Jupyter is a powerful platform for experimentation and analysis. You can execute code cells and view the results, e.g., numbers, messages, graphs, tables, files, etc., instantly within the notebook. Each cell can contain code written in Python or explanations in plain English. Jupyter Notebooks: This tutorial is a Jupyter notebook - a document made of cells. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions. We recommend using the Conda distribution of Python. To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. Option 2: Running on your computer locally You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms. The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder. Option 1: Running using free online resources (1-click, recommended) You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer. It serves as a unique, practical guide to Data Visualization, in a plethora of tools you might use in your career.This tutorial is an executable Jupyter notebook hosted on Jovian. More specifically, over the span of 11 chapters this book covers 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. It serves as an in-depth, guide that'll teach you everything you need to know about Pandas and Matplotlib, including how to construct plot types that aren't built into the library itself.ĭata Visualization in Python, a book for beginner to intermediate Python developers, guides you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. ✅ Updated with bonus resources and guidesĭata Visualization in Python with Matplotlib and Pandas is a book designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and allow them to build a strong foundation for advanced work with theses libraries - from simple plots to animated 3D plots with interactive buttons. ✅ Updated regularly for free (latest update in April 2021) ✅ 30-day no-question money-back guarantee However, when we run this code, it's obvious that the x and y ticks, nor the x and y labels didn't change in size: ![]() ![]() This approach will change everything that's specified as a font by the font kwargs object. ![]() You have to set these before the plot() function call since if you try to apply them afterwards, no change will be made. One way is to modify them directly: import matplotlib.pyplot as pltĪx.plot(y, color= 'blue', label= 'Sine wave')Īx.plot(z, color= 'black', label= 'Cosine wave') We can get to this parameter via rcParams. We'll want to set the font_size parameter to a new size. There are two ways we can set the font size globally. In such cases, we can turn to setting the font size globally. However, while we can set each font size like this, if we have many textual elements, and just want a uniform, general size - this approach is repetitive.
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