Lets say i have multiple numpy arrays like this: [1, 2, 4, 7, 1, 4, 6, 8, 1, 8, 2, 5]
I want to count the number of times each item appears in the array, and store the result in a dictionary:
{1: 3, 2: 2, 3: 0, 4: 2, 5: 1, 6: 1, 7: 1, 8: 2}
Is there a faster way to do this than to simply loop over the array and counting the items and storing them in dictionary?
You can do that using count:
MyList = [1, 2, 4, 7, 1, 4, 6, 8, 1, 8, 2, 5]
my_dict = {i:MyList.count(i) for i in MyList}
print(my_dict)
this will definetely work you will find more information here
I think the below one is the easiest way
from collections import Counter
MyList = [1, 2, 4, 7, 1, 4, 6, 8, 1, 8, 2, 5]
print(Counter(MyList))
output would be Counter({1: 3, 2: 2, 4: 2, 8: 2, 7: 1, 6: 1, 5: 1})
Numpy, in its wisdom, has functionality for this:
np.unique(x, return_counts=True)
It does not return a dictionary, but you can easily convert the result into one.
collections.Counter is faster.You can do this in pandas
>>> import pandas as pd
>>> a = pd.Series([1, 2, 4, 7, 1, 4, 6, 8, 1, 8, 2, 5])
>>> a.value_counts().to_dict()
{1: 3, 8: 2, 4: 2, 2: 2, 7: 1, 6: 1, 5: 1}
np.bincount.