数据可视化matplotlib

import matplotlib.pyplot as plt

import numpy as np

import numpy.random as randn

import pandas as pd

from pandas import Series,DataFrame

from pylab import mpl

mpl.rcParams['axes.unicode_minus'] = False # 我自己配置的问题

plt.rc('figure', figsize=(10, 6)) # 设置图像大小

%matplotlib inline

fig = plt.figure()

ax1 = fig.add_subplot(2,2,1)

ax2 = fig.add_subplot(2,2,2)

ax3 = fig.add_subplot(2,2,3)

ax4 = fig.add_subplot(2,2,4)

random_arr = randn.rand(50)

# 默认是在最后一次使用subplot的位置上作图

plt.plot(random_arr,'ro--') # r:表示颜色为红色，o:表示数据用o标记 ，--:表示虚线

# 等价于：

# plt.plot(random_arr,linestyle='--',color='r',marker='o')

plt.show()

# hist：直方图：统计分布情况

plt.hist(np.random.rand(8), bins=6, color='b', alpha=0.3)

# bins:数据箱子个数

(array([ 3.,  0.,  0.,  0.,  2.,  3.]),

 array([ 0.10261627,  0.19557319,  0.28853011,  0.38148703,  0.47444396,

         0.56740088,  0.6603578 ]),

 <a list of 6 Patch objects>)

# 散点图

plt.scatter(np.arange(30), np.arange(30) + 3 * randn.randn(30))

# 柱状图

fig, ax = plt.subplots()

x = np.arange(5)

y1, y2 = np.random.randint(1, 25, size=(2, 5))

width = 0.25

ax.bar(x, y1, width, color='r')

# 画柱子ax.bar(x+width, y2, width, color='g')

# 画柱子ax.set_xticks(x+width)

ax.set_xticklabels(['a', 'b', 'c', 'd', 'e']) # 下标注明

fig, axes = plt.subplots(2, 2, sharex=True, sharey=True) # 共享轴坐标

plt.subplots_adjust(left=0.5,top=0.5)

fig, axes = plt.subplots(2, 2)

random_arr = randn.randn(8)

fig, axes = plt.subplots(2, 2)

axes[0, 0].hist(random_arr, bins=16, color='k', alpha=0.5)

axes[0, 1].plot(random_arr,'ko--')

x = np.arange(8)

y = x + 5 * np.random.rand(8)

axes[1,0].scatter(x, y)

x = np.arange(5)

y1, y2 = np.random.randint(1, 25, size=(2, 5))

width = 0.25axes[1,1].bar(x, y1, width, color='r') # 画柱子

axes[1,1].bar(x+width, y2, width, color='g') # 画柱子

axes[1,1].set_xticks(x+width)

axes[1,1].set_xticklabels(['a', 'b', 'c', 'd', 'e']) # 下标注明

random_arr1 = randn.randn(8)

random_arr2 = randn.randn(8)

fig, ax = plt.subplots()

ax.plot(random_arr1,'ko--',label='A')

ax.plot(random_arr2,'b^--',label='B')

plt.legend(loc='best') #　自动选择放置图例的最佳位置

fig, ax = plt.subplots(1)

ax.plot(np.random.randn(380).cumsum())

# 设置刻度范围a

x.set_xlim([0, 500])

# 设置显示的刻度(记号)

ax.set_xticks(range(0,500,100))

# 设置刻度标签

ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],

rotation=30, fontsize='small')

# 设置坐标轴标签ax.set_xlabel('X:...')

ax.set_ylabel('Y:...')

# 设置标题

ax.set_title('Example')

plt.close('all')

s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))

s

fig,ax = plt.subplots(1)

s.plot(ax=ax,style='ko--')

fig, axes = plt.subplots(2, 1)

data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))

data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)

data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)

df = DataFrame(np.random.randn(10, 4).cumsum(0),

               columns=['A', 'B', 'C', 'D'],

               index=np.arange(0, 100, 10))

df

df.plot() # 列索引为图例，行索引为横坐标，值为纵坐标

df = DataFrame(np.random.randint(0,2,(10, 2)),

               columns=['A', 'B'],

               index=np.arange(0, 10, 1))

df

df.plot(kind='bar')

df.A.value_counts().plot(kind='bar')

df.A[df.B == 1].plot(kind='kde')  

df.A[df.B == 0].plot(kind='kde')    # 密度图

df = DataFrame(np.random.rand(6, 4),

               index=['one', 'two', 'three', 'four', 'five', 'six'],

               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))

df

df.plot(kind='bar',stacked='True') #行索引：横坐标

values = Series(np.random.normal(0, 1, size=200))

values.hist(bins=100, alpha=0.3, color='k', normed=True)

values.plot(kind='kde', style='k--')

df = DataFrame(np.random.randn(10,2),

               columns=['A', 'B'],

               index=np.arange(0, 10, 1))

df

plt.scatter(df.A, df.B)