創(chuàng)建category
使用Series創(chuàng)建
在創(chuàng)建Series的同時添加dtype="category"就可以創(chuàng)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(chuàng)建好一個pandas.Categorical ，將其作為參數(shù)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(chuàng)建
創(chuàng)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(chuàng)建控制
默認情況下傳入dtype='category' 創(chuàng)建出來的category使用的是默認值：
1.Categories是從數(shù)據中推斷出來的。
2.Categories是沒有大小順序的。
可以顯示創(chuàng)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(shù)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(chuàng)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(xiàn)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(chuàng)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(shù)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(xù)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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