从Python的2个数据框列中减去数字

Question

I am a beginner at Python and have searched the forum for the answer to my question without success.

I have a matrix and would like to subtract the numbers from one column from the numbers from another column and create a new column with the result.

I tried:

df['new column]=df['column 1']-df['column 2']

my output is: TypeError: unsupported operand type(s) for -: 'str' and 'str'

so then I tried to convert these columns to integers before performing subtraction with the following line:

df['column 2']=df['column 2'].astype(int)

my output is: ValueError: cannot convert float NaN to integer

(I have some NaN in my dataframe). I then tried to replace all of the NaN with an empty string using the following code:

def remove_nan(s):
    import math
    """ remove np.nan"""
    if math.isnan(s) == True:
        s.replace( np.nan,"")
    else:
        return s

df['column 1'] = df.apply(remove_nan, axis=0)

My output is: TypeError: ("cannot convert the series to <class 'float'>", 'occurred at index ID Number')

I would greatly appreciate it if someone could provide insight as to where I am making errors.

Thank you for the help.

Answer 1

Use pd.to_numeric to convert to numbers with parameter errors='coerce' to give nan when it isn't a number

Consider the df

df = pd.DataFrame(dict(A=list('456 8'), B=list('1 345')))

print(df)

   A  B
0  4  1
1  5   
2  6  3
3     4
4  8  5

After pd.to_numeric

df = df.apply(pd.to_numeric, errors='coerce')

print(df)

     A    B
0  4.0  1.0
1  5.0  NaN
2  6.0  3.0
3  NaN  4.0
4  8.0  5.0

Now we can do our column math

df['C'] = df.A - df.B

print(df)

     A    B    C
0  4.0  1.0  3.0
1  5.0  NaN  NaN
2  6.0  3.0  3.0
3  NaN  4.0  NaN
4  8.0  5.0  3.0

If you want to assume missing values are zero

df['C'] = df.A.sub(df.B, fill_value=0)

print(df)



    A    B    C
0  4.0  1.0  3.0
1  5.0  NaN  5.0
2  6.0  3.0  3.0
3  NaN  4.0 -4.0
4  8.0  5.0  3.0