Pandas的标签处理需要分成多种情况来处理,Series和DataFrame根据标签索引数据的操作方法是不同的,单列索引和双列索引的操作方法也是不同的。
单列索引
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.ones((2, 4)), index=list("AB"), columns=list("abcd"))
In [5]: df.iloc[0,0]=100
In [6]: df
Out[6]:
a b c d
A 100.0 1.0 1.0 1.0
B 1.0 1.0 1.0 1.0
reindex所插入的标签如果是原来的标签中没有的，就会将该行的值全部置为NaN
In [7]: df.reindex(["A", "f"])
Out[7]: ssss
a b c d
A 100.0 1.0 1.0 1.0
f NaN NaN NaN NaN
In [8]: df
Out[8]:
a b c d
A 100.0 1.0 1.0 1.0
B 1.0 1.0 1.0 1.0
使用index修改标签
In [9]: df.index = ["a", "b"]
In [10]: df
Out[10]:
a b c d
a 100.0 1.0 1.0 1.0
b 1.0 1.0 1.0 1.0
使用set_index将某一列变为标签
In [11]: df.set_index("a")
Out[11]:
b c d
a
100.0 1.0 1.0 1.0
1.0 1.0 1.0 1.0
In [12]: df
Out[12]:
a b c d
a 100.0 1.0 1.0 1.0
b 1.0 1.0 1.0 1.0
# 使用drop参数控制将某一列作为索引后是否删除原数据
In [13]: df.set_index("a", drop=False)
Out[13]:
a b c d
a
100.0 100.0 1.0 1.0 1.0
1.0 1.0 1.0 1.0 1.0
# 使用unique函数可以去除重复值
In [14]: df.set_index("b", drop=False).index.unique()
Out[14]: Float64Index([1.0], dtype='float64', name='b')
In [15]: df.set_index("b", drop=False).index
Out[15]: Float64Index([1.0, 1.0], dtype='float64', name='b')
In [16]: len(df.set_index("b", drop=False).index.unique())
Out[16]: 1
双列索引
In [17]: df.set_index(["a","b"])
Out[17]:
c d
a b
100.0 1.0 1.0 1.0
1.0 1.0 1.0 1.0
# levels这个列表中含有两个列表,分别是双列索引的每一列
In [18]: df.set_index(["a","b"]).index
Out[18]:
MultiIndex(levels=[[1.0, 100.0], [1.0]],
labels=[[1, 0], [0, 0]],
names=['a', 'b'])
In [19]: a = pd.DataFrame({'a': range(7),'b': range(7, 0, -1),'c': ['one','one','one','two','two','two', 'two'],'d': list("hjklmno")})
In [20]: a
Out[20]:
a b c d
0 0 7 one h
1 1 6 one j
2 2 5 one k
3 3 4 two l
4 4 3 two m
5 5 2 two n
6 6 1 two o
In [21]: b = a.set_index(["c","d"])
In [22]: b
Out[22]:
a b
c d
one h 0 7
j 1 6
k 2 5
two l 3 4
m 4 3
n 5 2
o 6 1
In [23]: c = b["a"]
In [24]: c
Out[24]:
c d
one h 0
j 1
k 2
two l 3
m 4
n 5
o 6
Name: a, dtype: int64
双列索引取值
In [25]: c["two"]["l"]
Out[25]: 3
In [26]: c["one"]
Out[26]:
d
h 0
j 1
k 2
Name: a, dtype: int64
In [27]: d = a.set_index(["d","c"])
In [28]: d = d["a"]
In [43]: d
Out[43]:
d c
h one 0
j one 1
k one 2
l two 3
m two 4
n two 5
o two 6
Name: a, dtype: int64
# 对于索引数少的列在后的情况,如果直接取会发生错误
In [44]: d["one"]
---------------------------------------------------------------------------
KeyError Traceback (most recent call
...
KeyError: 'one'
swaplevel()函数进行标签列换位
In [45]: d.swaplevel()
Out[45]:
c d
one h 0
j 1
k 2
two l 3
m 4
n 5
o 6
Name: a, dtype: int64
In [46]: d = d.swaplevel()
In [47]: d["one"]
Out[47]:
d
h 0
j 1
k 2
Name: a, dtype: int64
In [48]: b
Out[48]:
a b
c d
one h 0 7
j 1 6
k 2 5
two l 3 4
m 4 3
n 5 2
o 6 1
对于DataFrame类型数组的双列索引,取值时应该加上loc或iloc
In [49]: b.loc["one"]
Out[49]:
a b
d
h 0 7
j 1 6
k 2 5
In [51]: d.loc["two"].loc["m"]
Out[51]: 4