在matplotlib.pyplot中，如何使用交錯條形圖繪製兩個數據集？

Question

我正在通過Think Stats，我想直觀地比較多個數據集。從書中的例子中我可以看到，通過使用本書作者提供的模塊，可以爲每個數據集生成不同顏色的交錯條形圖，如何在pyplot中獲得相同的結果？

感謝
Tunnuz

Answer 1

呼叫酒吧功能多次，每一個系列。您可以使用左側參數控制條的左側位置，並且可以使用它來防止重疊。

完全未經測試的代碼：

pyplot.bar(numpy.arange(10) * 2, data1, color = 'red') 
pyplot.bar(numpy.arange(10) * 2 + 1, data2, color = 'red') 

數據2將被吸引偏移了的權利相比於數據的一個將被繪製。

Answer 2

還有的文檔中提供了光輝的榜樣/演示：

http://matplotlib.sourceforge.net/examples/api/barchart_demo.html

Answer 3

我碰到這個問題前一段時間來創造一個包裝函數，它接受一個二維數組，並自動創建從多條形圖它：

代碼：

import matplotlib.pyplot as plt 
import matplotlib.cm as cm 
import operator as o 

import numpy as np 

dpoints = np.array([['rosetta', '1mfq', 9.97], 
      ['rosetta', '1gid', 27.31], 
      ['rosetta', '1y26', 5.77], 
      ['rnacomposer', '1mfq', 5.55], 
      ['rnacomposer', '1gid', 37.74], 
      ['rnacomposer', '1y26', 5.77], 
      ['random', '1mfq', 10.32], 
      ['random', '1gid', 31.46], 
      ['random', '1y26', 18.16]]) 

fig = plt.figure() 
ax = fig.add_subplot(111) 

def barplot(ax, dpoints): 
    ''' 
    Create a barchart for data across different categories with 
    multiple conditions for each category. 

    @param ax: The plotting axes from matplotlib. 
    @param dpoints: The data set as an (n, 3) numpy array 
    ''' 

    # Aggregate the conditions and the categories according to their 
    # mean values 
    conditions = [(c, np.mean(dpoints[dpoints[:,0] == c][:,2].astype(float))) 
        for c in np.unique(dpoints[:,0])] 
    categories = [(c, np.mean(dpoints[dpoints[:,1] == c][:,2].astype(float))) 
        for c in np.unique(dpoints[:,1])] 

    # sort the conditions, categories and data so that the bars in 
    # the plot will be ordered by category and condition 
    conditions = [c[0] for c in sorted(conditions, key=o.itemgetter(1))] 
    categories = [c[0] for c in sorted(categories, key=o.itemgetter(1))] 

    dpoints = np.array(sorted(dpoints, key=lambda x: categories.index(x[1]))) 

    # the space between each set of bars 
    space = 0.3 
    n = len(conditions) 
    width = (1 - space)/(len(conditions)) 

    # Create a set of bars at each position 
    for i,cond in enumerate(conditions): 
     indeces = range(1, len(categories)+1) 
     vals = dpoints[dpoints[:,0] == cond][:,2].astype(np.float) 
     pos = [j - (1 - space)/2. + i * width for j in indeces] 
     ax.bar(pos, vals, width=width, label=cond, 
       color=cm.Accent(float(i)/n)) 

    # Set the x-axis tick labels to be equal to the categories 
    ax.set_xticks(indeces) 
    ax.set_xticklabels(categories) 
    plt.setp(plt.xticks()[1], rotation=90) 

    # Add the axis labels 
    ax.set_ylabel("RMSD") 
    ax.set_xlabel("Structure") 

    # Add a legend 
    handles, labels = ax.get_legend_handles_labels() 
    ax.legend(handles[::-1], labels[::-1], loc='upper left') 

barplot(ax, dpoints) 
plt.show() 

如果您對此功能的功能及其背後的邏輯感興趣，請致電here's a (shamelessly self-promoting) link to the blog post describing it.