目录
  • 1 Series
    • 1.1创造一个serise数据
    • 1.2 指定index
    • 1.3 用dictionary构造一个series
    • 1.4 用numpy ndarray构造一个Series
    • 1.5 选择数据
    • 1.6 操作数据
    • 1.7 查找
    • 1.8 Series赋值
    • 1.9 满足条件的统一赋值

1 Series

线性的数据结构, series是一个一维数组

Pandas 会默然用0到n-1来作为series的index, 但也可以自己指定index( 可以把index理解为dict里面的key )

1.1创造一个serise数据

 import pandas as pd import numpy as np ​s = pd.Series([9, 'zheng', 'beijing', 128]) ​print(s)

打印

0 9
1 zheng
2 beijing
3 128
dtype: object

访问其中某个数据

 print(s[1:2]) ​ # 打印 1 zheng dtype: object

Series类型的基本操作：

 In [14]: a = pd.Series({'a':1,'b':5}) In [15]: a.index Out[15]: Index(['a', 'b'], dtype='object') In [16]: a.values #返回一个多维数组numpy对象 Out[16]: array([1, 5], dtype=int64) 

 #自动索引和自定义索引并存，但不能混用 In [17]: a[0] #自动索引 Out[17]: 1 #自定义索引 In [18]: a['a'] Out[18]: 1 #不能混用 In [20]: a[['a',1]] Out[20]: a 1.0 1 NaN dtype: float64 

 #通过自定义索引访问 #对索引保留字in操作，值不可以 In [21]: 'a' in a Out[21]: True In [22]: 1 in a Out[22]: False

Series类型在运算中会自动对齐不同索引的数据

 In [29]: a = pd.Series([1,3,5],index = ['a','b','c']) In [30]: b = pd.Series([2,4,5,6],index = ['c,','d','e','b']) In [31]: a+b Out[31]: a NaN b 9.0 c NaN c, NaN d NaN e NaN dtype: float64

Series对象可以随时修改并即刻生效

 In [32]: a.index = ['c','d','e'] In [33]: a Out[33]: c 1 d 3 e 5 dtype: int64 In [34]: a+b Out[34]: b NaN c NaN c, NaN d 7.0 e 10.0 dtype: float64

1.2 指定index

 import pandas as pd import numpy as np ​s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])​ print(s)

打印

1 9
2 zheng
3 beijing
e 128
f usa
g 990
dtype: object

根据索引找出值

 print(s['f']) # usa

1.3 用dictionary构造一个series

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "car": None} sa = pd.Series(s, name="age")​ print(sa)

打印

car NaN
jack 19.0
mary 18.0
ton 20.0
Name: age, dtype: float64

检测类型

 print(type(sa)) # <class 'pandas.core.series.Series'>

1.4 用numpy ndarray构造一个Series

生成一个随机数

 import pandas as pd import numpy as np ​ num_abc = pd.Series(np.random.randn(5), index=list('abcde')) num = pd.Series(np.random.randn(5)) ​ print(num) print(num_abc) ​ # 打印 0 -0. 1 -1. 2 1. 3 -0. 4 1. dtype: float64 a -0. b 1. c 0. d 0. e -1. dtype: float64

1.5 选择数据

 import pandas as pd import numpy as np ​ s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g']) ​ print(s[1:3]) # 选择第1到3个, 包左不包右 zheng beijing print(s[[1,3]]) # 选择第1个和第3个, zheng 128 print(s[:-1]) # 选择第1个到倒数第1个, 9 zheng beijing 128 usa

1.6 操作数据

 import pandas as pd import numpy as np​ s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g']) sum = s[1:3] + s[1:3] sum1 = s[1:4] + s[1:4] sum2 = s[1:3] + s[1:4] sum3 = s[:3] + s[1:]​ print(sum) print(sum1) print(sum2) print(sum3)

打印

2 zhengzheng
3 beijingbeijing
dtype: object
2 zhengzheng
3 beijingbeijing
e 256
dtype: object
2 zhengzheng
3 beijingbeijing
e NaN
dtype: object
1 NaN
2 zhengzheng
3 beijingbeijing
e NaN
f NaN
g NaN
dtype: object

1.7 查找

是否存在

 USA in s # true

范围查找

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa[sa>19])


中位数

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa.median()) # 20

判断是否大于中位数

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa>sa.median())


找出大于中位数的数

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa[sa > sa.median()])


中位数

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") more_than_midian = sa>sa.median() print(more_than_midian) print('---------------------') print(sa[more_than_midian])


1.8 Series赋值

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(s) print('----------------') sa['ton'] = 99 print(sa)


1.9 满足条件的统一赋值

 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(s) # 打印原字典 print('---------------------') # 分割线 sa[sa>19] = 88 # 将所有大于19的同一改为88 print(sa) # 打印更改之后的数据 print('---------------------') # 分割线 print(sa / 2) # 将所有数据除以2

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