数据科学
数据产生
网站：用户每次点击
手机：位置和速度
智能手表、手环：心率、行动、饮食、睡眠
智能汽车：驾驶习惯
智能家居：生活习惯
数据科学家
从混乱数据中理出价值的人
案例：寻找关键联系人
根据用户网络关系数据识别关键联系人
用户列表
users = [{"id": 0, "name": "Hero"}, {"id": 1, "name": "Dunn"},
{"id": 2, "name": "Sue"}, {"id": 3, "name": "Chi"},
{"id": 4, "name": "Thor"}, {"id": 5, "name": "Clive"},
{"id": 6, "name": "Hicks"}, {"id": 7, "name": "Devin"},
{"id": 8, "name": "Kate"}, {"id": 9, "name": "Klein"},
{"id": 10, "name": "Jen"}]
用户好友关系
friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),
(4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9),(8,10),(10,3),(4,8)]
用户关系图谱
为每个用户创建朋友列表
for user in users:
user["friends"] = []
填充好友数据
for i, j in friendships:
users[i]["friends"].append(users[j])
users[j]["friends"].append(users[i])
填充好友数据
问题： 平均朋友联系数是多少？
答： 全部联系数除以用户个数
def number_of_friends(user):
return len(user["friends"])
total_connections = sum(number_of_friends(user)for user in users)
num_users = len(users)
avg_connections = total_connections / num_users
平均朋友系数
按朋友数多少排序
# 取(user_id, number_of_friends)
num_friends_by_id = [(user['id'], number_of_friends(user)) for user in users]
sorted(num_friends_by_id, key=lambda item: item[1], reverse=True)
以朋友数为基准降序排序
案例: 你可能知道的人
找朋友的朋友
def friends_of_friend_ids_bad(user):
# 双层循环
return [foaf["id"]for friend in user["friends"]for foaf in friend["friends"]]
查看9号的好友，发现结果不如意。它包含了它本身以及它的朋友，我们下一步的工作是在此基础上取出查询结果中的对象本身及对象的朋友，只保留对象朋友的朋友即可
查找共同的朋友
from collections import Counter
# 判断是否是自己
def not_the_same(user, other_user):
return user["id"] != other_user["id"]
# 判断是否是自己的朋友
def not_friends(user, other_user):
return all(not_the_same(friend, other_user)for friend in user["friends"])
# 在之前的基础上进行判断即可
def friends_of_friend_ids(user):
return Counter(foaf["id"]for friend in user["friends"]for foaf in friend["friends"]
if not_the_same(user, foaf) and not_friends(user, foaf))
查找朋友的朋友
找共同兴趣的人
interests = [(0, "Hadoop"), (0, "Big Data"), (0, "HBase"), (0, "Java"),(0, "Spark"), (0, "Storm"), (0, "Cassandra"),
(1, "NoSQL"), (1, "MongoDB"), (1, "Cassandra"), (1, "HBase"),(1, "Postgres"),
(2, "Python"), (2, "scikit-learn"), (2, "scipy"),(2, "numpy"), (2, "statsmodels"), (2, "pandas"),
(3, "R"), (3, "Python"),(3, "statistics"), (3, "regression"), (3, "probability"),
(4, "machine learning"), (4, "regression"), (4, "decision trees"),(4, "libsvm"),
(5, "Python"), (5, "R"), (5, "Java"), (5, "C++"),(5, "Haskell"), (5, "programming languages"),
(6, "statistics"),(6, "probability"), (6, "mathematics"), (6, "theory"),
(7, "machine learning"), (7, "scikit-learn"), (7, "Mahout"),(7, "neural networks"),
(8, "neural networks"), (8, "deep learning"),(8, "Big Data"), (8, "artificial intelligence"),
(9, "Hadoop"), (9, "Java"), (9, "MapReduce"), (9, "Big Data") ]
def data_scientists_who_like(target_interest):
return [user_idfor user_id, user_interest in interestsif user_interest == target_interest]
查找相同兴趣的人
每次搜索都要遍历列表，性能差，建立一个字典
from collections import defaultdict
user_ids_by_interest = defaultdict(list)
for user_id, interest in interests:
user_ids_by_interest[interest].append(user_id)
建立兴趣为键的字典
interests_by_user_id = defaultdict(list)
for user_id, interest in interests:
interests_by_user_id[user_id].append(interest)
建立用户为键的字典
此处所生成的两个字典采用以内存换时间的计算机基本原理，极大程度上提高了查找的速度。
找与指定用户爱好最多相似的用户
def most_common_interests_with(user_id):
return Counter(interested_user_id
for interest in interests_by_user_id[user_id]
for interested_user_id in user_ids_by_interest[interest]
if interested_user_id != user_id)
查找与指定用户爱好相似的用户
案例：工资与工作年限
salaries_and_tenures = [(83000, 8.7), (88000, 8.1),
(48000, 0.7), (76000, 6),
(69000, 6.5), (76000, 7.5),
(60000, 2.5), (83000, 10),
(48000, 1.9), (63000, 4.2)]
绘图
def make_chart_salaries_by_tenure():
tenures = [tenure for salary, tenure in salaries_and_tenures]
salaries = [salary for salary, tenure in salaries_and_tenures]
plt.scatter(tenures, salaries)
plt.xlabel("Years Experience")
plt.ylabel("Salary")
plt.show()
绘制年限与薪水之间的散点图
按工作年线算平均收入
salary_by_tenure = defaultdict(list)
for salary, tenure in salaries_and_tenures:
salary_by_tenure[tenure].append(salary)
average_salary_by_tenure = {
tenure : sum(salaries) / len(salaries)
for tenure, salaries in salary_by_tenure.items() }
生成年限为键薪水为值的字典
分组后计算
def tenure_bucket(tenure):
if tenure < 2: return "less than two"
elif tenure < 5: return "between two and five"
else: return "more than five"
salary_by_tenure_bucket = defaultdict(list)
for salary, tenure in salaries_and_tenures:
bucket = tenure_bucket(tenure)
salary_by_tenure_bucket[bucket].append(salary)
average_salary_by_bucket = {
tenure_bucket : sum(salaries) / len(salaries)
for tenure_bucket, salaries in salary_by_tenure_bucket.items() }
对各年限进行分组平均薪水
案例：兴趣主题
简单方法：数兴趣词汇个数
words_and_counts = Counter(word
for user, interest in interests
for word in interest.lower().split())
for word, count in words_and_counts.most_common():
if count > 1:
print(word, count)
统计兴趣爱好出现的次数并输出不止一次出现的爱好
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