Matplotlib学习笔记2

1、条形图绘制：
首先导入库：

import numpy as np
import matplotlib
matplotlib.use('nbagg')
import matplotlib.pyplot as plt
#如果不输入魔法指令，用jupyter notebook得在每个画图指令后加上指令
#plt.show()

指定数据：

np.random.seed(0)
x = np.arange(5)
y = np.random.randint(-5,5,5)
print (y)

画图：

fig,axes = plt.subplots(ncols = 2)           #2列
v_bars = axes[0].bar(x,y,color='red')          #竖着画第一个图
h_bars = axes[1].barh(x,y,color='red')        #横着画第二个图

axes[0].axhline(0,color='grey',linewidth=2)        #在0位置加一条分界线
axes[1].axvline(0,color='grey',linewidth=2)        #在0位置加一条分界线
plt.show()

自定义图的颜色和布局：

fig,ax = plt.subplots()
v_bars = ax.bar(x,y,color='lightblue')
for bar,height in zip(v_bars,y):
    if height < 0:
        bar.set(edgecolor = 'darkred',color = 'green',linewidth = 3)       #小于0改绿色
plt.show()

2、对折线图填充颜色：

x = np.random.randn(100).cumsum()
y = np.linspace(0,10,100)

fig,ax = plt.subplots()
ax.fill_between(x,y,color='lightblue')
plt.show()

指定范围填充：

x = np.linspace(0,10,200)
y1 = 2*x +1
y2 = 3*x +1.2
y_mean = 0.5*x*np.cos(2*x) + 2.5*x +1.1
fig,ax = plt.subplots()
ax.fill_between(x,y1,y2,color='red')        #在y1和y2之间填充红色
ax.plot(x,y_mean,color='black')              #线是黑色
plt.show()

3、误差棒绘制：

mean_values = [1,2,3]
variance = [0.2,0.4,0.5]
bar_label = ['bar1','bar2','bar3']

x_pos = list(range(len(bar_label)))
plt.bar(x_pos,mean_values,yerr=variance,alpha=0.3)
max_y = max(zip(mean_values,variance))
plt.ylim([0,(max_y[0]+max_y[1])*1.2])
plt.ylabel('variable y')
plt.xticks(x_pos,bar_label)
plt.show()

4、背靠背地画：

x1 = np.array([1,2,3])
x2 = np.array([2,2,3])

bar_labels = ['bat1','bar2','bar3']
fig = plt.figure(figsize = (8,6))
y_pos = np.arange(len(x1))
y_pos = [x for x in y_pos]

plt.barh(y_pos,x1,color='g',alpha = 0.5)
plt.barh(y_pos,-x2,color='b',alpha = 0.5)

plt.xlim(-max(x2)-1,max(x1)+1)
plt.ylim(-1,len(x1)+1)
plt.show()

5、一个x值多个y柱子：

green_data = [1, 2, 3]
blue_data = [3, 2, 1]
red_data = [2, 3, 3]
labels = ['group 1', 'group 2', 'group 3']

pos = list(range(len(green_data))) 
width = 0.2 
fig, ax = plt.subplots(figsize=(8,6))

plt.bar(pos,green_data,width,alpha=0.5,color='g',label=labels[0])
plt.bar([p+width for p in pos],blue_data,width,alpha=0.5,color='b',label=labels[1])
plt.bar([p+width*2 for p in pos],red_data,width,alpha=0.5,color='r',label=labels[2])
plt.show()

也可以改变一下条形图柱子的颜色：

mean_values = range(10,18)
x_pos = range(len(mean_values))

import matplotlib.colors as col
import matplotlib.cm as cm

cmap1 = cm.ScalarMappable(col.Normalize(min(mean_values),max(mean_values),cm.hot))
cmap2 = cm.ScalarMappable(col.Normalize(0,20,cm.hot))

plt.subplot(121)
plt.bar(x_pos,mean_values,color = cmap1.to_rgba(mean_values))

plt.subplot(122)
plt.bar(x_pos,mean_values,color = cmap2.to_rgba(mean_values))

plt.show()

闲着没事干的时候甚至也可改一下每个柱子的填充形状：

patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.')
fig = plt.gca()
mean_value = range(1,len(patterns)+1)
x_pos = list(range(len(mean_value)))
bars = plt.bar(x_pos,mean_value,color='white')
for bar,pattern in zip(bars,patterns):
    bar.set_hatch(pattern)
plt.show()

6、条形图的注释：

data = range(200, 225, 5)
bar_labels = ['a', 'b', 'c', 'd', 'e']
fig = plt.figure(figsize=(10,8))
y_pos = np.arange(len(data))
plt.yticks(y_pos, bar_labels, fontsize=16)
bars = plt.barh(y_pos,data,alpha = 0.5,color='g')
plt.vlines(min(data),-1,len(data)+0.5,linestyle = 'dashed')           #dashed虚线
for b,d in zip(bars,data):
    plt.text(b.get_width()+b.get_width()*0.05,b.get_y()+b.get_height()/2,'{0:.2%}'.format(d/min(data)))
plt.show()