Skip to main content

Seaborn catplot with two categories [duplicate]

I'm trying to build a plot like one from excel in seaborn. I basically have 12 functions that can have x number of errors and I want to distinguish between active and inactive users. Here's the chart in excel:

enter image description here

I tried this in Colab:

ax = sns.catplot(data=df_grp_pass.melt(id_vars =['function', 'M1_pass', 'study'], value_vars=['error_message']),kind="bar", x='variable', y='M1_pass',col='study', ci=None, estimator=sum)

but it doesn't work. I just get this:

enter image description here

I also tried the following but doesn't work either:

ax = sns.barplot(data=df_grp_pass, x='function', y='M1_pass', hue='study', estimator=sum)

I get the following:

enter image description here

Any help would be greatly appreciated.

thanks!



source https://stackoverflow.com/questions/72902103/seaborn-catplot-with-two-categories

Comments

Popular posts from this blog

How to split a rinex file if I need 24 hours data

Trying to divide rinex file using the command gfzrnx but getting this error. While doing that getting this error msg 'gfzrnx' is not recognized as an internal or external command Trying to split rinex file using the command gfzrnx. also install'gfzrnx'. my doubt is I need to run this program in 'gfzrnx' or in 'cmdprompt'. I am expecting a rinex file with 24 hrs or 1 day data.I Have 48 hrs data in RINEX format. Please help me to solve this issue. source https://stackoverflow.com/questions/75385367/how-to-split-a-rinex-file-if-i-need-24-hours-data

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings