绘图和可视化

Posted 2022-12-01 wangshuang1631

写在前面的话：

实例中的所有数据都是在GitHub上下载的，打包下载即可。
地址是：http://github.com/pydata/pydata-book

还有一定要说明的：

我使用的是Python2.7，书中的代码有一些有错误，我使用自己的2.7版本调通。

# coding: utf-8
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from numpy.random import randn
fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
plt.plot(randn(50).cumsum(),'k--')
_ = ax1.hist(randn(100),bins=20,color='k',alpha=0.3)
ax2.scatter(np.arange(30),np.arange(30) + 3 * randn(30))
fig,axes = plt.subplots(2,3)
axes

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)
plt.plot(randn(30).cumsum(),'ko--')
data = randn(30).cumsum()
plt.plot(data,'k--',label='Default')
plt.plot(data,'k--',drawstyle='steps-post',label='steps-post')
plt.legend(loc='best')

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('aadadada dsadad sdad')

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')
plt.show()

from datetime import datetime
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
data = pd.read_csv('D:\\Source Code\\pydata-book-master\\ch08\\spx.csv',index_col = 0,parse_dates = True)
spx = data['SPX']
spx.plot(ax = ax,style = 'k-')
crisis_data = [
(datetime(2007,10,11),'Peak of bull market'),
(datetime(2008,3,12),'Bear Stearns Fails'),
(datetime(2008,9,15),'Lehman Bankruptcy')
]
for date,label in crisis_data:
    ax.annotate(label,xy = (date,spx.asof(date) + 50),
        xytext = (date,spx.asof(date) + 200),
        arrowprops = dict(facecolor = 'black'),
        horizontalalignment = 'left',verticalalignment = 'top')
ax.set_xlim(['1/1/2007','1/1/2011'])
ax.set_ylim([600,1800])
ax.set_title('Important dates in 2008-2009 finacial crisis')
plt.show()

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)
circ = plt.Circle((0.7,0.2),0.15,color = 'b',alpha = 0.3)
pgon = plt.Polygon([[0.15,0.15],[0.35,0.4],[0.2,0.6]],color = 'g',alpha = 0.5)
ax.add_patch(rect)
ax.add_patch(circ)
ax.add_patch(pgon)
plt.show()

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

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

df = DataFrame(np.random.randn(6,4),index = ['one','two','three','four','five','six'],
    columns = pd.Index(['A','B','C','D'],name = 'Genus'))
df
df.plot(kind = 'bar')
df.plot(kind = 'bar',stacked = True,alpha = 0.5)

tips = pd.read_csv('D:\\Source Code\\pydata-book-master\\ch08\\\\tips.csv')
party_counts = pd.crosstab(tips.day,tips.size)
party_counts
party_counts = party_counts.ix[:,2:5]
party_pcts = party_counts.div(party_counts.sum(1).astype(float),axis = 0)
party_pcts
party_pcts.plot(kind = 'bar',stacked = True)

tips['tip_pct'] = tips['tip'] / tips['total_bill']
tips['tip_pct'].hist(bins = 50)
tips['tip_pct'].plot(kind = 'kde')
comp1 = np.random.normal(0,1,size = 200)
comp2 = np.random.normal(10,2,size = 200)
values = Series(np.concatenate([comp1,comp2]))
values
values.hist(bins = 100,alpha = 0.3,color = 'k',normed = True)
values.plot(kind = 'kde',style = 'k--')

macro = pd.read_csv('D:\\Source Code\\pydata-book-master\\ch08\\macrodata.csv')
data = macro[['cpi','m1','tbilrate','unemp']]
trans_data = np.log(data).diff().dropna()
trans_data[-5:]
plt.scatter(trans_data['m1'],trans_data['unemp'])
plt.title('Changes in log %s vs. log %s'%('m1','unemp'))
pd.scatter_matrix(trans_data,diagonal = 'kde',color = 'k',alpha = 0.3)
pd.scatter_matrix(trans_data,diagonal = 'hist',color = 'k',alpha = 0.3)

data = pd.read_csv('D:\\Source Code\\pydata-book-master\\ch08\\Haiti.csv')
data
data[['INCIDENT DATE','LATITUDE','LONGITUDE']][:10]
data['CATEGORY'][:6]
data.describe()
data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) & (data.LONGITUDE > -75) &
           (data.LONGITUDE < -70) & data.CATEGORY.notnull()]
def to_cat_list(catstr):
    stripped = (x.strip() for x in catstr.split(','))
    return [x for x in stripped if x]
def get_all_categoties(cat_series):
    cat_sets = (set(to_cat_list(x)) for x in cat_series)
    return sorted(set.union(*cat_sets))
def get_english(cat):
    code,names = cat.split('.')
    if '|' in names:
        names = names.split('|')[1]
    return code,names.strip()
all_cats = get_all_categoties(data.CATEGORY)
english_mapping = dict(get_english(x) for x in all_cats)
english_mapping['2a']
english_mapping['6c']
def get_code(seq):
    return [x.split('.')[0] for x in seq if x]
all_codes = get_code(all_cats)
code_index = pd.Index(np.unique(all_codes))
dummy_frame = DataFrame(np.zeros((len(data),len(code_index))),index = data.index,columns = code_index)
dummy_frame.ix[:,:6]
for row,cat in zip(data.index,data.CATEGORY):
    codes = get_code(to_cat_list(cat))
    dummy_frame.ix[row,codes] = 1
data = data.join(dummy_frame.add_prefix('category_'))
from mpl_toolkits.basemap import Basemap
def basic_haiti_map(ax = None,lllat = 17.25,urlat = 20.25,lllon = -75,urlon = -71):
    m  = Basemap(ax = ax,projection = 'stere',
        lon_0 = (urlon + lllon) / 2,
        lat_0 = (urlat + lllat) / 2,
        llcrnrlat = lllat,urcrnrlat = urlat,
        llcrnrlon = lllon,urcrnrlon = urlon,
        resolution = 'f' )
    m.drawcoastlines()
    m.drawstates()
    m.drawcountries()
    return m
fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,10))
fig.subplots_adjust(hspace=0.05,wspace=0.05)
to_plot = ['2a','1','3c','7a']
lllat=17.25;urlat=20.25;lllon=-75;urlon=-71
for code,ax in zip(to_plot,axes.flat):
    m = basic_haiti_map(ax,lllat=lllat,urlat=urlat,lllon=lllon,urlon=urlon)
    cat_data = data[data['category_%s' % code] == 1]
    x,y = m(cat_data.LONGITUDE,cat_data.LATITUDE)
    m.plot(x,y,'k.',alpha=0.5)
    ax.set_title('%s:%s'%(code,english_mapping[code]))

shapefile_path = 'D:\\Source Code\\pydata-book-master\\ch08\\PortAuPrince_Roads'
m.readshapefile(shapefile_path,'roads')