我正在嘗試預填充類似于以下內容的數據框：在示例中，我隨機刪除了一些行以突出挑戰。我正在嘗試*優雅地計算 dti 值。第一行中的 dti 值將為 0（即使第一行按照腳本被刪除）但由于 dti 序列中出現間隙需要跳過缺失的行。一種合乎邏輯的方法是將 dt/delta 相除以創建一個唯一的整數來表示桶，但我嘗試過的任何東西都感覺不到或看起來很優雅。一些代碼來幫助模擬問題：from datetime import datetime, timedeltaimport pandas as pdimport numpy as npstart = datetime.now()nin = 24delta='4H'df = pd.date_range( start, periods=nin, freq=deltadf, name ='dt') # remove some random data pointsfrac_points = 8/24 # Fraction of points to retainr = np.random.rand(nin)df = df[r <= frac_points] # reduce the number of pointsdf = df.to_frame(index=False) # reindexdf['dti'] = ...先感謝您，
1 回答
萬千封印
TA貢獻1891條經驗 獲得超3個贊
一種解決方案是將每行之間的時間差除以 timedelta：
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
start = datetime.now()
nin = 24
delta='4H'
df = pd.date_range(start, periods=nin, freq=delta, name='dt')
# Round to nearest ten minutes for better readability
df = df.round('10min')
# Ensure reproducibility
np.random.seed(1)
# remove some random data points
frac_points = 8/24 # Fraction of points to retain
r = np.random.rand(nin)
df = df[r <= frac_points] # reduce the number of points
df = df.to_frame(index=False) # reindex
df['dti'] = df['dt'].diff() / pd.to_timedelta(delta)
df['dti'] = df['dti'].fillna(0).cumsum().astype(int)
df
dt dti
0 2019-03-17 18:10:00 0
1 2019-03-17 22:10:00 1
2 2019-03-18 02:10:00 2
3 2019-03-18 06:10:00 3
4 2019-03-18 10:10:00 4
5 2019-03-19 10:10:00 10
6 2019-03-19 18:10:00 12
7 2019-03-20 10:10:00 16
8 2019-03-20 14:10:00 17
9 2019-03-21 02:10:00 20
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