Saloua Litayem slitayem

## postgres_sheet_cheat.md

      
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                slitayem
                / postgres_sheet_cheat.md
            
            
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              August 7, 2023 07:55
            
              
                Postgres Cheat Sheet
              
          
    Note: the commands were tested on Postgres 9.5.4
PSQL

Connect with the user USER_NAME
psql -h REMOTE_SERVER_ADDRESS -U USER_NAME

  
## classification_report_as_df.py
from sklearn import metrics

def get_classification_as_df(y_test: list, y_pred: list, sort_by: list =[])-> pd.DataFrame:
    ''' Get the classification report as a DataFrame'''
    report = metrics.classification_report(y_test, y_pred, output_dict=True)
    df_classification_report = pd.DataFrame(report).transpose()
    df_classification_report = df_classification_report.sort_values(
        by=sort_by, ascending=False)
    return df_classification_report

## cka.md

      
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                slitayem
                / cka.md
            
            
              Created
              December 25, 2021 10:32
                — forked from tuannvm/cka.md
            
              
                #Helm #Kubernetes #cheatsheet, happy helming!
              
          
    Kubernetes cheatsheet


Kubernetes cheatsheet

Getting Started
Sample yaml
Workflow
Physical components

Master
Node


Everything is an object - persistent entities


## bash_strict_mode.md

      
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                slitayem
                / bash_strict_mode.md
            
            
              Created
              December 25, 2021 10:30
                — forked from mohanpedala/bash_strict_mode.md
            
              
                set -e, -u, -o, -x pipefail explanation
              
          
    Table of Contents


set -e, -u, -x, -o pipefail
set -e
set -x
set -u
set -o pipefail
Setting IFS
Original Reference

set -e, -u, -x, -o pipefail


## spark2.2_hadoop2.7.4.md

      
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                slitayem
                / spark2.2_hadoop2.7.4.md
            
            
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              January 30, 2021 07:36
            
              
                Install Spark 2.2 and Hadoop 2.7.4 with Jupyter and zeppelin on macOS Sierra
              
          
Install Homebrew if you don't have it yet

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
The script will explain what changes it will make and prompt you before the installation begins. Once you’ve installed Homebrew, insert the Homebrew directory at the top of your PATH environment variable. You can do this by adding the following line at the bottom of your ~/.bash_profile file
export PATH=/usr/local/bin:/usr/local/sbin:$PATH

Install Python 3:


## Spark Dataframe Cheat Sheet.py
# A simple cheat sheet of Spark Dataframe syntax
# Current for Spark 1.6.1

# import statements
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *

#creating dataframes
df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

## models.py
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Integer, Column
from postgresql_json import JSON
Base = declarative_base()

class Document(Base):
    id = Column(Integer(), primary_key=True)
    data = Column(JSON)

#do whatever other work

## s3_buffered_writer.py
import contextlib
import os
import tempfile


half_lambda_memory = 10**6 * (
    int(os.getenv('AWS_LAMBDA_FUNCITON_MEMORY_SIZE', '0')) / 2)


@contextlib.contextmanager

## Simple-S3Bucket-SNS
---
AWSTemplateFormatVersion: '2010-09-09'

Description: Simple S3 Bucket with SNS Trigger

Parameters:

  BucketName:
    Type: String
    Description: The name of the S3 Bucket to create

## get_docker_tf_version.sh
#!/bin/bash
set -e

docker run -dit --rm --name test tensorflow/tensorflow:latest-gpu-py3-jupyter

TF_VERSION=`docker exec -it test bash -c "pip freeze | grep tensorflow-gpu | cut -d'=' -f3"`
TF_VERSION=`echo $TF_VERSION | tr -d '\r'`

docker stop test
	from sklearn import metrics

	def get_classification_as_df(y_test: list, y_pred: list, sort_by: list =[])-> pd.DataFrame:
	''' Get the classification report as a DataFrame'''
	report = metrics.classification_report(y_test, y_pred, output_dict=True)
	df_classification_report = pd.DataFrame(report).transpose()
	df_classification_report = df_classification_report.sort_values(
	by=sort_by, ascending=False)
	return df_classification_report
	# A simple cheat sheet of Spark Dataframe syntax
	# Current for Spark 1.6.1

	# import statements
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	from pyspark.sql.functions import *

	#creating dataframes
	df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data
	from sqlalchemy.ext.declarative import declarative_base
	from sqlalchemy import Integer, Column
	from postgresql_json import JSON
	Base = declarative_base()

	class Document(Base):
	id = Column(Integer(), primary_key=True)
	data = Column(JSON)

	#do whatever other work
	import contextlib
	import os
	import tempfile


	half_lambda_memory = 10*6 (
	int(os.getenv('AWS_LAMBDA_FUNCITON_MEMORY_SIZE', '0')) / 2)


	@contextlib.contextmanager
	---
	AWSTemplateFormatVersion: '2010-09-09'

	Description: Simple S3 Bucket with SNS Trigger

	Parameters:

	BucketName:
	Type: String
	Description: The name of the S3 Bucket to create
	#!/bin/bash
	set -e

	docker run -dit --rm --name test tensorflow/tensorflow:latest-gpu-py3-jupyter

	TF_VERSION=`docker exec -it test bash -c "pip freeze \| grep tensorflow-gpu \| cut -d'=' -f3"`
	TF_VERSION=`echo $TF_VERSION \| tr -d '\r'`

	docker stop test