import time
from random import randint
for i in range(1,85):
print('')
space = ''
for i in range(1,1000):
count = randint(1, 100)
while(count > 0):
space += ' '
count -= 1
if(i%10==0):
print(space + 'Happy New Year 2025🎉')
elif(i%9 == 0):
print(space + "🪅")
elif(i%5==0):
print(space +"🎈")
elif(i%8==0):
print(space + "🎈")
elif(i%7==0):
print(space + "🍁")
elif(i%6==0):
print(space + "❤️")
else:
print(space + "🔸")
space = ''
time.sleep(0.2)
import numpy as np
x = np.arange(7,16);
y = np.arange(1,18,2);
z = np.column_stack((x[:: -1],y))
for i,j in z:
print(' '*i+'*'*j)
for r in range(3):
print(' '*13, ' || ')
print(' '*12, end = '\======/')
print('')
데이터 세트의 숫자 변수 간의 쌍 관계를 보여주는 산점도 그리드입니다. 일반적으로 데이터 분포와 변수 간의 관계를 이해하는 데 사용됩니다.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import sklearn
print(sklearn.__version__)
# Sample dataset (Iris dataset)
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['species'] = [iris.target_names[i] for i in iris.target]
# Create a pair plot
sns.set(style="ticks")
pairplot = sns.pairplot(df, hue="species", diag_kind="kde")
# Save the pair plot as an image
output_file = "pair_plot.png"
pairplot.savefig(output_file)
print(f"Pair plot saved as {output_file}")
# Show the pair plot
plt.show()
[VSCODE] 14 VS Code Extensions Every Data Engineer Should Swear By for Maximum Productivity
1. Jupyter (85M+ downloads)
For Interactive Notebooks and Data Exploration
If you’re working with Python and data science, chances are you’ve used Jupyter notebooks. Well, this extension bringsJupyterfunctionality directly into VS Code. Whether you’re exploring datasets, running Python scripts, or testing ETL pipelines, this extension allows you to work in an interactive, notebook-style environment without leaving your code editor. It’s perfect for ad-hoc analysis, experimenting with new ideas, and visualizing your data right within your development setup.
Python is the lingua franca of data engineering, andPylancesupercharges your coding experience with advanced IntelliSense features. It provides type-checking, better autocompletion, and more accurate suggestions, all of which help you write cleaner, more efficient Python code. As a data engineer, you’re likely juggling multiple libraries, so having robust type information can prevent bugs and improve your productivity.