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Python Basics

This Repository Contains Files that hold the Basics Codes of Python. I Have Used Spyder and Jupyter Notebooks IDE's available in Anaconda Navigator. Those who want to Learn Python From Scratch can go through these Codes.

Download Anaconda Navigator

In order to perform These Codes, You need to Download and Install Anaconda Navigator.

Available For:

  1. Windows
  2. MacOS
  3. ubuntu

Download and Install the Python 3.7 Version.

Click Here to re-direct to the Download Page.

After the Installation, Open Anaconda Navigator. You will see Different Tools Offered by Anaconda Navigator.

Click on the Launch Button Under Spyder or Jupyter Notebook to Use it. I would Recommend to use the Jupyter Notebook for Beginners as it's Easy and Simple to use.

If you Launch Spyder, You will be able to see the GUI similar to Rstudio(Those who are Familiar with rstudio.). The GUI is Divided into 3 Sections:

1. The Left part Consists of the Editor.

2. The Right Part is Divided into Top and Bottom Section. The Top Section Consists of the Variable Explorer and File Explorer.

3. The Right Bottom Part Consists of the Ipython Console and History Log.

The Extension for Saving Spyder file is ".py"

If you Click on Launch Jupyter Notebook, a Web Browser will be Opened displaying the Localhost page with a port number. Jupyter Notebooks Runs on the Localhost Server. You can See all the Files that are Present in the Localhost server. On the Right side Top you will See a DropDown Button "New", Click it and select Python 3. The Extension used to save Python Notebook is ".ipynb"

Now the Python Notebook will Open and you can start your Coding part.

Lets start with the Basics of Python

1. String and Datatype Functions

Name Spyder Jupyter Notebook
String and Datatype .py .ipynb

2. Operators

Name Spyder Jupyter Notebook
Operators .py .ipynb

3. Conditional Statements and Loops

Name Spyder Jupyter Notebook
Conditional Statements .py .ipynb
Loops .py .ipynb

4. User Defined Functions

Name Spyder Jupyter Notebook
Functions .py .ipynb

5. Libraries

You Would need Data for Visualization. I have Used Cars.csv, mba.csv, car-sales.csv, car-sales-missing-data.csv, car-sales-extended.csv, car-sales-extended-missing-data.csv,heart-disease.csv and panda.png.

A. NumPy - Basic Numeric functions
Name Jupyter Notebook
01. NumPy .ipynb
02. NumPy DataTypes and Attributes .ipynb
03. Creating NumPy Arrays .ipynb
04. NumPy Random Seeds .ipynb
05. Viewing Arrays and Matrices .ipynb
06. Manipulating Arrays .ipynb
07. Standard Deviation and variance .ipynb
08. Reshape and Transpose .ipynb
09. Dot Product Vs Element Wise .ipynb
10. Turning Images into NumPy Arrays .ipynb
B. Pandas - Data Manipulation and Analysis
Name Jupyter Notebook
01. Pandas .ipynb
02. Series, Data Frame sand CSV's .ipynb
03. Describe Data with Pandas 03. .ipynb
04. Selecting and Viewing Data .ipynb
05. Manipulating Data .ipynb
C. Matplotlib and Seaborn - Various Visualization Functions
Name Jupyter Notebook
01. Matplotlib & Seaborn .ipynb
02. Importing and Using Matplotlib.ipynb .ipynb
03. Scatter Plot and Bar Plot .ipynb
04. Histogram and Subplots .ipynb
05. Plotting From Pandas DataFrame with Customization .ipynb
D. Scikit-Learn(sklearn) - Various Machine Learning Algorithms
Name Jupyter Notebook
01. Introduction to Scikit-Learn .ipynb
02. Getting Data Ready .ipynb
03. Choosing the Rght Estimator-Algorithm for Our Problem .ipynb
04. Fitting a Model to the Data .ipynb
05. Evaluating a Machine Learning Model ipynb
06. Improving a Machine Learning Model .ipynb
07. Save and Load a Trained Model .ipynb
08. Putting it all Together .ipynb

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