Jun 22, 2022 · print(sns.get_dataset_names()) #prints the inbuilt dataset names in Seaborn. #loading dataset df = sns.load_dataset("diamonds") #loads dataset into dataframe 'df' df.head()

Visualizing the multidimensional relationships among the samples is as easy as calling sns.pairplot (see the following figure): Sometimes the best way to view data is via histograms of subsets,...

Mar 21, 2024 · Learn how to use the free Google Colab tool and perform data analysis with Python programming language in this tutorial for digital marketers and data analysts.

Jul 13, 2017 · sns.distplot(x, bins=50, kde=False) plt.show() P.S: your code works for me using: numpy: 1.11.3, scipy: 0.18.1, statsmodels: 0.6.1

import seaborn as sns #visualisation import matplotlib.pyplot as plt #visualisation %matplotlib inline sns. set (color_codes= True)

Oct 9, 2021 · Indentation is part of python's syntax. Everything that should be inside the function must be indented. thanks to the @MichaelSzczesny I did fix the code. it was needed a little space before the below lines. and the labels="Training data" was incorrect, the correct form is label="Training data". here is the correct and fixed code:

Dec 24, 2024 · If you're just starting out with machine learning, data science, or even if you're a seasoned pro looking to streamline your workflow, Google Colab is an absolute game-changer. In this guide, we'll dive deep into what Google Colab is, how to get started, and some tips and tricks to make the most out of this powerful tool.

Discover how to use Google Colab, a powerful interactive Python environment. Learn about its features, setup, data management, computational resources, troubleshooting, and expert coding tips for seamless workflows.

Apr 7, 2025 · This blog post will take you through the fundamental concepts, usage methods, common practices, and best practices of using SNS in Python. In the realm of data visualization and statistical analysis in Python, Seaborn (sns) stands out as a powerful library.

Mar 1, 2025 · Intensity-Duration-Frequency (IDF) curves are crucial in hydrology for understanding rainfall patterns. This post demonstrates how to visualize IDF curves using Python in Google Colab, applying different styles, palettes, and hues for a modern and appealing look.