問題描述

我有一個包含數值的 csv 文件，例如 1524.449677.總有 6 位小數.

I have a csv file containing numerical values such as 1524.449677. There are always exactly 6 decimal places.

當我通過 pandas read_csv 導入 csv 文件(和其他列)時，該列會自動獲取數據類型 object.我的問題是這些值顯示為 2470.6911370000003 實際上應該是 2470.691137.或者值 2484.30691 顯示為 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.

這在某種程度上似乎是一個數據類型問題.在通過 read_csv 導入時，我嘗試通過將 dtype 參數作為 {'columnname': np.float64} 來顯式提供數據類型.問題仍然沒有消失.

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.

如何獲取導入的值并完全按照它們在源 csv 文件中的樣子顯示?

How can I get the values imported and shown exactly as they are in the source csv file?

推薦答案

Pandas 使用專用的 dec 2 bin 轉換器，該轉換器會犧牲準確性而不是速度.

Pandas uses a dedicated dec 2 bin converter that compromises accuracy in preference to speed.

將 float_precision='round_trip' 傳遞給 read_csv 可以解決此問題.

Passing float_precision='round_trip' to read_csv fixes this.

查看 此頁面 了解更多詳情.

Check out this page for more detail on this.

處理完你的數據后，如果你想把它保存回一個csv文件，你可以將
float_format = "%.nf"傳給對應的方法.

After processing your data, if you want to save it back in a csv file, you can pass
float_format = "%.nf" to the corresponding method.

一個完整的例子:

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

這篇關于Pandas 讀取具有浮點值的 csv 文件會導致奇怪的舍入和小數位數的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！