import numpy as np
import pandas as pd
from pandas import Series,DataFrame
s=Series([1,2,3],index=['a','b','c'])
print(s)
'''
a 1
b 2
c 3
dtype: int64
'''
print(np.max(s))#可以进行np运算
s.name='rank'
s.index.name='name'
print(s)
#创建DataFrame
sdata1={'name':['a','b','c'],'rank':[1,2,3],'score':[98,89,54]}
print(sdata1)#字典
df1=DataFrame(sdata1)
print(df1)
'''
name rank score
0 a 1 98
1 b 2 89
2 c 3 54
'''
df2=DataFrame(sdata1,columns=['score','name','rank'])
print(df2)
'''
可以自动对齐，只是位置变化
score name rank
0 98 a 1
1 89 b 2
2 54 c 3
'''
df3=DataFrame(sdata1,columns=['score','name','rank','class'],index=['1','2','3'])
print(df3)
'''
class这列是缺失值
score name rank class
1 98 a 1 NaN
2 89 b 2 NaN
3 54 c 3 NaN
'''
df4=df3.reindex(['1','2','3','4'])
print(df4)
'''
重新索引
score name rank class
1 98.0 a 1.0 NaN
2 89.0 b 2.0 NaN
3 54.0 c 3.0 NaN
4 NaN NaN NaN NaN
'''
print(df4['score'])
print(df4.ix['1'])
print(df2[df2['score']>60])#返回df2中score大于60的值
'''
score name rank
0 98 a 1
1 89 b 2
'''
del df3['class']
print(df3)#删除class这列
sdata1={'name':['a','b','c'],'rank':[1,2,3],'score':[98,89,54]}
print(sdata1)
df3=DataFrame(sdata1,columns=['score','name','rank','class'],index=['1','2','3'])
del df3['class']
print(df3)
print(df3.reindex(['1','2','3','4']))
print(df3.reindex(['1','2','3','4'],fill_value=0))#缺失值赋值为0
'''
score name rank
1 98 a 1
2 89 b 2
3 54 c 3
4 0 0 0
'''
print(df3.reindex(['0','1','2','3']))
'''
score name rank
0 NaN NaN NaN
1 98.0 a 1.0
2 89.0 b 2.0
3 54.0 c 3.0
'''
print(df3.reindex(['0','1','2','3'],method='bfill'))#向后填充
'''
score name rank
0 98 a 1
1 98 a 1
2 89 b 2
3 54 c 3
'''
print(df3.drop('1'))#删除第一行
print(df3.drop('score',axis=1))#删除指定列，axis是维数，0是行，1是列
print(df3.T)#转置
