second is in increments of 1. I would like to assign one column of first to another of second.

e.g., something like below

import pandas as pd

first = pd.DataFrame({"index": [0, 15, 30], "value": [2.2, 2.2, 2.2]})
second = pd.DataFrame({"value": [3.2, 3.2, 3.2]})
first = first.set_index("index")
first.value = second.value

however, the indices are disparate so the above gives NaNs for first.value after the first row. I think one approach is to call reset_index() prior to assignment, but I believe this is a costly op? Is there an approach that doesn't involve resetting the index?

Ynjxsjmh · Accepted Answer · 2023-01-15 02:27:47Z

2

IIUC, you can assign numpy array to dataframe column

first['value'] = second['value'].values
# or
first['value'] = second['value'].to_numpy()

print(first)

       value
index
0        3.2
15       3.2
30       3.2

answered Jan 15, 2023 at 2:27

Ynjxsjmh

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2 Comments

roulette01 Over a year ago

ah I see, but is calling values or to_numpy() more costly than reset_index()?

Ynjxsjmh Over a year ago

@roulette01 It won't, values wipes index from Series while reset_index() involves index and column conversion.

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