Pandas #5 : Multi-Index Dataframe in Pandas || Python Pandas Tutorial for Data Science
MultiIndexing or Hierarchical Indexing or Advanced Indexing is used to represent higher dimensional dataset into 2-d dataframe making it easier to introspect and draw insights from.
Popularity of #pandas is growing at a rapid rate and saw a 100% growth over the last 5 years.
Its rich functionality, enabling data preprocessing, data wrangling and building data summaries.
It is also one of the favorite #interview topics in #python for #datascience
In this 5 module pandas tutorial, we shall learn from the basics, going to attribute level functionality, table level functionality, multiindexing and discuss important interview questions
Follow the tutorial in sequence to become a "Pandas master" :)
Resources:
notebook: https://colab.research.google.com/dri...
multiindexing pandas doc: https://pandas.pydata.org/pandas-docs...
getting started guide: https://pandas.pydata.org/docs/gettin...
data source: https://www.kaggle.com/datasets/iabhi...
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Why learn #datascience ??
"Data Science is the sexiest job of 21st century"
If you are a #btech student, #diploma in #computerscience, #graduate looking to #career #transition into #datascience , #machinelearning, #dataanalysis, this is a place for you
Everyone of us use social media platforms such as instagram, facebook, or binge watch on OTT platforms such as #youtube , netflix, prime, hotstar or window shop on e-commerce sites such as amazon, flipkart, myntra
It certainly amazes us to see the products of our choice and interest being #recommended to us, our favorite shows being filtered among millions of others
However, not many get to understand how it works. Not to worry, we shall help you understand the concepts in a #simplified and #concise fashion, helping in career transition into machine learning or data science
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Additionally, here are few more useful resources for beginners and curious data enthusiasts
1. kaggle: a platform to learn, practice, compete and win cash prizes [https://www.kaggle.com/]
2. paperswithcode: website presenting the latest in machine learning and data science research and the code implementations [https://paperswithcode.com/]
3. google colab: a platform to run code, build machine learning solutions, explore the capabilities of GPU and TPU without any hassle in setting up the environment: [https://colab.research.google.com/]
Remember, "the greatest investment ever made is investment in self growth and learning"