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Python Fundamentals, PyWedge, SQL Connection, and Dashboards

Python serves as a programming language, allowing us to convey ideas much like other forms of communication. In the realm of programming languages, Python provides a platform to express concepts and accomplish a multitude of tasks. As a data analyst, I'll be your guide through the basics in this blog post, initiating your journey. We'll explore fundamental topics such as file reading, essential data analysis functions, and even delve into captivating elements like SQL connectivity and the incorporation of screenshots. And then, the pinnacle: crafting dashboards. This blog encapsulates a wow-worthy all-in-one experience! I'm eagerly anticipating the opportunity to share my learning journey with each and every one of you.

Let's start with the topic of variables. A variable is essentially a container or placeholder used to store values.

In programming, variables play a fundamental role. Instead of directly working with raw values, you can assign those values to variables and manipulate them using the variable names. This not only enhances the readability of your code but also makes it more adaptable to changes and easier to manage.

so here is an example

String Concatenation:

String concatenation is the process of combining multiple strings into one. In Python, you can concatenate strings using the + operator. This operation joins the strings together in the order they appear. Here's an example:

String Multiplication:

You can also multiply a string by an integer to repeat it multiple times. This operation creates a new string that contains the original string repeated a certain number of times.

Comparison Operation (Equality):

The == operator is used for comparison in Python. It checks if two values are equal and returns a Boolean value (True or False).

Data structure

Certainly, I'd be happy to explain lists, sets, tuples, and dictionaries with examples!


A list is an ordered collection of items,

and each item can be of a different data type. Lists are denoted by square brackets [].


we can create a set by enclosing comma-separated values in curly braces {}. Sets automatically remove duplicate elements, and they are unordered collections.


A tuple is an ordered collection of elements enclosed in parentheses ().

Unlike lists, tuples are immutable, meaning their elements cannot be changed after creation and it will follow the order


A dictionary is an unordered collection of key-value pairs.

Each key is unique and maps to a corresponding value. Dictionaries are enclosed in curly braces {}.

Connecting jupyter notebook with pgadmin

With the basics covered, the next step involves delving into the realm of data analysis. In this segment, I'll walk you through how to execute data analysis tasks. Specifically, I'll guide you through the process of connecting Jupyter Notebook with pgdmi (PostgreSQL database management interface

Here, I've utilized a hospital dataset to drive the exploration. This dataset serves as the foundation for the upcoming analyses and insights.

In this scenario, my exploration involves accessing patient details from a SQL database, specifically utilizing a hospital dataset. To initiate this process, the first step is to install the psycopg2 library via pip, which offers a Python interface to facilitate connectivity and interaction with PostgreSQL databases

Once you've successfully installed psycopg2, you can proceed by importing the necessary libraries:

import pandas as pd

import psycopg2 as ps

from sqlalchemy import create_engine

Subsequently, you'll connect to the PostgreSQL database using the provided connection string. The connection string is structured as follows:

conn_string = 'postgresql://postgres:postgres@localhost/postgres'

db = create_engine(conn_string)

conn = db.connect()

With the connection established, you can transfer the patient data to the SQL database table named 'pats':

pats.to_sql('pats', con=conn, if_exists='replace',


Next, you'll execute a SQL query to retrieve all patient records:

sql = """SELECT * From pats"""

df = pd.read_sql(sql,conn)

Finally, once the data extraction is complete, remember to close the database connection:


By employing this process, you can effectively fetch and analyze patient details from the PostgreSQL database using Python.

conn_string = 'postgresql://username:password@server_address@localhost/database'

Remember that the schema, username, password, server address, and database should be formatted in the correct order.

Here is a glimpse within the Jupyter Notebook.

Effortless Data Visualization with PyWedge

Continuing our learning journey, I dived into the world of pywedge. After installing the library using pip install pywedge, the next step was to import it using the line import pywedge as pw.

With just two lines of code, a wide variety of possibilities opened up. The library offered a seamless way to generate eight distinct charts. These charts, accessible through a simple interface, significantly expedited the process and enhanced user efficiency. To illustrate, I used the 're_adm' sheet.

This compact code efficiently generated the diverse array of charts, providing a valuable visual perspective on the data at hand.

By employing pywedge, I was able to streamline the chart generation process, saving valuable time and resources. The library's user-friendly nature facilitated the exploration of data insights, thus bolstering the analysis journey

one final addition

Crafting a Hospital Patient Dashboard

Welcome back to our exploration of Python's incredible capabilities! We've dived into the basics, connected to databases, and even witnessed PyWedge's visual magic. Now, we're going a step further by creating interactive dashboards that make data come alive!

Here I used a tool called Panel. With just a bit of code, Panel and hvplot turns our data into something interactive. Imagine having data that you can touch and play with – that's what we're about to discover!

Creating Your Dashboard Playground

To get started, let's bring in Panel and set the stage for our dashboard:

Here i am creating an interactive input field called patient_id_input that allows users to enter a Patient ID, with a range from 0 to 1000.

Bringing Dashboard to Life

Here I’m merging different datasets to create a special view just for you:

Now filtering the DataFrame df to include only rows where the PatientID matches the value entered in the patient_id_input, then creating an interactive table named patient_table using the filtered data with pagination and resizing options.

also making a special table and a chart to show why patients visit the hospital:

Now, introducing the most exciting part – My interactive dashboard! It's like a playground that lets you explore data in different and exciting ways. Let's take a closer look and see how it works.


In this blog Python's basics to PyWedge, SQL connectivity, and interactive dashboards, leaving readers inspired and equipped to embark on their own data-driven adventures. Adapt and personalize it to align with your style and the overarching narrative of your blog.

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