Python Packages •Efficient and reusable –Avoid re-writing code –More flexibility •Use the “import” command to use a package import numpy as np •Packages covered in this workshop: –NumPy –Pandas –Graphical: matplotlib, plotly and seaborn 2

Learn data visualization with Python using Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh. Hands-on examples and case studies included.

Numpy,Scipy,Matplotlib(today) IPythonnotebooks,Pandas,Statsmodels,SKLearn ... plt.savefig(’boxplot.pdf’) 5: Numpy, Scipy, Matplotlib 5-50. Box Plot sample 1 sample 2 sample 3 " 4 " 2 0 2 4 6 ... CME 193: Introduction to Scientific Python Lecture 5: …

This is a book for beginner to intermediate Python developers and will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, as well as experimental libraries like Altair.

Setting Up a Python Data Analysis Environment. What is Anaconda? What is Conda? 2. Diving into NumPY. 3. Operations on NumPy Arrays Selecting elements explicitly. 4. pandas are Fun! What is pandas? What does pandas do? 5. Arithmetic, Function Application, and Mapping with pandas Arithmetic.

Apr 12, 2018 · • Python can be used to import datasets quickly • Python’s importable libraries make it an attractive language for data analysis • NumPy • SciPy • Statsmodels • Pandas • Matplotlib • Natural Language Toolkit (NLTK) • Python can import and export common data formats such as CSV files

Aug 19, 2023 · Some of the popular libraries in Python for data analytics include Pandas for data manipulation, NumPy for numerical computing, Matplotlib for data visualization, and Scikit-learn for machine learning.

We will cover the basics of using Jupyter Notebooks, followed by an introduction to the python NumPy package, then Pandas and finally onto the Grammar of Graphics approach to presenting your data using the plotnine Python package. The format of the course comprises of demonstrated examples followed by hands-on exercises.

At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

– import matplotlib.pyplot as plt – import numpy as np • Create the X-Data: – X = np.arange(0., 5., 0.2) • Please use X, X2, and X3 as Y-Data to draw the line chart: – plt.plot(X, X, "r--") – plt.plot(X, X**2, "bs") – plt.plot(X, X**3, "g^") • Here is the code for reference: 18