Python

Intermediate Importing Data in Python

1. Importing flat files from the web: your turn! # Import package from urllib.request import urlretrieve # Import pandas import pandas as pd # Assign url of file: url url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv' # Save file locally urlretrieve(url, 'winequality-red.csv') # Read file into a DataFrame and print its head ## ('winequality-red.csv', <http.client.HTTPMessage object at 0x000000001FBF52C8>) df = pd.read_csv('winequality-red.csv', sep=';') print(df.head()) ## fixed acidity volatile acidity citric acid ... sulphates alcohol quality ## 0 7.

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Introduction to Importing Data in Python

1. Importing entire text files # Open a file: file file = open('c:/blogdown/moby_dick.txt', mode='r') # Print it print(file.read()) # Check whether file is closed ## CHAPTER 1. Loomings. ## ## Call me Ishmael. Some years ago--never mind how long precisely--having ## little or no money in my purse, and nothing particular to interest me on ## shore, I thought I would sail about a little and see the watery part of ## the world.

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Supervised Learning with Scikit-Learn

1. The Iris dataset in scikit-learn from sklearn import datasets import pandas as pd import numpy as np import matplotlib.pyplot as plt plt.style.use('ggplot') iris = datasets.load_iris() type(iris) ## <class 'sklearn.utils.Bunch'> print(iris.keys()) ## dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']) print(iris.DESCR) ## .. _iris_dataset: ## ## Iris plants dataset ## -------------------- ## ## **Data Set Characteristics:** ## ## :Number of Instances: 150 (50 in each of three classes) ## :Number of Attributes: 4 numeric, predictive attributes and the class ## :Attribute Information: ## - sepal length in cm ## - sepal width in cm ## - petal length in cm ## - petal width in cm ## - class: ## - Iris-Setosa ## - Iris-Versicolour ## - Iris-Virginica ## ## :Summary Statistics: ## ## ============== ==== ==== ======= ===== ==================== ## Min Max Mean SD Class Correlation ## ============== ==== ==== ======= ===== ==================== ## sepal length: 4.

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Python Data Science - part 1

1. Single Parameter Function # Define shout with the parameter, word def shout(word): """Print a string with three exclamation marks""" # Concatenate the strings: shout_word shout_word = word + '!!!' # Print shout_word print(shout_word) # Call shout with the string 'congratulations' shout("Congratulations") ## Congratulations!!! 2. Functions that return single values # Define shout with the parameter, word def shout(word): """Return a string with three exclamation marks""" # Concatenate the strings: shout_word shout_word = word + "!

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Linear Models - Scikit Learn

1. Linear Models The target value is expected to be a linear combination of the features. 1.1. Ordinary Least Squares (OLS) The OLS is a optimization math technique that aim to find the better adjustment for a set data and try to minimize the residual sum of squares between the observed targets in the dataset and the targets predicted by the linear approximation. from sklearn import linear_model reg = linear_model.

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TensorFlow 2 - Quickstart for Beginners

from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) predictions = model(x_train[:1]).numpy() ## WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.

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Iterables versus Iterators

1. Defining a list flash1 = ['jay garrick', 'barry allen', 'wally west', 'bart allen'] a = 1

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A Game of Chance

"""Simulating the dice game Craps""" ## 'Simulating the dice game Craps' import random def roll_dice(): """Roll two dice and return their face values as a tuple.""" die1 = random.randrange(1,7) die2 = random.randrange(1,7) return (die1, die2) def display_dice(dice): """Display one roll of the two dice.""" die1, die2 = dice print(f'Player rolled {die1} + {die2} = {sum(dice)}') die_values = roll_dice() #first roll display_dice(die_values) # determine game status and point, based on first roll.

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Functions

1. Defining functions def square(number): print("The square of", number, "is", number ** 2) square(7) ## The square of 7 is 49 2. Functions with multiple parameters def maximum(value1, value2, value3): max_value = value1 if value2 > max_value: max_value = value2 if value3 > max_value: max_value = value3 return max_value maximum(12, 27, 36) ## 36 maximum('yellow', 'red', 'orange') ## 'yellow' 3. Random-Number Generation import random random.

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Binary Search Algorithm

def binary_search(lista, item): low = 0 # low and high are part of the list thar you are searching for high = len(lista) - 1 while low <= high: #while you are not achieving one unique element middle = (low + high) // 2 # checking the central element guess = lista[middle] if guess == item: return middle if guess > item: # the guess are too high high = middle - 1 else: # the guess are too low low = middle + 1 return None my_list = [1, 3, 5, 7, 9] print(binary_search(my_list, 3)) ## 1 print(binary_search(my_list, -1)) ## None

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