1.pandas的数据结构

Series

• series是一维数据

import pandas as pd
s = pd.Series([1,2,3,4,5])
print(s.index) #获取series的索引
print(s.values)#获取series的值

DataFrame

• DataFrame为二维数据

df.values #获取数据的值
df.index  #获取行索引
df.columns #获取列索引

axis = 1/axis = columns #沿着列索引的方向进行运算
axis = 0/axis = index   #沿着行索引的方向进行运算

2.创建数据对象

Series

• 创建series一般有以下6种方法
  • 通过list创建
  • 通过字典创建
  • 通过ndrray创建
  • 通过标量创建
  • 创建空的Series
  • 通过读取数据文件创建

通过list创建

import pandas as pd
#直接通过列表进行创建
s = pd.Series([1,2,3,4,5])
#指定索引
s = pd.Series([1,2,3],index = ["a","b","c"])#values与index的个数必须相同，否则会报错
#指定表明
s = pd.Series([1,2,3],index = ["a","b","c"],name="hello")

通过字典创建

• 此时字典的keys便是Series的index

import pandas as pd
d1 = {"a":1,"b":2,"c":3}
s = pd.Series(d1)
print(s)

通过numpy的ndrray创建

import numpy as np
import pandas as pd

np.random.seed(0)
s = pd.Series(np.random.randint(5,size=3))
print(s)

#此处还不太懂哎，等学到numpy时再回过头来看看吧

通过标量创建

• 标量：具体的单个数据
• 通过设置索引的长度控制行数

import pandas as pd
s = pd.Series(1,index = ["a","b","c"])
print(s)

'''
输出结果
a    1
b    1
c    1
'''

创建空的Series

import pandas as pd
s = pd.Series()
print(s)

DataFrame

• 与Series类似DataFrame同样有6种创建数据的方法

  • 通过list创建

  • 通过字典创建

  • 通过ndrray创建

  • 通过标量创建

  • 创建空的DataFrame

  • 通过读取数据文件创建

创建空的DataFrame

import pandas as pd
df = pd.DataFrame()
print(df)

通过list创建

import pandas as pd
#一维列表
lst = [1,2,3,4]
df = pd.DataFrame(lst)
print(df)


#二维列表，二维列表有很多累

#**************list of list***************
lst = [["lemo","长沙",80,90],
       ["jack","上海",90,75],
       ["peter","深圳",60,80],]
df = pd.DataFrame(data=lst,columns=["name","city","math","chem"])
print(df)
'''
输出结果
    name city  math  chem
0   lemo   长沙    80    90
1   jack   上海    90    75
2  peter   深圳    60    80
'''

# **************************************list of dict*****************************************
lst = [ {"name":"lemo","city":"长沙","math":80,"chem":90},
       {"name":"jack","city":"上海","math":90,"chem":75},
       {"name":"peter","city":"深圳","math":60,"chem":80}]
df = pd.DataFrame(data=lst,columns=["name","city","math","chem"])
print(df)

'''
输出结果
    name city  math  chem
0   lemo   长沙    80    90
1   jack   上海    90    75
2  peter   深圳    60    80
'''

#最后的字典少了一个元素，最后生成的结果为NaN
lst = [ {"name":"lemo","city":"长沙","math":80,"chem":90},
       {"name":"jack","city":"上海","math":90,"chem":75},
       {"name":"peter","city":"深圳","math":60}]
df = pd.DataFrame(data=lst,columns=["name","city","math","chem"])
print(df)
"""
输出结果
  name city  math  chem
0   lemo   长沙    80  90.0
1   jack   上海    90  75.0
2  peter   深圳    60   NaN
"""

#创建数据框时，只选取特定的列，生成时只生成指定的列
lst = [ {"name":"lemo","city":"长沙","math":80,"chem":90},
       {"name":"jack","city":"上海","math":90,"chem":75},
       {"name":"peter","city":"深圳","math":60}]
df = pd.DataFrame(data=lst,columns=["name","city","math"])
print(df)
"""
输出结果
    name city  math
0   lemo   长沙    80
1   jack   上海    90
2  peter   深圳    60
"""

#设置列索引时，与字典的key值不匹配，创建的数据框会有nan值
lst = [ {"name":"lemo","city":"长沙","math":80,"chem":90},
       {"name":"jack","city":"上海","math":90,"chem":75},
       {"name":"peter","city":"深圳","math":60}]
df = pd.DataFrame(data=lst,columns=["name","city","math","化学"])
print(df)
"""
输出结果
    name city  math  化学
0   lemo   长沙    80 NaN
1   jack   上海    90 NaN
2  peter   深圳    60 NaN
"""
# **************************************list of tuple*****************************************
#此时是和list of list是非常相似的
lst = [ ("lemo","长沙",80,90),
        ("jack","上海",90,75),
       ("peter","深圳",60,85)]
df = pd.DataFrame(data=lst,columns=["name","city","math","化学"])
print(df)
"""
输出结果
    name city  math  化学
0   lemo   长沙    80  90
1   jack   上海    90  75
2  peter   深圳    60  85
"""

#通过zip方式将列表整合成元组后再生成数据
list1 = ["lemo","jack","peter","yang"]
list2 = ["长沙","上海","深圳","宁波"]
list3 = [80,90,60,20]
list4 = [90,75,80,10]
lis = zip(list1,list2,list3,list4)#zip把其压缩成一个元组包含在列表中
df = pd.DataFrame(data=lis,columns=("name","city","chem","mach"))
print(df)
'''
输出结果
   name city  chem  mach
0   lemo   长沙    80    90
1   jack   上海    90    75
2  peter   深圳    60    80
3   yang   宁波    20    10
'''

通过字典创建数据框

#普通模式
d = {"name":["lemo","jack","peter","yang"],"city":["长沙","上海","深圳","宁波"],
     "chem":[80,90,60,20],"mach":[90,75,80,10]}
df = pd.DataFrame(data=d)
#或者df = pd.DataFrame.from_dict(data=d)
print(df)
"""
输出结果
    name city  chem  mach
0   lemo   长沙    80    90
1   jack   上海    90    75
2  peter   深圳    60    80
3   yang   宁波    20    10
"""

#通过嵌套型的字典，此时可指定index的次序
d = {"name":{1:"lemo",2:"jack",3:"peter",4:"yang"},"city":{0:"长沙",1:"上海",2:"深圳",3:"宁波"},
     "chem":{0:80,1:90,2:60,3:20},"mach":{0:90,1:75,2:80,3:10}}
df = pd.DataFrame(data=d)
print(df)
"""
输出结果
    name city  chem  mach
1   lemo   上海  90.0  75.0
2   jack   深圳  60.0  80.0
3  peter   宁波  20.0  10.0
4   yang  NaN   NaN   NaN
0    NaN   长沙  80.0  90.0
"""

通过标量创建数据框

df = pd.DataFrame(1,index=[1,2,3],columns=list("abcde"))
print(df)
'''
输出结果
   a  b  c  d  e
1  1  1  1  1  1
2  1  1  1  1  1
3  1  1  1  1  1
'''

通过读取数据文件创建