Let’s say I have 600,000 data points in column for age. In the data there are values 0 and -1, which is not relevant for age. How can I change both 0 and -1 values in my data to the column mean value using python? The code so far: Answer You can find the mean separatly and then use the
Tag: mean
How would I sort averages by row and/or column of an array?
I’ve been having trouble with finding the average of an array of lists, specifically by row and by column. I know what I want to do with it, but I’m struggling with finding what kind of code to write for it. The array is as follows: By row, I want to essentially find the averages of each individual list within
python pandas dataframe : fill nans with a conditional mean of previous and next value
I have the following dataframe: And I want value NaN to be filled with the conditional mean of previous and next value based on the same column. Just like this, value 6 is the mean with 5 and 7. And this is a little part of my dataframe, so I need to replace all the NaN. Answer EDIT: For replace
groupby with diff function
I have a groupby with a diff function, however I want to add an extra mean column for heart rate, how can I do this the best way? this is the code where should I add in the piece of code to calculate the average heart rate? output will be the amount of seconds in high power zone and then
calculate sum of squares with rows above
I have a dataset that looks like this: I want to iterate through each row and calculate a sum of squares value for each row above (only if the Type matches). I want to put this value in the X.sq column. So for example, in the first row, there’s nothing above. So only (-1.975767 x -1.975767). In the second row,
Calculate the average of list of lists based on two elements in the list?
I have the following list: I want to calculate the average of the items which have the same “first and the second elements”. E.g., from the below example, I want to take the average of the elements which have ‘5’ and ‘1’ in the first two elements of the list. So, my desired output should be like this: If I
Pandas groupby datetime columns by periods
I have the following dataframe: I would like to get for each row (e.g a,b,c,d …) the mean vale between specific hours. The hours are between 9-15, and I want to groupby period, for example to calculate the mean value between 09:00:00 to 11:00:00, between 11- 12, between 13-15 (or any period I decide to). I was trying first to
Pandas DataFrame mean of data in columns occurring before certain date time
I have a dataframe with ID’s of clients and their expenses for 2014-2018. What I want is to have the mean of the expenses per ID but only the years before a certain date can be taken into account when calculating the mean value (so column ‘Date’ dictates which columns can be taken into account for the mean). Example: for
looking for the difference between ocurrences in a datframe
I have a dataframe like this (the real one is 7 million records and 345 features) the following image is only a small fraction related to if a cliente make an operation in a month. What I want to do is create a column at the end with the mean difference between each operation. For example in the first record
Why is statistics.mean() so slow?
I compared the performance of the mean function of the statistics module with the simple sum(l)/len(l) method and found the mean function to be very slow for some reason. I used timeit with the two code snippets below to compare them, does anyone know what causes the massive difference in execution speed? I’m using Python 3.5. The code above executes