If your $2$D numpy array has a regular structure, i$.$e$.$ each row and column has a fixed number of values, complicated ways of subsetting become very easy. Have a look at the code below where the elements $"$a$"$ and $"$c$"$ are extracted from a list of lists.

# regular list of lists

x $=[["$a$","$b$"],["$c$","$d$"]]$

$[$x$[0][0],$ x$[1][0]]$

# numpy

import numpy as np

np$\_$x $=$ np$.$array$($x$)$

np$\_$x$[$:$,0]$

For regular Python lists, this is a real pain. For $2$D numpy arrays, however, it's pretty intuitive! The indexes before the comma refer to the rows, while those after the comma refer to the columns. The : is for slicing; in this example, it tells Python to include all rows.

The code that converts the pre$-$loaded baseball list to a $2$D numpy array is already in the script. The first column contains the players' height in inches and the second column holds player weight, in pounds. Add some lines to make the correct selections. Remember that in Python, the first element is at index $0!$ baseball is available as a regular list of lists.

Instructions

$100$ XP

Print out the $50$th row of np$\_$baseball.

Make a new variable, np$\_$weight$\_$lb$,$ containing the entire second column of np$\_$baseball.

Select the height $($first column$)$ of the $124$th baseball player in np$\_$baseball and print it out.