import pandas as pd import matplotlib.pyplot as plt import matplotlib.patches as patches %matplotlib inline

def create_football_field(): # Create a rectangle defined via an anchor point *xy* and its *width* and *height* rect = patches.Rectangle((0, 0), 120, 53.3, facecolor='darkgreen', zorder=0) # Creating a subplot to plot our field on fig, ax = plt.subplots(1, figsize=(12, 6.33)) # Adding the rectangle to the plot ax.add_patch(rect) # Plotting a line plot for marking the field lines plt.plot([10, 10, 20, 20, 30, 30, 40, 40, 50, 50, 60, 60, 70, 70, 80, 80, 90, 90, 100, 100, 110, 110, 120, 0, 0, 120, 120], [0, 53.3, 53.3, 0, 0, 53.3, 53.3, 0, 0, 53.3, 53.3, 0, 0, 53.3, 53.3, 0, 0, 53.3, 53.3, 0, 0, 53.3, 53.3, 53.3, 0, 0, 53.3], color='white', zorder = 0) # Creating the left end-zone left_end_zone = patches.Rectangle((0, 0), 10, 53.3, facecolor='blue', alpha=0.2, zorder=0) # Creating the right end-zone right_end_zone = patches.Rectangle((110, 0), 120, 53.3, facecolor='blue', alpha=0.2, zorder=0) # Adding the patches to the subplot ax.add_patch(left_end_zone) ax.add_patch(right_end_zone) # Setting the limits of x-axis from 0 to 120 plt.xlim(0, 120) # Setting the limits of y-axis from -5 to 58.3 plt.ylim(-5, 58.3) # Removing the axis values from the plot plt.axis('off') # Plotting the numbers starting from x = 20 and ending at x = 110 # with a step of 10 for x in range(20, 110, 10): # Intializing another variable named 'number' number = x # If x exceeds 50, subtract it from 120 if x > 50: number = 120 - x # Plotting the text at the bottom plt.text(x, 5, str(number - 10), horizontalalignment='center', fontsize=20, color='white') # Plotting the text at the top plt.text(x - 0.95, 53.3 - 5, str(number - 10), horizontalalignment='center', fontsize=20, color='white', rotation=180) # Making ground markings for x in range(11, 110): ax.plot([x, x], [0.4, 0.7], color='white', zorder = 0) ax.plot([x, x], [53.0, 52.5], color='white', zorder = 0) ax.plot([x, x], [22.91, 23.57], color='white', zorder = 0) ax.plot([x, x], [29.73, 30.39], color='white', zorder = 0) # Returning the figure and axis return fig, ax

# Calling the plotting function fig, ax = create_football_field() # Plotting the figure plt.show()

# Reading the data as a Pandas DataFrame df = pd.read_csv('week_data.csv')

# Looking at the first five rows of the DataFrame df.head()

# Looking at the shape of the DataFrame df.shape

# Converting to Time values df['time'] = pd.to_datetime(df['time']).dt.time # Looking at the first five rows of the DataFrame df.head()

# Sorting the values of the DataFrame by time in an ascending order df = df.sort_values(by='time', ascending=True).reset_index(drop=True) # Looking at the first five rows of the DataFrame df.head()

# Selecting the data for the given game and play based on their Id sel_df = df.query('gameId == 2018111900 and playId == 5577') # Looking at the shape of the DataFrame print(f'The shape of the DataFrame is: {sel_df.shape}') # Looking at the DataFrame sel_df

# Selecting the home and away team home_team = sel_df.query('team == "home"') away_team = sel_df.query('team == "away"') # Selecting the football football = sel_df.query('team == "football"')

# Creating the football field fig, ax = create_football_field() # Plotitng the home team home_team.plot(x='x', y='y', kind='scatter', ax=ax, color='blue', s=20, zorder=2) # Plotting the away team away_team.plot(x='x', y='y', kind='scatter', ax=ax, color='orange', s=20, zorder=2) # Plotting the football football.plot(x='x', y='y', kind='scatter', ax=ax, color='brown', s=20, zorder=2) # Displaying the plot plt.show()

sel_df['event'].unique()

# Creating the football field fig, ax = create_football_field() # Plotitng the home team home_team.query('event == "ball_snap"').plot(x='x', y='y', kind='scatter', ax=ax, color='blue', s=20, zorder=2) # Plotting the away team away_team.query('event == "ball_snap"').plot(x='x', y='y', kind='scatter', ax=ax, color='orange', s=20, zorder=2) # Plotting the football football.query('event == "ball_snap"').plot(x='x', y='y', kind='scatter', ax=ax, color='brown', s=20, zorder=2) # Displaying the plot plt.show()