DEV Community: Joseph Ngigi

Demystifying React: How does React actually render JSX?

Joseph Ngigi — Thu, 14 Mar 2024 22:38:53 +0000

Checkout this project React First Project

Also, check this Try React locally - Add React to a Website

When explaining react, we say that it is a set of JS functions that can allow you to create front end reusable UIs and components. The React library provides all the set functions that can be used to manipulate how the UI behaves. The React Library can put views to the DOM, mobile apps and the desktop apps. The ReactDOM and ReactNative Are used to render the UIs on a screen like the browsers and mobile devices.

With the code below, we can use it to access the HTML container, which is basically marked with Id, in this case we give the ID the name root. So now using the reactDOM function from react, we can now render the UI libraries. To enable all these to work, we have some scripts in the HTML file. These scripts enable you to write React code directly in your HTML page using the development versions of React and React DOM libraries. Normally, we need a JS environment like node.js, but we can run bare React.js without node by adding the scripts below, which are available in the React.js Documentation referenced above.

<!-- tags am talking about  in the **head**-->
<script src="https://unpkg.com/react@18/umd/react.development.js" crossorigin></script>
<script src="https://unpkg.com/react-dom@18/umd/react-dom.development.js" crossorigin></script>

<!-- In the **body** -->
<div id="root"></div>
<div id="newElement"></div>

JS File

const reactContentRoot = document.getElementById('root');
ReactDOM.render('Hello, This is REACTHTML', reactContentRoot)

//React is a library that allows one to cleate react elements
const myElement = React.createElement('li', null, 'item1')
ReactDOM.render(myElement ,elementNew)

React is a JavaScript library for building user interfaces. ReactDOM is the library that interacts with the DOM. React focuses on creating UI components and managing application logic, while ReactDOM handles rendering those components to the actual DOM. In simple terms, developers use React to define elements, manage state, and implement lifecycle methods, while ReactDOM is specifically used for mounting components to the DOM.

To run the react code, we need a transpiler that can compile react code. We use the babel. A transpiler is a tool that converts code written in one programming language into another programming language while maintaining a similar level of abstraction. Since we use JSX, we can convert JSX, which is just like HTML. You should note that JSX is not HTML, they just work the like 90% of the time. Babel is a tool that allows developers to write code using the latest version of JavaScript, which may not be supported by all browsers. It is a JavaScript compiler that transforms newer ECMAScript features into older versions of JavaScript that are widely supported across different browsers.

So a list element will be transformed like this. So, in the background, React Uses the babel transpiler to compile the JSX code.


// jsx
 <ul>
    <li>Item 1</li>
    <li>Item 1</li>
    <li>Item 1</li>
</ul>

// Java Script
React.createElement('li', null,'List item one'),
React.createElement('li', {}, 'List item two')

JSX Support Scripts

With the following script, we can use it to compile our JS file by adding it to the header of our HTML file. After which we can now compile the JSX files in react-html. You can check out @babel/standalone.
So this will compile the JSX into JavaScript. When we add this script, we can use the live server on Visual Studio Code or just right-click on the HTML file and open with default browser. It opens your file on a port.

<script src="https://unpkg.com/@babel/standalone/babel.min.js"></script>

<!-- We then label our script tag with the type as shown -->
<script type="text/babel" src="./index.js"></script>

So now we can render the JSX element in the place of the code below. This code is how we can create elements in React JS

const arraylist = React.createElement('ul', {} ,[
React.createElement('p', null,'This is not a JSX Element'),
React.createElement('li', null,'List item one'),
React.createElement('li', {}, 'List item two')
])
ReactDOM.render(arraylist, listItems)

This is an example of the JSX code; a simplified version of the above code. So before this JSX code is run in the browser, babel will first interpret it into JavaScript.

const myJSX = (
    <>
        <p>This is a JSX file</p>
        <ul>
        <li>List item one</li>
        <li>List item two</li>
        <li>{myNewItem}</li>
        </ul>
    </>
)
ReactDOM.render(myJSX, jsxElement)

So now when it comes to writing a React Component, we can now return the JSX inside a function, and now we can return the function as a JSX argument in the reactDOM.render like shown below. Note the similarities with the above code. You can just Say <App /> or the function App()

const App = () =>{
    let userName = 'Joseph N'
   return(
    <>
    <big>RETURNING JSX</big>
    <p>This is an arrow function. They are used to return JSX in react</p>
    <p>my name is {userName}</p>
    </>
   )
}
ReactDOM.render(<App/>, returnElement)

Essential SQL Commands for Data Science

Joseph Ngigi — Sat, 25 Mar 2023 20:16:55 +0000

As a data scientist, having a strong understanding of SQL commands is important for working with large datasets and performing complex analyses. SQL provides a standardized way to interact with databases, making it a universal language for data and database management.

In this article, we will explore some most important SQL commands for data scientists. From querying data to manipulating tables, we will cover the key commands that every data scientist should know. We will also discuss best practices for using SQL, including how to optimize queries and avoid common errors.

I have some SQL script you can use for your queries here. These will create a table for your database.

Data Retrieval

SELECT * FROM

This is used to query data; in simple terms to list or get the data in a given Table. One can manupulate it to get a specific field in a record like shown

SELECT * FROM agent;

Data Retrieval with Simple Conditions Where

SELECT * FROM `agent` WHERE zip = 37119

Distinct

There are certain times where someone wants to get unique data, eliminating the data that is duplicate. SQL is used to eliminate duplicates from the result set of a SELECT statement. For example one may want to get a list of departments where they are duplicate in a table.

SELECT DISTINCT SKILL1 FROM employeeskills;

ORDER BY

This is quite a useful when someone us wants to retrieve Data while it is sorted. ORDER BY is used to sort the result set of a SELECT statement in ascending or descending order based on one or more columns. One can specify one or more columns to sort the result set by. The sorting is done in ascending order by default, but one can also specify using the the DESC to set the result in descending order.

SELECT EMPLOYEENAME,TITLE,SKILL2,HIREDATE FROM employeeskills ORDER BY HIREDATE

LIMIT

Sometimes we are only interested in a limited number of records. We may want to retrive like just 7 records from a table. When you use the LIMIT keyword in a SELECT query, you can specify the maximum number of rows to return from the result set.

SELECT * FROM skill LIMIT 7;

Aggregations

These are functions that operate on a set of values and return a single value. They are used to get a summary of the dataset to gain insights from the underlying data set. Aggregations are often used with the GROUP BY clause. The GROUP BY clause allows you to group data based on one or more columns, and then apply an aggregation function to each group.

GROUP BY & COUNT()

For the group and count, I chose to get the counts of employees, since it has repeated titles with different employees(I took the last name which will display only for one employee). I have grouped the count with the employee_id, so it returned the count of employees grouped by title and displayed the employee titles.

SELECT EMPLOYEE_LNAME,EMPLOYEE_TITLE, COUNT(EMPLOYEE_ID) FROM employee GROUP BY EMPLOYEE_TITLE;

SELECT AMT, COUNT(C_NAME) FROM customer GROUP BY AMT

It's important to note that when using the COUNT() function with GROUP BY, you should use the "*" wildcard character instead of a specific column name. This ensures that you are counting the number of rows in each group, rather than the number of non-null values in a specific column.

SELECT AMT, COUNT(*) FROM customer GROUP BY AMT

SUM()

SUM() is an aggregation function in SQL that is used to calculate the sum of all the values in a specific column.

SELECT SUM(AMT) FROM customer;

We can also use the SUM() function in conjunction with other SQL functions such as GROUP BY, ORDER BY, and HAVING, to perform more complex queries and analyses.

SELECT A_NAME, SUM(AMT) FROM customer GROUP BY A_NAME ORDER BY AMT DESC;

AVG()

AVG() is an aggregation function in SQL that calculates the average value of a specific column.

SELECT AVG(AMT) FROM customer;

SELECT A_NAME, AVG(AMT) FROM customer GROUP BY A_NAME;

Having

HAVING is a clause in SQL that is used to filter the results of a GROUP BY query based on a given condition. HAVING clause is similar to the WHERE clause, but it is used with GROUP BY queries.

SELECT A_NAME, AVG(AMT) as AVG_AMT FROM customer GROUP BY A_NAME HAVING AVG_AMT > 200;

WHERE is used to filter data before it is grouped and aggregated. HAVING is used to filter data after it has been grouped and aggregated.

Alias is a temporary name given to a table or column in a query result set.It can be used to give a more descriptive name to a column or table oe differentiate columns with same name from different tables

MIN()

Sometimes someone may want to get the least or the minimum value of a specific column. We can also use it with other SQL functions

SELECT A_NAME, MIN(AMT) FROM customer

This query will group the amount by name and then find the minimum price for each name using the MIN() function.

SELECT A_NAME, MIN(AMT) FROM customer GROUP BY A_NAME;

Joins

In some cases we may want to do some more complex data manipulation and analysis with data from different tables. In SQL, a join is a method for combining data from two or more tables into a single result set. A join can be performed by joining tables based on a common key.

You can view this example from Datacamp

SELECT column1, column2, ...
FROM table1
JOIN table2
ON table1.column = table2.column;

These are the different types of joins

INNER JOIN returns only the matching rows between two tables, based on a specified join condition. Non-matching rows are not returned.
LEFT JOIN returns all the rows from the left table and the matching rows from the right table. If there is no match in the right table, NULL values will be returned for the corresponding columns.
RIGHT JOIN returns all the rows from the right table and the matching rows from the left table. If there is no match in the left table, NULL values will be returned for the corresponding columns.
FULL OUTER JOIN returns all the rows from both tables being joined, along with any matching rows. If there is no match in either table, NULL values will be returned for the corresponding columns.

Inner join is the most common, but if you want to use any of them joins, they all have the same syntax you will just need to replace it with the join based on your query requirements.

INNER JOIN

INNER JOIN is a join where it returns only the rows where there is a match between the specified columns (can be a common key between the tables) in both tables being joined.INNER JOIN is a type of join in SQL that returns only the rows where there is a match between the specified columns in both tables being joined.

SELECT employee.EMPLOYEE_ID, employee.EMPLOYEE_LNAME, employee.EMPLOYEE_FNAME, certified.CERTIFIED_DATE FROM employee INNER JOIN certified ON employee.EMPLOYEE_ID = certified.EMPLOYEE_ID;

We can also get data from multiple tables

SELECT employee.EMPLOYEE_ID, employee.EMPLOYEE_LNAME, employee.EMPLOYEE_FNAME, employeeskills.SKILL2, certified.CERTIFIED_DATE FROM employee INNER JOIN certified ON employee.EMPLOYEE_ID = certified.EMPLOYEE_ID INNER JOIN employeeskills ON employeeskills.EMPLOYEENUMBER = employee.EMPLOYEE_ID GROUP BY employeeskills.ID;

Changing Data Types

ALTER TABLE COLUMN DATATYPE

One can change the data type of a column using the ALTER TABLE statement with the MODIFY or ALTER COLUMN clause, depending on the database system you are using. Changing the datatype of a column happens on rare occassions, as because it can result to conversion errors

ALTER TABLE customer ALTER COLUMN AMT DECIMAL(10,2);

CAST()

CAST() is another SQL function that allows one to convert the data type of a column or an expression into a different data type

SELECT CAST(order_date AS DATE) FROM orders;

You should note that although both the cast and alter table column have used to change the data of a given column, alter table column is used to modify the data type of a column permanently while cast is used to temporarily convert data for a specific query or calculation.

Round()

This SQL function that allows one to round a numeric value to a specified precision. It can be used with both integer and decimal data types. This sql statement is used to round the revenue column in the sales table to two decimal places.

SELECT ROUND(revenue, 2) FROM sales;

Complex Conditions

By combining different functions and clauses we have looked at, it is possible to perform complex queries within the database. We can use complex conditions in your queries by combining multiple conditions using logical operators such as AND, OR, and NOT. There ither situations where there is need to implement more logical operations to perform the requirements of our problems. This can be done in a bit more advanced SQL, like PLSQL. Some of the sql conditions we can use include

Case Statement

A case statement is a powerful way to perform conditional logic in your queries. It allows one to test more conditions. This is a basic syntax for the case statement

SELECT column1,
       CASE
           WHEN condition1 THEN result1
           WHEN condition2 THEN result2
           ELSE default_result
       END AS result_column
FROM table_name;

SELECT customer_id,
       SUM(CASE WHEN order_date >= '2022-01-01' THEN order_total ELSE 0 END) AS total_sales_2022,
       SUM(CASE WHEN order_date < '2022-01-01' THEN order_total ELSE 0 END) AS total_sales_before_2022
FROM orders
GROUP BY customer_id;

In a practical example

SELECT C_NAME, C_ADDRESS, A_PHONE, AMT,
 CASE
     WHEN AMT < 250 THEN 'Junior Engineer'
        WHEN AMT < 600 THEN 'Senior Enginner'
        WHEN AMT > 800 THEN 'CTO'
        ELSE 'Project Manager'
 END AS CUSTOMER_POSITION
FROM customer

Subqueries

A subquery is a query that is nested inside another query. The results of the subquery are used by the outer query to perform further operations or filtering. Subqueries can be used for filtering, joining, or aggregating data.

SELECT product_name, unit_price FROM products WHERE category_id = (SELECT category_id FROM categories WHERE category_name = 'Beverages');

Exploratory Data Analysis(EDA)

Joseph Ngigi — Mon, 27 Feb 2023 21:05:23 +0000

Introduction on EDA

EDA is an important stepping stone before one begins on the exploration of Data Analysis. EDA is done once there is a defined problem in which can be typically a normal business problem, or a company is looking forward to look for a business opportunity. So, before conducting an analysis, there is a verification of whether the data is appropriate. A quality analysis of data depends on how well one understands the data they are dealing with. EDA is very helpful as it helps one discover and resolve Data Quality issues for example:

Duplicates

Unwanted datapoints

Missing Data

Incorrect Values

Detect Outliers and Anomalies

EDA generates data summaries like Mean, median, sum, count and other statistical information. The main purpose of Exploratory data analysis is to Analyze and summarize a dataset.

A Dataset is a collection of related data, typically organized in a structured format which can be be accessed individually or in combination or managed as a whole entity.

What is EDA?

It is an approach to analyzing data that focuses on analyzation and investigating data sets and summarize their main characteristics, often employing data visualization methods. EDA is aimed to discover the underlying structure and nature of the data, rather than simply confirming preconceived hypotheses. It uses statistical and graphical techniques, such as histograms so as to visualize the data.

After completing the process of EDA and extracting insights, the characteristics of the data can be utilized for more advanced data analysis or modeling tasks, such as machine learning.

The Data Analysis Process

Defining an Objective
In data analytics, the first step is defining Problem Statement. This is the process of coming up with a hypothesis and figuring out how to test it. One can start by defining a business problem(eg. Why are we losing customers as Xiaomi). As a data analyst, you will look into depth by trying to find the factors that are negatively impacting the customer experience. The analysis is not limited to only this, of course there are other factors. Defining the objective is mostly about the soft skills, lateral thinking and the business model knowledge. Business Metrics and Key Performance Indicators(KPIs) can help track problem points.

Data Collection
Can be Qualitative on Quantitative Data. The data can be categorized into:

First-Party Data: This is the data that is directly collected from customers through various channels such as website, mobile apps, social media accounts, customer relationship management (CRM) software. This data is clear and structured.
Second-Party Data: Second-party data is data that a company acquires directly from another company or organization, rather than collecting it themselves. A car manufacturer might acquire second-party data from a car dealership that has customer data from its own sales and service interactions.
Third-Party Data: Tt is data that is collected by one company and then sold or licensed to other companies for their own use. It can include demographic information, browsing behavior, purchase history, and more.

Data Collection tool
There are various tools that can be used for data collection. One important tool that can be used for this is a Data Management Platform(DMP). It allows one to identify and aggregate data from numerous sources before you can manipulate it. Examples include Salesforce, SAS, Xplenty(Data Integration Platform). Some open source platforms can be Pymcore and Dswarm

Cleaning The Data
This is an important step in [EDA] Exploratory Data Analysis so as to get it ready for Analysis and make sure that you are working with high quality data. Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. Key cleaning data tasks include:

Removing major Error, duplicates and Outliers

Removing unwanted data points

Bringing Structure to the data eg. Fixing typos ot layout issues

Filling or removing unwanted

There are various tools that can be used for data cleaning. OpenRefine is one of the open source tools that one can use for data cleaning. This might not be suitable for large datasets. Python library Pandas and other R libraries can be a much better suit for cleaning the large datasets. There are much more DataCleaningTools tools. Check them out

Data Analyzation
Data analyis typically depends on your objective from the data. Some techniques of Data analysis include; Univariate or Bivariate analysis, Data Mining analysis, Time series analysi and Machine Learning. Categorically, all data analyzation can fit into:

Descriptive Analysis which uses current and historical data to identify relationships and trends

Diagnostic Analysis is an advanced analytics that examines data and diagnosing problems or issues to understand why it happened

Predictive Analysis is the analytics that makes predictions about future outcomes using statistical and machine learning techniques to analyze historical data

Prescriptive Analysis is the analysis where one analyzes data and content to recommend the optimal course of action to achieve the objective

Visualization of The Data
This involves the interpretation of the outcomes and present them. The results are expected to be unambiguous. Data visualization is the practice of presenting information or data in a visual form, such as charts, graphs, maps, and other interactive visual representations. There are many tools that can used for data visualization. They include; Data wrapper, Tableau, Google Charts, Power BI, etc. These don't require one to have coding skills. Plotty, Seaborn and Matplotlib are some python data visualization tools.

Types of exploratory data analysis

There are four primary types of EDA

Univariate analysis This EDA is Non-graphical. It is the simplest and it deals with analyzing a single variable mainly to identify patterns, trends, and outliers in the data.
Univariate graphical This method provide graphical presentation, providing Histograms, a bar plot, Box plots(minimum, first quartile, median, third quartile, and maximum.)
Multivariate nongraphical This involves analyzing multiple variables simultaneously to identify patterns, trends, and correlations in the data. Usually, it displays the correlation between two or more data variables by using cross-tabulation or statistical techniques.
Multivariate graphical This one uses uses graphics to display relationships. The most used is a grouped bar plot or bar chart.

Scatterplot, Multivariate chart, Bubble chart, Run chart and Heatmap

EDA Tools

These are various common tools that can b e used for EDA. Python and R are the most common used for data Analysis and Visualization

Pandas is a Python library that can be used for data manipulation and analysis and it provides tools that are suitable to manipulate large datasets. Pandas is built on top of NumPy, another popular Python library for scientific computing. Pandas can be used for Data cleaning and preparation, Data exploration, Data analysis and Time series analysis
NumPy This is a python library that used in scientific computing, providing tools to work with Array and Matrices.
MatplotlibA Python library for data visualization, including scatter plots, line graphs, and histograms.
Seaborn A Python library for statistical data visualization, including heatmaps, cluster maps, and box plots.

Other common tools include; Tableau, Power BI, RStudio, SAS and IBM SPSS

EDA tools provide a range of statistical functions and techniques that can be used to gain insights and make data-driven decisions. Some of these functions include Data visualization, Correlation analysis, egression analysis, Clustering and dimension reduction techniques and clustering analysis

Linking Github repositories

Joseph Ngigi — Sun, 26 Feb 2023 19:39:37 +0000

Git Submodules

Someone may come across a situation where they might want to link or associate two Github repositories. You may want to do this, probably for referencing on something. You need to have your two repositories ready in Github, In this tutorial, I will not go to much details on handling advanced git commands.
For both, one has the required submodule with you will put in the other that does not have it.
This is a picture of how the targeted repos

Navigate to the local git-repository that you want to add the git-submodule: In this case, I added Python_Codebase as a submodule to to the lux_data_scince repo. If you type git submodule, it will show you a few commands.

To link the repos, use git submodule add <add_your_target_url>. This will clone your targeted URL into the new repo. After wards you can now push your changes to the main repository.

git clone <add_your_url> --recurssive This will clone the Github repo lux_data_scince that contains the submodules. If not the gitsubmodule you added are left behind

.gitmodules is a file that shows how it links to the repo, and you can use tree to view your repo tree

Alternatively, you can setup git config submodule.recursive true This is if the incase of a pull/clone and you want to get all your modules
You can used git submodule update --init to download/update the repo submodules

The branches will not have any problems, and if you make any changes to the targeted repository, it will not affected your main repository.

Stuck on updating start menu link

Joseph Ngigi — Wed, 22 Feb 2023 08:54:52 +0000

I recently came across problem while installing MYSQL on my Windows(I am on windows 11), where I got stuck in the step, it could not resolve updating start menu link. I found this post on Stackoverflow.

If you have troubles in while on Windows 11, here is a guide to solve the problem

Go to the control panel, and find the Windows Tools. IF you want to easily find the make sure to sort using small icons
On windows tools, scroll and find the Services
Once in services, Scroll through and find MYSQL services for my case I am using the latest version so it is MYSQL80. Right click on it and go to Properties
On properties, click on Log On tab, choose Local System Account and click on apply.
Go run the MYSQL installer and MySQL from the start and re-configure the MYSQL server. This worked on my side.
Remember to go back to step 4 and choose the default This Account. You can now enjoy MYSQL from the Workbench or the MySQL Shell

Learning SQL and an Introduction on Python for Data Science

Joseph Ngigi — Sat, 18 Feb 2023 20:46:32 +0000

SQL For Data Analysis

SQL is a powerful tool for analyzing data, thanks to its ability to work with multiple databases simultaneously and its use of relational databases. It's a widely used and versatile language that is both user-friendly for beginners and highly advanced for creating sophisticated data analysis tools and dashboards.
In order to dive into SQL for data analysis, wel will dive into some basic questions that one may ask themselves when diving into this field

What Data Analysis

What is data and big Data, and data processing

What is SQL and how is is used in Data Analysis

Dabase and SQL

Data Analysis

Data analysis is the process of inspecting, transforming, and modeling data in order to extract useful information and draw conclusions from it. Data analysis is a valuable procedure that can minimize the risks associated with decision-making by offering useful insights and statistical information. This information is typically presented through charts, images, tables, and graphs. An everyday example of data analysis occurs when we assess past events or anticipate future outcomes to make decisions. In essence, this involves analyzing the past or the future and using that analysis to make an informed decision.

With the help of data analytics, businesses can better keep track of and understand how they run and what services they offer. It gives them a deep understanding of the problems and experiences of their clients. When businesses shift their focus from data to insights and then take action, they can make their products and services fit the needs of each customer, streamline their processes, and increase output across the board.

Data analysis plays a crucial role in processing big data into useful information.

Data

Data

Data is all information that may be collected, stored, and analyzed for the goal of making decisions or gaining insights is referred to as data. Text, numbers, photos, audio, and video are all examples of data.

Big Data

Big data refers to extremely large and complex data sets that cannot be easily processed or analyzed using traditional data processing techniques. These data sets typically contain a wide variety of data types, including structured data (such as data stored in databases or spreadsheets), unstructured data (such as text or multimedia content), and semi-structured data (such as web log files or sensor data).

Big data analytics follows five steps to analyze any large datasets:

Data collection
Data storage
Data processing
Data cleansing
Data analysis

SQL And SQL in Data Analysis

SQL, which stands for "Structured Query Language," is a type of programming language used to manage data in a relational database. A database is an organized collection of data. SQL is useful for conducting aggregations like sums, counts, minimums, and maximums that you would typically perform in an Excel pivot table, but over much bigger datasets and on numerous tables at once.

SQL is widely used in data analysis as it provides a powerful and efficient way to extract, filter, and transform data in a structured and consistent manner.

SQL is used in data analysis to query and manipulate big data sets to derive useful insights. Data analysts can use SQL to collect data from several tables, apply filters to that data based on user-defined criteria, and conduct computations and aggregations on the retrieved data.

Filtering data: SQL can be used to pull out information that satisfies specific parameters, like only including records that are inside a specified date range or that belong to a specific client.
Aggregating data: SQL can be used to get the sum, average, and maximum of a group of numbers.
Data transformation: In order to facilitate analysis, SQL can be used to transform data into a more usable form, for as by transforming dates into a standard format or by adding new variables depending on existing data.
Joining data: Joining from different sources or tables and it is a powerful feature of SQL.

Database and SQL Syntax

Database is an electronic filing system. We will use a relational database system. It is a software system from which we can manipulate the data in a database. DB maria will be our database management system. The datatypes used are;

Int For storing integers
Decimal(m, n) For storing the decimals, m is the main number and n is number of digits after decimals
BLOB For storing the large files (Binary large objects) like photos and videos
VARCHAR For storing strings characters or values
DATE For storing time date
TIEMSTAMP For storing the time stamp when something was installed

The SQL syntax

CREATE is used to create a database and table in a database. You start with the command and then the name of the database or the table. After describing the table, the other information describing the attributes of the table are found in brackets, and the statements end with the semicolon.

CREATE DATABASE {name_of_database}
CREATE TABLE {name} (student VARCHAR (20) PRIMARY KEY, name VARCHAR );
DROP is the command for deleting the anything on the Database or the database for that case
DESCRIBE is for displaying information about the table
ALTER is used for altering the table in the database
ALTER TABLE {name} ADD (name of column to add) >ALTER TABLE {name} DROP COLUMN (name of column to add)

Inserting Information

This is inserting the information in the database tables

INSERT INTO {table name} VALUES (‘Joseph’, ’Physics’,’ Sc200’). The data must be put in the older by Which the database table is already.

SELECT*FROM {table name} This one displays a table how it looks like or the tables in the databaseAnd how they are laid out.

When inserting information in the database, there some of the members who may not have all the information as required by the database. How to solve this, is just after the table name, you open the brackets and put in the name of the attributes by which all the information is available for input.

INSERT INTO employee(`emp_id`, `first_name`, `last_name`, `birth_date`, `sex`, `salary`, `super_id`, `branch_id`) VALUES
('103','Tabby','Mutharu','1999,16,24,','f','60550','3','2')’

Constraint Commands

NULL This means that an entity can have no value under the attribute.
These are the unique codes in the SQL syntax from which they affect how the data in the tables are affected. These commands are put when one is creating the tables and their attributes.

NOT NULL This means that for an entity, the attribute has to have a value for the data inserted to be valid, otherwise there is an error.
UNIQUE This means that the values for an entity put for an attribute with this parameter is and is only for that entity. No other entity can possess such attributes.
DEFAULT This is when one can put in something that appears once a column is left blank. Example;table_name (major VARCHAR (20) DEFAULT ‘undecided’); In the column this appears as “Undecided” once the pace is left unfilled.
AUTO_INCREMENT This can be used instead of the primary keys. What happens is that it increments in the series 1, 2, 3…. Once each of the data is inserted in the database. Example; table_name VARCHAR (20) AUTO_INCTREMENT);

Updating and Deleting

Updating can be used to correct certain data or insert new data of an existing entity into a table. Example, the syntax for updating the data for an entity is UPDATE table_name SET major = ‘Bio’ WHERE major = ‘Biology’; This line of SQL will update the table mentioned where the Biology is changed to Bio. Also, we can update other places of the table UPDATE table_name SET major = ‘Chemistry’ WHERE student_id = ‘4’.
We can change maybe two or more entities have the different majors but are in the same table

UPDATE table_name SET major = ‘Bio-Chemistry’ WHERE major = ‘Biology’ OR major=’Chemistry’

Where is used for comparison. Other comparisons are =(Equals), not equal (<>), Greater than (>), Less than (<), less than equals to(<=) and greater than equals to(>=).

Deleting is when you want to completely remove a certain entity from the table all delete all the data from the table. The syntax for the above is:

DELETE FROM table_name WHERE student_id = ‘3’
DELETE FROM student

Basic Queries:

These using various SQL statements so as to get information from a database.

SELECT statement in SQL. The select statement tells the database that we want to get some information from it.
SELECT * FROM table_name This means that we are going to tell the DB to display the whole of the table. The asterisk means that get everything from the table.

This implies that we can also use select statement to perform actions like Selecting a column in a table. That is SELECT Student_name FROM table_name
SELECT Student.Student_name, student.major FROM <table_name>

In the table we can use the ORDER BY command to sort the names in any way like the alphabetical order or the numbered way. You can add the DESC or ASC after specifying on which column you want the name sort.
SELECT Student.Student_name, student.major FROM table_name ORDER BY student_name ASC
You can also use another command called Limit. It limits the results from which you have specified.
SELECT * FROM student LIMIT 2
We can also filter the data in the table using the specific information we want. Like for example we want to display a name of a certain student in a table. We can filter this using their student ID by which is Unique for them.
SELECT student_name FROM student WHERE student_id = 2
In this case one can use the common operators where the “=” is. The operators are <, >, <>, AND, OR <= and >=
SELECT * FROM student WHERE Student_id <= 4 AND major <> 'Software Engineering'

This creates a table with the named columns. There below is a way by which the foreign key is set with the reference to the named table and then the column with which it’s set. This give two tables a relation.

CREATE TABLE branch
(
branch_id INT PRIMARY KEY,
branch_name VARCHAR(40),
mgr_id INT,
mgr_start_date DATE,
FOREIGN KEY(mgr_id) REFERENCES employee(emp_id) ON DELETE SET NULL
);

SELECT STATEMENT

We can change how the name of the databases are changed to whatever we want displayed based on the circumstances one is in.
SELECT student_name AS forename FROM student

DISTINCT

In an Example there could be information stored with same data. It gives a list of the different individual data in a column data given like male, female and others.

SELECT DISTINCT emp_id FROM works_with ORDER BY emp_id

SQL Functions

These are lines of codes that do perform different functions for us like doing averages, adding up the data or counting certain data. They are useful when we are collection and doing analyzation of data to get the information about it. Here are some functions in SQL:

COUNT()

This will count the number of employees in the column name and give the number of entities as the function says.

SELECT COUNT(emp_id)

SELECT COUNT(emp_id) WHERE sex = ‘F’ AND birth_date = ‘1980-01-01’

AVG()
This gives the average of whatever you’d want to calculate in any column of any table in the database. Below is the syntax for the average

SELECT AVG (salary) FROM employee

SUM()

This one finds the sum of the selected column for specified table. You can filter to what is needed.

SELECT sum(salary) FROM employee

One can filter to the desired way of how the’d want present on analyze data.

AGGREGATION

This is going to specify the name of the sex available on the name. To be more precise like shown below. This is going to show the number of females or males in the given table.
SELECT COUNT (sex), sex FROM EMPLOYEE

SELECT COUNT (sex), FROM employee GROUP BY sex

Python for Data Science

Python's ease of use, adaptability, and general-purpose nature have led to the language's widespread adoption in the field of data science. In spite of the fact that it wasn't the first major programming language, its status as one has steadily increased over time. About two-thirds of data scientists in 2018 said they used Python on a daily basis, making it the most popular language among analytics experts.

In order to get started on Python for dta science, it is important to first learn and understand the basics of Python as a Programming language. TO pratices this one need to install an environment to allow us to code Python. There are various ways to achieve this. I will discus two major ways. The first step involves installing a Python environment. This is the best way to learn how to do this as it will give you a chance on how to setup environments especially if you are using a Linux system. Linux system usually comes pre-installed with Python.

Installation of a Python Environment

Manual Installation
Download a the latest version of python.Python. Install it on your Windows or MAC. Install a VS code text Editor and navigate to Extensions. Find necessary extensions. My most preffered is Pylance as it gives me an intellisense for Python. Here you can practice the Python basics through tutorials, but the best way to learn is through doing simple projects as you progress and make use of the free documentations from the internet.

Note: To use Python files have an extension of .py. To make a project or a file, On your text editor Click on files>New file and name it as -anyname.py- then save it in your favorite place or your projects folder

Setup and installation of the Python environment for data science(Anaconda, Jupyter Notebook)
Download and install Anaconda: Anaconda is a free and open-source distribution of Python that includes a large number of data science packages and libraries.Anaconda. Jupyter Notebook is included with Anaconda, so you can start using it right away. The Jupyter Notebook is an open-source web application that lets data scientists make and share documents that include live code, equations, computational output, visualizations, and other multimedia resources, along with explanatory text JupyterNotebooks. To launch Jupyter Notebook, open Anaconda Navigator and click on the "Launch" button next to Jupyter Notebook. This will open Jupyter Notebook in your default web browser. Click on the "New" button in the top right corner of the Jupyter Notebook interface and select "Python 3".

Python data science basics:

DataTypes
To learn practice data science, there need to be knowledge for the datatypes used in Python and how to manipulate the. There are four basics datatypes of Python which include

Lists: A list is a collection of items that can be of any type. Lists are mutable, meaning they can be changed after they are created.

Dictionaries: A dictionary is a collection of key-value pairs. Each key is associated with a value, which can be of any type. Dictionaries are mutable, and you can add, remove, or modify key-value pairs.

Tuples: A tuple is similar to a list, but it is immutable, meaning it cannot be changed after it is created. Tuples are often used to represent a collection of related values.

Tools
There are many tools and libraries available in Python for data science. The common tools used for Data science in Python include:

NumPy: NumPy is a Python library that is often used to work with numerical data. It gives you tools that are fast and easy to use for working with arrays, which are groups of data that can be any size. NumPy also has a number of functions, like addition, multiplication, and trigonometric functions, that can be used to do math operations on arrays.

Pandas: Pandas is a library for Python that lets you work with data in tables. It has data structures like the Series and the DataFrame, which are like improved lists and dictionaries, respectively. Pandas also has tools that let you read and write data in different formats, like CSV and Excel.

Matplotlib: Matplotlib is a Python library that lets you make visuals of data. It has many tools for making charts and plots, such as line charts, scatter plots, and histograms. Matplotlib can be used to make both static and interactive visualizations that can be saved as image files or put into web applications.

Other common used for machine Learning include

Scikit-learn is a library that is used to teach machines how to do things. It has a lot of algorithms for things like regression, classification, clustering, and reducing the number of dimensions.

TensorFlow is a library that is used to build and train deep learning models. It gives you a strong and flexible base on which to build a wide range of machine learning models.

Data Cleaning
Cleaning and manipulating data are important parts of any data science project, since data is rarely clean and organized enough to be analyzed right away.

Aggregating data: Pandas has methods for aggregating data, like groupby(), which groups data by a given column, and aggregate(), which does calculations on grouped data.

Combining data: Methods like merge() and concat can be used to combine data from different sources ().

Handling missing data: Pandas gives you ways to find missing data in a DataFrame, filter it, and fill it in.

Renaming columns: You can change the names of columns in a DataFrame by using the rename() method.

Getting rid of duplicates: The drop duplicates() method can be used to find and get rid of duplicates.

Filtering and sorting data: Methods like loc[, iloc[, and sort values can be used to filter and sort data ().