Series的基本使用
Series的基本使用
一、定义
Series类似于一维数组, 由一组数据（类似于一维numpy对象）及一组标签（索引）组成
创建方法
Series(可迭代一维数据 [, index=索引列表 [, dtype=数据类型]])
注：可迭代对象可以使用一维链表，一维numpy对象，字典（使用字典时，index为字典的key），可迭代对象必须是一维，否则报错：Data must be 1-dimensional
举例说明
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import numpy as np
from pandas import Series
print(Series(range(3)))
print("#" * 30)
print(Series(range(3), index = ["first", "second", "third"]))
print("#" * 30)
print(Series(range(3), index = ["first", "second", "third"], dtype=int))
print("#" * 30)
print(Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int))
print("#" * 30)
print(Series({"first": 1, "second": 2, "third": 3}, dtype=int))复制代码
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0
0
1
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2
dtype: int64
##############################
first
0
second
1
third
2
dtype: int64
##############################
first
0
second
1
third
2
dtype: int32
##############################
first
0
second
1
third
2
dtype: int32
##############################
first
1
second
2
third
3
dtype: int32属性
Series对象的属性有：dtype, index, values, name
Series.index有属性:name
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
series0.index = ["语文", "数学", "英语"]
print(series0.dtype)
print("##############################")
print(series0.index)
print("##############################")
print(series0.values)
series0.name = "Series0"
series0.index.name = "idx"
print("##############################")
print(series0)复制代码
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int32
##############################
Index(['语文', '数学', '英语'], dtype='object')
##############################
[0 1 2]
##############################
idx
语文
0
数学
1
英语
2
Name: Series0, dtype: int32Series的增删查改
Series查询
常规查询
可以使用索引，也可以使用序号
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
print(series0[1])
print("#" * 30)
print(series0["first"])复制代码
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1
##############################
0切片查询
1、索引切片，闭区间
2、序号切片，前闭后开
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
print(series0["second": "third"])
print("#" * 30)
print(series0[1:2])复制代码
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second
1
third
2
dtype: int32
##############################
second
1
dtype: int32条件查询
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
print(series0[series0 > 0])复制代码
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second
1
third
2
dtype: int32新增
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
series0["fourth"] = 3
print(series0)复制代码
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first
0
second
1
third
2
fourth
3
dtype: int64删除
只能根据索引进行删除，无法直接删除值
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
series0 = series0.drop("third")
print(series0)复制代码
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first
0
second
1
dtype: int32修改
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
series0["first"] = "first-modify"
print(series0)
series0[1] = "second-modify"
print(series0)复制代码
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first
first-modify
second
1
third
2
dtype: object
first
first-modify
second
second-modify
third
2
dtype: object缺失值检测
1、使用新索引，原有索引 –> 值对应关系不变，新索引对应的值为np.nan, 显示为NaN
2、缺失值检测方法：pd.isnull(series对象) or series对象.isnull(), pd.notnull(series对象) or series对象.notnull()
3、缺失值过滤：series对象[pd.notnull(series对象)]
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scores = Series({"Tom": 90, "Jim": 98, "Zera": 59})
print(scores)
print("#" * 30)
new_index = ["Joe", "Tom", "Jim", "Zera"]
scores = Series(scores, index=new_index)
print(scores)
print("#" * 30)
print(pd.isnull(scores))
print("#" * 30)
print(pd.notnull(scores))
print("#" * 30)
print("scores[pd.isnull(scores)] n", scores[pd.isnull(scores)])
print("scores[scores.isnull()] n", scores[scores.isnull()])
print("#" * 30)
print("scores[pd.notnull(scores)] n", scores[pd.notnull(scores)])
print("scores[scores.notnull()] n", scores[scores.notnull()])复制代码
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Jim
98
Tom
90
Zera
59
dtype: int64
##############################
Joe
NaN
Tom
90.0
Jim
98.0
Zera
59.0
dtype: float64
##############################
Joe
True
Tom
False
Jim
False
Zera
False
dtype: bool
##############################
Joe
False
Tom
True
Jim
True
Zera
True
dtype: bool
##############################
scores[pd.isnull(scores)]
Joe
NaN
dtype: float64
scores[scores.isnull()]
Joe
NaN
dtype: float64
##############################
scores[pd.notnull(scores)]
Tom
90.0
Jim
98.0
Zera
59.0
dtype: float64
scores[scores.notnull()]
Tom
90.0
Jim
98.0
Zera
59.0
dtype: float64Series的自动对齐
两个索引未对齐(索引顺序不一样)的Series对象，进行运算后会自动对齐，索引相同的值对应做运算
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product_num = Series([1, 2, 3, 4], index=['p1', 'p2', 'p3', 'p4'])
product_price = Series([3, 2, 1, 6], index=['p3', 'p2', 'p5', 'p1'])
product_sum = product_num * product_price
print(product_sum)复制代码
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p1
6.0
p2
4.0
p3
9.0
p4
NaN
p5
NaN
dtype: float64运算不改变键值对应关系
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series0 = Series(np.array(range(3)), index = ["first", "second", "third"], dtype=int)
print(series0/5)
print("#" * 30)
print(np.exp(series0))复制代码
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first
0.0
second
0.2
third
0.4
dtype: float64
##############################
first
1.000000
second
2.718282
third
7.389056
dtype: float64最后
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