Counting values

Question

I work with Pandas data frame. I want to count data by one column. You can see example below:

data = {'name': ['Company1', 'Company2', 'Company1', 'Company2', 'Company1'], 'employee': [1, 1, 1, 1, 1], } df = pd.DataFrame(data, columns = ['name', 'employee']) df

Now I want to count all employees from column employee into new column. I try to do this with this line of code

 df['employees']=df.groupby(['name']).agg({'employee':np.count,})

But after execution this line of code I got this error:

module 'numpy' has no attribute 'count'

So can anybody help me how to solve this problem ?

Will you please add a sample dataframe containing your expected output? — user17242583
– user17242583, Commented Jan 30, 2022 at 18:44

user17242583 · Accepted Answer · 2022-01-30 18:48:07Z

4

Are you looking for something like this?

df['employee'] = df.groupby('name')['employee'].transform('count')

Output:

>>> df name employee 0 Company1 3 1 Company2 2 2 Company1 3 3 Company2 2 4 Company1 3

answered Jan 30, 2022 at 18:48

user17242583

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2 Comments

gilf0yle Over a year ago

does using map slow down the process anyway

user17242583 Over a year ago

@gilf0yle I'm not really sure...I once saw a post where replace was faster than map, but I'm not sure about here. Anyway, your answer is good, I'll upvote it tomorrow since I'm out of votes :|

gilf0yle · Accepted Answer · 2022-01-30 18:59:17Z

My answer is based on my understanding of the question. please be clear and crisp from next time you ask a question. initial Dataframe:

 name employee 0 Company1 1 1 Company2 1 2 Company1 1 3 Company2 1 4 Company1 1

use this

df['employees']=df['name'].map(df.name.value_counts().to_dict())

output:-

 name employee employees 0 Company1 1 3 1 Company2 1 2 2 Company1 1 3 3 Company2 1 2 4 Company1 1 3

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