我有一系列的时间序列数据，记录了到访某建筑的人员，每个人都有唯一的ID。对于时间序列中的每条记录，我想知道在过去的 365 天内到访该建筑的独立人数（即一个滚动的、窗口为 365 天的唯一人数计数）。
pandas 没有似乎没有内置的方法可以进行这种计算。当存在大量独立访问者和/或大窗口时，计算变得计算密集。（实际数据比此示例更大。）
是否有比我下面做的更好的计算方法？我不确定我制作的快速方法 windowed_nunique（"Speed test 3" 下）为什么会少 1。
感谢任何帮助！
相关链接：
- 源 Jupyter Notebook：https://gist.github.com/stharrold/17589e6809d249942debe3a5c43d38cc
- 相关
pandas问题：https://github.com/pandas-dev/pandas/issues/14336
初始化
In [1]:
# Import libraries.
import pandas as pd
import numba
import numpy as np
In [2]:
# Create data of people visiting a building.
np.random.seed(seed=0)
dates = pd.date_range(start='2010-01-01', end='2015-01-01', freq='D')
window = 365 # days
num_pids = 100
probs = np.linspace(start=0.001, stop=0.1, num=num_pids)
df = pd\
.DataFrame(
data=[(date, pid)
for (pid, prob) in zip(range(num_pids), probs)
for date in np.compress(np.random.binomial(n=1, p=prob, size=len(dates)), dates)],
columns=['Date', 'PersonId'])\
.sort_values(by='Date')\
.reset_index(drop=True)
print("Created data of people visiting a building:")
df.head() # 9181 rows × 2 columns
Out[2]:
Created data of people visiting a building:
| | Date | PersonId |
|---|------------|----------|
| 0 | 2010-01-01 | 76 |
| 1 | 2010-01-01 | 63 |
| 2 | 2010-01-01 | 89 |
| 3 | 2010-01-01 | 81 |
| 4 | 2010-01-01 | 7 |
速度参考
In [3]:
%%timeit
# This counts the number of people visiting the building, not the number of unique people.
# Provided as a speed reference.
df.rolling(window='{:d}D'.format(window), on='Date').count()
3.32毫秒±124微秒每次循环（平均值±7次运行的标准差，每个循环100次）
速度测试1
In [4]:
%%timeit
df.rolling(window='{:d}D'.format(window), on='Date').apply(lambda arr: pd.Series(arr).nunique())
每次循环的平均时间为2.42秒，标准偏差为282毫秒（7次运行的平均值和标准差，每次运行1次）
In [5]:
# Save results as a reference to check calculation accuracy.
ref = df.rolling(window='{:d}D'.format(window), on='Date').apply(lambda arr: pd.Series(arr).nunique())['PersonId'].values
速度测试 2
在 [6]:
# Define a custom function and implement a just-in-time compiler.
@numba.jit(nopython=True)
def nunique(arr):
return len(set(arr))
In [7]:
%%timeit
df.rolling(window='{:d}D'.format(window), on='Date').apply(nunique)
每次循环平均需要430毫秒±31.1毫秒（7次运行，每次循环1次）
In [8]:
# Check accuracy of results.
test = df.rolling(window='{:d}D'.format(window), on='Date').apply(nunique)['PersonId'].values
assert all(ref == test)
速度测试 3
In [9]:
# Define a custom function and implement a just-in-time compiler.
@numba.jit(nopython=True)
def windowed_nunique(dates, pids, window):
r"""Track number of unique persons in window,
reading through arrays only once.
Args:
dates (numpy.ndarray): Array of dates as number of days since epoch.
pids (numpy.ndarray): Array of integer person identifiers.
window (int): Width of window in units of difference of `dates`.
Returns:
ucts (numpy.ndarray): Array of unique counts.
Raises:
AssertionError: Raised if `len(dates) != len(pids)`
Notes:
* May be off by 1 compared to `pandas.core.window.Rolling`
with a time series alias offset.
"""
# Check arguments.
assert dates.shape == pids.shape
# Initialize counters.
idx_min = 0
idx_max = dates.shape[0]
date_min = dates[idx_min]
pid_min = pids[idx_min]
pid_max = np.max(pids)
pid_cts = np.zeros(pid_max, dtype=np.int64)
pid_cts[pid_min] = 1
uct = 1
ucts = np.zeros(idx_max, dtype=np.int64)
ucts[idx_min] = uct
idx = 1
# For each (date, person)...
while idx < idx_max:
# If person count went from 0 to 1, increment unique person count.
date = dates[idx]
pid = pids[idx]
pid_cts[pid] += 1
if pid_cts[pid] == 1:
uct += 1
# For past dates outside of window...
while (date - date_min) > window:
# If person count went from 1 to 0, decrement unique person count.
pid_cts[pid_min] -= 1
if pid_cts[pid_min] == 0:
uct -= 1
idx_min += 1
date_min = dates[idx_min]
pid_min = pids[idx_min]
# Record unique person count.
ucts[idx] = uct
idx += 1
return ucts
In [10]:
# Cast dates to integers.
df['DateEpoch'] = (df['Date'] - pd.to_datetime('1970-01-01'))/pd.to_timedelta(1, unit='D')
df['DateEpoch'] = df['DateEpoch'].astype(int)
输入[11]:
%%timeit
windowed_nunique(
dates=df['DateEpoch'].values,
pids=df['PersonId'].values,
window=window)
107微秒±63.5微秒每个循环（平均值±7次运行的标准差，每个循环1次）
In [12]:
# Check accuracy of results.
test = windowed_nunique(
dates=df['DateEpoch'].values,
pids=df['PersonId'].values,
window=window)
# Note: Method may be off by 1.
assert all(np.isclose(ref, np.asarray(test), atol=1))
在 [13] 中：
# Show where the calculation doesn't match.
print("Where reference ('ref') calculation of number of unique people doesn't match 'test':")
df['ref'] = ref
df['test'] = test
df.loc[df['ref'] != df['test']].head() # 9044 rows × 5 columns
Out[13]:
Where reference ('ref') calculation of number of unique people doesn't match 'test':
| | Date | PersonId | DateEpoch | ref | test |
|----|------------|----------|-----------|------|------|
| 78 | 2010-01-19 | 99 | 14628 | 56.0 | 55 |
| 79 | 2010-01-19 | 96 | 14628 | 56.0 | 55 |
| 80 | 2010-01-19 | 88 | 14628 | 56.0 | 55 |
| 81 | 2010-01-20 | 94 | 14629 | 56.0 | 55 |
| 82 | 2010-01-20 | 48 | 14629 | 57.0 | 56 |
df.rolling(365)['PersonId'].apply(lambda x: len(set(x)))？？？ - Woody Pridenunique）。计算是正确的，但效率低下，因为set每次窗口计算执行时都会对窗口中的每个元素进行操作。更有效的方法是保持每个元素的运行总数，就像“速度测试3”中一样（在示例数据上比较“速度测试2”和“速度测试3”，效率提高了约4000倍）。然而，我的实现windowed_nunique有误差1，我想知道是否有人能帮忙找到问题。 - Samuel HarroldpersonId是一个数字。但是如果我们尝试将其应用于非数字类型，我们会得到DataError: No numeric types to aggregate的错误。有什么办法可以让它工作吗？ - David Davó