Well, I decided to workout myself on my question to solve the above problem. What I wanted is to implement a simple OCR using KNearest or SVM features in OpenCV. And below is what I did and how. (it is just for learning how to use KNearest for simple OCR purposes). 1) My first question … Read more
Use .shape to obtain a tuple of array dimensions: >>> a.shape (2, 2)
Use numpy.set_printoptions to set the precision of the output: import numpy as np x = np.random.random(10) print(x) # [ 0.07837821 0.48002108 0.41274116 0.82993414 0.77610352 0.1023732 # 0.51303098 0.4617183 0.33487207 0.71162095] np.set_printoptions(precision=3) print(x) # [ 0.078 0.48 0.413 0.83 0.776 0.102 0.513 0.462 0.335 0.712] And suppress suppresses the use of scientific notation for small numbers: … Read more
np.random.seed(0) makes the random numbers predictable >>> numpy.random.seed(0) ; numpy.random.rand(4) array([ 0.55, 0.72, 0.6 , 0.54]) >>> numpy.random.seed(0) ; numpy.random.rand(4) array([ 0.55, 0.72, 0.6 , 0.54]) With the seed reset (every time), the same set of numbers will appear every time. If the random seed is not reset, different numbers appear with every invocation: >>> … Read more
If you only have two choices to select from: df[‘color’] = np.where(df[‘Set’]==’Z’, ‘green’, ‘red’) For example, import pandas as pd import numpy as np df = pd.DataFrame({‘Type’:list(‘ABBC’), ‘Set’:list(‘ZZXY’)}) df[‘color’] = np.where(df[‘Set’]==’Z’, ‘green’, ‘red’) print(df) yields Set Type color 0 Z A green 1 Z B green 2 X B red 3 Y C red If … Read more
import numpy as np def find_nearest(array, value): array = np.asarray(array) idx = (np.abs(array – value)).argmin() return array[idx] Example usage: array = np.random.random(10) print(array) # [ 0.21069679 0.61290182 0.63425412 0.84635244 0.91599191 0.00213826 # 0.17104965 0.56874386 0.57319379 0.28719469] print(find_nearest(array, value=0.5)) # 0.568743859261
The purpose of meshgrid is to create a rectangular grid out of an array of x values and an array of y values. So, for example, if we want to create a grid where we have a point at each integer value between 0 and 4 in both the x and y directions. To create … Read more
To sort by the second column of a: a[a[:, 1].argsort()]
The deprecated low_memory option The low_memory option is not properly deprecated, but it should be, since it does not actually do anything differently[source] The reason you get this low_memory warning is because guessing dtypes for each column is very memory demanding. Pandas tries to determine what dtype to set by analyzing the data in each … Read more
Use numpy.genfromtxt() by setting the delimiter kwarg to a comma: from numpy import genfromtxt my_data = genfromtxt(‘my_file.csv’, delimiter=”,”)