Wie kann ich vermeiden oder Abhilfe: Typeerror: nicht unterstützte Operandtyp (e) für: - 'float' und 'str'

Question

Ich erhalte:

TypeError: unsupported operand type(s) for -: 'float' and 'str' 

in:

line 525, in <module> 
     df3['D'] = df3['O2'].sub(df3['O1'], fill_value=0) 
     File "C:\Users\AC\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\ops.py", line 1070, in flex_wrapper 
     return self._binop(other, op, level=level, fill_value=fill_value) 
     File "C:\Users\AC\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\series.py", line 1715, in _binop 
     result = func(this_vals, other_vals) 
    TypeError: unsupported operand type(s) for -: 'float' and 'str' 

In:

df1 = pd.read_csv('C:\13.csv', 
        index_col=False, 
        usecols=[0, 1, 2, 3], 
        names=["EW", "WE", "DA", "DD"], 
        header=None) 
# print(df1.head()) 


df2 = pd.read_csv('C:\\14.csv', 
        index_col=False, 
        usecols=[0, 1, 2, 3, 4], 
        names=["AA", "AB", "AC", "AD", "AE"], 
        header=None) 

df1['EW'] = df1['EW'] + ' v ' + df1['WE'] 

df1['WE'] = df1['DA'] 
df1['DA'] = df1['DD'] 

df2['EW'] = df2['AB'] + ' v ' + df2['AC'] 

df2['EW'] = df2['AB'] + ' v ' + df2['AC'] 

df3 = pd.merge(df2, df1, on='EW') 
df3 = df3.rename(columns={'WE': 'O1', 'AD': 'O2'}) 
df3['D'] = df3['O2'].sub(df3['O1'], fill_value=0) 

Meine Datenrahmen wie folgt aussehen:

df1

    EW   WE DA \ 
0  KV Mechelen Club Brugge 4.30 
1    Gent Charleroi 1.75 
2  Royal Antwerp  Lokeren 1.68 
3  Anderlecht  Eupen 1.31 
4 Waasland-Beveren  Oostende 2.20 



                DD 
0 https://www.topsport.com.au/Soccer/Belgian_Jup... 
1 https://www.topsport.com.au/Soccer/Belgian_Jup... 
2 https://www.topsport.com.au/Soccer/Belgian_Jup... 
3 https://www.topsport.com.au/Soccer/Belgian_Jup... 
4 https://www.topsport.com.au/Soccer/Belgian_Jup... 

df2

   AA     AB      AC AD \ 
0  Thu 18:50  Brisbane Roar    Perth Glory 2.24 
1 Dec 22 18:50  Newcastle Jets Western Sydney Wanderers 2.42 
2 Dec 23 16:35 Wellington Phoenix     Sydney 7.20 
3 Dec 23 18:50  Melbourne City   Melbourne Victory 3.00 
4 Dec 26 18:50  Adelaide United Central Coast Mariners 2.14 

                AE 
0 https://www.betfair.com.au/exchange/plus/footb... 
1 https://www.betfair.com.au/exchange/plus/footb... 
2 https://www.betfair.com.au/exchange/plus/footb... 
3 https://www.betfair.com.au/exchange/plus/footb... 
4 https://www.betfair.com.au/exchange/plus/footb... 

Wie vermeide ich diesen Fehler, die auftauchen hält?

Answer 1

Ich denke, es gibt String-Spalte O1.

müssen Spalte float So konvertieren:

df3 = pd.merge(df2, df1, on='EW') 
df3 = df3.rename(columns={'WE': 'O1', 'AD': 'O2'}) 

df3['O1'] = df3['O1'].astype(float) 

df3['D'] = df3['O2'].sub(df3['O1'], fill_value=0) 

Aber wenn es einige nicht numerische Werte verwenden to_numeric mit errors='coerce' für NaN s für nicht parseable Werte:

df3 = pd.merge(df2, df1, on='EW') 
df3 = df3.rename(columns={'WE': 'O1', 'AD': 'O2'}) 

df3['O1'] = pd.to_numeric(df3['O1'], errors='coerce') 

df3['D'] = df3['O2'].sub(df3['O1'], fill_value=0)