pandas dataframe.sum()结果是否已更改？

6l7fqoea 于 2023-04-28 发布在其他

关注(0)|答案(1)|浏览(101)

我有以下代码，最后一次使用是在几个月前：

opendf = pd.DataFrame(
    rdbin[0],
    columns=[
        "highpoint_sum",
        "highpoint_mean",
        "highpoint diff_sum",
        "highpoint diff_mean",
        "name",
        "bin",
    ],
)
opendf.index = opendf["bin"]
opendf.drop(
    columns=["highpoint_sum", "highpoint_mean", "highpoint diff_mean", "name", "bin"],
    inplace=True,
)
opendf["Bresser_sum"] = brbinarr[:, 2]
opendf["open gauge_sum"] = rdbin[21][:, 2]
opendf["br_open_sum"] = opendf[["Bresser_sum", "open gauge_sum"]].sum(
    axis=1, min_count=1
)

当cols 'Bresser_sum'和'open gauge_sum'都是NaN时，它会导致列'br_open_sum'具有NaN。

highpoint diff_sum Bresser_sum open gauge_sum br_open_sum
bin                                                                          
2021-07-19 00:00:00                0.0         NaN            NaN        None
2021-07-19 01:00:00                0.0         NaN            NaN        None
2021-07-19 02:00:00                0.0         NaN            NaN        None
2021-07-19 03:00:00                0.0         NaN            NaN        None
2021-07-19 04:00:00                0.0         NaN            NaN        None
2021-07-19 05:00:00                0.0         NaN            NaN        None
2021-07-19 06:00:00                0.0         NaN            NaN        None
2021-07-19 07:00:00                0.0         NaN            NaN        None
2021-07-19 08:00:00                0.0         NaN            NaN        None
2021-07-19 09:00:00                0.0         NaN            NaN        None
2021-07-19 10:00:00                0.0         NaN            NaN        None
2021-07-19 11:00:00                0.0           0            NaN           0
2021-07-19 12:00:00                0.0         0.0            NaN         0.0
2021-07-19 13:00:00                0.0         0.0            NaN         0.0
2021-07-19 14:00:00                0.0         0.0            NaN         0.0
2021-07-19 15:00:00                0.0         0.0            NaN         0.0
2021-07-19 16:00:00                0.0         0.0            NaN         0.0
2021-07-19 17:00:00                0.0         0.0            NaN         0.0
2021-07-19 18:00:00                0.0         0.0            NaN         0.0
2021-07-19 19:00:00                0.0         0.0            NaN         0.0
2021-07-19 20:00:00                0.0         0.0            NaN         0.0
2021-07-19 21:00:00                0.0         0.0            NaN         0.0
2021-07-19 22:00:00                0.0         0.0            NaN         0.0
2021-07-19 23:00:00                0.0         0.0            NaN         0.0

现在，当我在数据没有任何变化的情况下运行代码时，但是我已经从python3.9升级到了3.11，我在列中得到'None'（一个NoneType对象）（如上所示）
我如何让.sum（）语句返回到以前的行为？我已经阅读了文档pandas dataframe.sum()，并尝试了skipna，numeric_only和min_count的所有组合。
用NaN替换'None'是另一种选择，我尝试使用.fillna（），但它抛出了大量错误！

pandas

来源：https://stackoverflow.com/questions/76075321/pandas-dataframe-sum-result-has-changed

1条答案

按热度按时间

k10s72fa1#

使用以下玩具数据框：

import pandas as pd

df = pd.DataFrame(
    {
        "col1": [0.0, 0.0, 0.0],
        "col2": [np.NaN, np.NaN, np.NaN],
        "col3": [None, None, "0.0"],
    }
)

print(df)
# Output

   col1  col2  col3
0   0.0   NaN  None
1   0.0   NaN  None
2   0.0   NaN   0.0

您可以使用Pandas to_numeric方法转换None值：

df["col3"] = pd.to_numeric(df["col3"])

print(df)
# Output

   col1  col2  col3
0   0.0   NaN   NaN
1   0.0   NaN   NaN
2   0.0   NaN   0.0

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pandas dataframe.sum()结果是否已更改？

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