I have written the following code to plot 6 pie charts in different subplots, but I get an error. This code works correctly if I use it to plot only 2 charts, but produces an an error for anything more than that. I have 6 categorical variables in my dataset, the names of which are stored in the list cat_cols.
Tag: numpy
Pandas get column values based on duplicate rows
I have a sample DF: OP: I am trying to get the values of columns – “A” and “B” wherever there are duplicate values in column col. For example the column col has value Apple in index – 0,1,3,5 and I am trying to get the respective values in column – A and B, ie I have a iterative approach
Why do coefficient of determination, R², implementations produce different results?
When attempting to implement a python function for calculating the coefficient of determination, R², I noticed I got wildly different results depending on whose calculation sequence I used. The wikipedia page on R² gives a seemingly very clear explanation as to how R² should be calculated. My numpy interpretation of what is being said on the wiki page is the
Numpy find maximum tuple in array of windows
I’m starting our with list of tuples (each tuple is an (X,Y)). My end result is I want to find the maximum Y-value within EACH window/bin of length 4 using numpy. Expected output I want from each window/blocks using max y-value is below. Alternatively format could be regular list of tuples, doesn’t strictly need to be numpy array: Answer Here
is there any way to calculate L2 norm of multiple 2d matrices at once, in python?
for example, I have a matrix of dimensions (a,b,c,d). I want to calculate L2 norm of all d matrices of dimensions (a,b,c). Is there any way to use numpy.linalg.norm with out any looping structure? I mean, the resultant array should be 1 x d Answer How about this?
Data_Frame Error – ValueError: Can only compare identically-labeled Series objects
I have two data_frames, as below: The task is to create a data_frame(below one), where I need to add df_marks[‘Int1/40’] & df_marks[‘Int2/40’], if df_name[‘Student_ID’] == df_marks[‘Student_ID’] I tried But its giving error as, Do we have any simple way to do this? Regards, Deepak Dash Answer Use DataFrame.join with aggregated sum for new column in df_name: Or solution without helper
TypeError: only size-1 arrays can be converted to Python scalars when using trapz method
This is my python code And am getting this error: Why is that so? Integration using quad method worked fine but not with trapz method Answer As a user commented, you can only use math functions with scalar values, for arrays you have to use math functions provided in NumPy library.
Smoothing Categorical Output
I have a list of outputs obtained from a cow behavior detection model. Even in a video when a cow is laying, often time it identifies as standing and vice versa. In each video frame, a classification result is given by the model and we are appending it into a list. Let’s assume after 20 frames, we have a series
Create an array of repeating values with numpy
Given the following array, where the elements in the array are a value at index [0], and its frequency at index [1]. I need an array that is the length of the sum of the frequencies, filled with v, based on their respective frequency. This can be done with How can I do this with a vectorized numpy method? No
‘BACKTICK_QUOTED_STRING__AT_key’ is not defined for Pandas Querying function
Trying to get a hold of pd.query function Getting an error UndefinedVariableError: name ‘BACKTICK_QUOTED_STRING__AT_key’ is not defined for the below pandas Python code. What did I do wrong? Answer Try this : or