text: # --------------
  #Header files
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  
  #path of the data file - path
  #Code starts here 
  ##Loading the dataframe and saving the same as a variable
  data = pd.read_csv(path)
  
  ##Replacing '-' with 'Agender' using the replace() function in column Gender and saving it as variable gender_count
  data["Gender"].replace('-', 'Agender', inplace = True)
  gender_count = data["Gender"].value_counts()
  
  ##Plotting a bar graph of gender_count
  gender_count.plot.bar()
  
  #Code ends here
  
  
  # --------------
  #Code starts here 
  ##Creating a value-count variable for Alignment, named alignment
  alignment = data["Alignment"].value_counts()
  
  ##Plotting a pie-chart of alignment and adding a label to it
  alignment.plot.pie()
  plt.title('Character Alignment')
  
  #Code ends here
  
  
  # --------------
  #Code starts here
  ##Creating a new dataframe sc_df comprising only Strength and Combat
  sc_df = data[["Strength", "Combat"]]
  
  ##Computing the covariance between the above two variables and the std.dev. of both and saving all three as variables
  sc_covariance = sc_df["Strength"].cov(sc_df["Combat"])
  sc_strength, sc_combat = sc_df.std()[0], sc_df.std()[1]
  
  ##Computing the Pearson's correlation coefficient between the above two variablesand saving it as a variable
  sc_pearson = sc_covariance/(sc_strength*sc_combat)
  
  ##Repeating the same process for Intelligence and Combat
  ic_df = data[["Intelligence", "Combat"]]
  ic_covariance = ic_df["Intelligence"].cov(ic_df["Combat"])
  ic_intelligence, ic_combat = ic_df.std()[0], ic_df.std()[1]
  ic_pearson = ic_covariance/(ic_intelligence*ic_combat)
  
  ##Outputing the Pearson's correlation coefficient between Strength and Combat, and that between Intelligence and Combat
  print("Strength--Combat Perason's Corr.: ", sc_pearson)
  print("Intelligence--Combat Perason's Corr.: ", ic_pearson)
  
  ##Things to ponder upon:
  #Q: Based on the above calculations, which attribute is more correlated to combat? Strength or Intelligence?
  #A: Intelligence is more correlated with combat.
  #Q: Why did we use Pearson's correlation instead of Spearman?
  #A: We use Pearson's as we are dealing with quantitative data.
  
  
  # --------------
  #Code starts here
  ##Calculating the 99th percentile of Total and saving it as a variable
  total_high = data["Total"].quantile(0.99)
  
  ##Subseting the dataframe for only those entries where the Total score is in the top-1%
  super_best = data[data.Total > total_high]
  
  ##Outputing the top-1% Total score Names as a list
  super_best_names = [super_best.Name]
  print(super_best_names)
  
  #Code ends here
  
  
  # --------------
  #Code starts here
  ##
  ax_1, ax_2, ax_3 = super_best.Intelligence, super_best.Speed, super_best.Power
  
  axes_list = [ax_1, ax_2, ax_3]
  
  plt.boxplot(axes_list)
  
  
  
  