import pandas as pd

# Load the dataset
file_path = '/mnt/data/Dynasty Knowledge Dataset 2.1 - 2023 IDP.csv'
df_idp = pd.read_csv(file_path)

# Check the first few rows of the dataframe to understand its structure
df_idp.head()

             Player   Tm  Age Pos   G  GS  Int   Yds  TD   Lng  ...   FR  \
0     Bobby Wagner*  SEA   33  LB  17  16  0.0   0.0   0   0.0  ...  1.0   
1    Zaire Franklin  IND   27  LB  16  16  0.0   0.0   0   0.0  ...  0.0   
2    Alex Singleton  DEN   30  LB  17  16  0.0   0.0   0   0.0  ...  2.0   
3  Foyesade Oluokun  JAX   28  LB  17  17  1.0  24.0   1  24.0  ...  3.0   
4   Azeez Al-Shaair  TEN   26  LB  17  17  0.0   0.0   0   0.0  ...  1.0   

   Yds.1  TD.1   Sk  Comb▼  Solo  Ast  TFL  QBHits  Sfty  
0    0.0     0  3.5    183    96   87   11       6   NaN  
1    0.0     0  1.5    179   107   72    3       3   NaN  
2    0.0     0  2.0    177   106   71    6       6   NaN  
3    0.0     0  2.5    173   111   62    8      10   NaN  
4    0.0     0  2.0    163    84   79    9       6   NaN  

[5 rows x 23 columns]

# Selecting the top 10 players based on total combined tackles
top_10_tackles = df_idp.nlargest(10, 'Comb▼')[['Player', 'Comb▼']].reset_index(drop=True)
top_10_tackles.columns = ['Player', 'Total Tackles']

# Display the top 10 players
top_10_tackles

             Player  Total Tackles
0     Bobby Wagner*            183
1    Zaire Franklin            179
2    Alex Singleton            177
3  Foyesade Oluokun            173
4   Azeez Al-Shaair            163
5     Roquan Smith*            158
6      T.J. Edwards            155
7       C.J. Mosley            152
8     Bobby Okereke            149
9   Robert Spillane            148

import matplotlib.pyplot as plt

# Plotting the bar chart for the top 10 tackle leaders
plt.figure(figsize=(12, 6))
plt.barh(top_10_tackles['Player'], top_10_tackles['Total Tackles'], color='skyblue')
plt.xlabel('Total Tackles')
plt.ylabel('Player')
plt.title('Top 10 Tackle Leaders in 2024')
plt.gca().invert_yaxis()  # Invert y-axis to have the player with the most tackles on top
plt.show()

# Enhancing the bar chart with more colors and dynamic features

# Creating a color palette with a different color for each bar
colors = plt.cm.viridis(np.linspace(0, 1, 10))

plt.figure(figsize=(14, 7))
bars = plt.barh(top_10_tackles['Player'], top_10_tackles['Total Tackles'], color=colors)

# Adding value labels to each bar
for bar in bars:
    plt.text(bar.get_width(), bar.get_y() + bar.get_height()/2, 
             f'{bar.get_width()}', 
             va='center', ha='left', color='black')

plt.xlabel('Total Tackles')
plt.ylabel('Player')
plt.title('Top 10 Tackle Leaders in 2024 - Colorful and Dynamic Chart')
plt.gca().invert_yaxis()  # Invert y-axis to have the player with the most tackles on top
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 4
      1 # Enhancing the bar chart with more colors and dynamic features
      2 
      3 # Creating a color palette with a different color for each bar
----> 4 colors = plt.cm.viridis(np.linspace(0, 1, 10))
      6 plt.figure(figsize=(14, 7))
      7 bars = plt.barh(top_10_tackles['Player'], top_10_tackles['Total Tackles'], color=colors)

NameError: name 'np' is not defined


import numpy as np

# Creating a color palette with a different color for each bar
colors = plt.cm.viridis(np.linspace(0, 1, 10))

plt.figure(figsize=(14, 7))
bars = plt.barh(top_10_tackles['Player'], top_10_tackles['Total Tackles'], color=colors)

# Adding value labels to each bar
for bar in bars:
    plt.text(bar.get_width(), bar.get_y() + bar.get_height()/2, 
             f'{bar.get_width()}', 
             va='center', ha='left', color='black')

plt.xlabel('Total Tackles')
plt.ylabel('Player')
plt.title('Top 10 Tackle Leaders in 2024 - Colorful and Dynamic Chart')
plt.gca().invert_yaxis()  # Invert y-axis to have the player with the most tackles on top
plt.show()

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