Pandas 读取带有浮点值的 csv 文件会导致奇怪的四舍五入和十进制数字 [英] Pandas read csv file with float values results in weird rounding and decimal digits
问题描述
我有一个包含数值的 csv 文件,例如 1524.449677
.总是精确到 6 位小数.
I have a csv file containing numerical values such as 1524.449677
. There are always exactly 6 decimal places.
当我通过 pandas read_csv
导入 csv 文件(和其他列)时,该列会自动获取数据类型 object
.我的问题是这些值显示为 2470.6911370000003
,实际上应该是 2470.691137
.或者值 2484.30691
显示为 2484.3069100000002
.
When I import the csv file (and other columns) via pandas read_csv
, the column automatically gets the datatype object
. My issue is that the values are shown as 2470.6911370000003
which actually should be 2470.691137
. Or the value 2484.30691
is shown as 2484.3069100000002
.
这在某种程度上似乎是数据类型问题.在通过 read_csv
导入时,我尝试通过将 dtype
参数指定为 {'columnname': np.float64}
来明确提供数据类型.问题仍然没有消失.
This seems to be a datatype issue in some way. I tried to explicitly provide the data type when importing via read_csv
by giving the dtype
argument as {'columnname': np.float64}
. Still the issue did not go away.
我怎样才能让导入的值与源 csv 文件中的值完全一样?
How can I get the values imported and shown exactly as they are in the source csv file?
推荐答案
Pandas 使用专用的 dec 2 bin
转换器,该转换器会影响准确性而不是速度.
Pandas uses a dedicated dec 2 bin
converter that compromises accuracy in preference to speed.
将 float_precision='round_trip'
传递给 read_csv
可解决此问题.
Passing float_precision='round_trip'
to read_csv
fixes this.
查看这个页面 了解更多详情.
处理完您的数据后,如果您想将其保存回 csv 文件,您可以将float_format = "%.nf"
传递给相应的方法.
After processing your data, if you want to save it back in a csv file, you can passfloat_format = "%.nf"
to the corresponding method.
一个完整的例子:
import pandas as pd
df_in = pd.read_csv(source_file, float_precision='round_trip')
df_out = ... # some processing of df_in
df_out.to_csv(target_file, float_format="%.3f") # for 3 decimal places
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