Consider the dataframe below, where there are several variables, each with the same number of values (in this case, 4). I would like to create a function that returns the proportion of values that are greater/less than the specified threshold values for several variables. The main goal is to create a function…
Tag: subset
cannot search value in dataframe althought the value exists
I have a data frame with location data. I know a value for a certain location exists and I even know its index location. When I search using index location the values is shown correctly but if I search using a combination of other columns(lat and lon), the value does not show. I am attaching the screenshot be…
Selecting rows based on condition in python pandas
I have a data-frame with columns as [‘ID’,’Title’,’Category’,’Company’,’Field’] and it has both blank values and at some places missing values are put as N/A. I have to pick the row which has maximum information available. For example one case could …
Grouping / clustering a list of numbers so that the min-max gap of each subset is always less than a cutoff in Python
Say I have a list of 50 random numbers. I want to group the numbers in a way that each subset has a min-max gap less than a cutoff 0.05. Below is my code. Check if all subsets have min-max gaps less than the cutoff: Output: Obviously my code is not working. Any suggestions? Answer Following @j_random_hacker&#…
Splitting a dataframe with many labels
I’m trying to split my data by different labels, like this: And this works fine for small amounts of numbers. However, I want to do this for many values. for example: This spits outs an error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). I…
Generating Maximal Subsets of a Set Under Some Constraints in Python
I have a set of attributes A= {a1, a2, …an} and a set of clusters C = {c1, c2, … ck} and I have a set of correspondences COR which is a subset of A x C and |COR|<< A x C. Here is a sample set of correspondences COR = {(a1, c1), (a1, c2), (a2, c1), (a3, c3), (a4,