$1.$ Write Python code to read your CSV file in Jupyter Notebook.

$2.$ Write Python code to read the first $5$ records and the last $5$ records of your CSV file.

$3.$ Write Python code to get the summary of your dataset. The result should look like below

$4.$ Write Python code to get the statistics of your dataset as below $($approx$.)$

$5.$ There is a column named age$\_$sex $($see fig. below$).$ Could you split that column and make two extra columns called sex and age?

$6.$ Now the column age$\_$sex is no longer needed. Can you remove that column and produce the updated output?

$7.$ Write Python code to Re$-$order the columns as below

$8.$ Replace the following negative values $($see fig. below$)$ to its positive values, such replace $60\mathrm{.}0$ to $60\mathrm{.}0$ etc.

$9.$ You might have noticed from the above screen shot that one of the person$$s height is $0\mathrm{.}0,$ and other ones$$ weight is $160$kg$.$ Obviously, these are outliers$/$wrong data. Can you replace them with their respective average please?

$10.$ Write your python code to calculate the null values of each column?

$11.$ Replace the NaN values with their respective average$/$median values and display the whole table and take a screen shot.

$12.$ Write your python code to calculate the null values of each column again $($after replacing NaN with average$)$

$13.$ Create a Bar chart for weight.

$14.$ Create a histogram for height.

$15.$ Create a scatter plot between height $($x$)$ and weight $($y$).$