将DataFrame值转换为int，添加它们并创建带有结果的新列? [英] Converting DataFrame values to int, adding them and create new column with result?

问题描述

我有一个非常大的字符串数字数据框，例如:

I have a very large dataframe of string numbers, something like for example:

a,b,c
"1","2","3"
"4","5","6"
"7","8","9"

我想创建一个新列d并添加a + c，所以最终结果将是:

And I want to create a new column d with the addition of a + c so the end result would be:

a,b,c,d
1,2,3,4
4,5,6,10
7,8,9,16

我仍在尝试仅将a + c的列转换为字符串，但是我不知道如何将它们加在一起并创建结果的新列.请帮助解决最后一个问题！

I'm still trying to convert just the columns of a + c to strings, but I have no idea how I'll add them together and create a new column of the result. Please help with this last problem!

推荐答案

我认为read_csv将列转换为整数.

In my opinion read_csv convert columns to integers.

因此使用:

df = pd.read_csv(file)
df['d'] = df['a'] + df['c']

但是如果失败，则尝试转换为整数或浮点数:

But if failed, then try convert to integer or floats:

df = pd.read_csv(file)
df['d'] = df['a'].astype(int) + df['c'].astype(int)
#floats 
#df['d'] = df['a'].astype(float) + df['c'].astype(float)

如果数字之间也可能存在一些字符串，则可以将问题值转换为NaN s并求和:

If there are also some strings between numeric is possible convert problems values to NaNs and sum:

df = pd.read_csv(file)
df['d'] = pd.to_numeric(df['a'], errors='coerce') + pd.to_numeric(df['c'],  errors='coerce')

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