In the dataframe, one of the columns is duration. It was given as a string. How can I convert this column into seconds? Answer Use pd.Timedelta to parse each item: Output:
Tag: datetime
Add hours to datetime of rows in a specific time interval
Let it be the following Python Panda DataFrame: Given a date and time range (start and end) and a country_ID, I want to add 2 hours to the rows that are in that range: Example: Answer Try your logic with boolean indexing (date must also be a datetime object, not a string):
Taking the recency time of client purchase with PySpark
The sample of the dataset I am working on: I’d like to take the customer’s most recent purchase (the customer’s date.max()) and the previous maximum purchase (the penultimate purchase) and take the difference between the two (I’m assuming the same product on all purchases). I still haven’t found something in pyspark that does this. One example of my idea was
MyPy complains about datetime difference
MyPy complains about the line of code where bar is assigned the difference of two datetimes. The type of foo is datetime.timedelta. The error message from MyPy is “int not callable”. What am I missing? Answer This turned out to be a known issue. For details, see the following: https://github.com/python/mypy/issues/11613
Attempting to introduce dynamic variables causes a toordinal error
I’m trying to make the generation of charts a little bit more dynamic so other users can get a bit more value out of my code. Currently, I do this (and variations of it) to produce a variety of charts: And this works perfectly well, but involves people scrolling through the code to individually change the data. What I’ve attempted
Manipulate the Dataframe to start from the nearest varying Midnight timestamp
My goal: I have a dataset that gets generated every day at random hours leading to the first row to start at a random time. I want to make this dataset start from the nearest midnight date. For example, if the date on the first row is 2022-05-09 15:00:00, I would have to slice the data to make it start
subtracting time intervals from column dates in dataframes Pandas Python
How would I be able to subtract 1 second and 1 minute and 1 month from data[‘date’] column? Answer Your date column is of type string. Convert it to pd.Timestamp and you can use pd.DateOffset:
How can I fix the list index out of range problem?
So I wanna fill NaN value of the pay date with the date one month after the join date. Join date Payday1 Okt’10 NaN Des’10 NaN My expectation output is: Join date Payday1 Okt’10 Nov’10 Des’10 Jan’11 I try this code: This code is error in this code month=months[months.index(month)+1], it said list index out of range. So how to fix
Problem: Pandas – Slicing a datetime64[ns] column returns a list of 19-digit integers
I am trying to convert a column of dates in a dataframe to a list, but something is happening when I slice it that turns the dates into 19-digit integers. This is the original df along with the column dtypes: This is the function that turns a dataframe column into a list: This is what is printed after the column
convert month of dates into sequence
i want to combine months from years into sequence, for example, i have dataframe like this: i want to sequence the months of the date. the desired output is: which means feb’15 is the first month in the date list and jan’2016 is the 12th month after feb’2015 Answer If your date column is a datetime (if it’s not, cast