This Repo include Notes for 3 main libraries in Python used for Data Science & Machine Learning
# Import numpy
import numpy as np
# Calculate the BMI: bmi
height_in = [74, 74, 72, 72, 73, 69, 69, 71, 76, 71]
weight_lb = [180, 215, 210, 210, 188, 176, 209, 200, 231, 180]
np_height_m = np.array(height_in) * 0.0254
np_weight_kg = np.array(weight_lb) * 0.453592
bmi = np_weight_kg / (np_height_m**2)
# Create the light array (boolean array)
light = bmi < 25
print(light)
# BMIs of all baseball players whose BMI is below 21
print(bmi[light])[ True False False False True False False False False False]
[23.11037639 24.80333518]
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])
# my_house greater than or equal to 18
print(my_house>=18)
# my_house less than your_house
print(my_house<your_house)[ True True False False]
[False True True False]
np.logical_and()
np.logical_or()
np.logical_not()
# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])
# my_house greater than 18.5 or smaller than 10
print(np.logical_or(my_house>18.5,my_house<10))
# Both my_house and your_house smaller than 11
print(np.logical_and(my_house<11,your_house<11))[False True False True]
[False False False True]
baseball = [[180, 78.4], [215, 102.7], [210, 98.5], [188, 75.2]]
# 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)<class 'numpy.ndarray'>
(4, 2)
# Import numpy package
import numpy as np
np_mat = np.array([[1, 2], [3, 4], [5, 6]])
# Create numpy array: conversion
# Multiply the first col by 2 & the second col by 3
conversion = np.array([2, 3])
# Print out product of np_baseball and conversion
print(np_mat * conversion)[[ 2 6]
[ 6 12]
[10 18]]
import numpy as np
positions = ["GK", "M", "A", "D"]
heights = [191, 184, 185, 180]
# 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
print("Median height of goalkeepers: " + str(np.median(gk_heights)))
# Print out the median height of other players
print("Median height of other players: " + str(np.median(other_heights)))Median height of goalkeepers: 191.0
Median height of other players: 184.0
plt.clf() to clear
import matplotlib.pyplot as plt
year = [2000, 2010, 2020, 2030, 2040]
pop = [2000, 4000, 1000, 3000, 5000]
# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year, pop)
# Customization
# ---------------------------------------------
plt.xlabel("Year")
plt.ylabel("Population")
plt.title("World Population Projections")
plt.yticks([0, 1000, 2000, 3000, 4000, 5000, 6000],[0,'1k','2k','3k','4k','5k','6k'])
plt.text(2010,50,"Text")
plt.grid(True)
plt.xlim(2000,None)
# ---------------------------------------------
# Display the plot with plt.show()
plt.show()
Use log scale
plt.xscale('log')
import matplotlib.pyplot as plt
year = [2000, 2010, 2020, 2030, 2040]
pop = [20, 40, 10, 30, 50]
# Make a line plot: year on the x-axis, pop on the y-axis
plt.scatter(year, pop, color="red", marker="x")
# Display the plot with plt.show()
plt.show()
plt.hist(list,bins)
bins = 10 by default
import matplotlib.pyplot as plt
x = [
43.828,
76.423,
72.301,
42.731,
75.32,
81.235,
79.829,
75.635,
64.062,
79.441,
56.728,
65.554,
74.852,
50.728,
72.39,
73.005,
52.295,
49.58,
59.723,
50.43,
80.653,
50.651,
78.553,
72.961,
72.889,
65.152,
46.462,
]
plt.hist(x, bins=6, color="red", edgecolor="black", linewidth=2)
plt.show()
# Pre-defined lists
names = ["United States", "Australia", "Japan", "India", "Russia", "Morocco", "Egypt"]
dr = [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
# Import pandas as pd
import pandas as pd
# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {"country": names, "drives_right": dr, "cars_per_cap": cpc}
# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)
# Definition of row_labels
row_labels = ["US", "AUS", "JPN", "IN", "RU", "MOR", "EG"]
# Specify row labels of cars
cars.index = row_labels
# Print cars
print(cars)# Import pandas as pd
import pandas as pd
# Fix import by including index_col
cars = pd.read_csv("datasets/cars.csv")
# Print out cars
print(cars)
print("----------------------------------------------")
# Specify the index_col argument inside pd.read_csv(): set it to 0, so that the first column is used as row labels.
cars2 = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars2)import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars)
print("----------------------------------------------")
print(cars[["country", "drives_right"]])
print("----------------------------------------------")
print(cars[["country"]])
print("----------------------------------------------")import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars)
print("----------------------------------------------")
print(cars[1:4])
print("----------------------------------------------")[Subsetting DataFrame]
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars)
print("----------------------------------------------")
# Access row by label
print(cars.loc[["RU"]])
print("----------------------------------------------")
print(cars.iloc[[4]])
print("----------------------------------------------")
# print as series
print(cars.loc["RU"])
print("----------------------------------------------")
# print as dataframe
print(cars.loc[["RU"]])
print("----------------------------------------------")
# Access multiple rows by label
print(cars.loc[["RU", "IN", "EG"]])
print("----------------------------------------------")
print(cars.iloc[[4, 3, 6]])
print("----------------------------------------------")
# Access row and column by label
print(cars.loc[["RU", "IN", "EG"], ["country", "drives_right"]])
print("----------------------------------------------")
print(cars.iloc[[4, 3, 6], [1, 2]])
print("----------------------------------------------")
# All rows, some columns
print(cars.loc[:, ["country", "drives_right"]])
print("----------------------------------------------")
print(cars.iloc[:, [1, 2]])
print("----------------------------------------------")
# specific cell
print(cars.loc[["RU"], ["country"]])
print("----------------------------------------------")
# Sub-dataframe
print(cars.loc[["RU", "IN", "EG"], ["country", "drives_right"]])
print("----------------------------------------------")# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
# Extract drives_right column as Series: dr
dr = cars["drives_right"]
print(dr)
print("----------------------------------------------")
# Use dr to subset cars: sel
sel = dr == True
# Print sel
print(cars[sel])# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
# Create car_maniac: observations that have a cars_per_cap over 500
cpc = cars["cars_per_cap"]
many_cars = cpc > 500
car_maniac = cars[many_cars]
# Print car_maniac
print(car_maniac)# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
# Import numpy, you'll need this
import numpy as np
# Create medium: observations with cars_per_cap between 100 and 500
cpc = cars["cars_per_cap"]
between = np.logical_and(cpc > 100, cpc < 500)
medium = cars[between]
# Print medium
print(medium)# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
# Iterate over rows of cars
for label, row in cars.iterrows():
print(label)
print(row)# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
# Adapt for loop
for lab, row in cars.iterrows():
print(f"{lab}: {row['cars_per_cap']}")# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars)
print("----------------------------------------------")
# Code for loop that adds COUNTRY column
for label, row in cars.iterrows():
cars.loc[label, "COUNTRY"] = row["country"].upper()
# Print cars
print(cars)# Import cars data
import pandas as pd
cars = pd.read_csv("datasets/cars.csv", index_col=0)
print(cars)
print("----------------------------------------------")
# Use .apply(str.upper) beacuse .upper() is a method
cars["COUNTRY"] = cars["country"].apply(str.upper)
# use .apply(len) because len() is a function
cars["country length"] = cars["country"].apply(len)
print(cars)