pandas 中不一致的日期格式计数

Question

I have a column of type object it contains 500 rows of dates. I converted the column type to date and I am trying to get a count of the incorrect values, in order to fix them.

Sample of the column, you can see examples of the wrong values in rows: 3 and 5

0      2018-06-14
1      2018-11-12
2      2018-10-09
3      2018-24-08
4      2018-11-12
5      11-02-2018
6      2018-12-31

I can fix the dates if I use this code:

dirtyData['date'] = pd.to_datetime(dirtyData['date'],dayfirst=True)

But I would like to check that the format in every row is %Y-%m-%d' and get the count of the inconsistent formats first. Then change the values.

Is it possible to achieve this?

Answer 1

The below code will work. However, as Michael Gardner mentioned it wont distinguish between days and months if the day 12 or less

import datetime
import pandas as pd

date_list = ["2018-06-14", "2018-11-12", "2018-10-09", "2018-24-08",
"2018-11-12", "11-02-2018", "2018-12-31"]

series1 = pd.Series(date_list)
print(series1)
#The above code is to replicate your date series


count = 0
for item in series1:
    try:
        datetime.datetime.strptime(item, "%Y-%m-%d") #checks if the date format is Year, Month,Day.
    except ValueError: #if there is a value error then it will count these errors
        count += 1 

print(count)