Pandas一行代码绘制25种美图

妖狐艹你老母 2022-09-11 08:30 130阅读 0赞

![2c47dea45e2e5331c3a775fba6e6fb2e.gif][]

**导读：**今天介绍一下，如何用Pandas的一行代码绘制 25 种美图。

作者 / 来源：pythonic生物人

![212e3d6b36523f09db91e7124c22049f.png][]

本文涉及：

> 单组折线图、多组折线图、单组条形图、多组条形图、堆积条形图、水平堆积条形图、直方图、分面直方图、箱图、面积图、堆积面积图、散点图、单组饼图、多组饼图、分面图、hexbin图、andrews\_curves图、核密度图、parallel\_coordinates图、autocorrelation\_plot图、radviz图、bootstrap\_plot图、子图（subplot）、子图任意排列、图中绘制数据表格

Pandas可视化主要依赖下面两个函数：

*  pandas.DataFrame.plot

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html?highlight=plot\#pandas.DataFrame.plot

*  pandas.Series.plot

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html?highlight=plot\#pandas.Series.plot

可绘制下面几种图，注意Dataframe和Series的细微差异：'area', 'bar', 'barh', 'box', 'density', 'hexbin', 'hist', 'kde', 'line', 'pie', 'scatter'

![595a42d50f6329310314d606a23aeb22.png][]

导入依赖包

import matplotlib.pyplot as plt 
    import numpy as np
    import pandas as pd
    from pandas import DataFrame,Series
    plt.style.use('dark_background')#设置绘图风格

**01 单组折线图**

np.random.seed(0)#使得每次生成的随机数相同
    ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
    ts1 = ts.cumsum()#累加
    ts1.plot(kind="line")#默认绘制折线图

![0981058bf990de5d4ca1d450a3073df3.png][]

**02 多组折线图**

np.random.seed(0)
    df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
    df = df.cumsum()
    df.plot()#默认绘制折线图

![ae1205d4431de1207e1f7a57372ff43f.png][]

**03 单组条形图**

df.iloc[5].plot(kind="bar")

![0edbc3e82393a419662af8d549fe135e.png][]

**04 多组条形图**

df2 = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])
    df2.plot.bar()

![52eccd1b73e8d4ab574c5cbe5235eb00.png][]

### **05 堆积条形图** ###

df2.plot.bar(stacked=True)

![1c4847196d6dffa95ed81c7986c5665b.png][]

### **06 水平堆积条形图** ###

df2.plot.barh(stacked=True)

![2dd73d2e52fe9cdc15b91bc8d6fa96e9.png][]

### **07 直方图** ###

df4 = pd.DataFrame(
        {
            "a": np.random.randn(1000) + 1,
            "b": np.random.randn(1000),
            "c": np.random.randn(1000) - 1,
        },
        columns=["a", "b", "c"],
    )
    df4.plot.hist(alpha=0.8)

![ecc3485f47b6e132a49417c958b5c969.png][]

### **08 分面直方图** ###

df.diff().hist(color="r", alpha=0.9, bins=50)

![9b415a3b35c8b1078931cffe77a9ac2d.png][]

### **09 箱图** ###

df = pd.DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])
    df.plot.box()

![232ae907699f7190f3e1b8e42c8e1643.png][]

### **10 面积图** ###

df = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])
    df.plot.area()

![c5ecfb3f1e4cfe224c86da692c90cd9f.png][]

### **11 堆积面积图** ###

df.plot.area(stacked=False)

![4e4890f0dfb7368cb4000d854bc4ca04.png][]

### **12 散点图** ###

ax = df.plot.scatter(x="a", y="b", color="r", label="Group 1",s=90)
    df.plot.scatter(x="c", y="d", color="g", label="Group 2", ax=ax,s=90)

![977d45af04e3b414b8d669ec721c8c41.png][]

### **13 单组饼图** ###

series = pd.Series(3 * np.random.rand(4), index=["a", "b", "c", "d"], name="series")
    series.plot.pie(figsize=(6, 6))

![e081bc8225ed0374a761fbb953b57383.png][]

### **14 多组饼图** ###

df = pd.DataFrame(
        3 * np.random.rand(4, 2), index=["a", "b", "c", "d"], columns=["x", "y"]
    )
    df.plot.pie(subplots=True, figsize=(8, 4))

![2e4d536b3571048836d51fe15fd690b5.png][]

### **15 分面图** ###

import matplotlib as mpl
    mpl.rc_file_defaults()
    plt.style.use('fivethirtyeight')
    from pandas.plotting import scatter_matrix
    df = pd.DataFrame(np.random.randn(1000, 4), columns=["a", "b", "c", "d"])
    scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal="kde")
    plt.show()

![6cbe8bc9eb683c30784b7ed8cb2f7296.png][]

### **16 hexbin图** ###

df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
    df["b"] = df["b"] + np.arange(1000)
    df.plot.hexbin(x="a", y="b", gridsize=25)

![c44868cbc21ce375fe8eb51bdd34804d.png][]

### **17 andrews\_curves图** ###

from pandas.plotting import andrews_curves
    mpl.rc_file_defaults()
    data = pd.read_csv("iris.data.txt")
    plt.style.use('dark_background')
    andrews_curves(data, "Name")

![816137dd7b4e30848f31b18d703bff07.png][]

### **18 核密度图** ###

ser = pd.Series(np.random.randn(1000))
    ser.plot.kde()

![a83f12b394524d19557527e4866f771a.png][]

### **19 parallel\_coordinates图** ###

from pandas.plotting import parallel_coordinates
    data = pd.read_csv("iris.data.txt")
    plt.figure()
    parallel_coordinates(data, "Name")

![8858ade9f366142308366b3c00fe5497.png][]

### **20 autocorrelation\_plot图** ###

from pandas.plotting import autocorrelation_plot
    plt.figure();
    spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
    data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
    autocorrelation_plot(data)

![c85d0062cce5624a4454677e051a088c.png][]

### **21 radviz图** ###

from pandas.plotting import radviz
    data = pd.read_csv("iris.data.txt")
    plt.figure()
    radviz(data, "Name")

![df85b088943b000a51662bfca281d492.png][]

### **22 bootstrap\_plot图** ###

from pandas.plotting import bootstrap_plot
    data = pd.Series(np.random.rand(1000))
    bootstrap_plot(data, size=50, samples=500, color="grey")

![d49bfc2c66b75a89318e8ba913e6194c.png][]

### **23 子图（subplot）** ###

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
    df.plot(subplots=True, figsize=(6, 6))

![7e05831a8200d74eeca9be0aaac6c77a.png][]

### **24 子图任意排列** ###

df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False)

![786bf81deef199886814e0c7bc18f89b.png][]

fig, axes = plt.subplots(4, 4, figsize=(9, 9))
    plt.subplots_adjust(wspace=0.5, hspace=0.5)
    target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]
    target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]
    df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False);
    (-df).plot(subplots=True, ax=target2, legend=False, sharex=False, sharey=False)

![04cddeba2bbc763490899479473dba26.png][]

### **25 图中绘制数据表格** ###

from pandas.plotting import table
    mpl.rc_file_defaults()
    #plt.style.use('dark_background')
    fig, ax = plt.subplots(1, 1)
    table(ax, np.round(df.describe(), 2), loc="upper right", colWidths=[0.2, 0.2, 0.2]);
    df.plot(ax=ax, ylim=(0, 2), legend=None);

![7f48993346860d4272d983663ec48a60.png][]

更多pandas可视化精进资料：

https://pandas.pydata.org/pandas-docs/stable/user\_guide/cookbook.html\#cookbook-plotting

![音符][483eb946c8b5d8397880d0e6094f664e.gif]

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