Introduction to Python

import pandas as pd data = pd.read_csv("baseball.csv") index_col = 0 data = pd.read_csv("baseball.csv", index_col = 0)

1. What do you need to do first before importing a csv file? (import panda library) 2. What is the formula to read CSV file? (Import baseball csv) 3. How to you make sure the first row is not counted? 4. How do you save a column as a variable? (Save height column from baseball)

Numphy entire

An alternative to the regular python list: the Nu___________ array. The Nu___________ array is pretty similar to the list, but has one additional feature: you can perform calculations over en_____________ arrays. It's really easy, and super-fast as well.

compound group

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

np_height_in = np.array(height_in) print(np_height_in) np_height_m = np.array(np_height_in * 0.0254) print(np_height_m)

Create a numpy array from height_in. Name this new array np_height_in. Print np_height_in. Multiply np_height_in 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.

savings = 100 growth_multiplier = 1.1 result = (savings * growth_multiplier ** 7) print(result)

Create a variable savings with the value 100. Create a variable growth_multiplier, equal to 1.1. Create a variable, result, equal to the amount of money you saved after 7 years. Print out the value of result.

object methods capitalize append replace

Each one of these values or data structures are so-called Python obj______________. This string is an obj__________________, this float is an obj______________, but this list is also, you got it, an obj______________. Python objects also come with a bunch of so-called "met____________". You can think of met____________ as functions that "belong to" Python obj____________ A Python obj____________ of type string has met____________, An example of met__________ is capit____________, app____________, repl____________, etc they modify an obj____________ When adding a method to an object, place it at the end. Example: name.capitalize()

4 ** 2

Exponentiation: * *. This operator raises the number to its left to the power of the number to its right. For example ____ * * _____ will give 16.

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

Finish the code that creates the areas list. Build the list so that the list first contains the name of each room as a string and then its area. In other words, add the strings "hallway", "kitchen" and "bedroom" at the appropriate locations. Print areas again; is the printout more informative this time? areas = [hall, kit, "living room", liv, bed, "bathroom", bath]

elements different coercion arithmetic

First of all, numpy arrays cannot contain ele____________ with diff______________ 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 coe__________ Second, the typical arit_______________ operators, such as +, -, * and / have a different meaning for regular Python lists and numpy arrays.

import numpy numpy.array([1,2,3])

How to do you import a package "numpy"? How do you get array([1,2,3]) from numpy?

1 2 3

If you ask for the type of these arrays, Python tells you that they are numpy-dot-ndarray. numpy dot tells you it's a type that was defined in the numpy package. ndarray stands for n-dimensional array. The arrays np_height and np_weight are ________ (#) -dimensional arrays, but it's perfectly possible to create___________(#) dimensional, ___________(#) dimensional, etc

int() float() bool()

If you want to convert types, you can use: interger = float = boolean =

list areas_copy = list(areas)

If you want to prevent changes in a copied list from also taking effect in your main list, you'll have to do a more explicit copy of the areas list. You can do this with li_____() or by using [:]. Make an explicit copy of the areas list areas_copy = areas

1 0

In [1]:np.array([True, 1, 2]) + np.array([3, 4, False]) Out[1]:array([4, 5, 2]) True is converted to _______(#), False is converted to _______(#). Numpy: In [4]: np.array([4, 3, 0]) + np.array([0, 2, 2]) Out[4]: array([4, 5, 2])

3 4 5

List slicing [1, 2, 3, 4 ,5,6] 2:5 = ?

%

Modulo: ______. This operator returns the remainder of the division of the number to the left by the number on its right. For example 18 ______ 7 equals 4.

single methods

Numpy can do all of this so easily because it assumes that your Numpy array can only contain values of a sin______________ type. It's either an array of floats, either an array of booleans, and so on. Numpy array is simply a new kind of Python type, like the float, string and list types from before. This means that it comes with its own me________________, which can behave differently than you'd expect.

print(np_weight_lb[50]) print(np_height_in[100:111])

Subsetting x = ["a", "b", "c"] x[1] Subsetting with Numpy np_x = np.array(x) np_x[1] np_weight_lb = np.array(weight_lb) np_height_in = np.array(height_in) Subset np_weight_lb by printing out the element at index 50. Print out a sub-array of np_height_in that contains the elements at index 100 up to and including index 110.

print(np_baseball[49,:]) np_weight_lb = np_baseball[:,1] print(np_baseball[123,0])

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. np_baseball = np.array(baseball) Print out the 50th 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 124th baseball player in np_baseball and print it out.

light = bmi < 21 bmi[light] print(light) print(bmi[light])

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] 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. Don't forget to put bmi outside of the square brackets.

1 2 3 4

[1, 2, 3, 4 ,5,6] :4 = ?

append remove reverse

ap____________(), that adds an element to the list it is called on, rem_________(), that deletes the first element of a list that matches the input, and rev_________(), that changes the order of the elements in the list it is called on.

print(areas[1]) print(areas[-1]) print(areas[5])

areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50] Print out the second element from the areas list (it has the value 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 (20.0) and print it out.

areas[-1] = 10.50 areas[4] = "chill zone"

areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50] 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".

downstairs = areas[:6] upstairs = areas[-4:] print(downstairs) print(upstairs)

areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50] 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().

eat_sleep_area = (areas[3] + areas[-3])

areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50] 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. Use only whole numbers

areas.append(24.50) areas.append(15.45) areas.reverse() print(areas)

areas = [11.25, 18.0, 20.0, 10.75, 9.50] 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. (Not the other one) Print out areas once more.

print(areas.index(20.0)) print(areas.count(9.50))

areas = [11.25, 18.0, 20.0, 10.75, 9.50] 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.

import numpy as np np_baseball = np.array(baseball) print(type(np_baseball))

baseball = [180, 215, 210, 210, 188, 176, 209, 200] 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.

print(type(np_baseball)) print(np_baseball.shape)

baseball = [[180, 78.4], [215, 102.7], [210, 98.5], [188, 75.2]] 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.

full = (first + second) full_sorted = sorted(full, reverse=True) print(full_sorted)

first = [11.25, 18.0, 20.0] second = [10.75, 9.50] 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.

areas = [hall, kit, liv, bed, bath]

hall = 11.25 kit = 18.0 liv = 20.0 bed = 10.75 bath = 9.50 Save the values above. Create a list, areas, that contains the area of the hallway (hall), kitchen (kit), living room (liv), bedroom (bed) and bathroom (bath), in this order. Use the predefined variables. Print areas with the print() function.

house = [["hallway", hall], ["kitchen", kit], ["living room", liv], ["bedroom", bed], ["bathroom", bath]] print(type(house))

hall = 11.25 kit = 18.0 liv = 20.0 bed = 10.75 bath = 9.50 list of lists house = [["hallway", hall], ["kitchen", kit]] Finish the list of lists so that it also contains the living room, bedroom and bathroom data. Make sure you enter these in order! Print out house then print the type of house.

np_weight_kg = np.array(weight_lb) * 0.453592 bmi = np_weight_kg / np_height_m ** 2 print(bmi)

import numpy as np Create a numpy array from the weight_lb 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. equation:BMI=weight(kg) / height(m)2 Save the resulting numpy array as bmi. Print out bmi.

np_height_in = np_baseball[:,0] print(np.mean(np_height_in)) print(np.median(np_height_in))

import numpy as np x = [1, 4, 8, 10, 12] np.mean(x) np.median(x) Create numpy array np_height_in that is equal to first column of np_baseball. Print out the mean of np_height_in. Print out the median of np_height_in.

place_up = place.upper() print(place) print(place_up) print(place.count("o"))

place = "poolhouse" 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"!

import math C = 2*math.pi * r A = math.pi * r**2

r = 0.43 For a fancy clustering algorithm, you want to find the circumference, C, and area, A, of a circle. When the radius of the circle is r, you can calculate C and A 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. C=2πr Calculate the area of the circle and store it in A. A = πr² C = ? A = ? Print what is below print("Circumference: " + str(C)) print("Area: " + str(A))

from math import radians dist = radians(12) * r

r = 192500 Perform a selective import from the math package where you only import the radians function. Example: fr______ ma____ imp_________ rad__________ 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 with 12 degrees and multiply by r. Print out dist.

1.7 2

round(1.68, 1) = ? round(1.68) = ?

print("I started with $" + str(savings) + " and now have $" + str(result) + "Awesome!")

savings = 100 result = 100 * 1.10 ** 7 convert the integer to strings print("I started with $" + savings + " and now have $" + result + ". Awesome!")

x[1] = "r" x[2:] = ["s", "t"]

x = ["a", "b", "c", "d"] change B to R change C to S and D to T in one code

areas_1 = areas + ["poolhouse", 24.5] areas_2 = areas_1 + ["garage", 15.45]

x = ["a", "b", "c", "d"] y = x + ["e", "f"] areas = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0, "bedroom", 10.75, "bathroom", 10.50] 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.

G G H

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

print(type(year1))

year1 = (savings * growth_multiplier) find the type of year 1