利用Python代码实现数据可视化的5种方法详解

import matplotlib.pyplot as pltimport numpy as npdef scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False): 
 # Create the plot object 
 _, ax = plt.subplots() # Plot the data, set the size (s), color and transparency (alpha) 
 # of the points 
 ax.scatter(x_data, y_data, s = 10, color = color, alpha = 0.75) if yscale_log == True: 
 ax.set_yscale('log') # Label the axes and provide a title 
 ax.set_title(title) 
 ax.set_xlabel(x_label) 
 ax.set_ylabel(y_label) 

def lineplot(x_data, y_data, x_label="", y_label="", title=""): 
 # Create the plot object 
 _, ax = plt.subplots() # Plot the best fit line, set the linewidth (lw), color and 
 # transparency (alpha) of the line 
 ax.plot(x_data, y_data, lw = 2, color = '#539caf', alpha = 1) # Label the axes and provide a title 
 ax.set_title(title) 
 ax.set_xlabel(x_label) 
 ax.set_ylabel(y_label) 

def histogram(data, n_bins, cumulative=False, x_label = "", y_label = "", title = ""): 
 _, ax = plt.subplots() 
 ax.hist(data, n_bins = n_bins, cumulative = cumulative, color = '#539caf') 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title) 

# Overlay 2 histograms to compare themdef overlaid_histogram(data1, data2, n_bins = 0, data1_name="", data1_color="#539caf", data2_name="", data2_color="#7663b0", x_label="", y_label="", title=""): 
 # Set the bounds for the bins so that the two distributions are fairly compared 
 max_nbins = 10 
 data_range = [min(min(data1), min(data2)), max(max(data1), max(data2))] 
 binwidth = (data_range[1] - data_range[0]) / max_nbins if n_bins == 0 
 bins = np.arange(data_range[0], data_range[1] + binwidth, binwidth) else: 
 bins = n_bins # Create the plot 
 _, ax = plt.subplots() 
 ax.hist(data1, bins = bins, color = data1_color, alpha = 1, label = data1_name) 
 ax.hist(data2, bins = bins, color = data2_color, alpha = 0.75, label = data2_name) 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title) 
 ax.legend(loc = 'best') 

def barplot(x_data, y_data, error_data, x_label="", y_label="", title=""): 
 _, ax = plt.subplots() 
 # Draw bars, position them in the center of the tick mark on the x-axis 
 ax.bar(x_data, y_data, color = '#539caf', align = 'center') 
 # Draw error bars to show standard deviation, set ls to 'none' 
 # to remove line between points 
 ax.errorbar(x_data, y_data, yerr = error_data, color = '#297083', ls = 'none', lw = 2, capthick = 2) 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title) 
 
def stackedbarplot(x_data, y_data_list, colors, y_data_names="", x_label="", y_label="", title=""): 
 _, ax = plt.subplots() 
 # Draw bars, one category at a time 
 for i in range(0, len(y_data_list)): 
 if i == 0: 
  ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i]) 
 else: 
  # For each category after the first, the bottom of the 
  # bar will be the top of the last category 
  ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i]) 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title) 
 ax.legend(loc = 'upper right') 
 
def groupedbarplot(x_data, y_data_list, colors, y_data_names="", x_label="", y_label="", title=""): 
 _, ax = plt.subplots() 
 # Total width for all bars at one x location 
 total_width = 0.8 
 # Width of each individual bar 
 ind_width = total_width / len(y_data_list) 
 # This centers each cluster of bars about the x tick mark 
 alteration = np.arange(-(total_width/2), total_width/2, ind_width) 
 
 # Draw bars, one category at a time 
 for i in range(0, len(y_data_list)): 
 # Move the bar to the right on the x-axis so it doesn't 
 # overlap with previously drawn ones 
 ax.bar(x_data + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width) 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title) 
 ax.legend(loc = 'upper right') 

def boxplot(x_data, y_data, base_color="#539caf", median_color="#297083", x_label="", y_label="", title=""): 
 _, ax = plt.subplots() 
 # Draw boxplots, specifying desired style 
 ax.boxplot(y_data 
    # patch_artist must be True to control box fill 
    , patch_artist = True 
    # Properties of median line 
    , medianprops = {'color': median_color} 
    # Properties of box 
    , boxprops = {'color': base_color, 'facecolor': base_color} 
    # Properties of whiskers 
    , whiskerprops = {'color': base_color} 
    # Properties of whisker caps 
    , capprops = {'color': base_color}) 
 
 # By default, the tick label starts at 1 and increments by 1 for 
 # each box drawn. This sets the labels to the ones we want 
 ax.set_xticklabels(x_data) 
 ax.set_ylabel(y_label) 
 ax.set_xlabel(x_label) 
 ax.set_title(title)