通常实时的数据包括重复的文本列。例如：性别，国家和代码等特征总是重复的。 这些是分类数据的例子。
分类变量只能采用有限且通常是固定的数量。除了固定长度， 分类数据可能有顺序，但不能执行数字操作。 分类是Pandas数据类型。
分类数据类型在以下情况下非常有用：
- 一个字符串变量，只包含几个不同的值。将这样的字符串变量转换为分类变量将会节省一些内存。
- 变量的词法顺序与逻辑顺序("one"，"two"，"three")不同。 通过转换为分类并指定类别上的顺序，排序和最小/最大将使用逻辑顺序，而不是词法顺序。
- 作为其他python库的一个信号，这个列应该被当作一个分类变量(例如，使用合适的统计方法或
plot类型)。
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import pandas as pd
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s = pd.Series(["a","b","c","a"], dtype="category")
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print (s)
0 a 1 b 2 c 3 a dtype: category Categories (3, object): ['a', 'b', 'c']
pandas.Categorical(values, categories, ordered)举个例子：
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import pandas as pd
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cat = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
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print (cat)
['a', 'b', 'c', 'a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c']
再举一个例子：
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import pandas as pd
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cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a'])
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print (cat)
['a', 'b', 'c', 'a', 'b', 'c', NaN] Categories (3, object): ['c', 'b', 'a']
这里，第二个参数表示类别。因此，在类别中不存在的任何值将被视为 NaN 。
现在，看看下面的例子：
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import pandas as pd
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cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a'],ordered=True)
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print (cat)
['a', 'b', 'c', 'a', 'b', 'c', NaN] Categories (3, object): ['c' < 'b' < 'a']
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import pandas as pd
import numpy as np
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cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
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df = pd.DataFrame({"cat":cat, "s":["a", "c", "c", np.nan]})
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print (df.describe())
cat s count 3 3 unique 2 2 top c c freq 2 2
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print ("=============================")
=============================
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print (df["cat"].describe())
count 3 unique 2 top c freq 2 Name: cat, dtype: object
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import pandas as pd
import numpy as np
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s = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
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print (s.categories)
Index(['b', 'a', 'c'], dtype='object')
obj.ordered 命令用于获取对象的顺序。
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import pandas as pd
import numpy as np
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cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])
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print (cat.ordered)
False
该函数返回结果为False，因为没有指定任何顺序。
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import pandas as pd
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s = pd.Series(["a","b","c","a"], dtype="category")
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s = s.cat.rename_categories({"a": "Group a",
"b": "Group b",
"c": "Group c"})
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print(s.cat.categories)
Index(['Group a', 'Group b', 'Group c'], dtype='object')
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import pandas as pd
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s = pd.Series(["a","b","c","a"], dtype="category")
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s = s.cat.add_categories([4])
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print (s.cat.categories)
Index(['a', 'b', 'c', 4], dtype='object')
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import pandas as pd
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s = pd.Series(["a","b","c","a"], dtype="category")
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print ("Original object:")
Original object:
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s
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0 a 1 b 2 c 3 a dtype: category Categories (3, object): ['a', 'b', 'c']
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print ("After removal:")
After removal:
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s.cat.remove_categories("a")
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0 NaN 1 b 2 c 3 NaN dtype: category Categories (2, object): ['b', 'c']
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import pandas as pd
创建有序分类数据
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cat = pd.Series(pd.Categorical([1,2,3], categories=[1,2,3], ordered=True))
cat1 = pd.Series(pd.Categorical([2,2,2], categories=[1,2,3], ordered=True))
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print(cat > cat1)
0 False 1 False 2 True dtype: bool