利用Python进行数据分析(11)-高阶应用category
2020-05-17 本文已影响0人
皮皮大
本文中介绍的是pandas的高阶应用-分类数据category
image
分裂数据Categorical
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
使用背景和目标
一个列中经常会包含重复值，这些重复值是一个小型的不同值的集合。
unique()和value_counts()能够从数组中提取到不同的值并分别计算它们的频率
values = pd.Series(["apple","orange","apple","apple"] * 2)
values
0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
dtype: object
pd.unique(values) # 查看不同的取值情况
array(['apple', 'orange'], dtype=object)
pd.value_counts(values) # 查看每个值的个数
apple 6
orange 2
dtype: int64
维度表
维度表包含了不同的值，将主要观测值存储为引用维度表的整数键
values = pd.Series([0,1,0,0] * 2)
dim = pd.Series(["apple","orange"])
values
0 0
1 1
2 0
3 0
4 0
5 1
6 0
7 0
dtype: int64
dim
0 apple
1 orange
dtype: object
take方法-分类（字典编码展现）
不同值的数组被称之为数据的类别、字典或者层级
dim.take(values)
0 apple
1 orange
0 apple
0 apple
0 apple
1 orange
0 apple
0 apple
dtype: object
使用Categorical类型
fruits = ["apple","orange","apple","apple"] * 2
N = len(fruits)
df = pd.DataFrame({"fruit":fruits, # 指定每列的取值内容
"basket_id":np.arange(N),
"count":np.random.randint(3,15,size=N),
"weight":np.random.uniform(0,4,size=N)},
columns=["basket_id","fruit","count","weight"]) # 4个属性值
df
image.png
df["fruit"]
0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
Name: fruit, dtype: object
如何生成Categorical实例
fruit_cat = df["fruit"].astype("category") # 调用函数改变
fruit_cat # 变成pd.Categorical的实例
0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
Name: fruit, dtype: category
Categories (2, object): [apple, orange]
c = fruit_cat.values
c
[apple, orange, apple, apple, apple, orange, apple, apple]
Categories (2, object): [apple, orange]
<span class="burk">两个属性：categories + codes</span>
print(c.categories)
print("-----")
print(c.codes)
Index(['apple', 'orange'], dtype='object')
-----
[0 1 0 0 0 1 0 0]
# 将DF的一列转成Categorical对象
df["fruit"] = df["fruit"].astype("category")
df.fruit
0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
Name: fruit, dtype: category
Categories (2, object): [apple, orange]
从其他序列生成pd.Categorical对象
my_categories = pd.Categorical(['foo','bar','baz','foo','bar'])
my_categories
[foo, bar, baz, foo, bar]
Categories (3, object): [bar, baz, foo]
已知分类编码数据的情况：from_codes
categories = ["foo","bar","baz"]
codes = [0,1,0,0,1,0,1,0]
my_code = pd.Categorical.from_codes(codes,categories)
my_code
[foo, bar, foo, foo, bar, foo, bar, foo]
Categories (3, object): [foo, bar, baz]
<span class="mark">显式指定分类顺序：ordered = True</span>
如果不指定顺序，分类转换是无序的。我们可以自己显式地指定
ordered_cat = pd.Categorical.from_codes(codes,categories # 指定分类用的数据
,ordered=True)
ordered_cat
[foo, bar, foo, foo, bar, foo, bar, foo]
Categories (3, object): [foo < bar < baz]
未排序的实例通过as_ordered排序
# 未排序的实例通过as_ordered来进行排序
my_categories.as_ordered()
[foo, bar, baz, foo, bar]
Categories (3, object): [bar < baz < foo]
Categorical对象来进行计算
np.random.seed(12345) # 设置随机种子
draws = np.random.randn(1000)
draws[:5]
array([-0.20470766, 0.47894334, -0.51943872, -0.5557303 , 1.96578057])
qcut()函数-四分位数
# 计算四位分箱
bins = pd.qcut(draws,4)
bins
[(-0.684, -0.0101], (-0.0101, 0.63], (-0.684, -0.0101], (-0.684, -0.0101], (0.63, 3.928], ..., (-0.0101, 0.63], (-0.684, -0.0101], (-2.9499999999999997, -0.684], (-0.0101, 0.63], (0.63, 3.928]]
Length: 1000
Categories (4, interval[float64]): [(-2.9499999999999997, -0.684] < (-0.684, -0.0101] < (-0.0101, 0.63] < (0.63, 3.928]]
四分位数名称 labels
bins = pd.qcut(draws,4,labels=["Q1","Q2","Q3","Q4"])
bins
[Q2, Q3, Q2, Q2, Q4, ..., Q3, Q2, Q1, Q3, Q4]
Length: 1000
Categories (4, object): [Q1 < Q2 < Q3 < Q4]
bins.codes[:10]
array([1, 2, 1, 1, 3, 3, 2, 2, 3, 3], dtype=int8)
结合groupby提取汇总信息
bins = pd.Series(bins, name="quartile")
results = (pd.Series(draws)
.groupby(bins)
.agg(["count","min","max"]).reset_index()
)
results
image.png
results["quartile"] # 保留原始中的分类信息
0 Q1
1 Q2
2 Q3
3 Q4
Name: quartile, dtype: category
Categories (4, object): [Q1 < Q2 < Q3 < Q4]
分类提高性能
如果在特定的数据集上做了大量的数据分析，将数据转成分类数据有大大提高性能
N = 10000000
draws = pd.Series(np.random.randn(N))
labels = pd.Series(["foo","bar","baz","qux"] * (N // 4))
labels
0 foo
1 bar
2 baz
3 qux
4 foo
...
9999995 qux
9999996 foo
9999997 bar
9999998 baz
9999999 qux
Length: 10000000, dtype: object
转成分类数据
# 转成分类数据
categories = labels.astype("category")
categories
0 foo
1 bar
2 baz
3 qux
4 foo
...
9999995 qux
9999996 foo
9999997 bar
9999998 baz
9999999 qux
Length: 10000000, dtype: category
Categories (4, object): [bar, baz, foo, qux]
内存比较
labels.memory_usage()
80000128
categories.memory_usage()
10000320
分类转换的开销
%time _ = labels.astype("category")
CPU times: user 374 ms, sys: 34.8 ms, total: 409 ms
Wall time: 434 ms
<span class="burk">分类方法</span>
s = pd.Series(["a","b","c","d"] * 2)
cat_s = s.astype("category")
cat_s
0 a
1 b
2 c
3 d
4 a
5 b
6 c
7 d
dtype: category
Categories (4, object): [a, b, c, d]
cat属性
特殊属性cat提供了对分类方法的访问
- codes
- categories
- set_categories
cat_s.cat.codes
0 0
1 1
2 2
3 3
4 0
5 1
6 2
7 3
dtype: int8
cat_s.cat.categories
Index(['a', 'b', 'c', 'd'], dtype='object')
数据的实际类别超出给定的个数
actual_categories = ["a","b","c","d","e"]
cat_s2 = cat_s.cat.set_categories(actual_categories)
cat_s2
0 a
1 b
2 c
3 d
4 a
5 b
6 c
7 d
dtype: category
Categories (5, object): [a, b, c, d, e]
cat_s2.value_counts()
d 2
c 2
b 2
a 2
e 0
dtype: int64
去除不在数据中的类别
cat_s3 = cat_s[cat_s.isin(["a","b"])]
cat_s3
0 a
1 b
4 a
5 b
dtype: category
Categories (4, object): [a, b, c, d]
# c、d没有出现，直接删除
cat_s3.cat.remove_unused_categories()
0 a
1 b
4 a
5 b
dtype: category
Categories (2, object): [a, b]
如何创建虚拟变量：get_dummies()
在机器学习或统计数据中，通常需要将分类数据转成虚拟变量，也称之为one-hot编码
cat_s = pd.Series(["a","b","c","d"] * 2, dtype="category")
cat_s
0 a
1 b
2 c
3 d
4 a
5 b
6 c
7 d
dtype: category
Categories (4, object): [a, b, c, d]
pd.get_dummies(cat_s)
image.png
