Show the values in columns $2$ and $5$ ONLY.

Change the names of column "TransID" and "Product" to "TransactionNumber" and "ItemBought"

respectively.

Using list comprehension, generate a new list that has values for each: TransactionNumber modulo

Insert the list as a new column between CustomerName and TransactionNumber in the existing

DataFrame. The column name should be called TransactionHash.

Show the values in the DataFrame sorted by Age then by CustomerName $($for viewing purposes

only$).$

Delete the TransactionNumber series from the dataset.

Show the observations $($rows$)$ that meet the following criteria:

Transactions in which $3$ or more of any items were bought.

More than $$10$ was spent without a Coupon.

Replace all instances of "Cash" in the PaymentType column with "Debit Card".

Calculate the total value of all transactions where a Coupon was used.

Determine the average age of customers who paid with a Credit Card.

Create a new column "TotalCost" that is the product of "Quantity" and "Value".

Group the DataFrame by "Product" and calculate the sum of "Quantity" for each product.

Create a pivot table showing the sum of "Value" for each "PaymentType" and "Coupon"

combination.

Visualize the distribution of ages using a histogram.

Export the DataFrame to a CSV file named "transactions.csv$".$

Calculate the correlation matrix for the numerical columns in the DataFrame.

Identify and display the rows with duplicate 'Customer Name' entries, if any.

Convert the 'Value' column to a different currency $($e$.$g$.,$ from USD to EUR$),$ assuming a conversion

rate of $1$USD$=0\mathrm{.}85$EUR. Add this as a new column named 'Value$\_$EUR'.

Filter and display transactions made by customers who are over $40$ years old.

Create a summary table that shows the mean, median, and standard deviation of the 'Value' column

for each 'Product'.

Use a bar plot to visualize the total 'Quantity' sold for each 'Product'.

For each 'PaymentType', calculate the total 'Value' of transactions and display it using a pie chart. Implement a function that takes a product name as an argument and returns all transactions

involving that product. Demonstrate the function with an example.

Determine the most frequent 'Product' in the dataset and show its frequency.

Calculate the cumulative sum of 'Value' for transactions sorted by 'TransID'. Add this as a new

column named 'CumulativeValue'.

Normalize the 'Age' column using Min$-$Max normalization. Add the normalized values as a new

column named 'NormalizedAge'.

Extract and display all transactions where the 'Customer Name' contains the substring 'Brown'.

Generate a scatter plot to visualize the relationship between 'Age' and 'Quantity'.

Create a new column 'AgeGroup' that categorizes customers into 'Youth' $($below $30),$ 'Adult'

$(30-60),$ and 'Senior' $($above $60).$

Implement a method to handle missing values: assume some values in 'Coupon' column are

missing, and replace them with $\text{'}$N$\text{'}.$ Demonstrate this on a modified dataset.Help need