<jupyter_start><jupyter_text>Q1). How many apps are there in GAME?<jupyter_code>df_pr = df[df['Category'] == 'GAME']
len(df_pr[df_pr['Type'] == 'Free'])<jupyter_output><empty_output><jupyter_text>Q2) How many paid apps are there in Shopping?<jupyter_code>df_pr = df[df['Category'] == 'SHOPPING']
df_pr[df_pr['Type'] == 'Paid']<jupyter_output><empty_output><jupyter_text>Q3) How many free apps are there in BEAUTY and having rating more than 4.5?<jupyter_code>df_pr = df[df['Category'] == 'BEAUTY']
df_pr = df_pr[df_pr['Type'] == 'Free']
len(df_pr[df_pr['Rating'] > 4.5])<jupyter_output><empty_output><jupyter_text>Q4) Which category is having most number of paid apps?<jupyter_code>lst = []

for i in df['Category'].unique():

  df_pr = df[df['Category'] == i]
  df_pr = df_pr[df_pr['Type'] == 'Paid']

  lst.append([i, len(df_pr)])
df_pr = pd.DataFrame(lst, columns = ['Category', 'Qn']).sort_values(by = 'Qn', ascending = False)
df_pr.plot(kind = 'bar')
df_pr<jupyter_output><empty_output><jupyter_text>Q5) How many Paid apps are there in SOCIAL category and Teen Content Rating?<jupyter_code>df_pr = df[df['Category'] == 'SOCIAL']
df_pr = df_pr[df_pr['Type'] == 'Paid']
df_pr = df_pr[df_pr['Content Rating'] == 'Teen']

df_pr
df.head()
df.groupby('Category')['Rating'].mean()
df.groupby('Category').size().sort_values().plot(kind = 'bar', figsize = (10,6))
df.groupby('Type').size().plot(kind = 'pie', autopct = "%0.1f")
df.head()
df.groupby('Content Rating').size().sort_values(ascending = False).plot(kind = 'pie')<jupyter_output><empty_output>
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