mteb/BrightProStackoverflowRetrieval huggingface dataset:
Is there a melt command in Snowflake?

Is there a Snowflake command that will transform a table like this:
```
a,b,c
1,10,0.1
2,11,0.12
3,12,0.13
```
to a table like this:
```
key,value
a,1
a,2
a,3
b,10
b,11
b,13
c,0.1
c,0.12
c,0.13
```
?

This operation is often called melt in other tabular systems, but the basic idea is to convert the table into a list of key value pairs.

There is an UNPIVOT in SnowSQL, but as I understand it UNPIVOT requires to manually specify every single column. This doesn't seem practical for a large number of columns.
Python split a column value into multiple columns and keep remaining column same

The task is to split the values of column A to into different columns and have values of corresponding column2 values and need column3 to contain the corresponding value of the group.

```
Column1 Column2 Column3
Group1  Value1  V1
Group1  Value2  V2
Group1  Value3  V3
Group1  Value4  V4
Group2  Value1  x1
Group2  Value2  x2
Group2  Value3  x3
Group2  Value4  x4
Group3  Value1  y1
Group3  Value2  y2
```

to a table like this:

```
Group1 Group2 Group3 Column3.Group1 Column3.Group2 Column3.Group3
Value1 Value1 Value1  v1             x1                y1
Value2 Value2 Value2  v2             x2                y1
Value3 Value3 NaN     v3             x3                NaN
Value4 Value4 NaN     v4             x4                NaN
```

Any way to achieve this output in python?
Python type hints: what should I use for a variable that can be any iterable sequence?

```
def mysum(x)->int:
    s = 0
    for i in x:
        s += i
    return s
```

The argument x can be list[int] or set[int], it can also be d.keys() where d is a dict, it can be range(10), as well as possibly many other sequences, where the item is of type int. What is the correct type-hint for x?
I have below scenario where list str columns need to be merged with the dataframe.

```
columns = ["server", "ip"]
 
dataframes = [
        df1,
        df2,
        df3,
        df4,
]

df_res = pd.merge(dataframes, columns )
```

df1 name , server , df1,df2,df3 and df4 contains column "server" and "ip" and other columns too.

So, i want to merge all the columns with server and ip in the df_res.

But i am getting issue as below:

Can only merge Series or DataFrame objects, a <class 'list'> was passed. Please help.
I have a custom PyTorch model that bottlenecks my application due to how it is currently used.

The application is a web server built in Flask that receives job submissions for the PyTorch model to process. Due to the processing time of each job, I use Celery to handle the computation, where Flask queues the tasks for Celery to execute.

Each job consists of loading the PyTorch model from the disk, moving the model and data to a GPU, and making a prediction on the data submitted. However, loading the model takes around 6 seconds. In many instances, that is a magnitude or two larger than prediction time.

Thus, is it possible to load the model and move it to a GPU on server startup (specifically when the Celery worker starts), avoiding the time needed to load the model and copy it to the GPU every job? Ideally, I'd want to load the model and copy it to every available GPU on server startup, leaving each Celery job to choose an available GPU and copy the data over. Currently, I only have one GPU, so a multi-GPU solution is not a requirement at the moment, but I'm planning ahead.

Further, the memory constraints of the model and data allow for only one job per GPU at a time, so I have a single Celery worker that processes jobs sequentially. This could reduce the complexity of the solution due to avoiding multiple jobs attempting to use the model in shared memory at the same time, so I figured I'd mention it.

How can I deal with it?
I'm trying to write an LLM class as below:

```
from langchain_openai import ChatOpenAI

class LLM:
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name  

class OpenAILLM(LLM,ChatOpenAI):
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name
```
which works fine but when I try to add another variable in __init__ of OpenAILLM class as below

```
from langchain_openai import ChatOpenAI

class LLM:
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name  

class OpenAILLM(LLM,ChatOpenAI):
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name
        self.var = 'var'
```

The object creation for OpenAILLM fails and gives ValueError: "OpenAILLM" object has no field "var"

Can anyone help me how I can add more variables?

Another thing I tried is the get_var function define below works but the set_var function defined below gives same error as above

```
from langchain_openai import ChatOpenAI

class LLM():
    var = 1
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name  

class OpenAILLM(LLM,ChatOpenAI):
    def __init__(self,model_name):
        super().__init__(model_name=model_name)
        self.model_name = model_name
    def get_var(self):
        return self.var
    def set_var(self, var):
        print('self var',self.var)
        self.var = var
        return True
```

The mro of OpenAILLM is as below

```
(__main__.OpenAILLM,
 __main__.LLM,
 langchain_openai.chat_models.base.ChatOpenAI,
 langchain_core.language_models.chat_models.BaseChatModel,
 langchain_core.language_models.base.BaseLanguageModel,
 langchain_core.runnables.base.RunnableSerializable,
 langchain_core.load.serializable.Serializable,
 pydantic.v1.main.BaseModel,
 pydantic.v1.utils.Representation,
 langchain_core.runnables.base.Runnable,
 typing.Generic,
 abc.ABC,
 object)
```

I think this has to do something with pydantic, but I'm not sure. And I don't know how to resolve it. Please help.
Create a New Column Based on the Value of two Columns in Pandas with conditionals

I have a dataframe with two different columns i need to use to calculate a score:
```
id        Pos        Player        GW        VP        Final        Drop        TournamentPoints
0        1        1        Alessio Bianchi        2        7.0        5.0        NaN
1        2        2        Gianluca Bianco        2        7.0        0.0        NaN
2        3        2        Sara Rossi        1        5.0        0.0        NaN
3        4        2        Gabriele Verdi        1        4.5        0.0        NaN
4        5        2        Luca Gialli        1        3.0        0.0        NaN
```

Tournament points is calculated from GW and VP with a formula:
```
df['TournamentPoints'] = ((number_of_players / 10) * (df["VP"] + 1)) + (df['GW'] * x)
```
where number_of_players and X are calculated previously.

However i need another step:
1. add 50 to the row with the highest value in "Final" columns (in this case Alessio Bianchi)
2. if two rows have the same value in "Final" and it's the highest, only the row with the lowest "Pos" must receive the 50 boost
I set up a local simple http server in python on windows.

I set up an HTML file with javascript that uses setInterval() to request a JSON in the local directory every second. (Then updates the webpage with json content.)

Great, this works. Until after 15-60 seconds, the browser stops making requests, based on the server log in the terminal. The response 304 always precedes the break.

How can I avoid this and force the server always serve the json file every second?

Very new to this attempt and had to do tricks to make the browser even make the requests for local jsons or so due to security protocols. In part why the server is necessary, as via file:/// it can't be done.

My thought is change something in the server so if it wants to respond with 304, it will give a 200. I hope if it's always 200, the browser won't pause requests.

My other thought is ensure the json is always changing, hopefully avoiding the idea of it cached.
I have a dataframe with people and the food they like:

```
df_class = pl.DataFrame(
    {
        'people': ['alan', 'bob', 'charlie'],
        'food': [['orange', 'apple'], ['banana', 'cherry'], ['banana', 'grape']]
    }
)
print(df_class)

shape: (3, 2)
┌──────┬─────┐
│ people    ┆ food      │
│ ---           ┆ ---          │
│ str           ┆ list[str]   │
╞═════╪═════════════╡
│ alan     ┆ ["orange", "apple"]   │
│ bob      ┆ ["banana", "cherry"] │
│ charlie ┆ ["banana", "grape"]  │
└─────┴─────────────┘
```

And, I have a data structure with animals and the things they like to eat:

```
animals = [
    ('squirrel', ('acorn', 'almond')),
    ('parrot', ('cracker', 'grape', 'guava')),
    ('dog', ('chicken', 'bone')),
    ('monkey', ('banana', 'plants'))
]
```

I want to add a new column pets in df_class, such that pets is a list of the animals that have at least one food in common with the corresponding person:

```
df_class.with_columns(pets=???)   # <-- not sure what to do here

shape: (3, 3)
┌─────────┬─────────────┬──────────┐
│ people            ┆ fruits                          ┆ pets                 │
│ ---                   ┆ ---                               ┆ ---                     │
│ str                  ┆ list[str]                         ┆ list[str]              │
╞═════════╪══════════════╪══════════╡
│ alan               ┆ ["orange", "apple"]      ┆ []                       │
│ bob                ┆ ["banana", "cherry"]    ┆ ["monkey"]        │
│ charlie           ┆ ["banana", "grape"]      ┆ ["monkey", "parrot"] │
└─────────┴──────────────┴─────────────┘
```

1. I have some flexibility in the data structure for animals, in case for e.g. this is easier to do with some sort of set intersection
2. the order of pets is unimportant
3. pets should contain unique values
4. I'm looking for a single expression that would achieve the desired result, so as to fit within a larger framework of a list of expressions that perform transformations on other columns of my actual dataset

Side note: my title seems kind of clunky and I'm open to suggestions to reword so that it's easier to find by others that might be trying to solve a similar problem.
I´ve got the following DataFrame:

                        value
A   B
111 2024-03-22 00:00:00 1
111 2024-03-22 01:00:00 2
111 2024-03-22 02:00:00 3
222 2024-03-22 00:00:00 4
222 2024-03-22 01:00:00 5
222 2024-03-22 02:00:00 6
Now I want to resample and sum index B to days and would expect the following result:

                        value
A   B
111 2024-03-22 00:00:00 6
222 2024-03-22 00:00:00 15
How can I achieve something like that?

Another Example would be the following:

                        value
A   B
111 2024-03-22 00:00:00 1
111 2024-03-22 01:00:00 2
111 2024-03-22 02:00:00 3
222 2024-03-22 00:00:00 4
222 2024-03-22 01:00:00 5
222 2024-03-22 02:00:00 6
333 2024-03-22 05:00:00 7
Of which I want the following result with resampling by 1h:

                        value
A   B
111 2024-03-22 00:00:00 1
111 2024-03-22 01:00:00 2
111 2024-03-22 02:00:00 3
111 2024-03-22 03:00:00 0
111 2024-03-22 04:00:00 0
111 2024-03-22 05:00:00 0
222 2024-03-22 00:00:00 4
222 2024-03-22 01:00:00 5
222 2024-03-22 02:00:00 6
222 2024-03-22 03:00:00 0
222 2024-03-22 04:00:00 0
222 2024-03-22 05:00:00 0
333 2024-03-22 00:00:00 0
333 2024-03-22 01:00:00 0
333 2024-03-22 02:00:00 0
333 2024-03-22 03:00:00 0
333 2024-03-22 04:00:00 0
333 2024-03-22 05:00:00 7
Pandas Version: 2.0.1

I tried using level on resample but that way I lose Index A.

I have the same issue when I have two timestamps in the index and want one to be resampled to days and the other to hours.

I´ve looked at other answers of related questions here but couldn´t find a way to get them working for me.
My dataframe looks like

```
data = {
    "ReportName": ["Sample cycle", 'Message',  "ID", "m1", "Uncertainty m1", "Message", "Sample cycle", 'Message', "ID", "m0", "Uncertainty m0", "Message", "ID", "m1", "Uncertainty m1", "Message"],
    "Values": [ "1","NO", "II", None, None, "NO", "1", "NO", "ID1", "1.8", "0.43", "NO", "ID2", "1.5", "0.41", "NO"],
}

df = pd.DataFrame(data)
```

I I created a new "ID" column with this function

```
def extract_id(row):
    if row['ReportName'] == 'ID':
        return row['Values']
    return None
```

Now I want fill Na with ID from ReportName == 'Sample cycle' to next 'Sample cycle'.

Desired output
```
        ReportName Values    ID
0     Sample cycle      1  None
1          Message     NO    II
2               ID     II    II
3               m1   None    II
4   Uncertainty m1   None    II
5          Message     NO    II
6     Sample cycle      1  None
7          Message     NO   ID1
8               ID    ID1   ID1
9               m0    1.8   ID1
10  Uncertainty m0   0.43   ID1
11         Message     NO   ID1
12              ID    ID2   ID2
13              m1    1.5   ID2
14  Uncertainty m1   0.41   ID2
15         Message     NO   ID2
```
I am working on a huge denormalized table on a SQL server (10 columns x 130m rows). Take this as data example :

```
import pandas as pd
import numpy as np
data = pd.DataFrame({
    'status' :  ['pending', 'pending','pending', 'canceled','canceled','canceled', 'confirmed', 'confirmed','confirmed'],
    'clientId' : ['A', 'B', 'C', 'A', 'D', 'C', 'A', 'B','C'],
    'partner' :  ['A', np.nan,'C', 'A',np.nan,'C', 'A', np.nan,'C'],
    'product' : ['afiliates', 'pre-paid', 'giftcard','afiliates', 'pre-paid', 'giftcard','afiliates', 'pre-paid', 'giftcard'],
    'brand' : ['brand_1', 'brand_2', 'brand_3','brand_1', 'brand_2', 'brand_3','brand_1', 'brand_3', 'brand_3'],
    'gmv' : [100,100,100,100,100,100,100,100,100]})

data = data.astype({'partner':'category','status':'category','product':'category', 'brand':'category'})
```

As you can see, many of it columns are categories/strings that could be factorize (replaced by a small int identification to another x.1 join).

My question is if there is a easy way to extract another "dataframe" from each category columns and factory factorize the main table, so the bytes transmitted over a single query could be faster! Is there any easy library for it?

I would expect to get this output:

```
    data = pd.DataFrame({
        'status' :  ['1', '1','1', '2','2','2', '3', '3','3'],
        'clientId' : ['1', '2', '3', '1', '4', '3', '1', '2','3'],
        'partner' :  ['A', np.nan,'C', 'A',np.nan,'C', 'A', np.nan,'C'],
        'product' : ['afiliates', 'pre-paid', 'giftcard','afiliates', 'pre-paid', 'giftcard','afiliates', 'pre-paid', 'giftcard'],
        'brand' : ['brand_1', 'brand_2', 'brand_3','brand_1', 'brand_2', 'brand_3','brand_1', 'brand_3', 'brand_3'],
        'gmv' : [100,100,100,100,100,100,100,100,100]})
    
status_df = {1 : 'pending', 2:'canceled', 3:'confirmed'} 
clientid = {1 : 'A', 2:'B', 3:'C', 4:'D'}
```
Is any supertype and also a subtype of all types in TypeScript?

I am teaching a class on TypeScript fundamentals and I am trying to really understand the relationships between basic TS types. In all articles I saw, they put the any type very close to the top of the type hierarchy. Either

+---------+
| unknown |
+---------+
     |
     v
  +-----+
  | any |
  +-----+
     |
     v
all other types
Or maybe

+--------------+
| any, unknown |
+--------------+
     |
     v
all other types
But I do not see it that way. You can assign value of all other types to an unknown variable but you cannot assign unknown value to anything. That puts it on top. But with any, you can assign all values of all types to any and also assign an any value to variables of all types. So it behaves as uknown and never at the same time. From this I would argue, that any stands completely aside of the type hierarchy tree not fitting anywhere in it. I would say any sidesteps the type system completely and in essence means "turn off typechecking"

Am I wrong?
Better option than pandas iterrows

I have following table in pandas. the table contains time and the price of the product.

For analysis purposes, I want to have 2 columns which would contain the next time when the product is more than $100 price change & less than $100 price change.

e.g. if I am at cell 09:19 cell the next price more than $100 would be 14:02 & less than $100 would be 11:39 so 14:02 & 11:39 should come in 09:19 row in respective columns.

Same way against cell 09:56, next price more than $100 would be 14:02 & less than $100 would be 12:18 so these 2 values would come in against the row of 09:56.

```
Table
Time        Price    Up_Time   Down_Time
09:19:00    3252.25     
09:24:00    3259.9      
09:56:00    3199.4      
10:17:00    3222.5      
10:43:00    3191.25     
11:39:00    3143        
12:18:00    2991.7      
13:20:00    3196.35     
13:26:00    3176.1      
13:34:00    3198.85     
13:37:00    3260.75     
14:00:00    3160.85     
14:02:00    3450        
14:19:00    3060.5      
14:30:00    2968.7      
14:31:00    2895.8      
14:52:00    2880.7      
14:53:00    2901.55     
14:55:00    2885.55     
14:57:00    2839.05     
14:58:00    2871.5      
15:00:00    2718.95
```
     
I am using following code, which works but takes 15-20 mins for 1 dataset.

```
for i, row in df.iterrows():
    time_up = np.nan
    time_down = np.nan

    for j in range(i+1, len(df)):
        diff = df.iloc[j]['Price'] - row['Price']
        if diff > 100:
            time_up = df.iloc[j]['Time']
        elif diff < -100:
            time_down = df.iloc[j]['Time']

        if not pd.isna(time_up) or not pd.isna(time_down):
            break

    df.at[i, 'Up_Time'] = time_up
    df.at[i, 'Down_Time'] = time_down
```

Is there any efficient ways to do it?
Trying to interact with a contract using is function selector, how do i get the block hash?

I have the below code in a index.js file, but when i run it i get an error message: The method eth_sendTransaction does not exist/is not available. Please can anyone help?

```
require('dotenv').config();
require('events').EventEmitter.defaultMaxListeners = 0

const { ethers } = require('ethers');

// Provider and contract address
const provider = new ethers.providers.JsonRpcProvider('https://polygon-mumbai.infura.io/v3/7d803b173d114ba8a1bffafff7ff541a');
const wallet = new ethers.Wallet(process.env.KEY, provider)
//const signer = wallet.provider.getSigner(wallet.address);
const contractAddress = '0x57C98f1f2BC34A0054CBc1257fcc9333c1b6730c';

// Function selector
const functionSelector = '0x838ad0ee';


// Call the contract
const send = async () => {
  try {
    const result = await wallet.call({
        to: contractAddress,
        data: functionSelector
    });
    console.log("Result:", result);
} catch (error) {
    console.error("Error:", error);
}
}

send()
```
Selenium Python how to run an XPath error handling exception on loop with multiple def?

is it possible to schedule my code by running it with schedule.every(10).seconds.do(example) or similar? I'm trying to schedule the Error Handling part in my code (XPath) which works with a While loop although because its in a While loop it refuses to run the other def functions, I want the XPath/error handling part to run in a loop with my Selenium window but detect and not interfere with the other def functions. it should just detect the XPath being there or not being there then run an exception if it doesn't detect.. Does anyone have a solution?

```
def example():
    options = Options()
    options.add_experimental_option("detach", True)

    driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()),
                              options=options)

    driver.get("example.com")
    driver.set_window_position(0, 0)
    driver.set_window_size(750, 512)

    while True:
        e = driver.find_elements(By.XPATH,"/html/body/div/div/div/div/div[2]/div/div/div[2]/div[2]/div[1]/span/button/span")
        if not e:
            print("Element not found")
            pyautogui.moveTo(89, 56)
            time.sleep(1)
            pyautogui.click()

        time.sleep(10)


def example2():
    options = Options()
    options.add_experimental_option("detach", True)

    driver = webdriver.Firefox(service=FirefoxService(GeckoDriverManager().install()))

    driver.get("example.com")
    driver.set_window_position(750, 0)
    driver.set_window_size(750, 512)

    while True:
        e = driver.find_elements(By.XPATH,"/html/body/div/div/div/div/div[2]/div/div/div[2]/div[2]/div[1]/span/button/span")
        if not e:
            print("Element not found")
            pyautogui.moveTo(850, 57)
            time.sleep(1)
            pyautogui.click()

schedule.every().day.at("22:59").do(example)
schedule.every().day.at("22:59").do(example2)

while True:
    schedule.run_pending()
    time.sleep(1)
```
How to create an image from a string in python

I'm currently having trouble creating an image from a binary string of data in my Python program. I receive the binary data via a socket but when I try the methods I read about in Imaging Library Handbook like this:

···
buff = StringIO.StringIO() #buffer where image is stored
#Then I concatenate data by doing a 
buff.write(data) #the data from the socket
im = Image.open(buff)
···
I get an exception to the effect of "image type not recognized". I know that I am receiving the data correctly because if I write the image to a file and then open a file it works:

···
buff = StringIO.StringIO() #buffer where image is stored
buff.write(data) #data is from the socket
output = open("tmp.jpg", 'wb')
output.write(buff)
output.close()
im = Image.open("tmp.jpg")
im.show()
···
I figure I am probably doing something wrong in using the StringIO class but I'm not sure
Retrieve bounded time periods as a step function where beginning of period returns 1, end of period returns 0

I have a table in Microsoft SQL Server called Downtime that looks like this (I have omitted some irrelevant columns and many entries):

```
ID        StartTime                                        EndTime
5        2024-03-27 09:07:20.653        2024-03-27 09:09:07.690
17        2024-03-27 09:59:50.557        2024-03-27 10:59:50.137
24        2024-03-27 11:04:07.497        2024-03-27 11:07:02.657
```

I need to write a query that turns the above data into this format:

```
t_stamp                                   CurrentlyDown
2024-03-27 09:07:20.653        1
2024-03-27 09:09:07.690        0
2024-03-27 09:59:50.557        1
2024-03-27 10:59:50.137        0
2024-03-27 11:04:07.497        1
2024-03-27 11:07:02.657        0
```

In words, this query should split each original entry into two entries (one t_stamp for StartTime and one t_stamp for EndTime) and return a value (CurrentlyDown) of 1 (if t_stamp is from the StartTime column) or 0 (if t_stamp is from the EndTime column).

I can think to try two things:

A self join around the ID field with a CASE statement checking the timestamp fields
Two CTE's (one focused on grabbing StartTimes and the other focused on EndTimes) with a final query to join these two CTE's together around the ID column. Maybe just one CTE is needed here?

I am concerned with performance so I want to do this as efficiently as possible. I am far from a SQL expert, so I don't really know which path is best to take (if either).
Why does JavaScript return different results for RegExp test() method with empty object?

I recently came across a code snippet (a joke) where the test() method of JavaScript's RegExp object was used with an empty object as an argument. Here's the code:

```
console.log(new RegExp({}).test('mom')); // true
console.log(new RegExp({}).test('dad')); // false
```

Can someone explain why is it happens?