如何使用 Python Pandas 将 csv 中的列转换为年、月、周的字符串值？

Question

我已经尝试使用 python 如下：

import pandas as pd

df = pd.read_csv("master_list.csv", usecols = ['Duration'])
#month manipulation starts
df[df['Duration'].fillna('').str.contains('Months') & df['Duration'].fillna('').str.len().gt(0)]
df["Duration"] = df["Duration"].str.replace("\sMonths", "")
df['Duration'] = pd.to_numeric(df['Duration'], errors='coerce').fillna(0).astype(float)
df['Duration'] *= 30.436875
#month manipulation end
df[df['Duration'].fillna('').str.contains('Weeks') & df['Duration'].fillna('').str.len().gt(0)]
df["Duration"] = df["Duration"].str.replace("\sWeeks", "")
#df['Duration'] *= 30.436875

print(df)

Sample_Data = ["7 Months","1 Year","8 Weeks","3 Days"]
Expected Output = ["210 Days","365 Days","56 Days","3 Days"]

Answer 1

模块dateparser可以解析带有weeks 、 years 、 months 、 days等的文本。

import dateparser
import datetime

sample_data = ["7 Months","1 Year","8 Weeks","3 Days"]

today = datetime.datetime.now()

for item in sample_data:
    result = dateparser.parse(item)
    diff = today.date() - result.date()
    print(item, '|', diff.days)

结果：

7 Months | 212
1 Year | 365
8 Weeks | 56
3 Days | 3