Let’s create a Python NIL (Name, Image, Likeness) deal analysis using pandas. In this analysis, we can use pandas to manage and analyze data related to NIL deals. Let’s break it down into a few steps:

1. Importing Libraries
2. Loading Data
3. Exploring Data
4. Analyzing Data
5. Visualizing Results

1. Importing Libraries

First, you’ll need to import the necessary libraries. pandas is essential for data manipulation, and matplotlib or seaborn can be used for visualization.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

2. Loading Data

Assuming you have a dataset in a CSV file containing information about NIL deals, you can load it into a pandas DataFrame.

# Load the data
df = pd.read_csv('nil_deals.csv')

# Display the first few rows of the dataframe
print(df.head())

3. Exploring Data

It’s important to understand the structure of your data. Look at the column names, data types, and check for any missing values.

# Summarize total deal amount by athlete
total_deals_by_athlete = df.groupby('athlete_name')['deal_amount'].sum().sort_values(ascending=False)
print(total_deals_by_athlete)

4. Analyzing Data

Here are some common analyses you might perform:

a. Total Deal Amount by…