import numpy as np
import pandas as pd
from pandas import Series, DataFrame
np.random.seed(666)
# series reindex
s1 = Series([1, 2, 3, 4], index=['A', 'B', 'C', 'D'])
print(s1)
'''
A 1
B 2
C 3
D 4
dtype: int64
'''
# 重新指定 index， 多出来的index，可以使用fill_value 填充
print(s1.reindex(index=['A', 'B', 'C', 'D', 'E'], fill_value = 10))
'''
A 1
B 2
C 3
D 4
E 10
dtype: int64
'''
s2 = Series(['A', 'B', 'C'], index = [1, 5, 10])
print(s2)
'''
1 A
5 B
10 C
dtype: object
'''
# 修改索引，
# 将s2的索引增加到15个
# 如果新增加的索引值不存在，默认为 Nan
print(s2.reindex(index=range(15)))
'''
0 NaN
1 A
2 NaN
3 NaN
4 NaN
5 B
6 NaN
7 NaN
8 NaN
9 NaN
10 C
11 NaN
12 NaN
13 NaN
14 NaN
dtype: object
'''
# ffill ： foreaward fill 向前填充，
# 如果新增加索引的值不存在，那么按照前一个非nan的值填充进去
print(s2.reindex(index=range(15), method='ffill'))
'''
0 NaN
1 A
2 A
3 A
4 A
5 B
6 B
7 B
8 B
9 B
10 C
11 C
12 C
13 C
14 C
dtype: object
'''
# reindex dataframe
df1 = DataFrame(np.random.rand(25).reshape([5, 5]), index=['A', 'B', 'D', 'E', 'F'], columns=['c1', 'c2', 'c3', 'c4', 'c5'])
print(df1)
'''
c1 c2 c3 c4 c5
A 0.700437 0.844187 0.676514 0.727858 0.951458
B 0.012703 0.413588 0.048813 0.099929 0.508066
D 0.200248 0.744154 0.192892 0.700845 0.293228
E 0.774479 0.005109 0.112858 0.110954 0.247668
F 0.023236 0.727321 0.340035 0.197503 0.909180
'''
# 为 dataframe 添加一个新的索引
# 可以看到 自动 扩充为 nan
print(df1.reindex(index=['A', 'B', 'C', 'D', 'E', 'F']))
''' 自动填充为 nan
c1 c2 c3 c4 c5
A 0.700437 0.844187 0.676514 0.727858 0.951458
B 0.012703 0.413588 0.048813 0.099929 0.508066
C NaN NaN NaN NaN NaN
D 0.200248 0.744154 0.192892 0.700845 0.293228
E 0.774479 0.005109 0.112858 0.110954 0.247668
F 0.023236 0.727321 0.340035 0.197503 0.909180
'''
# 扩充列， 也是一样的
print(df1.reindex(columns=['c1', 'c2', 'c3', 'c4', 'c5', 'c6']))
'''
c1 c2 c3 c4 c5 c6
A 0.700437 0.844187 0.676514 0.727858 0.951458 NaN
B 0.012703 0.413588 0.048813 0.099929 0.508066 NaN
D 0.200248 0.744154 0.192892 0.700845 0.293228 NaN
E 0.774479 0.005109 0.112858 0.110954 0.247668 NaN
F 0.023236 0.727321 0.340035 0.197503 0.909180 NaN
'''
# 减小 index
print(s1.reindex(['A', 'B']))
''' 相当于一个切割效果
A 1
B 2
dtype: int64
'''
print(df1.reindex(index=['A', 'B']))
''' 同样是一个切片的效果
c1 c2 c3 c4 c5
A 0.601977 0.619927 0.251234 0.305101 0.491200
B 0.244261 0.734863 0.569936 0.889996 0.017936
'''
# 对于一个 serie 来说，可以使用 drop，来丢掉某些 index
print(s1.drop('A'))
''' 就只剩下 三个了
B 2
C 3
D 4
dtype: int64
'''
# dataframe drop(A) 直接去掉一行
print(df1.drop('A', axis=0))
''' axis 默认 是 行
c1 c2 c3 c4 c5
B 0.571883 0.254364 0.530883 0.295224 0.352663
D 0.858452 0.379495 0.593284 0.786078 0.949718
E 0.556276 0.643187 0.808664 0.289422 0.501041
F 0.737993 0.286072 0.332714 0.873371 0.421615
'''
print(df1.drop('c1', axis=1))
''' 将 c1 的列 去掉
c2 c3 c4 c5
A 0.326681 0.247832 0.601982 0.145905
B 0.373961 0.393819 0.439284 0.926706
D 0.558490 0.617851 0.461280 0.373102
E 0.030434 0.566498 0.383103 0.739243
F 0.982220 0.989826 0.957863 0.411514
'''
