import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pandas import Series, DataFrame

# 線形圖
s = Series(np.random.randn(10).cumsum(),index=np.arange(0,100,10)).plot()
df = DataFrame(np.random.randn(10,4).cumsum(0),columns=['A','B','C','D'],index=np.arange(0,100,10)).plot()

# 柱狀圖
fig,axes = plt.subplots(2,1)
data 
 
= Series(np.random.randn(16),index=list('abcdefghijklmnop'))
data.plot(kind='bar',ax=axes[0],color='g',alpha=0.7)
data.plot(kind='barh',ax=axes[1],color='b',alpha=0.7)

# rand [0, 1)
df = DataFrame(np.random.rand(6,4),
               index=['one','two','three','four','five','six'],
               columns
 
=pd.Index(['A','B','C','D'],name='name'))
df.plot(kind='barh',alpha=0.5)
df.plot(kind='barh',stacked=True,alpha=0.5) # stacked=True 每行值堆積在一起

# 直方圖和密度圖
comp1 = np.random.normal(0,1,size=200)
comp2 = np.random.normal(10,2,size=200)
values = Series(np.concatenate([comp1,comp2]))
#values.hist(bins=100,alpha=0.3,color='g',density=True)
 

values.hist(bins=100,alpha=0.3,color='g',density=True,stacked=True)
values.plot(kind='kde',style='k--',alpha=0.3)

# 散佈圖
macro = pd.read_csv('macrodata.csv')
data = macro[['cpi','m1','tbilrate','unemp']]
trans_data = np.log(data).diff().dropna()

plt.scatter(trans_data['m1'],trans_data['unemp'])
plt.title('Change in log %s vs. log %s' %('m1','unemp'))

pd.plotting.scatter_matrix(trans_data,diagonal='kde',color='b',alpha=0.3)