I have a df like this I want a output like below The goal is calculate column C by mulytiplying A and B only when the count value is “yes” but if the column People values are same that is yes for dia and no for also dia , then we have to calculate for the count value “no” I
Tag: pandas-groupby
Groupby and count only how many times customer was called at specific point of time
my problem is closely related to Groupby count only when a certain value is present in one of the column in pandas. Let’s say I have a dataframe which is sorted by not_unique_id and date_of_call. Now I want to add a new column which tells me, how often the customer was called successfully in the past. In other words: count
Pandas Assign Same Value to All Unique Values in Column
H, I have a dataset with two columns, one of them is target. If I group all the unique values in target, I get an array of 826 elements. My problem is when trying to assign some values based on this uniqueness. I have a second array, called array with contains a total of 826 values (of string type) to
Python: How to add groupby but not affect ngroup()?
per user I want an unique item order (as they click through them). If a item already has been seen, then don’t cumulative count, but place the already assigned value there. For example, c,d, g & b in the tables below. I used the function below, but its not getting the job done at the moment. If I add the
Removing one source value when there are multiple sources
I have the following dataset: I wanna reorganize the dataset in the following way: In word, in each year, for each category if we have value from multiple source (A,B), we will drop the source value of B. But if there is only source value of B, then we will keep that. I have tried to fix it using df.groupby().count().replace(‘count’)
pandas groupby column to list and keep certain values
I have the following dataframe: I create a new column with a list of the all the occupations: How do I only include teacher and student values in occupation_list? Answer You can filter before groupby: Output:
pandas groupby dataframes, calculate diffs between consecutive rows
Using pandas, I open some csv files in a loop and set the index to the cycleID column, except the cycleID column is not unique. See below: This prints the 2 columns (cycleID and mean) of the dataframe I am interested in for further computations: The objective is to use the rows corresponding to the same cycleID and calculate the
How to groupby 2 columns but order descending by count()
i have a dataframe and want to group 2 columns, which is working fine. Now the grouped dataframe is sorted by the CustomerID values. But i want to sort it by the count(). So that i have the Sektor then the CustomerIDs but the CustomerIds that occure the most should be at the top. So descending. Expected Output should be:
Enumerate rows in each group starting from one
I have a dataframe (which is sorted on date, date column is not included in the example for simplicity) that looks like this: I want to create a new column that counts the occurrence of each value in the letters column, increasing 1 by 1 as the value occurs in the letters column. The data frame I want to reach
How to groupby multiple columns with count unique value in Python Pandas
I have a DataFrame df_data: I have a function and parameter like this: Explain Parameters: with CustID = 1 the parameters should be list_minor = [3,1] (position is not important), list_major = [1] because with LocationID = 324 he get 3 times and LocationID = 490 he get 1 time (324,490 gets isMajor = 0 so it should be into