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Articles by Susan Maina
The sustainability of social enterprises in Nairobi, Kenya: A micro-level agency focus on metacognitive awareness
español Investigaciones recientes sugieren que la sostenibilidad está estrechamente vinculada a las empresas de economía social, las cuales, al ser iniciativas impulsadas por una misión, generan un cambio sistémico y ofrecen soluciones sostenibles a las comunidades locales. Si bien la literatura sobre sostenibilidad se centra principalmente en el cambio a gran escala, a menudo se pasa por alto la capacidad de acción a nivel micro.
13 Python Code Blocks for Exploratory Data Analysis- Classification Task
Explore and analyse any dataset for a machine learning classification task with this quick guide. EDA or Exploratory Data Analysis is an important machine learning step that involves learning about the data without spending too much time or getting lost in it. Here, you become familiar with the structure and general characteristics of the dataset, including its dependent and independent features, as well as their interactions.
Exploratory Data Analysis in Python: 11 Essential Code Blocks for a Regression Task
A practical guide to exploring any data science project and gaining valuable insights Exploratory Data Analysis (EDA) is one of the first steps in the data science process. It involves learning as much as possible about the data, without spending too much time. Here, you get an instinctive and practical understanding of the data. By the end of this process, you should have a general idea of the dataset's structure, some cleaning ideas, the target variable, and possible modeling techniques.
The Python Tutorial You Need on Lists, Tuples, Dictionaries, and Sets
An in-depth guide to Python’s built-in data structures We use lists every day. We write down to-dos in the morning and check off the grocery list at the store. Lists are just a simple way to track tasks and items. Similarly, computers need a way to store and manage collections of data. Data structures are Python’s method for efficiently storing, organizing, and managing data in a computer. Now, if only lists worked for all our coding projects. But computers don’t work like that.
Effectively Use %timeit, %lprun, and %mprun to Write Efficient Python Code
A hands-on guide on how to use magic commands to test the most efficient python code I recently did a coding interview where one of the passing considerations was how optimized the code was. Unfortunately, I failed the test not because the code did not work (which it did) or the logic was wrong (which it was not) but because my code was not optimized. As a data scientist, efficient code: Saves time when processing and analyzing large or complex data.
Pivot tables in Pandas and Handling Multi-Index Data with Hands-On Examples in Python
Learn how to pivot a Pandas DataFrame and get meaningful insights A pivot table is a data manipulation tool that rearranges a table and sometimes aggregates the values for easy analysis. In this article, we’ll look at the Pandas pivot_table function and how to use the various parameters it offers. We'll explore a real-world dataset from Kaggle to illustrate when and how to use the pivot_table function.
Pivot tables in Pandas and Handling Multi-Index Data with Hands-On Examples in Python
Learn how to pivot a Pandas DataFrame and get meaningful insights A pivot table is a data manipulation tool that rearranges a table and sometimes aggregates the values for easy analysis. In this article, we’ll look at the Pandas pivot_table function and how to use the various parameters it offers. We'll explore a real-world dataset from Kaggle to illustrate when and how to use the pivot_table function.
Regular Expressions (Regex) with Examples in Python and Pandas
A hands-on practical tutorial on Regex in Python For the longest time, I used regular expressions with copy-pasted stackoverflow code and never bothered to understand it, so long as it worked. However, I soon realized that to tackle software and data-related tasks such as web scraping, sentiment analysis, and string manipulation, regex was a must-have tool.
Regular Expressions (Regex) with Examples in Python and Pandas
For the longest time, I used regular expressions with copy-pasted stackoverflow code and never bothered to understand it, so long as it worked. However, I soon realized that to tackle software and data-related tasks such as web scraping, sentiment analysis, and string manipulation, regex was a must-have tool. This article aims to demystify regex characters, and the python functions for handling them, by tackling them one at a time while providing clear and simple examples.
Can You Put a For Loop in an If Statement?
Control flow structures like ifstatements and for loops are powerful ways to create logical, clean and well organized code in Python.If statements test a conditionand then complete an action if the test is true. For loops do something for a defined number of elements. List comprehensions are Python’s way of creating lists on the fly using a single line of code. Can You Put a For Loop in an If Statement?
10 Must-know Seaborn Visualization Plots for Multivariate Data Analysis in Python
Learn how to visualize data using Seaborn’s axes-level and figure-level plots Many beginner courses dwell on Matplotlib for visualization, and the reason is the underlying functionalities and ability to customize every plot detail. But, I found myself bogged down by all the documentation, community discussions, and many ways of creating simple plots, and thank goodness I found Seaborn.
8 Seaborn Plots for Univariate Exploratory Data Analysis (EDA) in Python
Learn how to visualize and analyze one variable at a time using seaborn and matplotlib Photo by Pixabay from Pexels Data exploration, particularly exploratory data analysis (EDA), is my favorite part of the data science project. There’s a lingering curiosity and excitement that comes with a new set of data with the opportunity of discovering subtle relationships and unexpected trends. In an older post, I covered the 11 essential EDA code blocks that I use every time on a new data set.
8 Ways To Filter a Pandas DataFrame by a Partial String or Pattern
Filtering a DataFrame refers to checking its contents and returning only those that fit certain criteria. It is part of the data analysis task known as data wrangling and is efficiently done using the Pandas library of Python. The idea is that once you have filtered this data, you can analyze it separately and gain insights that might be unique to this group, and inform the predictive modeling steps of the project moving forward.
A Pandas Tutorial Highlighting 8 Essential Functionalities
Whether you have a messy dataset to clean and analyze for insights, or you want to prepare data for a machine learning model, pandas is the library for you. It is simple to use, fast, and highly intuitive. Pandas was authored specifically for data science and is packaged with additional features from other libraries. This means that you can work only in pandas without importing other libraries as we’ll see in the article. A big disadvantage with pandas is that it fails with big data.
Lambda Functions with Practical Examples in Python
Introduction When I first came across lambda functions in python, I was very much intimated and thought they were for advanced Pythonistas. Beginner python tutorials applaud the language for its readable syntax, but lambdas sure didn’t seem user-friendly. However, once I understood the general syntax and examined some simple use cases, using them was less scary.
What is Data Extraction? A Python Guide to Real-World Datasets
Data extraction involves pulling data from different sources and converting it into a useful format for further processing or analysis. It is the first step of the Extract-Transform-Load pipeline (ETL) in the data engineering process. As a data scientist, you might need to combine data that is available in multiple file formats such as JSON, XML, CSV and SQL.
The Ultimate 4 Step Guide to Clean Data
Data cleaning is the process of removing inconsistencies and errors from data that would undermine the efficiency of a machine learning model. Though time-intensive, the process is worth it because good quality data is better than fancy algorithms. That said, different data cleaning procedures apply to different situations. A good understanding of the data and its properties is essential. Below, I outline a ‘template’ you can use to identify unclean data and different ways to efficiently clean it.
Demystify Deep Learning Terminologies and Build Your First Neural Network
Deep learning is a branch of machine learning whereby you feed a machine with data and answers, and the machine figures out the rules by which the answers are derived. The answers are the labels for which the data represents for example for data about house prices, the label is the price and the data is the various aspects of a house that affect the price. Another example is image-data about cats and dogs, and the labels are whether an animal is a cat or a dog.
11 Essential Code Blocks for Complete EDA (Exploratory Data Analysis)
By Susan Maina, Passionate about data, machine learning enthusiast, writer at Medium Exploratory Data Analysis, or EDA, is one of the first steps of the data science process. It involves learning as much as possible about the data, without spending too much time. Here, you get an instinctive as well as a high-level practical understanding of the data.
When You Find ‘the One’, and What To Do About It
Everyone should be with someone who makes them feel great about themselves, happy and giddy at the thought of them. You want someone who does not criticize you but critiques you healthily so that you always want to be a better version of you. You want to be excited when he calls and get chills when he kisses you. I experience this everyday with my husband. We met 10 years ago and have been married 7.
4 Ways to Easily Fool your Deep Neural Net
Neural networks are now being used for a variety of tasks. ChatBots, Image Recognition , Speech to Text, and Automated Translation are some of the areas where Deep Learning is actively replacing other methods. This is mostly because of their great generalisation capabilities when given tons of data. But what about targeted attacks? Is it possible to exploit their workings, and create artificial data that can make them misclassify?
What is Model Complexity? Compare Linear Regression to Decision Trees to Random Forests
A machine learning model is a system that learns the relationship between the input (independent) features and the target (dependent) feature of a dataset to be useful in making predictions in the future. To test the effectiveness of the model, a completely new similar dataset is introduced which only contains the input features, and the model should predict the target variable based on the insights it gained during training.
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