Learn KAGGLE-KERNELS with Real Code Examples

print("Hello World")

A simple Kaggle Kernel cell printing 'Hello World'.

import pandas as pd

# Example: load dataset (assuming file is added to Kaggle Kernel input)
df = pd.read_csv("../input/sample-dataset/data.csv")
print(df.head())

Load a Kaggle dataset (CSV) into a Pandas DataFrame and display the first rows.

import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
print(arr.sum())
print(arr.T)

Create a NumPy array and perform basic operations.

import matplotlib.pyplot as plt
x = [1,2,3,4]
y = [10,20,25,30]
plt.plot(x, y)
plt.title('Line Graph')
plt.show()

Plot a simple line graph using matplotlib in a Kaggle Kernel.

import seaborn as sns
import pandas as pd
import numpy as np

data = pd.DataFrame({'x': np.random.rand(50), 'y': np.random.rand(50)})
sns.scatterplot(x='x', y='y', data=data)

Create a Seaborn scatter plot in Kaggle Kernel.

import pandas as pd

df = pd.DataFrame({'Category': ['A','A','B','B'], 'Value': [10,20,30,40]})
print(df.groupby('Category').mean())

Group dataset by a column and calculate mean.

import tensorflow as tf
hello = tf.constant('Hello Kaggle!')
print(hello.numpy().decode('utf-8'))

Create a TensorFlow constant and evaluate it in Kaggle Kernel.

import torch
tensor = torch.randn(3,3)
print(tensor.shape)

Create a PyTorch tensor and print its shape.

# Hello Kaggle
This is a **Markdown** cell in a Kaggle Kernel.

A Markdown cell displaying formatted text in Kaggle Kernel.

import plotly.express as px
import pandas as pd

df = pd.DataFrame({'x':[1,2,3,4], 'y':[10,20,25,30]})
fig = px.line(df, x='x', y='y', title='Interactive Line Plot')
fig.show()

Create an interactive Plotly chart in a Kaggle Kernel.