你的问题有几个方面。
您可以使用您拥有的数据构建什么
有几种保留。为简单起见，我们将仅提及两个：
- 第 N 天保留：如果用户在第 0 天注册，她是否在第 N 天登录？（在第 N+1 天登录不会影响此指标）。要衡量它，您需要跟踪用户的所有日志。
- 滚动保留：如果用户在第 0 天注册，她是在第 N 天还是之后的任何一天登录？（在第 N+1 天登录会影响此指标）。要衡量它，您只需要用户的最后知道日志。
如果我正确理解您的表格，那么您有两个相关变量来构建您的群组表格：注册日期和上次日志（访问周）。每周访问次数似乎无关紧要。
因此，您只能选择选项 2，即滚动保留。
如何建表
首先，让我们构建一个虚拟数据集，以便我们有足够的工作量并且您可以重现它：
import pandas as pd
import numpy as np
import math
import datetime as dt
np.random.seed(0) # so that we all have the same results
def random_date(start, end,p=None):
# Return a date randomly chosen between two dates
if p is None:
p = np.random.random()
return start + dt.timedelta(seconds=math.ceil(p * (end - start).days*24*3600))
n_samples = 1000 # How many users do we want ?
index = range(1,n_samples+1)
# A range of signup dates, say, one year.
end = dt.datetime.today()
from dateutil.relativedelta import relativedelta
start = end - relativedelta(years=1)
# Create the dataframe
users = pd.DataFrame(np.random.rand(n_samples),
index=index, columns=['signup_date'])
users['signup_date'] = users['signup_date'].apply(lambda x : random_date(start, end,x))
# last logs randomly distributed within 10 weeks of singing up, so that we can see the retention drop in our table
users['last_log'] = users['signup_date'].apply(lambda x : random_date(x, x + relativedelta(weeks=10)))
所以现在我们应该有一些看起来像这样的东西：
users.head()
下面是一些构建队列表的代码：
### Some useful functions
def add_weeks(sourcedate,weeks):
return sourcedate + dt.timedelta(days=7*weeks)
def first_day_of_week(sourcedate):
return sourcedate - dt.timedelta(days = sourcedate.weekday())
def last_day_of_week(sourcedate):
return sourcedate + dt.timedelta(days=(6 - sourcedate.weekday()))
def retained_in_interval(users,signup_week,n_weeks,end_date):
'''
For a given list of users, returns the number of users
that signed up in the week of signup_week (the cohort)
and that are retained after n_weeks
end_date is just here to control that we do not un-necessarily fill the bottom right of the table
'''
# Define the span of the given week
cohort_start = first_day_of_week(signup_week)
cohort_end = last_day_of_week(signup_week)
if n_weeks == 0:
# If this is our first week, we just take the number of users that signed up on the given period of time
return len( users[(users['signup_date'] >= cohort_start)
& (users['signup_date'] <= cohort_end)])
elif pd.to_datetime(add_weeks(cohort_end,n_weeks)) > pd.to_datetime(end_date) :
# If adding n_weeks brings us later than the end date of the table (the bottom right of the table),
# We return some easily recognizable date (not 0 as it would cause confusion)
return float("Inf")
else:
# Otherwise, we count the number of users that signed up on the given period of time,
# and whose last known log was later than the number of weeks added (rolling retention)
return len( users[(users['signup_date'] >= cohort_start)
& (users['signup_date'] <= cohort_end)
& pd.to_datetime((users['last_log']) >= pd.to_datetime(users['signup_date'].map(lambda x: add_weeks(x,n_weeks))))
])
有了这个，我们可以创建实际的功能：
def cohort_table(users,cohort_number=6,period_number=6,cohort_span='W',end_date=None):
'''
For a given dataframe of users, return a cohort table with the following parameters :
cohort_number : the number of lines of the table
period_number : the number of columns of the table
cohort_span : the span of every period of time between the cohort (D, W, M)
end_date = the date after which we stop counting the users
'''
# the last column of the table will end today :
if end_date is None:
end_date = dt.datetime.today()
# The index of the dataframe will be a list of dates ranging
dates = pd.date_range(add_weeks(end_date,-cohort_number), periods=cohort_number, freq=cohort_span)
cohort = pd.DataFrame(columns=['Sign up'])
cohort['Sign up'] = dates
# We will compute the number of retained users, column-by-column
# (There probably is a more pythonesque way of doing it)
range_dates = range(0,period_number+1)
for p in range_dates:
# Name of the column
s_p = 'Week '+str(p)
cohort[s_p] = cohort.apply(lambda row: retained_in_interval(users,row['Sign up'],p,end_date), axis=1)
cohort = cohort.set_index('Sign up')
# absolute values to percentage by dividing by the value of week 0 :
cohort = cohort.astype('float').div(cohort['Week 0'].astype('float'),axis='index')
return cohort
现在您可以调用它并查看结果：
cohort_table(users)
希望能帮助到你