Showing posts with label Pandas. Show all posts
Showing posts with label Pandas. Show all posts

Sunday, 18 June 2023

Data Analytics Course Handwritten Notes


In today's digital age, where typing on keyboards and tapping on screens has become the norm, there is something truly magical about the simplicity and authenticity of handwritten notes. Handwritten notes have a unique charm that digital text cannot replicate. They are a reflection of our personality, creativity, and individuality. In this blog, I want to share my newfound love for handwritten notes and the joy they bring.

The Art of Handwriting:

Handwriting is an art form that allows us to express ourselves in a personal and intimate way. Each stroke of the pen carries a piece of our emotions, thoughts, and ideas. Whether it's elegant cursive, playful doodles, or colorful illustrations, our handwriting reveals a glimpse of our character. Handwritten notes offer a tangible connection between the writer and the reader, creating a more intimate and meaningful experience.

Unleashing Creativity:

Writing by hand stimulates our creativity and imagination. As we put pen to paper, ideas flow more freely, and we are more likely to explore new perspectives and insights. The act of writing itself becomes a therapeutic process, allowing us to slow down, focus, and fully engage with our thoughts. Handwritten notes offer a canvas for our creativity to flourish, enabling us to experiment with different styles, fonts, and embellishments.

A Personal Touch:

When we receive a handwritten note, it feels like a precious gift. The time and effort invested in crafting the note make it a unique and personal gesture. Whether it's a heartfelt letter, a thoughtful thank-you card, or a quick reminder, handwritten notes show that we care. They create a deeper connection and leave a lasting impression on the recipient, unlike impersonal digital messages that can easily be forgotten.

Preserving Memories:

Handwritten notes have an enduring quality that transcends time. They become treasured keepsakes, reminding us of special moments, important milestones, and cherished relationships. Stumbling upon a box of old handwritten letters can evoke a wave of nostalgia and bring back vivid memories. In a world where digital files can be lost or corrupted, handwritten notes stand as tangible and irreplaceable mementos of our lives.

Sharing Handwritten Notes:

In the spirit of celebrating the beauty of handwritten notes, I am excited to share my own collection of handwritten notes with you all. Through my blog and social media platforms, I will be posting images and stories behind my notes, discussing different techniques and styles, and even providing tips on improving handwriting skills. I hope to inspire others to rediscover the joy of writing by hand and to embrace the personal touch that handwritten notes bring to our lives.


Handwritten notes are not merely pieces of paper; they are vessels of our thoughts, emotions, and creativity. They allow us to connect on a deeper level, create lasting memories, and express ourselves in a way that digital text cannot replicate. So, let's bring back the beauty of handwritten notes, one stroke of the pen at a time, and embrace the power of personal expression. Together, let's make the world a little brighter with our handwritten notes.

Tuesday, 5 October 2021

Operations On Dataframe in Python

Checking data types of each Column in a Data Frame.

  • If you want to check the data type of each column, because whenever you have been given a data, you want to really check what is the structure of the data; that means, which variable has which data type?
  • In, that case you can use dtypes, because that returns a series with the data type of each column and the syntax would be you use dtypes along with the Data Frame name.
  • So, Data Frame.dtypes will give you a series with the data type of each column

Here is the syntax.

Syntax = DataFrame.dtypes


Count of unique data types

  • So, now we have an overall idea about what are the data types that we are going to work with using the cars_data. 
  • There is also an option where you can get the count of unique data types available in your Data Frame.
  • So, in that case get_dtype_counts, returns the counts of unique data types in the data frame.

Here is the syntax.

Syntax = get_dtype_counts()

Selecting data based on data types

  • So, now we also have an overall idea about the count of unique data types that we are going to handle with. 

  • So, now, we know about how to get the data type of each variables. So, there might be cases where you want to perform the operations only on a numerical data type.

  • Similarly, there can be cases where you are going to work with only categorical data type.

Here is the syntax.

Syntax =

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