[英]How to plot a grouped bar chart from multiple datasets
我正在瀏覽Think Stats ,我想直觀地比較多個數據集。 從書中的例子中可以看到,通過使用本書作者提供的模塊,可以為每個數據集生成不同顏色的交錯條形圖,如何在pyplot
獲得相同的結果?
文檔中提供了一個精彩的示例/演示:
http://matplotlib.sourceforge.net/examples/api/barchart_demo.html
多次調用條形函數,每個系列一個。 您可以使用left參數控制條的左側位置,您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' )
與繪制數據的位置相比,Data2將在右側繪制。
我不久前遇到過這個問題並創建了一個包裝器函數,該函數采用2D數組並自動創建一個多條形圖:
編碼:
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()
如果你對這個功能的作用及其背后的邏輯感興趣, 這里有一個(無恥地自我推銷)鏈接到描述它的博客文章。
Matplotlib的交錯條形圖的示例代碼適用於任意實值x坐標(如@ db42所述)。
但是,如果您的x坐標是分類值(如鏈接問題中的詞典),則從分類x坐標到實際x坐標的轉換是麻煩且不必要的。
您可以使用matplotlib的api直接並排繪制兩個詞典。 繪制兩個相互偏移的條形圖的技巧是設置align=edge
和正寬度( +width
)以繪制一個條形圖,而繪制另一個條形圖的負寬度( -width
)。
為繪制兩個字典而修改的示例代碼如下所示:
"""
========
Barchart
========
A bar plot with errorbars and height labels on individual bars
"""
import matplotlib.pyplot as plt
# Uncomment the following line if you use ipython notebook
# %matplotlib inline
width = 0.35 # the width of the bars
men_means = {'G1': 20, 'G2': 35, 'G3': 30, 'G4': 35, 'G5': 27}
men_std = {'G1': 2, 'G2': 3, 'G3': 4, 'G4': 1, 'G5': 2}
rects1 = plt.bar(men_means.keys(), men_means.values(), -width, align='edge',
yerr=men_std.values(), color='r', label='Men')
women_means = {'G1': 25, 'G2': 32, 'G3': 34, 'G4': 20, 'G5': 25}
women_std = {'G1': 3, 'G2': 5, 'G3': 2, 'G4': 3, 'G5': 3}
rects2 = plt.bar(women_means.keys(), women_means.values(), +width, align='edge',
yerr=women_std.values(), color='y', label='Women')
# add some text for labels, title and axes ticks
plt.xlabel('Groups')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.legend()
def autolabel(rects):
"""
Attach a text label above each bar displaying its height
"""
for rect in rects:
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width()/2., 1.05*height,
'%d' % int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()
結果:
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