If your column is a string, you will need to first use `pd.to_datetime',

Copydf['Date'] = pd.to_datetime(df['Date'])

Then, use .dt datetime accessor with strftime:

Copydf = pd.DataFrame({'Date':pd.date_range('2017-01-01', periods = 60, freq='D')})

df.Date.dt.strftime('%Y%m%d').astype(int)

Or use lambda function:

Copydf.Date.apply(lambda x: x.strftime('%Y%m%d')).astype(int)

Output:

Copy0     20170101
1     20170102
2     20170103
3     20170104
4     20170105
5     20170106
6     20170107
7     20170108
8     20170109
9     20170110
10    20170111
11    20170112
12    20170113
13    20170114
14    20170115
15    20170116
16    20170117
17    20170118
18    20170119
19    20170120
20    20170121
21    20170122
22    20170123
23    20170124
24    20170125
25    20170126
26    20170127
27    20170128
28    20170129
29    20170130
30    20170131
31    20170201
32    20170202
33    20170203
34    20170204
35    20170205
36    20170206
37    20170207
38    20170208
39    20170209
40    20170210
41    20170211
42    20170212
43    20170213
44    20170214
45    20170215
46    20170216
47    20170217
48    20170218
49    20170219
50    20170220
51    20170221
52    20170222
53    20170223
54    20170224
55    20170225
56    20170226
57    20170227
58    20170228
59    20170301
Name: Date, dtype: int32

Let pandas to parse dates, but then some days with months should be swapped:

df['accepted_date'] = pd.to_datetime(df['accepted_date'])

So better is use to_datetime with format and parameter errors='coerce', what return only matched datetimes with NaT for non matched. Last use combine_first for join all Series - NaT are replaced by values from another Series:

df = pd.DataFrame({'accepted_date':['2017-01-02','07-08-2017','20-03-2017','2017-01-04']})

d1 = pd.to_datetime(df['accepted_date'], format='%d-%m-%Y', errors='coerce')
d2 = pd.to_datetime(df['accepted_date'], format='%Y-%m-%d', errors='coerce')

df['accepted_date1'] = d1.combine_first(d2)
df['accepted_date2'] = pd.to_datetime(df['accepted_date'])
print (df)
  accepted_date accepted_date1 accepted_date2
0    2017-01-02     2017-01-02     2017-01-02
1    07-08-2017     2017-08-07     2017-07-08 <-swapped dd-mm
2    20-03-2017     2017-03-20     2017-03-20
3    2017-01-04     2017-01-04     2017-01-04

Detail:

print (d1)
0          NaT
1   2017-08-07
2   2017-03-20
3          NaT
Name: accepted_date, dtype: datetime64[ns]

print (d2)
0   2017-01-02
1          NaT
2          NaT
3   2017-01-04
Name: accepted_date, dtype: datetime64[ns]

EDIT:

Another solution is use parameter dayfirst=True:

df['accepted_date3'] = pd.to_datetime(df['accepted_date'], dayfirst=True)
print (df)
  accepted_date accepted_date3
0    2017-01-02     2017-01-02
1    07-08-2017     2017-08-07
2    20-03-2017     2017-03-20
3    2017-01-04     2017-01-04

You can use the parse_dates and dayfirst arguments of pd.read_csv, see: the docs for read_csv()

df = pd.read_csv('myfile.csv', parse_dates=['Date'], dayfirst=True)

This will read the Date column as datetime values, correctly taking the first part of the date input as the day. Note that in general you will want your dates to be stored as datetime objects.

Then, if you need to output the dates as a string you can call dt.strftime():

df['Date'].dt.strftime('%d/%m/%Y')

Use strptime and strftime of datetime.datetime methods:

from datetime import datetime

def change_format(val):
    val = str(val)
    date = datetime.strptime(val, "%Y%M%d")
    new_date = date.strftime("%Y-%M-%d")
    return new_date

Example :

change_format("20220622")
>>> "2022-06-22"

Then, you can apply this function to you dataframe series:

df['Valuation Date'].apply(change_format)