I want to count number of times each values is appearing in dataframe.
Here is my dataframe - df:
status
1 N
2 N
3 C
4 N
5 S
6 N
7 N
8 S
9 N
10 N
11 N
12 S
13 N
14 C
15 N
16 N
17 N
18 N
19 S
20 N
I want to dictionary of counts:
ex. counts = {N: 14, C:2, S:4}
I have tried df['status']['N'] but it gives keyError and also df['status'].value_counts but no use.
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}
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)}
See my response in this thread for a Pandas DataFrame output,
count the frequency that a value occurs in a dataframe column
For dictionary output, you can modify as follows:
def column_list_dict(x):
column_list_df = []
for col_name in x.columns:
y = col_name, len(x[col_name].unique())
column_list_df.append(y)
return dict(column_list_df)
