---------------------------------------------------------------------------AttributeError Traceback (most recent call last)Cell In[12], line 5 2 a = df.groupby(['City', 'Cuisines']).size().reset_index(name='Counts') 4 # Find the most prevalent cuisines in each city----> 5 n = a.loc[df.count.groupby('City')['Counts'].idxmax()].head(5)AttributeError: 'function' object has no attribute 'groupby'

KeyError Traceback (most recent call last)Cell In[94], line 2 1 # Sales by customer type----> 2 sales_by_customer_type = july.groupby('Customer Type')['Amount'].sum().sort_values(ascending=False) 4 # Plotting 5 plt.figure(figsize=(12, 8))File ~\anaconda3\lib\site-packages\pandas\core\frame.py:9183, in DataFrame.groupby(self, by, axis, level, as_index, sort, group_keys, observed, dropna) 9180 if level is None and by is None: 9181 raise TypeError("You have to supply one of 'by' and 'level'")-> 9183 return DataFrameGroupBy( 9184 obj=self, 9185 keys=by, 9186 axis=axis, 9187 level=level, 9188 as_index=as_index, 9189 sort=sort, 9190 group_keys=group_keys, 9191 observed=observed, 9192 dropna=dropna, 9193 )File ~\anaconda3\lib\site-packages\pandas\core\groupby\groupby.py:1329, in GroupBy.__init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna) 1326 self.dropna = dropna 1328 if grouper is None:-> 1329 grouper, exclusions, obj = get_grouper( 1330 obj, 1331 keys, 1332 axis=axis, 1333 level=level, 1334 sort=sort, 1335 observed=False if observed is lib.no_default else observed, 1336 dropna=self.dropna, 1337 ) 1339 if observed is lib.no_default: 1340 if any(ping._passed_categorical for ping in grouper.groupings):File ~\anaconda3\lib\site-packages\pandas\core\groupby\grouper.py:1043, in get_grouper(obj, key, axis, level, sort, observed, validate, dropna) 1041 in_axis, level, gpr = False, gpr, None 1042 else:-> 1043 raise KeyError(gpr) 1044 elif isinstance(gpr, Grouper) and gpr.key is not None: 1045 # Add key to exclusions 1046 exclusions.add(gpr.key)KeyError: 'Customer Type'

3. import pandas as pddata = pd.DataFrame({'name':['Tatenda','Hazel','Carlos','Tinaye','Judah','Tawanda','Lebo','Chenge','Solomon','Simba'], 'rating':[60,60,98,100,96,96,96,80,94,50]})mask = data.duplicated()filtered_data = data.loc[data.duplicated(keep= False)]Which of the following statements correctly describes the results of mask and filtered_data?mask will be a Series indicating whether each row is a duplicate, including the first occurrence, and filtered_data will contain only rows that are unique.mask will be a Series indicating whether each row is a duplicate, excluding the first occurrence, and filtered_data will be a DataFrame including all rows that are duplicates, including both occurrences of each duplicate.mask will be a DataFrame with boolean values indicating duplicate status, and filtered_data will be a DataFrame containing rows that are unique.mask will be a Series with True for rows that are duplicates including the first occurrence, and filtered_data will be a DataFrame including only the first occurrence of each duplicate.

data = pd.DataFrame({ 'name': ['Tatenda', 'Hazel', 'Carlos', 'Tinaye', 'Judah', 'Tawanda', 'Lebo', 'Chenge', 'Solomon', 'Simba'], 'department': ['Data Analyst', 'Data Analyst', 'Actuarial', 'Actuarial', 'Development', 'Development', 'Data Analyst', 'Data Analyst', 'Actuarial', 'Data Analyst'], 'project_count': [8, 10, 20, 30,20 , 15, 20, 10, 20, 11]})data.shape, data.describe()Which of the following statements correctly distinguishes between methods and attributes in the context of data.shape and data.describe()?data.shape is a method that returns the number of rows and columns in the DataFrame, while data.describe() is an attribute that shows a summary of the DataFrame's numeric data.Both data.shape and data.describe() are methods that perform operations on the DataFrame, with data.shape showing dimensions and data.describe() computing summary statistics.data.shape is an attribute that returns a tuple representing the dimensions of the DataFrame, while data.describe() is a method that generates descriptive statistics of the DataFrame's numeric columns.data.describe() is an attribute that returns descriptive statistics, while data.shape is a method that computes the dimensions of the DataFrame.

---------------------------------------------------------------------------TypeError Traceback (most recent call last)Cell In[20], line 2 1 # df['1-Do you spend your time on Netflix or binge watching shows or anime?']=df['1-Do you spend your time on Netflix or binge watching shows or anime?'].cat.codes()----> 2 df['2-At which time do you prefer watching binge-watching/Netflix? ']=df['2-At which time do you prefer watching binge-watching/Netflix? '].cat.codes() 3 df['3-What type of genre do you prefer?']=df['3-What type of genre do you prefer?'].cat.codes() 4 df['4-How many hours do you sleep daily?']=df['4-How many hours do you sleep daily?'].cat.codes()TypeError: 'Series' object is not callable

22. You have dataframe called food_prices below:ItemStorePriceDatePearA0.992017PearB1.992017PeachA2.992017PeachB3.492017BananaA0.392017BananaB0.492017SteakA5.992017SteakB 6.992017steakB4.992015 and another called food_transactions below: IndexCustIDItemStoreQuantity01PearA511PearB1022PeachA332PeachB142BananaA252BananaB162SteakA4 Which of the following line of code will give the ouput below?IndexCustIDItemStoreQuantityPriceDate01PearA50.99201711PearB101.99201722PeachA32.99201732PeachB13.49201742BananaA20.39201752BananaB10.49201762SteakA45.992017food_transactions.merge(food_prices,on = ['item','store'])food_transactions.join(food_prices,on = ['item','store'])food_transactions.concat(food_prices,on = ['item','store'])food_prices.concat(food_transactions,on = ['item','store'])None of the above