Where is Machine Learning not used?🤖 Machine Learning is an integral part of our everyday lives. Because of the digital transformation and computerization in the last years, we have adapted to this new digital era. Therefore, we use machine learning every day; why? Because we want to learn from data rather than compute hardcore solutions.
How this happened too fast? Well, computers became cheaper. Forget those times when computer were rather big calculators. Yeah, big calculators which processed numbers. But numbers represented data, hence, computers were, even at that time, able to process all kinds of data (Figure 1).
Why GIT is used. (Git cheatsheet)
Why Git is used? With my everyday job as a Software Developer, I can tell you that GIT is used to manage software development in a better way.
P.S. I should tell you that not just software, everything, literally.
I encounter that working with GIT is a repetitive task. I, personally speaking, forget most of the time the commands. Therefore, I am providing the commands that I use the most for the last month's workings as a developer with a short explanation of the command. Use it wisely and enjoy it.
How to Kibana? Well, let's clarify it. Data Science works closely with DataBases. Kibana gives you the visual analysis that you have built from your Searchengine or database, in this case, Elasticsearch. Let me explain to you my story. In my second week as a Full Stack and Data Science Developer, I met Kibana.
Kibana is highly developed to work together with elasticsearch. The development has given us the possibility of searching, viewing and visualising data indexed in Elasticsearch. We can analyse data through our search engine and create bar charts, pie charts, tables, histograms, and maps.
The kibana dashboard…
When working with unstructured data, you should ask yourself, How Elasticsearch works?.
In my second week as a Software Developer & Data Scientist, I met Elasticsearch. Elasticsearch is a search engine. It is open and provides you with an analytical engine. The queries for Elasticsearch are in real-time. Elasticsearch provides features as autocomplete, geo-localisation, based filters and multilevel aggregations.
Our company scraps the web 🐱💻 . Therefore, scraping unstructured data as any web page and transforming it into structured data seems to fit Elasticsearch and our duties perfectly.
The structure of Elasticsearch could be similar to a SQL…
Tutorial (Part 1) — Web Scraping for Beginners
We can get data when scraping the web with Python. Solving real problems in Software Development and Data Science requires data. Therefore, Data means everything for Data scientists, also for Software Developers. In this first Series of web scraping with python, we will learn how to get it.
In my third day as a Software Developer & Data Scientist (My path as Software Developer), I met Scrapy. Scrapy (Scraping Python) module, allows you to extract, process and store data from websites. However, that noise comes unstructured. What if we can collect that…
Renaming Pandas columns it's a basic Pandas manipulation, yet powerful—Lets program.
Let's start creating a simple DataFrame that contains 2 Pandas Series.
#1 In the code block section that corresponds to # 1, the creation of the DataFrame is set. The first Series corresponds to Animal and the second Series correspond to the speed of that animal.
#2 In the code block section # 2, we print the DataFrame to corroborate the information and the DataFrame creation.
#3 In the code block section # 3, the DataFrame is displayed. …
Variables are the names that you assign to data. To print variables python values that you assigned, you must use print(). Lets code!
#Code Block 1.0x = 1 #(1)
y = 3.0 #(2)
z = "Ramirez" #(3)
In the code block 1.0, we have created a variables “x” with an integer value 1 (1). The variable “y” has the float value 3.0 (2), and lastly, the value “z” has the string value “Ramirez” (3).
#Code Block 2.0 print (x) #(1)
print (y) #(2)
print (z) #(3)>>> 1
In the code block 2.0, we printed the value…
A Pandas DataFrame is the concatenation of a group of Pandas Series. That's how to create PandasDataFrame, simple.
#Code block 1.0#Data #(1)data_foo = ["one", "one", "one", "two", "two", "two"]
data_bar = ["A", "B", "C", "A", "B", "C"]
data_baz = [1, 2, 3, 4, 5, 6]
data_zoo = ["x", "y", "z", "q", "w", "t"]index = [0, 1, 2, 3, 4, 5] #(2)
In the code block 1.0, we have created four lists (1) with the information that will be contained in our columns, the columns foo, bar, baz and zoo. …
When to use Python Pandas For Loop? There are at least 5 methods to loop over a DataFrame, today; we explain python pandas for loop with iterrows().
The DataFrame structure, it's always associated with an index. In the same way, the Pandas Series. The index helps us to perform operations within the DataFrame. The iterrows() generator, loop from the index and extract the row values of the DataFrame.
The python data frame structure is mainly mutable. Therefore, to the question, are pandas data frames immutable? Yes, they are mutable. Nevertheless, not always.
Python Pandas package provides a fast and flexible relational data analysis module. The module`s concept is to create practical data analysis from labelled data correlated to an index. The DataFrame is therefore mutable; data can be added, updated or deleted. However, when additional information to a DataFrame is carried by adding a Pandas Series to the DataFrame, the Pandas Series length cannot be changed. That is when the DataFrame is immutable. Lets code!