pandas
pandas数据结构
pandas 有 2 个常用的数据结构：Series 和 Dataframe
一. Series
Series 是一个一维的数组型对象，包括了索引和值序列。
- 列表，元组，字典和数组都能转换成 Series
import pandas as pd
list1=[4,7,5,-2]
s1=pd.Series(list1)
s1
'''
0 4
1 7
2 5
3 -2
dtype: int64
'''
#最左边一列为index列，默认为数字序列。第二列为值序列
s2=pd.Series(list1,index=['a','b','c','d'],name='number')
'''
a 4
b 7
c 5
d -2
Name: number, dtype: int64
'''
#也可以在生成Series时指定索引和列索引的名称
dict1={'Ohio':35000,'Texas':71000,'Oregon':16000,'Utah':5000}
s3=pd.Series(dict1)
s3
'''
Ohio 35000
Texas 71000
Oregon 16000
Utah 5000
dtype: int64
'''
#字典转换成Series时，index为排序好的键值
s4=pd.Series(dict1,index=['Ohio','California','Oregon','Texas'])
s4
'''
Ohio 35000.0
California NaN
Oregon 16000.0
Texas 71000.0
dtype: float64
'''
#在创建时，也可以修改index，当index中出现key不存在的值时，得到Series该项对象的值为NaN缺失值
- Series 的属性
s2.values
#array([ 4, 7, 5, -2], dtype=int64)
s2.index
#Index(['a', 'b', 'c', 'd'], dtype='object')
s2.name
#'number'
s2.index.name='order' #设置总索引名
s2
'''
order
a 4
b 7
c 5
d -2
Name: number, dtype: int64
'''
- Series 的索引和切片
s2['a'] #4
s2[0] #4 通过标签或者数字索引进行索引
s2[['a','c']] #索引多个值用列表的形式传入
'''
order
a 4
c 5
Name: number, dtype: int64
'''
s2['a':'c'] #Series 的包含左括号处和右括号处的值
'''
order
a 4
b 7
c 5
Name: number, dtype: int64
'''
s2[1:2] #不包含右括号的值
'''
order
b 7
Name: number, dtype: int64
'''
二. DataFrame
DataFrame 相当于共用索引的Series的字典，其中字典的键值为列索引
- DataFrame 的构建
dict2={'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]}
df1=pd.DataFrame(dict2)
df1
'''
state year pop
0 Ohio 2000 1.5
1 Ohio 2001 1.7
2 Ohio 2002 3.6
3 Nevada 2001 2.4
4 Nevada 2002 2.9
5 Nevada 2003 3.2
'''
#默认列索引为字典的键值，行索引为数字索引
df2=pd.DataFrame(dict2,columns=['year','state','pop','debt'],index=['one','two','three','four','five','six'])
df2
'''
year state pop debt
one 2000 Ohio 1.5 NaN
two 2001 Ohio 1.7 NaN
three 2002 Ohio 3.6 NaN
four 2001 Nevada 2.4 NaN
five 2002 Nevada 2.9 NaN
six 2003 Nevada 3.2 NaN
'''
#将列索引重新排列，原先不存在的列名中的值为NaN,对行索引进行重新赋值
#包含字典的嵌套字典也可以转换成DataFrame
dict2={'Nevada':{2001:2.4,2002:2.9},'Ohio':{2000:1.5,2001:1.7,2002:3.6}}
df3=pd.DataFrame(dict2)
df3
'''
Nevada Ohio
2001 2.4 1.7
2002 2.9 3.6
2000 NaN 1.5
'''
#将外层字典的key作为列索引，将内层字典的键作为行索引
#包含Series的字典转换成DataFrame
df4=pd.DataFrame({'Ohio':df3['Ohio'][:2],'Nevada':df3['Nevada'][:-1]})
df4
'''
Ohio Nevada
2001 1.7 2.4
2002 3.6 2.9
'''
#将key值作为列索引，行索引由Series自带，也可以在创建过程中指定行索引
可以通过pd.DataFrame函数生成DataFrame的对象类型和索引特征
| 类型 | 注释 | 代码实现 |
|---|---|---|
| 2D ndarray，列表或者元组构成的列表 | 数据的矩阵，行列的索引可选 | ndarray_2d=np.arange(20).reshape(4,5) ddf=pd.DataFrame(ndarray_2d,columns=[1,2,3,4,5],index=['a','b','c','d']) |
| 数组、列表、元组构成的字典 | 每个序列为DataFrame的一列，所有的序列必须长度一致 | 如上 |
| Series构成的字典 | 每个Series为一列，行索引由所有的Series决定，列索引为key值 | 如上 |
| 字典嵌套字典 | 字典外部的Key为列索引，内部key为行索引 | 如上 |
| 字典或者Series构成的列表 | 以列表中的每个元素为一行，字典键或者Series的索引为列索引 | dict2={'Nevada':{2001:2.4,2002:2.9},'Ohio':{2000:1.5,2001:1.7,2002:3.6}} df3=pd.DataFrame(dict2) list_s=[df3['Ohio'],df3['Nevada']] ddf2=pd.DataFrame(list_s) |
- DataFrame 的属性
df2.index
#Index(['one', 'two', 'three', 'four', 'five', 'six'], dtype='object')
df2.columns
#Index(['year', 'state', 'pop', 'debt'], dtype='object')
df2.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)
'''
- DataFrame 的列索引
df2['year']
df2.year #以字典键值或者属性的方式检索
df2[['year','state']]
- 列值的修改
#赋值标量值
df2['debt']=1
#赋值值数组/值列表
df2['debt']=[x for x in range(6)]
df2
'''
year state pop debt
one 2000 Ohio 1.5 0
two 2001 Ohio 1.7 1
three 2002 Ohio 3.6 2
four 2001 Nevada 2.4 3
five 2002 Nevada 2.9 4
six 2003 Nevada 3.2 5
'''
df2['debt']=np.arange(6.0)
df2
'''
year state pop debt
one 2000 Ohio 1.5 0.0
two 2001 Ohio 1.7 1.0
three 2002 Ohio 3.6 2.0
four 2001 Nevada 2.4 3.0
five 2002 Nevada 2.9 4.0
six 2003 Nevada 3.2 5.0
'''
#赋值Series
arr2=np.full(3,3)
s5=pd.Series(arr2,index=['one','three','six'])
df2['debt']=s5
df2
'''
year state pop debt
one 2000 Ohio 1.5 3.0
two 2001 Ohio 1.7 NaN
three 2002 Ohio 3.6 3.0
four 2001 Nevada 2.4 NaN
five 2002 Nevada 2.9 NaN
six 2003 Nevada 3.2 3.0
'''
#如果Series中的index和DataFrame中的index不在交集范围内，该索引对应的信息会被过滤掉
s6=pd.Series(arr2,index=['one','two','seven'])
df2['debt']=s6
df2
'''
year state pop debt
one 2000 Ohio 1.5 3.0
two 2001 Ohio 1.7 3.0
three 2002 Ohio 3.6 NaN
four 2001 Nevada 2.4 NaN
five 2002 Nevada 2.9 NaN
six 2003 Nevada 3.2 NaN
'''
#列的增加,通过对列索引的引用直接赋值
df2['eastern']=df2['state']=='Ohio' #不能通过df2.eastern的方式赋值
df2
'''
year state pop debt eastern
one 2000 Ohio 1.5 3.0 True
two 2001 Ohio 1.7 3.0 True
three 2002 Ohio 3.6 NaN True
four 2001 Nevada 2.4 NaN False
five 2002 Nevada 2.9 NaN False
six 2003 Nevada 3.2 NaN False
'''
#列的删除
del df2['eastern']
