Romi romicofre

## init_gists.sh
#!/bin/bash

# clone all gists of the accou

# with url id
#for name in $( gh gist list | awk '{print $2}'); do gh gist clone $name; done;


# with description folder
#gh api \

## aws_create_user_by_list_terraform.tf
# Variables

variable "region"{
  type = string
}

variable "email_list"{
  type    = list(string)
}

## var_class.scala
println(1.getClass)
println("Hello").getClass
println(println("Hello").getClass)

## Carga_datos_bigquery_Colab.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                romicofre
                / Carga_datos_bigquery_Colab.ipynb
            
            
              Last active
              February 1, 2021 01:24
            
              
                Cargar datos desde Google Colab a Bigquery
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## add_partition_expiration.sql
 ALTER TABLE `dataset.table`
 SET OPTIONS (
   partition_expiration_days=30
 );

## firestore_to_pandas_dataframe.py
import pandas as pd
from google.cloud import firestore

db = firestore.Client()
users = list(db.collection(u'users').stream())

users_dict = list(map(lambda x: x.to_dict(), users))
df = pd.DataFrame(users_dict)

## memory_usage.py
def memory_usage():
    """

    :return: memory usage in MB
    """
    mem = psutil.Process(os.getpid()).memory_info().rss
    return mem / 1024 ** 2

## parallel_with_pool.py
# Function to apply a function over multiple cores
@print_timing
def parallel_apply(apply_func, groups, nb_cores):
    with Pool(nb_cores) as p:
        results = p.map(apply_func, groups)
    return pd.concat(results)

# Parallel apply using 1 core
parallel_apply(take_mean_age, athlete_events.groupby('Year'), 1)

## date_convertion.py
fecha = "20200413191418"
objeto_fecha = datetime.strptime(fecha, '%Y%m%d%H%M%S').strftime('%Y-%m-%dT%H:%M:%S')
print(objeto_fecha)
print(type(objeto_fecha))

#result:
# 2020-04-13T19:14:18
# <class 'str'>

## add_environment_var.py
os.environ.setdefault("DEPLOY", "DEV")
	#!/bin/bash

	# clone all gists of the accou

	# with url id
	#for name in $( gh gist list \| awk '{print $2}'); do gh gist clone $name; done;


	# with description folder
	#gh api \
	# Variables

	variable "region"{
	type = string
	}

	variable "email_list"{
	type = list(string)
	}
	println(1.getClass)
	println("Hello").getClass
	println(println("Hello").getClass)
	ALTER TABLE `dataset.table`
	SET OPTIONS (
	partition_expiration_days=30
	);
	import pandas as pd
	from google.cloud import firestore

	db = firestore.Client()
	users = list(db.collection(u'users').stream())

	users_dict = list(map(lambda x: x.to_dict(), users))
	df = pd.DataFrame(users_dict)
	def memory_usage():
	"""

	:return: memory usage in MB
	"""
	mem = psutil.Process(os.getpid()).memory_info().rss
	return mem / 1024 ** 2
	# Function to apply a function over multiple cores
	@print_timing
	def parallel_apply(apply_func, groups, nb_cores):
	with Pool(nb_cores) as p:
	results = p.map(apply_func, groups)
	return pd.concat(results)

	# Parallel apply using 1 core
	parallel_apply(take_mean_age, athlete_events.groupby('Year'), 1)
	fecha = "20200413191418"
	objeto_fecha = datetime.strptime(fecha, '%Y%m%d%H%M%S').strftime('%Y-%m-%dT%H:%M:%S')
	print(objeto_fecha)
	print(type(objeto_fecha))

	#result:
	# 2020-04-13T19:14:18
	# <class 'str'>