5.2 Pandas基本功能_增加series的索引个数时,如果新增加的索引值不存在,则默认索引值为-优快云博客

5.2.1 reindex(pandas重要方法）为Series和DataFrame创建符合索引的新对象

obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['c', 'b', 'a', 'd'])
print(obj)
obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])
print(obj2)

结果：reindex 会将索引进行排序，如果索引值之前不存在，则会引入缺失值。

c    4.5
b    7.2
a   -5.3
d    3.6
dtype: float64
a   -5.3
b    7.2
c    4.5
d    3.6
e    NaN
dtype: float64

1.对于需要插值填值的数据，比如时间序列这些顺序数据，利用reindex中的method参数中的ffill等方法，ffill方法会将值进行向前填充：

obj3 = pd.Series(['white', 'blue', 'purple'], index=[0, 2, 4])
print(obj3)
obj4 = obj3.reindex(range(6))
print(obj4)
obj5 = obj3.reindex(range(6), method='ffill')
print(obj5)

结果：

0     white
2      blue
4    purple
dtype: object
0     white
1       NaN
2      blue
3       NaN
4    purple
5       NaN
dtype: object
0     white
1     white
2      blue
3      blue
4    purple
5    purple
dtype: object

2.在DataFrame中，reindex可以改变行索引和列索引，利用reindex中的column参数可以实现对列索引的修改

frame = pd.DataFrame(np.arange(9).reshape(3, 3),
                     index=['a', 'c', 'd'],
                     columns=['Ohio', 'Texas', 'California'])
print(frame)
frame2 = frame.reindex(['a', 'b', 'c', 'd'])
print(frame2)
state = ['Texas', 'Utah', 'California']
frame3 = frame.reindex(columns=state)
print(frame3)
#也可以直接这样：用loc直接改变索引
print(frame.loc[['a', 'b', 'c', 'd'], ['Texas', 'Utah', 'California']])

结果：

  Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8
   Ohio  Texas  California
a   0.0    1.0         2.0
b   NaN    NaN         NaN
c   3.0    4.0         5.0
d   6.0    7.0         8.0
   Texas  Utah  California
a      1   NaN           2
c      4   NaN           5
d      7   NaN           8
   Texas  Utah  California
a    1.0   NaN         2.0
b    NaN   NaN         NaN
c    4.0   NaN         5.0
d    7.0   NaN         8.0

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
'''