R语言日期格式化：宽格式降雪数据转为年月单列长格式求助

以下分别提供R语言tidyverse生态和Python pandas生态的实现代码，适配你的雪季统计规则： #

R语言实现方案 #

library(tidyverse)

# 预定义月份映射规则：1-6月归属雪季标注的后一年，7-12月归属雪季标注的前一年
month_rule <- tibble(
  month_abb = c("JAN","FEB","MAR","APR","MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC"),
  month_num = 1:12,
  is_next_year = c(rep(TRUE,6), rep(FALSE,6))
)

# 宽转长处理逻辑
snow_long <- snow_wide %>%
  # 保留雪季和总降雪量列，其余月份列转长
  pivot_longer(cols = -c(SEASON, TOTAL),
               names_to = "month_abb",
               values_to = "monthly_snowfall") %>%
  left_join(month_rule, by = "month_abb") %>%
  # 拆分雪季年份计算对应年月
  mutate(season_start = as.numeric(substr(SEASON, 1, 4)),
         cal_year = ifelse(is_next_year, season_start + 1, season_start),
         year_month = paste0(cal_year, "-", str_pad(month_num, 2, side = "left", pad = "0"))) %>%
  # 按需调整输出列顺序
  select(SEASON, year_month, monthly_snowfall, TOTAL)

转换后输出示例：

Python pandas实现方案 #

import pandas as pd
import numpy as np

# 预定义月份映射规则
month_map = {
    "JAN": (1, True), "FEB": (2, True), "MAR": (3, True), "APR": (4, True), "MAY": (5, True), "JUN": (6, True),
    "JUL": (7, False), "AUG": (8, False), "SEP": (9, False), "OCT": (10, False), "NOV": (11, False), "DEC": (12, False)
}

# 宽转长处理
snow_long = snow_wide.melt(
    id_vars=["SEASON", "TOTAL"],
    var_name="month_abb",
    value_name="monthly_snowfall"
)

# 生成年月字段
snow_long[["month_num", "is_next_year"]] = snow_long["month_abb"].apply(lambda x: pd.Series(month_map[x]))
snow_long["season_start"] = snow_long["SEASON"].str[:4].astype(int)
snow_long["cal_year"] = np.where(snow_long["is_next_year"], snow_long["season_start"] + 1, snow_long["season_start"])
snow_long["year_month"] = snow_long["cal_year"].astype(str) + "-" + snow_long["month_num"].astype(str).str.zfill(2)

# 调整输出列
snow_long = snow_long[["SEASON", "year_month", "monthly_snowfall", "TOTAL"]]

内容的提问来源于stack exchange，提问作者Ben