Select and/or replace specific array inside pandas dataframe

Question

Here is my reproducible example:

import pandas as pd
import numpy as np
df = pd.DataFrame({'x' : [np.zeros(2), np.array([1,2])], 'y' : [np.array([3,2]),0], 'z' : [np.array([4,5]),np.zeros(2)], 't' : [np.array([3,4]),np.array([4,5])]})

My goal is to change np.zeros(2) to np.Nan so as to be able to compute the mean two-dimensional array for each row excluding 0.

I have tried:

df.replace(np.zeros(2),np.NaN)

df[df.eq(np.zeros(2)).any(axis=1)]

df.where(df == [np.zeros(2)])

df[df == np.zeros(2)]

all of which are expected to worked had the item I am looking not been an array.

Obviously, being new at Python, there must be a concept that I am not grasping.

Asked By: user1627466

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Source

Answer 1

You can’t vectorize with objects as values.

Use applymap and numpy.array_equal:

df[df.applymap(lambda x: np.array_equal(x, np.zeros(2)))] = np.nan

Updated df:

        x       y       z       t
0     NaN  [3, 2]  [4, 5]  [3, 4]
1  [1, 2]       0     NaN  [4, 5]

Alternative with allclose:

df[df.applymap(lambda x: np.allclose(x, 0))] = np.nan

Output:

        x       y       z       t
0     NaN  [3, 2]  [4, 5]  [3, 4]
1  [1, 2]     NaN     NaN  [4, 5]

Answered By: mozway

Select and/or replace specific array inside pandas dataframe

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