1.echart可做出更精美的图片，matplotlib和seaborn可以对数据进行简单的可视化操作
2.series或dateframe转化成array使用方法.values，在可视化过程中尽量用np数组的形式
3.%matplotlib inline 图是嵌入在notebook里面的不是跳出来一个框的形式
4.matplotlib.pyplot作图的子模块
5.x = np.arange(0., 10, 0.2)，0-10之间，间隔0.2取一个数
6.numpy是具有boardcast的特性
7.plt.rcParams["figure.figsize"] = (12,8)设置图的尺寸
8.plt.plot(x, y1, color='blue', linewidth=1.5, linestyle='-', marker='.', label=r'$y = cos{x}$')作图的轮廓，其中label是取名作用，在这里可以不指定，下面区分图例使用
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)
10.plt一般是全局的做图
11.常用图形：曲线图、灰度图、散点图、箱式图、饼状图
12.可视化详细的网站：http://matplotlib.org/api/pyplot_api.html
更详细的网站：http://matplotlib.org/api/index.html
13.自行车租赁的案例
##加载相应的包
import pandas as pd # 读取数据到DataFrame
import urllib # 获取网络数据
import shutil # 文件操作
import zipfile # 压缩解压
import os#和文件，目录等打交道的一个库
## 建立临时目录
try:
os.system('mkdir bike_data')
except:
os.system('rm -rf bike_data; mkdir bike_data')
data_source = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip' # 网络数据地址
zipname = 'bike_data/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.extractall('bike_data')
zip_ref.close()
daily_path = 'bike_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
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
fontP = font_manager.FontProperties()
fontP.set_family('SimHei')
fontP.set_size(1000)
# 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'].values
, y_data = daily_data['cnt'].values
, x_label = 'temperature (C)'
, y_label = 'Check outs'
, title = 'Number of Check Outs vs Temperature')
####做曲线图
# 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]
print(data)
print(fitted_values)
print(ss2)
# 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')
########做带置信区间的曲线图
# 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')
###### 双坐标轴的曲线图
# 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')
2.series或dateframe转化成array使用方法.values，在可视化过程中尽量用np数组的形式
3.%matplotlib inline 图是嵌入在notebook里面的不是跳出来一个框的形式
4.matplotlib.pyplot作图的子模块
5.x = np.arange(0., 10, 0.2)，0-10之间，间隔0.2取一个数
6.numpy是具有boardcast的特性
7.plt.rcParams["figure.figsize"] = (12,8)设置图的尺寸
8.plt.plot(x, y1, color='blue', linewidth=1.5, linestyle='-', marker='.', label=r'$y = cos{x}$')作图的轮廓，其中label是取名作用，在这里可以不指定，下面区分图例使用
9.完整的绘图
#加载包
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()
savefig('../figures/plot3d_ex.png',dpi=48) # 保存，前提目录存在，dpi分辨率10.plt一般是全局的做图
11.常用图形：曲线图、灰度图、散点图、箱式图、饼状图
12.可视化详细的网站：http://matplotlib.org/api/pyplot_api.html
更详细的网站：http://matplotlib.org/api/index.html
13.自行车租赁的案例
##加载相应的包
import pandas as pd # 读取数据到DataFrame
import urllib # 获取网络数据
import shutil # 文件操作
import zipfile # 压缩解压
import os#和文件，目录等打交道的一个库
## 建立临时目录
try:
os.system('mkdir bike_data')
except:
os.system('rm -rf bike_data; mkdir bike_data')
data_source = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip' # 网络数据地址
zipname = 'bike_data/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.extractall('bike_data')
zip_ref.close()
daily_path = 'bike_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
daily_data.head() # 看一看数据~
#####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')
######做散点图，查看关联性
from matplotlib import font_managerfontP = font_manager.FontProperties()
fontP.set_family('SimHei')
fontP.set_size(1000)
# 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'].values
, y_data = daily_data['cnt'].values
, x_label = 'temperature (C)'
, y_label = 'Check outs'
, title = 'Number of Check Outs vs Temperature')
####做曲线图
# 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]
print(data)
print(fitted_values)
print(ss2)
# 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')
########做带置信区间的曲线图
# 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')
###### 双坐标轴的曲线图
# 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')