《利用python进行数据分析》学习记录——使用Pandas和seaborn绘图

matplotlib实际上是一种比较低级的工具。要绘制一张图表，你组装一些基本组件就行：数据展示（即图表类型：线型图、柱状图、盒形图、散布图、等值线图等）、图例、标题、刻度标签以及其他注解型信息。

在pandas中，我们有多列数据，还有行和列标签。pandas自身就有内置的方法，用于简化从DataFrame和Series绘制图形。另一个库seaborn（https://seaborn.pydata.org/），由Michael Waskom创建的静态图形库。Seaborn简化了许多常见可视类型的创建。

In [194]: import matplotlib.pyplot as plt

In [195]: plt.figure()   #创建一个幕布
Out[195]: 


In [196]:  s = pd.Series(np.random.randn(10).cumsum(),index = np.arange(0,100,10))

In [197]: s.plot()
Out[197]: 

In [198]: plt.show()    #显示图形

注：在ipython中要写成如下代码才会显示图像：

import matplotlib.pyplot as plt
plt.figure() 
df.plot()
plt.show()

该Series对象的索引index会被传给matplotlib，并用以绘制X轴。可以通过use_index=False禁用该功能。
DataFrame的plot方法会在一个subplot中为各列绘制一条线，并自动创建图例（如图9-14所示）：

In [200]: df = pd.DataFrame(np.random.rand(10,4).cumsum(0),columns=['A','B','C','D'],index=n
     ...: p.arange(0,100,10))

In [201]: df
Out[201]:
           A         B         C         D
0   0.685788  0.297513  0.651090  0.445658
10  1.557753  0.813536  1.338988  0.993341
20  2.433134  1.193840  1.840116  1.262187
30  3.356239  1.889819  2.715777  1.779696
40  3.831967  2.843798  3.342842  2.269416
50  4.688547  3.843593  3.982264  2.882778
60  5.546727  4.640172  4.857665  3.075847
70  6.233858  5.135775  5.343059  3.692292
80  6.597572  5.355745  5.709290  3.919498
90  7.127534  6.151853  5.731094  4.640323

In [202]: df.plot()
Out[202]: 

In [203]: plt.show()

柱状图

plot.bar()和plot.barh()分别绘制水平和垂直的柱状图。这时，Series和DataFrame的索引将会被用作X（bar）或Y（barh）刻度（如图9-15所示）：

In [206]: fig,axes = plt.subplots(2,1)

In [207]: data = pd.Series(np.random.randn(16),index = list('abcdefghijklmnop'))

In [208]: data.plot.bar(ax=axes[0],color='k',alpha=0.7)  #alpha为图表的填充不透明度
Out[208]: 

In [209]: data.plot.barh(ax=axes[1],color='k',alpha=0.7)
Out[209]: 

In [210]: plt.show()

注意：plt.subplots(2,1)可以一下产生2x1个子窗口（两行一列），并且以numpy数组的方式保存在axes中，而fig仍然是整个图像对象，这样我们可以通过对axes进行索引来访问每个子窗口。

color='k’和alpha=0.7设定了图形的颜色为黑色，并使用部分的填充透明度。对于DataFrame，柱状图会将每一行的值分为一组，并排显示：

In [211]: df = pd.DataFrame(np.random.rand(6, 4), index=['one', 'two', 'three', 'four', 'fiv
     ...: e', 'six'],columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))

In [212]: df
Out[212]:
Genus         A         B         C         D
one    0.783765  0.973372  0.397304  0.668468
two    0.849481  0.883813  0.059709  0.620467
three  0.188168  0.387766  0.975336  0.781791
four   0.996210  0.188114  0.205050  0.492547
five   0.404493  0.192918  0.305952  0.436618
six    0.475883  0.312828  0.720343  0.637083

In [213]: df.plot.bar()
Out[213]: 

In [215]: plt.show()

传入stacked=True产生堆叠效果：

In [216]: df.plot.bar(stacked=True)
Out[216]: 
In [217]: plt.show()

直方图和密度图

seaborn的distplot方法绘制直方图和密度图更加简单，还可以同时画出直方图和连续密度估计图。作为例子，考虑一个双峰分布，由两个不同的标准正态分布组成

In [219]: import seaborn as sns

In [220]: comp1 = np.random.normal(0,1,size=200)

In [221]: comp2 = np.random.normal(10,2,size=200)

In [222]: values = pd.Series(np.concatenate([comp1,comp2]))

In [223]: sns.distplot(values,bins=100,color='k')

Out[223]: 

In [224]: plt.show()

以下是别的资料中的介绍：

Seaborn vs Matplotlib

正如你所知道的，Seaborn是比Matplotlib更高级的免费库，特别地以数据可视化为目标，但他要比这一切更进一步：他解决了用Matplotlib的2个最大问题，正如Michael Waskom所说的：Matplotlib试着让简单的事情更加简单，困难的事情变得可能，那么Seaborn就是让困难的东西更加简单。

用Matplotlib最大的困难是其默认的各种参数，而Seaborn则完全避免了这一问题。

In [227]: tips = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv ")
    In [233]: tips
    Out[233]:
         total_bill   tip     sex smoker   day    time  size
    0         16.99  1.01  Female     No   Sun  Dinner     2
    1         10.34  1.66    Male     No   Sun  Dinner     3
    2         21.01  3.50    Male     No   Sun  Dinner     3
    3         23.68  3.31    Male     No   Sun  Dinner     2
    4         24.59  3.61  Female     No   Sun  Dinner     4
    5         25.29  4.71    Male     No   Sun  Dinner     4
    6          8.77  2.00    Male     No   Sun  Dinner     2
    7         26.88  3.12    Male     No   Sun  Dinner     4
    8         15.04  1.96    Male     No   Sun  Dinner     2
    9         14.78  3.23    Male     No   Sun  Dinner     2
    10        10.27  1.71    Male     No   Sun  Dinner     2
    11        35.26  5.00  Female     No   Sun  Dinner     4
    12        15.42  1.57    Male     No   Sun  Dinner     2
    13        18.43  3.00    Male     No   Sun  Dinner     4
    14        14.83  3.02  Female     No   Sun  Dinner     2
    15        21.58  3.92    Male     No   Sun  Dinner     2
    16        10.33  1.67  Female     No   Sun  Dinner     3
    17        16.29  3.71    Male     No   Sun  Dinner     3
    18        16.97  3.50  Female     No   Sun  Dinner     3
    19        20.65  3.35    Male     No   Sat  Dinner     3
    20        17.92  4.08    Male     No   Sat  Dinner     2
    21        20.29  2.75  Female     No   Sat  Dinner     2
    22        15.77  2.23  Female     No   Sat  Dinner     2
    23        39.42  7.58    Male     No   Sat  Dinner     4
    24        19.82  3.18    Male     No   Sat  Dinner     2
    25        17.81  2.34    Male     No   Sat  Dinner     4
    26        13.37  2.00    Male     No   Sat  Dinner     2
    27        12.69  2.00    Male     No   Sat  Dinner     2
    28        21.70  4.30    Male     No   Sat  Dinner     2
    29        19.65  3.00  Female     No   Sat  Dinner     2
    ..          ...   ...     ...    ...   ...     ...   ...
    214       28.17  6.50  Female    Yes   Sat  Dinner     3
    215       12.90  1.10  Female    Yes   Sat  Dinner     2
    216       28.15  3.00    Male    Yes   Sat  Dinner     5
    217       11.59  1.50    Male    Yes   Sat  Dinner     2
    218        7.74  1.44    Male    Yes   Sat  Dinner     2
    219       30.14  3.09  Female    Yes   Sat  Dinner     4
    220       12.16  2.20    Male    Yes   Fri   Lunch     2
    221       13.42  3.48  Female    Yes   Fri   Lunch     2
    222        8.58  1.92    Male    Yes   Fri   Lunch     1
    223       15.98  3.00  Female     No   Fri   Lunch     3
    224       13.42  1.58    Male    Yes   Fri   Lunch     2
    225       16.27  2.50  Female    Yes   Fri   Lunch     2
    226       10.09  2.00  Female    Yes   Fri   Lunch     2
    227       20.45  3.00    Male     No   Sat  Dinner     4
    228       13.28  2.72    Male     No   Sat  Dinner     2
    229       22.12  2.88  Female    Yes   Sat  Dinner     2
    230       24.01  2.00    Male    Yes   Sat  Dinner     4
    231       15.69  3.00    Male    Yes   Sat  Dinner     3
    232       11.61  3.39    Male     No   Sat  Dinner     2
    233       10.77  1.47    Male     No   Sat  Dinner     2
    234       15.53  3.00    Male    Yes   Sat  Dinner     2
    235       10.07  1.25    Male     No   Sat  Dinner     2
    236       12.60  1.00    Male    Yes   Sat  Dinner     2
    237       32.83  1.17    Male    Yes   Sat  Dinner     2
    238       35.83  4.67  Female     No   Sat  Dinner     3
    239       29.03  5.92    Male     No   Sat  Dinner     3
    240       27.18  2.00  Female    Yes   Sat  Dinner     2
    241       22.67  2.00    Male    Yes   Sat  Dinner     2
    242       17.82  1.75    Male     No   Sat  Dinner     2
    243       18.78  3.00  Female     No  Thur  Dinner     2
    
    [244 rows x 7 columns]
In [226]: fig,ax = plt.subplots()
In [229]: ax.violinplot(tips['total_bill'],vert=False)
Out[229]:
{'bodies': [],
 'cmaxes': ,
 'cmins': ,
 'cbars': }

In [230]: plt.show()

In [231]: sns.violinplot(x ='total_bill',data=tips)

Out[231]: 

In [232]: plt.show()

In [235]: iris = sns.load_dataset('iris')

In [236]: sns.swarmplot(x='species',y='petal_length',data=iris)
Out[236]: 

In [237]: plt.show()