code: # --------------
  #Header files
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  #path of the data file- path
  data = pd.read_csv(path)
  #Code starts here 
  data['Gender'].replace('-','Agender',inplace=True)
  gender_count = data['Gender'].value_counts()
  gender_count.plot(kind='bar', title ="Gender",figsize=(15,10),legend=True, fontsize=12)
  plt.show()
  
  
  # --------------
  #Code starts here
  alignment=data['Alignment'].value_counts()
  alignment.plot.pie()
  plt.title('Character Alignment')
  plt.show()
  
  
  # --------------
  #Code starts here
  import pandas as pd
  #strength and combat
  sc_df = data[['Strength','Combat']].copy()
  sc_covariance= round((sc_df['Strength'].cov(sc_df['Combat'])),2)
  sc_strength = round((sc_df['Strength'].std()),2)
  sc_combat = round((sc_df['Combat'].std()),2)
  sc_pearson = round((sc_covariance/(sc_combat*sc_strength)),2)
  #intelligence and combat
  ic_df = round((data[['Intelligence','Combat']].copy()),2)
  ic_covariance = round((ic_df['Intelligence'].cov(ic_df['Combat'])),2)
  ic_intelligence = round((ic_df['Intelligence'].std()),2)
  ic_combat = round((ic_df['Combat'].std()),2)
  ic_pearson = round((ic_covariance/(ic_combat*ic_intelligence)),2)
  
  
  
  # --------------
  #Code starts here
  total_high = np.quantile(data['Total'], .99)
  #print(total_high)
  super_best = data[data['Total']>total_high]
  
  super_best_names = super_best['Name'].tolist()
  print(super_best_names)
  
  
  # --------------
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  import seaborn as sns
  fig,(ax_1,ax_2,ax_3) = plt.subplots(3,1)
  ax_1.plot(data['Intelligence'])
  ax_1.set_title('Intelligence')
  ax_1.set_xlabel('Intelligence')
  ax_1.legend()
  
  ax_2.plot(data['Speed'])
  ax_2.set_title('Speed')
  ax_2.set_xlabel('Speed')
  ax_2.legend()
  
  ax_3.plot(data['Power'])
  ax_3.set_title('Power')
  ax_3.set_xlabel('Power')
  ax_3.legend()
  plt.tight_layout()
  plt.show()
  
  
  
apis: numpy.quantile pandas.read_csv matplotlib.pyplot.tight_layout matplotlib.pyplot.subplots matplotlib.pyplot.title matplotlib.pyplot.show