演示如何实现此目的的示例

import random
from datetime import timedelta

import pandas as pd

# Set the number of dates and the starting year
num_dates = 50
start_year = 2022

# Generate random dates for column 1
dates_column1 = pd.date_range(start=f"1/1/{start_year}", periods=num_dates)

# Function to add a specific number of days to a given date
def add_days(date, num_days):
    return date + timedelta(days=num_days)

# Generate dates for column 2 based on column 1
dates_column2 = [add_days(date, random.randint(1, 10)) for date in dates_column1]

# Generate dates for column 3 based on column 2
dates_column3 = [add_days(date, random.randint(1, 10)) for date in dates_column2]

# Create a DataFrame with the three columns
df = pd.DataFrame({"Date of sale": dates_column1,
                   "Date of shipment": dates_column2,
                   "Date of delivery": dates_column3})

# Convert the dates to the desired format
df["Date of sale"] = df["Date of sale"].dt.strftime("%d.%m.%Y")
df["Date of shipment"] = df["Date of shipment"].dt.strftime("%d.%m.%Y")
df["Date of delivery"] = df["Date of delivery"].dt.strftime("%d.%m.%Y")

# Print the resulting DataFrame
print(df)
#    Date of sale Date of shipment Date of delivery
# 0    01.01.2022       04.01.2022       06.01.2022
# 1    02.01.2022       09.01.2022       13.01.2022
# 2    03.01.2022       11.01.2022       15.01.2022
# 3    04.01.2022       10.01.2022       12.01.2022
# ...

可以使用pd.to_timedelta

num = 10  # 50
origin = pd.Timestamp('2023-01-01')

days1 = pd.to_timedelta(np.random.randint(0, 366, num), unit='D')
days2 = days1 + pd.to_timedelta(np.random.randint(1, 10, num), unit='D')
days3 = days2 + pd.to_timedelta(np.random.randint(1, 10, num), unit='D')
df = pd.DataFrame({'Sales': origin + days1,
                   'Shipment': origin + days2,
                   'Delivery': origin + days3})

输出：

>>> df
       Sales   Shipment   Delivery
0 2023-08-12 2023-08-18 2023-08-22
1 2023-07-09 2023-07-14 2023-07-20
2 2023-07-07 2023-07-10 2023-07-19
3 2023-11-26 2023-12-02 2023-12-08
4 2023-11-17 2023-11-22 2023-11-29
5 2023-12-01 2023-12-05 2023-12-14
6 2023-07-09 2023-07-12 2023-07-16
7 2023-10-07 2023-10-16 2023-10-20
8 2023-02-28 2023-03-05 2023-03-14
9 2023-10-22 2023-10-23 2023-11-01