問題描述

我有一個 DataFrame.以下是兩個相關的列:一個是 int 的列，另一個是 str 的列.

I have a DataFrame. Two relevant columns are the following: one is a column of int and another is a column of str.

我知道如果我將 NaN 插入 int 列，Pandas 會將所有 int 轉換為 float 因為 int 沒有 NaN 值.

I understand that if I insert NaN into the int column, Pandas will convert all the int into float because there is no NaN value for an int.

但是，當我將 None 插入 str 列時，Pandas 會將我的所有 int 轉換為 float 為好.這對我來說沒有意義 - 為什么我在第 2 列中輸入的值會影響第 1 列?

However, when I insert None into the str column, Pandas converts all my int to float as well. This doesn't make sense to me - why does the value I put in column 2 affect column 1?

這是一個簡單的工作示例(Python 2):

Here's a simple working example (Python 2):

import pandas as pd
df = pd.DataFrame()
df["int"] = pd.Series([], dtype=int)
df["str"] = pd.Series([], dtype=str)
df.loc[0] = [0, "zero"]
print df
print
df.loc[1] = [1, None]
print df

輸出是

   int   str
0    0  zero

   int   str
0  0.0  zero
1  1.0   NaN

有沒有辦法讓輸出如下:

Is there any way to make the output the following:

   int   str
0    0  zero

   int   str
0    0  zero
1    1   NaN

不將第一列重鑄為 int.

• 我更喜歡使用 int 而不是 float 因為實際數據在該列是整數.如果沒有解決方法，我只會使用 float.

• I prefer using int instead of float because the actual data in that column are integers. If there's not workaround, I'll just use float though.

我不喜歡重鑄，因為在我的實際代碼中，我不需要
存儲實際的dtype.

I prefer not having to recast because in my actual code, I don't
store the actual dtype.

我還需要逐行插入數據.

I also need the data inserted row-by-row.

推薦答案

如果你設置dtype=object，你的系列就可以包含任意數據類型:

If you set dtype=object, your series will be able to contain arbitrary data types:

df["int"] = pd.Series([], dtype=object)
df["str"] = pd.Series([], dtype=str)
df.loc[0] = [0, "zero"]
print(df)
print()
df.loc[1] = [1, None]
print(df)

   int   str
0    0  zero
1  NaN   NaN

  int   str
0   0  zero
1   1  None

這篇關于阻止 Pandas 將 int 轉換為 float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！