Pandas数据类型之category的用法

创建category

使用series创建

在创建series的同时添加dtype="category"就可以创建好category了。category分为两部分，一部分是order，一部分是字面量：

in [1]: s = pd.series(["a", "b", "c", "a"], dtype="category")

in [2]: s
out[2]: 
0    a
1    b
2    c
3    a
dtype: category
categories (3, object): ['a', 'b', 'c']

可以将df中的series转换为category：

in [3]: df = pd.dataframe({"a": ["a", "b", "c", "a"]})

in [4]: df["b"] = df["a"].astype("category")

in [5]: df["b"]
out[32]: 
0    a
1    b
2    c
3    a
name: b, dtype: category
categories (3, object): [a, b, c]

可以创建好一个pandas.categorical ，将其作为参数传递给series：

in [10]: raw_cat = pd.categorical(
   ....:     ["a", "b", "c", "a"], categories=["b", "c", "d"], ordered=false
   ....: )
   ....: 

in [11]: s = pd.series(raw_cat)

in [12]: s
out[12]: 
0    nan
1      b
2      c
3    nan
dtype: category
categories (3, object): ['b', 'c', 'd']

使用df创建

创建dataframe的时候，也可以传入 dtype="category"：

in [17]: df = pd.dataframe({"a": list("abca"), "b": list("bccd")}, dtype="category")

in [18]: df.dtypes
out[18]: 
a    category
b    category
dtype: object

df中的a和b都是一个category:

in [19]: df["a"]
out[19]: 
0    a
1    b
2    c
3    a
name: a, dtype: category
categories (3, object): ['a', 'b', 'c']

in [20]: df["b"]
out[20]: 
0    b
1    c
2    c
3    d
name: b, dtype: category
categories (3, object): ['b', 'c', 'd']

或者使用df.astype("category")将df中所有的series转换为category:

in [21]: df = pd.dataframe({"a": list("abca"), "b": list("bccd")})

in [22]: df_cat = df.astype("category")

in [23]: df_cat.dtypes
out[23]: 
a    category
b    category
dtype: object

创建控制

默认情况下传入dtype='category' 创建出来的category使用的是默认值：

1.categories是从数据中推断出来的。

2.categories是没有大小顺序的。

可以显示创建categoricaldtype来修改上面的两个默认值：

in [26]: from pandas.api.types import categoricaldtype

in [27]: s = pd.series(["a", "b", "c", "a"])

in [28]: cat_type = categoricaldtype(categories=["b", "c", "d"], ordered=true)

in [29]: s_cat = s.astype(cat_type)

in [30]: s_cat
out[30]: 
0    nan
1      b
2      c
3    nan
dtype: category
categories (3, object): ['b' < 'c' < 'd']

同样的categoricaldtype还可以用在df中：

in [31]: from pandas.api.types import categoricaldtype

in [32]: df = pd.dataframe({"a": list("abca"), "b": list("bccd")})

in [33]: cat_type = categoricaldtype(categories=list("abcd"), ordered=true)

in [34]: df_cat = df.astype(cat_type)

in [35]: df_cat["a"]
out[35]: 
0    a
1    b
2    c
3    a
name: a, dtype: category
categories (4, object): ['a' < 'b' < 'c' < 'd']

in [36]: df_cat["b"]
out[36]: 
0    b
1    c
2    c
3    d
name: b, dtype: category
categories (4, object): ['a' < 'b' < 'c' < 'd']

转换为原始类型

使用series.astype(original_dtype) 或者 np.asarray(categorical)可以将category转换为原始类型：

in [39]: s = pd.series(["a", "b", "c", "a"])

in [40]: s
out[40]: 
0    a
1    b
2    c
3    a
dtype: object

in [41]: s2 = s.astype("category")

in [42]: s2
out[42]: 
0    a
1    b
2    c
3    a
dtype: category
categories (3, object): ['a', 'b', 'c']

in [43]: s2.astype(str)
out[43]: 
0    a
1    b
2    c
3    a
dtype: object

in [44]: np.asarray(s2)
out[44]: array(['a', 'b', 'c', 'a'], dtype=object)

categories的操作

获取category的属性

categorical数据有 categories 和 ordered 两个属性。可以通过s.cat.categories 和 s.cat.ordered来获取：

in [57]: s = pd.series(["a", "b", "c", "a"], dtype="category")

in [58]: s.cat.categories
out[58]: index(['a', 'b', 'c'], dtype='object')

in [59]: s.cat.ordered
out[59]: false

重排category的顺序：

in [60]: s = pd.series(pd.categorical(["a", "b", "c", "a"], categories=["c", "b", "a"]))

in [61]: s.cat.categories
out[61]: index(['c', 'b', 'a'], dtype='object')

in [62]: s.cat.ordered
out[62]: false

重命名categories

通过给s.cat.categories赋值可以重命名categories:

in [67]: s = pd.series(["a", "b", "c", "a"], dtype="category")

in [68]: s
out[68]: 
0    a
1    b
2    c
3    a
dtype: category
categories (3, object): ['a', 'b', 'c']

in [69]: s.cat.categories = ["group %s" % g for g in s.cat.categories]

in [70]: s
out[70]: 
0    group a
1    group b
2    group c
3    group a
dtype: category
categories (3, object): ['group a', 'group b', 'group c']

使用rename_categories可以达到同样的效果：

in [71]: s = s.cat.rename_categories([1, 2, 3])

in [72]: s
out[72]: 
0    1
1    2
2    3
3    1
dtype: category
categories (3, int64): [1, 2, 3]

或者使用字典对象：

# you can also pass a dict-like object to map the renaming
in [73]: s = s.cat.rename_categories({1: "x", 2: "y", 3: "z"})

in [74]: s
out[74]: 
0    x
1    y
2    z
3    x
dtype: category
categories (3, object): ['x', 'y', 'z']

使用add_categories添加category

可以使用add_categories来添加category:

in [77]: s = s.cat.add_categories([4])

in [78]: s.cat.categories
out[78]: index(['x', 'y', 'z', 4], dtype='object')

in [79]: s
out[79]: 
0    x
1    y
2    z
3    x
dtype: category
categories (4, object): ['x', 'y', 'z', 4]

使用remove_categories删除category

in [80]: s = s.cat.remove_categories([4])

in [81]: s
out[81]: 
0    x
1    y
2    z
3    x
dtype: category
categories (3, object): ['x', 'y', 'z']

删除未使用的cagtegory

in [82]: s = pd.series(pd.categorical(["a", "b", "a"], categories=["a", "b", "c", "d"]))

in [83]: s
out[83]: 
0    a
1    b
2    a
dtype: category
categories (4, object): ['a', 'b', 'c', 'd']

in [84]: s.cat.remove_unused_categories()
out[84]: 
0    a
1    b
2    a
dtype: category
categories (2, object): ['a', 'b']

重置cagtegory

使用set_categories()可以同时进行添加和删除category操作：

in [85]: s = pd.series(["one", "two", "four", "-"], dtype="category")

in [86]: s
out[86]: 
0     one
1     two
2    four
3       -
dtype: category
categories (4, object): ['-', 'four', 'one', 'two']

in [87]: s = s.cat.set_categories(["one", "two", "three", "four"])

in [88]: s
out[88]: 
0     one
1     two
2    four
3     nan
dtype: category
categories (4, object): ['one', 'two', 'three', 'four']

category排序

如果category创建的时候带有 ordered=true ， 那么可以对其进行排序操作：

in [91]: s = pd.series(["a", "b", "c", "a"]).astype(categoricaldtype(ordered=true))

in [92]: s.sort_values(inplace=true)

in [93]: s
out[93]: 
0    a
3    a
1    b
2    c
dtype: category
categories (3, object): ['a' < 'b' < 'c']

in [94]: s.min(), s.max()
out[94]: ('a', 'c')

可以使用 as_ordered() 或者 as_unordered() 来强制排序或者不排序：

in [95]: s.cat.as_ordered()
out[95]: 
0    a
3    a
1    b
2    c
dtype: category
categories (3, object): ['a' < 'b' < 'c']

in [96]: s.cat.as_unordered()
out[96]: 
0    a
3    a
1    b
2    c
dtype: category
categories (3, object): ['a', 'b', 'c']

重排序

使用categorical.reorder_categories() 可以对现有的category进行重排序：

in [103]: s = pd.series([1, 2, 3, 1], dtype="category")

in [104]: s = s.cat.reorder_categories([2, 3, 1], ordered=true)

in [105]: s
out[105]: 
0    1
1    2
2    3
3    1
dtype: category
categories (3, int64): [2 < 3 < 1]

多列排序

sort_values 支持多列进行排序：

in [109]: dfs = pd.dataframe(
   .....:     {
   .....:         "a": pd.categorical(
   .....:             list("bbeebbaa"),
   .....:             categories=["e", "a", "b"],
   .....:             ordered=true,
   .....:         ),
   .....:         "b": [1, 2, 1, 2, 2, 1, 2, 1],
   .....:     }
   .....: )
   .....: 

in [110]: dfs.sort_values(by=["a", "b"])
out[110]: 
   a  b
2  e  1
3  e  2
7  a  1
6  a  2
0  b  1
5  b  1
1  b  2
4  b  2

比较操作

如果创建的时候设置了ordered==true ，那么category之间就可以进行比较操作。支持 ==, !=, >, >=, <, 和 <=这些操作符。

in [113]: cat = pd.series([1, 2, 3]).astype(categoricaldtype([3, 2, 1], ordered=true))

in [114]: cat_base = pd.series([2, 2, 2]).astype(categoricaldtype([3, 2, 1], ordered=true))

in [115]: cat_base2 = pd.series([2, 2, 2]).astype(categoricaldtype(ordered=true))
in [119]: cat > cat_base
out[119]: 
0     true
1    false
2    false
dtype: bool

in [120]: cat > 2
out[120]: 
0     true
1    false
2    false
dtype: bool

其他操作

cagetory本质上来说还是一个series，所以series的操作category基本上都可以使用，比如： series.min(), series.max() 和 series.mode()。

value_counts：

in [131]: s = pd.series(pd.categorical(["a", "b", "c", "c"], categories=["c", "a", "b", "d"]))

in [132]: s.value_counts()
out[132]: 
c    2
a    1
b    1
d    0
dtype: int64

dataframe.sum()：

in [133]: columns = pd.categorical(
   .....:     ["one", "one", "two"], categories=["one", "two", "three"], ordered=true
   .....: )
   .....: 

in [134]: df = pd.dataframe(
   .....:     data=[[1, 2, 3], [4, 5, 6]],
   .....:     columns=pd.multiindex.from_arrays([["a", "b", "b"], columns]),
   .....: )
   .....: 

in [135]: df.sum(axis=1, level=1)
out[135]: 
   one  two  three
0    3    3      0
1    9    6      0

groupby：

in [136]: cats = pd.categorical(
   .....:     ["a", "b", "b", "b", "c", "c", "c"], categories=["a", "b", "c", "d"]
   .....: )
   .....: 

in [137]: df = pd.dataframe({"cats": cats, "values": [1, 2, 2, 2, 3, 4, 5]})

in [138]: df.groupby("cats").mean()
out[138]: 
      values
cats        
a        1.0
b        2.0
c        4.0
d        nan

in [139]: cats2 = pd.categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])

in [140]: df2 = pd.dataframe(
   .....:     {
   .....:         "cats": cats2,
   .....:         "b": ["c", "d", "c", "d"],
   .....:         "values": [1, 2, 3, 4],
   .....:     }
   .....: )
   .....: 

in [141]: df2.groupby(["cats", "b"]).mean()
out[141]: 
        values
cats b        
a    c     1.0
     d     2.0
b    c     3.0
     d     4.0
c    c     nan
     d     nan

pivot tables：

in [142]: raw_cat = pd.categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])

in [143]: df = pd.dataframe({"a": raw_cat, "b": ["c", "d", "c", "d"], "values": [1, 2, 3, 4]})

in [144]: pd.pivot_table(df, values="values", index=["a", "b"])
out[144]: 
     values
a b        
a c       1
  d       2
b c       3
  d       4

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