Perform geospatial visualization on confirmed corona cases in just 10 line of code

Hello Guys on this tutorial we learn out how we can perform Geo Spatial visualization of confirmed corona virus cases.

Geo-spatial visuals involve correlating different variables with their specific geographical locations and are usually in form of maps.

Throughout this tutorial we will learn out how to automatically generate those Visuals with confirmed corona virus cases as our main variable.

https://kalebujordan.com/geospatial-visualization-corona-cases-python/

#python #visualization #data-science #folium

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Perform geospatial visualization on confirmed corona cases in just 10 line of code

Perform geospatial visualization on confirmed corona cases in just 10 line of code

Hello Guys on this tutorial we learn out how we can perform Geo Spatial visualization of confirmed corona virus cases.

Geo-spatial visuals involve correlating different variables with their specific geographical locations and are usually in form of maps.

Throughout this tutorial we will learn out how to automatically generate those Visuals with confirmed corona virus cases as our main variable.

https://kalebujordan.com/geospatial-visualization-corona-cases-python/

#python #visualization #data-science #folium

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

COMO USAR e trabalhar com Code Review no Visual Studio Code

Não é todo programador que gosta de compartilhar o seu trabalho ou até mesmo receber feedbacks de como o seu código foi escrito, mas o Code Review é cada vez mais comum em empresas do mundo todo.

Conheça uma extensão para Visual Studio Code e comece a trabalhar com Code Review em seu próximo projeto. Essa é a sua chance de saber COMO USAR e trabalhar com Code Review no Visual Studio Code.

#visual studio code #code review #visual studio #code

Justyn  Ortiz

Justyn Ortiz

1597777200

Backtest and optimize your trading strategies with only 3 lines of code

fastquant

fastquant — Backtest and optimize your trading strategies with only 3 lines of code!

Features

  1. Easily access historical stock data
  2. Backtest and optimize trading strategies with only 3 lines of code

* - Both Yahoo Finance and Philippine stock data data are accessible straight from fastquant

Check out our blog posts in the fastquant website and this intro article on Medium!

Installation

Python

pip install fastquant

R

R support is pending development and lagging in features, but you may install the R package by typing the following:

## To install the stable version: 
install.packages("fastquant")

## To install the development version: 
## install.packages("remotes")
remotes::install_github("enzoampil/fastquant", subdir = "R")

Get stock data

All symbols from Yahoo Finance and Philippine Stock Exchange (PSE) are accessible via get_stock_data.

Python

from fastquant import get_stock_data
df = get_stock_data("JFC", "2018-01-01", "2019-01-01")
print(df.head())

##           dt  close
##   2019-01-01  293.0
##   2019-01-02  292.0
##   2019-01-03  309.0
##   2019-01-06  323.0
##   2019-01-07  321.0

R

library(fastquant)

get_stock_data("JFC", "2018-01-01", "2018-02-01")

#> ## A tibble: 22 x 7
#>    symbol dt         name     currency close percent_change  volume
#>    <chr>  <date>     <chr>    <chr>    <dbl>          <dbl>   <dbl>
#>  1 JFC    2018-01-03 Jollibee PHP       255\.             NA  745780
#>  2 JFC    2018-01-04 Jollibee PHP       255              NA  617010
#>  3 JFC    2018-01-05 Jollibee PHP       255              NA  946040
#>  4 JFC    2018-01-08 Jollibee PHP       256              NA  840630
#> ...

Get crypto data

The data is pulled from Binance, and all the available tickers are found here.

Python

from fastquant import get_crypto_data
crypto = get_crypto_data("BTC/USDT", "2018-12-01", "2019-12-31")
crypto.head()

##             open    high     low     close    volume
## dt                                                          
## 2018-12-01  4041.27  4299.99  3963.01  4190.02  44840.073481
## 2018-12-02  4190.98  4312.99  4103.04  4161.01  38912.154790
## 2018-12-03  4160.55  4179.00  3827.00  3884.01  49094.369163
## 2018-12-04  3884.76  4085.00  3781.00  3951.64  48489.551613
## 2018-12-05  3950.98  3970.00  3745.00  3769.84  44004.799448

#data visualization #visual studio code #visual studio #code

Samanta  Moore

Samanta Moore

1621137960

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review