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.
In order to perform These Codes, You need to Download and Install Anaconda Navigator.
- Windows
- MacOS
- 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.
| Name | Spyder | Jupyter Notebook |
|---|---|---|
| String and Datatype | .py | .ipynb |
| Name | Spyder | Jupyter Notebook |
|---|---|---|
| Operators | .py | .ipynb |
| Name | Spyder | Jupyter Notebook |
|---|---|---|
| Conditional Statements | .py | .ipynb |
| Loops | .py | .ipynb |
| Name | Spyder | Jupyter Notebook |
|---|---|---|
| Functions | .py | .ipynb |
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.
| 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 |
| 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 |
| 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 |
| 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 |
Keep Checking for Updates