Pandas库学习

学习笔记手札及单元小结Pandas库的介绍Pandas库的引用Pandas库小测 Pandas库的Series类型从标量值创建从字典类型创建从ndarray类型创建Series类型的基本操作Series类型对齐操作Series类型的那么属性Series类型的修改 Pandas库的DataFrame类型DataFrame类型从二维ndarray对象创建从列表类型的字典创建 Pandas库的数据类型运算数据类型的算术运算数据类型的比较运算

学习笔记手札及单元小结

Pandas库的介绍

Pandas库网址：https://pandas.pydata.org/

Pandas库的引用

import pandas as pd

Pandas基于Numpy实现，常与Numpy和Matplotlib一同使用

Pandas库小测

请在Anaconda的IPython平台运行代码

import pandas as pd d = pd.Series(range(20)) d Out[3]: 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 dtype: int64 d.cumsum() #计算前N项累加和 Out[4]: 0 0 1 1 2 3 3 6 4 10 5 15 6 21 7 28 8 36 9 45 10 55 11 66 12 78 13 91 14 105 15 120 16 136 17 153 18 171 19 190 dtype: int64

Pandas库的Series类型

Series类型由一组数据及与之相关的数据索引组成

import pandas as pd a = pd.Series([9,8,7,6]) a Out[3]: 0 9 1 8 2 7 3 6 dtype: int64 #NumPy中数据类型 import pandas as pd b = pd.Series([9,8,7,6],index=['a','b','c','d']) #作为第二个参数时，可以省略index= b Out[6]: a 9 b 8 c 7 d 6 dtype: int64

从标量值创建

import pandas as pd s = pd.Series(25,index=['a','b','c']) #此处不能省略index s Out[9]: a 25 b 25 c 25 dtype: int64

从字典类型创建

import pandas as pd d = pd.Series({'a':9,'b':8,'c':7}) d Out[12]: a 9 b 8 c 7 dtype: int64 e = pd.Series({'a':9,'b':8,'c':7},index=['c','a','b','d']) #index从字典中进行选择操作 e Out[14]: c 7.0 a 9.0 b 8.0 d NaN dtype: float64

从ndarray类型创建

import pandas as pd import numpy as np n = pd.Series(np.arange(5)) n Out[4]: 0 0 1 1 2 2 3 3 4 4 dtype: int32 m = pd.Series(np.arange(5),index=np.arange(9,4,-1)) m Out[6]: 9 0 8 1 7 2 6 3 5 4 dtype: int32

Series类型的基本操作

import pandas as pd b = pd.Series([9,8,7,6],['a','b','c','d']) b Out[3]: a 9 b 8 c 7 d 6 dtype: int64 b.index #.index 获得索引 Out[4]: Index(['a', 'b', 'c', 'd'], dtype='object') b.values #.values 获得数据 Out[5]: array([9, 8, 7, 6], dtype=int64) b['b'] #自动索引 Out[6]: 8 b[1] #自定义索引 Out[7]: 8 b[['c','d',0]] #两套索引并存，但不能混用 --------------------------------------------------------------------------- KeyError Traceback (most recent call last) b[['c','d','a']] Out[10]: c 7 d 6 a 9 dtype: int64 import pandas as pd b = pd.Series([9,8,7,6],['a','b','c','d']) b Out[3]: a 9 b 8 c 7 d 6 dtype: int64 b[3] Out[4]: 6 b[:3] Out[5]: a 9 b 8 c 7 dtype: int64 b[b > b.median()] Out[6]: a 9 b 8 dtype: int64 np.exp(b) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-7-a3137cbb6ea4> in <module> ----> 1 np.exp(b) import pandas as pd b = pd.Series([9,8,7,6],['a','b','c','d']) b['b'] Out[3]: 8 'c' in b Out[4]: True 0 in b Out[5]: False b.get('f',100) Out[6]: 100

Series类型对齐操作

Series + Series

import pandas as pd a = pd.Series([1,2,3],['c','d','e']) b = pd.Series([9,8,7,6],['a','b','c','d']) a + b Out[10]: a NaN b NaN c 8.0 d 8.0 e NaN dtype: float64

Series类型在运算中会自动对齐不同索引的数据

Series类型的那么属性

import pandas as pd b = pd.Series([9,8,7,6],['a','b','c','d']) b.name b.name = 'Series对象' b.index.name = '索引列' b Out[16]: 索引列 a 9 b 8 c 7 d 6 Name: Series对象, dtype: int64

Series类型的修改

Series对象可以随时修改并即刻生效

import pandas as pd b = pd.Series([9,8,7,6],['a','b','c','d']) b.name = "Series" b Out[20]: a 9 b 8 c 7 d 6 Name: Series, dtype: int64 b.name = "New Series" b['b','c'] = 20 b Out[23]: a 9 b 20 c 20 d 6 Name: New Series, dtype: int64

Pandas库的DataFrame类型

DataFrame类型

从二维ndarray对象创建

import pandas as pd import numpy as np d = pd.DataFrame(np.arange(10).reshape(2,5)) d Out[4]: 0 1 2 3 4 0 0 1 2 3 4 1 5 6 7 8 9

从列表类型的字典创建

import pandas as pd dl = {'one':[1,2,3,4],'two':[9,8,7,6]} d = pd.DataFrame(dl,index = ['a','b','c','d']) d Out[7]: one two a 1 9 b 2 8 c 3 7 d 4 6

Pandas库的数据类型运算

import pandas as pd import numpy as np a = pd.DataFrame(np.arange(12).reshape(3,4)) a Out[4]: 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 b = pd.DataFrame(np.arange(20).reshape(4,5)) b Out[6]: 0 1 2 3 4 0 0 1 2 3 4 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 a + b Out[7]: 0 1 2 3 4 0 0.0 2.0 4.0 6.0 NaN 1 9.0 11.0 13.0 15.0 NaN 2 18.0 20.0 22.0 24.0 NaN 3 NaN NaN NaN NaN NaN a * b Out[8]: 0 1 2 3 4 0 0.0 1.0 4.0 9.0 NaN 1 20.0 30.0 42.0 56.0 NaN 2 80.0 99.0 120.0 143.0 NaN 3 NaN NaN NaN NaN NaN #自动补齐，缺项补NAN

数据类型的算术运算

import pandas as pd import numpy as np a = pd.DataFrame(np.arange(12).reshape(3,4)) a Out[12]: 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 b = pd.DataFrame(np.arange(20),.reshape(4,5)) File "<ipython-input-13-58d77ec75842>", line 1 b = pd.DataFrame(np.arange(20),.reshape(4,5)) ^ SyntaxError: invalid syntax b = pd.DataFrame(np.arange(20).reshape(4,5)) b Out[15]: 0 1 2 3 4 0 0 1 2 3 4 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 b.add(a,fill_value = 100) Out[16]: 0 1 2 3 4 0 0.0 2.0 4.0 6.0 104.0 1 9.0 11.0 13.0 15.0 109.0 2 18.0 20.0 22.0 24.0 114.0 3 115.0 116.0 117.0 118.0 119.0 a.mul(b,fill_value = 0) Out[17]: 0 1 2 3 4 0 0.0 1.0 4.0 9.0 0.0 1 20.0 30.0 42.0 56.0 0.0 2 80.0 99.0 120.0 143.0 0.0 #fill_value参数替代NAN，替代后参与运算

不同维度间为广播运算，一维Series默认在轴1参与运算

import pandas as pd import numpy as np b = pd.DataFrame(np.arange(20).reshape(4,5)) b Out[21]: 0 1 2 3 4 0 0 1 2 3 4 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 c = pd.Series(np.arange(4)) c Out[23]: 0 0 1 1 2 2 3 3 dtype: int32 c -10 Out[24]: 0 -10 1 -9 2 -8 3 -7 dtype: int32 b - c Out[25]: 0 1 2 3 4 0 0.0 0.0 0.0 0.0 NaN 1 5.0 5.0 5.0 5.0 NaN 2 10.0 10.0 10.0 10.0 NaN 3 15.0 15.0 15.0 15.0 NaN

使用运算方法可以令一维Series参与轴0运算

import pandas as pd import numpy as np b = pd.DataFrame(np.arange(20).reshape(4,5)) b Out[29]: 0 1 2 3 4 0 0 1 2 3 4 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 c = pd.Series(np.arange(4)) c Out[31]: 0 0 1 1 2 2 3 3 dtype: int32 b.sub(c,axis=0) Out[32]: 0 1 2 3 4 0 0 1 2 3 4 1 4 5 6 7 8 2 8 9 10 11 12 3 12 13 14 15 16

数据类型的比较运算

同维度运算，尺寸一致

import pandas as pd import numpy as np a = pd.DataFrame(np.arange(12).reshape(3,4)) a Out[36]: 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 d = pd.DataFrame(np.arange(12,0,-1).reshape(3,4)) d Out[38]: 0 1 2 3 0 12 11 10 9 1 8 7 6 5 2 4 3 2 1 a > d Out[39]: 0 1 2 3 0 False False False False 1 False False False True 2 True True True True a == d Out[40]: 0 1 2 3 0 False False False False 1 False False True False 2 False False False False

不同维度，广播运算，默认在1轴

import pandas as np import numpy as np a = pd.DataFrame(np.arange(12).reshape(3,4)) a Out[44]: 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 c = pd.Series(np.arange(4)) c Out[46]: 0 0 1 1 2 2 3 3 dtype: int32 a > c Out[47]: 0 1 2 3 0 False False False False 1 True True True True 2 True True True True c >0 Out[48]: 0 False 1 True 2 True 3 True dtype: bool