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<s>[INST] Instruction:
Create a function to calculate the sum of a sequence of integers.
Input:
[1, 2, 3, 4, 5]
[/INST] Output:
# Python code
def sum_sequence(sequence):
sum = 0
for num in sequence:
sum += num
return sum</s> |
<s>[INST] Instruction:
Generate a Python code for crawling a website for a specific type of data.
Input:
website: www.example.com
data to crawl: phone numbers
[/INST] Output:
import requests
import re
def crawl_website_for_phone_numbers(website):
response = requests.get(website)
phone_numbers = re.findall('\d{3}-\d{3}-\d{4}', response.text)
return phone_numbers
if __name__ == '__main__':
print(crawl_website_for_phone_numbers('www.example.com'))</s> |
<s>[INST] Instruction:
Create a Python list comprehension to get the squared values of a list [1, 2, 3, 5, 8, 13].
Input:
[/INST] Output:
[x*x for x in [1, 2, 3, 5, 8, 13]]</s> |
<s>[INST] Instruction:
Generate a python script to perform this action.
Input:
Given a string, remove all the consecutive duplicates from the string.
Input: "AAABBCCCD"
[/INST] Output:
def remove_duplicates(string):
result = ""
prev = ''
for char in string:
if char != prev:
result += char
prev = char
return result
result = remove_duplicates("AAABBCCCD")
print(result)</s> |
<s>[INST] Instruction:
Write a python script to generates random numbers between 0 and 9 that are divisible by 3.
Input:
[/INST] Output:
def generate_random_divisible_number():
import random
while True:
# Generate a random number
process = random.randint(0, 9)
# Check if the number is divisible by 3
if process % 3 == 0:
# If it is divisible, return it
return process</s> |
<s>[INST] Instruction:
Write a Python code to get the third largest element in a given row.
Input:
[12, 13, 13, 45, 22, 99]
[/INST] Output:
def third_largest(lst):
if len(lst) < 3:
return
distinct = []
for i in lst:
if i not in distinct:
distinct.append(i)
distinct.sort(reverse=True)
return distinct[2]</s> |
<s>[INST] Instruction:
Generate a REST API with Python and Flask that allows users to create, read, update, and delete records from a database.
Input:
Not applicable
[/INST] Output:
from flask import Flask, request
from flask_sqlalchemy import SQLAlchemy
app = Flask(name)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'
db = SQLAlchemy(app)
class Record(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(120), unique=True)
db.create_all()
@app.route('/records', methods=['GET'])
def get_records():
records = Record.query.all()
return {'records':[record.name for record in records]}
@app.route('/records', methods=['POST'])
def create_record():
record = Record(name=request.json['name'])
db.session.add(record)
db.session.commit()
return {'id': record.id}
@app.route('/records/int:id', methods=['PUT'])
def update_record(id):
record = Record.query.get(id)
record.name = request.json['name']
db.session.commit()
return {'message': 'Record updated'}
@app.route('/records/int:id', methods=['DELETE'])
def delete_record(id):
Record.query.filter_by(id=id).delete()
db.session.commit()
return {'message': 'Record deleted'}
if name == 'main':
app.run(debug=True)</s> |
<s>[INST] Instruction:
Create a Python function that takes in a string and a list of words and returns true if the string contains all the words in the list.
Input:
'This is a test', ['test', 'this', 'is']
[/INST] Output:
def contains_words(input_string, words):
for word in words:
if word not in input_string:
return False
return True
print(contains_words('This is a test', ['test', 'this', 'is'])) # True</s> |
<s>[INST] Instruction:
What should this Python program do?
Input:
def is_palindrome(s):
'''Write your code here.'''
[/INST] Output:
def is_palindrome(s):
'''This function returns True if the string s is a palindrome and False if it isn't.'''
return s == s[::-1] # Checks if s is the same as its reverse.</s> |
<s>[INST] Instruction:
Create a Python program to sort and print out the elements of an array of integers.
Input:
[17, 41, 5, 22, 54, 6, 29, 3, 13]
[/INST] Output:
arr = [17, 41, 5, 22, 54, 6, 29, 3, 13]
arr.sort()
print(arr)
# Output: [3, 5, 6, 13, 17, 22, 29, 41, 54]</s> |
<s>[INST] Instruction:
Generate a Python program that adds two then doubles the result.
Input:
[/INST] Output:
def double_add(num1, num2):
return (num1 + num2) * 2</s> |
<s>[INST] Instruction:
Write a python program to extract the non-vowel words in a sentence.
Input:
This machine learning project is challenging.
[/INST] Output:
def extract_words(sentence):
non_vowel_words = []
words = sentence.split()
vowels = ['a','e','i','o','u','A','E','I','O','U']
for word in words:
if not any(char in vowels for char in word):
non_vowel_words.append(word)
return non_vowel_words</s> |
<s>[INST] Instruction:
Write a Python program to calculate the average of a list of positive integers and output the result.
Input:
List of positive integers: [1, 5, 6, 7, 8]
[/INST] Output:
list_of_positive_integers = [1, 5, 6, 7, 8]
total = sum(list_of_positive_integers)
avg = total/len(list_of_positive_integers)
print("The average of the list of positive integers is {}".format(avg))</s> |
<s>[INST] Instruction:
Develop a function in Python that prints out the Pascal's triangle for a given number of rows.
Input:
[/INST] Output:
def pascal_triangle(n):
trow = [1]
y = [0]
for x in range(max(n, 0)):
print(trow)
trow=[l+r for l,r in zip(trow + y, y + trow)]
return n>=1
pascal_triangle(5)</s> |
<s>[INST] Instruction:
Implement a decision-making algorithm for a rock-paper-scissors game using Python.
Input:
[/INST] Output:
# Rock, paper, scissors game!
def play_game(player1, player2):
# Define the winning rules
rules = {'rock': 'scissors', 'scissors': 'paper', 'paper': 'rock'}
if player1 == player2:
return 'It is a draw!'
elif rules[player1] == player2:
return 'Player 1 wins!'
elif rules[player2] == player1:
return 'Player 2 wins!'
# Get user input
player1 = input('Player 1, make a choice: rock, paper, or scissors? ').lower()
player2 = input('Player 2, make a choice: rock, paper, or scissors? ').lower()
# Start the game and get the result
result = play_game(player1, player2)
print(result)</s> |
<s>[INST] Instruction:
Collate a machine learning model in Python that distinguishes between cats and dogs.
Input:
A dataset of 800 images of cats and dogs
[/INST] Output:
import numpy as np
import keras
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
# Create the neural network model
model = Sequential()
# Input layer
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Hidden layers
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(64, (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(64, (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.3))
# Output layer
model.add(Flatten())
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))
# Compile the model
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
# Create data generator
datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)
# Fit the model
train_generator = datagen.flow_from_directory(directory = '/path/to/dataset', target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = 32)
model.fit_generator(generator = train_generator, steps_per_epoch = 800, epochs = 5, validation_data = test_generator, validation_steps = 200)</s> |
<s>[INST] Instruction:
Write a Python program to find all the numbers divisible by a certain number within a certain range.
Input:
int m=36, int n=74, int divisor=6
[/INST] Output:
def find_numbers_divisible_by(m, n, divisor):
divisible_numbers = []
for i in range(m, n+1):
if i % divisor == 0:
divisible_numbers.append(i)
return divisible_numbers</s> |
<s>[INST] Instruction:
Implement a Python function that will take two strings and return true if the first string contains the second.
Input:
query = "catering"
word = "cat"
[/INST] Output:
def check_contains(query, word):
if query.find(word) != -1:
return True
return False
result = check_contains(query, word)</s> |
<s>[INST] Instruction:
Write an algorithm in Python 3 that calculates the factorial of a number.
Input:
Not applicable
[/INST] Output:
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n-1)</s> |
<s>[INST] Instruction:
Develop a Python function to predict the class labels of a given set of data.
Input:
The dataset contains 4 features: Length, Width, Height and Weight.
[/INST] Output:
# Load the dataset
import pandas as pd
csv_url = 'http://test.test/test.csv'
df = pd.read_csv(csv_url)
# Split the dataset into features and label
X = df.iloc[:, 0:4].values
y = df.iloc[:, 4].values
# Import the ML model
from sklearn.tree import DecisionTreeClassifier
# Create and fit the model
model = DecisionTreeClassifier()
model.fit(X, y)
# Predict the label
def predict(data):
predicted_label = model.predict([data])
return predicted_label</s> |
<s>[INST] Instruction:
Develop a Python program to generate random passwords that consist of 8 characters.
Input:
Not applicable
[/INST] Output:
import string
import random
def generate_password():
chars = string.ascii_letters + string.digits
return ''.join(random.choice(chars) for _ in range(8))
password = generate_password()
print(password)</s> |
<s>[INST] Instruction:
Generate a list using a while loop in Python and appends the current number to the list.
Input:
[/INST] Output:
my_list = []
num = 0
while num < 10:
my_list.append(num)
num += 1
print(my_list)</s> |
<s>[INST] Instruction:
Write a Python program to query Twitter to get the most recent tweets from a user.
Input:
username: @elonmusk
[/INST] Output:
import tweepy
# Authenticate to Twitter
auth = tweepy.OAuthHandler("CONSUMER_KEY", "CONSUMER_SECRET")
auth.set_access_token("ACCESS_TOKEN", "ACCESS_TOKEN_SECRET")
# Create API object
api = tweepy.API(auth)
# Get the most recent tweets from the given user
tweets = api.user_timeline(screen_name="@elonmusk")
for tweet in tweets:
print(tweet.text)</s> |
<s>[INST] Instruction:
Create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day.
Input:
[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]
[/INST] Output:
import numpy as np
import tensorflow as tf
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
tf.keras.layers.Dense(1)
])
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['accuracy'])
return model
if __name__ == "__main__":
model = create_model()
input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
predictions = model.predict(input_data)
print(f"Predicted stock price for the next day is: {predictions[0][0]}")</s> |
<s>[INST] Instruction:
Create a way to encrypt a message using a key in Python.
Input:
message = "Hello world!"
key = "secret"
[/INST] Output:
import hashlib
def encrypt(message, key):
encrypted_msg = ""
for char in message:
encrypted_msg += chr(ord(char) + ord(key[0]))
return encrypted_msg
encrypted_msg = encrypt(message, key)
print(encrypted_msg)</s> |
<s>[INST] Instruction:
Create a function in Python that takes two parameters and prints out the larger of them.
Input:
parameter_1 = 7
parameter_2 = 9
[/INST] Output:
def printMax(parameter_1, parameter_2):
if parameter_1 > parameter_2:
print(parameter_1)
else:
print(parameter_2)</s> |
<s>[INST] Instruction:
Rewrite the following Python code by introducing list comprehensions.
Input:
even_numbers = []
for i in range(1,21):
if i % 2 == 0:
even_numbers.append(i)
[/INST] Output:
even_numbers = [i for i in range(1, 21) if i % 2 == 0]</s> |
<s>[INST] Instruction:
Develop an algorithm in Python to print Fibonacci series up to a given number.
Input:
[/INST] Output:
def fibonacci(n):
a = 0
b = 1
if n < 0:
print("Incorrect input")
elif n == 0:
return a
elif n == 1:
return b
else:
for i in range(2,n):
c = a + b
a = b
b = c
return b
n = int(input("Enter a number: "))
print(fibonacci(n))</s> |
<s>[INST] Instruction:
Make the following code snippet into a function in Python3 capable of solving Any Sudoku puzzle
Input:
board = [
[7,8,0,4,0,0,1,2,0],
[6,0,0,0,7,5,0,0,9],
[0,0,0,6,0,1,0,7,8],
[0,0,7,0,4,0,2,6,0],
[0,0,1,0,5,0,9,3,0],
[9,0,4,0,6,0,0,0,5],
[0,7,0,3,0,0,0,1,2],
[1,2,0,0,0,7,4,0,0],
[0,4,9,2,0,6,0,0,7]
]
[/INST] Output:
def solve_sudoku(board):
# Utility function to find vacant cells
# Returns a boolean
def find_vacant_cell(board, l):
for row in range(9):
for col in range(9):
if board[row][col] == 0:
l[0] = row
l[1] = col
return True
return False
# Utility function to check if a number
# is not currently placed in a particular
# row, column and block
def is_safe(board, row, col, num):
# Check if 'num' is not already placed
# in current row, current column
# and current 3x3 box
for i in range(9):
# Check the rows
if(board[row][i] == num):
return False
# Check the columns
if(board[i][col] == num):
return False
# Check the 3x3 boxes
if(board[(row//3)*3 + i//3][(col//3)*3 + i%3] == num):
return False
return True
# Solve the sudoku
def solve(board):
# 'l' is a list variable that keeps
# the record of row and col in
# find_vacant_cell function
l = [0, 0]
# If there is no unassigned location,
# we are done
if (not find_vacant_cell(board, l)):
return True
# Assigning the list values
row = l[0]
col = l[1]
# consider digits 1 to 9
for num in range(1, 10):
# if it is a safe position
if (is_safe(board, row, col, num)):
# make tentative assignment
board[row][col] = num
# return if succcess
if (solve(board)):
return True
# failure, unmake and try again
board[row][col] = 0
# trigger backtracking
return False
#Driver Program
if (solve(board)):
for row in board:
print(row)
else:
print("No solution")</s> |
<s>[INST] Instruction:
Write a function in Python to calculate the sum of the digits in a given number.
Input:
[/INST] Output:
def sum_digits(num):
total = 0
while num > 0:
digit = num % 10
total += digit
num = num // 10
return total</s> |
<s>[INST] Instruction:
Take the list of people below and create a Python dictionary with the names as keys and the ages as values.
Input:
John (20), Jane (40), Alex (18), Peter (45)
[/INST] Output:
people_dict = {'John': 20, 'Jane': 40, 'Alex': 18, 'Peter': 45}</s> |
<s>[INST] Instruction:
Develop a Python program to clearly understand the concept of recursion.
Input:
[/INST] Output:
# This program is to understand recursion in Python.
# Define a recursive function to calculate the factorial of a number
def factorial(n):
if n == 1:
return 1
else:
return n * factorial(n - 1)
# Calculate factorial of number 5
result = factorial(5)
print("Factorial of 5 is ", result)
# Output: Factorial of 5 is 120</s> |
<s>[INST] Instruction:
Generate a random 6-digits number in Python.
Input:
[/INST] Output:
import random
number = random.randint(100000, 999999)
print(number)</s> |
<s>[INST] Instruction:
Create a sorting algorithm in Python to sort strings by their length.
Input:
["cat", "dog", "apple", "banana"]
[/INST] Output:
def sort_by_length(strings):
for i in range(len(strings) - 1):
for j in range(i + 1, len(strings)):
if len(strings[i]) > len(strings[j]):
strings[i], strings[j] = strings[j], strings[i]
return strings
unsorted_list = ["cat", "dog", "apple", "banana"]
sorted_list = sort_by_length(unsorted_list)
print(sorted_list)</s> |
<s>[INST] Instruction:
Write a Python function to get the maximum and minimum values in a given list.
Input:
Not applicable
[/INST] Output:
def get_max_min(lst):
min_value = lst[0]
max_value = lst[0]
for i in range(1, len(lst)):
if lst[i] > max_value:
max_value = lst[i]
if lst[i] < min_value:
min_value = lst[i]
return (min_value, max_value)</s> |
<s>[INST] Instruction:
Create a for loop in Python to iterate over a list of numbers and print only the even numbers.
Input:
my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
[/INST] Output:
for num in my_list:
if num % 2 == 0:
print(num)</s> |
<s>[INST] Instruction:
Can you suggest a pure python alternative to declaring a Float32 array with numbers from 0 to 10?
Input:
[/INST] Output:
myArray = [float(i) for i in range(0,11)]</s> |
<s>[INST] Instruction:
Construct a neural network model in Python to classify the MNIST data set correctly.
Input:
[/INST] Output:
import keras
# Input layer
input_layer = keras.layers.Input(shape=(784,))
# Hidden layer
hidden_layer = keras.layers.Dense(128,
activation='relu')(input_layer)
# Output layer
output_layer = keras.layers.Dense(10,
activation='softmax')(hidden_layer)
# Model
model = keras.Model(inputs=input_layer, outputs=output_layer)
# Compile model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])</s> |
<s>[INST] Instruction:
Create a machine learning classifier in Python for categorizing user input.
Input:
[/INST] Output:
from sklearn.naive_bayes import MultinomialNB
# Assuming an input string is passed to the function as a parameter
def classify_string(input_string):
# Feature extraction and transformation from string to numeric
features = . . .
# Create an instance of the classifier
model = MultinomialNB()
# Train the classifier on the data
model.fit(data, target)
# Make a prediction based on the input
prediction = model.predict([features])
# Return the prediction
return prediction</s> |
<s>[INST] Instruction:
Implement a algorithm in Python for sorting two large lists A and B. The algorithm should take the first element of list A and compare it with the first element of list B. If it is greater, then it should add the element to the result list and move the pointer of B to the next element. If the element of A is smaller, the pointer should move to the next element of A, and so on.
Input:
A = [3, 6, 8, 10, 11]
B = [2, 5, 7, 12]
[/INST] Output:
def merge_sort(A, B):
result = []
i, j = 0, 0
# Compare elements and add lower one to result
while i < len(A) and j < len(B):
if A[i] < B[j]:
result.append(A[i])
i += 1
else:
result.append(B[j])
j += 1
# Add remaining elements
result += A[i:]
result += B[j:]
return result
#Test the algorithm
A = [3, 6, 8, 10, 11]
B = [2, 5, 7, 12]
print(merge_sort(A, B))
# Output: [2, 3, 5, 6, 7, 8, 10, 11, 12]</s> |
<s>[INST] Instruction:
Develop a program in Python for finding the longest common subsequence between two strings.
Input:
"ABCDF", "ABECD"
[/INST] Output:
def longest_common_subsequence(string1, string2):
table = [[0]*(len(string2)+1) for _ in range(len(string1)+1)]
for i, x in enumerate(string1):
for j, y in enumerate(string2):
if x == y:
table[i+1][j+1] = table[i][j]+1
else:
table[i+1][j+1] = max(table[i+1][j], table[i][j+1])
result = ""
x, y = len(string1), len(string2)
while x != 0 and y != 0:
if table[x][y] == table[x-1][y]:
x -= 1
elif table[x][y] == table[x][y-1]:
y -= 1
else:
result = string1[x-1] + result
x -= 1
y -= 1
return result
result = longest_common_subsequence("ABCDF", "ABECD")
print(result)</s> |
<s>[INST] Instruction:
Convert a given Python program to a C# program.
Input:
def power(x, y):
if y == 0:
return 1
else:
return x * power(x, y - 1)
[/INST] Output:
public int Power(int x, int y)
{
if (y == 0)
{
return 1;
}
else
{
return x * Power(x, y - 1);
}
}</s> |
<s>[INST] Instruction:
Rewrite the following code in a different programming language.
Input:
// JavaScript code
let arr = [1, 2, 3, 4, 5];
arr.forEach(item => {
console.log(item * 2);
});
[/INST] Output:
# Python code
arr = [1, 2, 3, 4, 5]
for item in arr:
print(item * 2)</s> |
<s>[INST] Instruction:
Create a TicTacToe game in Python.
Input:
[/INST] Output:
class TicTacToe:
def __init__(self):
self.board = [['_' for _ in range(3)]
for _ in range(3)]
self.turn = 'x'
def display(self):
for row in self.board:
print(' '.join(row))
def check_win(self):
board = self.board
# check if the player with the current turn has a row of three
for row in self.board:
if row.count(self.turn) == 3:
return True
# check if the player with the current turn has a column of three
for col in range(3):
if board[0][col] == board[1][col] and board[1][col] == board[2][col] and board[2][col] == self.turn:
return True
# check for the two diagonals
if board[0][0] == board[1][1] and board[1][1] == board[2][2] and board[2][2] == self.turn:
return True
if board[0][2] == board[1][1] and board[1][1] == board[2][0] and board[2][0] == self.turn:
return True
return False</s> |
<s>[INST] Instruction:
Create a Python program to store the strings "Hello" and "World" and print them in the same line.
Input:
[/INST] Output:
string1 = "Hello"
string2 = "World"
print(string1 + " " + string2)</s> |
<s>[INST] Instruction:
Use the inputs provided, write Python code to check if two integers are equal.
Input:
First Integer: 10
Second Integer: 10
[/INST] Output:
if int(10) == int(10):
print("The two integers are equal")
else:
print("The two integers are not equal")</s> |
<s>[INST] Instruction:
Create a Python program for analyzing the sentiment of given text. The program should categorize the text into positive, negative, and neutral sentiments.
Input:
Not applicable
[/INST] Output:
import nltk
from nltk.tokenize import sent_tokenize
from nltk.classify import NaiveBayesClassifier
def analyze_sentiment(text):
sents = sent_tokenize(text)
neg = 0
pos = 0
for sentence in sents:
sentiment_analysis = NaiveBayesClassifier.classify(sentence)
if sentiment_analysis == 'neg':
neg += 1
if sentiment_analysis == 'pos':
pos += 1
if neg > pos:
sentiment = 'negative'
elif pos > neg:
sentiment = 'positive'
else:
sentiment = 'neutral'
return sentiment
# End analyze sentiment definition</s> |
<s>[INST] Instruction:
Create a Python program to calculate the average of five numbers entered by a user.
Input:
[/INST] Output:
nums = []
for i in range(5):
nums.append(int(input("Please enter number: ")))
#Calculate average
average = sum(nums) / len(nums)
#Print average
print("The average is ", average)</s> |
<s>[INST] Instruction:
Write a Python program to generate a Markov chain given a text input.
Input:
Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, `and what is the use of a book,' thought Alice `without pictures or conversation?'
[/INST] Output:
import random
def generateMarkov(text):
words = text.split(' ')
markovChain = {}
for i in range(len(words) - 2):
current = words[i]
next = words[i + 1]
if current in markovChain:
if next in markovChain[current]:
markovChain[current][next] += 1
else:
markovChain[current][next] = 1
else:
markovChain[current] = {next: 1}
generatedText = ""
current = random.choice(list(markovChain))
generatedText += current
for i in range(100):
choices = markovChain[current]
choices = [
key for key, value in choices.items()
for i in range(value)
]
try:
current = random.choice(choices)
generatedText += " " + current
except:
break
return generatedText
text = 'Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, `and what is the use of a book,' thought Alice `without pictures or conversation?'
print(generateMarkov(text))</s> |
<s>[INST] Instruction:
Write a Python program to determine if a number provided by the user is prime or not.
Input:
17
[/INST] Output:
# Ask the user for a number
num = int(input('Enter a number: '))
# Set initial values
is_prime = True
# Check for prime
for i in range(2, num):
if num % i == 0:
is_prime = False
break
# Output result
if is_prime:
print(f'{num} is a prime number.')
else:
print(f'{num} is not a prime number.')</s> |
<s>[INST] Instruction:
Write a Python program to calculate the difference between the lowest and highest value for an array of integers.
Input:
[12, 24, 3, 8, -1]
[/INST] Output:
def diffHighestLowest(arr):
lowest = min(arr)
highest = max(arr)
return highest - lowest
arr = [12, 24, 3, 8, -1]
difference = diffHighestLowest(arr)
print(difference) # 25</s> |
<s>[INST] Instruction:
Rewrite the following code snippet to find prime numbers using Python.
Input:
Not applicable
[/INST] Output:
def is_prime(num):
# Iterate from 2 to the square root of the number
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
return False
return True
# Iterate from 2 to n
for n in range(2, n+1):
# Check if the number is prime
if is_prime(n):
print(n)</s> |
<s>[INST] Instruction:
Write a Python program to create a dictionary where the keys are numbers between 1 and 15 (both included) and the values are square of keys.
Input:
[/INST] Output:
d = {}
for i in range(1, 16):
d[i] = i**2
print(d)</s> |
<s>[INST] Instruction:
Write a Python program to calculate the sum of all even numbers less than or equal to n.
Input:
[/INST] Output:
def sum_of_even_numbers(n):
result = 0
for i in range(2, n+1, 2):
result += i
return result</s> |
<s>[INST] Instruction:
Create a Python script to calculate the Fibonacci numbers up to a given number.
Input:
[/INST] Output:
def Fibonacci(n):
if n==0:
return 0
if n==1:
return 1
else:
return Fibonacci(n-1)+Fibonacci(n-2)</s> |
<s>[INST] Instruction:
Write a Python program to calculate the total cost of a shopping cart. The program should consider the item prices, quantity, shipping cost, and any applicable taxes.
Input:
[
{"name": "widget", "price": 10, "quantity": 5 },
{"name": "thingy", "price": 7, "quantity": 3 },
{"name": "doodad", "price": 5, "quantity": 2 },
]
Shipping cost: 7.50
Tax rate: 9.5%
[/INST] Output:
items = [
{"name": "widget", "price": 10, "quantity": 5 },
{"name": "thingy", "price": 7, "quantity": 3 },
{"name": "doodad", "price": 5, "quantity": 2 },
]
taxRate = 0.095
shippingCost = 7.50
totalCost = 0
for item in items:
totalCost += item['price'] * item['quantity']
totalCost += totalCost * taxRate
totalCost += shippingCost
print('Total cost:', totalCost)</s> |
<s>[INST] Instruction:
In Python, create a function to find the maximum value within a nested dictionary.
Input:
sample_dict = {
"key1": {
"value1": 10,
"value2": 20
},
"key2": {
"value3": 40
},
}
[/INST] Output:
def findMaxValue(dict):
max_val = None
for key, value in dict.items():
if isinstance(value, dict):
max_val = max(findMaxValue(value), max_val)
else:
max_val = max(value, max_val)
return max_val
print(findMaxValue(sample_dict))</s> |
<s>[INST] Instruction:
Write a function in Python that returns an object of the following time and date: Sunday March 7 5:30 PM.
Input:
[/INST] Output:
from datetime import datetime
def get_date_time():
dt = datetime(year=2021, month=3, day=7, hour=17, minute=30)
return dt
date_time = get_date_time()
print(date_time) # will output 2021-03-07 17:30:00</s> |
<s>[INST] Instruction:
Generate a Python program to perform matrix multiplication on two given matrices.
Input:
[/INST] Output:
def matrix_multiplication(A, B):
rows_A = len(A)
cols_A = len(A[0])
rows_B = len(B)
cols_B = len(B[0])
result = [[0 for i in range(cols_B)] for j in range(rows_A)]
for i in range(rows_A):
for j in range(cols_B):
for k in range(cols_A):
result[i][j] += A[i][k] * B[k][j]
return result</s> |
<s>[INST] Instruction:
Write a Python script to read an array from the user and then determine the mean and median of the array.
Input:
arr = [10, 15, 20, 25, 30, 35]
[/INST] Output:
def mean_median(arr):
arr_sum = 0
for i in range(len(arr)):
arr_sum += arr[i]
mean = arr_sum/len(arr)
arr = sorted(arr)
if len(arr) % 2 != 0:
median = arr[floor(len(arr)/2)]
else:
median = (arr[len(arr)//2] + arr[len(arr)//2 - 1]) / 2
return mean, median
mean, median = mean_median(arr)
print("Mean =", mean)
print("Median =", median)</s> |
<s>[INST] Instruction:
Create an algorithm in Python to check whether an array has even or odd number of elements.
Input:
[1, 2, 3, 4]
[/INST] Output:
def is_array_even(arr):
if len(arr)%2 == 0:
return True
else:
return False</s> |
<s>[INST] Instruction:
Implement a Python function to calculate the Gini coefficient of a given dataset.
Input:
[2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,21]
[/INST] Output:
def gini(x):
"""Calculate Gini Coefficient of a given dataset."""
# calculate the frequency of each item in x
x_value_counts = x.value_counts().sort_index()
# calculate the cumulative sum of x_value_counts
x_cum_sum = x_value_counts.cumsum()
# calculate the Lorenz values
n = len(x)
lorenz = x_cum_sum / n
lorenz = np.insert(lorenz, 0, 0) # add 0 to the beginning of array
# calculate the Gini Coefficient
width = lorenz[:-1] - lorenz[1:]
gini_coef = (1 / n) * sum(width * (lorenz[1:] + lorenz[:-1]))
return gini_coef
if __name__ == '__main__':
x = np.array([2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,21])
gini_coef = gini(x)
print(gini_coef) # should print 0.605</s> |
<s>[INST] Instruction:
Generate a neural network model with TensorFlow and Python for classifying hand-written digits from the MNIST dataset.
Input:
Not applicable
[/INST] Output:
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1
# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_classes])
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# Create model
def multilayer_perceptron(x):
# Hidden fully connected layer with 256 neurons
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
# Hidden fully connected layer with 256 neurons
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
# Output fully connected layer with a neuron for each class
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
# Construct model
logits = multilayer_perceptron(X)
# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
# Initializing the variables
init = tf.global_variables_initializer()
# Train model
model = tf.Session()
model.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = model.run([train_op, loss_op], feed_dict={X: batch_x,
Y: batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
pred = tf.nn.softmax(logits) # Apply softmax to logits
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}, model))</s> |
<s>[INST] Instruction:
Write a function to generate a prime number sequence using Python.
Input:
[/INST] Output:
def generate_prime_numbers(n):
primes = [True for i in range(n+ 1)]
p = 2
while (p * p <= n):
if (primes[p] == True):
for i in range(p * p, n + 1, p):
primes[i] = False
p += 1
primes[0] = False
primes[1] = False
for p in range(n + 1):
if primes[p]:
print(p)</s> |
<s>[INST] Instruction:
Write a Python program that removes duplicates from a list.
Input:
Input List: [1, 2, 3, 4, 3, 2, 1]
[/INST] Output:
def remove_duplicates(my_list):
return list(dict.fromkeys(my_list))
my_list = [1, 2, 3, 4, 3, 2, 1]
print(remove_duplicates(my_list)) # output [1, 2, 3, 4]</s> |
<s>[INST] Instruction:
Generate a Python script to sort the given list of words.
Input:
['bubble', 'selection', 'quicksort', 'insertion']
[/INST] Output:
# function to sort the given list of words
def sort_words(words):
# sort the list of words in alphabetical order
words.sort()
# print the sorted list of words
print(words)
# driver code
words = ['bubble', 'selection', 'quicksort', 'insertion']
sort_words(words)
# output
['bubble', 'insertion', 'quicksort', 'selection']</s> |
<s>[INST] Instruction:
Generate a function for calculating the average of a list of numbers in Python.
Input:
Not applicable
[/INST] Output:
def avg_list(lst):
sum = 0
count = 0
for i in lst:
sum += i
count += 1
avg = sum / count
return avg</s> |
<s>[INST] Instruction:
Generate a Python program that compares two binary trees to check if they are similar.
Input:
[/INST] Output:
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
def are_similar(root1, root2):
if root1 is None and root2 is None:
return True
if (root1 and root2) is None or (root1 or root2) is None:
return False
if root1.val != root2.val:
return False
else:
return are_similar(root1.left, root2.left) and are_similar(root1.right, root2.right)</s> |
<s>[INST] Instruction:
Create a program for solving a quadratic equation in Python.
Input:
Equation: x2 + 3x - 4 = 0
[/INST] Output:
import math
def solveQuadratic(a, b, c):
discriminant = (b**2) - (4*a*c);
# If discriminant is negative there are no real roots.
if (discriminant < 0):
return {
"status": false,
"roots": []
}
# If discriminant is zero, there is only one real roots.
elif (discriminant == 0):
x = -b/(2*a)
return {
"status": true,
"roots": [x]
}
# Otherwise there are 2 real roots
else:
x1 = (-b + math.sqrt(discriminant)) / (2*a);
x2 = (-b - math.sqrt(discriminant)) / (2*a);
return {
"status": true,
"roots": [x1, x2]
}
a = 1
b = 3
c = -4
print(solveQuadratic(a, b, c)) #{'status': True, 'roots': [-4.0, 1.0]}</s> |
<s>[INST] Instruction:
Construct a Python code for adding two matrices.
Input:
[/INST] Output:
def add_matrix(matrix1, matrix2):
if len(matrix1) != len(matrix2):
return 'Matrices not compatible'
if len(matrix1[0]) != len(matrix2[0]):
return 'Matrices not compatible'
rows = len(matrix1)
cols = len(matrix1[0])
result = [[sum(row) for row in zip(*size)] for size in zip(matrix1, matrix2)]
return result</s> |
<s>[INST] Instruction:
Generate a Python program which takes a string as input and prints all of its substrings in order from longest to shortest length.
Input:
[/INST] Output:
def substring_in_order(string):
result = []
n = len(string)
for l in range(n, 0, -1):
for i in range(n-l + 1):
result.append(string[i:i+l])
return result
string = input("Enter the string: ")
print ("All substrings in order from longest to shortest:")
print (substring_in_order(string))</s> |
<s>[INST] Instruction:
Generate a Python function which takes an array and a value and returns the index at which this value is present in the array.
Input:
arr = [2, 5, 9, 15], x = 5
[/INST] Output:
def find_index(arr, x):
for i in range(len(arr)):
if arr[i] == x:
return i
return -1
print(find_index(arr, x))</s> |
<s>[INST] Instruction:
Develop a Python program to find the common elements in two lists.
Input:
List 1: [3, 9, 15, 21, 27]
List 2: [21, 7, 13, 29, 31]
[/INST] Output:
common_elements = []
# using loop
for i in [3, 9, 15, 21, 27]:
for j in [21, 7, 13, 29, 31]:
if i == j:
common_elements.append(i)
# printing common elements
print(common_elements)
# Output: [21]</s> |
<s>[INST] Instruction:
Create a Python program that takes in two strings and returns an integer indicating the number of common substrings between the two strings.
Input:
s1 = "abcd", s2 = "bae"
[/INST] Output:
def num_common_substrings(s1, s2):
count = 0
for i in range(len(s1)):
for j in range(len(s2)):
if s1[i] == s2[j]:
count += 1
return count
result = num_common_substrings("abcd", "bae")
print(result)</s> |
<s>[INST] Instruction:
Generate a Python program to convert a string to its lowercase version.
Input:
[/INST] Output:
def str_to_lower(string):
new_string = ""
for char in string:
new_string += char.lower()
return new_string</s> |
<s>[INST] Instruction:
Can you generate a function in Python that shuffles a given list?
Input:
[/INST] Output:
import random
def shuffle_list(list):
# Initialize the random number generator
random.seed()
# Shuffle the list
random.shuffle(list)
# Return the list
return list</s> |
<s>[INST] Instruction:
Write a Python function to validate an a list of emails. The function should return a boolean indicating whether a given email address is valid.
Input:
mail_list = ["example.user@gmail.com", "bad@example"]
[/INST] Output:
def validate_mail_list(mail_list):
for mail in mail_list:
if not re.match("[^@]+@[^@]+\.[^@]+", mail):
return False
return True</s> |
<s>[INST] Instruction:
Create a Python script that will perform several simple math operations. The script should accept two integers as inputs and output the results of the following operations: addition, subtraction, multiplication, division, and modulo.
Input:
int1 = 10
int2 = 5
[/INST] Output:
int1 = 10
int2 = 5
addition = int1 + int2
print('Addition:', addition)
subtraction = int1 - int2
print('Subtraction:', subtraction)
multiplication = int1 * int2
print('Multiplication:', multiplication)
division = int1 / int2
print('Division:', division)
modulo = int1 % int2
print('Modulo:', modulo)</s> |
<s>[INST] Instruction:
Create a Python program to perform sentiment analysis on product reviews. The program should use natural language processing techniques like tokenization and n-grams to generate features for sentiment analysis and use a classification model to assign sentiments to the reviews.
Input:
Not applicable
[/INST] Output:
import numpy as np
import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report
# Read in the data
data = pd.read_csv('reviews.csv')
# Tokenise the texts
data['tokenised'] = data['review'].apply(word_tokenize)
# Remove stopwords
stop_words = set(stopwords.words('english'))
data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
# Normalize words
wordnet_lemmatizer = WordNetLemmatizer()
data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
# Generate features using CountVectorizer
cv = CountVectorizer()
data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
# Split into training and test sets
X = data['features']
y = data['sentiment']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train model
model = MultinomialNB(alpha=0.1)
model.fit(X_train, y_train)
# Predict using the test set
predictions = model.predict(X_test)
# Generate a classification report
report = classification_report(y_test, predictions)
print(report)</s> |
<s>[INST] Instruction:
Write a python program to clean up a list of emails and put them into a new list.
Input:
The original list of emails:
['bob@example.com', 'alice+bob@cafe.com', 'bob@gmail.com.uk', 'bob+alice@example']
[/INST] Output:
original_list = ['bob@example.com', 'alice+bob@cafe.com', 'bob@gmail.com.uk', 'bob+alice@example']
clean_list = []
for email in original_list:
email_without_plus = email.split('+')[0]
email_without_dot_uk = email_without_plus.split('.uk')[0]
clean_list.append(email_without_dot_uk)
print(clean_list) # prints ['bob@example.com', 'alice@cafe.com', 'bob@gmail.com', 'bob@example']</s> |
<s>[INST] Instruction:
Generate sample data for a web application using Python's faker library. The output should contain three columns of data: name, email, and age.
Input:
Not applicable
[/INST] Output:
from faker import Faker
fake = Faker()
for _ in range(10):
print(fake.name(), fake.email(), fake.random_int(min=18, max=60))</s> |
<s>[INST] Instruction:
Create a Python algorithm to classify positive and negative sentiment in tweets.
Input:
[/INST] Output:
def sentiment_analysis(tweets):
# Initialize a list to store the result
results = []
# Iterate through each tweet
for tweet in tweets:
# Clean the tweet
tweet = preprocessing(tweet)
# Vectorize the tweet and obtain the prediction
tweet_vector = vectorize(tweet)
prediction = predict_sentiment(tweet_vector)
# Append the prediction to the result list
if prediction == 1:
results.append("Positive")
elif preduction == 0:
results.append("Negative")
return results</s> |
<s>[INST] Instruction:
Parse the given URL and print out the protocol, domain name, and path using Python.
Input:
https://www.example.com/path/to/file
[/INST] Output:
import urllib.parse
url = 'https://www.example.com/path/to/file'
parsed_url = urllib.parse.urlparse(url)
# Get the protocol
protocol = parsed_url.scheme
# Get the domain name
domain_name = parsed_url.netloc
# Get the path
path = parsed_url.path
# Print the results
print('Protocol:', protocol)
print('Domain name:', domain_name)
print('Path:', path)
# Output:
# Protocol: https
# Domain name: www.example.com
# Path: /path/to/file</s> |
<s>[INST] Instruction:
Write a recursive Python function to generate Fibonacci numbers.
Input:
[/INST] Output:
def fibonacci(n):
if n == 1 or n == 2:
return 1
else:
return fibonacci(n - 1) + fibonacci(n - 2)
print(fibonacci(10))</s> |
<s>[INST] Instruction:
Create a Kubernetes pod setup that publishes a tweet every 5 minutes.
Input:
Not applicable
[/INST] Output:
apiVersion: v1
kind: Pod
metadata:
name: tweet-publisher
spec:
containers:
- name: tweet-publisher
image: <INSERT_PUBLIC_PULLABLE_IMAGE_PATH_HERE>
command:
- sh
- -c
- 'crontab -lcrontab && while true; do python publish-tweet.py && crontab -l | { cat; echo "*/5 * * * * python publish-tweet.py"; } | crontab -; sleep 31622400; done'
volumeMounts:
- mountPath: /crontab/crontab
subPath: crontab
name: cronmount
volumes:
- name: cronmount
configMap:
name: tweet-cron
---
apiVersion: v1
kind: ConfigMap
metadata:
name: tweet-cron
data:
crontab: "* * * * * python publish-tweet.py"</s> |
<s>[INST] Instruction:
Write a code to generate a two-dimensional array with zeros shapes in Python
Input:
Row = 3, Column = 3
[/INST] Output:
"""
Write a code to generate a two-dimensional array with zeros shapes in Python
"""
def generate_zeros_array(row,col):
return [[0] * col for _ in range(row)]
# Example
row = 3
col = 3
array = generate_zeros_array(row, col)
print(array)
# Output: [[0, 0, 0], [0, 0, 0], [0, 0, 0]]</s> |
<s>[INST] Instruction:
Write a Python code to create a dictionary which contains a data structrue in the form of [x:x^2] where x is the key and x^2 is the value.
Input:
numbers = [1, 2, 3, 4, 5]
[/INST] Output:
numbers = [1, 2, 3, 4, 5]
dict_numbers = {num: num**2 for num in numbers}
print(dict_numbers)</s> |
<s>[INST] Instruction:
Create a Python script to print the numbers from 1 to 50, but for multiples of 3 print "Fizz" instead of the number, for multiples of 5 print "Buzz" instead of the number and for multiples of both 3 and 5 print "FizzBuzz".
Input:
Not applicable
[/INST] Output:
for i in range(1, 51):
if i % 3 == 0 and i % 5 == 0:
print("FizzBuzz")
elif i % 3 == 0:
print("Fizz")
elif i % 5 == 0:
print("Buzz")
else:
print(i)</s> |
<s>[INST] Instruction:
Create a Python program to calculate the square root of a user-defined positive number.
Input:
n = 100
[/INST] Output:
import math
n = 100
#Calculate the square root of a number
square_root = math.sqrt(n)
print(f'Square root of {n} is {square_root}')</s> |
<s>[INST] Instruction:
Create a Python class called Rectangle to represent a two-dimensional rectangle with a length and width.
Input:
[/INST] Output:
class Rectangle:
def __init__(self, length, width):
self.length = length
self.width = width
def area(self):
return self.length * self.width</s> |
<s>[INST] Instruction:
Create a data visualization dashboard in Python using Matplotlib.
Input:
[/INST] Output:
import matplotlib
import matplotlib.pyplot as plt
# Generate the data for the dashboard
x = [1,2,3,4,5]
y = [10,25,30,50,80]
# Create the figure and set the size
fig = plt.figure(figsize=(10,6))
# Configure the plot
plt.plot(x, y, color='green', marker='o', linestyle='dashed', linewidth=2, markersize=12)
plt.title("My Data Visualization Dashboard")
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Show the plot
plt.show()</s> |
<s>[INST] Instruction:
Edit the following python code to prompt the user to enter the value of radius to calculate the area of a circle.
Input:
[/INST] Output:
# Calculate area of a circle
Pi = 3.14
# Get radius from user
radius = float(input("Please enter the radius of the circle: "))
# Calculate the circle area
area = Pi*radius**2
# Print the area
print("The area of the circle is:", area)</s> |
<s>[INST] Instruction:
Create a Python algorithm to identify the longest word in a sentence.
Input:
“The rain in Spain stays mainly in the plain.”
[/INST] Output:
def find_longest_word(sentence):
words = sentence.split()
# initialise longest_word variable with 0
longest_word = 0
for word in words:
if len(word) > longest_word:
longest_word = len(word)
longest_word_str = word
return longest_word_str</s> |
<s>[INST] Instruction:
Write a Python script that can convert degrees Fahrenheit to degrees Celsius.
Input:
[/INST] Output:
def FahrenheitToCelsius(temp_fahrenheit):
temp_celsius = (temp_fahrenheit - 32) * 5/9
return temp_celsius</s> |
<s>[INST] Instruction:
Generate a Python function that checks if a given number is an Armstrong number.
Input:
[/INST] Output:
def is_armstrong_number(num):
n = len(str(num))
# initialize sum
temp = num
sum_of_digits = 0
# find sum of nth power of individual digits
while temp > 0:
digit = temp % 10
sum_of_digits += digit ** n
temp //= 10
# if num is equal to sum_of_digits then the number is an Armstrong number
if num == sum_of_digits:
return True
else:
return False</s> |
<s>[INST] Instruction:
Write a code in Python to print the following pattern:
Input:
[/INST] Output:
for i in range(5):
for j in range(i+1):
print("*", end="")
print("")
for i in range(5, 0, -1):
for j in range(i):
print("*", end="")
print("")</s> |
<s>[INST] Instruction:
Program a web scraper in Python to extract the latest stock prices from specific websites.
Input:
["AAPL stocks", "MSFT stocks", "FB stocks", "GOOG stocks"]
[/INST] Output:
import requests
from bs4 import BeautifulSoup
# input
stocks = ["AAPL stocks", "MSFT stocks", "FB stocks", "GOOG stocks"]
# process
def get_stock_price(stock):
url = f"https://finance.yahoo.com/quote/{stock}"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
price = soup.find('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}).find('span').text
return price
# output
for stock in stocks:
price = get_stock_price(stock)
print(f'The price of {stock} is {price}.')</s> |
<s>[INST] Instruction:
The code provides Python wrappers around TensorFlow ops for decoding LibSVM input to tensors. To use it, call the `decode_libsvm` function with the following arguments: `input` (a `Tensor` of type `string`), `num_features` (an `int` that is `>= 1`), `dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`), `label_dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`), and `name` (a name for the operation, optional). The function returns a tuple of `Tensor` objects `(label, feature_indices, feature_values, feature_shape)`, where `label` is a `Tensor` of type `label_dtype`, `feature_indices` is a `Tensor` of type `int64`, `feature_values` is a `Tensor` of type `dtype`, and `feature_shape` is a `Tensor` of type `int64`.
Input:
[/INST] Output:
"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
Original C++ source file: libsvm_ops.cc
"""
import collections as _collections
import six as _six
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.tf_export import tf_export
_decode_libsvm_outputs = ["label", "feature_indices", "feature_values",
"feature_shape"]
_DecodeLibsvmOutput = _collections.namedtuple(
"DecodeLibsvm", _decode_libsvm_outputs)
@tf_export('decode_libsvm')
def decode_libsvm(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None):
r"""Convert LibSVM input to tensors. The output consists of
a label and a feature tensor. The shape of the label tensor
is the same as input and the shape of the feature tensor is
`[input_shape, num_features]`.
Args:
input: A `Tensor` of type `string`. Each string is a record in the LibSVM.
num_features: An `int` that is `>= 1`. The number of features.
dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`.
label_dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (label, feature_indices, feature_values, feature_shape).
label: A `Tensor` of type `label_dtype`. A tensor of the same shape as input.
feature_indices: A `Tensor` of type `int64`. A 2-D int64 tensor of dense_shape [N, ndims].
feature_values: A `Tensor` of type `dtype`. A 1-D tensor of any type and dense_shape [N].
feature_shape: A `Tensor` of type `int64`. A 1-D int64 tensor of dense_shape [ndims].
"""
_ctx = _context._context
if _ctx is None or not _ctx._eager_context.is_eager:
num_features = _execute.make_int(num_features, "num_features")
if dtype is None:
dtype = _dtypes.float32
dtype = _execute.make_type(dtype, "dtype")
if label_dtype is None:
label_dtype = _dtypes.int64
label_dtype = _execute.make_type(label_dtype, "label_dtype")
_, _, _op = _op_def_lib._apply_op_helper(
"DecodeLibsvm", input=input, num_features=num_features, dtype=dtype,
label_dtype=label_dtype, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "label_dtype",
_op.get_attr("label_dtype"), "num_features",
_op.get_attr("num_features"))
_execute.record_gradient(
"DecodeLibsvm", _inputs_flat, _attrs, _result, name)
_result = _DecodeLibsvmOutput._make(_result)
return _result
else:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "DecodeLibsvm",
name, _ctx._post_execution_callbacks, input, "dtype", dtype,
"label_dtype", label_dtype, "num_features", num_features)
_result = _DecodeLibsvmOutput._make(_result)
return _result
except _core._FallbackException:
return decode_libsvm_eager_fallback(
input, dtype=dtype, label_dtype=label_dtype,
num_features=num_features, name=name, ctx=_ctx)
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
def decode_libsvm_eager_fallback(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function decode_libsvm
"""
_ctx = ctx if ctx else _context.context()
num_features = _execute.make_int(num_features, "num_features")
if dtype is None:
dtype = _dtypes.float32
dtype = _execute.make_type(dtype, "dtype")
if label_dtype is None:
label_dtype = _dtypes.int64
label_dtype = _execute.make_type(label_dtype, "label_dtype")
input = _ops.convert_to_tensor(input, _dtypes.string)
_inputs_flat = [input]
_attrs = ("dtype", dtype, "label_dtype", label_dtype, "num_features",
num_features)
_result = _execute.execute(b"DecodeLibsvm", 4, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodeLibsvm", _inputs_flat, _attrs, _result, name)
_result = _DecodeLibsvmOutput._make(_result)
return _result
_ops.RegisterShape("DecodeLibsvm")(None)
def _InitOpDefLibrary(op_list_proto_bytes):
op_list = _op_def_pb2.OpList()
op_list.ParseFromString(op_list_proto_bytes)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
op_def_lib.add_op_list(op_list)
return op_def_lib
# op {
# name: "DecodeLibsvm"
# input_arg {
# name: "input"
# type: DT_STRING
# }
# output_arg {
# name: "label"
# type_attr: "label_dtype"
# }
# output_arg {
# name: "feature_indices"
# type: DT_INT64
# }
# output_arg {
# name: "feature_values"
# type_attr: "dtype"
# }
# output_arg {
# name: "feature_shape"
# type: DT_INT64
# }
# attr {
# name: "dtype"
# type: "type"
# default_value {
# type: DT_FLOAT
# }
# allowed_values {
# list {
# type: DT_FLOAT
# type: DT_DOUBLE
# type: DT_INT32
# type: DT_INT64
# }
# }
# }
# attr {
# name: "label_dtype"
# type: "type"
# default_value {
# type: DT_INT64
# }
# allowed_values {
# list {
# type: DT_FLOAT
# type: DT_DOUBLE
# type: DT_INT32
# type: DT_INT64
# }
# }
# }
# attr {
# name: "num_features"
# type: "int"
# has_minimum: true
# minimum: 1
# }
# }
_op_def_lib = _InitOpDefLibrary(b"\n\311\001\n\014DecodeLibsvm\022\t\n\005input\030\007\032\024\n\005label\"\013label_dtype\032\023\n\017feature_indices\030\t\032\027\n\016feature_values\"\005dtype\032\021\n\rfeature_shape\030\t\"\033\n\005dtype\022\004type\032\0020\001:\010\n\0062\004\001\002\003\t\"!\n\013label_dtype\022\004type\032\0020\t:\010\n\0062\004\001\002\003\t\"\027\n\014num_features\022\003int(\0010\001")
</s> |
<s>[INST] Instruction:
Write a Python program to compute the sum of the values of a given dictionary
Input:
dictionary = {'A':5, 'B':6, 'C':10}
[/INST] Output:
def dict_sum(dict):
sum = 0
for key, value in dict.items():
sum += value
return sum</s> |
<s>[INST] Instruction:
Create a REST API in Python that can be used to delete an item from a list.
Input:
Not applicable
[/INST] Output:
from flask import Flask, request
import json
app = Flask(__name__)
items = ["Apple", "Banana", "Orange"]
@app.route('/items', methods=["GET"])
def get_items():
return json.dumps(items)
@app.route('/items/<item>', methods=["DELETE"])
def delete_item(item):
if item in items:
items.remove(item)
message = "Item successfully deleted."
else:
message = "Item not found."
return json.dumps({"message": message})</s> |
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