Exclusion from the queue
The collections.deque() class is a generalization of stacks and queues, and represents a deque. A deque() supports thread-safe, memory-efficient operations for inserting and removing elements of a sequence from either side, with roughly the same O(1) performance in either direction.
The collections.deque() class is a generalization of stacks and queues, and represents a deque. A deque() supports thread-safe, memory-efficient operations for inserting and removing elements of a sequence from either side, with roughly the same O(1) performance in either direction.
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gui_calender.py
1.3 KB
GUI Calender in Python 📅
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Creating Virtual Environment for Python
» Download Python
» Steps to create '
1. Navigate to the folder where you want to make your project
Example:
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
Command for 'Powershell':
Command for Git Bash or WSL:
If Powershell gives you error like
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
» Download Python
First you need python installed in your local machine to create virtual environment.
Download Python from Here
» Steps to create '
.env' folder (virtual environment for python)1. Navigate to the folder where you want to make your project
Example:
cd D:/code/
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
python --version # Type this and hit enter to verify the python version
# Now use these commands
python -m venv .env
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
.\env\Scripts\activate
Command for 'Powershell':
.\env\Scripts\Activate.ps1
Command for Git Bash or WSL:
source \.env\bin\activate
If Powershell gives you error like
File cannot be loaded because running scripts is disabled then use this command!Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
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from turtle import *
import turtle as t
def my_turtle():
# Choices
sides = str(3)
loops = str(450)
pen = 1
for i in range(int(loops)):
forward(i * 2/int(sides) + i)
left(360/int(sides) + .350)
hideturtle()
pensize(pen)
speed(30)
my_turtle()
t.done()👍9👎1
Scrap Image from google using BeautifulSoup
import requests
from bs4 import BeautifulSoup as BSP
def get_image_urls(search_query):
url = f"https://www.google.com/search?q={search_query}&tbm=isch"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
}
rss = requests.get(url, headers=headers)
soup = BSP(rss.content, "html.parser")
all_img = []
for img in soup.find_all('img'):
src = img['src']
if not src.endswith("gif"):
all_img.append(src)
return all_img
print(get_image_urls("boy"))
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
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Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]
1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using
- Tip: Mention specific functions you used, like
2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with
- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).
3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used
- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like
4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used
- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.
Like this post if you want next part of this interview series 👍❤️
1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using
fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.- Tip: Mention specific functions you used, like
dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with
describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).
3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used
apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like
groupby(), merge(), concat(), or pivot().4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used
sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.
Like this post if you want next part of this interview series 👍❤️
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