如何在DataFrame中用指定值替换负值并消除代码警告？

我有一个df，需要将其中的负值替换为指定值（1e-13），如何简化代码同时避免出现SettingWithCopyWarning警告？

原始数据 #

datetime        a0        a1        a2
0 2022-01-01  0.097627  0.430379  0.205527
1 2022-01-02  0.089766 -0.152690  0.291788
2 2022-01-03 -0.124826  0.783546  0.927326
3 2022-01-04 -0.233117  0.583450  0.057790
4 2022-01-05  0.136089  0.851193 -0.857928
5 2022-01-06 -0.825741 -0.959563  0.665240
6 2022-01-07  0.556314  0.740024  0.957237
7 2022-01-08  0.598317 -0.077041  0.561058
8 2022-01-09 -0.763451  0.279842 -0.713293
9 2022-01-10  0.889338  0.043697 -0.170676

目标结果 #

datetime            a0            a1            a2
0 2022-01-01  9.762701e-02  4.303787e-01  2.055268e-01
1 2022-01-02  8.976637e-02  1.000000e-13  2.917882e-01
2 2022-01-03  1.000000e-13  7.835460e-01  9.273255e-01
3 2022-01-04  1.000000e-13  5.834501e-01  5.778984e-02
4 2022-01-05  1.360891e-01  8.511933e-01  1.000000e-13
5 2022-01-06  1.000000e-13  1.000000e-13  6.652397e-01
6 2022-01-07  5.563135e-01  7.400243e-01  9.572367e-01
7 2022-01-08  5.983171e-01  1.000000e-13  5.610584e-01
8 2022-01-09  1.000000e-13  2.798420e-01  1.000000e-13
9 2022-01-10  8.893378e-01  4.369664e-02  1.000000e-13

现有代码及警告 #

现有代码 #

import numpy as np
import pandas as pd

np.random.seed(0)


# 生成演示数据
def generate_data():
    datetime1 = pd.date_range(start='20220101', end='20220110')
    df = pd.DataFrame(data=datetime1, columns=['datetime'])
    col = [f'a{x}' for x in range(3)]
    df[col] = np.random.uniform(-1, 1, (10, 3))
    return df


def main():
    df = generate_data()
    print(df)
    col = list(df.columns)[1:]
    df2 = df[col]
    df2[df2 < 0] = float(1e-13)
    df[col] = df2
    print(df)
    return


if __name__ == '__main__':
    main()

运行警告 #

<ipython-input-5-887189ce29a9>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2[df2 < 0] = float(1e-13)
<ipython-input-5-887189ce29a9>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2[df2 < 0] = float(1e-13)

警告原因 #

代码里的df2 = df[col]是原DataFrame的切片引用，不是独立副本。直接修改df2时，pandas无法判断你要修改原数据还是副本，因此抛出警告。

方法1：用df.loc直接修改（最简洁） #

无需创建中间变量，直接定位目标列完成替换：

def main():
    df = generate_data()
    print(df)
    col = list(df.columns)[1:]
    # 用mask替换负值
    df.loc[:, col] = df.loc[:, col].mask(df.loc[:, col] < 0, 1e-13)
    print(df)
    return

或者用更直观的where方法（保留满足条件的值，替换不满足的）：

df.loc[:, col] = df.loc[:, col].where(df.loc[:, col] >= 0, 1e-13)

方法2：显式创建副本修改 #

如果需要用中间变量，给切片加.copy()创建独立副本，避免引用问题：

def main():
    df = generate_data()
    print(df)
    col = list(df.columns)[1:]
    df2 = df[col].copy()  # 显式创建副本
    df2[df2 < 0] = 1e-13
    df[col] = df2
    print(df)
    return

方法3：用replace批量替换 #

通过条件匹配批量替换负值：

def main():
    df = generate_data()
    print(df)
    col = list(df.columns)[1:]
    df[col] = df[col].replace(df[col] < 0, 1e-13)
    print(df)
    return

以上方法都能消除警告，同时简化代码逻辑。

内容的提问来源于stack exchange，提问作者jaried