pandasNote1

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

obj = pd.Series([4, 7, 8, -1])
obj

0    4
1    7
2    8
3   -1
dtype: int64

# 指定索引值
obj1 = pd.Series([1, 2, 3, 4], 
                 index=['a', 'b', 'c', 'd'])
obj1

a    1
b    2
c    3
d    4
dtype: int64

# 通过字典的形式创建Series数据
# S中的索引就是原来字典中的键
data1 = {"city": "shenzhen", "age": 25, "number": 123456}
obj2 = pd.Series(data1)
obj2

city      shenzhen
age             25
number      123456
dtype: object

# 通过自己创建的索引来获取数据
obj1[['a', 'c', 'b']]

a    1
c    3
b    2
dtype: int64

# 默认数值索引来获取数据
obj1[:3]

a    1
b    2
c    3
dtype: int64

# 布尔型数组过滤掉不满足要求的数据
obj1[obj1 >= 2]

b    2
c    3
d    4
dtype: int64

# 上面的obj1
obj1

a    1
b    2
c    3
d    4
dtype: int64

obj1['b']

2

# 通过切片形式，包含末端，和Python1不同
obj1["a":"c"]

a    1
b    2
c    3
dtype: int64

obj1[["b", "a", "d"]]

b    2
a    1
d    4
dtype: int64

obj1[1]

2

obj1[1:4]

b    2
c    3
d    4
dtype: int64

obj1[[1, 3]]

b    2
d    4
dtype: int64

obj1[obj1 > 2]

c    3
d    4
dtype: int64

# 上面的obj2
obj2

city      shenzhen
age             25
number      123456
dtype: object

# sex对应的值找不到，用NaN表示
data2 = ["sex", "city", "age", "number"]
obj3 = pd.Series(obj2, index=data2)
obj3

sex            NaN
city      shenzhen
age             25
number      123456
dtype: object

pd.isnull(obj3)

sex        True
city      False
age       False
number    False
dtype: bool

# Series的实例方法isnull和notnull
obj3.isnull()

sex        True
city      False
age       False
number    False
dtype: bool

# S的name属性
obj3.name = "person"
# S索引的name属性
obj3.index.name = "information"
obj3

information
sex            NaN
city      shenzhen
age             25
number      123456
Name: person, dtype: object

# 上面栗子中的number修改为phone_num
obj3.index = ["sex", "city", "age", "phone_num"]
obj3

sex               NaN
city         shenzhen
age                25
phone_num      123456
Name: person, dtype: object

# 1.通过传入等长列表或者Numpy数组组成的字典
data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
        'year': [2000, 2001, 2002, 2001, 2002, 2003],
        'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
frame = pd.DataFrame(data)
frame

# 2. 指定列序列创建，通过columns参数
# 结果中3个列属性和上面的顺序不同
pd.DataFrame(data, columns=["year", "state", "pop"])

# 3、columns中的属性如果不存在，则结果中用缺失值代替，debt属性
# 4、在DF中传入指定的index，有one-six
frame2 = pd.DataFrame(data, columns=["year", "state", "pop", "debt"],
                      index=["one", "two", "three", "four", "five", "six"])
frame2

# 1、获取DF中的列属性和索引
print(frame2.columns)
print(frame2.index)

Index(['year', 'state', 'pop', 'debt'], dtype='object')
Index(['one', 'two', 'three', 'four', 'five', 'six'], dtype='object')

# 2、查看values
frame2.values

array([[2000, 'Ohio', 1.5, nan],
       [2001, 'Ohio', 1.7, nan],
       [2002, 'Ohio', 3.6, nan],
       [2001, 'Nevada', 2.4, nan],
       [2002, 'Nevada', 2.9, nan],
       [2003, 'Nevada', 3.2, nan]], dtype=object)

# 3、查看列数据
frame2.year

one      2000
two      2001
three    2002
four     2001
five     2002
six      2003
Name: year, dtype: int64

frame2["year"]

one      2000
two      2001
three    2002
four     2001
five     2002
six      2003
Name: year, dtype: int64

# 4、赋值修改列
# 注意是整列的修改
frame2["debt"] = 18
frame2

# 传入numpy数据
frame2["debt"] = np.arange(6.0)
frame2

# 传入S型数据
val = pd.Series([1.2, 1.9, 2], index=["one", "three", "five"])
frame2["debt"] = val
frame2

# 1、2

# 先判断state属性的值是否为Ohio
# 如果等于，将eastern属性的值设为T，否则为F
# eastern属性是新建的，只能通过字典标记的形式
frame2["eastern"] = (frame2.state == "Ohio")
frame2

# 3 删除数据
del frame2["eastern"]

frame2

frame2["pop"]

one      1.5
two      1.7
three    3.6
four     2.4
five     2.9
six      3.2
Name: pop, dtype: float64

frame2.pop

<bound method NDFrame.pop of        year   state  pop  debt
one    2000    Ohio  1.5   1.2
two    2001    Ohio  1.7   NaN
three  2002    Ohio  3.6   1.9
four   2001  Nevada  2.4   NaN
five   2002  Nevada  2.9   2.0
six    2003  Nevada  3.2   NaN>

# 4  嵌套字典创建DF：外层为列属性，内层为行
pop = {'Nevada': {2001: 2.4, 2002: 2.9},
       'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
frame3 = pd.DataFrame(pop)
frame3

# 5、转置操作
frame3.T

# 6 、DF中传入S型数据
pdata = {'Ohio': frame3['Ohio'][:-1],
         'Nevada': frame3['Nevada'][:2]}
pd.DataFrame(pdata)

# 获取DF中的所有数据
frame2.values

array([[2000, 'Ohio', 1.5, 1.2],
       [2001, 'Ohio', 1.7, nan],
       [2002, 'Ohio', 3.6, 1.9],
       [2001, 'Nevada', 2.4, nan],
       [2002, 'Nevada', 2.9, 2.0],
       [2003, 'Nevada', 3.2, nan]], dtype=object)