# Numpy

| September 16th, 2015

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?

### Instructions

• 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.
import numpy as np # Create list baseball baseball = [180, 215, 210, 210, 188, 176, 209, 200] # Import the numpy package as np # Create a Numpy array from baseball: np_baseball # Print out type of np_baseball  # 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))  msg = "You don't have to change or remove the predefined variables." test_object("baseball", undefined_msg = msg, incorrect_msg = msg) test_import("numpy") test_object("np_baseball", do_eval = False) test_function("numpy.array", not_called_msg = "Be sure to call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array).", incorrect_msg = "You should call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array) as follows: np.array(baseball).") test_object("np_baseball", incorrect_msg = "Assign the correct value to np_baseball.") msg = "Make sure to print out the type of np_baseball like this: print(type(np_baseball))." test_function("type", 1, incorrect_msg = msg) test_function("print", 1, incorrect_msg = msg) success_msg("Great job!") 
• import numpy as np will do the trick. Now, you have to use np.fun_name() whenever you want to use a Numpy function.
• np.array() should take on input baseball. Assign the result of the function call to np_baseball.
• To print out the type of a variable x, simply type print(type(x)).

## Baseball players' height

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 centimeters?

height is already available and the numpy package is loaded, so you can start straight away (Source: stat.ucla.edu).

### Instructions

• 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.
import pandas as pd mlb = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv") height = mlb['Height'].tolist() import numpy as np # height is available as a regular list # Import numpy import numpy as np # Create a Numpy array from height: np_height # Print out np_height # Convert np_height to m: np_height_m # Print np_height_m  # 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)  # sct code test_import("numpy", same_as = False) test_object("np_height", do_eval = False) test_function("numpy.array", not_called_msg = "Be sure to call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array).", incorrect_msg = "You should call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array) as follows: np.array(np_height).") test_object("np_height", incorrect_msg = "Assign the correct value to np_height.") test_function("print", 1, incorrect_msg = "Print out np_height with print(np_height).") test_object("np_height_m", incorrect_msg = "Your calculation of np_height_m is not quite correct, be sure to multily np_height with 0.0254.") test_function("print", 2, incorrect_msg = "Print out np_height_m with print(np_height_m).") success_msg("Nice! In the blink of an eye, Numpy performs multiplications on more than 1000 height measurements.") 
• Use np.array() and pass it height. Store the result in np_height.
• To print out a variable x, type print(x) in the Python script.
• Perform calculations as if np_height is a single number: np_height * factor is part of the answer.
• To print out a variable x, type print(x) in the Python script.

## Baseball player's BMI

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!

### Instructions

• 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: $$\mathrm{BMI} = \frac{\mathrm{weight (kg)}}{\mathrm{height (m)}^2}$$ Save the resulting numpy array as bmi.
• Print out bmi.
import pandas as pd mlb = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv") height = mlb['Height'].tolist() weight = mlb['Weight'].tolist() import numpy as np # height and weight are available as a 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 # Calculate the BMI: bmi # Print out bmi  # height and weight are available as a 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)  test_import("numpy", same_as = False) msg = "The variable np_height_m was defined for you. You don't have to change or remove it!" test_object("np_height_m", incorrect_msg = msg, undefined_msg = msg) test_object("np_weight_kg", do_eval = False) test_function("numpy.array", 2, not_called_msg = "Be sure to call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array).", incorrect_msg = "To assign np_weight_kg, use np.array(weight).") test_operator(2, incorrect_result_msg = "Are you calculating np_weight_kg correctly? Be sure to multiply np.array(weight) with 0.453592.") test_object("np_weight_kg", incorrect_msg = "Assign the result of your calculation to np_weight_kg.") test_object("bmi", do_eval = False) test_operator(3, incorrect_result_msg = "Are you calculating np_weight_kg correctly? Be sure to multiply np.array(weight) with 0.453592.") test_object("bmi", incorrect_msg = "Assign the result of your calculation to bmi.") test_function("print", 1, incorrect_msg = "Print out bmi with print(bmi).") success_msg("Cool! Time to step up your game!") 
• Use a similar approach as the code that calculates np_height_m. This time, though, the you have to work with weight and multiply with 0.453592.
• To calculate the bmi, you will need the / and ** operators.
• To print out a variable x, type print(x) in the script.

## Lightweight baseball players

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!

### Instructions

• 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.
import pandas as pd mlb = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv") height = mlb['Height'].tolist() weight = mlb['Weight'].tolist() import numpy as np # 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 # Print out light # Print out BMIs of all baseball players whose BMI is below 21  # 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])  msg = "You don't have to change or remove the predefined variables." test_object("np_height_m", undefined_msg = msg, incorrect_msg = msg) test_object("np_weight_kg", undefined_msg = msg, incorrect_msg = msg) test_object("bmi", undefined_msg = msg, incorrect_msg = msg) test_object("light", incorrect_msg = "Use the < boolean operator to define light. bmi should be smaller than 21.") test_function("print", 1, incorrect_msg = "Print out light with print(light).") test_function("print", 2, incorrect_msg = "For the second printout, use light as an index for bmi.") success_msg("Wow! It appears that only 11 of the more than 1000 baseball players have a BMI under 21!") 
• bmi > 30 will give you a boolean Numpy array in which the elements are True if the corresponding player's BMI is over 30.
• To print out a variable x, type print(x) in the Python script.
• bmi[light] will return the array you need. Don't forget to wrap a print() call around it!

## Subsetting Numpy Arrays

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.

### Instructions

• Subset np_weight: print out the element at index 50.
• Print out a sub-array of np_height: It contains the elements at index 100 up to and including index 110
import pandas as pd mlb = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv") height = mlb['Height'].tolist() weight = mlb['Weight'].tolist() import numpy as np # 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 out sub-array of np_height: 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])  test_import("numpy", same_as = False) msg = "You don't have to change or remove the predefined variables." test_object("np_height", undefined_msg = msg, incorrect_msg = msg) test_object("np_weight", undefined_msg = msg, incorrect_msg = msg) test_function("print", 1, incorrect_msg = "For the first printout, subset np_weight to select the 50th element.") test_function("print", 2, incorrect_msg = "For the second printout, subset np_height to select the 100th to 110th element, included. You can use the slicing operator: :, just make sure to put in the correct ending index.") success_msg("Nice! Time to learn something new: 2D Numpy arrays!") 
• Make sure to wrap a print() call around your subsetting operations.
• Use [100:111] to get the elements from index 100 up to and including index 110.

## Your First 2D Numpy Array

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.

### Instructions

• 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.
import numpy as np # 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 # Print out the type of np_baseball # Print out the shape of np_baseball  # 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)  msg = "You don't have to change or remove the predefined variables." test_object("baseball", undefined_msg = msg, incorrect_msg = msg) test_import("numpy", same_as = False) test_object("np_baseball", do_eval = False) test_function("numpy.array", not_called_msg = "Be sure to call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array).", incorrect_msg = "You should call np.array(baseball) to make a 2D numpy array out of baseball.") test_object("np_baseball", incorrect_msg = "Assign the correct value to np_baseball.") msg = "Make sure to print out the type of np_baseball like this: print(type(np_baseball))." test_function("type", 1, incorrect_msg = msg) test_function("print", 1, incorrect_msg = msg) test_function("print", 2, incorrect_msg = "You can print the shape of np_baseball like this: np_baseball.shape.") success_msg("Great! You're ready to convert the actual MLB data to a 2D Numpy array now!") 

## Baseball data in 2D form

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?

### Instructions

• Use np.array() to create a 2D Numpy array from baseball. Name it np_baseball.
• Print out the shape attribute of np_baseball.
import pandas as pd baseball = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv")[['Height', 'Weight']].as_matrix().tolist() import numpy as np # 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 # Print out the shape 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)  test_import("numpy", same_as = False) test_object("np_baseball", do_eval = False) test_function("numpy.array", not_called_msg = "Be sure to call [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array).", incorrect_msg = "You should call np.array(baseball) to make a 2D numpy array out of baseball.") test_object("np_baseball", incorrect_msg = "Assign the numpy array you created to np_baseball.") test_function("print", incorrect_msg = "Print the shape field of the np_baseball object using the dot notation: ..") success_msg("Slick! Time to show off some killer features of multi-dimensional Numpy arrays!") 
• baseball is already available in the Python environment. Call np.array() on it and store the resulting 2D Numpy array in np_baseball.
• np_baseball.shape will give the dimensions of the np_baseball. Make sure to wrap a print() call around it.

## Subsetting 2D Numpy Arrays

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. Add some lines to make the correct selections. Remember that in Python, the first element is at index 0!

### Instructions

• 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.
import pandas as pd baseball = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv")[['Height', 'Weight']].as_matrix().tolist() import numpy as np # 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 # Select the entire second column of np_baseball: np_weight # Print out height of 124th player  # 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])  test_import("numpy", same_as = False) msg = "You don't have to change or remove the predefined variables." test_object("np_baseball", undefined_msg = msg, incorrect_msg = msg) test_function("print", 1, incorrect_msg = "For the first printout, subset the np_baseball object using [49,:]. This will select the 50th row completely.") test_object("np_weight", incorrect_msg = "Define np_weight by subsetting the np_baseball object with [:,1]. This will select the first column, completely.") test_function("print", 2, incorrect_msg = "For the second printout, subset the np_baseball object using [123,0]. This will select the first column of the 123th row.") success_msg("This is going well!") 
• You need row index 49 in the first instruction! More specifically, you'll want to use [49,:].
• To select the entire second column, you'll need [:,1].
• For the last instruction, use [123, 0]; don't forget to wrap it all in a print() statement.

## 2D Arithmetic

Remember how you calculated the Body Mass Index for all baseball players? Numpy was able to perform all calculations element-wise. 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, weight and age.

### Instructions

• You managed to get hold on the changes in weight, height and age of all baseball players. It is available as a 2D Numpy array, update. Add np_baseball and update and print out the result.
• You want to convert the units of height and weight. 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.
import pandas as pd import numpy as np baseball = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv")[['Height', 'Weight', 'Age']].as_matrix().tolist() n = len(baseball) update = np.array(pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/update.csv", header = None)) import numpy as np # baseball is available as a regular list of lists # update 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 update # Create Numpy array: conversion # Print out product of np_baseball and conversion  # baseball is available as a regular list of lists # update 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 update print(np_baseball + update) # Create Numpy array: conversion conversion = np.array([0.0254, 0.453592, 1]) # Print out product of np_baseball and conversion print(np_baseball * conversion)  test_import("numpy") msg = "You don't have to change or remove the predefined variables." test_object("np_baseball", undefined_msg = msg, incorrect_msg = msg) test_operator(1, not_found_msg = "Use the + operator to add update to np_baseball.", incorrect_result_msg = "Are you sure you correctly added update to np_baseball? The resulting numpy array seems to be incorrect.") test_function("print", 1, incorrect_msg = "Print out the result of np_baseball + update using print(np_baseball + update).") msg = "Create the conversion object using np.array(...). Fill in the correct list on the dots." test_function("numpy.array", not_called_msg = msg, incorrect_msg = msg) test_object("conversion", incorrect_msg = "Assign the object you created with [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array) to conversion.") test_operator(2, not_found_msg = "Use the * operator to multiply np_baseball with conversion.", incorrect_result_msg = "Are you sure you correctly miltiplied np_baseball with conversion? The resulting numpy array seems to be incorrect.") test_function("print", 2, incorrect_msg = "Print out the result of np_baseball * conversion using print(np_baseball * conversion).") success_msg("Great job! Notice how with very little code, you can change all values in your Numpy data structure in a very specific way. This will be very useful in your future as a data scientist!") 
• np_baseball + update will do a element-wise summation of the two Numpy arrays.
• Create a Numpy array with np.array(); the input is a regular Python list with three elements.
• np_baseball * conversion will work, without extra work. Try out it! Make sure to wrap it in a print() call.

## Average versus median

You now know how to use Numpy functions to a 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.

### Instructions

• 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.
import pandas as pd np_baseball = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv")[['Height', 'Weight', 'Age']].as_matrix() np_baseball[slice(0, 1015, 50), 0] = np_baseball[slice(0, 1015, 50), 0]*1000 import numpy as np # np_baseball is available # Import numpy import numpy as np # Create np_height from 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_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))  test_import("numpy", same_as = False) test_object("np_height", incorrect_msg = "Make sure to use the correct subsetting operation to define np_height.") test_function("numpy.mean", not_called_msg = "Don't forget to call [np.mean()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.mean.html).", incorrect_msg = "Pass np_height as an argument to the mean function of np to print out the correct value for the first printout. Don't forget to use the dot notation: ..") test_function("print", 1, incorrect_msg = "Print out the result of your calculations using print(np.mean(np_height)).") test_function("numpy.median", not_called_msg = "Don't forget to call [np.median()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.median.html).", incorrect_msg = "Pass np_height as an argument to the median function of np to print out the correct value for the second printout. Don't forget to use the dot notation: ..") test_function("print", 2, incorrect_msg = "Print out the result of your calculations using print(np.median(np_height)).") success_msg("An average length of 1586 inches, that doesn't sound right, does it? However, the median does not seem affected by the outliers: 74 inches makes perfect sense. It's always a good idea to check both the median and the mean, to get a first hunch for the overall distribution of the entire dataset.") 

## Explore the baseball data

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?

### Instructions

• 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.
import pandas as pd np_baseball = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/baseball.csv")[['Height', 'Weight', 'Age']].as_matrix() import numpy as np # 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 = None print("Median: " + str(med)) # Print out the standard deviation on height. Replace 'None' stddev = None print("Standard Deviation: " + str(stddev)) # Print out correlation between first and second column. Replace 'None' corr = None print("Correlation: " + str(corr)) # 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[:,1]) print("Correlation: " + str(corr))  # sct code test_import("numpy") msg = "You don't have to change or remove the predefined variables." test_object("avg", undefined_msg = msg, incorrect_msg = msg) test_function("print", 1, not_called_msg = msg, incorrect_msg = msg) test_function("numpy.median", 1, not_called_msg = "Don't forget to call [np.median()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.median.html).", incorrect_msg = "To assign med, use [np.median()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.median.html). Make sure to pass it the correct column of np_baseball.") test_object("med") test_function("print", 2, not_called_msg = msg, incorrect_msg = msg) test_function("numpy.std", 1, not_called_msg = "Don't forget to call [np.std()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.std.html).", incorrect_msg = "To assign stddev, use [np.std()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.std.html). Make sure to pass it the correct column of np_baseball.") test_object("stddev") test_function("print", 3, not_called_msg = msg, incorrect_msg = msg) test_object("corr", incorrect_msg = "To assign corr, use [np.corrcoef()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.corrcoef.html). Make sure to pass it the correct columns of np_baseball. You can pass it two columns.") test_function("print", 4, not_called_msg = msg, incorrect_msg = msg) success_msg("Great! Time to use all of your new data science skills in the last exercise!") 

## Blend it all together

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 the 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, ...]  Each element in the lists corresponds to a player. The first list, positions, contains strings representing each player's position. 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.

### Instructions

• 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 the 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.
import pandas as pd fifa = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/intro_to_python/fifa.csv", skipinitialspace=True, usecols=['position', 'height']) positions = list(fifa.position) heights = list(fifa.height) # heights and positions are available as lists # Import numpy import numpy as np # Convert positions and heights to numpy arrays: np_positions, np_heights # Heights of the goalkeepers: gk_heights # Heights of the other players: other_heights # Print out the median height of goalkeepers. Replace 'None' print("Median height of goalkeepers: " + str(None)) # Print out the median height of other players. Replace 'None' print("Median height of other players: " + str(None)) # 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)))  test_import("numpy") msg = "Convert the regular lists to numpy lists using [np.array()](http://docs.scipy.org/doc/numpy-1.10.0/glossary.html#term-array). This function takes one argument: the regular list itself!" test_object("np_positions", do_eval = False) test_function("numpy.array", 1, not_called_msg = msg, incorrect_msg = msg) test_object("np_positions", incorrect_msg = "Assign the converted numpy array of positions to np_positions.") test_object("np_heights", do_eval = False) test_function("numpy.array", 2, not_called_msg = msg, incorrect_msg = msg) test_object("np_heights", incorrect_msg = "Assign the converted numpy array of heights to np_heights.") test_object("gk_heights", incorrect_msg = "You can use [np_positions == 'GK'] as an index of np_heights to find the heights of all goalkeepers, gk_heights. You can use a hint if you're stuck!") test_object("other_heights", incorrect_msg = "You can use [np_positions != 'GK'] as an index of np_heights to find the heights of all other players, other_heights. You can use a hint if you're stuck!") msg = "Use np.median(%s) to find the median height of %s." gk_msg = msg % ("gk_heights", "goalkeepers") test_function("numpy.median", 1, not_called_msg = gk_msg, incorrect_msg = gk_msg) test_function("print", 1, incorrect_msg = "Don't forget to print out the result for the goalkeepers.") other_msg = msg % ("other_heights", "other players") test_function("numpy.median", 2, not_called_msg = other_msg, incorrect_msg = other_msg) test_function("print", 2, incorrect_msg = "Don't forget to print out the result for the other players.") success_msg("Wonderful! You were right and the disbelievers were wrong! This exercise marks the end of the Intro to Python for Data Science course. See you in another course!") 
• Use np.array() to convert the lists to numpy arrays.
• You should use np_heights[np_positions == 'GK'] to extract the heights of all goalkeepers. Don't forget to assign the result to gk_heights.
• You should use np_heights[np_positions != 'GK'] to extract the heights of all other players. Don't forget to assign the result to other_heights.
• Print out the median height of the goalkeepers using np.median(gk_heights).
• Print out the median height of the other players using np.median(other_heights).