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Introduction to Python

Run the hidden code cell below to import the data used in this course.

# Importing course packages; you can add more too!
import numpy as np
import math

# Import columns as numpy arrays
baseball_names = np.genfromtxt(
    fname="baseball.csv",  # This is the filename
    delimiter=",",  # The file is comma-separated
    usecols=0,  # Use the first column
    skip_header=1,  # Skip the first line
    dtype=str,  # This column contains strings
)
baseball_heights = np.genfromtxt(
    fname="baseball.csv", delimiter=",", usecols=3, skip_header=1
)
baseball_weights = np.genfromtxt(
    fname="baseball.csv", delimiter=",", usecols=4, skip_header=1
)
baseball_ages = np.genfromtxt(
    fname="baseball.csv", delimiter=",", usecols=5, skip_header=1
)

soccer_names = np.genfromtxt(
    fname="soccer.csv",
    delimiter=",",
    usecols=1,
    skip_header=1,
    dtype=str,
    encoding="utf", 
)
soccer_ratings = np.genfromtxt(
    fname="soccer.csv",
    delimiter=",",
    usecols=2,
    skip_header=1,
    encoding="utf", 
)
soccer_positions = np.genfromtxt(
    fname="soccer.csv",
    delimiter=",",
    usecols=3,
    skip_header=1,
    encoding="utf", 
    dtype=str,
)
soccer_heights = np.genfromtxt(
    fname="soccer.csv",
    delimiter=",",
    usecols=4,
    skip_header=1,
    encoding="utf", 
)
soccer_shooting = np.genfromtxt(
    fname="soccer.csv",
    delimiter=",",
    usecols=8,
    skip_header=1,
    encoding="utf", 
)

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Methods and Functions

É a mesma coisa, mas as funções aplicadas a objetos se comportam de maneiras diferentes dependendo do tipo #str, bool, list, int#. Para chamar um method específico para um determinado objeto, deve utilizar object.method

EX:

List = [0, 1, 2]
List.index(2)

Lists Starts from 0 The first selection countes, the last doesn't.

All elements to a list Use the + operator. Ex areas = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0, "bedroom", 10.75, "bathroom", 10.50] areas_1 = areas + ["poolhouse", 24.5] areas_2 = areas_1 + ["garage", 15.45] print(areas_2)

areas_2 = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0, "bedroom", 10.75, "bathroom", 10.50, "poolhouse", 24.5, "garage", 15.45]

**Delete ** x = ["a", "b", "c", "d"] del(x[1]) x =["a", "c", "d"]

The ; sign is used to place commands on the same line

# Add your code snippets here

Explore Datasets

Use the arrays imported in the first cell to explore the data and practice your skills!

  • Print out the weight of the first ten baseball players.
  • What is the median weight of all baseball players in the data?
  • Print out the names of all players with a height greater than 80 (heights are in inches).
  • Who is taller on average? Baseball players or soccer players? Keep in mind that baseball heights are stored in inches!
  • The values in soccer_shooting are decimals. Convert them to whole numbers (e.g., 0.98 becomes 98).
  • Do taller players get higher ratings? Calculate the correlation between soccer_ratings and soccer_heights to find out!
  • What is the average rating for attacking players ('A')?