Convert Datetime to Date Format Dd/Mm/Yyyy

convert datetime to date format dd/mm/yyyy

DateTime dt = DateTime.ParseExact(yourObject.ToString(), "MM/dd/yyyy hh:mm:ss tt", CultureInfo.InvariantCulture);

string s = dt.ToString("dd/M/yyyy", CultureInfo.InvariantCulture);

How to Convert Datetime to Date in dd/MM/yyyy format

Give a different alias

SELECT  Convert(varchar,A.InsertDate,103) as converted_Tran_Date from table as A
order by A.InsertDate 

Need to convert datetime to dd/mm/yyyy

Yes, I would make df_sales_sum['Date'] a string: str(df_sales_sum['Date']). See if that helps. However, I'm not sure what df_sales_sum['Date'] output is.

df_date = str(df_sales_sum['Date'])

convert_date = df_date.strftime("%d/%m/%Y")

Good luck.

Why is it not possible to store DD-MM-YYYY format in date format?

Following the format of the solutions you have tried, a correct way to do it (not the most efficient) is:

import datetime

df["Date"] = [datetime.datetime.strptime(s, '%Y-%m-%d').strftime('%d-%m-%Y') for s in df["Date"]]

If you need more efficiency in the solution, the ideal would be to use vectorized operations, for example:

df['Date'] = pd.to_datetime(df['Date'])
df['Date'] = df["Date"].dt.strftime("%d-%m-%Y")

Or in a single line:

df['Date'] = pd.to_datetime(df['Date']).dt.strftime("%d-%m-%Y")

Converting dd/mm/yyyy formatted string to Datetime

You need to use DateTime.ParseExact with format "dd/MM/yyyy"

DateTime dt=DateTime.ParseExact("24/01/2013", "dd/MM/yyyy", CultureInfo.InvariantCulture);

Its safer if you use d/M/yyyy for the format, since that will handle both single digit and double digits day/month. But that really depends if you are expecting single/double digit values.

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Your date format day/Month/Year might be an acceptable date format for some cultures. For example for Canadian Culture en-CA DateTime.Parse would work like:

DateTime dt = DateTime.Parse("24/01/2013", new CultureInfo("en-CA"));

Or

System.Threading.Thread.CurrentThread.CurrentCulture = new CultureInfo("en-CA");
DateTime dt = DateTime.Parse("24/01/2013"); //uses the current Thread's culture

Both the above lines would work because the the string's format is acceptable for en-CA culture. Since you are not supplying any culture to your DateTime.Parse call, your current culture is used for parsing which doesn't support the date format. Read more about it at DateTime.Parse.

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Another method for parsing is using DateTime.TryParseExact

DateTime dt;
if (DateTime.TryParseExact("24/01/2013", 
                            "d/M/yyyy", 
                            CultureInfo.InvariantCulture, 
                            DateTimeStyles.None,
    out dt))
{
    //valid date
}
else
{
    //invalid date
}

The TryParse group of methods in .Net framework doesn't throw exception on invalid values, instead they return a bool value indicating success or failure in parsing.

Notice that I have used single d and M for day and month respectively. Single d and M works for both single/double digits day and month. So for the format d/M/yyyy valid values could be:

• "24/01/2013"
• "24/1/2013"
• "4/12/2013" //4 December 2013
• "04/12/2013"

For further reading you should see: Custom Date and Time Format Strings

SQL order by datetime formatted as dd.MM.yyyy is incorrect

Group the datetime by converting to date and format the date after grouping:

SELECT CONVERT(VARCHAR(10), CONVERT(date, recievedDate), 104)
FROM t
GROUP BY CONVERT(date, recievedDate)
ORDER BY CONVERT(date, recievedDate)

Convert data from datetime format in format dd.mm.YYYY in sqlalchemy

To convert dates to the dd.mm.yyyy format, use:

result = date.strftime("%d.%.m.%Y")

Source: Converting date between DD/MM/YYYY and YYYY-MM-DD?

Pandas - Converting date column from dd/mm/yy hh:mm:ss to yyyy-mm-dd hh:mm:ss

If you know you will have a consistent format in your column, you can pass this to to_datetime:

df['sale_date'] = pd.to_datetime(df['sale_date'], format='%d/%m/%y %H:%M:%S')

If your formats aren't necessarily consistent but do have day before month in each case, it may be enough to use dayfirst=True though this is difficult to say without seeing the data:

df['sale_date'] = pd.to_datetime(df['sale_date'], dayfirst=True)

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