toys | Storage for my snippets, toy programs, etc | Learning library
kandi X-RAY | toys Summary
kandi X-RAY | toys Summary
Storage for my snippets, toy programs, etc.
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QUESTION
Say I have a dictionary
...ANSWER
Answered 2022-Apr-16 at 14:52Like that:
QUESTION
Im trying to learn form validation and I cannot figure out what is going on here. I am following a W3Schools tutorial and the validate form function is giving an error but it is not doing that on their example.
I tried copy and pasting the example into my project and just changing the property name and it still gives an error.
...ANSWER
Answered 2022-Mar-12 at 09:52I don't know if this will solve your problem (what was the error?) but the following line:
QUESTION
I have a task where I need to change multiple times data in my data frame. I wrote the answer in Jupyter notebook, using loops and it's take around 2,5min to run.
However, when I rewrite my code to pycharm using modules and definitions it takes around 20min and I do not know where I made a mistake.
Here is explanation of my task and my idea which was written in Jupyter, maybe you will have some ideas how I could write it better.
I have a data frame with weekly qty of sold toys in factory when 0w last week.
...ANSWER
Answered 2022-Mar-11 at 08:49You can use clip:
QUESTION
I have a collection in MongoDB that represents the chores kids needs to do each day and whether they are done. Here is an example of a document.
...ANSWER
Answered 2022-Feb-21 at 15:44You could use the update method with aggregation pipeline to update the chores array. You would need to use the following pipeline to achieve the desired results:
QUESTION
I'm trying to define generic types for domain/aggregate and entities that would enable me to build aggregates that include a number of domains. All in all this would lead to nested value object classes.
In order to simplify API response I would like to provide a toObject() function that would return a somewhat flatten object with strings (or better yet; ValueType).
In the example below I would like order.toObject() to return the type and object as specified in the end.
I would also like to be able to define a recursive/deep generic type for BaseDomain.toObject()
Any ideas? I welcome feedback and thoughts
...ANSWER
Answered 2022-Jan-29 at 01:26It looks FlattenProperties should be a recursive conditional type, where the base cases are if T is a primitive type or if T extends IBaseEntity for some U. If T extends IBaseDomain or IBaseList for some U then you can recurse into U. Or if T is an object type, you can map its properties recursively. Something like this:
QUESTION
I have a single Dataframe and I need to find how many toys with different color are same and how many are changing across years.
For Example: Toy1 color remain intact from 2019 to 2020 but in year 2021 there were two toys one with red and other with green color. Hence there is no change in 2019 to 2020 stating overlap of 1 and new count as 0. However for year 2020 to 2021 overlap count though will remain 1 (due to red color), new count will get the value as 1 (due to addition of green color of toy)
Attaching a sample data, original data has million of records.
Input data -
...ANSWER
Answered 2022-Jan-28 at 15:44A few steps here. First we unstack the data to have a cross table of toy/year vs color, where 1 indicates that that color was in force for that toy/year
QUESTION
I've been at this problem for so long that I'm not even sure where I am with it. I've written the following management command which generates some JSON and saves it using a Django model. Later I have a view which retrieves this JSON and displays it in a URL.
The model is as follows:
...ANSWER
Answered 2022-Jan-18 at 10:51Don't use json.dumps()
If you use it it will add / and other stuff.
You can achieve using dictionary JSONField will handle the serialization like this
QUESTION
I extracted a temporary table by using the following query in mysql select player_id, obj_category from table order by player_id;
the output is as shown, each id is associated with multiple object categories
Current output
player_id obj_category created_time 19855. Electronics 2021-09-21 18:02:17 19855. House. 2021-09-21 18:03:20 19855. Car. 2021-09-21 18:03:54 19855. Toys. 2021-09-21 18:04:17 19855 Sweets. 2021-09-21 18:05:13 19907 Business 2021-09-21 15:02:17 19907 Books. 2021-09-21 15:02:40 19907 House. 2021-09-21 15:04:14 19907 Books 2021-09-21 15:05:34 19908 Toys 2021-09-21 14:04:17 19908 Mobile 2021-09-21 14:07:19 19908 Sports 2021-09-21 14:08:43 19908 Electronics. 2021-09-21 14:02:17In this sample, i have 3 ids,(19855,19907,19908). I want to create a query where I would retrieve only the first 2 obj_category associated by each of the ids. They would be ordered according to the created time of each of the objects per each id
Expected output:
player_id obj_category 19855. Electronics 19855. House. 19907 Business 19907 Books 19908 Toys 19908 MobileI tried multiple codes such as
, however i was not able to reach that output and i couldn't find any guidance to help me.
thank you in advance
...ANSWER
Answered 2021-Dec-14 at 23:19You need to decide what determines which one of the n items related to an ID get selected as "the first two". When you do that, you can try a ROW_NUMBER() function combined with a common table expression (CTE):
QUESTION
Say I have two pandas dataframes (df_a & df_b), where each row represents a toy and features about that toy. Some pretend features:
- Was_Sold (Y/N)
- Color
- Size_Group
- Shape
- Date_Made
Say df_a is relatively small (10s of thousands of rows) and df_b is relatively large (>1 million rows).
Then for every row in df_a, I want to:
- Find all the toys from df_b with the same type as the one from df_a (e.g. the same color group)
- The df_b toys must also be made before the given df_a toy
- Then find the ratio of those sold (So count sold / count all matched)
What is the most efficient means to make those per-row calculations above?
The best I've came up with so far is something like the below. (Note code might have an error or two as I'm rough typing from a different use case)
...ANSWER
Answered 2021-Dec-15 at 02:20First of all, I think your computation would be much more efficient using relational database and SQL query. Indeed, the filters can be done by indexing columns, performing a database join, some advance filtering and count the result. An optimized relational database can generate an efficient algorithm based on a simple SQL query (hash-based row grouping, binary search, fast intersection of sets, etc.). Pandas is sadly not very good to perform efficiently advanced requests like this. It is also very slow to iterate over pandas dataframe although I am not sure this can be alleviated in this case using only pandas. Hopefully you can use some Numpy and Python tricks and (partially) implement what fast relational database engines would do.
Additionally, pure-Python object types are slow, especially (unicode) strings. Thus, **converting column types to efficient ones in a first place can save a lot of time (and memory). For example, there is no need for the Was_Sold column to contains "Y"/"N" string objects: a boolean can just be used in that case. Thus let us convert that:
QUESTION
I wanted to output the following based on regex expressions. I wanted to have some kind of reusability based on filename formats
Filename formats
...ANSWER
Answered 2021-Nov-23 at 07:35If I'm understanding your question correctly, you want to parse/extract from a string, i.e. "toy-blue_wide.jpg", a productCode everything before the "_" character, i.e. "toy-blue", and a slotCode, everything after the "_" excluding the file extension. You can use a single REGEX and capture these two parts into their respective groups.
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