matplotlib

问题 I have defined a PairGrid function, which works fine grid = sns.PairGrid(data= df, vars = ['sugarpercent', 'pricepercent', 'winpercent'], size = 3) # Map the plots to the locations grid = grid.map_upper(plt.scatter, color = 'darkred') grid = grid.map_upper(corr) grid = grid.map_upper(sns.regplot, line_kws={'color': 'darkred'}, lowess=True) However, I want to use RGBA (12, 50, 196, 0.6) instead of darkred for both scatter and regplot function. I tried to replace the tuple 'color': (12, 50, 196

问题 I am trying to plot some 2D empirical probability distributions using matplotlib's histogram2d. I want the colours to be on the same scale across several different plots, but cannot find a way to set a scale even if I know a global upper and lower bound on the resulting distribution. As is, each color scale will run from the minimum height to the maximum height of the histogram bins, but this range will be different for each plot. One potential solution would be to force one bin to take the

问题 I'm using this code below to plot a catplot with Seaborn. Its working ok. sns.catplot(data=df_gol, x='MODALIDADE_DO_VOO', y='VALOR',row='DESTINO', kind='box',height=3, aspect=1, color='red') But I am getting each box-plot stacked. I'd like to plot it side by side. How can I do that? My DF (df_gol) has these values: Column DESTINO - ['JPA', 'FOR', 'SSA', 'AJU'] Column MODALIDADE_DO_VOO - ['Direto', '1 parada'] Columns VALOR is float type I've been looking for some examples but I didn't

问题 I am trying to use what I learned from plotting multiple plots but whith offset ranges python, but I can't seem to make the appropriate adjustments for my Legendre plotting code: import numpy as np import pylab from numpy.polynomial.legendre import leggauss, legval def f(x): if 0 <= x <= 1: return 1 if -1 <= x <= 0: return -1 f = np.vectorize(f) deg = 1000 x, w = leggauss(deg) # len(x) == deg L = np.polynomial.legendre.legval(x, np.identity(deg)) integral = (L * (f(x) * w)[None,:]).sum(axis=1

问题 I make following Python Code to calculate center and size of Gaussian-like distribution basis of moment method. But, I can't make the code to calculate the angle of gaussian. Please look at pictures. First Picture is original data. Second picture is reconstruct data from the result of moment method. But, second picture is insufficient reconstruction. Because, original data is inclined distribution. I have to, I think, calculate the angle of axis for Gaussian-like distribution. To assume that

问题 I'm using this code below to plot a catplot with Seaborn. Its working ok. sns.catplot(data=df_gol, x='MODALIDADE_DO_VOO', y='VALOR',row='DESTINO', kind='box',height=3, aspect=1, color='red') But I am getting each box-plot stacked. I'd like to plot it side by side. How can I do that? My DF (df_gol) has these values: Column DESTINO - ['JPA', 'FOR', 'SSA', 'AJU'] Column MODALIDADE_DO_VOO - ['Direto', '1 parada'] Columns VALOR is float type I've been looking for some examples but I didn't

问题 I have a pandas.dataframe that looks like this: columns 0 1 2 3 4 5 A A A A B B B B B C C D D D E E F F I want to plot this using pyplot.imshow() , specifying the following colormap: color_dict = { "A": "#DA291E", "B": "#83DF39", "C": "#E8132d", "D": "#008933", "E": "#006CB3", "F": "#52BFEC" } If I was plotting a bar or a scatter I could just call with the argument color=a_list_of_colors but this doesn't work with imshow() . Instead I need to call with a cmap but as far as I understand it isn

问题 I have a pandas.dataframe that looks like this: columns 0 1 2 3 4 5 A A A A B B B B B C C D D D E E F F I want to plot this using pyplot.imshow() , specifying the following colormap: color_dict = { "A": "#DA291E", "B": "#83DF39", "C": "#E8132d", "D": "#008933", "E": "#006CB3", "F": "#52BFEC" } If I was plotting a bar or a scatter I could just call with the argument color=a_list_of_colors but this doesn't work with imshow() . Instead I need to call with a cmap but as far as I understand it isn

问题 I have a plot with a background grid. I need grid cells to be square (both major grid and minor grid cells) even though the limits of X and Y axes are different. My current code is as follows: import matplotlib.pyplot as plt import matplotlib.ticker as plticker import numpy as np data = [0.014, 0.84, 0.95, -0.42, -0.79, 0.84, 0.98, 1.10, 0.56, -0.49] fig, ax = plt.subplots(figsize=(20, 5)) ax.minorticks_on() # Set major and minor grid lines on X ax.set_xticks(np.arange(0, 10, 0.2)) ax.xaxis

问题 can someone explain why this simple code wont execute the facecolor command while setting the axis off? fig = plt.figure(1) ax = fig.add_subplot(211, facecolor=(0,0,0), aspect='equal') ax.scatter(np.random.random(10000), np.random.random(10000), c="gray", s=0.25) ax.axes.set_axis_off() Thanks in advance! 回答1: The background patch is part of the axes. So if the axes is turned off, so will the background patch. Some options: Re-add the background patch ax = fig.add_subplot(211, facecolor=(0,0,0