7 Awesome Python Packages and Example Projects to Build!

7 Awesome Python Packages and Example Projects to Build!

Introduction. There are almost 100 python packages being created each day. On PyPI, at the time of writing this article, there are 254,216 projects. So how do you know which packages are most useful? This article hopes to address that issue, by presenting 7 awesome python packages, example use cases, and example code.

Introduction

There are almost 100 python packages being created each day. On PyPI, at the time of writing this article, there are 254,216 projects. So how do you know which packages are most useful? This article hopes to address that issue, by presenting 7 awesome python packages, example use cases, and example code.

This list is mostly geared towards data science, with an emphasis on python packages that make cool projects easy to do.

Slacker — easy to use Slack API

I love Slack’s easy to use API. Slack makes it super easy to make automated bots, to increase the productivity of your team. Simply, we can use Slack bots to send alerts, such as algorithm performance over time. More complex apps can be made that take inputs from a user via a modal which can trigger a job.

The Slacker package can be found here, and a great example of complex apps can be found on Glitch.

Example — using Slacker to send alerts and files

Slacker is super simple to use and combined with automation, can be very useful. Let’s say you want to use it to connect to SQL every morning to get a daily sales snapshot and send a graph to a channel:

from slacker import Slacker
import matplotlib.pyplot as plt
import tempfile
from pathlib import Path
## input slack credentials
slack = Slacker('<your-slack-api-token-goes-here>')
## plot graph of sales by day
fig = plt.figure(figsize=(15,10))plt.plot(y=orders_df['sales'], 
             x=orders_df['date'], 
             color = '#2ae8bf', 
             linewidth = 2,
             label='Total Orders by day',
             alpha=0.2)

## use a temporary directory to send the graph to slack
with tempfile.TemporaryDirectory() as td:
    local_path = Path(td, f"output graph {psku}.png") 
    plt.savefig(local_path, pad_inches=1)
            ## send graph to slack
    slack.files.upload(file=local_path.as_posix(), 
                    title='Daily Sales', 
                    initial_comment='Sales by day')

Prophet — Simple Time Series Forecasting

While there are definitely more complex time series forecasting methods, such as using LSTM Neural Networks, a great place to start when first beginning a time series forecasting project is with Facebook’s open-source package called Prophet.

Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

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Data Science Projects | Data Science | Machine Learning | Python

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.