Buddy Bass Tournament 2020

Introduction

The Buddy Bass Tournament on Williamstown Lake was started in 1985 by twin brothers: Bob and Sam Perry. They held their final tournament season in 2020.

In this notebook, I'll take a look at the dataset from the Buddy Bass Tournament 2020 Season. I will use the following dataset: dataset/Buddy_Bass_Tournament_2020.xlsx, that I received from my dad, Sam Perry.

How to Run this Project

via Anaconda

1. Install Anaconda if you don't have it installed (https://docs.anaconda.com/anaconda/install/index.html)
2. Clone this repository from Github https://github.com/istarlet/buddy_bass_tournament/
3. Open Jupyter Notebook in Anaconda
4. Open cloned repo and run 'buddy_bass_.ipynb'

Python packages used in this project:

• matplotlib
• pandas

Project Requirements

1. Read Data In

Read in data from a local csv, excel file, json, or any other file type.

I read in an excel file with data from my dad's "Buddy Bass Tournament".

2. Manipulate and Clean Your Data

Use built-in pandas or numpy functions to do things like remove 0’s and null values where they don’t belong in your dataset.

1. skiprows - I used skiprows when reading the datasets in to skip over the title so the dataframe will start at the column headings.
2. pd.Series(pd.date_range()) - I created a series from date_range that starts on 06/10/2022 and then every Wednesday for the next 15 periods.
3. .drop() - I used drop to remove the "Big Fish/Year (LBS)" column from the dataframe.
4. .fillna - I used .fillna to replace all instances of NaN in the dataframe with 0.

3. Analyze Your Data

Use at least 5 different built-in Python functions to find out something about your data.

Do 5 basic calculations with Pandas

1. .info() - I used .info() to display information about the columns including data type and number of missing values, if any
2. shape - I used .shape to return the number of rows and columns in the data
3. .describe() - I used .describe to display a summary of statistics calculated for each column
4. .groupby + .sum() - I used .groupby to group the data by "Month" and calculate the sum for "Number of Boats", "Total No of Fish Caught", and "Total Weight in Pounds" by month
5. Find tournament dates where number of boats was less than 30 - buddy_bass_2020[buddy_bass_2020["Number of Boats"] < 30]
6. .median() - I used .median to find the median number of fish caught

4. Visualize Your Data

Make 2 basic plots with matplotlib, seaborn, or any other kind of visualization library that you think looks interesting.

Fig.1 - Scatter Plot

Fig. 2 - Bar Plot

5. Interpret Your Data and Graphical Output

Write markdown cells in Jupyter explaining your thought process and code.

Throughout my Jupyter Notebook you will find comments and markdown cells where I explain my thought process and code.