Python之pandas，series，可视化

一时失言乱红尘 2021-10-31 16:42 509阅读 0赞

### 七月在线之python数据处理 ###

*  python常用导入函数
 *  ndarray
 *   *  np之常用函数创建
     *  ndarray之聚合操作
 *  pandas
 *   *  pandas之series
     *   *  Series的创建
         *  Series的索引和切片
 *  可视化

# python常用导入函数 #

from IPython.display import display
    import numpy as np
    import pandas as pd
    from pandas import Series,DataFrame
    from PIL import Image  
    
    import matplotlib.pyplot as plt
    %matplotlib inline  
                  
    %config ZMQInteractiveShell.ast_node_interactivity='all'    # nootbook使用
    from scipy import interp             # 线性插值

# ndarray #

## np之常用函数创建 ##

np.ones(shape, dtype=None, order=‘C’)  
\#ones–>创建指定长度或形状全部为1的数组  
参数说明：  
shape:维度  
dtype:数据类型，默认是float  
order: 可选规定返回数组元素在内存的存储顺序：看源码两个选项：\{‘C’, ‘F’\},  
C（C语言）-rowmajor；F（Fortran《FormulaTranslation）的缩写，是一种编程语言》）column-major

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 3]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 4]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 5]

## ndarray之聚合操作 ##

以三维数组求和为例：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 6]  
%config ZMQInteractiveShell.ast\_node\_interactivity=‘all’  
a = np.random.randint(0,10,size = \[3,2,3\])  
a.shape  
b = a.min(axis = 0) \# 按三维，对应位置比较  
c = a.min(axis = 1) \# 按两维，行比较  
d = a.min(axis = 2) \# 按1维，列比较  
e = a.min(axis = -1) \# 按1维，列比较  
display(b,c,d,e)  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 7]  
其他聚合操作：

Function Name	NaN-safe Version	Description
        np.sum	np.nansum	Compute sum of elements
        np.prod	np.nanprod	Compute product of elements
        np.mean	np.nanmean	Compute mean of elements
        np.std	np.nanstd	Compute standard deviation
        np.var	np.nanvar	Compute variance
        np.min	np.nanmin	Find minimum value
        np.max	np.nanmax	Find maximum value
        np.argmin	np.nanargmin	Find index of minimum value
        np.argmax	np.nanargmax	Find index of maximum value
        np.median	np.nanmedian	Compute median of elements
        np.percentile	np.nanpercentile	Compute rank-based statistics of elements
        np.any	N/A	Evaluate whether any elements are true
        np.all	N/A	Evaluate whether all elements are true
        np.power 幂运算

# pandas #

## pandas之series ##

### Series的创建 ###

Series是一种类似与一维数组的对象，由下面两个部分组成：

values：一组数据（ndarray类型）  
index：相关的数据索引标签  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 8]

### Series的索引和切片 ###

import numpy as np
        import pandas as pd
        from pandas import Series,DataFrame
        import matplotlib.pyplot as plt
        %matplotlib inline 
        # %matplotlib inline这一句是IPython的魔法函数，
        # 可以在IPython编译器里直接使用，作用是内嵌画图，省略掉plt.show()这一步，直接显示图像
        
    	s = Series(nd,index = ['a','b','c','d','e'])
        # 显示索引
        s[['a','d']]
        s.loc[['a','d']]
        
        # 隐式索引
        s[[0,3]]
        s.iloc[[0,3]]
        
        # 以隐式索引为例，取一段连续的
        s.iloc[[0,1,2,3]]
        
        # 索引如果要取一段连续的值，就要多个索引，
        # 对索引稍加修改，去掉一个中括号，逗号改冒号，引入切片
        # 切片
        
        s['a':'d']#左闭右闭
        s.loc['a':'d']
        s[0:4]
        s.iloc[0:4]#左闭右开

执行结果：  
![在这里插入图片描述][20190822120153299.png]  
后四行代码，运用切片方法  
![在这里插入图片描述][20190822120254986.png]

# 可视化 #

十分钟掌握Seaborn，进阶Python数据可视化分析：[https://zhuanlan.zhihu.com/p/49035741][https_zhuanlan.zhihu.com_p_49035741]

matlplob官网：[https://matplotlib.org/api/\_as\_gen/matplotlib.pyplot.plot.html\#matplotlib.pyplot.plot][https_matplotlib.org_api_as_gen_matplotlib.pyplot.plot.html_matplotlib.pyplot.plot]

matlplob中文文档：[https://www.matplotlib.org.cn/tutorials/introductory/usage.html][https_www.matplotlib.org.cn_tutorials_introductory_usage.html]

Matplotlib可视化最有价值的 50 个图表[http://liyangbit.com/pythonvisualization/matplotlib-top-50-visualizations/][http_liyangbit.com_pythonvisualization_matplotlib-top-50-visualizations]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70]: /images/20211031/bc49d827288a4612bfcb9665a4b33321.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 1]: /images/20211031/6ea7043b6c904e5aabd54425cd396a01.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 2]: /images/20211031/ccf825dcc8694028be8f2c49d93c851b.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 3]: /images/20211031/7cb12cc6f3ad426a86377a73778bf829.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 4]: /images/20211031/a7f92543c2ba4d8d8f9820382ad8624c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 5]: /images/20211031/900b3c49f1b0408594dca05478d8ee36.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 6]: /images/20211031/97f7acf6f0e6480c8f73175e60506a67.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 7]: /images/20211031/da898325ad8b4f03aa58201267b271df.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0hlYXJ0X1NlYQ_size_16_color_FFFFFF_t_70 8]: /images/20211031/770b2d6da54d4dc09b85076e6bddb6cf.png
[20190822120153299.png]: /images/20211031/faa132a90eaf4260a10a28cb6baa47f9.png
[20190822120254986.png]: /images/20211031/1c2f34b97f7e4e72978857cba866ed06.png
[https_zhuanlan.zhihu.com_p_49035741]: https://zhuanlan.zhihu.com/p/49035741
[https_matplotlib.org_api_as_gen_matplotlib.pyplot.plot.html_matplotlib.pyplot.plot]: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot
[https_www.matplotlib.org.cn_tutorials_introductory_usage.html]: https://www.matplotlib.org.cn/tutorials/introductory/usage.html
[http_liyangbit.com_pythonvisualization_matplotlib-top-50-visualizations]: http://liyangbit.com/pythonvisualization/matplotlib-top-50-visualizations/