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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