This article is a part of “Data Science from Scratch — Can I to I Can”series.

Click here for the previous article/lecture on “A10: Pandas (Part-3): Merging, Combining & GroupBy”.

✅ A Suggestion: Open a new jupyter notebook and type the code while reading this article, doing is learning, and yes, “PLEASE Read the comment, they are very useful…..!”

So, let’s have a quick look at some of the most important methods that you will be using all the time. Along with which gives the basic statistics on your data, we have already covered etc.


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A9: Hierarchical Indexing (MultiIndex) & Handling Missing Data”.

✅ A Suggestion: Open a new jupyter notebook and type the code while reading this article, doing is learning, and yes, “PLEASE Read the comment, they are very useful…..!”

So, it’s time to talk about data wrangling and aggregation:

Let’s learn how to Merging/Joining, Combining/Concatenation, and use GroupBy method for this purpose.

Merging and combining datasets:

Merging and combining are common operations that we perform on our data from different tables. …


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A8: Series & DataFrames — Index & Slicing”.

✅ A Suggestion: Open a new jupyter notebook and type the code while reading this article, doing is learning, and yes, “PLEASE Read the comment, they are very useful…..!”

Hello guys,
In the previous article, we have learned about pandas data structures. Let’s move on and talk about hierarchical indexing (MultiIndex) and handling missing data in this article.

Hierarchical Indexing (MultiIndex):

Hierarchical or Mulit-level Indexing is another very important feature in…


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A7: NumPy (Practice Exercises)”.

✅ A Suggestion: Open a new jupyter notebook and type the code while reading this article, doing is learning, and yes, “PLEASE Read the comment, they are very useful…..!”

Pandas Essentials

Welcome to the pandas essentials!

Pandas is state-of-the-art tool/library in Python for Data Science ecosystem. This high-performance and easy-to-use open source library is a must have skill for any data scientist. …


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A6: NumPy (Part-2): Indexing, slicing, broadcasting, boolean masking & universal function (ufuncs).”

Hello Guys,

So, it’s time to test your knowledge on NumPy. Lets start with simple task and move on to more challenging ones!

☞ Please note, there are several ways to get the required output, so your code could be different. As long as you are getting the tasks done, it is fine at this stage.

Exercises:

(Solutions are provided at the end.)

  1. What is…

This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A5: NumPy (Part-1): Arrays, random module, array methods & attributes.”

Hi Guys,
In the previous article/lecture, we learned about NumPy arrays along with other basic concepts in NumPy. Let’s move on and talk about indexing, slicing, broadcasting, fancy indexing, and boolean masking. We will also talk about arithmetic operation on NumPy arrays along with universal function (ufuncs) at the end of this article.

In NumPy arrays, indexes are zero based — first element in a row…


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A4: Python Essentials (Practice Exercises)”.

Hello guys,
Welcome to the NumPy Essentials!

NumPy is a fundamental package for scientific computing, which provides the foundations of mathematical, scientific, engineering and data science programming within the Python echo-system. NumPy’s main object is the homogeneous multidimensional array. NumPy is extremely important for Data Science because it’s a Linear algebra library. It is powerful and incredibly fast and can also integrate C/C++ and Fortran codes. …


This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A3: Python Essentials (Part-2): Operators, conditional statements, loops, list comprehension, functions, lambda expression, map & filter”.

Hello guys,

It’s time to test your understanding of Python basics. Create a new jupyter notebook and try to solve the given tasks, if given, please follow the instructions.

Good luck!

Exercises:

(Solutions are provided at the end.)

  1. How to compute to the power of ?
  2. Write python code toe get remainder if is divided by .

This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A2: Python Essentials (Part-1): Data types”.

In Python Essentials (Part 1), we have explored Python’s data types. Let’s talk about:

  • Comparisons & Logical Operators
  • Conditional Statements
  • Loops (while & for) & range()
  • List Comprehension
  • Functions and Lambda Expressions
  • Map and Filter

Please note, the video lectures for this article are embedded at the end!

Comparison Operators:

Comparison operators allow to compare two elements. These operators include,

  • greater than( 3>4: three greater than 4)
  • less than (3<4: three less…

This article is a part of “Data Science from Scratch — Can I to I Can” series.

Click here for the previous article/lecture on “A1: course introduction and environment setup”.

We all know that Python is the mainstream language for Data Science. We also know that learning Python for development purpose is very different than learning for Data Science. Without involving you guys into any unnecessary discussion, let’s move on to the primary goal and learn the most important and key concepts of Python that are absolute necessity to do Data Science.

Please note, the video lectures for this article…

Dr. Junaid Qazi

Data Science Consultant, Mentor and a Professional Development Coach. https://www.linkedin.com/in/jqazi/ www.scienceacademy.ca

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