I have the following function: def create_col4(df): df['col4'] = df['col1'] + df['col2'] If I apply this function within my jupyter notebook as in create_c
can somebody tell me how i can round down to the nearest thousand. So far I tried it with math.round(), the truncate function but i couldn't f
First I asked on gitter, though I got help they were not sure, see link Numpy release v1.21.3 states: Note a few oddities about Python 3.10: There are no 32 bi
I'm getting an NameError in jupyter notebook even after importing numpy as np. Any idea how to go about it will be appreciated %matplotlib inline %config Inline
i am using HampelFilter to detect outliers by SKTIME on my dataset but i faced a problem after applied the filter . My dataset contains Timeseries (signals) the
I have made a Pandas dataframe from several NumPy arrays and tried to format columns heads using LaTex, but it looks awful. I'm working with Jupyter Notebook. i
Let's say I have a 2D array: L = np.array([[1,2,3], [4,5,6], [7,8,9]]) I would like to make a 3D array from this, using a parameter
I am having problems with while loops in python right now: while(j < len(firstx)): trainingset[j][0] = firstx[j] trainingset[j][1] = firsty[j] tr
So this is my python code import numpy as np n = 3 T = 100 ts = .2*(100/(2*n-3))
My Jupiter notebook was crushed, so I have to reinstall the notebook, but in the new Jupiter notebook, I cannot run pandas. import pandas as pd AttributeError
I know that when assigning to a double indexed-array gives bad results because you're assigning to a view rather then to an array directly, but I cannot figure
I have a list of points let's say 5 points. I want to crop the area that this polygon is covering from the image. Here, red areas are the points and I want to c
I want to map each numpy array to a color to create an image. For example: if I have the numpy array: [ [0 0 1 3] [0 2 4 5] [1 2 3 6] ] I want to make an ima
Suppose I have a numpy array from which I want to remove a specific element. # data = np.array([ 97 32 98 32 99 32 100 32 101]) # collect indices where th
I'm trying to reshape an array of bitmap images that has a shape of (50,50,90000). How can I modify it so that I can get an array of (90000,50,50)? - I tried ar
I have dataframe where new columns need to be added based on existing column values conditions and I am looking for an efficient way of doing. For Ex: df = pd.D
You can see my dataframe below, x values are different value, but other values are same with left values, for example, column 15 and column 16 are same value. I
I have an array of indices like a = [2, 4, 1, 0, 3] and I want to transform it into np.argsort(a) = [3, 2, 0, 4, 1]. The problem is that argsort has O(n*log(n))
I have a numpy array of agents positions: positions = np.array([[row_0, col_0], [row_1, col_1], [row_2, col_2]]) I
Most similar questions relating to calculating this involve a single correlation value for each feature column, showing how the features in a dataset correlate