Str Error When Replacing Values In Pandas Dataframe

My code scrapes information from the website and puts it into a dataframe. But i'm not certain why the order of the code will give rise to the error: AttributeError: Can only use .

Solution 1:

For me works double replace - first with regex=True for replace substrings and second for all values:

np.random.seed(23)
df = pd.DataFrame(np.random.choice(['(2,333)','n.a.','-',2.34], size=(3,3)), 
                  columns=list('ABC'))
print (df)
      A     B        C
0  2.34     -  (2,333)
1  n.a.     -  (2,333)
2  2.34  n.a.  (2,333)

df1 = df.replace(['\(','\)','\,'], ['-','',''], regex=True).replace(['-','n.a.'], np.nan)
print(df1)
      A   B      C
0  2.34 NaN  -2333
1   NaN NaN  -2333
2  2.34 NaN  -2333

df1 = df.replace(['-','n.a.'], np.nan).replace(['\(','\)','\,'], ['-','',''], regex=True)
print(df1)  
      A   B      C
0  2.34 NaN  -2333
1   NaN NaN  -2333
2  2.34 NaN  -2333

EDIT:

Your error means you want replace some non string column (e.g. all columns are NaNs in column B) by str.replace:

df1 = df.apply(lambda x: x.str.replace('\(','-').str.replace('\)','')
                           .str.replace(',','')).replace(['-','n.a.'], np.nan)
print(df1)
      A   B      C
0  2.34 NaN  -2333
1   NaN NaN  -2333
2  2.34 NaN  -2333 

---

df1 = df.replace(['-','n.a.'], np.nan)
       .apply(lambda x: x.str.replace('\(','-')
                         .str.replace('\)','')
                         .str.replace(',',''))
print(df1)

AttributeError: ('Can only use .str accessor with string values, which use np.object_ dtype in pandas', 'occurred at index B')

dtype of column B is float64:

df1 = df.replace(['-','n.a.'], np.nan)
print(df1)
      A   B        C
0  2.34 NaN  (2,333)
1   NaN NaN  (2,333)
2  2.34 NaN  (2,333)

print (df1.dtypes)
A     object
B    float64
C     object
dtype: object