替换 Pandas Dataframe 中的特殊符号

Question

Can anyone help me to remove extra characters from the dataframe column? Should I have to use replace string method?

For example,

`$7.99` -> `7.99`
`$16.99` -> `16.99`
`$0.99` -> `0.99`

Answer 1

IIUC: consider the following dataframe:

df = pd.DataFrame(['$7.99', '$3.45', '$56.99'])

you can use replace to do:

df[0].str.replace('$', '', regex=False)

Output:

0     7.99
1     3.45
2    56.99
Name: 0, dtype: object

Answer 2

You can remove the first character with .str[1:] :

df["Column1"] = df["Column1"].str[1:].astype(float)
print(df)

Prints:

   Column1
0     7.99
1    16.99
2     0.99

---

Dataframe used:

  Column1
0   $7.99
1  $16.99
2   $0.99

Answer 3

You can use pandas.DataFrame.mask to replace certain values, for example, as first positional parameter I provide a conditonal that is used in df.mask(df.str[0]=='$','') as if-them conditional.

df.mask(df.str[0]=='$',df.str[1:])