Use the 'Symbol' column as the index.

LOAD

Use Pandas to load the data into a DataFrame. Display the data in the DataFrame.

How many non$-$null values exist for each column and what each column$$s type is$.$

Utilizing the shape property and the len$()$ function, find the number of rows and columns.

Display the first and last $10$ rows.

ACCESS

$5.$ Show the Company column.

$6.$ Show the columns named Company and Industry in a DataFrame.

$7.$ Present the information for the row that has the symbol AAPL.

$8.$ Show the Exchange for the row with the Symbol AAPL.

FILTER

$9.$ Sort $($and show$)$ the data by Industry, then Company.

$10.$ Show the rows where Industry is Information Technology.

MISSING

$11.$ Show the rows where Notes has a value $($other than missing$).$

DROPPING

$12.$ Create and display a DataFrame from the full DataFrame but excluding the Notes column.

JOIN

$13.$ The file Stock$-$Prices.csv has additional data, including Symbol and Price. Use join$()$ to augment the DataFrame with the Price data. python code