Counting the occurences a particular item in a column

Question

ctr = df['gender'].value_counts()

ctr

**Output:**

female       102

male          83

nonbinary      5

trans          2

Name: gender, dtype: int64

This is the output I got and I understand that this is how the output should look like. But I'm interested to know the frequency of a particular item under the 'gender' column, in this case for example; female or male, and want to store each of those frequencies to two separate variables. I'm relatively a newbie in this field. Any help is appreciated.

score 0 · Answer 1 · answered Nov 28 '20 at 13:11

0

pd.DataFrame({"a":["a","a","b"]}).a.value_counts(normalize=True)

All you have to do is add normalize. I am pretty sure the question is a duplicate but I cant find it.

answered Nov 28 '20 at 13:11

ombk

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score 0 · Answer 2 · edited Nov 30 '20 at 04:57

0

I found this as a solution to what I was looking for....

fe =len(df[df['gender']=='female'])

fe

OUTPUT :102

edited Nov 30 '20 at 04:57

Joe Ferndz

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answered Nov 30 '20 at 04:41

KK47

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use `value_counts()` instead – Joe Ferndz Nov 30 '20 at 04:58

score 0 · Accepted Answer · answered Nov 30 '20 at 05:12

The fastest way to do this will be to use value_counts().

As you already know, df['gender'].value_counts() will give you the distribution. However, you can also specify which one you are looking for.

import pandas as pd
df = pd.DataFrame({'gender':['male','female','trans','nonbinary',
                             'male','female','male','female',
                             'male','female','male','female',
                             'male','female','female','trans','nonbinary',
                             'male','male','male','trans',
                             'male','male','female','female',
                             'male','male','nonbinary','trans','male']})
#print (df)
m = df['gender'].value_counts().male
f = df['gender'].value_counts().female
t = df['gender'].value_counts().trans
b = df['gender'].value_counts().nonbinary

print (df['gender'].value_counts())
print ('male      :', m)
print ('female    :', f)
print ('trans     :', t)
print ('nonbinary :', b)

The output of this will be:

values_counts() will give you an output as:

male         14
female        9
trans         4
nonbinary     3
Name: gender, dtype: int64

However, specific prints will give you output as:

male      : 14
female    : 9
trans     : 4
nonbinary : 3

Counting the occurences a particular item in a column

3 Answers3