Count frequency of values in pandas DataFrame column

You can use value_counts and to_dict:

print df['status'].value_counts()
N    14
S     4
C     2
Name: status, dtype: int64

counts = df['status'].value_counts().to_dict()
print counts
{'S': 4, 'C': 2, 'N': 14}

An alternative one liner using underdog Counter:

In [3]: from collections import Counter

In [4]: dict(Counter(df.status))
Out[4]: {'C': 2, 'N': 14, 'S': 4}

You can try this way.

df.stack().value_counts().to_dict()

Can you convert df into a list?

If so:

a = ['a', 'a', 'a', 'b', 'b', 'c']
c = dict()
for i in set(a):
    c[i] = a.count(i)

Using a dict comprehension:

c = {i: a.count(i) for i in set(a)}