Count frequency of values in pandas DataFrame column
121,374
Solution 1
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}
Solution 2
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}
Solution 3
You can try this way.
df.stack().value_counts().to_dict()
Solution 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)}
Solution 5
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)
Author by
Kishan Mehta
Curious learner. Believe in hard work. Mostly code in Python, sometimes JS.
Updated on July 05, 2022Comments
-
Kishan Mehta almost 2 years
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 giveskeyError
and alsodf['status'].value_counts
but no use. -
Say OL over 2 yearsWhy do we stack?
-
Say OL over 2 yearsNice solution - using only pandas