How to perform bincount on an array of strings?

user_3564164 0
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I have NumPy array containing string values.

For instance: ["bus", "bar", "bar", "café" .....]

What is the best way of counting the number of occurrences of each element in my array. My current solution is:

# my_list contains my data.
bincount = []
for name in set(my_list.tolist()):
    count = sum([1 for elt in my_list if elt == name])
    bincount.append(count)

I have tried bincount but it does not work with this type of data.

Do you know a better solution?

pandas numpy arrays python string
2 Answers

np.unique

l = ['bus', 'bar', 'bar', 'café', 'bus', 'bar', 'café']
a, b = np.unique(l, return_counts=True)

a
# array(['bar', 'bus', 'café'], dtype='<U4')

b
# array([3, 2, 2])

pd.value_counts

pd.value_counts(l)

bar     3
bus     2
café    2
dtype: int64

# <=0.23
pd.value_counts(l).values
# 0.24+
pd.value_counts(l).to_numpy()
# array([3, 2, 2])

Make sure pandas is imported (import pandas as pd).

pd.factorize

np.bincount(pd.factorize(l)[0])
# array([2, 3, 2])

This converts the string to numeric categories (or factors, if you prefer), and counts them.

pd.get_dummies

pd.get_dummies(l).sum()

bar     3
bus     2
café    2
dtype: int64

Slightly roundabout, but interesting nevertheless.

user_49090870

You can also use dictionary in python.

result_dict = {}
l = ['bus', 'bar', 'bar', 'café', 'bus', 'bar', 'café']
for ele in l:
    result_dict[ele] = 1 if ele not in result_dict else result_dict[ele] + 1

Then print the result:

for key in result_dict:
    print key, result_dict[key]

reuslt:

bus 2
bar 3
café 2
user_60889380
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