text: # --------------
  #Importing header files
  import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns
  from sklearn.preprocessing import MinMaxScaler, LabelEncoder
  
  
  
  
  #Loading the data
  data=pd.read_csv(path)
  
  # Histogram of Rating column
  
  data.Rating.hist()
  plt.show()
  
  data=data[data['Rating']<=5]
  
  # Histogram of Rating column
  
  data.Rating.hist()
  plt.show()
  
  data.isnull().sum()
  data.dropna(inplace=True)
  total_null = data.isnull().sum()
  print(total_null)
  #Category vs Rating
  
  #Setting the figure size
  plt.figure()
  
  #Plotting boxplot between Rating and Category
  cat = sns.catplot(x="Category",y="Rating",data=data,kind='box',height=10)
  
  #Rotating the xlabel rotation
  cat.set_xticklabels(rotation=90)
  
  #Setting the title of the plot
  plt.title("Rating vs Category",size=20)
  
  #Splitting the column to include only the first genre of each app
  data['Genres'] = data['Genres'].str.split(';').str[0]
  
  #Grouping Genres and Rating
  gr_mean = data[['Genres','Rating']].groupby(['Genres'], as_index=False).mean() 
  print(gr_mean.describe())
  
  #Sorting the grouped dataframe by Rating
  gr_mean_short = gr_mean.sort_values('Rating')
  print(gr_mean_short.min(),' \n',gr_mean_short.max())
  
  # Rating vs Installs
  #Removing `,` from the column
  data['Installs'] = data['Installs'].str.replace(',','')
  
  #Removing `+` from the column
  data['Installs'] = data['Installs'].str.replace('+','')
  
  #Converting the column to 'int' datatype
  data['Installs'] = data['Installs'].astype(int)
  
  #Creating a label encoder object
  le_en = LabelEncoder()
  
  #Label encoding the column to reduce the effect of a large range of values
  data['Installs'] = le_en.fit_transform(data['Installs'])
  
  #Setting figure size
  
  plt.figure(figsize = (10,6))
  
  #Plotting Regression plot between Rating and Installs
  sns.regplot(x='Installs',y='Rating',color='teal',data=data)
  
  #Setting the title of the plot
  plt.title('Rating vs Installment[RegPlot]',size=20)
  #Code starts here
  
  # Ratings vs Price
  #Removing the dollar sign from the column
  
  data['Price'] = data['Price'].str.replace('$','')
  
  #Converting the column to float
  data['Price'] = data['Price'].astype(float)
  
  #Setting the figure size
  plt.figure(figsize = (10,6))
  
  #Plotting Regression plot between Rating and Price
  sns.regplot(x="Price",y="Rating",data=data)
  
  #Setting the plot title
  plt.title('Rating vs Price[Reg Plot]',size = 20)
  
  data['Last Updated']=  pd.to_datetime(data['Last Updated'], format='%B %d, %Y' )
  
  #Setting the figure size
  
  #plt.figure()
  #data.plot.scatter(x='Last Updated',y='Rating')
  #plt.show()
  
  
  
  
  
  
  
  
  