###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()
复制代码>#####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)
<br>
####【课程3.7】 柱状图、堆叠图
    plt.plot(kind='bar/barh') , plt.bar()
>##### 柱状图与堆叠图
复制代码柱状图与堆叠图
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.
<br>
>##### 柱状图 plt.bar()
复制代码柱状图 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 = y10.1) plt.bar(x, y2, width = 1, facecolor = "lightskyblue", edgecolor = "white", yerr = y20.1)
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")
>##### 外嵌图表plt.table()
复制代码外嵌图表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
<br>
####【课程3.8】 面积图、填图、饼图
    plt.plot.area()
    plt.fill(), plt.fill_between()
    plt.pie()
>#####面积图
复制代码面积图
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
<br>
>#####填图
复制代码填图
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
<br>
>##### 饼图 plt.pie()
复制代码饼图 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
<br>
####【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
复制代码直方图+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
<br>
>##### 堆叠直方图
复制代码堆叠直方图
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
<br>
####【课程3.10】 散点图、矩阵散点图
    plt.scatter(), pd.scatter_matrix()
>##### plt.scatter()散点图
复制代码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
<br>
>##### pd.scatter_matrix()散点矩阵
复制代码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
<br>
####【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
复制代码创建极坐标轴
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, theta3, linestyle = "--", lw = 1) ax1.plot(s, linestyle = "--", marker = ".", lw = 2) ax2.plot(theta, theta3, 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
<br>
>##### 极坐标参数设置
复制代码极坐标参数设置
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
<br>
>##### 雷达图1 - 极坐标的折线图/填图 - plt.plot()
复制代码雷达图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, 2np.pi, 2np.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
<br>
>##### 雷达图2 - 极坐标的折线图/填图 - plt.polar()
复制代码雷达图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
<br>
>##### 极轴图 - 极坐标的柱状图
复制代码极轴图 - 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, 2np.pi, 2np.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）
<br>
####【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
复制代码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
<br>
>##### plt.boxplot()绘制
复制代码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,
<br>
> #### plt.boxplot()绘制
复制代码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
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#####最后：
[以上完整代码](https://github.com/ChaoRenYuan/Python/tree/master/Python数据分析工具/图表绘制工具：Matplotlib)
复制代码转载于:https://juejin.im/post/5b0e6a036fb9a009f87252ee