Pandas replace change so values to NaN-CodePudding

I have the the following dataframe:

# dictionary with list object in values
details = {
    'Name' : ['D', 'C', 'F', 'G','A','N'],
    'values' : ['21%','45%','10%',12,14,15],
}

df = pd.DataFrame(details)

The column value has values in %, however, some were originally saves as string with symbol % and some as number. I would like to get rid of the % and have them all as int type. For that I have used replace and then as_type. however, when I repalce the '%', the values that son't have % change to Nan values:

df['values']=df['values'].str.replace('%', '')

df

>>> Name    values
0   D        21
1   C        45
2   F        10
3   G       NaN
4   A       NaN
5   N       NaN

My reuired output should be:

>>> Name    values
0   D        21
1   C        45
2   F        10
3   G        12
4   A        14
5   N        15

My question is, how can I get rid of the % and get the column with the values , without getting these NaN values? and why is this happenning?

CodePudding user response：

There are numeric values, so if use str function get missing values for numeric, possible solution is use Series.replace with regex=True for replace by substring and then because get numeric with strings convert output to integers:

df['values']=df['values'].replace('%', '', regex=True).astype(int)
print (df)
  Name  values
0    D      21
1    C      45
2    F      10
3    G      12
4    A      14
5    N      15

Or your solution with replace missing values:

df['values']=df['values'].str.replace('%', '').fillna(df['values']).astype(int)