Suppose, I have the following data-set as numpy array:
import numpy as np
x = np.array([0, 5, 10, 15, 20, 25, 30])
y = np.array([0, 0.13157895, 0.31578947, 0.40789474, 0.46052632, 0.5, 0.53947368])
and I want to read only values corresponding to x = 20, 25, 30. How can I read that using numpy ?
numpy.searchsorted can do the job:
idx = np.searchsorted(x,[20,25,30])
part = y[idx]
Note that x has to be sorted. In case x is not sorted try:
idx_sort = np.argsort(x)
xsorted = x[idx_sort]
ysorted = y[idx_sort]
idx = np.searchsorted(xsorted, [20,25,30])
part = y[idx]
We can filter y using zip and list comprehension.
np.array([v for i, v in zip(x, y) if i in [20, 25, 30]])
#array([0.46052632, 0.5 , 0.53947368])
Alternative with pandas.
import pandas as pd
pd.Series(index=x, data=y).loc[[20, 25, 30]].values
An alternative using numpy.any to create the boolean mask:
import numpy as np
x = np.array([0, 5, 10, 15, 20, 25, 30])
y = np.array([0, 0.13157895, 0.31578947, 0.40789474, 0.46052632, 0.5, 0.53947368])
values = [20,25,30]
m = np.any([x==v for v in values], axis=0)
y[m]
array([0.46052632, 0.5 , 0.53947368])
this code will do that:
import numpy as np
ind = [np.where(x == num)[0] for num in [20, 25, 30]]
corresponding = y[ind]
i believe there is no need to explain, but if you need anything, comment please
Not with numpy but with a dict:
import numpy as np
x = np.array([0, 5, 10, 15, 20, 25, 30])
y = np.array([0, 0.13157895, 0.31578947, 0.40789474, 0.46052632, 0.5, 0.53947368])
xy_dict = {}
for j, k in zip(x, y):
xy_dict[j] = k
print(xy_dict)
