pandas 中的 .sum() 方法给出的结果不一致

问题描述

我有一个大的 DataFrame(大约 4e+07 行).

I have a large DataFrame (circa 4e+07 rows).

求和时，我得到 2 个明显不同的结果，无论我是在在列选择之前还是之后进行求和.
此外，类型从 float32 更改为到 float64，即使总数都低于 2**31

When summing it, I get 2 significantly different results whether I do the sum before or after the column selection.
Also, the type changes from float32 to float64 even though totals are all below 2**31

df[[col1, col2, col3]].sum()
Out[1]:
col1         9.36e+07
col2         1.39e+09
col3         6.37e+08
dtype: float32

df.sum()[[col1, col2, col3]]
Out[2]:
col1         1.21e+08
col2         1.70e+09
col3         7.32e+08
dtype: float64

我显然遗漏了一些东西，有人遇到过同样的问题吗?

I am obviously missing something, has anybody had the same issue?

感谢您的帮助.

推荐答案

使用 np.float32 相对于 np.float64 可能会丢失精度

You can lose precision with np.float32 relative to np.float64

np.finfo(np.float32)

finfo(resolution=1e-06, min=-3.4028235e+38, max=3.4028235e+38, dtype=float32)

和

np.finfo(np.float64)

finfo(resolution=1e-15, min=-1.7976931348623157e+308, max=1.7976931348623157e+308, dtype=float64)

一个人为的例子

df = pd.DataFrame(dict(
    x=[-60499999.315, 60500002.685] * int(2e7),
    y=[-60499999.315, 60500002.685] * int(2e7),
    z=[-60499999.315, 60500002.685] * int(2e7),
)).astype(dict(x=np.float64, y=np.float32, z=np.float32))

print(df.sum()[['y', 'z']], df[['y', 'z']].sum(), sep='

')

y    80000000.0
z    80000000.0
dtype: float64

y    67108864.0
z    67108864.0
dtype: float32