# coding: utf-8

# In[1]:

import matplotlib.pyplot as plt

from pylab import *

get_ipython().magic('matplotlib inline')

# In[2]:

fig = plt.figure()

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

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

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

from numpy.random import randn

plt.plot(randn(50).cumsum(),'k--')

ax1.hist(randn(1000),bins = 15 , color = 'k' ,alpha = 0.8)

ax2.scatter(np.arange(30),np.arange(30)+3*randn(30))

# In[3]:

fig,axes = plt.subplots(2,2,sharex = True,sharey=True)

for i in range(2):

for j in range(2):

axes[i,j].hist(randn(500),bins = 50,color = 'k',alpha = 0.5)

plt.subplots_adjust(wspace =0, hspace = 0)

# In[4]:

plt.plot(randn(30).cumsum(),'k*--')

# In[5]:

plot(randn(30).cumsum(),color = 'k',linestyle = 'dashed',marker = 'o')

# In[6]:

data = randn(30).cumsum()

plt.plot(data,'k--',label = 'Default')

plt.plot(data,'k-',drawstyle = 'steps-post',label = 'step-post')

plt.legend(loc='best')

# In[7]:

fig = plt.figure();

ax = fig.add_subplot(1,1,1)

ax.plot(randn(1000).cumsum())

ticks = ax.set_xticks([0,250,500,750,1000])

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

rotation =30,fontsize='small')

ax.set_title('My first matplotlib plot')

ax.set_xlabel('Stages')

# In[8]:

fig = plt.figure();

ax = fig.add_subplot(1,1,1)

ax.plot(randn(1000).cumsum(),'k',label = 'one')

ax.plot(randn(1000).cumsum(),'k--',label ='two')

ax.plot(randn(1000).cumsum(),'k.',label = 'three')

ax.legend(loc = 'best')

ax.text(100,0,'Hello World!',

family = 'monospace',fontsize = 10)

# In[9]:

from datetime import datetime

import pandas as pd

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

data = pd.read_csv('D:\\Github\\pydata-book-master\\ch08\\spx.csv',index_col = 0, parse_dates = True)

spx = data['SPX']

spx.plot(ax =ax,style = 'k-')

crisis_data = [

]

for data,label in crisis_data:

ax.annotate(label,xy = (data,spx.asof(data)+50),

xytext=(data,spx.asof(data)+200),

arrowprops = dict(facecolor = 'black'),

horizontalalignment = 'left',verticalalignment = 'top')

ax.set_xlim(['1/1/2007','1/1/2011'])

ax.set_ylim([600,1800])

plt.savefig('D:\\Python SaveFile\\1.png',dpi = 400,bbox_inches = 'tight')

# In[10]:

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

rect = plt.Rectangle((0.2,0.75),0.4,0.15,color = 'k',alpha = 0.3)

ax.add_patch(rect)

plt.savefig('D:\\Python SaveFile\\figpath.svg')

plt.savefig('D:\\Python SaveFile\\figpath.png',dpi = 400,bbox_inches = 'tight')

# In[11]:

from pandas import Series,DataFrame

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

print(s)

s.plot()

# In[12]:

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

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

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

print(df)

df.plot()

# In[13]:

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

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

print(data)

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

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

# In[14]:

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

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

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

print(df)

df.plot(kind = 'bar')

# In[15]:

df.plot(kind = 'bar',stacked = True,alpha = 0.5)

# In[16]:

tips = pd.read_csv('D:\\Github\\pydata-book-master\\ch08\\tips.csv')

party_counts = pd.crosstab(tips.day,tips.size)

party_counts

# In[19]:

party_counts = party_counts.ix[:,2:5]

party_pcts = party_counts.div(party_counts.sum(1).astype(float),axis = 0)

party_pcts

# In[20]:

comp1 = np.random.normal(0,1,size = 200) #N(0,1)

comp2 = np.random.normal(10,2,size = 200)#N(10,4)

values = Series(np.concatenate([comp1,comp2]))

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

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

# In[23]:

macro = pd.read_csv('D:\\Github\\pydata-book-master\\ch08\\macrodata.csv')

data = macro[['cpi','m1','tbilrate','unemp']]

trans_data = np.log(data).diff().dropna()

trans_data

# In[29]:

plt.scatter(trans_data['m1'],trans_data['unemp'])

plt.title(' Changes in log%s vs. log%s ' %('m1','unemp'))

# In[30]:

pd.scatter_matrix(trans_data,diagonal = 'kde',c = 'k',alpha = 0.3)