Monday, 18 March 2024
Sunday, 17 March 2024
Python Coding challenge - Day 151 | What is the output of the following Python Code?
Python Coding March 17, 2024 Python Coding Challenge No comments
Let's break down the code:
s = 'clcoding'
index = s.find('n', -1)
print(index)
s = 'clcoding': This line initializes a variable s with the string 'clcoding'.
index = s.find('n', -1): This line uses the find() method on the string s. The find() method searches for the specified substring within the given string. It takes two parameters: the substring to search for and an optional parameter for the starting index. If the starting index is negative, it counts from the end of the string.
In this case, 'n' is the substring being searched for.
The starting index -1 indicates that the search should start from the end of the string.
Since the substring 'n' is not found in the string 'clcoding', the method returns -1.
print(index): This line prints the value stored in the variable index, which is the result of the find() method. In this case, it will print -1, indicating that the substring 'n' was not found in the string 'clcoding'.
So, the overall output of this code will be -1.
Saturday, 16 March 2024
Python Coding challenge - Day 150 | What is the output of the following Python Code?
Python Coding March 16, 2024 Python Coding Challenge No comments
Let's break down each line:
my_tuple = (1, 2, 3): This line creates a tuple named my_tuple containing three elements: 1, 2, and 3.
x, y, z, *rest = my_tuple: This line uses tuple unpacking to assign values from my_tuple to variables x, y, z, and rest. The *rest syntax is used to gather any extra elements into a list called rest.
x is assigned the first element of my_tuple, which is 1.
y is assigned the second element of my_tuple, which is 2.
z is assigned the third element of my_tuple, which is 3.
*rest gathers any remaining elements of my_tuple (if any) into a list named rest. In this case, there are no remaining elements, so rest will be an empty list.
print(x, y, z, rest): This line prints the values of x, y, z, and rest.
x, y, and z are the values assigned earlier, which are 1, 2, and 3 respectively.
rest is an empty list since there are no remaining elements in my_tuple.
Therefore, when you run this code, it will output:
1 2 3 []
Operators - Lecture 2
Python Coding March 16, 2024 Python Coding Challenge No comments
Q:- What is Operator ?
Operators are symbol or special characters that perform specific
operations on one or more operands (Values or Variables).
Assignment Question
1. Write a program that prompts the user to enter their name, age, and
favorite number. Calculate and print the product of their age and
favorite number.
2. Write a program that prompts the user for enter a sentence and then
check the length of the sentence and prints the sentence also.
3. Write a program that takes two sentences from user and then checks for
the length of both sentences using “Identity Operators”.
4. Write a program that takes a integer value from the user and checks that
the number is between 10 and 20 then it will print true or else false , use
Logical and & or operator both for checking the result.
5. Write the uses of all the operators which comes inside these operators
use comments in python for writing the uses :-
Arithmetic operators
Assignment operators
Comparison operators
Logical operators
Identity operators
Basics of Coding - Lecture 1
Python Coding March 16, 2024 Python Coding Challenge No comments
1. What is coding?
->Coding refers to the process of creating instructions for a computer to
perform specific tasks. It involves writing lines of code using a programming
language that follows a defined syntax and set of rules.
Coding can be used to create software applications, websites, algorithms, and
much more. It is a fundamental skill in the field of computer science and in
essential for anyone interested in software development, data analysis,
machine learning, and various other technological domains.
2. What is algorithm?
->An algorithm is a set of clear and specific instructions that guide the
computer to solve a problem or complete a task efficiently and accurately. It’s
like a recipe that tells the computer exactly what do to achieve a desired
outcome.
3. Who created Python?
-> Python was created by Guido van Rossum. He started developing Python in
the late 1980s, and the first version of the programming language was released
in 1991.
4. What is Python?
->Python is a popular and easy to learn programming language. It is known for
it’s simplicity and readability, making it a great choice for beginners. Python is
versatile and can be used for a wide range of tasks, from web development to
data analysis and artificial intelligence. It’s clear syntax and extensive library
support make it efficient and productive for software development. Overall,
Python is a powerful yet user-friendly language that is widely used in the tech
industry.
Assignment Questions
1. Declare two variables, x and y, and assign them the values 5
and 3, respectively. Calculate their sum and print the result.
2. Declare a variable radius and assign it a value of 7. Calculate the
area of a circle with that radius and print the result.
3. Declare a variable temperature and assign it a value of 25.
Convert the temperature from Celsius to Fahrenheit and print the
result.
4. Declare three variables a, b, and c and assign them the values
10, 3.5, and 2, respectively. Calculate the result of a divided by the
product of b and c and print the result.
5. Declare a variable initial_amount and assign it a value of 1000.
Calculate the compound interest after one year with an interest rate
of 5% and print the result.
6. Declare a variable seconds and assign it a value of 86400.
Convert the seconds into hours, minutes, and seconds, and print the
result in the format: "hh:mm:ss".
7. Declare a variable numerator and assign it a value of 27.
Declare another variable denominator and assign it a value of 4.
Calculate the integer division and remainder of numerator divided by
denominator and print both results.
8. Declare a variable length and assign it a value of 10. Calculate
the perimeter and area of a square with that length and print the
results.
Friday, 15 March 2024
The json library in Python
Python Coding March 15, 2024 Python No comments
The json library in Python
1. Encoding Python Data to JSON:
import json
# Python dictionary to be encoded to JSON
data = {
"name": "John",
"age": 30,
"city": "New York"
}
# Encode the Python dictionary to JSON
json_data = json.dumps(data)
print("Encoded JSON:", json_data)
#clcoding.com
Encoded JSON: {"name": "John", "age": 30, "city": "New York"}
2. Decoding JSON to Python Data:
import json
# JSON data to be decoded to Python
json_data = '{"name": "John", "age": 30, "city": "New York"}'
# Decode the JSON data to a Python dictionary
data = json.loads(json_data)
print("Decoded Python Data:", data)
#clcoding.com
Decoded Python Data: {'name': 'John', 'age': 30, 'city': 'New York'}
3. Reading JSON from a File:
clcoding
import json
# Read JSON data from a file
with open('clcoding.json', 'r') as file:
data = json.load(file)
print("JSON Data from File:", data)
#clcoding.com
JSON Data from File: {'We are supporting freely to everyone. Join us for live support. \n\nWhatApp Support: wa.me/919767292502\n\nInstagram Support : https://www.instagram.com/pythonclcoding/\n\nFree program: https://www.clcoding.com/\n\nFree Codes: https://clcoding.quora.com/\n\nFree Support: pythonclcoding@gmail.com\n\nLive Support: https://t.me/pythonclcoding\n\nLike us: https://www.facebook.com/pythonclcoding\n\nJoin us: https://www.facebook.com/groups/pythonclcoding': None}
4. Writing JSON to a File:
import json
# Python dictionary to be written to a JSON file
data = {
"name": "John",
"age": 30,
"city": "New York"
}
# Write the Python dictionary to a JSON file
with open('output.json', 'w') as file:
json.dump(data, file)
#clcoding.com
5. Handling JSON Errors:
import json
# JSON data with syntax error
json_data = '{"name": "John", "age": 30, "city": "New York"'
try:
# Attempt to decode JSON data
data = json.loads(json_data)
except json.JSONDecodeError as e:
# Handle JSON decoding error
print("Error decoding JSON:", e)
#clcoding.com
Error decoding JSON: Expecting ',' delimiter: line 1 column 47 (char 46)
Thursday, 14 March 2024
Python Coding challenge - Day 149 | What is the output of the following Python Code?
Python Coding March 14, 2024 Python Coding Challenge No comments
Let's break down the given code:
for i in range(1, 3):
print(i, end=' - ')
This code snippet is a for loop in Python. Let's go through it step by step:
for i in range(1, 3)::
This line initiates a loop where i will take on values from 1 to 2 (inclusive). The range() function generates a sequence of numbers starting from the first argument (1 in this case) up to, but not including, the second argument (3 in this case).
So, the loop will iterate with i taking on the values 1 and 2.
print(i, end=' - '):
Within the loop, this line prints the current value of i, followed by a dash (-), without moving to the next line due to the end=' - ' parameter.
So, during each iteration of the loop, it will print the value of i followed by a dash and space.
When you execute this code, it will output:
1 - 2 -
Explanation: The loop runs for each value of i in the range (1, 3), which are 1 and 2. For each value of i, it prints the value followed by a dash and space. So, the output is 1 - 2 - .
Learn hashlib library in Python
Python Coding March 14, 2024 Python No comments
1. Hashing Strings:
import hashlib
# Hash a string using SHA256 algorithm
string_to_hash = "Hello, World!"
hashed_string = hashlib.sha256(string_to_hash.encode()).hexdigest()
print("Original String:", string_to_hash)
print("Hashed String:", hashed_string)
#clcoding.com
Original String: Hello, World!
Hashed String: dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f
2. Hashing Files:
#clcoding.com
import hashlib
def calculate_file_hash(file_path, algorithm='sha256'):
# Choose the hash algorithm
hash_algorithm = getattr(hashlib, algorithm)()
# Read the file in binary mode and update the hash object
with open(file_path, 'rb') as file:
for chunk in iter(lambda: file.read(4096), b''):
hash_algorithm.update(chunk)
# Get the hexadecimal representation of the hash value
hash_value = hash_algorithm.hexdigest()
return hash_value
# Example usage
file_path = 'example.txt'
file_hash = calculate_file_hash(file_path)
print("SHA-256 Hash of the file:", file_hash)
#clcoding.com
SHA-256 Hash of the file: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
3. Using Different Hash Algorithms:
import hashlib
# Hash a string using different algorithms
string_to_hash = "Hello, World!"
# MD5
md5_hash = hashlib.md5(string_to_hash.encode()).hexdigest()
# SHA1
sha1_hash = hashlib.sha1(string_to_hash.encode()).hexdigest()
# SHA512
sha512_hash = hashlib.sha512(string_to_hash.encode()).hexdigest()
print("MD5 Hash:", md5_hash)
print("SHA1 Hash:", sha1_hash)
print("SHA512 Hash:", sha512_hash)
#clcoding.com
MD5 Hash: 65a8e27d8879283831b664bd8b7f0ad4
SHA1 Hash: 0a0a9f2a6772942557ab5355d76af442f8f65e01
SHA512 Hash: 374d794a95cdcfd8b35993185fef9ba368f160d8daf432d08ba9f1ed1e5abe6cc69291e0fa2fe0006a52570ef18c19def4e617c33ce52ef0a6e5fbe318cb0387
4. Hashing Passwords (Securely):
import hashlib
# Hash a password securely using a salt
password = "my_password"
salt = "random_salt"
hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode(), salt.encode(), 100000)
hashed_password_hex = hashed_password.hex()
print("Salted and Hashed Password:", hashed_password_hex)
#clcoding.com
Salted and Hashed Password: b18597b62cda4415c995eaff30f61460da8ff4d758d3880f80593ed5866dcf98
5. Verifying Passwords:
import hashlib
# Verify a password against a stored hash
stored_hash = "stored_hashed_password"
def verify_password(password, stored_hash):
input_hash = hashlib.sha256(password.encode()).hexdigest()
if input_hash == stored_hash:
return True
else:
return False
password_to_verify = "password_to_verify"
if verify_password(password_to_verify, stored_hash):
print("Password is correct!")
else:
print("Password is incorrect.")
#clcoding.com
Password is incorrect.
6. Hashing a String using SHA-256:
import hashlib
# Create a hash object
hash_object = hashlib.sha256()
# Update the hash object with the input data
input_data = b'Hello, World!'
hash_object.update(input_data)
# Get the hexadecimal representation of the hash value
hash_value = hash_object.hexdigest()
print("SHA-256 Hash:", hash_value)
#clcoding.com
SHA-256 Hash: dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f
7. Hashing a String using MD5:
import hashlib
# Create a hash object
hash_object = hashlib.md5()
# Update the hash object with the input data
input_data = b'Hello, World!'
hash_object.update(input_data)
# Get the hexadecimal representation of the hash value
hash_value = hash_object.hexdigest()
print("MD5 Hash:", hash_value)
#clcoding.com
MD5 Hash: 65a8e27d8879283831b664bd8b7f0ad4
Wednesday, 13 March 2024
Learn psutil library in Python 🧵:
Python Coding March 13, 2024 Python No comments
Learn psutil library in Python
pip install psutil
1. Getting CPU Information:
import psutil
# Get CPU information
cpu_count = psutil.cpu_count()
cpu_percent = psutil.cpu_percent(interval=1)
print("CPU Count:", cpu_count)
print("CPU Percent:", cpu_percent)
#clcoding.com
CPU Count: 8
CPU Percent: 6.9
2. Getting Memory Information:
import psutil
# Get memory information
memory = psutil.virtual_memory()
total_memory = memory.total
available_memory = memory.available
used_memory = memory.used
percent_memory = memory.percent
print("Total Memory:", total_memory)
print("Available Memory:", available_memory)
print("Used Memory:", used_memory)
print("Memory Percent:", percent_memory)
#clcoding.com
Total Memory: 8446738432
Available Memory: 721600512
Used Memory: 7725137920
Memory Percent: 91.5
3. Listing Running Processes:
import psutil
# List running processes
for process in psutil.process_iter():
print(process.pid, process.name())
#clcoding.com
0 System Idle Process
4 System
124 Registry
252 chrome.exe
408 PowerToys.Peek.UI.exe
436 msedge.exe
452 svchost.exe
504 smss.exe
520 svchost.exe
532 RuntimeBroker.exe
544 TextInputHost.exe
548 svchost.exe
680 csrss.exe
704 fontdrvhost.exe
768 wininit.exe
776 chrome.exe
804 chrome.exe
848 services.exe
924 lsass.exe
1036 WUDFHost.exe
1100 svchost.exe
1148 svchost.exe
1160 SgrmBroker.exe
1260 dllhost.exe
1284 PowerToys.exe
1328 svchost.exe
1392 svchost.exe
1400 svchost.exe
1408 svchost.exe
1488 svchost.exe
1504 svchost.exe
1512 svchost.exe
1600 SmartAudio3.exe
1608 svchost.exe
1668 svchost.exe
1716 svchost.exe
1724 IntelCpHDCPSvc.exe
1732 svchost.exe
1752 svchost.exe
1796 TiWorker.exe
1828 svchost.exe
1920 chrome.exe
1972 svchost.exe
1992 svchost.exe
2016 svchost.exe
2052 svchost.exe
2060 svchost.exe
2068 IntelCpHeciSvc.exe
2148 igfxCUIService.exe
2168 svchost.exe
2224 svchost.exe
2260 svchost.exe
2316 svchost.exe
2360 chrome.exe
2364 svchost.exe
2400 MsMpEng.exe
2420 svchost.exe
2428 svchost.exe
2448 PowerToys.FancyZones.exe
2480 screenrec.exe
2488 svchost.exe
2496 svchost.exe
2504 svchost.exe
2552 svchost.exe
2604 svchost.exe
2616 MemCompression
2716 svchost.exe
2792 chrome.exe
2796 dasHost.exe
2804 chrome.exe
2852 svchost.exe
2876 svchost.exe
2932 CxAudioSvc.exe
3016 svchost.exe
3240 svchost.exe
3416 svchost.exe
3480 svchost.exe
3536 spoolsv.exe
3620 svchost.exe
3660 svchost.exe
3700 svchost.exe
3752 RuntimeBroker.exe
3848 taskhostw.exe
3976 svchost.exe
3984 svchost.exe
3992 svchost.exe
4000 svchost.exe
4008 svchost.exe
4016 svchost.exe
4024 svchost.exe
4032 svchost.exe
4100 svchost.exe
4132 OneApp.IGCC.WinService.exe
4140 AnyDesk.exe
4148 armsvc.exe
4156 CxUtilSvc.exe
4208 WMIRegistrationService.exe
4284 msedge.exe
4312 svchost.exe
4320 AGMService.exe
4340 svchost.exe
4488 chrome.exe
4516 svchost.exe
4584 svchost.exe
4720 jhi_service.exe
4928 chrome.exe
5004 chrome.exe
5176 dwm.exe
5348 svchost.exe
5368 Flow.exe
5380 svchost.exe
5536 chrome.exe
5540 chrome.exe
5584 audiodg.exe
5620 svchost.exe
5724 svchost.exe
5776 svchost.exe
5992 ctfmon.exe
6032 CompPkgSrv.exe
6056 SearchProtocolHost.exe
6076 msedge.exe
6120 SearchIndexer.exe
6128 RuntimeBroker.exe
6156 svchost.exe
6192 MoUsoCoreWorker.exe
6380 PowerToys.PowerLauncher.exe
6424 PowerToys.Awake.exe
6480 msedge.exe
6596 svchost.exe
6740 svchost.exe
6792 winlogon.exe
6856 TrustedInstaller.exe
6872 svchost.exe
6888 igfxEM.exe
6908 svchost.exe
6948 chrome.exe
7140 csrss.exe
7296 PowerToys.KeyboardManagerEngine.exe
7336 WhatsApp.exe
7348 chrome.exe
7416 chrome.exe
7440 MusNotifyIcon.exe
7444 StartMenuExperienceHost.exe
7480 svchost.exe
7520 chrome.exe
7556 SearchApp.exe
7560 SecurityHealthService.exe
7720 msedge.exe
8220 MmReminderService.exe
8316 RuntimeBroker.exe
8636 svchost.exe
8836 python.exe
9088 ShellExperienceHost.exe
9284 svchost.exe
9344 NisSrv.exe
9560 msedge.exe
9664 chrome.exe
9736 chrome.exe
9784 SearchApp.exe
9808 svchost.exe
9868 python.exe
9884 svchost.exe
9908 chrome.exe
9936 chrome.exe
9996 QtWebEngineProcess.exe
10012 taskhostw.exe
10024 chrome.exe
10148 svchost.exe
10228 svchost.exe
10236 PowerToys.CropAndLock.exe
10304 Taskmgr.exe
10324 Video.UI.exe
10584 svchost.exe
10680 chrome.exe
10920 LockApp.exe
11064 chrome.exe
11176 chrome.exe
11188 msedge.exe
11396 msedge.exe
11500 QtWebEngineProcess.exe
11592 svchost.exe
12132 msedge.exe
12212 RuntimeBroker.exe
12360 RuntimeBroker.exe
12500 chrome.exe
12596 python.exe
12704 chrome.exe
12744 svchost.exe
12832 svchost.exe
12848 MicTray64.exe
12852 fontdrvhost.exe
12992 chrome.exe
13092 chrome.exe
13268 chrome.exe
13332 chrome.exe
13388 sihost.exe
13572 chrome.exe
13760 SecurityHealthSystray.exe
13792 msedge.exe
13880 fodhelper.exe
13900 chrome.exe
14160 UserOOBEBroker.exe
14220 RuntimeBroker.exe
14260 chrome.exe
14356 msedge.exe
14572 chrome.exe
14648 chrome.exe
14696 PowerToys.AlwaysOnTop.exe
14852 chrome.exe
14868 PowerToys.ColorPickerUI.exe
14876 conhost.exe
14888 PowerToys.PowerOCR.exe
14948 chrome.exe
15324 explorer.exe
4. Getting Process Information:
252
import psutil
# Get information for a specific process
pid = 252 # Replace with the process ID of interest
process = psutil.Process(pid)
print("Process Name:", process.name())
print("Process Status:", process.status())
print("Process CPU Percent:", process.cpu_percent(interval=1))
print("Process Memory Info:", process.memory_info())
#clcoding.com
Process Name: chrome.exe
Process Status: running
Process CPU Percent: 0.0
Process Memory Info: pmem(rss=29597696, vms=24637440, num_page_faults=14245, peak_wset=37335040, wset=29597696, peak_paged_pool=635560, paged_pool=635560, peak_nonpaged_pool=21344, nonpaged_pool=17536, pagefile=24637440, peak_pagefile=33103872, private=24637440)
5. Killing a Process:
import psutil
# Kill a process
pid_to_kill = 10088
# Replace with the process ID to kill
process_to_kill = psutil.Process(pid_to_kill)
process_to_kill.terminate()
#clcoding.com
6. Getting Disk Usage:
import psutil
# Get disk usage information
disk_usage = psutil.disk_usage('/')
total_disk_space = disk_usage.total
used_disk_space = disk_usage.used
free_disk_space = disk_usage.free
disk_usage_percent = disk_usage.percent
print("Total Disk Space:", total_disk_space)
print("Used Disk Space:", used_disk_space)
print("Free Disk Space:", free_disk_space)
print("Disk Usage Percent:", disk_usage_percent)
#clcoding.com
Total Disk Space: 479491600384
Used Disk Space: 414899838976
Free Disk Space: 64591761408
Disk Usage Percent: 86.5
Tuesday, 12 March 2024
Python Coding challenge - Day 148 | What is the output of the following Python Code?
Python Coding March 12, 2024 Python Coding Challenge No comments
Let's break down the provided code:
d = {'Milk': 1, 'Soap': 2, 'Towel': 3}
if 'Soap' in d:
print(d['Soap'])
d = {'Milk': 1, 'Soap': 2, 'Towel': 3}: This line initializes a dictionary named d with three key-value pairs. Each key represents an item, and its corresponding value represents the quantity of that item. In this case, there are items such as 'Milk', 'Soap', and 'Towel', each associated with a quantity.
if 'Soap' in d:: This line checks whether the key 'Soap' exists in the dictionary d. It does this by using the in keyword to check if the string 'Soap' is a key in the dictionary. If 'Soap' is present in the dictionary d, the condition evaluates to True, and the code inside the if block will execute.
print(d['Soap']): If the key 'Soap' exists in the dictionary d, this line will execute. It retrieves the value associated with the key 'Soap' from the dictionary d and prints it. In this case, the value associated with 'Soap' is 2, so it will print 2.
So, in summary, this code checks if the dictionary contains an entry for 'Soap'. If it does, it prints the quantity of soap available (which is 2 in this case).
Plots using Python
Python Coding March 12, 2024 Python No comments
1. Line Plot:
#clcoding.com
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Create a line plot
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot Example')
plt.show()
#clcoding.com
2. Bar Plot:
import matplotlib.pyplot as plt
# Sample data
categories = ['A', 'B', 'C', 'D']
values = [10, 20, 15, 25]
# Create a bar plot
plt.bar(categories, values)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot Example')
plt.show()
3. Histogram:
import matplotlib.pyplot as plt
import numpy as np
# Generate random data
data = np.random.randn(1000)
# Create a histogram
plt.hist(data, bins=30)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram Example')
plt.show()
4. Scatter Plot:
import matplotlib.pyplot as plt
import numpy as np
# Generate random data
x = np.random.randn(100)
y = 2 * x + np.random.randn(100)
# Create a scatter plot
plt.scatter(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot Example')
plt.show()
5. Box Plot:
import seaborn as sns
import numpy as np
# Generate random data
data = np.random.randn(100)
# Create a box plot
sns.boxplot(data=data)
plt.title('Box Plot Example')
plt.show()
6. Violin Plot:
import seaborn as sns
import numpy as np
# Generate random data
data = np.random.randn(100)
# Create a violin plot
sns.violinplot(data=data)
plt.title('Violin Plot Example')
plt.show()
7. Heatmap:
#clcoding.com
import seaborn as sns
import numpy as np
# Generate random data
data = np.random.rand(10, 10)
#clcoding.com
# Create a heatmap
sns.heatmap(data)
plt.title('Heatmap Example')
plt.show()
8. Area Plot:
import matplotlib.pyplot as plt
# Sample data #clcoding.com
x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 3, 5, 7, 9]
# Create an area plot
plt.fill_between(x, y1, color="skyblue", alpha=0.4)
plt.fill_between(x, y2, color="salmon", alpha=0.4)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Area Plot Example')
plt.show()
9. Pie Chart:
import matplotlib.pyplot as plt
# Sample data
sizes = [30, 20, 25, 15, 10]
labels = ['A', 'B', 'C', 'D', 'E']
# Create a pie chart
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart Example')
plt.show()
10. Polar Plot:
g
import matplotlib.pyplot as plt
import numpy as np
# Sample data
theta = np.linspace(0, 2*np.pi, 100)
r = np.sin(3*theta)
# Create a polar plot #clcoding.com
plt.polar(theta, r)
plt.title('Polar Plot Example')
plt.show()
11. 3D Plot:
import matplotlib.pyplot as plt
import numpy as np
# Sample data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
# Create a 3D surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z)
ax.set_title('3D Plot Example')
plt.show()
12. Violin Swarm Plot:
#clcoding.com
import seaborn as sns
import numpy as np
# Generate random data
data = np.random.randn(100)
#clcoding.com
# Create a violin swarm plot
sns.violinplot(data=data, inner=None, color='lightgray')
sns.swarmplot(data=data, color='blue', alpha=0.5)
plt.title('Violin Swarm Plot Example')
plt.show()
13. Pair Plot:
import seaborn as sns
import pandas as pd
# Load sample dataset
iris = sns.load_dataset('iris')
# Create a pair plot
sns.pairplot(iris)
plt.title('Pair Plot Example')
plt.show()
Monday, 11 March 2024
Python Coding challenge - Day 147 | What is the output of the following Python Code?
Python Coding March 11, 2024 Python Coding Challenge No comments
In Python, the is operator checks whether two variables reference the same object in memory, while the == operator checks for equality of values. Now, let's analyze the given code:
g = (1, 2, 3)
h = (1, 2, 3)
print(f"g is h: {g is h}")
print(f"g == h: {g == h}")
Explanation:
Identity (is):
The g is h expression checks if g and h refer to the same object in memory.
In this case, since tuples are immutable, Python creates separate objects for g and h with the same values (1, 2, 3).
Equality (==):
The g == h expression checks if the values contained in g and h are the same.
Tuples are compared element-wise. In this case, both tuples have the same elements (1, 2, 3).
Output:
The output of the code will be:
g is h: False
g == h: True
Explanation of Output:
g is h: False: The is operator returns False because g and h are distinct objects in memory.
g == h: True: The == operator returns True because the values inside g and h are the same.
In summary, the tuples g and h are different objects in memory, but they contain the same values, leading to == evaluating to True.
Cybersecurity using Python
Python Coding March 11, 2024 Python No comments
1. Hashing Passwords:
import hashlib
def hash_password(password):
hashed_password = hashlib.sha256(password.encode()).hexdigest()
return hashed_password
# Example
password = "my_secure_password"
hashed_password = hash_password(password)
print("Hashed Password:", hashed_password)
#clcoding.com
Hashed Password: 2c9a8d02fc17ae77e926d38fe83c3529d6638d1d636379503f0c6400e063445f
2. Generating Random Passwords:
import random
import string
def generate_random_password(length=12):
characters = string.ascii_letters + string.digits + string.punctuation
password = ''.join(random.choice(characters) for _ in range(length))
return password
# Example
random_password = generate_random_password()
print("Random Password:", random_password)
#clcoding.com
Random Password: zH7~ANoO:7#S
3. Network Scanning with Scapy:
from scapy.all import IP, ICMP, sr1
def ping(host):
packet = IP(dst=host)/ICMP()
response = sr1(packet, timeout=2, verbose=0)
if response:
return f"{host} is online"
else:
return f"{host} is offline"
# Example
host_to_scan = "example.com"
result = ping(host_to_scan)
print(result)
#clcoding.com
4. Web Scraping for Security Research:
import requests
from bs4 import BeautifulSoup
def scrape_security_news():
url = "https://example-security-news.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
headlines = soup.find_all('h2', class_='security-headline')
return [headline.text for headline in headlines]
# Example
security_headlines = scrape_security_news()
print("Security Headlines:", security_headlines)
#clcoding.com
5. Password Cracking Simulation:
import hashlib
def simulate_password_cracking(hashed_password, password_list):
for password in password_list:
if hashlib.sha256(password.encode()).hexdigest() == hashed_password:
return f"Password cracked: {password}"
return "Password not found"
# Example
hashed_password_to_crack = "d033e22ae348aeb5660fc2140aec35850c4da997"
common_passwords = ["password", "123456", "qwerty", "admin"]
result = simulate_password_cracking(hashed_password_to_crack, common_passwords)
print(result)
#clcoding.com
6. Secure File Handling:
import os
def secure_file_deletion(file_path):
with open(file_path, 'w') as file:
file.write(os.urandom(1024))
# Overwrite the file with random data
os.remove(file_path)
print(f"{file_path} securely deleted")
# Example
file_path_to_delete = "example.txt"
secure_file_deletion(file_path_to_delete)
#clcoding.com
Sunday, 10 March 2024
Python Coding challenge - Day 146 | What is the output of the following Python Code?
Python Coding March 10, 2024 Python Coding Challenge No comments
Let's go through the code step by step:
years = 5: Initializes a variable named years with the value 5.
if True or False:: This is an if statement with a condition. The condition is True or False, which will always be True because the logical OR (or) operator returns True if at least one of the operands is True. In this case, True is always True, so the condition is satisfied.
years = years + 2: Inside the if block, there's an assignment statement that adds 2 to the current value of the years variable. Since the condition is always True, this line of code will always be executed.
print(years): Finally, this line prints the current value of the years variable.
As a result, the code will always enter the if block, increment the value of years by 2 (from 5 to 7), and then print the final value of years, which is 7.
Saturday, 9 March 2024
try and except in Python
Python Coding March 09, 2024 Python Coding Challenge No comments
Example 1: Handling a Specific Exception
try:
# Code that might raise an exception
num = int(input("Enter a number: "))
result = 10 / num
print("Result:", result)
except ZeroDivisionError:
# Handle the specific exception (division by zero)
print("Error: Cannot divide by zero.")
except ValueError:
# Handle the specific exception (invalid input for conversion to int)
print("Error: Please enter a valid number.")
#clcoding.com
Enter a number: 5
Result: 2.0
Example 2: Handling Multiple Exceptions
try:
file_name = input("Enter the name of a file: ")
# Open and read the contents of the file
with open(file_name, 'r') as file:
contents = file.read()
print("File contents:", contents)
except FileNotFoundError:
# Handle the specific exception (file not found)
print("Error: File not found.")
except PermissionError:
# Handle the specific exception (permission error)
print("Error: Permission denied to access the file.")
except Exception as e:
# Handle any other exceptions not explicitly caught
print(f"An unexpected error occurred: {e}")
#clcoding.com
Enter the name of a file: clcoding
Error: File not found.
Example 3: Using a Generic Exception
try:
# Code that might raise an exception
x = int(input("Enter a number: "))
y = 10 / x
print("Result:", y)
except Exception as e:
# Catch any type of exception
print(f"An error occurred: {e}")
#clcoding.com
Enter a number: 5
Result: 2.0
Python Coding challenge - Day 145 | What is the output of the following Python Code?
Python Coding March 09, 2024 Python Coding Challenge No comments
Let's evaluate the provided Python code:
a = 20 or 40
if 30 <= a <= 50:
print('Hello')
else:
print('Hi')
Here's a step-by-step breakdown:
Assignment of a:
a = 20 or 40: In Python, the or operator returns the first true operand or the last operand if none are true. In this case, 20 is considered true, so a is assigned the value 20.
Condition Check:
if 30 <= a <= 50:: Checks whether the value of a falls within the range from 30 to 50 (inclusive).
Print Statement Execution:
Since a is assigned the value 20, which is outside the range 30 to 50, the condition is not met.
Therefore, the else block is executed, and the output will be Hi.
Let's run through the logic:
Is 30 <= 20 <= 50? No.
So, the else block is executed, and 'Hi' is printed.
The output of this code will be:
Hi
Friday, 8 March 2024
Lambda Functions in Python
Python Coding March 08, 2024 Python Coding Challenge No comments
Example 1: Basic Syntax
# Regular function
def add(x, y):
return x + y
# Equivalent lambda function
lambda_add = lambda x, y: x + y
# Using both functions
result_regular = add(3, 5)
result_lambda = lambda_add(3, 5)
print("Result (Regular Function):", result_regular)
print("Result (Lambda Function):", result_lambda)
#clcoding.com
Result (Regular Function): 8
Result (Lambda Function): 8
Example 2: Sorting with Lambda
# List of tuples
students = [("Alice", 25), ("Bob", 30), ("Charlie", 22)]
# Sort by age using a lambda function
sorted_students = sorted(students, key=lambda student: student[1])
print("Sorted Students by Age:", sorted_students)
#clcoding.com
Sorted Students by Age: [('Charlie', 22), ('Alice', 25), ('Bob', 30)]
Example 3: Filtering with Lambda
# List of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# Filter even numbers using a lambda function
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print("Even Numbers:", even_numbers)
#clcoding.com
Even Numbers: [2, 4, 6, 8]
Example 4: Mapping with Lambda
# List of numbers
numbers = [1, 2, 3, 4, 5]
# Square each number using a lambda function
squared_numbers = list(map(lambda x: x**2, numbers))
print("Squared Numbers:", squared_numbers)
#clcoding.com
Squared Numbers: [1, 4, 9, 16, 25]
Example 5: Using Lambda with max function
# List of numbers
numbers = [10, 5, 8, 20, 15]
# Find the maximum number using a lambda function
max_number = max(numbers, key=lambda x: -x) # Use negation for finding the minimum
print("Maximum Number:", max_number)
#clcoding.com
Maximum Number: 5
Example 6: Using Lambda with sorted and Multiple Criteria
# List of dictionaries representing people with names and ages
people = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}, {"name": "Charlie", "age": 22}]
# Sort by age and then by name using a lambda function
sorted_people = sorted(people, key=lambda person: (person["age"], person["name"]))
print("Sorted People:", sorted_people)
#clcoding.com
Sorted People: [{'name': 'Charlie', 'age': 22}, {'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
Example 7: Using Lambda with reduce from functools
from functools import reduce
# List of numbers
numbers = [1, 2, 3, 4, 5]
# Calculate the product of all numbers using a lambda function and reduce
product = reduce(lambda x, y: x * y, numbers)
print("Product of Numbers:", product)
#clcoding.com
Product of Numbers: 120
Example 8: Using Lambda with Conditional Expressions
# List of numbers
numbers = [10, 5, 8, 20, 15]
# Use a lambda function with a conditional expression to filter and square even numbers
filtered_and_squared = list(map(lambda x: x**2 if x % 2 == 0 else x, numbers))
print("Filtered and Squared Numbers:", filtered_and_squared)
#clcoding.com
Filtered and Squared Numbers: [100, 5, 64, 400, 15]
Example 9: Using Lambda with key in max and min to Find Extremes
# List of tuples representing products with names and prices
products = [("Laptop", 1200), ("Phone", 800), ("Tablet", 500), ("Smartwatch", 200)]
# Find the most and least expensive products using lambda functions
most_expensive = max(products, key=lambda item: item[1])
least_expensive = min(products, key=lambda item: item[1])
print("Most Expensive Product:", most_expensive)
print("Least Expensive Product:", least_expensive)
#clcoding.com
Most Expensive Product: ('Laptop', 1200)
Least Expensive Product: ('Smartwatch', 200)
Python Coding challenge - Day 144 | What is the output of the following Python Code?
Python Coding March 08, 2024 Python Coding Challenge No comments
The code print(()*3) in Python will print an empty tuple three times.
Let's break down the code:
print(): This is a built-in function in Python used to output messages to the console.
(): This represents an empty tuple. A tuple is an ordered collection of items similar to a list, but unlike lists, tuples are immutable, meaning their elements cannot be changed after creation.
*3: This is the unpacking operator. In this context, it unpacks the empty tuple three times.
Since an empty tuple by itself doesn't contain any elements to print, it essentially prints nothing three times. So the output of this code will be an empty line repeated three times.
Fractal Data Science Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
What you'll learn
Apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios.
Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.
Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.
Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.
Join Free: Fractal Data Science Professional Certificate
Professional Certificate - 8 course series
CertNexus Certified Data Science Practitioner Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science No comments
Advance your career with in-demand skills
Receive professional-level training from CertNexus
Demonstrate your technical proficiency
Earn an employer-recognized certificate from CertNexus
Prepare for an industry certification exam
Join Free: CertNexus Certified Data Science Practitioner Professional Certificate
Professional Certificate - 5 course series
IBM Data Engineering Professional Certificate
Python Coding March 08, 2024 Coursera, Data Science, IBM No comments
What you'll learn
Master the most up-to-date practical skills and knowledge data engineers use in their daily roles
Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2
Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming
Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards
Join Free: IBM Data Engineering Professional Certificate
Professional Certificate - 13 course series
Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Python Coding March 08, 2024 Books, Data Science, Google No comments
What you'll learn
Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
Employ BigQuery to carry out interactive data analysis.
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
Choose between different data processing products on Google Cloud.
Join Free: Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Professional Certificate - 6 course series
Tableau Business Intelligence Analyst Professional Certificate
Python Coding March 08, 2024 Books, Data Science No comments
What you'll learn
Gain the essential skills necessary to excel in an entry-level Business Intelligence Analytics role.
Learn to use Tableau Public to manipulate and prepare data for analysis.
Craft and dissect data visualizations that reveal patterns and drive actionable insights.
Construct captivating narratives through data, enabling stakeholders to explore insights effectively.
Join Free: Tableau Business Intelligence Analyst Professional Certificate
Professional Certificate - 8 course series
Thursday, 7 March 2024
Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples
Python Coding March 07, 2024 Books, Machine Learning, Python No comments
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.
Purchase of the print or Kindle book includes a free eBook in PDF format.
Key Features
Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores
Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods
Analyze and extract insights from complex models from CNNs to BERT to time series models
Book Description
Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.
Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.
In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.
By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.
What you will learn
Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty
Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers
Use monotonic and interaction constraints to make fairer and safer models
Understand how to mitigate the influence of bias in datasets
Leverage sensitivity analysis factor prioritization and factor fixing for any model
Discover how to make models more reliable with adversarial robustness
Who this book is for
This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
Table of Contents
Interpretation, Interpretability and Explainability; and why does it all matter?
Key Concepts of Interpretability
Interpretation Challenges
Global Model-agnostic Interpretation Methods
Local Model-agnostic Interpretation Methods
Anchors and Counterfactual Explanations
Visualizing Convolutional Neural Networks
Interpreting NLP Transformers
Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Feature Selection and Engineering for Interpretability
Bias Mitigation and Causal Inference Methods
Monotonic Constraints and Model Tuning for Interpretability
Adversarial Robustness
What's Next for Machine Learning Interpretability?
Hard Copy: Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples
Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs
Python Coding March 07, 2024 AI, Books, Python No comments
Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.
Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.
Purchase of the print or Kindle book includes a free PDF eBook.
Key Features
Learn how to leverage LLMs' capabilities and work around their inherent weaknesses
Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges
Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality
Book Description
ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.
Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.
What you will learn
Understand LLMs, their strengths and limitations
Grasp generative AI fundamentals and industry trends
Create LLM apps with LangChain like question-answering systems and chatbots
Understand transformer models and attention mechanisms
Automate data analysis and visualization using pandas and Python
Grasp prompt engineering to improve performance
Fine-tune LLMs and get to know the tools to unleash their power
Deploy LLMs as a service with LangChain and apply evaluation strategies
Privately interact with documents using open-source LLMs to prevent data leaks
Who this book is for
The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.
Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.
Table of Contents
What Is Generative AI?
LangChain for LLM Apps
Getting Started with LangChain
Building Capable Assistants
Building a Chatbot like ChatGPT
Developing Software with Generative AI
LLMs for Data Science
Customizing LLMs and Their Output
Generative AI in Production
The Future of Generative Models
Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs
Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster
Python Coding March 07, 2024 AI, data management No comments
Printed in Color
Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact
Leverage the power of Generative AI with Kaggle Models
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models
Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound
Improve the style and readability of your Notebooks, making them more impactful and compelling
Book Description
Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.
For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.
Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.
What you will learn
Approach a dataset or competition to perform data analysis via a notebook
Learn data ingestion and address issues arising with the ingested data
Structure your code using reusable components
Analyze in depth both small and large datasets of various types
Distinguish yourself from the crowd with the content of your analysis
Enhance your notebook style with a color scheme and other visual effects
Captivate your audience with data and compelling storytelling techniques
Who this book is for
This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:
Beginners on Kaggle from any background
Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization
Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings
Professionals who already use Kaggle for learning and competing
Table of Contents
Introducing Kaggle and Its Basic Functions
Getting Ready for Your Kaggle Environment
Starting Our Travel - Surviving the Titanic Disaster
Take a Break and Have a Beer or Coffee in London
Get Back to Work and Optimize Microloans for Developing Countries
Can You Predict Bee Subspecies?
Text Analysis Is All You Need
Analyzing Acoustic Signals to Predict the Next Simulated Earthquake
Can You Find Out Which Movie Is a Deepfake?
Unleash the Power of Generative AI with Kaggle Models
Closing Our Journey: How to Stay Relevant and on Top
Hard Copy: Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster
Wednesday, 6 March 2024
Data Analysis and Visualization Foundations Specialization
Python Coding March 06, 2024 Coursera, Data Science, IBM No comments
What you'll learn
Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis
Explain basic functionality of spreadsheets and utilize Excel to perform a variety of data analysis tasks like data wrangling and data mining
List various types of charts and plots and create them in Excel as well as work with Cognos Analytics to generate interactive dashboards
Join Free: Data Analysis and Visualization Foundations Specialization
Specialization - 4 course series
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