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I need to forward-fill nan values in a numpy array along the columns (axis=0). I am looking for a pure numpy solution that yields the same result as pd.fillna(method="ffill").

import numpy as np import pandas as pd arr = np.array( [ [5, np.nan, 3], [4, np.nan, np.nan], [6, 2, np.nan], [2, np.nan, 6], ] ) expected = pd.DataFrame(arr).fillna(method="ffill", axis=0) # I need this line in pure numpy print(f"Original array:\n {arr}\n") print(f"Expected array:\n {expected.values}\n") Original array: [[ 5. nan 3.] [ 4. nan nan] [ 6. 2. nan] [ 2. nan 6.]] Expected array: [[ 5. nan 3.] [ 4. nan 3.] [ 6. 2. 3.] [ 2. 2. 6.]]
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  • I had this same issue. Frustrated the hell out of me. Ended up using a chained pandas instruction. [pd.Series(array).bfill().to_numpy()]. Commented Jul 30, 2023 at 7:27

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No inbuilt function in numpy to do this. Below simple code will generate desired result using numpy array only.

row,col = arr.shape mask = np.isnan(arr) for i in range(1,row): for j in range(col): if mask[i][j]: arr[i][j] =arr[i-1][j]
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Bottleneck push function is a good option to forward fill. It's normally used internally in packages like Xarray.

from bottleneck import push push(arr, axis=0)
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Usually this answer would be sufficient, however, I am not able to install the package in my environment. Hence, I am looking for a pure numpy (or maybe numba) solution.
This link might help with various : stackoverflow.com/questions/41190852/…

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