binning

问题 I have a dataframe that I want to bin (i.e., group into sub-ranges) by one column, and take the mean of the second column for each of the bins: import pandas as pd import numpy as np data = pd.DataFrame(columns=['Score', 'Age']) data.Score = [1, 1, 1, 1, 0, 1, 2, 1, 0, 1, 1, 0, 2, 1, 1, 2, 1, 0, 1, 1, -1, 1, 0, 1, 1, 0, 1, 0, -2, 1] data.Age = [29, 59, 44, 52, 60, 53, 45, 47, 57, 54, 35, 32, 48, 31, 49, 43, 67, 32, 31, 42, 37, 45, 52, 59, 56, 57, 48, 45, 56, 31] _, bins = np.histogram(data

问题 Now i use optbinning module to binning all logstic regression modeling varible. however optbinning module need to use only one variable ,such as variable = "REGION_POPULATION_RELATIVE" x = df[variable].values y = df.TARGET.values from optbinning import OptimalBinning optb = OptimalBinning(name=variable, dtype="numerical", solver="ls", max_n_prebins=100, min_prebin_size=0.001, time_limit=50) optb.fit(x, y) how can i use loop to get binning result for all variable ? i try to codeing variable

问题 Now i use optbinning module to binning all logstic regression modeling varible. however optbinning module need to use only one variable ,such as variable = "REGION_POPULATION_RELATIVE" x = df[variable].values y = df.TARGET.values from optbinning import OptimalBinning optb = OptimalBinning(name=variable, dtype="numerical", solver="ls", max_n_prebins=100, min_prebin_size=0.001, time_limit=50) optb.fit(x, y) how can i use loop to get binning result for all variable ? i try to codeing variable

问题 I am using scipy.stats.binned_statistic_2d to bin irregular data onto a uniform grid by finding the mean of points within every bin. x,y = np.meshgrid(sort(np.random.uniform(0,1,100)),sort(np.random.uniform(0,1,100))) z = np.sin(x*y) statistic, xedges, yedges, binnumber = sp.stats.binned_statistic_2d(x.ravel(), y.ravel(), values=z.ravel(), statistic='mean',bins=[np.arange(0,1.1,.1), np.arange(0,1.1,.1)]) plt.figure(1) plt.pcolormesh(x,y,z, vmin = 0, vmax = 1) plt.figure(2) plt.pcolormesh

问题 I am using scipy.stats.binned_statistic_2d to bin irregular data onto a uniform grid by finding the mean of points within every bin. x,y = np.meshgrid(sort(np.random.uniform(0,1,100)),sort(np.random.uniform(0,1,100))) z = np.sin(x*y) statistic, xedges, yedges, binnumber = sp.stats.binned_statistic_2d(x.ravel(), y.ravel(), values=z.ravel(), statistic='mean',bins=[np.arange(0,1.1,.1), np.arange(0,1.1,.1)]) plt.figure(1) plt.pcolormesh(x,y,z, vmin = 0, vmax = 1) plt.figure(2) plt.pcolormesh

问题 What formula does Google Charts use to construct its histogram? For example, does it use Sturge's rule? Doane's rule? Scott's rule? etc. Is there any documentation on how it constructs it default bin size, min, and max? Here is a link to the Histogram page for Google Charts. Google Charts automatically chooses the number of bins for you. All bins are equal width and have a height proportional to the number of data points in the bin. In other respects, histograms are similar to column charts.

问题 Looking for a quick and elegant way to bin based on 2 columns in Pandas. Here's my data frame filename height width 0 shopfronts_23092017_3_285.jpg 750.0 560.0 1 shopfronts_200.jpg 4395.0 6020.0 2 shopfronts_25092017_eateries_98.jpg 414.0 621.0 3 shopfronts_101.jpg 480.0 640.0 4 shopfronts_138.jpg 3733.0 8498.0 5 shopfronts_25092017_eateries_95.jpg 187.0 250.0 6 shopfronts_25092017_neon_33.jpg 100.0 200.0 7 shopfronts_322.jpg 682.0 1024.0 8 shopfronts_171.jpg 800.0 600.0 9 shopfronts_23092017

问题 Looking for a quick and elegant way to bin based on 2 columns in Pandas. Here's my data frame filename height width 0 shopfronts_23092017_3_285.jpg 750.0 560.0 1 shopfronts_200.jpg 4395.0 6020.0 2 shopfronts_25092017_eateries_98.jpg 414.0 621.0 3 shopfronts_101.jpg 480.0 640.0 4 shopfronts_138.jpg 3733.0 8498.0 5 shopfronts_25092017_eateries_95.jpg 187.0 250.0 6 shopfronts_25092017_neon_33.jpg 100.0 200.0 7 shopfronts_322.jpg 682.0 1024.0 8 shopfronts_171.jpg 800.0 600.0 9 shopfronts_23092017

问题 I’m trying to represent some continuous data via binning. Continuous weighting data of an area should be binned as: VeryHigh, High, Low, VeryLow. The weighting values are based on an interaction between certain Types of events grouped by an Area and so can change depending on the Type selected by the report user. I have included some sample data below and an outline of what’s been done so far. Start with five sets of area data (A-E). Within each is one or more incident Types. Each incident

问题 How to convert two datasets X and Y to histograms whose x-axes/index are identical, instead of the x-axis range of variable X being collectively lower or higher than the x-axis range of variable Y (like how the code below generates)? I would like the numpy histogram output values to be ready to plot in a shared histogram-plot afterwards. import numpy as np from numpy.random import randn n = 100 # number of bins #datasets X = randn(n)*.1 Y = randn(n)*.2 #empirical distributions a = np