This is a code / query task — you are asked to write Python (pandas) code for each of the seven sub-parts (a through g) based on the given table.

(a) To create the DataFrame for the above table

import pandas as pd

data = {
    'Item': ['TV', 'TV', 'TV', 'AC'],
    'Company': ['LG', 'VIDEOCON', 'LG', 'SONY'],
    'Rupees': [12000, 10000, 15000, 14000],
    'USD': [700, 650, 800, 750]
}

df = pd.DataFrame(data)

The dictionary keys become column names, and each list provides the column values in order. This is the most direct way to create a DataFrame from tabular data.

(b) To add new rows in the DataFrame

new_row = pd.DataFrame({'Item': ['AC'], 'Company': ['LG'], 'Rupees': [13000], 'USD': [720]})
df = pd.concat([df, new_row], ignore_index=True)

pd.concat() appends the new row DataFrame to the original. ignore_index=True resets the index so it runs sequentially. You can also use df.loc[len(df)] = ['AC', 'LG', 13000, 720] as a simpler alternative.

(c) To display the maximum price of LG TV

max_price = df[(df['Item'] == 'TV') & (df['Company'] == 'LG')]['Rupees'].max()
print(max_price)

The boolean mask (df['Item'] == 'TV') & (df['Company'] == 'LG') filters rows where both conditions hold. .max() then returns the highest value in the Rupees column of that filtered set.

(d) To display the sum of all products (Rupees)

total_rupees = df['Rupees'].sum()
print(total_rupees)

.sum() on a Series adds all values. If the question meant "sum of all products" as in total Rupees across all rows, this is it. If it meant sum per product group, you would use df.groupby('Item')['Rupees'].sum().

(e) To display the median of the USD of Sony products

median_usd_sony = df[df['Company'] == 'SONY']['USD'].median()
print(median_usd_sony)

Filter for Sony rows, then call .median() on the USD column. With only one Sony row in the original data (USD = 750), the median is 750. If more Sony rows were added in part (b), the median would adjust accordingly.

(f) To sort the data according to Rupees and transfer the data to MySQL

import mysql.connector

df_sorted = df.sort_values(by='Rupees', ascending=True)

conn = mysql.connector.connect(host='localhost', user='root', password='password', database='mydb')
cursor = conn.cursor()

for _, row in df_sorted.iterrows():
    cursor.execute("INSERT INTO product (Item, Company, Rupees, USD) VALUES (%s, %s, %s, %s)",
                   (row['Item'], row['Company'], row['Rupees'], row['USD']))

conn.commit()
cursor.close()
conn.close()
``` …

The core idea here is that a DataFrame is the right tool because it gives us tabular data with labelled columns and row indices, plus a rich set of built-in methods for aggregation, filtering, and sorting. We'll use Pandas for the in-memory work and SQLAlchemy (or pymysql) to push data to MySQL.

Let's build this step by step.

(a) Create the DataFrame

We construct the DataFrame directly from a dictionary of lists — each key becomes a column name, each list becomes the column's values. This is the most readable way when you have the data in front of you.

import pandas as pd

data = {
    'Item': ['TV', 'TV', 'TV', 'AC'],
    'Company': ['LG', 'VIDEOCON', 'LG', 'SONY'],
    'Rupees': [12000, 10000, 15000, 14000],
    'USD': [700, 650, 800, 750]
}

df = pd.DataFrame(data)
print(df)

Output:

  Item   Company  Rupees  USD
0   TV        LG   12000  700
1   TV  VIDEOCON   10000  650
2   TV        LG   15000  800
3   AC      SONY   14000  750

(b) Add new rows

We use pd.concat() to append a new DataFrame (or a list of rows) to the existing one. The ignore_index=True parameter resets the index so we don't carry over old indices.

new_rows = pd.DataFrame([
    ['AC', 'LG', 18000, 900],
    ['TV', 'SONY', 13000, 720]
], columns=['Item', 'Company', 'Rupees', 'USD'])

df = pd.concat([df, new_rows], ignore_index=True)
print(df)

Output:

  Item   Company  Rupees  USD
0   TV        LG   12000  700
1   TV  VIDEOCON   10000  650
2   TV        LG   15000  800
3   AC      SONY   14000  750
4   AC        LG   18000  900
5   TV      SONY   13000  720

(c) Display maximum price of LG TV

We filter the DataFrame for rows where Item == 'TV' and Company == 'LG', then take the max of the Rupees column.

max_lg_tv = df[(df['Item'] == 'TV') & (df['Company'] == 'LG')]['Rupees'].max()
print("Maximum price of LG TV:", max_lg_tv)

Output:

Maximum price of LG TV: 15000

(d) Display sum of all products (Rupees)

The sum() method on a Series gives the total. We call it on the Rupees column.

total_rupees = df['Rupees'].sum()
print("Sum of all products (Rupees):", total_rupees)

Output:

Sum of all products (Rupees): 82000

(e) Display median of USD for Sony products

First filter for Company == 'SONY', then compute the median of the USD column.

sony_usd_median = df[df['Company'] == 'SONY']['USD'].median()
print("Median USD of Sony products:", sony_usd_median)

Output:

Median USD of Sony products: 735.0

(f) Sort by Rupees and transfer to MySQL

We sort the DataFrame using sort_values(), then use to_sql() to write it to a MySQL table. You'll need sqlalchemy and a MySQL driver (pymysql or mysql-connector-python).

# Sort by Rupees ascending
df_sorted = df.sort_values(by='Rupees')

# Connect to MySQL and write …