Is there an easy equivalent way to convert all columns in a dask df(converted from a pandas df) using a dictionary. I have a dictionary as follows: and would like to convert the pandas|dask df dtypes all at once to the suggested dtypes in the dictionary. Answer Not sure if I understand the question correctly, but the conversion of dtypes
How do web scrape more underlying data from a websites map location?
Currently, I have successfully used python to scrape data from a competitor’s website to find out store information. The website has a map where you can enter a zip code and it will tell you all the stores in the area of a my current location. The website sends a GET request to pull store data by using this link:
reset form to initial data in invalid_form and display error in Django
I have a profile form that shows email, user name and first name. user only allowed to change first name field and the others are read only, if user change HTML value in email and username then submit it, it returns error but fill the fields with invalid value entered. I tried create a new instance of form and render
How to perform Cumulative sum based on groupby() output in Python
Below is the data in which I am facing the issue: id electronics date 101 Mobile 2022-05-30 101 Laptop 2022-05-30 101 Laptop 2022-05-30 101 Laptop 2022-05-30 101 TV 2022-05-30 102 Mobile 2022-05-31 102 Laptop 2022-05-31 I need to find Cumulative sum of ID(Count) based on Month i.e. if the month end then it should start with 0. I have used
Pandas dataframe: select list items in a column, then transform string on the items
One of the columns I’m importing into my dataframe is structured as a list. I need to pick out certain values from said list, transform the value and add it to one of two new columns in the dataframe. Before: Name Listed_Items Tom [“dr_md_coca_cola”, “dr_od_water”, “potatoes”, “grass”, “ot_other_stuff”] Steve [“dr_od_orange_juice”, “potatoes”, “grass”, “ot_other_stuff”, “dr_md_pepsi”] Phil [“dr_md_dr_pepper”, “potatoes”, “grass”, “dr_od_coffee”,”ot_other_stuff”] From
Aggregating Pandas DF – Losing Data
I’m trying to aggregate a pandas df in a way an excel pivot table would. I have one quantitative variable called “Count”. I would like the same qualitative variables to combine and the “Count” data to sum. However, when I am trying to do this with the below code, I see that I am somehow losing data. Any idea why
Parsing a Pandas Dataframe
I have a dataframe like this; Looking like this in Jupyter notebook output; I want to parse this table so that table name repeats with each field name and column counts remain the same such as the output dataframe will look like; I tried this code from a stackoverfow solution; But it did not work. In the solution; Unlist multiple
Error with a player turn ending loop in the Zombie Dice game
I’m trying to develop a code for the zombie dice game. My problem is when the first player replies that he doesn’t want to continue playing. The game ends and I can’t get the second player to play. Can someone help me? Does anyone know what I’m doing wrong? I’m new as a programmer. If anyone knows how to help
Where does xarrays time.hour start and end?
I am not sure where xarray starts and ends the hour. For example: When I get a value for 1 o’clock, are those values form 00:00-01:00 or from 00:30-01:30 or from 01:00-02:00? In my specific case I have datas form several year token every minute and I need to know what exact timeslice the mean is when its plotted at
Detect strings containing only digits, letters and one or more question marks
I am writing a python regex that matches only string that consists of letters, digits and one or more question marks. For example, regex1: ^[A-Za-z0-9?]+$ returns strings with or without ? I want a regex2 that matches expressions such as ABC123?A, 1AB?CA?, ?2ABCD, ???, 123? but not ABC123, ABC.?1D1, ABC(a)?1d on mysql, I did that and it works: Answer How