Pandas系列（一）-Series详解

一、初始Series

　　Series 是一个带有 名称 和索引的一维数组，既然是数组，肯定要说到的就是数组中的元素类型，在 Series 中包含的数据类型可以是整数、浮点、字符串、Python对象等。

 pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

• 创建第一个Series

import pandas as pd
user_age = pd.Series(data=[18, 30, 25, 40])
user_age.index = ["Tom", "Bob", "Mary", "James"] #加索引
user_age.index.name = "name"  #索引加名字
user_age.name="user_age_info"  #series加名字
user_age
Out[4]: 
name
Tom      18
Bob      30
Mary     25
James    40
Name: user_age_info, dtype: int64

• 创建Series的方式

1. 列表方式创建

   pd.Series([],index=[])　

2. 字典方式创建

   pd.Series({}

# 方式一
t = pd.Series([1,2,3,4,43],index=list('asdfg'))
print(t)
a     1
s     2
d     3
f     4
g    43
dtype: int64

#方式二
temp_dict = {'name':'xiaohong','age':30,'tel':10086}
t2 = pd.Series(temp_dict)
t2
Out[10]: 
name    xiaohong
age           30
tel        10086
dtype: object

import string
#字典推导式
a = {string.ascii_uppercase[i]:i for i in range(10)}
print(a)
print(pd.Series(a))
print(pd.Series(a,index=list(string.ascii_uppercase[5:15])))
{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9}
A    0
B    1
C    2
D    3
E    4
F    5
G    6
H    7
I    8
J    9
dtype: int64
F    5.0
G    6.0
H    7.0
I    8.0
J    9.0
K    NaN
L    NaN
M    NaN
N    NaN
O    NaN
dtype: float64

• 手动指定数据类型

#手动指定类型
name = ["Tom", "Bob", "Mary", "James"] 
user_age = pd.Series(data=[18, 30, 25, 40], index=name, name="user_age_info", dtype=float)
user_age
Out[7]: 
Tom      18.0
Bob      30.0
Mary     25.0
James    40.0
Name: user_age_info, dtype: float64

　二、Series的索引

• 　series索引有五种方式：索引、序号、逻辑值查找、切片，数组

name = pd.Index(["Tom", "Bob", "Mary", "James"], name="name")
user_age = pd.Series(data=[18, 30, 25, 40], index=name, name="user_age_info")
"""按索引、序号，逻辑值查找，切片"""
user_age['Tom']   # 索引
Out[14]: 18
user_age.get('Tom')   # get方式
Out[15]: 18
user_age[0]　　# 序号
Out[16]: 18
user_age[:3]   # 切片
Out[17]: 
name
Tom     18
Bob     30
Mary    25
Name: user_age_info, dtype: int64
user_age[user_age>25]    # 逻辑值查找
Out[18]: 
name
Bob      30
James    40
Name: user_age_info, dtype: int64
user_age[[3,1]]     # 取多个值
Out[19]: 
name
James    40
Bob      30
Name: user_age_info, dtype: int64
user_age[::2]   # 切片
Out[20]: 
name
Tom     18
Mary    25
Name: user_age_info, dtype: int64

　三、基本属性　

user_age.shape
Out[21]: (4,)
user_age.index
Out[22]: Index(['Tom', 'Bob', 'Mary', 'James'], dtype='object', name='name')
user_age.values
Out[23]: array([18, 30, 25, 40], dtype=int64)
user_age.unique()
Out[24]: array([18, 30, 25, 40], dtype=int64)
user_age.nunique()
Out[25]: 4
user_age.dropna()
Out[26]: 
name
Tom      18
Bob      30
Mary     25
James    40
Name: user_age_info, dtype: int64
user_age.isin(list(range(30)))
Out[28]: 
name
Tom       True
Bob      False
Mary      True
James    False
Name: user_age_info, dtype: bool
user_age.sort_index()
Out[29]: 
name
Bob      30
James    40
Mary     25
Tom      18
Name: user_age_info, dtype: int64
user_age.sort_values()
Out[30]: 
name
Tom      18
Mary     25
Bob      30
James    40
Name: user_age_info, dtype: int64
user_age.sort_values(ascending=False)
Out[31]: 
name
James    40
Bob      30
Mary     25
Tom      18
Name: user_age_info, dtype: int64

• 　数字类型

•  字符串类型

 四、画图

Series.plot(kind='line', ax=None, figsize=None, use_index=True, title=None, grid=None, legend=False, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds)[source]

　　

官方文档：http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html

• series转为dataframe

series.to_frame()