CODEWITHSUNDEEP
pandaspandas-groupby
dsnOwhiskey
dsnOwhiskey

Reputation: 171

How to count the number of times a value occurs in each column (per column), using Pandas?

Is there anyway to use pandas to count the number of times that a certain value occurs in each column?

data = pd.DataFrame({'userID':['Luis', 'Mike', 'Harvey'], 'category1':[True, False, True], 'category2': [True, True, False], 'category3':[False, False, False]})

Let's say I want to count the number of 'True' boolean per category to get a return of:

 Category 1  -- Category 2 -- Category 3
      2             2              0

How would I go about doing this?

Upvotes: 1

Views: 100

Answers (2)

jezrael
jezrael

Reputation: 863481

You can count occurence by sum of boolean mask - True values are processes like 1s:

#count all columns without first to Series
print (data.iloc[:, 1:].sum().astype(int))

category1    2
category2    2
category3    0
dtype: int32

#or to one row DataFrame
df = data.iloc[:, 1:].sum().astype(int).to_frame().T

General solution with eq (==):

val = True
df = data.iloc[:, 1:].eq(val).sum().astype(int).to_frame().T
print (df)

   category1  category2  category3
0          2          2          0

Upvotes: 1

Venkatesh Garnepudi
Venkatesh Garnepudi

Reputation: 316

a = dict(df.category1.value_counts()) This gives you output like {True : 20, False : 15}

Upvotes: 0

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