count on a Boolean variable

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I have a dataframe (events) having several columns: id, ...,click,... The column "click" is a Boolean variable which has value of 'True' or 'False'. I use the code in Python below in order to count the number of 'True' for each individual "id":

click_yes = events.groupby("id")["click"].count(True).reset_index()

It came out an error: TypeError: count() takes 1 positional argument but 2 were given


Example Output:

If the case is like this original dataframe:

   id  click
0   0   True
1   1   False
2   1   True
3   1   True
4   2   True
5   2   False
6   3   False

I want the result is a new dataframe, like this (count on the number of 'True'):

   id  click_count
0   0   1
1   1   2
2   2   1
3   3   0

How to modify my following code (not working) or write a new code to make my expectation happen?

click_yes = events.groupby("id")["click"].count(True).reset_index() Thanks !!!

What is the right way to do this?

Thank you very much!

You can use sum() instead - since True value is 1 and False is 0.

Counting the number of True Booleans in a Python List, I generated 50,000 random booleans and called sum and count on them. Here are my results: >>> a = [bool(  hi , i have a variable named sumClient of Calculation Type = count , i want to count the number of client in My report basing on Boolean variable CL, so i have put in the variable Expression

>>> sum(map(bool,[True, True, False, False, False, True]))
3

Answer is 3, because True == 1

or only Sum:

>>> sum([True, True, False, False, False, True])
3

Or using count():

lst = [True, True, False, False, False, True] 
print(count(lst)

Python, Python3 program to count True booleans in a list. def count(lst):. return sum ( bool (x) for x in lst). # Driver code. lst = [ True , False , True , True , False ]. You could do this in a table visualization by adding Element A and then Count of Element B or Count of Element A. If you wanted a measure, you could do something like: TrueCount = CALCULATE(COUNT([Element A]),FILTER(Table,[Element A] = "True"))

Thanks for all answers! Appreciate it!

If the case is like this:

Original dataframe:

   id  click
0   0   True
1   1   False
2   1   True
3   1   True
4   2   True
5   2   False
6   3   False

I want the result is a new dataframe, like this (count on the number of 'True'):

   id  click_count
0   0   1
1   1   2
2   2   1
3   3   0

How to modify my following code (not working) or write a new code to make my expectation happen?

click_yes = events.groupby("id")["click"].count(True).reset_index()

Thanks !!!

Iteration using a count or Boolean condition, Iteration using a count or Boolean condition. You can instruct a model to iterate until a Boolean or long value variable is false. If you use a long  The column "click" is a Boolean variable which has value of 'True' or 'False'. I use the code in Python below in order to count the number of 'True' for each individual "id": click_yes = events.groupby("id")["click"].count(True).reset_index() It came out an error: TypeError: count() takes 1 positional argument but 2 were given

I assume you are using panda dataframes, so I've added a quick approach. BTW: Actually your use of count is wrong. It can only return the length of something (e. g. a list) but can't be used as a filter.

As you have added the desired output i now understand what you try to achieve. I have added a new snippet. Again I filter for 'True' Values. In the next line I start summing lines depending on it's column content.

import pandas as pd

# Create your list
# initialize list of lists
data = [ [0, True], [1, False], [1, True], [5, True], [2, True], 
         [2, False], [3, False], [2, True], [4, False], [1, True],
         [6, True], [2, True]]

# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['id', 'click'])
df = df.sort_values(by=['id'])


#------------------------------------------------------------------------------#

# Filter for key value true
df = df[df.click == True]
# Merge lines depending of it's column content
filtered =  df.groupby('id').agg({ 'click':'sum'}).reset_index()
# If we need it, rename the column
filtered = filtered.rename(columns={"click": "click_count"})


# Print out the list
print(filtered)

If this is your input (dataframe):

    id  click
0    0   True
1    1  False
2    1   True
9    1   True
4    2   True
5    2  False
7    2   True
11   2   True
6    3  False
8    4  False
3    5   True
10   6   True

with the snippet you will get the following output:

    id  click_count
0   0          1.0
1   1          2.0
2   2          3.0
3   5          1.0
4   6          1.0

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