So yesterday i was having issues trying to define all column via data frame’s to_sql method. For the most part this works just fine.

I try to filter a dataframe using the following expression: I am seeking the times “col” changes to “val”:

I have the following data-frame (read from a csv file):

I have the following data-frame (read from a csv file):

I am trying to automate sth but struggling with it. It is actually my first code in Python and what I would like to order this data frame by Hotel Id (which is already done) and then create a column that says:

I have a dataframe with two columns, the first one has a date and time (type: datetime64[ns]) and the second column has the timezone that it is in. I cannot figure out how to convert each of the times to UTC based on their individual timezone.

I am looking for a solution to read previous two rows where C_1=104 and next two rows where C_1=105 includes these 2 rows as well. Below are the different dataframe inputs.
The challange I am facing is-

So i have a data frame with row labels as months and the columns are amounts in float format, i need to sum and multiply the columns in such a way, in which the numbers of rows when the code takes to calculate the sum changes are according to the user, which means the user should be able to take 12 months values 24 etc. The summation of the columns should be divided by the previous column, so the division should be Current_sum(Column) / Previous_sum(Column). The sum should be in such a way in which the code should exclude the last row of the DataFrame when it sums to 12 or 24 values according to user.

I’m trying to access specific info from the data I received after calling an API, but rather than being ready available, it is instead given as a link to another API. How do I call specifically what I need from this secondary API?

I tried to apply my newly acquired pandas skills to a typical ANOVA calculation on large dataset with 5, partially nested indices. The corresponding dataframe “dSchema” looks like this: