python数据分析(十)

# -*- coding: utf-8 -*-

from __future__ import division

from numpy.random import randn

import numpy as np

import os

import matplotlib.pyplot as plt

np.random.seed(12345)

plt.rc('figure', figsize=(10, 6))

from pandas import Series, DataFrame

import pandas as pd

np.set_printoptions(precision=4)

get_ipython().magic(u'matplotlib inline')

get_ipython().magic(u'pwd')

#####matplotlib创建图表

plt.plot([1,2,3,2,3,2,2,1])

plt.show()

plt.plot([4,3,2,1],[1,2,3,4])

plt.show()

#更多简单的图形

x = [1,2,3,4]

y = [5,4,3,2]

plt.figure()

plt.subplot(2,3,1)

plt.plot(x, y)

plt.subplot(232)

plt.bar(x, y)

plt.subplot(233)

plt.barh(x, y)

plt.subplot(234)

plt.bar(x, y)

y1 = [7,8,5,3]

plt.bar(x, y1, bottom=y, color = 'r')

plt.subplot(235)

plt.boxplot(x)

plt.subplot(236)

plt.scatter(x,y)

plt.show()

#####figure与subplot

#figure对象

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.show()

from numpy.random import randn

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

fig.show()

_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)

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

plt.close('all')

fig, axes = plt.subplots(2, 3)

axes

#调整subplot周围的间距

plt.subplots_adjust(left=None, bottom=None, right=None, top=None,

wspace=None, hspace=None)

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)

#####matplotlib基本设置

#颜色、标记和线型

plt.figure()

plt.plot(x,y,linestyle='--',color='g')

plt.plot(randn(30).cumsum(), 'ko--')

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

plt.close('all')

data = randn(30).cumsum()

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

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

plt.legend(loc='best')

#设置标题、轴标签、刻度以及刻度标签

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')

#添加图例

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')

#注释以及在subplot上绘图

from datetime import datetime

fig = plt.figure()

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

data = pd.read_csv('d:/data/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 financial crisis')

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)

#图表的保存

fig

fig.savefig('figpath.svg')

fig.savefig('figpath.png', dpi=400, bbox_inches='tight')

from io import BytesIO

buffer = BytesIO()

plt.savefig(buffer)

plot_data = buffer.getvalue()

#matplotlib配置

plt.rc('figure', figsize=(10, 10))

font_options={'family':'monospace',

'weight':'bold','size':'small'}

plt.rc('font',**font_options)

#####pandas中的绘图函数

#线图

plt.close('all')

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

s.plot()

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

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

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

df.plot()

#柱形图

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.rand(6, 4),

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

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

df

df.plot(kind='bar')

plt.figure()

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

tips = pd.read_csv('d:/data/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)

#直方图和密度图

plt.figure()

tips['tip_pct'] = tips['tip'] / tips['total_bill']

tips['tip_pct'].hist(bins=50)

plt.figure()

tips['tip_pct'].plot(kind='kde')

plt.figure()

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--')

#散点图

macro = pd.read_csv('d:/data/macrodata.csv')

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

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

trans_data[-5:]

plt.figure()

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)

#####Matplotlib作图

#误差条形图

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

y = np.log(x)

xe = 0.1 * np.abs(np.random.randn(len(y)))

plt.bar(x, y, yerr=xe, width=0.4, align='center', ecolor='r', color='cyan',

label='experiment #1');

plt.xlabel('# measurement')

plt.ylabel('Measured values')

plt.title('Measurements')

plt.legend(loc='upper left')

plt.show()

#饼图

plt.figure(1, figsize=(8, 8))

ax = plt.axes([0.1, 0.1, 0.8, 0.8])

labels = 'Spring', 'Summer', 'Autumn', 'Winter'

values = [15, 16, 16, 28]

explode =[0.1, 0.1, 0.1, 0.1]

plt.pie(values, explode=explode, labels=labels,

autopct='%1.1f%%', startangle=67)

plt.title('Rainy days by season')

plt.show()

#等高线图

import matplotlib as mpl

def process_signals(x, y):

return (1 - (x ** 2 + y ** 2)) * np.exp(-y ** 3 / 3)

x = np.arange(-1.5, 1.5, 0.1)

y = np.arange(-1.5, 1.5, 0.1)

X, Y = np.meshgrid(x, y)

Z = process_signals(X, Y)

N = np.arange(-1, 1.5, 0.3)

CS = plt.contour(Z, N, linewidths=2, cmap=mpl.cm.jet)

plt.clabel(CS, inline=True, fmt='%1.1f', fontsize=10)

plt.colorbar(CS)

plt.title('My function: $z=(1-x^2+y^2) e^{-(y^3)/3}$')

plt.show()

###3D图像

#3d柱形图

import matplotlib.dates as mdates

from mpl_toolkits.mplot3d import Axes3D

mpl.rcParams['font.size'] = 10

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

for z in [2011, 2012, 2013, 2014]:

xs = xrange(1,13)

ys = 1000 * np.random.rand(12)

color = plt.cm.Set2(random.choice(xrange(plt.cm.Set2.N)))

ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8)

ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs))

ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))

ax.set_xlabel('Month')

ax.set_ylabel('Year')

ax.set_zlabel('Sales Net [usd]')

plt.show()

#3d直方图

mpl.rcParams['font.size'] = 10

samples = 25

x = np.random.normal(5, 1, samples)

y = np.random.normal(3, .5, samples)

fig = plt.figure()

ax = fig.add_subplot(211, projection='3d')

hist, xedges, yedges = np.histogram2d(x, y, bins=10)

elements = (len(xedges) - 1) * (len(yedges) - 1)

xpos, ypos = np.meshgrid(xedges[:-1]+.25, yedges[:-1]+.25)

xpos = xpos.flatten()

ypos = ypos.flatten()

zpos = np.zeros(elements)

dx = .1 * np.ones_like(zpos)

dy = dx.copy()

dz = hist.flatten()

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', alpha=0.4)

ax.set_xlabel('X Axis')

ax.set_ylabel('Y Axis')

ax.set_zlabel('Z Axis')

ax2 = fig.add_subplot(212)

ax2.scatter(x, y)

ax2.set_xlabel('X Axis')

ax2.set_ylabel('Y Axis')

plt.show()

推荐阅读更多精彩内容