#### Category : scalar

I am trying to use plotly to graph a huge function which is in turn a sum of other functions – I want to append the smaller functions to arrays and add them up to the final function. Process with simple functions works, e.g.: import numpy as np import plotly.graph_objects as go pi = math.pi ..

I am trying to learn NumPy basic concepts but unable to wrap my head around few things. I am following this NumPy documentation Scalars Python defines only one type of a particular data class (there is only one integer type, one floating-point type, etc.). This can be convenient in applications that don’t need to be .. Following my previous question , all is working well but when the list it’s long it’s showing this erreur, anyone knows how can i fix it ? thank you in advance. NB: I’ve tried several solutions from responses here but they hasn’t work for me Source: Python..

If we define scalar multiplication given a point 𝑃P and a scalar 𝑎a as 𝑎𝑃=𝑎(𝑥,𝑦)=(𝑎𝑥,𝑎𝑦)aP=a(x,y)=(ax,ay) and we can define the inner product for points 𝑃,𝑄P,Q as 𝑃⋅𝑄=(𝑥1,𝑦1)⋅(𝑥2,𝑦2)=𝑥1𝑥2+𝑦1𝑦2P⋅Q=(x1,y1)⋅(x2,y2)=x1x2+y1y2 To test that you’ve implemented this correctly, compute 2(𝑥,𝑦)⋅(𝑥,𝑦)2(x,y)⋅(x,y) for a Point object. Source: Python..

In API calling a JSON object as a input parameter, I am giving as null for that object and run the python code throwing the error: "cannot call json_populate_recordset on a scalar". If I give any data to JSON object it’s working fine. Here is my python code. result = execute_function.put(params, db_functions.fn_upd_tasks) if (task_resource!=”): for ..

Here’s my csv file CSV I’m trying to take the mean of columns "Angle Min" and "Angle Max" and then multiply every row in the resulting dataframe with the "Angle Conversion Factor" in cell D8. Likewise I want to do the same with "Torque Min" and "Torque Max" (get the mean and then multiply the ..

I am trying to solve this issue in a specific way. Would love pointers on how to proceed. I have df1, which is: Columns colA colB colC Then there is df2, which is: Name colA colB colC A B C Then there is df3, which is: Name Property A colA B colC A colB C ..