I have a array X = np.random.randn(399,2). I want to add 1 in each element of first column. How can I get my desired result. After adding the shape of X should remain same as before means X.shape = (399,2). Source: Python..

#### Category : numpy-ndarray

I am trying to count the number of elements in a rolling window of a dataframe satisfying a condition. let’s say I have a column having data 1,2,3,4,5,6,7,8,9,10. I want to count the number of elements above their mean (i.e. 5.5) So far I tried this: df["count"] = df["O"].shift().rolling(10).apply(lambda x : x[x>=x.mean()].count()) expected value: 5 ..

I have pandas frame F and numpy ndarray a, of dimensions m x n and m x 1 respectively. a contains only 1’s and 0’s and I’m trying to get a new pandas frame that are the rows of F for which a is 1. I tried F2 = F[a,:] and F2 = F[a.tolist(),:], neither ..

I would like to convert this part into vector for faster computation like the NumPy Nd array. Due to this, actual FPS is getting dropped. How can we achieve or make it better? public static List<Double> rgbMean (Mat mat) { double R=0,G=0,B=0,ctr=0; for (int i=0; i<480; i++) { for (int j=0; j<640; j++) { double[] ..

I have a dataset to need to make a K-Means figure using machine learning. The following is my code from sklearn.cluster import KMeans import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets df = pd.read_csv(‘../6_to_12_Month_All_Data.csv’ ,encoding=’utf-8′) X = df[[‘Yellow’, ‘Blue’]].values X KM=KMeans(n_clusters=3,init=’random’,random_state=5) KM.fit(X) KM.predict(X) plt.figure(figsize=(25,6)) plt.style.use(‘ggplot’) plt.scatter(X[:,0],X[:,1],c=KM.predict(X)) The ..

I have a set of values a,b,c,d I want to create an nx1 vector that allocates the values to different rows based the size of n. If n=9 I want the vector to be [a+b, a, a+c, b, 0, c, b+c, d, c+d] or if n=4 my vector is [a+b, a+c, b+c, c+d]. The different ..

My data is about genome sequence basically a long string of "AAATTGCCAA…AA". Here is a pic of my dataFrame I converted the data into a NumPy array by using the function given below. def seq_conversion(matrix): vectorSize= 29907 print(‘vectorSize’, vectorSize) out_data=[] for i in range (len(matrix)): sample=np.zeros(vectorSize) for j in range (0, len(matrix[i])): if(matrix[i][j]==’C’): sample[j]=0.25 elif(matrix[i][j]==’T’): ..

Suppose I have the below nd and 1d array arr_nd = [[ 1 2 3] [ 4 5 6] [ 7 8 9] [21 22 23]] arr_1d = [[ 7.5 2.5 6.5]] When I compare arr_1d with arr_nd I should get the below output The next smaller elements comparing arr_1d with arr_nd is arr_smaller = ..

I have an error " operands could not be broadcast together with shapes" the first time this code is worked after that i have this error and I don’t understand how to solve this problem despite i tried with other topics on Stack Overflow with the same error but in different contexts Is there a ..

So I was just creating zero arrays using np.zeros: XMI= np.zeros((50,1)) I tried this code to different names X, ABC, XM, B, C5, XMI but only X, B and C5 are saved/registered/accepted in the variable list. I am wanting to use the array name XMI, XCR. Please help. Source: Python..

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