Projects from the Udacity Nanodegree AI Programming with Python in the form of Jupyter Notebooks . The first notebook uses NumPy to perform mean normalization. The Second notebook uses Pandas to extract useful information concerning Apple, Amazon, and Google stocks. The remaining notebooks demonstrate basic features of the MatPlotLib library.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
The Code inside the nptebooks is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.
To install pip run in the command Line
python -m ensurepip -- default-pip
to upgrade it
python -m pip install -- upgrade pip setuptools wheel
to upgrade Python
pip install python -- upgrade
You will also need to download the Numpy, Pandas and MatplotLib packages. Again in the command Line type
pip install numpy
pip install pandas
pip install matplotlib
to download them using pip or
conda install numpy pandas matplotlib
and type Yes when asked to to download them using Anaconda.
In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using
git clone https://github.com/fotisk07/NumPy-Pandas-MatPlotLib/
then in the command Line type, after you have downloaded jupyter notebook type
jyputer notebook
locate the notebook and run it.
The NumPy Project does not require any external dataset . However the Pandas one does require Stock data from 3 companies in order to work. Stock data are already provided in the Folder data in the form of 3 .csv files(APPL, AMZN, GOOG), but feel free to use any other data you wish by doing the appropraite modifications. Also the Bar_Chart_Practice , the Histogram_Practice and the Scales_and_Transformations_Practice notebooks require external data in order to create the relative plots. Two datasets are provided, Pokemon and fuel data, but you can use any data to plot the relative diagrams if you perform the necessery modifications.
Please read CONTRIBUTING.md for the process for submitting pull requests.
- Fotios Kapotos - Initial work
This project is licensed under the MIT License - see the LICENSE.md file for details