創建category
使用Series創建
在創建Series的同時添加dtype="category"就可以創建好category了。category分為兩部分，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']
可以將DF中的Series轉換為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]
可以創建好一個pandas.Categorical ，將其作為參數傳遞給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創建
創建DataFrame的時候，也可以傳入 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都是一個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")將DF中所有的Series轉換為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
創建控制
默認情況下傳入dtype='category' 創建出來的category使用的是默認值：
1.Categories是從數據中推斷出來的。
2.Categories是沒有大小順序的。
可以顯示創建CategoricalDtype來修改上面的兩個默認值：
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']
同樣的CategoricalDtype還可以用在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']
轉換為原始類型
使用Series.astype(original_dtype) 或者 np.asarray(categorical)可以將Category轉換為原始類型：
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
獲取category的屬性
Categorical數據有 categories 和 ordered 兩個屬性。可以通過s.cat.categories 和 s.cat.ordered來獲取：
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的順序：
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
通過給s.cat.categories賦值可以重命名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可以達到同樣的效果：
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]
或者使用字典對象：
# 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來添加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刪除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']
刪除未使用的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()可以同時進行添加和刪除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創建的時候帶有 ordered=True ， 那么可以對其進行排序操作：
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() 來強制排序或者不排序：
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() 可以對現有的category進行重排序：
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 支持多列進行排序：
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
比較操作
如果創建的時候設置了ordered==True ，那么category之間就可以進行比較操作。支持 ==, !=, >, >=, =這些操作符。
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本質上來說還是一個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
到此這篇關于Pandas數據類型之category的用法的文章就介紹到這了,更多相關category的用法內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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