A Summary of my August Articles: from Theory to Practice
August, the month of holidays: sun, sea, mountains and more. During the past month, I went holidays and I relaxed too, as many people did. Nevertheless, I continued to study (a bit…!) and continued my writing activity.
Firstly, I started a new blog, called Tips & Tricks for Data Science, where you can find my old articles, already published on Medium, without being a Medium subscriber. You can also follow the related Facebook Page.
Then, I published the 5 articles, covering the following topics:
- Python basics
- Data Science Theory
- Data Manipulation
1. Python Basics
1.1 How to Convert Your Python Project into a Package Installable through pip
In this tutorial, I describe how to convert a simple Python project into a Package, available in the Python Package Index.
The procedure is quite simple: it is sufficient to organise the project folder into a given structure and run some simple Python commands to build the distribution.
1.2 Three Tricks on Python Functions that You Should Know
This tutorial covers the following three advanced programming tricks on Python functions:
- nested functions — A nested function is a function within another function. Due to scope rules, usually a nested function cannot be invoked outside the container function.
- variable parameters — Python provides a mechanism, which permits to invoke a function with a potential unlimited number of parameters.
- lambda functions — A lambda function is an inline function, which can be used to run simple and repetitive operations, such well-known math operations.
Three Tricks on Python Functions that You Should Know
A quick overview of some tips which may improve your programming skills: nested functions, variable parameters and…
2. Data Science Theory
2.1 A Brief Introduction to the concept of Data
This article introduces the basic concept of data, which include quantitative and qualitative data. Quantitative analysis focuses on numbers, while qualitative analysis focuses on categories. A great effort has been done in both types of analysis, but the research is still open.
A Brief Introduction to the concept of Data
Every aspiring data scientist must know the concept of data and the kind of analysis they can run. This article…
2.2 Hypothesis Testing Made Easy through the easy-ht Python Package
In this article I illustrate a Python package, called
easy-ht, which permits to perform some statistical tests, such as correlation, normality, randomness, means and so on, without caring about the specific test to be used.
The package will configure itself automatically, according to the provided data.
In details, I describe the
easy-ht Python package, through the following steps:
- overview of the package
- example of usage in case of one single input dataset
- example of usage in case of two input datasets
Hypothesis Testing Made Easy through the easy-ht Python Package
Which Hypothesis Test should you use? Pearson or Spearman? T-Test or Z-Test? Chi Square? No problem with easy-ht.
3. Data Manipulation
3.1 How to Manipulate a Pandas Dataframe in SQL
In this article, I illustrate some tricks to manipulate a Python Pandas Dataframe, using SQL queries. In details, I cover the following topics:
- Missing Values (removal and replacement)
- Dataframe Ordering
- Dropping Duplicates
- Merge two Dataframe (Union and Intersection)
How to Manipulate a Pandas Dataframe in SQL
A ready-to-run code with some tricks to manipulate a Python Pandas Dataframe, using SQL queries.
In this article, I have described a quick summary of the articles I published in August. If you want to stay up-to-date, you can follow me and also read my new publications.
Stay tuned :)
A Summary of My July Articles: from Python/R Tutorials to Discussions
Here a quick recap of articles I wrote in July
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