Python Pandas 简明教程
Python Pandas - Indexing and Selecting Data
在本章中，我们将讨论如何对日期进行切片和划分，通常获得 Pandas 对象的子集。
In this chapter, we will discuss how to slice and dice the date and generally get the subset of pandas object.
Python 和 NumPy 索引运算符 “[ ]” 和属性运算符 “.” 可以在各种用例中快速简便地访问 Pandas 数据结构。但是，由于要访问的数据类型是事先不知道的，因此直接使用标准运算符会带来一些优化限制。对于生产代码，我们建议你利用本章中介绍的经过优化的 Pandas 数据访问方法。
The Python and NumPy indexing operators "[ ]" and attribute operator "." provide quick and easy access to Pandas data structures across a wide range of use cases. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommend that you take advantage of the optimized pandas data access methods explained in this chapter.
Pandas 现在支持三种多轴索引；以下表中提到了这三种类型：
Pandas now supports three types of Multi-axes indexing; the three types are mentioned in the following table −
Sr.No |
Indexing & Description |
1 |
.loc() Label based |
2 |
.iloc() Integer based |
3 |
.ix() Both Label and Integer based |
.loc()
Pandas 提供了多种方法来拥有纯粹的 label based indexing 。切片时，也包括起始边界。整数有效标签，但它们指的是该标签，而不是该位置。
Pandas provide various methods to have purely label based indexing. When slicing, the start bound is also included. Integers are valid labels, but they refer to the label and not the position.
.loc() 有多种访问方法，如 −
.loc() has multiple access methods like −
-
A single scalar label
-
A list of labels
-
A slice object
-
A Boolean array
loc 使用两个由“,”分隔的单一/列表/范围运算符。第一个指示行，而第二个指示列。
loc takes two single/list/range operator separated by ','. The first one indicates the row and the second one indicates columns.
Example 1
#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
#select all rows for a specific column
print df.loc[:,'A']它的 output 如下所示 −
Its output is as follows −
a 0.391548
b -0.070649
c -0.317212
d -2.162406
e 2.202797
f 0.613709
g 1.050559
h 1.122680
Name: A, dtype: float64Example 2
# import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
# Select all rows for multiple columns, say list[]
print df.loc[:,['A','C']]它的 output 如下所示 −
Its output is as follows −
A C
a 0.391548 0.745623
b -0.070649 1.620406
c -0.317212 1.448365
d -2.162406 -0.873557
e 2.202797 0.528067
f 0.613709 0.286414
g 1.050559 0.216526
h 1.122680 -1.621420Example 3
# import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
# Select few rows for multiple columns, say list[]
print df.loc[['a','b','f','h'],['A','C']]它的 output 如下所示 −
Its output is as follows −
A C
a 0.391548 0.745623
b -0.070649 1.620406
f 0.613709 0.286414
h 1.122680 -1.621420Example 4
# import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
# Select range of rows for all columns
print df.loc['a':'h']它的 output 如下所示 −
Its output is as follows −
A B C D
a 0.391548 -0.224297 0.745623 0.054301
b -0.070649 -0.880130 1.620406 1.419743
c -0.317212 -1.929698 1.448365 0.616899
d -2.162406 0.614256 -0.873557 1.093958
e 2.202797 -2.315915 0.528067 0.612482
f 0.613709 -0.157674 0.286414 -0.500517
g 1.050559 -2.272099 0.216526 0.928449
h 1.122680 0.324368 -1.621420 -0.741470Example 5
# import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
# for getting values with a boolean array
print df.loc['a']>0它的 output 如下所示 −
Its output is as follows −
A False
B True
C False
D False
Name: a, dtype: bool.iloc()
Pandas 提供多种方法来获取纯基于整数的索引。如同 python 和 numpy，这些是 0-based 索引。
Pandas provide various methods in order to get purely integer based indexing. Like python and numpy, these are 0-based indexing.
各种访问方法如下所示 −
The various access methods are as follows −
-
An Integer
-
A list of integers
-
A range of values
Example 1
# import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
# select all rows for a specific column
print df.iloc[:4]它的 output 如下所示 −
Its output is as follows −
A B C D
0 0.699435 0.256239 -1.270702 -0.645195
1 -0.685354 0.890791 -0.813012 0.631615
2 -0.783192 -0.531378 0.025070 0.230806
3 0.539042 -1.284314 0.826977 -0.026251Example 2
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
# Integer slicing
print df.iloc[:4]
print df.iloc[1:5, 2:4]它的 output 如下所示 −
Its output is as follows −
A B C D
0 0.699435 0.256239 -1.270702 -0.645195
1 -0.685354 0.890791 -0.813012 0.631615
2 -0.783192 -0.531378 0.025070 0.230806
3 0.539042 -1.284314 0.826977 -0.026251
C D
1 -0.813012 0.631615
2 0.025070 0.230806
3 0.826977 -0.026251
4 1.423332 1.130568Example 3
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
# Slicing through list of values
print df.iloc[[1, 3, 5], [1, 3]]
print df.iloc[1:3, :]
print df.iloc[:,1:3]它的 output 如下所示 −
Its output is as follows −
B D
1 0.890791 0.631615
3 -1.284314 -0.026251
5 -0.512888 -0.518930
A B C D
1 -0.685354 0.890791 -0.813012 0.631615
2 -0.783192 -0.531378 0.025070 0.230806
B C
0 0.256239 -1.270702
1 0.890791 -0.813012
2 -0.531378 0.025070
3 -1.284314 0.826977
4 -0.460729 1.423332
5 -0.512888 0.581409
6 -1.204853 0.098060
7 -0.947857 0.641358.ix()
除了基于纯标签和基于整数之外，Pandas 提供了一种混合方法来选择和子集化对象，使用 .ix() 运算符。
Besides pure label based and integer based, Pandas provides a hybrid method for selections and subsetting the object using the .ix() operator.
Example 1
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
# Integer slicing
print df.ix[:4]它的 output 如下所示 −
Its output is as follows −
A B C D
0 0.699435 0.256239 -1.270702 -0.645195
1 -0.685354 0.890791 -0.813012 0.631615
2 -0.783192 -0.531378 0.025070 0.230806
3 0.539042 -1.284314 0.826977 -0.026251Example 2
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
# Index slicing
print df.ix[:,'A']它的 output 如下所示 −
Its output is as follows −
0 0.699435
1 -0.685354
2 -0.783192
3 0.539042
4 -1.044209
5 -1.415411
6 1.062095
7 0.994204
Name: A, dtype: float64Use of Notations
使用多轴索引从 Pandas 对象获取值使用以下表示法 −
Getting values from the Pandas object with Multi-axes indexing uses the following notation −
Object |
Indexers |
Return Type |
Series |
s.loc[indexer] |
Scalar value |
DataFrame |
df.loc[row_index,col_index] |
Series object |
Panel |
p.loc[item_index,major_index, minor_index] |
p.loc[item_index,major_index, minor_index] |
Note − .iloc() & .ix() 应用相同的索引选项和返回值。
Note − .iloc() & .ix() applies the same indexing options and Return value.
现在让我们看看如何在 DataFrame 对象上执行每个操作。我们将使用基本的索引运算符“[ ]” −
Let us now see how each operation can be performed on the DataFrame object. We will use the basic indexing operator '[ ]' −
Example 1
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print df['A']它的 output 如下所示 −
Its output is as follows −
0 -0.478893
1 0.391931
2 0.336825
3 -1.055102
4 -0.165218
5 -0.328641
6 0.567721
7 -0.759399
Name: A, dtype: float64Note − 我们可以将一个值列表传递给 [ ] 来选择这些列。
Note − We can pass a list of values to [ ] to select those columns.
Example 2
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print df[['A','B']]它的 output 如下所示 −
Its output is as follows −
A B
0 -0.478893 -0.606311
1 0.391931 -0.949025
2 0.336825 0.093717
3 -1.055102 -0.012944
4 -0.165218 1.550310
5 -0.328641 -0.226363
6 0.567721 -0.312585
7 -0.759399 -0.372696