Assigning numpy array based on condition

Question

What is a better way to write this numpy python code?

age[age < 20.0] = 0.0
age[age > 0.0] = 1.0
mature = age

Here, mature contains 1.0 for all values of age > 20.0, else 0.0

do you want to change age?

Ruggero Turra
– Ruggero Turra

2016-02-08 23:54:31 +00:00
Commented Feb 8, 2016 at 23:54 — Ruggero Turra
– Ruggero Turra, Commented Feb 8, 2016 at 23:54
i do not care if it changes

user308827
– user308827

2016-02-09 00:02:39 +00:00
Commented Feb 9, 2016 at 0:02 — user308827
– user308827, Commented Feb 9, 2016 at 0:02

unutbu · Accepted Answer · 2016-02-09 00:19:38Z

4

mature = age = (age > 20.0).astype(float)

age > 20.0 is a boolean array. The astype(float) converts the array to float dtype, which changes True to 1.0 and False to 0.0. Note that this also converts NaNs to 0.

To preserve NaNs, like your original code, you could use np.clip:

mature = age = np.clip(age-20, 0, 1)

For example,

In [90]: age = np.array([np.nan, 30, 20, 10])

In [91]: (age > 20.0).astype(float)
Out[91]: array([ 0.,  1.,  0.,  0.])

In [92]: np.clip(age-20, 0, 1)
Out[92]: array([ nan,   1.,   0.,   0.])

edited Feb 9, 2016 at 0:19

answered Feb 8, 2016 at 23:21

unutbu

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