Python Pandas 简明教程

Python Pandas - Categorical Data

通常在实时数据中，将包含重复的文本列。性别、国家和代码等要素始终是重复的。这些是分类数据的示例。

Often in real-time, data includes the text columns, which are repetitive. Features like gender, country, and codes are always repetitive. These are the examples for categorical data.

分类变量只能取有限且通常是固定数量的可能值。除了固定长度外，分类数据可能存在顺序，但不能执行数值操作。分类是熊猫数据类型。

Categorical variables can take on only a limited, and usually fixed number of possible values. Besides the fixed length, categorical data might have an order but cannot perform numerical operation. Categorical are a Pandas data type.

分类数据类型在以下情况下有用−

The categorical data type is useful in the following cases −

A string variable consisting of only a few different values. Converting such a string variable to a categorical variable will save some memory.
The lexical order of a variable is not the same as the logical order (“one”, “two”, “three”). By converting to a categorical and specifying an order on the categories, sorting and min/max will use the logical order instead of the lexical order.
As a signal to other python libraries that this column should be treated as a categorical variable (e.g. to use suitable statistical methods or plot types).

Object Creation

分类对象可以通过多种方式创建。以下介绍了不同的方式-

Categorical object can be created in multiple ways. The different ways have been described below −

pd.Categorical

使用标准的 pandas 分类构造函数，我们可以创建一个类别对象。

Using the standard pandas Categorical constructor, we can create a category object.

pandas.Categorical(values, categories, ordered)

让我们举个例子-

Let’s take an example −

import pandas as pd

cat = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
print cat

它的 output 如下所示 −

Its output is as follows −

[a, b, c, a, b, c]
Categories (3, object): [a, b, c]

我们举另一个例子-

Let’s have another example −

import pandas as pd

cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a'])
print cat

它的 output 如下所示 −

Its output is as follows −

[a, b, c, a, b, c, NaN]
Categories (3, object): [c, b, a]

在此，第二个参数表示类别。因此，在类别中不存在的任何值都将被视为 NaN 。

Here, the second argument signifies the categories. Thus, any value which is not present in the categories will be treated as NaN.

现在，看下面的例子 -

Now, take a look at the following example −

import pandas as pd

cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a'],ordered=True)
print cat

它的 output 如下所示 −

Its output is as follows −

[a, b, c, a, b, c, NaN]
Categories (3, object): [c < b < a]

从逻辑上来说，该顺序表示 a 大于 b ， b 大于 c 。

Logically, the order means that, a is greater than b and b is greater than c.

Description

使用分类数据上的 .describe() 命令，我们得到一个类似于 type 字符串中的 Series 或 DataFrame 的输出。

Using the .describe() command on the categorical data, we get similar output to a Series or DataFrame of the type string.

import pandas as pd
import numpy as np

cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
df = pd.DataFrame({"cat":cat, "s":["a", "c", "c", np.nan]})

print df.describe()
print df["cat"].describe()

它的 output 如下所示 −

Its output is as follows −

       cat s
count    3 3
unique   2 2
top      c c
freq     2 2
count     3
unique    2
top       c
freq      2
Name: cat, dtype: object

Get the Properties of the Category

obj.cat.categories 命令用于获取 categories of the object 。

obj.cat.categories command is used to get the categories of the object.

import pandas as pd
import numpy as np

s = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
print s.categories

它的 output 如下所示 −

Its output is as follows −

Index([u'b', u'a', u'c'], dtype='object')

obj.ordered 命令用于获取对象的顺序。

obj.ordered command is used to get the order of the object.

import pandas as pd
import numpy as np

cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
print cat.ordered

它的 output 如下所示 −

Its output is as follows −

False

函数返回 false ，因为我们没有指定任何顺序。

The function returned false because we haven’t specified any order.

Renaming Categories

通过将新值赋值给 *series.cat.categories*series.cat.categories 属性来重新命名类别。

Renaming categories is done by assigning new values to the *series.cat.categories*series.cat.categories property.

import pandas as pd

s = pd.Series(["a","b","c","a"], dtype="category")
s.cat.categories = ["Group %s" % g for g in s.cat.categories]
print s.cat.categories

它的 output 如下所示 −

Its output is as follows −

Index([u'Group a', u'Group b', u'Group c'], dtype='object')

对象的 s.cat.categories 属性更新了初始类别 [a,b,c] 。

Initial categories [a,b,c] are updated by the s.cat.categories property of the object.

Appending New Categories

使用 Categorical.add.categories() 方法可以附加新类别。

Using the Categorical.add.categories() method, new categories can be appended.

import pandas as pd

s = pd.Series(["a","b","c","a"], dtype="category")
s = s.cat.add_categories([4])
print s.cat.categories

它的 output 如下所示 −

Its output is as follows −

Index([u'a', u'b', u'c', 4], dtype='object')

Removing Categories

使用 Categorical.remove_categories() 方法可以删除不需要的类别。

Using the Categorical.remove_categories() method, unwanted categories can be removed.

import pandas as pd

s = pd.Series(["a","b","c","a"], dtype="category")
print ("Original object:")
print s

print ("After removal:")
print s.cat.remove_categories("a")

它的 output 如下所示 −

Its output is as follows −

Original object:
0  a
1  b
2  c
3  a
dtype: category
Categories (3, object): [a, b, c]

After removal:
0  NaN
1  b
2  c
3  NaN
dtype: category
Categories (2, object): [b, c]

Comparison of Categorical Data

在三个情况下，分类数据与其他对象进行比较 -

Comparing categorical data with other objects is possible in three cases −

comparing equality (== and !=) to a list-like object (list, Series, array, …) of the same length as the categorical data.
all comparisons (==, !=, >, >=, <, and ⇐) of categorical data to another categorical Series, when ordered==True and the categories are the same.
all comparisons of a categorical data to a scalar.

请看以下示例：

Take a look at the following example −

import pandas as pd

cat = pd.Series([1,2,3]).astype("category", categories=[1,2,3], ordered=True)
cat1 = pd.Series([2,2,2]).astype("category", categories=[1,2,3], ordered=True)

print cat>cat1

它的 output 如下所示 −

Its output is as follows −

0  False
1  False
2  True
dtype: bool