序言：
Python的可视化工具,以下截图，均以展示图表实例，如需了解部分对象的输出结果，可参照我Github上的代码，3Q🌹
【课程3.6】 基本图表绘制 plt.plot()
图表类别：线形图、柱状图、密度图，以横纵坐标两个维度为主
同时可延展出多种其他图表样式
plt.plot(kind='line', ax=None, figsize=None, use_index=True, title=None, grid=None, legend=False,
style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None,
rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds)
Series直接生成图表
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Series直接生成图表
ts = pd.Series(np.random.randn(1000), index = pd.date_range("1/1/2000", periods = 1000))
ts = ts.cumsum()
ts.plot(kind = "line",
label = "heheheh", # Series需要Lable
style = "--g.",
color = "red",
alpha = 0.4,
use_index = True,
rot = 45,
grid = True,
ylim = [-50, 50],
yticks = list(range(-50, 50, 10)),
figsize = (8,4),
title = "test",
legend = True)
# plt.grid(True, linestyle = "--",color = "gray", linewidth = "0.5",axis = 'x') # 网格
plt.legend()
# Series.plot()：series的index为横坐标，value为纵坐标
# kind → line,bar,barh...（折线图，柱状图，柱状图-横...）
# label → 图例标签，Dataframe格式以列名为label
# style → 风格字符串，这里包括了linestyle（-），marker（.），color（g）
# color → 颜色，有color指定时候，以color颜色为准
# alpha → 透明度，0-1
# use_index → 将索引用为刻度标签，默认为True
# rot → 旋转刻度标签，0-360
# grid → 显示网格，一般直接用plt.grid
# xlim,ylim → x,y轴界限
# xticks,yticks → x,y轴刻度值
# figsize → 图像大小
# title → 图名
# legend → 是否显示图例，一般直接用plt.legend()
# 也可以 → plt.plot()
1.png
DataFrame直接生成图表
# DataFrame直接生成图表
df = pd.DataFrame(np.random.randn(1000, 4), index = ts.index, columns = list("ABCD"))
df = df.cumsum()
df.plot(kind = "line",
style = "--.",
alpha = 0.4,
use_index = True,
rot = 45,
grid = True,
figsize = (8,4),
title = "test",
legend = True,
subplots = False,
colormap = "Greens")
# subplots -> 是否将各个列绘制到不同图表，默认False
# 也可以 -> plt.plot(df)
2.png
【课程3.7】 柱状图、堆叠图
plt.plot(kind='bar/barh') , plt.bar()
柱状图与堆叠图
# 柱状图与堆叠图
fig, axes = plt.subplots(4,1,figsize = (10,10))
s = pd.Series(np.random.randint(0,10,16), index = list("abcdefghijklmnop"))
df = pd.DataFrame(np.random.rand(10,3), columns = ["a", "b", "c"])
s.plot(kind = "bar", color = "k", grid = True, alpha = 0.5, ax = axes[0]) # ax参数 -> 选择第几个子图
# 单系列柱状图方法一：plt.plot(kind = "bar/barh")
df.plot(kind = "bar", ax = axes[1], grid = True, colormap = "Reds_r")
# 多序列柱状图
df.plot(kind = "bar", ax = axes[2], grid = True, colormap = "Blues_r", stacked = True)
# 多系列堆叠图
# stacked -> 堆叠
df.plot.barh(ax = axes[3], grid = True, stacked = True, colormap = "BuGn_r")
# 新版本plt.plot.<kind>
3.png
柱状图 plt.bar()
# 柱状图 plt.bar()
plt.figure(figsize=(10,4))
x = np.arange(10)
y1 = np.random.rand(10)
y2 = -np.random.rand(10)
plt.bar(x, y1, width = 1, facecolor = "yellowgreen", edgecolor = "white", yerr = y1*0.1)
plt.bar(x, y2, width = 1, facecolor = "lightskyblue", edgecolor = "white", yerr = y2*0.1)
# x,y参数：x，y值
# width：宽度比例
# facecolor柱状图里填充的颜色、edgecolor是边框的颜色
# left-每个柱x轴左边界,bottom-每个柱y轴下边界 → bottom扩展即可化为甘特图 Gantt Chart
# align：决定整个bar图分布，默认left表示默认从左边界开始绘制,center会将图绘制在中间位置
# xerr/yerr ：x/y方向error bar
for i,j in zip(x,y1):
plt.text(i+0.3, j-0.15, "%.2f" %j, color = "k")
for i,j in zip(x,y2):
plt.text(i+0.3, j+0.05, "%.2f" %-j, color = "k")
4.png
外嵌图表plt.table()
# 外嵌图表plt.table()
# table(cellText=None, cellColours=None,cellLoc='right', colWidths=None,rowLabels=None, rowColours=None, rowLoc='left',
# colLabels=None, colColours=None, colLoc='center',loc='bottom', bbox=None)
data = [[ 66386, 174296, 75131, 577908, 32015],
[ 58230, 381139, 78045, 99308, 160454],
[ 89135, 80552, 152558, 497981, 603535],
[ 78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
df = pd.DataFrame(data,columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail'),
index = ['%d year' % x for x in (100, 50, 20, 10, 5)])
print(df)
df.plot(kind='bar',grid = True,colormap='Blues_r',stacked=True,figsize=(8,3))
# 创建堆叠图
plt.table(cellText = data,
cellLoc='center',
cellColours = None,
rowLabels = rows,
rowColours = plt.cm.BuPu(np.linspace(0, 0.5,5))[::-1], # BuPu可替换成其他colormap
colLabels = columns,
colColours = plt.cm.Reds(np.linspace(0, 0.5,5))[::-1],
rowLoc='right',
loc='bottom')
# cellText：表格文本
# cellLoc：cell内文本对齐位置
# rowLabels：行标签
# colLabels：列标签
# rowLoc：行标签对齐位置
# loc：表格位置 → left，right，top，bottom
plt.xticks([])
# 不显示x轴标注
5.png
【课程3.8】 面积图、填图、饼图
plt.plot.area()
plt.fill(), plt.fill_between()
plt.pie()
面积图
# 面积图
fig, axes = plt.subplots(2, 1, figsize = (8,6))
df1 = pd.DataFrame(np.random.rand(10, 4), columns = ["a", "b", "c", "d"])
df2 = pd.DataFrame(np.random.randn(10, 4), columns = ["a", "b", "c", "d"])
df1.plot.area(colormap = "Greens_r", alpha = 0.5, ax = axes[0])
df2.plot.area(stacked = False, colormap = "Set2", alpha = 0.5, ax = axes[1])
# 使用Series.plot.area()和DataFrame.plot.area()创建面积图
# stacked：是否堆叠，默认情况下，区域图被堆叠
# 为了产生堆积面积图，每列必须是正值或全部负值！
# 当数据有NaN时候，自动填充0，所以图标签需要清洗掉缺失值
6.png
填图
# 填图
fig, axes = plt.subplots(2, 1, figsize = (8,6))
x = np.linspace(0, 1, 500)
y1 = np.sin(4 * np.pi * x) * np.exp(-5 * x)
y2 = -np.sin(4 * np.pi * x) * np.exp(-5 * x)
axes[0].fill(x, y1, "r", alpha = 0.5, label = "y1")
axes[0].fill(x, y2, "g", alpha = 0.5, label = "y2")
# 对函数与坐标轴之间的区域进行填充，使用fill函数
# 也可写成：plt.fill(x, y1, "r", x, y2, "g", alpha = 0.5)
x = np.linspace(0, 5 * np.pi, 1000)
y1 = np.sin(x)
y2 = np.sin(2 * x)
axes[1].fill_between(x, y1, y2, color = "b", alpha = 0.5, label = "area")
# 填充两个函数之间的区域，使用fill_between函数
for i in range(2):
axes[i].legend()
axes[i].grid()
# 添加图例、网格
7.png
饼图 plt.pie()
# 饼图 plt.pie()
# plt.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,
# radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, hold=None, data=None)
s = pd.Series(3 * np.random.rand(4), index = ["a", "b", "c", "d"], name = "series")
plt.axis("equal") # 保证长宽相等
plt.pie(s,
explode = [0.1, 0, 0, 0],
labels = s.index,
colors = ["r", "g", "b", "c"],
autopct = ".2f%%",
pctdistance = 0.6,
labeldistance = 1.2,
shadow = True,
startangle = 0,
radius = 1.5,
frame = False)
print(s)
# 第一个参数：数据
# explode：指定每部分的偏移量
# labels：标签
# colors：颜色
# autopct：饼图上的数据标签显示方式
# pctdistance：每个饼切片的中心和通过autopct生成的文本开始之间的比例
# labeldistance：被画饼标记的直径,默认值：1.1
# shadow：阴影
# startangle：开始角度
# radius：半径
# frame：图框
# counterclock：指定指针方向，顺时针或者逆时针
8.png
【课程3.9】 直方图
plt.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None,
histtype='bar', align='mid', orientation='vertical',rwidth=None, log=False, color=None, label=None,
stacked=False, hold=None, data=None, **kwargs)
直方图+密度图
# 直方图+密度图
s = pd.Series(np.random.randn(1000))
s.hist(bins = 20,
histtype = "bar",
align = "mid",
orientation = "vertical",
alpha = 0.5,
normed = True)
# bin：箱子的宽度
# normed 标准化
# histtype 风格，bar，barstacked，step，stepfilled
# orientation 水平还是垂直{"horizontal", "vertical"}
# align：{"left", "mid", "right"}, optional(对齐方式)
s.plot(kind = "kde", style = "k--")
# 密度图
9.png
堆叠直方图
# 堆叠直方图
plt.figure(num=1)
df = pd.DataFrame({"a": np.random.randn(1000) + 1,
"b": np.random.randn(1000),
"c": np.random.randn(1000) - 1,
"d": np.random.randn(1000) - 2},
columns = ["a", "b", "c", "d"])
df.plot.hist(stacked = True,
bins = 20,
colormap = "Greens_r",
alpha = 0.5,
grid = True)
# 使用DataFrame.plot.hist()和Series.plot.hist()方法绘制
# stacked：是否堆叠
df.hist(bins = 50)
# 生成多个直方图
10.png
【课程3.10】 散点图、矩阵散点图
plt.scatter(), pd.scatter_matrix()
plt.scatter()散点图
# plt.scatter()散点图
# plt.scatter(x, y, s=20, c=None, marker='o', cmap=None, norm=None, vmin=None, vmax=None,
# alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)
plt.figure(figsize = (8,6))
x = np.random.randn(1000)
y = np.random.randn(1000)
plt.scatter(x, y, marker = ".",
s = np.random.randn(1000)*100,
cmap = "Reds",
c = y,
alpha = 0.8)
plt.grid()
# s：散点的大小
# c：散点的颜色
# vmin，vmax：高度设置，标量
# cmap：colormap
11.png
pd.scatter_matrix()散点矩阵
# pd.scatter_matrix()散点矩阵
# pd.scatter_matrix(frame, alpha=0.5, figsize=None, ax=None,
# grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwds)
df = pd.DataFrame(np.random.randn(100, 4), columns = ["a", "b", "c", "d"])
pd.scatter_matrix(df, figsize = (10,6),
marker = "o",
diagonal = "kde",
alpha = 0.5,
range_padding = 0.1)
# diagonal：({‘hist’, ‘kde’})，必须且只能在{‘hist’, ‘kde’}中选择1个 → 每个指标的频率图
# range_padding：(float, 可选)，图像在x轴、y轴原点附近的留白(padding)，该值越大，留白距离越大，图像远离坐标原点
12.png
【课程3.11】 极坐标图
调用subplot()创建子图时通过设置projection='polar',便可创建一个极坐标子图，然后调用plot()在极坐标子图中绘图
创建极坐标轴
# 创建极坐标轴
s = pd.Series(np.arange(20))
theta = np.arange(0, 2*np.pi, 0.02)
print(s.head())
print(theta[:10])
# 创建数据
fig = plt.figure(figsize = (8,4))
ax1 = plt.subplot(121, projection = "polar")
ax2 = plt.subplot(122)
# 创建极坐标子图
# 还可以写：ax = fig.add_subplot(111, polar = True)
ax1.plot(theta, theta*3, linestyle = "--", lw = 1)
ax1.plot(s, linestyle = "--", marker = ".", lw = 2)
ax2.plot(theta, theta*3, linestyle = "--", lw = 1)
ax2.plot(s)
plt.grid()
# 创建极坐标图，参数1为角度（弧度制），参数2为value
# lw -> 线宽
13.png
极坐标参数设置
# 极坐标参数设置
theta = np.arange(0, 2*np.pi, 0.02)
plt.figure(figsize = (8,4))
ax1 = plt.subplot(121, projection = "polar")
ax2 = plt.subplot(122, projection = "polar")
ax1.plot(theta, theta/6, "--", lw = 2)
ax2.plot(theta, theta/6, "--", lw = 2)
# 创建极坐标子图ax
ax2.set_theta_direction(-1)
# set_theta_direction()：坐标轴正方向，默认1逆时针，如果是-1为顺时针
ax2.set_thetagrids(np.arange(0.0, 360.0, 90), ["a", "b","c", "d"])
ax2.set_rgrids(np.arange(0.2, 2, 0.4))
# set_thetagrids()：设置极坐标角度网格线显示及标签 -> 网格和标签数量一致
# set_rgrids()：设置极经网格线显示，其中参数必须是正数
ax2.set_theta_offset(np.pi/2)
# set_theta_offset()：设置角度偏移，逆时针，弧度制
ax2.set_rlim(0.2, 1.2)
ax2.set_rmax(2)
ax2.set_rticks(np.arange(0.1, 1.5, 0.2))
# set_rlim()：设置显示的极径范围
# set_rmax()：设置显示的极径最大值
# set_rticks()：设置极径网格线的显示范围
14.png
雷达图1 - 极坐标的折线图/填图 - plt.plot()
# 雷达图1 - 极坐标的折线图/填图 - plt.plot()
plt.figure(figsize = (8,4))
ax1 = plt.subplot(111, projection = "polar")
ax1.set_title("radar map\n") # 创建标题
ax1.set_rlim(0, 12)
data1 = np.random.randint(1, 10, 10)
data2 = np.random.randint(1, 10, 10)
data3 = np.random.randint(1, 10, 10)
theta = np.arange(0, 2*np.pi, 2*np.pi/10)
# 创建数据
ax1.plot(theta, data1, ".--", label = "data1")
ax1.fill(theta, data1, alpha = 0.2)
ax1.plot(theta, data2, ".--", label = "data2")
ax1.fill(theta, data2, alpha = 0.2)
ax1.plot(theta, data3, ".--", label = "data3")
ax1.fill(theta, data3, alpha = 0.2)
# 绘制雷达线
15.png
雷达图2 - 极坐标的折线图/填图 - plt.polar()
# 雷达图2 - 极坐标的折线图/填图 - plt.polar()
# 首尾闭合
labels = np.array(["a", "b", "c", "d", "e", "f"]) # 标签
dataLenth = 6 # 数据长度
data1 = np.random.randint(0, 10, 6)
data2 = np.random.randint(0, 10, 6) # 数据
angles = np.linspace(0, 2*np.pi, dataLenth, endpoint = False) # 分割圆周长
data1 = np.concatenate((data1, [data1[0]])) # 闭合
data2 = np.concatenate((data2, [data2[0]])) # 闭合
angles = np.concatenate((angles, [angles[0]])) # 闭合
plt.polar(angles, data1, "o-", linewidth = 1) # 做极坐标系
plt.fill(angles, data1, alpha = 0.25) # 填充
plt.polar(angles, data2, "o-", linewidth = 1) # 做极坐标系
plt.fill(angles, data2, alpha = 0.25) # 填充
plt.thetagrids(angles * 180/np.pi, labels) # 设置网格、标签
plt.ylim(0, 10) # polar的极值设置为ylim
16.png
极轴图 - 极坐标的柱状图
# 极轴图 - 极坐标的柱状图
plt.figure(figsize = (8,4))
ax1 = plt.subplot(111, projection = "polar")
ax1.set_title("radar map\n") # 创建标题
ax1.set_rlim(0, 12)
data = np.random.randint(1, 10, 10)
theta = np.arange(0, 2*np.pi, 2*np.pi / 10)
# 创建数据
bar = ax1.bar(theta, data, alpha = 0.5)
for r,bar in zip(data, bar):
bar.set_facecolor(plt.cm.jet(r/10.)) # 设置颜色
plt.thetagrids(np.arange(0.0, 360.0, 90), []) # 设置网格、标签（这里是空标签，则不显示内容）
17.png
【课程3.12】 箱型图
箱型图：又称为盒须图、盒式图、盒状图或箱线图，是一种用作显示一组数据分散情况资料的统计图
包含一组数据的：最大值、最小值、中位数、上四分位数（Q3）、下四分位数（Q1）、异常值
① 中位数 → 一组数据平均分成两份，中间的数
② 上四分位数Q1 → 是将序列平均分成四份，计算(n+1)/4与(n-1)/4两种，一般使用(n+1)/4
③ 下四分位数Q3 → 是将序列平均分成四份，计算(1+n)/4*3=6.75
④ 内限 → T形的盒须就是内限，最大值区间Q3+1.5IQR,最小值区间Q1-1.5IQR （IQR=Q3-Q1）
⑤ 外限 → T形的盒须就是内限，最大值区间Q3+3IQR,最小值区间Q1-3IQR （IQR=Q3-Q1）
⑥ 异常值 → 内限之外 - 中度异常，外限之外 - 极度异常
plt.plot.box(),plt.boxplot()
plt.plot.box()绘制
# plt.plot.box()绘制
fig, axes = plt.subplots(2, 1, figsize = (10,6))
df = pd.DataFrame(np.random.rand(10, 5), columns = ["A", "B", "C", "D", "E"])
color = dict(boxes = "DarkGreen", whiskers = "DarkOrange", medians = "DarkBlue", caps = "Gray")
# 箱型图着色
# boxes → 箱线
# whiskers → 分位数与error bar横线之间竖线的颜色
# medians → 中位数线颜色
# caps → error bar横线颜色
df.plot.box(ylim = [0, 1.2],
grid = True,
color = color,
ax = axes[0])
# color：样式填充
df.plot.box(vert = False,
positions = [1, 4, 5, 6, 8],
ax = axes[1],
grid = True,
color = color)
# vert：是否垂直，默认True
# position：箱型图占位
18.png
plt.boxplot()绘制
# plt.boxplot()绘制
# pltboxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None,
# usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None,
# labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False,
# zorder=None, hold=None, data=None)
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
plt.figure(figsize=(10,4))
# 创建图表、数据
f = df.boxplot(sym = "o", # 异常点形状，参考marker
vert = True, # 是否垂直
whis = 1.5, # IQR，默认1.5， 也可以设置区间比如[5, 95], 代表强制上下边缘为数据95%和5%位置
patch_artist = True, # 上下四分位框内是否填充，True会填充
meanline = False, showmeans = True, # 是否有均值线及其形状
showbox = True, # 是否显示箱线
showcaps = True, # 是否显示边缘线
showfliers = True, # 是否显示异常值
notch = False, # 中间箱体是否缺口
return_type = 'dict' # 返回类型为字典
)
plt.title("boxplot")
print(f)
for box in f["boxes"]:
box.set(color = "b", linewidth = 1) # 箱体边框颜色
box.set(facecolor = "b", alpha = 0.5) # 箱体内部填充颜色
for whisker in f["whiskers"]:
whisker.set(color = "k", linewidth = 0.5, linestyle = "-")
for cap in f["caps"]:
cap.set(color = "red", linewidth = 2)
for median in f["medians"]:
median.set(color = "DarkBlue", linewidth = 2)
for flier in f["fliers"]:
flier.set(marker = "o", color = "y", alpha = 0.5)
# boxes, 箱线
# medians, 中位值的横线,
# whiskers, 从box到error bar之间的竖线.
# fliers, 异常值
# caps, error bar横线
# means, 均值的横线,
19.png
plt.boxplot()绘制
# plt.boxplot()绘制
# 分组汇总
df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])
print(df.head())
df.boxplot(by = 'X')
df.boxplot(column=['Col1','Col2'], by=['X','Y'])
# columns：按照数据的列分子图
# by：按照列分组做箱型图
20.png
