visualization——matplotlib

作者: 王侦 | 来源:发表于2017-08-06 14:04 被阅读222次

通过本手册，你将收获以下知识：

matplotlib 及环境配置
数据图的组成结构，与 matplotlib 对应的名称
常见的数据绘图类型，与绘制方法

您可能需要以下的准备与先修知识：

Python开发环境及matplotlib工具包
Python基础语法
Python numpy 包使用

1.matplotlib安装配置

linux可以通过以下方式安装matplotlib
sudo pip install numpy
sudo pip install scipy
sudo pip install matplotlib
windows墙裂推荐大家使用anaconda

2.一副可视化图的基本结构

通常，使用 numpy 组织数据, 使用 matplotlib API 进行数据图像绘制。一幅数据图基本上包括如下结构：

Data: 数据区，包括数据点、描绘形状
Axis: 坐标轴，包括 X 轴、 Y 轴及其标签、刻度尺及其标签
Title: 标题，数据图的描述
Legend: 图例，区分图中包含的多种曲线或不同分类的数据
其他的还有图形文本 (Text)、注解 (Annotate)等其他描述

image.png

3.画法

下面以常规图为例，详细记录作图流程及技巧。按照绘图结构，可将数据图的绘制分为如下几个步骤：

导入 matplotlib 包相关工具包
准备数据，numpy 数组存储
绘制原始曲线
配置标题、坐标轴、刻度、图例
添加文字说明、注解
显示、保存绘图结果

3.1 导包

会用到 matplotlib.pyplot、pylab 和 numpy

#coding:utf-8
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from pylab import *

3.2 准备数据

numpy 常用来组织源数据:

# 定义数据部分
x = np.arange(0., 10, 0.2)
y1 = np.cos(x)
y2 = np.sin(x)
y3 = np.sqrt(x)

#x = all_df['house_age']
#y = all_df['house_price']

3.3绘制基本曲线

使用 plot 函数直接绘制上述函数曲线，可以通过配置 plot 函数参数调整曲线的样式、粗细、颜色、标记等：

# 绘制 3 条函数曲线
# $y=\sqrt{x}$
plt.rcParams["figure.figsize"] = (12,8)
plt.plot(x, y1, color='blue', linewidth=1.5, linestyle='-', marker='.', label=r'$y = cos{x}$')
plt.plot(x, y2, color='green', linewidth=1.5, linestyle='-', marker='*', label=r'$y = sin{x}$')
plt.plot(x, y3, color='m', linewidth=1.5, linestyle='-', marker='x', label=r'$y = \sqrt{x}$')

3.3.1 关于颜色的补充

主要是color参数：

r 红色
g 绿色
b 蓝色
c cyan
m 紫色
y 土黄色
k 黑色
w 白色

image.png

3.3.2 linestyle参数

linestyle 参数主要包含虚线、点化虚线、粗虚线、实线，如下：

image.png

3.3.3 marker参数

marker参数设定在曲线上标记的特殊符号，以区分不同的线段。常见的形状及表示符号如下图所示：

image.png

3.4 设置坐标轴

可通过如下代码，移动坐标轴 spines

# 坐标轴上移
ax = plt.subplot(111)
#ax = plt.subplot(2,2,1)
ax.spines['right'].set_color('none')     # 去掉右边的边框线
ax.spines['top'].set_color('none')       # 去掉上边的边框线
# 移动下边边框线，相当于移动 X 轴
ax.xaxis.set_ticks_position('bottom')    
ax.spines['bottom'].set_position(('data', 0))
# 移动左边边框线，相当于移动 y 轴
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))

可通过如下代码，设置刻度尺间隔 lim、刻度标签 ticks

# 设置 x, y 轴的刻度取值范围
plt.xlim(x.min()*1.1, x.max()*1.1)
plt.ylim(-1.5, 4.0)
# 设置 x, y 轴的刻度标签值
plt.xticks([2, 4, 6, 8, 10], [r'two', r'four', r'6', r'8', r'10'])
plt.yticks([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0],
    [r'-1.0', r'0.0', r'1.0', r'2.0', r'3.0', r'4.0'])

可通过如下代码，设置 X、Y 坐标轴和标题：

# 设置标题、x轴、y轴
plt.title(r'$the \ function \ figure \ of \ cos(), \ sin() \ and \ sqrt()$', fontsize=19)
plt.xlabel(r'$the \ input \ value \ of \ x$', fontsize=18, labelpad=88.8)
plt.ylabel(r'$y = f(x)$', fontsize=18, labelpad=12.5)

3.5 设置文字描述、注解

可通过如下代码，在数据图中添加文字描述 text：

plt.text(0.8, 0.9, r'$x \in [0.0, \ 10.0]$', color='k', fontsize=15)
plt.text(0.8, 0.8, r'$y \in [-1.0, \ 4.0]$', color='k', fontsize=15)

可通过如下代码，在数据图中给特殊点添加注解 annotate：

# 特殊点添加注解
plt.scatter([8,],[np.sqrt(8),], 50, color ='m')  # 使用散点图放大当前点
plt.annotate(r'$2\sqrt{2}$', xy=(8, np.sqrt(8)), xytext=(8.5, 2.2), fontsize=16, color='#090909', arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=0.1', color='#090909'))

3.6 设置图例

可使用如下两种方式，给绘图设置图例：

1: 在 plt.plot 函数中添加 label 参数后，使用 plt.legend(loc=’up right’)
2: 不使用参数 label, 直接使用如下命令：

plt.legend(['cos(x)', 'sin(x)', 'sqrt(x)'], loc='upper right')

image.png

3.7 网格线开关

可使用如下代码，给绘图设置网格线：

# 显示网格线
plt.grid(True)

3.8 显示与图像保存

plt.show()    # 显示
#savefig('../figures/plot3d_ex.png',dpi=48)    # 保存，前提目录存在

4. 完整的绘制程序

#coding:utf-8

import numpy as np
import matplotlib.pyplot as plt
from pylab import *

# 定义数据部分
x = np.arange(0., 10, 0.2)
y1 = np.cos(x)
y2 = np.sin(x)
y3 = np.sqrt(x)

# 绘制 3 条函数曲线
plt.plot(x, y1, color='blue', linewidth=1.5, linestyle='-', marker='.', label=r'$y = cos{x}$')
plt.plot(x, y2, color='green', linewidth=1.5, linestyle='-', marker='*', label=r'$y = sin{x}$')
plt.plot(x, y3, color='m', linewidth=1.5, linestyle='-', marker='x', label=r'$y = \sqrt{x}$')

# 坐标轴上移
ax = plt.subplot(111)
ax.spines['right'].set_color('none')     # 去掉右边的边框线
ax.spines['top'].set_color('none')       # 去掉上边的边框线

# 移动下边边框线，相当于移动 X 轴
ax.xaxis.set_ticks_position('bottom')    
ax.spines['bottom'].set_position(('data', 0))

# 移动左边边框线，相当于移动 y 轴
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))

# 设置 x, y 轴的取值范围
plt.xlim(x.min()*1.1, x.max()*1.1)
plt.ylim(-1.5, 4.0)

# 设置 x, y 轴的刻度值
plt.xticks([2, 4, 6, 8, 10], [r'2', r'4', r'6', r'8', r'10'])
plt.yticks([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], 
    [r'-1.0', r'0.0', r'1.0', r'2.0', r'3.0', r'4.0'])

# 添加文字
plt.text(0.8, 0.8, r'$x \in [0.0, \ 10.0]$', color='k', fontsize=15)
plt.text(0.8, 0.9, r'$y \in [-1.0, \ 4.0]$', color='k', fontsize=15)

# 特殊点添加注解
plt.scatter([8,],[np.sqrt(8),], 50, color ='m')  # 使用散点图放大当前点
plt.annotate(r'$2\sqrt{2}$', xy=(8, np.sqrt(8)), xytext=(8.5, 2.2), fontsize=16, color='#090909', arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=0.1', color='#090909'))

# 设置标题、x轴、y轴
plt.title(r'$the \ function \ figure \ of \ cos(), \ sin() \ and \ sqrt()$', fontsize=19)
plt.xlabel(r'$the \ input \ value \ of \ x$', fontsize=18, labelpad=88.8)
plt.ylabel(r'$y = f(x)$', fontsize=18, labelpad=12.5)

# 设置图例及位置
plt.legend(loc='up right')    
# plt.legend(['cos(x)', 'sin(x)', 'sqrt(x)'], loc='up right')

# 显示网格线
plt.grid(True)    

# 显示绘图
plt.show()

5.常用图像

细节看这里，看这里，看这里
想成为可视化专家的你，工具手册在哪里？在这里！更全的在这里

曲线图：matplotlib.pyplot.plot(data)
灰度图：matplotlib.pyplot.hist(data)
散点图：matplotlib.pyplot.scatter(data)
箱式图：matplotlib.pyplot.boxplot(data)

x = np.arange(-5,5,0.1)
y = x ** 2
plt.plot(x,y)

x = np.random.normal(size=1000)
plt.hist(x, bins=10)

plt.rcParams["figure.figsize"] = (8,8)
x = np.random.normal(size=1000)
y = np.random.normal(size=1000)
plt.scatter(x,y)

plt.boxplot(x)

箱式图科普

上边缘（Q3+1.5IQR）、下边缘（Q1-1.5IQR）、IQR=Q3-Q1
上四分位数（Q3）、下四分位数（Q1）
中位数
异常值
处理异常值时与标准的异同：统计边界是否受异常值影响、容忍度的大小

6.案例：自行车租赁数据分析与可视化

step1. 导入数据，做简单的数据处理

import pandas as pd # 读取数据到DataFrame
import urllib # 获取网络数据
import tempfile # 创建临时文件系统
import shutil # 文件操作
import zipfile # 压缩解压

temp_dir = tempfile.mkdtemp() # 建立临时目录
data_source = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip' # 网络数据地址
zipname = temp_dir + '/Bike-Sharing-Dataset.zip' # 拼接文件和路径
urllib.urlretrieve(data_source, zipname) # 获得数据

zip_ref = zipfile.ZipFile(zipname, 'r') # 创建一个ZipFile对象处理压缩文件
zip_ref.extractall(temp_dir) # 解压
zip_ref.close()

daily_path = 'data/day.csv'
daily_data = pd.read_csv(daily_path) # 读取csv文件
daily_data['dteday'] = pd.to_datetime(daily_data['dteday']) # 把字符串数据传换成日期数据
drop_list = ['instant', 'season', 'yr', 'mnth', 'holiday', 'workingday', 'weathersit', 'atemp', 'hum'] # 不关注的列
daily_data.drop(drop_list, inplace = True, axis = 1) # inplace=true在对象上直接操作

shutil.rmtree(temp_dir) # 删除临时文件目录

daily_data.head() # 看一看数据~

step2. 配置参数

from __future__ import division, print_function # 引入3.x版本的除法和打印
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
# 在notebook中显示绘图结果
%matplotlib inline

# 设置一些全局的资源参数，可以进行个性化修改
import matplotlib
# 设置图片尺寸 14" x 7"
# rc: resource configuration
matplotlib.rc('figure', figsize = (14, 7))
# 设置字体 14
matplotlib.rc('font', size = 14)
# 不显示顶部和右侧的坐标线
matplotlib.rc('axes.spines', top = False, right = False)
# 不显示网格
matplotlib.rc('axes', grid = False)
# 设置背景颜色是白色
matplotlib.rc('axes', facecolor = 'white')

step3. 关联分析

散点图

分析变量关系

from matplotlib import font_manager
fontP = font_manager.FontProperties()
fontP.set_family('SimHei')
fontP.set_size(14)

# 包装一个散点图的函数便于复用
def scatterplot(x_data, y_data, x_label, y_label, title):

    # 创建一个绘图对象
    fig, ax = plt.subplots()

    # 设置数据、点的大小、点的颜色和透明度
    ax.scatter(x_data, y_data, s = 10, color = '#539caf', alpha = 0.75) # http://www.114la.com/other/rgb.htm

    # 添加标题和坐标说明
    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)

# 绘制散点图
scatterplot(x_data = daily_data['temp']
            , y_data = daily_data['cnt']
            , x_label = 'Normalized temperature (C)'
            , y_label = 'Check outs'
            , title = 'Number of Check Outs vs Temperature')

曲线图

拟合变量关系

# 线性回归
import statsmodels.api as sm # 最小二乘
from statsmodels.stats.outliers_influence import summary_table # 获得汇总信息
x = sm.add_constant(daily_data['temp']) # 线性回归增加常数项 y=kx+b
y = daily_data['cnt']
regr = sm.OLS(y, x) # 普通最小二乘模型，ordinary least square model
res = regr.fit()
# 从模型获得拟合数据
st, data, ss2 = summary_table(res, alpha=0.05) # 置信水平alpha=5%，st数据汇总，data数据详情，ss2数据列名
fitted_values = data[:,2]

# 包装曲线绘制函数
def lineplot(x_data, y_data, x_label, y_label, title):
    # 创建绘图对象
    _, ax = plt.subplots()

    # 绘制拟合曲线，lw=linewidth，alpha=transparancy
    ax.plot(x_data, y_data, lw = 2, color = '#539caf', alpha = 1)

    # 添加标题和坐标说明
    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)

# 调用绘图函数
lineplot(x_data = daily_data['temp']
         , y_data = fitted_values
         , x_label = 'Normalized temperature (C)'
         , y_label = 'Check outs'
         , title = 'Line of Best Fit for Number of Check Outs vs Temperature')

x.head()
type(regr)
st

带置信区间的曲线图

评估曲线拟合结果

# 获得5%置信区间的上下界
predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T

# 创建置信区间DataFrame，上下界
CI_df = pd.DataFrame(columns = ['x_data', 'low_CI', 'upper_CI'])
CI_df['x_data'] = daily_data['temp']
CI_df['low_CI'] = predict_mean_ci_low
CI_df['upper_CI'] = predict_mean_ci_upp
CI_df.sort_values('x_data', inplace = True) # 根据x_data进行排序

# 绘制置信区间
def lineplotCI(x_data, y_data, sorted_x, low_CI, upper_CI, x_label, y_label, title):
    # 创建绘图对象
    _, ax = plt.subplots()

    # 绘制预测曲线
    ax.plot(x_data, y_data, lw = 1, color = '#539caf', alpha = 1, label = 'Fit')
    # 绘制置信区间，顺序填充
    ax.fill_between(sorted_x, low_CI, upper_CI, color = '#539caf', alpha = 0.4, label = '95% CI')
    # 添加标题和坐标说明
    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)

    # 显示图例，配合label参数，loc=“best”自适应方式
    ax.legend(loc = 'best')

# Call the function to create plot
lineplotCI(x_data = daily_data['temp']
           , y_data = fitted_values
           , sorted_x = CI_df['x_data']
           , low_CI = CI_df['low_CI']
           , upper_CI = CI_df['upper_CI']
           , x_label = 'Normalized temperature (C)'
           , y_label = 'Check outs'
           , title = 'Line of Best Fit for Number of Check Outs vs Temperature')

双坐标曲线图

曲线拟合不满足置信阈值时，考虑增加独立变量
*分析不同尺度多变量的关系

# 双纵坐标绘图函数
def lineplot2y(x_data, x_label, y1_data, y1_color, y1_label, y2_data, y2_color, y2_label, title):
    _, ax1 = plt.subplots()
    ax1.plot(x_data, y1_data, color = y1_color)
    # 添加标题和坐标说明
    ax1.set_ylabel(y1_label, color = y1_color)
    ax1.set_xlabel(x_label)
    ax1.set_title(title)

    ax2 = ax1.twinx() # 两个绘图对象共享横坐标轴
    ax2.plot(x_data, y2_data, color = y2_color)
    ax2.set_ylabel(y2_label, color = y2_color)
    # 右侧坐标轴可见
    ax2.spines['right'].set_visible(True)

# 调用绘图函数
lineplot2y(x_data = daily_data['dteday']
           , x_label = 'Day'
           , y1_data = daily_data['cnt']
           , y1_color = '#539caf'
           , y1_label = 'Check outs'
           , y2_data = daily_data['windspeed']
           , y2_color = '#7663b0'
           , y2_label = 'Normalized windspeed'
           , title = 'Check Outs and Windspeed Over Time')

step4. 分布分析

灰度图

粗略区间计数

# 绘制灰度图的函数
def histogram(data, x_label, y_label, title):
    _, ax = plt.subplots()
    res = ax.hist(data, color = '#539caf', bins=10) # 设置bin的数量
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)
    return res

# 绘图函数调用
res = histogram(data = daily_data['registered']
           , x_label = 'Check outs'
           , y_label = 'Frequency'
           , title = 'Distribution of Registered Check Outs')
res[0] # value of bins
res[1] # boundary of bins

堆叠直方图

比较两个分布

# 绘制堆叠的直方图
def overlaid_histogram(data1, data1_name, data1_color, data2, data2_name, data2_color, x_label, y_label, title):
    # 归一化数据区间，对齐两个直方图的bins
    max_nbins = 10
    data_range = [min(min(data1), min(data2)), max(max(data1), max(data2))]
    binwidth = (data_range[1] - data_range[0]) / max_nbins
    bins = np.arange(data_range[0], data_range[1] + binwidth, binwidth) # 生成直方图bins区间

    # Create the plot
    _, ax = plt.subplots()
    ax.hist(data1, bins = bins, color = data1_color, alpha = 1, label = data1_name)
    ax.hist(data2, bins = bins, color = data2_color, alpha = 0.75, label = data2_name)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)
    ax.legend(loc = 'best')

# Call the function to create plot
overlaid_histogram(data1 = daily_data['registered']
                   , data1_name = 'Registered'
                   , data1_color = '#539caf'
                   , data2 = daily_data['casual']
                   , data2_name = 'Casual'
                   , data2_color = '#7663b0'
                   , x_label = 'Check outs'
                   , y_label = 'Frequency'
                   , title = 'Distribution of Check Outs By Type')

registered：注册的分布，正态分布，why
casual：偶然的分布，疑似指数分布，why

密度图

精细刻画概率分布
KDE: kernal density estimate

# 计算概率密度
from scipy.stats import gaussian_kde
data = daily_data['registered']
density_est = gaussian_kde(data) # kernal density estimate: https://en.wikipedia.org/wiki/Kernel_density_estimation
# 控制平滑程度，数值越大，越平滑
density_est.covariance_factor = lambda : .3
density_est._compute_covariance()
x_data = np.arange(min(data), max(data), 200)

# 绘制密度估计曲线
def densityplot(x_data, density_est, x_label, y_label, title):
    _, ax = plt.subplots()
    ax.plot(x_data, density_est(x_data), color = '#539caf', lw = 2)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)

# 调用绘图函数
densityplot(x_data = x_data
            , density_est = density_est
            , x_label = 'Check outs'
            , y_label = 'Frequency'
            , title = 'Distribution of Registered Check Outs')

type(density_est)

step5. 组间分析

组间定量比较
分组粒度
组间聚类

柱状图

一级类间均值方差比较

# 分天分析统计特征
mean_total_co_day = daily_data[['weekday', 'cnt']].groupby('weekday').agg([np.mean, np.std])
mean_total_co_day.columns = mean_total_co_day.columns.droplevel()

# 定义绘制柱状图的函数
def barplot(x_data, y_data, error_data, x_label, y_label, title):
    _, ax = plt.subplots()
    # 柱状图
    ax.bar(x_data, y_data, color = '#539caf', align = 'center')
    # 绘制方差
    # ls='none'去掉bar之间的连线
    ax.errorbar(x_data, y_data, yerr = error_data, color = '#297083', ls = 'none', lw = 5)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)

# 绘图函数调用
barplot(x_data = mean_total_co_day.index.values
        , y_data = mean_total_co_day['mean']
        , error_data = mean_total_co_day['std']
        , x_label = 'Day of week'
        , y_label = 'Check outs'
        , title = 'Total Check Outs By Day of Week (0 = Sunday)')

mean_total_co_day.columns
daily_data[['weekday', 'cnt']].groupby('weekday').agg([np.mean, np.std])

堆积柱状图

多级类间相对占比比较

mean_by_reg_co_day = daily_data[['weekday', 'registered', 'casual']].groupby('weekday').mean()
mean_by_reg_co_day

# 分天统计注册和偶然使用的情况
mean_by_reg_co_day = daily_data[['weekday', 'registered', 'casual']].groupby('weekday').mean()
# 分天统计注册和偶然使用的占比
mean_by_reg_co_day['total'] = mean_by_reg_co_day['registered'] + mean_by_reg_co_day['casual']
mean_by_reg_co_day['reg_prop'] = mean_by_reg_co_day['registered'] / mean_by_reg_co_day['total']
mean_by_reg_co_day['casual_prop'] = mean_by_reg_co_day['casual'] / mean_by_reg_co_day['total']


# 绘制堆积柱状图
def stackedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):
    _, ax = plt.subplots()
    # 循环绘制堆积柱状图
    for i in range(0, len(y_data_list)):
        if i == 0:
            ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i])
        else:
            # 采用堆积的方式，除了第一个分类，后面的分类都从前一个分类的柱状图接着画
            # 用归一化保证最终累积结果为1
            ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i])
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)
    ax.legend(loc = 'upper right') # 设定图例位置

# 调用绘图函数
stackedbarplot(x_data = mean_by_reg_co_day.index.values
               , y_data_list = [mean_by_reg_co_day['reg_prop'], mean_by_reg_co_day['casual_prop']]
               , y_data_names = ['Registered', 'Casual']
               , colors = ['#539caf', '#7663b0']
               , x_label = 'Day of week'
               , y_label = 'Proportion of check outs'
               , title = 'Check Outs By Registration Status and Day of Week (0 = Sunday)')

分组柱状图

多级类间绝对数值比较

# 绘制分组柱状图的函数
def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):
    _, ax = plt.subplots()
    # 设置每一组柱状图的宽度
    total_width = 0.8
    # 设置每一个柱状图的宽度
    ind_width = total_width / len(y_data_list)
    # 计算每一个柱状图的中心偏移
    alteration = np.arange(-total_width/2+ind_width/2, total_width/2+ind_width/2, ind_width)

    # 分别绘制每一个柱状图
    for i in range(0, len(y_data_list)):
        # 横向散开绘制
        ax.bar(x_data + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)
    ax.legend(loc = 'upper right')

# 调用绘图函数
groupedbarplot(x_data = mean_by_reg_co_day.index.values
               , y_data_list = [mean_by_reg_co_day['registered'], mean_by_reg_co_day['casual']]
               , y_data_names = ['Registered', 'Casual']
               , colors = ['#539caf', '#7663b0']
               , x_label = 'Day of week'
               , y_label = 'Check outs'
               , title = 'Check Outs By Registration Status and Day of Week (0 = Sunday)')

偏移前：ind_width/2
偏移后：total_width/2
偏移量：total_width/2-ind_width/2

箱式图

多级类间数据分布比较
柱状图 + 堆叠灰度图

# 只需要指定分类的依据，就能自动绘制箱式图
days = np.unique(daily_data['weekday'])
bp_data = []
for day in days:
    bp_data.append(daily_data[daily_data['weekday'] == day]['cnt'].values)

# 定义绘图函数
def boxplot(x_data, y_data, base_color, median_color, x_label, y_label, title):
    _, ax = plt.subplots()

    # 设置样式
    ax.boxplot(y_data
               # 箱子是否颜色填充
               , patch_artist = True
               # 中位数线颜色
               , medianprops = {'color': base_color}
               # 箱子颜色设置，color：边框颜色，facecolor：填充颜色
               , boxprops = {'color': base_color, 'facecolor': median_color}
               # 猫须颜色whisker
               , whiskerprops = {'color': median_color}
               # 猫须界限颜色whisker cap
               , capprops = {'color': base_color})

    # 箱图与x_data保持一致
    ax.set_xticklabels(x_data)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)

# 调用绘图函数
boxplot(x_data = days
        , y_data = bp_data
        , base_color = 'b'
        , median_color = 'r'
        , x_label = 'Day of week'
        , y_label = 'Check outs'
        , title = 'Total Check Outs By Day of Week (0 = Sunday)')

7. 简单总结

关联分析、数值比较：散点图、曲线图
分布分析：灰度图、密度图
涉及分类的分析：柱状图、箱式图

8.案例：2014世界杯决赛分析

step1. 预处理

准备好相应的数据，同时也引入需要的包。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from footyscripts.footyviz import draw_events, draw_pitch, type_names

#plotting settings
%matplotlib inline
pd.options.display.mpl_style = 'default'

df = pd.read_csv("../datasets/germany-vs-argentina-731830.csv", encoding='utf-8', index_col=0)
df.head()

df.index = range(1,len(df) + 1)
df.head()

#standard dimensions
x_size = 105.0
y_size = 68.0
box_height = 16.5*2 + 7.32
box_width = 16.5
y_box_start = y_size/2-box_height/2
y_box_end = y_size/2+box_height/2

#scale of dataset is 100 by 100. Normalizing for a standard soccer pitch size
df['x']=df['x']/100*x_size 
df['y']=df['y']/100*y_size
df['to_x']=df['to_x']/100*x_size
df['to_y']=df['to_y']/100*y_size

#creating some measures and classifiers from the original 
df['count'] = 1
df['dx'] = df['to_x'] - df['x']
df['dy'] = df['to_y'] - df['y']
df['distance'] = np.sqrt(df['dx']**2+df['dy']**2)
df['fivemin'] = np.floor(df['min']/5)*5
df['type_name'] = df['type'].map(type_names.get)
df['to_box'] = (df['to_x'] > x_size - box_width) & (y_box_start < df['to_y']) & (df['to_y'] < y_box_end)
df['from_box'] = (df['x'] > x_size - box_width) & (y_box_start < df['y']) & (df['y'] < y_box_end)
df['on_offense'] = df['x']>x_size/2

添加队名和球员的名字，翻遍后面进行统计和评估

df['team_name'] = np.where(df['team']==357, 'Germany', 'Argentina')

player_dic = {15207:"Philipp Lahm",44989:"Toni Kroos",15208:"Bastian Schweinsteiger",40691:"Jerome Boateng",37605:"Mesut Özil",32644:"Javier Mascherano",66842:"André Schürrle",41316:"Benedikt Höwedes",38392:"Mats Hummels",55634:"Thomas Müller",39462:"Lucas Biglia",28525:"Ezequiel Garay",15312:"Martín Demichelis",20658:"Pablo Zabaleta",19054:"Lionel Messi",58893:"Marcos Rojo",20388:"Manuel Neuer",55661:"Enzo Pérez",42899:"Sergio Agüero",37572:"Sergio Romero",5155:"Miroslav Klose",69600:"Fernando Gago",19975:"Mario Götze",40232:"Gonzalo Higuaín",45154:"Ezequiel Lavezzi",20153:"Rodrigo Palacio",100927:"Christoph Kramer",17127:"Per Mertesacker"}

def get_player_name(player_id):
    return player_dic[player_id]

df['player_name'] = df['player_id'].apply(get_player_name)

#preslicing of the main DataFrame in smaller DFs that will be reused along the notebook
dfPeriod1 = df[df['period']==1]
dfP1Shots = dfPeriod1[dfPeriod1['type'].isin([13, 14, 15, 16])]
dfPeriod2 = df[df['period']==2]
dfP2Shots = dfPeriod2[dfPeriod2['type'].isin([13, 14, 15, 16])]
dfExtraTime = df[df['period']>2]
dfETShots = dfExtraTime[dfExtraTime['type'].isin([13, 14, 15, 16])]

step2. 上半场

咱们快速过一下上半场，下面我们来做一个图标，看看进攻和防守的状况（大于0的上半部分表示德国队的进攻，小于0的部分表示德国队的防守），图中还标出了射球的点。

fig = plt.figure(figsize=(12,4))

avg_x = (dfPeriod1[dfPeriod1['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - 
         dfPeriod1[dfPeriod1['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'])

plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))
plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))

for i, shot in dfP1Shots.iterrows():
    x = shot['min']
    y = avg_x.ix[shot['min']]
    signal = 1 if shot['team_name']=='Germany' else -1
    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+")"), xy=(x, y), xytext=(x-5,y+30*signal), arrowprops=dict(facecolor='black'))

plt.gca().set_xlabel('minute')
plt.title("First Half Profile")

image.png

上半场很有意思的地方在于，德国队基本主导着比赛，使得阿根廷大多数时候都在自己的半场内传球。对于这个的一个可视化，可能更能说明问题，我们一起来看看，阿根廷上半场的传球路径。

draw_pitch()
draw_events(dfPeriod1[(dfPeriod1['type']==1) & (dfPeriod1['outcome']==1) & (dfPeriod1['team_name']=='Argentina')], mirror_away=True)
plt.text(x_size/4, -3, "Germany's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')
plt.text(x_size*3/4, -3, "Argentina's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')
plt.title("Argentina's passes during the first half")

image.png

dfPeriod1.groupby('team_name').agg({'x': np.mean, 'on_offense': np.mean})

dfPeriod1[dfPeriod1.type==1].groupby('team_name').agg({'outcome': np.mean})

上面还做了一个数据的分析，阿根廷大概只有28%的传球是在进攻阶段，而德国有61%是进攻阶段。同时即使是进攻阶段，你会发现德国队也保持着更高的传球准确率。
不过从进入禁区和射门的角度上看，德国队也并没有这么轻松，事实上，从下面我们做出的图里你可以看到，德国队在多次尝试进入禁区射门里，有效的很少。

draw_pitch()
draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==1) & (df['outcome']==1)], mirror_away=True)
draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==1) & (df['outcome']==0)], mirror_away=True, alpha=0.2)
draw_events(dfP1Shots, mirror_away=True, base_color='#a93e3e')
plt.text(x_size/4, -3, "Germany's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')
plt.text(x_size*3/4, -3, "Argentina's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')

image.png

dfPeriod1[(dfPeriod1['to_box']==True) & (dfPeriod1['from_box']==False) & (dfPeriod1['type']==1)].groupby(['team_name']).agg({'outcome': np.mean,  'count': np.sum})

step3. 关于克拉默的分析

大概19分钟的时候，克拉默受伤了，但是12分钟之后才真正换上替补球员。然后你会发现这段时间简直就是德国上半场的地狱期，在我们之前的图表里也可以看出来。
Reports say that he acted confused，相关数据表明在克拉默受伤以后直到替补上场，他基本是“无功能”状态：唯一做的可能就是有一个接应，同时穿了一次球，还失掉了一次球。

dfKramer = df[df['player_name']=='Christoph Kramer']
pd.pivot_table(dfKramer, values='count', index='type_name', columns='min', aggfunc=sum, fill_value=0)

dfKramer['action']=dfKramer['outcome'].map(str) + '-' + dfKramer['type_name']
dfKramer['action'].unique()

score = {'1-LINEUP': 0, '1-RUN WITH BALL': 0.5, '1-RECEPTION': 0, '1-PASS': 1, '0-PASS': -1,
       '0-TACKLE (NO CONTROL)': 0, '1-CLEAR BALL (OUT OF PITCH)': 0.5,
       '0-LOST CONTROL OF BALL': -1, '1-SUBSTITUTION (OFF)': 0}

dfKramer['score'] = dfKramer['action'].map(score.get)

dfKramer.groupby('min')['score'].sum().reindex(range(32), fill_value=0).plot(kind='bar')
plt.annotate('Injury', (19,0.5), (14,1.1), arrowprops=dict(facecolor='black'))
plt.annotate('Substitution', (31,0), (22,1.6), arrowprops=dict(facecolor='black'))
plt.gca().set_xlabel('minute')
plt.gca().set_ylabel('no. events')

image.png

step4. 下半场

相比之下，下半场就势均力敌多了，按照上半场的方式绘出图形，你会发现双方的控球确实是相当的。

fig = plt.figure(figsize=(12,4))

avg_x = (dfPeriod2[dfPeriod2['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - 
         dfPeriod2[dfPeriod2['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'])

plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))
plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))

for i, shot in dfP2Shots.iterrows():
    x = shot['min']
    y = avg_x.ix[shot['min']]
    signal = 1 if shot['team_name']=='Germany' else -1
    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+")"), xy=(x, y), xytext=(x-5,y+30*signal), arrowprops=dict(facecolor='black'))

plt.gca().set_xlabel('minute')
plt.title("Second Half Profile")

image.png

dfPeriod2.groupby('team_name').agg({'x': np.mean, 'on_offense': np.mean})

dfPeriod2[dfPeriod2['type']==1].groupby('team_name').agg({'outcome': np.mean})

draw_pitch()
draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==2) & (df['outcome']==1)], mirror_away=True)
draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==2) & (df['outcome']==0)], mirror_away=True, alpha=0.2)
draw_events(dfP2Shots, mirror_away=True, base_color='#a93e3e')
plt.text(x_size/4, -3, "Germany's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')
plt.text(x_size*3/4, -3, "Argentina's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')

image.png

dfPeriod2[(dfPeriod2['to_box']==True) & (dfPeriod2['from_box']==False) & (dfPeriod2['type']==1)].groupby(['team_name']).agg({'outcome': np.mean,  'count': np.sum})

step5. 加时部分

fig = plt.figure(figsize=(12,4))

avg_x = (dfExtraTime[dfExtraTime['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - 
         dfExtraTime[dfExtraTime['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'].reindex(dfExtraTime['min'].unique(), fill_value=0))

plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))
plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))

for i, shot in dfETShots.iterrows():
    x = shot['min']
    y = avg_x.ix[shot['min']]
    signal = 1 if shot['team_name']=='Germany' else -1
    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+")"), xy=(x, y), xytext=(x-5,y+20*signal), arrowprops=dict(facecolor='black'))

plt.gca().set_xlabel('minute')
plt.title("Extra Time Profile")

image.png

df.groupby(['team_name', 'period']).agg({'count': np.sum, 'x': np.mean, 'on_offense': np.mean})

我们发现德国队的第4段和其余阶段很不同，德国队明显减少了传球次数，他们在试图控制比赛，把节奏放慢（有点拖延时间的味道？）。你可以看看在德国队的上一记射门之后的数据，更能体现这一点。

goal_ix = df[df['type']==16].index[0]
df_after_shot = df.ix[goal_ix+1:]
df_after_shot.groupby(['team_name', 'period']).agg({'count': np.sum, 'x': np.mean, 'on_offense': np.mean})

draw_pitch()
draw_events(df_after_shot[(df_after_shot['to_box']==True) & (df_after_shot['type']==1) & (df_after_shot['from_box']==False) & (df_after_shot['outcome']==1)], mirror_away=True)
draw_events(df_after_shot[(df_after_shot['to_box']==True) & (df_after_shot['type']==1) & (df_after_shot['from_box']==False) & (df_after_shot['outcome']==0)], mirror_away=True, alpha=0.2)
draw_events(df_after_shot[df_after_shot['type'].isin([13,14,15,16])], mirror_away=True, base_color='#a93e3e')
plt.text(x_size/4, -3, "Germany's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')
plt.text(x_size*3/4, -3, "Argentina's defense", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')

image.png

df_after_shot[df_after_shot['type'].isin([13,14,15,16])][['min', 'player_name', 'team_name', 'type_name']]

德国队基本不打算继续射门了，只有一次是试图把球传入禁区的。但是他们的防守策略非常成功，以至于阿根廷基本很难进入他们的禁区。2记射门全都是禁区外射门的，而且都出自梅西之脚，然而梅西可能到这时候也深感绝望了。

step6. 射门

goal = int(df[df['type']==16].index[0])
dfGoal = df.ix[goal-30:goal]
#goal = np.where(df.type==16)[0][0]
#dfGoal = df.iloc[goal-30:goal+1]
draw_pitch()
draw_events(dfGoal[dfGoal.team_name=='Germany'], base_color='white')
draw_events(dfGoal[dfGoal.team_name=='Argentina'], base_color='cyan')

image.png

#Germany's players involved in the play
dfGoal['progression']=dfGoal['to_x']-dfGoal['x']
dfGoal[dfGoal['type'].isin([1, 101, 16])][['player_name', 'type_name', 'progression']]

step7. 一些基础数据

#passing accuracy
df.groupby(['player_name', 'team_name']).agg({'count': np.sum, 'outcome': np.mean}).sort('count', ascending=False)

#shots
pd.pivot_table(df[df['type'].isin([13,14,15,16])],
               values='count',
               aggfunc=sum,
               index=['player_name', 'team_name'], 
               columns='type_name',
               fill_value=0,
               margins=True).sort('All', ascending=False)

#defensive play
pd.pivot_table(df[df['type'].isin([7, 8, 49])],
               values='count',
               aggfunc=np.sum,
               index=['player_name', 'team_name'], 
               columns='type_name',
               fill_value=0,
               margins=True).sort('All', ascending=False)

visualization——matplotlib

1.matplotlib安装配置

2.一副可视化图的基本结构

3.画法

3.1 导包

3.2 准备数据

3.3绘制基本曲线

3.3.1 关于颜色的补充

3.3.2 linestyle参数

3.3.3 marker参数

3.4 设置坐标轴

3.5 设置文字描述、注解

3.6 设置图例

3.7 网格线开关

3.8 显示与图像保存

4. 完整的绘制程序

5.常用图像

6.案例：自行车租赁数据分析与可视化

step1. 导入数据，做简单的数据处理

step2. 配置参数

step3. 关联分析

step4. 分布分析

step5. 组间分析

7. 简单总结

8.案例：2014世界杯决赛分析

step1. 预处理

step2. 上半场

step3. 关于克拉默的分析

step4. 下半场

step5. 加时部分

step6. 射门

step7. 一些基础数据

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