Pandas: calculate average from numpy array for each row in column
I have a pandas dataframe, df , that contains columns where each row contains a numpy array of different sizes, eg.
column A
0 np.array([1,2,3])
1 np.array([1,2,3,4])
2 np.array([1,2])
Is there a built-in pandas function that will return the average of each array, i.e. rows, for the whole column? Something like:
df.A.mean()
But it works on every line. Thanks for any help.
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1 answer
You can use df.<column>.map
to apply a function to every item in a column:
df = pd.DataFrame({'a':
[np.array([1, 2, 3]),
np.array([4, 5, 6, 7]),
np.array([7, 8])]
})
df
Out[8]:
a
0 [1, 2, 3]
1 [4, 5, 6, 7]
2 [7, 8]
df['a'].map(lambda x: x.mean())
Out[9]:
0 2.0
1 5.5
2 7.5
Name: a, dtype: float64
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