Abdou Seck AbdouSeck

## too_many_open_files.py
import multiprocessing as mp


def RunPrice(items, price):
    print("There is %s items, price is: %s" % (items, price))

def GetTargetItemsAndPrice(cursor):
    res = cursor.execute("SELECT DISTINCT items, price FROM SELLS")
    results = dict()
    with mp.Pool() as pool:

## .gcloudignore
env.yaml

## playground.rs
fn main() {
    let mut arr = [12, 8, 10, 3, 5, 10, 45];
    if let Some(elem) = arr.get(10) {
        println!("Element requested: {}.", elem);
    } else {
        println!(
            "Element at index {} could not be found in an array of size {}.",
            10,
            arr.len()
        );

## expand_ballrooms.R
# There is really not a dynamic way of doing what you want.
# You will need to expand the dash and generate a sequence from it.
# I wrote an example function that converts something like 2-4 into seq(2, 4)

expand.dash <- function(dashed) {
    limits <- unlist(strsplit(dashed, '-'))
    seq(limits[1], limits[2])
}

# We can use the above function inside another function which expands ballrooms

## keybase.md

      
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                AbdouSeck
                / keybase.md
            
            
              Created
              October 21, 2018 00:14
            
          
    Keybase proof

I hereby claim:

I am abdouseck on github.
I am aseck (https://keybase.io/aseck) on keybase.
I have a public key ASDjVkM0h3zHkVUrkbaofaafZujw0FeGtxaYSAKgS4YnRQo

To claim this, I am signing this object:

  
## weird_string_to_datetime.py
# The arbitrary and opinionated conversion of strings to datetime in Series comparisons

# When comparing two values where the left side values is a pandas datetime object,
# Pandas casts the left side values to pd.DatetimeIndex
# Once the casting is successfully, Pandas then calls the __eq__ method on the result of the cast operation.
# In this case, you want to take a look at pd.DatetimeIndex.__eq__ to figure out how the comparison is being handled

# The following is how pd.DatetimeIndex.__eq__ is defined (it's a decorated function, so the function name doesn't come up):

def wrapper(self, other):

## subparsers.py
#!/usr/bin/env python3
"""
Script to test or run commands on given servers.
./this_script.py test  # To test all servers
./this_script.py run --id 127.0.0.1 --command "echo hello world"
"""
from argparse import ArgumentParser, RawDescriptionHelpFormatter as RDHF


def test_servers(servers):

## manipulate_weird_file.R
# persons.txt
# Person 1
# Class        Grade
# Math         A
# Science      C
# English      A
# Person 2
# Class       Grade
# Math        D
# English     A

## group_until.R
# https://stackoverflow.com/q/52613778/3135417

# Function to keep track of diff changes along the V1 column
get_ids <- function(x) {
  vrle <- rle(cummin(x))$lengths
  rep(1:length(vrle), vrle)
}

# Thesis data
thesis <- data.frame(V1=c(8628, 8626, 8624, 8622, 8620, 8618,

## flask_unittesting.py
# application.py

from flask import Flask, render_template_string
from flask_sqlalchemy import SQLAlchemy


db = SQLAlchemy()

class User(db.Model):
    """
	import multiprocessing as mp


	def RunPrice(items, price):
	print("There is %s items, price is: %s" % (items, price))

	def GetTargetItemsAndPrice(cursor):
	res = cursor.execute("SELECT DISTINCT items, price FROM SELLS")
	results = dict()
	with mp.Pool() as pool:
	fn main() {
	let mut arr = [12, 8, 10, 3, 5, 10, 45];
	if let Some(elem) = arr.get(10) {
	println!("Element requested: {}.", elem);
	} else {
	println!(
	"Element at index {} could not be found in an array of size {}.",
	10,
	arr.len()
	);
	# There is really not a dynamic way of doing what you want.
	# You will need to expand the dash and generate a sequence from it.
	# I wrote an example function that converts something like 2-4 into seq(2, 4)

	expand.dash <- function(dashed) {
	limits <- unlist(strsplit(dashed, '-'))
	seq(limits[1], limits[2])
	}

	# We can use the above function inside another function which expands ballrooms
	# The arbitrary and opinionated conversion of strings to datetime in Series comparisons

	# When comparing two values where the left side values is a pandas datetime object,
	# Pandas casts the left side values to pd.DatetimeIndex
	# Once the casting is successfully, Pandas then calls the __eq__ method on the result of the cast operation.
	# In this case, you want to take a look at pd.DatetimeIndex.__eq__ to figure out how the comparison is being handled

	# The following is how pd.DatetimeIndex.__eq__ is defined (it's a decorated function, so the function name doesn't come up):

	def wrapper(self, other):
	#!/usr/bin/env python3
	"""
	Script to test or run commands on given servers.
	./this_script.py test # To test all servers
	./this_script.py run --id 127.0.0.1 --command "echo hello world"
	"""
	from argparse import ArgumentParser, RawDescriptionHelpFormatter as RDHF


	def test_servers(servers):
	# persons.txt
	# Person 1
	# Class Grade
	# Math A
	# Science C
	# English A
	# Person 2
	# Class Grade
	# Math D
	# English A
	# https://stackoverflow.com/q/52613778/3135417

	# Function to keep track of diff changes along the V1 column
	get_ids <- function(x) {
	vrle <- rle(cummin(x))$lengths
	rep(1:length(vrle), vrle)
	}

	# Thesis data
	thesis <- data.frame(V1=c(8628, 8626, 8624, 8622, 8620, 8618,
	# application.py

	from flask import Flask, render_template_string
	from flask_sqlalchemy import SQLAlchemy


	db = SQLAlchemy()

	class User(db.Model):
	"""