如何简化DataFrame逐列生成正向连续streak值的重复代码?
简化DataFrame正向连续streak次数计算的代码
需要将DataFrame中的所有数值替换为对应的当前正向连续streak次数,目前已实现该功能,但因逐列手动指定列名编写代码,导致代码冗余杂乱,希望获得简化代码的方法。
原实现代码:
import pandas as pd import numpy as np df = pd.DataFrame([[9, 5, 2], [-2, 6, -4], [-5, 1, -1], [9, 6, -5], [7, -1, -3], [6, -4, 1], [2, -9, 3]], columns=['A', 'B', 'C'], index=[1, 2, 3, 4, 5, 6, 7]) def streaks(df, col): sign = np.sign(df[col]) s = sign.groupby((sign!=sign.shift()).cumsum()).cumsum() return df.assign(A=s.where(s>0, 0.0).abs()) df = streaks(df, 'A') def streaks(df, col): sign = np.sign(df[col]) s = sign.groupby((sign!=sign.shift()).cumsum()).cumsum() return df.assign(B=s.where(s>0, 0.0).abs()) df = streaks(df, 'B') def streaks(df, col): sign = np.sign(df[col]) s = sign.groupby((sign!=sign.shift()).cumsum()).cumsum() return df.assign(C=s.where(s>0, 0.0).abs()) df = streaks(df, 'C')
简化方案
方法一:通用函数+批量遍历
提取通用计算逻辑,遍历所有列批量处理:
import pandas as pd import numpy as np df = pd.DataFrame([[9, 5, 2], [-2, 6, -4], [-5, 1, -1], [9, 6, -5], [7, -1, -3], [6, -4, 1], [2, -9, 3]], columns=['A', 'B', 'C'], index=[1, 2, 3, 4, 5, 6, 7]) def calculate_positive_streak(series): sign = np.sign(series) # 标记连续相同符号的分组 group_id = (sign != sign.shift()).cumsum() # 计算分组内连续次数,负向时重置为0 streak = sign.groupby(group_id).cumsum() return streak.where(streak > 0, 0.0).abs() # 遍历所有列更新数据 for col in df.columns: df[col] = calculate_positive_streak(df[col])
方法二:使用apply一次性处理所有列
利用apply方法对整列批量应用计算逻辑:
import pandas as pd import numpy as np df = pd.DataFrame([[9, 5, 2], [-2, 6, -4], [-5, 1, -1], [9, 6, -5], [7, -1, -3], [6, -4, 1], [2, -9, 3]], columns=['A', 'B', 'C'], index=[1, 2, 3, 4, 5, 6, 7]) def calculate_positive_streak(series): sign = np.sign(series) group_id = (sign != sign.shift()).cumsum() streak = sign.groupby(group_id).cumsum() return streak.where(streak > 0, 0.0).abs() # 对所有列应用函数,直接生成结果 df = df.apply(calculate_positive_streak)
简化说明
- 把重复的计算逻辑封装成通用函数
calculate_positive_streak,接收单列数据(Series)作为输入,输出处理后的连续次数 - 两种方法都避免了重复定义函数和硬编码列名,新增数值列时无需修改代码,直接自动适配
- 保留原逻辑的核心:通过符号分组计算连续次数,仅保留正向连续计数,负向时重置为0
内容的提问来源于stack exchange,提问作者Nad545
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