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.
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