This repo contains all of my Datacamp source code and projects. Anyone use this code and program First Course of Data Analyst with Python

First Problem Exercise No = 1

Python is perfectly suited to do basic calculations. Apart from addition, subtraction, multiplication and division, there is also support for more advanced operations such as:

it's 100×1.1=110 100×1.1=110 dollars, and after two years it's 100×1.1×1.1=121 100×1.1×1.1=121 Add code on the right to calculate how much money you end up with after 7 years.

Remember how you calculated the money you ended up with after 7 years of investing $100? You did something like this: 100 * 1.10 ** 7 Instead of calculating with the actual values, you can use variables instead. The savings variable you've created in the previous exercise represents the $100 you started with. It's up to you to create a new variable to represent 1.10 and then redo the calculations!

In the previous exercise, you worked with two Python data types:

# Create a variable desc
desc = "compound interest"
# Create a variable profitable
profitable = True

To find out the type of a value or a variable that refers to that value, you can use the type() function. Suppose you've defined a variable a, but you forgot the type of this variable. To determine the type of a, simply execute: type(a) We already went ahead and created three variables: a, b and c. You can use the IPython shell on the right to discover their type. Which of the following options is correct?

Filip mentioned that different types behave differently in Python. When you sum two strings, for example, you'll get different behavior than when you sum two integers or two booleans. In the script some variables with different types have already been created. It's up to you to use them.

As opposed to int, bool etc., a list is a compound data type; you can group values together:

a = "is"
b = "nice"
my_list = ["my", "list", a, b]

After measuring the height of your family, you decide to collect some information on the house you're living in. The areas of the different parts of your house are stored in separate variables for now, as shown in the script.

A list can contain any Python type. Although it's not really common, a list can also contain a mix of Python types including strings, floats, booleans, etc. The printout of the previous exercise wasn't really satisfying. It's just a list of numbers representing the areas, but you can't tell which area corresponds to which part of your house. The code on the right is the start of a solution. For some of the areas, the name of the corresponding room is already placed in front. Pay attention here! "bathroom" is a string, while bath is a variable that represents the float 9.50 you specified earlier.

# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# Adapt list areas
areas = ["hallway", hall, "kitchen", kit, "living room", liv,"bedroom", bed, "bathroom", bath]

# Print areas
print (areas)

As a data scientist, you'll often be dealing with a lot of data, and it will make sense to group some of this data.

Instead of creating a flat list containing strings and floats, representing the names and areas of the rooms in your house, you can create a list of lists. The script on the right can already give you an idea.

Don't get confused here: "hallway" is a string, while hall is a variable that represents the float 11.25 you specified earlier.

• Finish the list of lists so that it also contains the bedroom and bathroom data. Make sure you enter these in order!
• Print out house; does this way of structuring your data make more sense?
• Print out the type of house. Are you still dealing with a list?

# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# house information as list of lists
house = [["hallway", hall],
         ["kitchen", kit],
         ["living room", liv],
         ["bedroom", bed],
         ["bathroom", bath]]
# Print out house
print (house)
# Print out the type of house
print (type(house))

Subsetting Python lists is a piece of cake. Take the code sample below, which creates a list x and then selects "b" from it. Remember that this is the second element, so it has index 1. You can also use negative indexing.

x = ["a", "b", "c", "d"]
x[1]
x[-3] # same result!

Remember the areas list from before, containing both strings and floats? Its definition is already in the script. Can you add the correct code to do some Python subsetting?

• Print out the second element from the areas list, so 11.25.
• Subset and print out the last element of areas, being 9.50. Using a negative index makes sense here!
• Select the number representing the area of the living room and print it out.

# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Print out second element from areas
print (areas[1])

# Print out last element from areas
print (areas[-1])

# Print out the area of the living room
print (areas[5])

After you've extracted values from a list, you can use them to perform additional calculations. Take this example, where the second and fourth element of a list x are extracted. The strings that result are pasted together using the + operator:

x = ["a", "b", "c", "d"]
print(x[1] + x[3])

• Using a combination of list subsetting and variable assignment, create a new variable, eat_sleep_area, that contains the sum of the area of the kitchen and the area of the bedroom.
• Print the new variable eat_sleep_area

# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Sum of kitchen and bedroom area: eat_sleep_area
eat_sleep_area = areas[3] + areas[7]

# Print the variable eat_sleep_area
print (eat_sleep_area)

Selecting single values from a list is just one part of the story. It's also possible to slice your list, which means selecting multiple elements from your list. Use the following syntax:

my_list[start:end]

The start index will be included, while the end index is not. The code sample below shows an example. A list with "b" and "c", corresponding to indexes 1 and 2, are selected from a list x:

x = ["a", "b", "c", "d"]
x[1:3]

The elements with index 1 and 2 are included, while the element with index 3 is not.

• Use slicing to create a list, downstairs, that contains the first 6 elements of areas.
• Do a similar thing to create a new variable, upstairs, that contains the last 4 elements of areas.
• Print both downstairs and upstairs using print().

# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Use slicing to create downstairs
downstairs = areas[0:6]
# Use slicing to create upstairs
upstairs = areas[6:11]

# Print out downstairs and upstairs
print (downstairs)x[2] results in a list, that you can subset again by adding additional square brackets.
print (upstairs)

You saw before that a Python list can contain practically anything; even other lists! To subset lists of lists, you can use the same technique as before: square brackets. Try out the commands in the following code sample in the IPython Shell:

x = [["a", "b", "c"],
     ["d", "e", "f"],
     ["g", "h", "i"]]
x[2][0]
x[2][:2]

What will house[-1][1] return? house, the list of lists that you created before, is already defined for you in the workspace. You can experiment with it in the IPython Shell.

Replacing list elements is pretty easy. Simply subset the list and assign new values to the subset. You can select single elements or you can change entire list slices at once. Use the IPython Shell to experiment with the commands below. Can you tell what's happening and why?

x = ["a", "b", "c", "d"]
x[1] = "r"
x[2:] = ["s", "t"]

For this and the following exercises, you'll continue working on the areas list that contains the names and areas of different rooms in a house.

• Update the area of the bathroom area to be 10.50 square meters instead of 9.50.
• Make the areas list more trendy! Change "living room" to "chill zone".

# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]
# Correct the bathroom area
areas[-1] = 10.50
# Change "living room" to "chill zone"
areas[4]="chill zone"
print (areas )

If you can change elements in a list, you sure want to be able to add elements to it, right? You can use the + operator:

x = ["a", "b", "c", "d"]
y = x + ["e", "f"]

You just won the lottery, awesome! You decide to build a poolhouse and a garage. Can you add the information to the areas list?

• Use the + operator to paste the list ["poolhouse", 24.5] to the end of the areas list. Store the resulting list as areas_1.
• Further extend areas_1 by adding data on your garage. Add the string "garage" and float 15.45. Name the resulting list areas_2.

# Create the areas list and make some changes
areas = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0,
         "bedroom", 10.75, "bathroom", 10.50]

# Add poolhouse data to areas, new list is areas_1
areas_1 = areas + ["poolhouse", 24.5]
# Add garage data to areas_1, new list is areas_2

areas_2 = areas_1 + ["garage", 15.45]

print (areas_2)

At the end of the video, Filip explained how Python lists work behind the scenes. In this exercise you'll get some hands-on experience with this. The Python code in the script already creates a list with the name areas and a copy named areas_copy. Next, the first element in the areas_copy list is changed and the areas list is printed out. If you hit Run Code you'll see that, although you've changed areas_copy, the change also takes effect in the areas list. That's because areas and areas_copy point to the same list. If you want to prevent changes in areas_copy from also taking effect in areas, you'll have to do a more explicit copy of the areas list. You can do this with list() or by using [:].

Change the second command, that creates the variable areas_copy, such that areas_copy is an explicit copy of areas. After your edit, changes made to areas_copy shouldn't affect areas. Hit Submit Answer to check this.

# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Create areas_copy
areas_copy = list(areas)

# Change areas_copy
areas_copy[0] = 5.0

# Print areas
print(areas)
print(areas_copy)

Out of the box, Python offers a bunch of built-in functions to make your life as a data scientist easier. You already know two such functions: print() and type(). You've also used the functions str(), int(), bool() and float() to switch between data types. These are built-in functions as well. Calling a function is easy. To get the type of 3.0 and store the output as a new variable, result, you can use the following

result = type(3.0)

The general recipe for calling functions and saving the result to a variable is thus:

output = function_name(input)

• Use print() in combination with type() to print out the type of var1.
• Use len() to get the length of the list var1. Wrap it in a print() call to directly print it out.
• Use int() to convert var2 to an integer. Store the output as out2.

# Create variables var1 and var2
var1 = [1, 2, 3, 4]
var2 = True

# Print out type of var1
print (type(var1))

# Print out length of var1
print (len(var1))

# Convert var2 to an integer: out2
out2 = int(var2)

In the previous exercise, the square brackets around imag in the documentation showed us that the imag argument is optional. But Python also uses a different way to tell users about arguments being optional. Have a look at the documentation of sorted() by typing help(sorted) in the IPython Shell. You'll see that sorted() takes three arguments: iterable, key and reverse. key=None means that if you don't specify the key argument, it will be None. reverse=False means that if you don't specify the reverse argument, it will be False. In this exercise, you'll only have to specify iterable and reverse, not key. The first input you pass to sorted() will be matched to the iterable argument, but what about the second input? To tell Python you want to specify reverse without changing anything about key, you can use =:

sorted(___, reverse = ___)

Two lists have been created for you on the right. Can you paste them together and sort them in descending order? Note: For now, we can understand an iterable as being any collection of objects, e.g. a List.

• Use + to merge the contents of first and second into a new list: full.
• Call sorted() on full and specify the reverse argument to be True. Save the sorted list as full_sorted.
• Finish off by printing out full_sorted.

# Create lists first and second
first = [11.25, 18.0, 20.0]
second = [10.75, 9.50]

# Paste together first and second: full
full = first + second

# Sort full in descending order: full_sorted
full_sorted = sorted(full,reverse=True)

# Print out full_sorted
print (full_sorted)

Strings come with a bunch of methods. Follow the instructions closely to discover some of them. If you want to discover them in more detail, you can always type help(str) in the IPython Shell. A string place has already been created for you to experiment with.

• Use the upper() method on place and store the result in place_up. Use the syntax for calling methods that you learned in the previous video.
• Print out place and place_up. Did both change?
• Print out the number of o's on the variable place by calling count() on place and passing the letter 'o' as an input to the method. * * We're talking about the variable place, not the word "place"!

# string to experiment with: room
room = "poolhouse"

# Use upper() on room: room_up
room_up = room.upper()

# Print out room and room_up
print (room)
print (room_up)
# Print out the number of o's in room
print (room.count("o"))

Strings are not the only Python types that have methods associated with them. Lists, floats, integers and booleans are also types that come packaged with a bunch of useful methods. In this exercise, you'll be experimenting with:

• index(), to get the index of the first element of a list that matches its input and count(), to get the number of times an element appears in a list.
• You'll be working on the list with the area of different parts of a house: areas.

• Use the index() method to get the index of the element in areas that is equal to 20.0. Print out this index.
• Call count() on areas to find out how many times 9.50 appears in the list. Again, simply print out this number.

# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Print out the index of the element 20.0
print (areas.index(20.0))

# Print out how often 14.5 appears in areas

print (areas.count(14.5))

Most list methods will change the list they're called on. Examples are:

• append(), that adds an element to the list it is called on,
• remove(), that removes the first element of a list that matches the input, and reverse(), that reverses the order of the elements in the list it is called on.
• You'll be working on the list with the area of different parts of the house: areas.

• Use append() twice to add the size of the poolhouse and the garage again: 24.5 and 15.45, respectively. Make sure to add them in this order.
• Print out areas
• Use the reverse() method to reverse the order of the elements in areas.
• Print out areas once more.

# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Use append twice to add poolhouse and garage size
areas.append(24.5)
areas.append(15.45)
# Print out areas
print (areas)
# Reverse the orders of the elements in areas
print (areas.reverse())

# Print out areas
print (areas)

As a data scientist, some notions of geometry never hurt. Let's refresh some of the basics. For a fancy clustering algorithm, you want to find the circumference, C=2πr A=πr2 To use the constant pi, you'll need the math package. A variable r is already coded in the script. Fill in the code to calculate C and A and see how the print() functions create some nice printouts.

• Import the math package. Now you can access the constant pi with math.pi.
• Calculate the circumference of the circle and store it in C.
• Calculate the area of the circle and store it in A.

# Definition of radius
r = 0.43

# Import the math package
import math

# Calculate C
C = 2 * r * math.pi

# Calculate A
A = math.pi * r ** 2

# Build printout
print("Circumference: " + str(C))
print("Area: " + str(A))

General imports, like import math, make all functionality from the math package available to you. However, if you decide to only use a specific part of a package, you can always make your import more selective: from math import pi Let's say the Moon's orbit around planet Earth is a perfect circle, with a radius r (in km) that is defined in the script.

• Perform a selective import from the math package where you only import the radians function.
• Calculate the distance travelled by the Moon over 12 degrees of its orbit. Assign the result to dist. You can calculate this as r * phi, where r is the radius and phi is the angle in radians. To convert an angle in degrees to an angle in radians, use the radians() function, which you just imported.
• Print out dist.

# Definition of radius
r = 192500
# Import radians function of math package
from math import radians
from math import pi
# Travel distance of Moon over 12 degrees. Store in dist.
dist = r *  radians(12)
# Print out dist
print (dist)

In this chapter, we're going to dive into the world of baseball. Along the way, you'll get comfortable with the basics of numpy, a powerful package to do data science. A list baseball has already been defined in the Python script, representing the height of some baseball players in centimeters. Can you add some code here and there to create a numpy array from it?

• Import the numpy package as np, so that you can refer to numpy with np.
• Use np.array() to create a numpy array from baseball. Name this array np_baseball.
• Print out the type of np_baseball to check that you got it right.

# Create list baseball
baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np
import numpy as np

# Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out type of np_baseball
print (type(np_baseball))

You are a huge baseball fan. You decide to call the MLB (Major League Baseball) and ask around for some more statistics on the height of the main players. They pass along data on more than a thousand players, which is stored as a regular Python list: height. The height is expressed in inches. Can you make a numpy array out of it and convert the units to meters? height is already available and the numpy package is loaded, so you can start straight away (Source: stat.ucla.edu)

• Create a numpy array from height. Name this new array np_height.
• Print np_height.
• Multiply np_height with 0.0254 to convert all height measurements from inches to meters. Store the new values in a new array, np_height_m.
• Print out np_height_m and check if the output makes sense.

# height is available as a regular list

# Import numpy
import numpy as np

# Create a numpy array from height: np_height
np_height = np.array(height)

# Print out np_height
print (np_height)

# Convert np_height to m: np_height_m
np_height_m = np_height * 0.0254
# Print np_height_m
print (np_height_m)

The MLB also offers to let you analyze their weight data. Again, both are available as regular Python lists: height and weight. height is in inches and weight is in pounds. It's now possible to calculate the BMI of each baseball player. Python code to convert height to a numpy array with the correct units is already available in the workspace. Follow the instructions step by step and finish the game!

Create a numpy array from the weight list with the correct units. Multiply by 0.453592 to go from pounds to kilograms. Store the resulting numpy array as np_weight_kg. Use np_height_m and np_weight_kg to calculate the BMI of each player. Use the following equation:

BMI = weight(kg)/height(m)^2

• Save the resulting numpy array as bmi. Print out bmi.

# height and weight are available as regular lists

# Import numpy
import numpy as np

# Create array from height with correct units: np_height_m
np_height_m = np.array(height) * 0.0254

# Create array from weight with correct units: np_weight_kg
np_weight_kg = np.array(weight) * 0.453592

# Calculate the BMI: bmi

bmi = np_weight_kg / np_height_m ** 2
# Print out bmi
print ((bmi))

To subset both regular Python lists and numpy arrays, you can use square brackets:

x = [4 , 9 , 6, 3, 1]
x[1]
import numpy as np
y = np.array(x)
y[1]

For numpy specifically, you can also use boolean numpy arrays:

high = y > 5
y[high]

The code that calculates the BMI of all baseball players is already included. Follow the instructions and reveal interesting things from the data!

• Create a boolean numpy array: the element of the array should be True if the corresponding baseball player's BMI is below 21. You can use the < operator for this. Name the array light.
• Print the array light.
• Print out a numpy array with the BMIs of all baseball players whose BMI is below 21.
• Use light inside square brackets to do a selection on the bmi array.

# height and weight are available as a regular lists

# Import numpy
import numpy as np

# Calculate the BMI: bmi
np_height_m = np.array(height) * 0.0254
np_weight_kg = np.array(weight) * 0.453592
bmi = np_weight_kg / np_height_m ** 2

# Create the light array
light = bmi < 21
# Print out light
print (light)

# Print out BMIs of all baseball players whose BMI is below 21
print (bmi[light])

As Filip explained before, numpy is great for doing vector arithmetic. If you compare its functionality with regular Python lists, however, some things have changed. First of all, numpy arrays cannot contain elements with different types. If you try to build such a list, some of the elements' types are changed to end up with a homogeneous list. This is known as type coercion. Second, the typical arithmetic operators, such as +, -, * and / have a different meaning for regular Python lists and numpy arrays. Have a look at this line of code:

np.array([True, 1, 2]) + np.array([3, 4, False])

Can you tell which code chunk builds the exact same Python object? The numpy package is already imported as np, so you can start experimenting in the IPython Shell straight away!

You've seen it with your own eyes: Python lists and numpy arrays sometimes behave differently. Luckily, there are still certainties in this world. For example, subsetting (using the square bracket notation on lists or arrays) works exactly the same. To see this for yourself, try the following lines of code in the IPython Shell:

x = ["a", "b", "c"]
x[1]

np_x = np.array(x)
np_x[1]

The script on the right already contains code that imports numpy as np, and stores both the height and weight of the MLB players as numpy arrays.

• Subset np_weight by printing out the element at index 50.
• Print out a sub-array of np_height that contains the elements at index 100 up to and including index 110

# height and weight are available as a regular lists

# Import numpy
import numpy as np

# Store weight and height lists as numpy arrays
np_weight = np.array(weight)
np_height = np.array(height)

# Print out the weight at index 50
print (np_weight[50])

# Print out sub-array of np_height: index 100 up to and including index 110
print (np_height[100:111])

Before working on the actual MLB data, let's try to create a 2D numpy array from a small list of lists. In this exercise, baseball is a list of lists. The main list contains 4 elements. Each of these elements is a list containing the height and the weight of 4 baseball players, in this order. baseball is already coded for you in the script.

• Use np.array() to create a 2D numpy array from baseball. Name it np_baseball.
• Print out the type of np_baseball.
• Print out the shape attribute of np_baseball. Use np_baseball.shape.

# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Import numpy
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the type of np_baseball
print (type(np_baseball))

# Print out the shape of np_baseball
print (np_baseball.shape)

You have another look at the MLB data and realize that it makes more sense to restructure all this information in a 2D numpy array. This array should have 1015 rows, corresponding to the 1015 baseball players you have information on, and 2 columns (for height and weight). The MLB was, again, very helpful and passed you the data in a different structure, a Python list of lists. In this list of lists, each sublist represents the height and weight of a single baseball player. The name of this embedded list is baseball. Can you store the data as a 2D array to unlock numpy's extra functionality?

• Use np.array() to create a 2D numpy array from baseball. Name it np_baseball.
• Print out the shape attribute of np_baseball

# baseball is available as a regular list of lists

# Import numpy package
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the shape of np_baseball
print (np_baseball.shape)

If your 2D 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 2D 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 2D 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!

• Print out the 50th row of np_baseball.
• Make a new variable, np_weight, containing the entire second column of np_baseball.
• Select the height (first column) of the 124th baseball player in np_baseball and print it out.

# baseball is available as a regular list of lists

# Import numpy package
import numpy as np

# Create np_baseball (2 cols)
np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball
print (np_baseball[49,:])

# Select the entire second column of np_baseball: np_weight
np_weight = np_baseball[:,1]

# Print out height of 124th player
print (np_baseball[123,0])

Remember how you calculated the Body Mass Index for all baseball players? numpy was able to perform all calculations element-wise (i.e. element by element). For 2D numpy arrays this isn't any different! You can combine matrices with single numbers, with vectors, and with other matrices. Execute the code below in the IPython shell and see if you understand:

import numpy as np
np_mat = np.array([[1, 2],
                   [3, 4],
                   [5, 6]])
np_mat * 2
np_mat + np.array([10, 10])
np_mat + np_mat

np_baseball is coded for you; it's again a 2D numpy array with 3 columns representing height (in inches), weight (in pounds) and age (in years).

• You managed to get hold of the changes in height, weight and age of all baseball players. It is available as a 2D numpy array, updated. Add np_baseball and updated and print out the result.

• You want to convert the units of height and weight to metric (meters and kilograms respectively). As a first step, create a numpy array with three values: 0.0254, 0.453592 and 1. Name this array conversion.

• Multiply np_baseball with conversion and print out the result.

# baseball is available as a regular list of lists
# updated is available as 2D numpy array

# Import numpy package
import numpy as np

# Create np_baseball (3 cols)
np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
print (np_baseball+updated)

# Create numpy array: conversion
conversion = np.array([0.0254,0.453592,1])

# Print out product of np_baseball and conversion
print (np_baseball * conversion)

You now know how to use numpy functions to get a better feeling for your data. It basically comes down to importing numpy and then calling several simple functions on the numpy arrays:

import numpy as np
x = [1, 4, 8, 10, 12]
np.mean(x)
np.median(x)

The baseball data is available as a 2D numpy array with 3 columns (height, weight, age) and 1015 rows. The name of this numpy array is np_baseball. After restructuring the data, however, you notice that some height values are abnormally high. Follow the instructions and discover which summary statistic is best suited if you're dealing with so-called outliers.

• Create numpy array np_height that is equal to first column of np_baseball.
• Print out the mean of np_height.
• Print out the median of np_height.

# np_baseball is available

# Import numpy
import numpy as np

# Create np_height from np_baseball
np_height = np.array(np_baseball[:,0])

# Print out the mean of np_height
print (np.mean(np_height))

# Print out the median of np_height
print (np.median(np_height))

Because the mean and median are so far apart, you decide to complain to the MLB. They find the error and send the corrected data over to you. It's again available as a 2D Numpy array np_baseball, with three columns. The Python script on the right already includes code to print out informative messages with the different summary statistics. Can you finish the job.

• The code to print out the mean height is already included. Complete the code for the median height. Replace None with the correct code.
• Use np.std() on the first column of np_baseball to calculate stddev. Replace None with the correct code.
• Do big players tend to be heavier? Use np.corrcoef() to store the correlation between the first and second column of np_baseball in corr. Replace None with the correct code.

# np_baseball is available

# Import numpy
import numpy as np

# Print mean height (first column)
avg = np.mean(np_baseball[:,0])
print("Average: " + str(avg))

# Print median height. Replace 'None'
med = np.median(np_baseball[:,0])
print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'
stddev = np.std(np_baseball[:,0])
print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'
corr = np.corrcoef(np_baseball[:,0],np_baseball[0:,1])
print("Correlation: " + str(corr))

In the last few exercises you've learned everything there is to know about heights and weights of baseball players. Now it's time to dive into another sport: soccer. You've contacted FIFA for some data and they handed you two lists. The lists are the following:

positions = ['GK', 'M', 'A', 'D', ...]
heights = [191, 184, 185, 180, ...]

The possible positions are: 'GK' (goalkeeper), 'M' (midfield), 'A' (attack) and 'D' (defense). The second list, heights, contains integers representing the height of the player in cm. The first player in the lists is a goalkeeper and is pretty tall (191 cm). You're fairly confident that the median height of goalkeepers is higher than that of other players on the soccer field. Some of your friends don't believe you, so you are determined to show them using the data you received from FIFA and your newly acquired Python skills.

• Convert heights and positions, which are regular lists, to numpy arrays. Call them np_heights and np_positions.
• Extract all the heights of the goalkeepers. You can use a little trick here: use np_positions == 'GK' as an index for np_heights. * * * Assign the result to gk_heights.
• Extract all the heights of all the other players. This time use np_positions != 'GK' as an index for np_heights. Assign the result to other_heights.
• Print out the median height of the goalkeepers using np.median(). Replace None with the correct code.
• Do the same for the other players. Print out their median height. Replace None with the correct code.

# heights and positions are available as lists

# Import numpy
import numpy as np

# Convert positions and heights to numpy arrays: np_positions, np_heights
np_positions = np.array(positions)
np_heights = np.array(heights)

# Heights of the goalkeepers: gk_heights
gk_heights = np_heights[np_positions == 'GK']

# Heights of the other players: other_heights
other_heights = np_heights[np_positions != 'GK']

# Print out the median height of goalkeepers. Replace 'None'
print("Median height of goalkeepers: " + str(np.median(gk_heights)))

# Print out the median height of other players. Replace 'None'
print("Median height of other players: " + str(np.median(other_heights)))