位置下标，类似序列
import numpy as np
import pandas as pd
s = pd.Series(np.random.rand(5))
print('s = \n', s)
print('\ns[0] = {0}, type(s[0]) = {1}, s[0].dtype = {2}'.format(s[0], type(s[0]), s[0].dtype))
print('\nfloat(s[0]) = {0}, type(float(s[0]) = {1})'.format(float(s[0]), type(float(s[0]))))
打印结果：
s =
0 0.221458
1 0.430786
2 0.096305
3 0.262580
4 0.993637
dtype: float64
s[0] = 0.22145841095529073, type(s[0]) = <class 'numpy.float64'>, s[0].dtype = float64
float(s[0]) = 0.22145841095529073, type(float(s[0]) = <class 'float'>)
Process finished with exit code 0
方法类似下标索引，用[]表示，内写上index，注意index是字符串
- 如果需要选择多个标签的值，用[[]]来表示（相当于[]中包含一个列表）
- 多标签索引结果是新的数组
import numpy as np
import pandas as pd
s = pd.Series(np.random.rand(5), index=['a', 'b', 'c', 'd', 'e'])
print('\ns = \n{0}, \n\ntype(s) = {1}'.format(s, type(s)))
print("\ns['a'] = {0}, type(s['a']) = {1}, s['a'].dtype = {2}".format(s['a'], type(s['a']), s['a'].dtype))
print('-' * 100)
sci = s[['a', 'b', 'e']]
print('\nsci = \n{0}, \n\ntype(sci) = {1}'.format(sci, type(sci)))
打印结果：
s =
a 0.648198
b 0.356047
c 0.375873
d 0.811611
e 0.070581
dtype: float64,
type(s) = <class 'pandas.core.series.Series'>
s['a'] = 0.6481978904955534, type(s['a']) = <class 'numpy.float64'>, s['a'].dtype = float64
s1[1:4] =
1 0.223454
2 0.591863
3 0.911600
dtype: float64
s2 =
a 0.398821
b 0.856505
c 0.795255
d 0.985476
e 0.724451
dtype: float64
s2['c'] =
0.7952552440685834
s2[3] =
0.9854755359882719
s2[::2] =
a 0.398821
c 0.795255
e 0.724451
dtype: float64
bs1 =
0 False
1 True
2 True
4 False
dtype: bool,
type(bs1) = <class 'pandas.core.series.Series'>,
bs1.dtype = bool
bs3 =
0 True
1 True
2 True
4 False
dtype: bool,
type(bs3) = <class 'pandas.core.series.Series'>, bs3.dtype = bool
s[bs3]
0 11.735721
1 62.482804
2 51.381651
dtype: object
Process finished with exit code 0
Original: https://blog.csdn.net/u013250861/article/details/124002657
Author: u013250861
Title: Pandas-数据结构-Series（二）：Series的索引【下标索引、标签索引、切片索引、布尔型索引】
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