Remember: You can find the datasets and databases used in the Notebooks in 'File' folder
| File Name | Brief info |
|---|---|
| Advanced Matplotlib Concepts.ipynb | Material to learn to use matplotlib library |
| Data Cleaning.ipynb | Examples of manipulating missing values in a dataset |
| Data Transformation.ipynb | Examples of making data normalization |
| Data_Compression_P.ipynb | A brief comparation of 4 different Lossless compression algorithms |
| Data_Transformation_-_Data_Creationipynb | Dataset Transformation in order to take the dataset to make predictions (https://www.machinehack.com/course/predict-the-flight-ticket-price-hackathon/) |
| Huffman_Codingipynb | Code used for lossless data compression. |
| Matplotlib Exercises - Solutions.ipynb | Excersises to improve skills using matplotlib |
| Pandas Built-in Data Visualization.ipynb | Material to learn to use pandas library |
| Tax Reform Project.ipynb | Determine if voters about Tax Reform in USA were influenced by their income level. |
| Tidy Data examples.ipynb | Tidy Data by Hadley Wickham (2014): the paper focuses on one aspect of cleaning up data. The examples in the Notebook are mentioned in the book |
| Working with HoyoDeCrimen API.ipynb | Using the HoyoDeCrimen's API (https://hoyodecrimen.com/api/) to extract data about crimes at CDMX, Mexico and visualize it |
You can email samuel040999@gmail.com or st1809173@upy.edu.mx
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