我在 Python 中有以下代碼：import pandas as pdimport numpy as npdate_rng = pd.date_range(start='5/18/2019', end='7/22/2020', freq='S')df = pd.DataFrame(date_rng, columns=['start_timestamp'])df['end_timestamp'] = date_rngdf['start_timestamp'] = np.random.randint(1589760000,1595376000,size=(len(date_rng)))df['end_timestamp'] = np.random.randint(1589760000,1595376000,size=(len(date_rng)))df = df[(df.end_timestamp/df.start_timestamp<=1.000009)&(df.end_timestamp/df.start_timestamp>=1.000001)]df = df.sort_values(by=['start_timestamp','end_timestamp'])df['start_timestamp'] = pd.to_datetime(df['start_timestamp'],unit='s')df['end_timestamp'] = pd.to_datetime(df['end_timestamp'],unit='s')結果，我有以下數據框： start_timestamp end_timestamp2020-05-18 00:00:30 2020-05-18 00:54:072020-05-18 00:01:40 2020-05-18 03:50:392020-05-18 00:02:08 2020-05-18 02:39:412020-05-18 00:04:01 2020-05-18 00:47:252020-05-18 00:04:01 2020-05-18 02:26:502020-05-18 00:04:44 2020-05-18 02:17:53 . . .我應該怎么做才能確保在我的數據集中每個end_timestamp都是在其下一行之前的日期時間start_timestamp？已實施的解決方案我基本上將數據集轉換為數組，將其按升序排列并將其轉換回數據框。它可能不是最優雅的解決方案，但它工作正常并為我打算使用的內容生成了一致的數據。import pandas as pdimport numpy as npdate_rng = pd.date_range(start='7/22/2019', end='7/22/2020', freq='S')df = pd.DataFrame(date_rng, columns=['start_timestamp'])df['end_timestamp'] = date_rngdf['start_timestamp'] = np.random.randint(1563753600,1595376000,size=(len(date_rng)))df['end_timestamp'] = np.random.randint(1563753600,1595376000,size=(len(date_rng)))df = df[(df.end_timestamp/df.start_timestamp<=1.0000009)&(df.end_timestamp/df.start_timestamp>=1.0000001)]df = df.to_numpy()df = df.reshape(df.shape[0]*2,1)df = np.sort(df,axis=0)df = df.reshape(int(df.shape[0]/2),2)df = pd.DataFrame(df,columns=['start_timestamp','end_timestamp'])df['start_timestamp'] = pd.to_datetime(df['start_timestamp'],unit='s')df['end_timestamp'] = pd.to_datetime(df['end_timestamp'],unit='s')
1 回答
揚帆大魚
TA貢獻1799條經驗 獲得超9個贊
編寫您的邏輯代碼，一切都很好
freq='S'沒有任何意義，您將生成與開始日期和結束日期之間的秒數一樣多的行在隨機化開始時間后，使用當前行和下一行作為結束時間隨機函數的種子。這是作為列表理解嗎
在范圍的開始和結束處獲取 UTC 秒數時更聰明一些
import pandas as pd
import numpy as np
from datetime import datetime
# date_rng = pd.date_range(start='5/18/2019', end='7/22/2020', freq='S')
date_rng = pd.date_range(start='5/18/2019', end='5/19/2019', freq='min')
sec = [(date_rng.min() - datetime(1970, 1, 1)).total_seconds(),
(date_rng.max() - datetime(1970, 1, 1)).total_seconds() ]
df = pd.DataFrame(date_rng, columns=['start_timestamp'])
df['start_timestamp'] = np.random.randint(sec[0],sec[1],size=(len(date_rng)))
df = df.sort_values(by="start_timestamp")
l = df["start_timestamp"].tolist() # get randomised start times
l[-1] = sec[1] # set last time to end of range
# randomise end time between two start times
df['end_timestamp'] = [np.random.randint(l[i], l[i+1]) if i<len(l)-1 and l[i]<l[i+1] else l[i] for i, s in enumerate(l)]
df['start_timestamp'] = pd.to_datetime(df['start_timestamp'],unit='s')
df['end_timestamp'] = pd.to_datetime(df['end_timestamp'],unit='s')
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