Millennial Suicides — an Analysis about Change

One profound claim and observations by the media is, that the rate of suicides for younger people in the UK have risen from the 1980s to the 2000s. You might find it generally on the news , in publications or it is just an accepted truth by the population. But how can you make this measurable?

Making an assumption tangible

In order to make this claim testable we look for data and find an overview of the suicide rates, specifically England and Wales, at the [Office for National Statistics (UK)]

Generally, one type of essential questions to ask — in everyday life, business or academia — is when changes have occurred. You are under the assumption that something has fundamentally changed over time. In order to prove that you have to quantify it. So you get some data on the subject-matter and build a model to display points at which changes in values have occurred as well as their magnitude. We are going to look at exactly how to do that.

Millennial Suicides Specifically

With our data at hand, we take the average for the age-group of Millennials. That entails ages in the range of 25 to 35, for the sake of simplicity. Given our dataset, we can visualize the average suicide count for Millennials since the 1980s like this:

suicide_millenials_df = suicide_data[suicide_data.Age.isin(range(23,35))].groupby(["Year"]).mean().reset_index()
# round values to closest integer
suicide_millenials_df["Suicides"] = suicide_millenials_df.Suicides.round()
suicide_millenials_df.head()

What the head of your dataframe should look like.

Fig. 1 — The average suicides (per 100.000 people) in the age group 25 to 35 for England and Wales from 1981 to 2017.

From this data we can clearly see a steady increase over time until the very early 2000s followed by a decline until the 2010s. The inquisitive side of us wants to know when exactly that happened so that we can go out and look for the root causes and the underlying issues associated with those impacting trends. Let’s build a model to find out exactly in which years to look for that substantial change. To look for a specific change-point we will use the Pyro PPL as a probabilistic framework.

#millenials #pyro #tutorial #timeseries #data-science #data analysis

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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:

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., 7, Bob, 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

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

…

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Millennial Suicides — an Analysis about Change

One profound claim and observations by the media is, that the rate of suicides for younger people in the UK have risen from the 1980s to the 2000s. You might find it generally on the news , in publications or it is just an accepted truth by the population. But how can you make this measurable?

Making an assumption tangible

In order to make this claim testable we look for data and find an overview of the suicide rates, specifically England and Wales, at the [Office for National Statistics (UK)]

Generally, one type of essential questions to ask — in everyday life, business or academia — is when changes have occurred. You are under the assumption that something has fundamentally changed over time. In order to prove that you have to quantify it. So you get some data on the subject-matter and build a model to display points at which changes in values have occurred as well as their magnitude. We are going to look at exactly how to do that.

Millennial Suicides Specifically

With our data at hand, we take the average for the age-group of Millennials. That entails ages in the range of 25 to 35, for the sake of simplicity. Given our dataset, we can visualize the average suicide count for Millennials since the 1980s like this:

suicide_millenials_df = suicide_data[suicide_data.Age.isin(range(23,35))].groupby(["Year"]).mean().reset_index()
# round values to closest integer
suicide_millenials_df["Suicides"] = suicide_millenials_df.Suicides.round()
suicide_millenials_df.head()

What the head of your dataframe should look like.

Fig. 1 — The average suicides (per 100.000 people) in the age group 25 to 35 for England and Wales from 1981 to 2017.

From this data we can clearly see a steady increase over time until the very early 2000s followed by a decline until the 2010s. The inquisitive side of us wants to know when exactly that happened so that we can go out and look for the root causes and the underlying issues associated with those impacting trends. Let’s build a model to find out exactly in which years to look for that substantial change. To look for a specific change-point we will use the Pyro PPL as a probabilistic framework.

#millenials #pyro #tutorial #timeseries #data-science #data analysis

Performing a Bayesian Analysis by Hand

Introduction

Bayesian analysis offers the possibility to get more insights from your data compared to the pure frequentist approach. In this post, I will walk you through a real life example of how a Bayesian analysis can be performed. I will demonstrate what may go wrong when choosing a wrong prior and we will see how we can summarize our results. For you to follow this post, I assume you are familiar with the foundations of Bayesian statistics and with Bayes’ theorem.

Scenario

As an example analysis, we will discuss a real life problem from a physics lab. No worries, you don’t need any physics knowledge for that. We want to determine the efficiency of a particle detector. A particle detector is a sensor that may produce a measurable signal when certain particles traverse it. The efficiency of the detector we want to evaluate is the chance that the detector actually measures the traversing particle. In order to measure this, we put the detector that we want to evaluate in between two other sensors in a sandwich-like structure. If we measure a signal in the top and bottom sensors we know that a particle should have also traversed the detector in the middle. A picture of the experimental setup is shown below.

We want to measure the efficiency of a particle detector (device under test). Two different sensors (triggers) are placed on top and below the detector in order to detect particles traversing the setup (in this case muons µ).

For the measurement, we count the number of traversing particles N in a certain time (as reported by the top and bottom sensors) as well as the number of signals measured in our detector r. For this example, we assume N=100 and r=98.

#confidence-interval #data-analysis #physics #bayesian-analysis #prior #data analysis