Visualization Cheatsheet
Matplotlib matplotlib-cheatsheet
2020-08-31
Plotly
Dash Dash, a member of Plotly’s open-source tools, is a Open Source Python library for creating reactive, Web-based applications. Dash is a user interface library for creating analytical web applications. Those who use Python for data analysis, data exploration, visualization, modelling, instrument control, and reporting will find immediate use for Dash.
2020-08-31
Matplotlib Getting Started
View in nbviewer: Visualization with matplotlib Simple line plots Simple scatter plots Visualizing errors Density and contour plots Histograms, binning and density Customizing plot legend Customizing colorbars Multiple subplots Text and annotation Customizing tricks Configuration and stylesheets 3D plots Seaborn
2020-08-31
Pandas Getting Started
Summary View in: nbviewer Notes Pandas basics Series DataFrame Index Data indexing and selection Operating on data Handle missing data Hierarchical indexing Combining Dataset concat and append Dataset merge and join Aggregation and grouping Pivot table Vectorized string operations Time series High performance Pandas
2020-08-31
Numpy 1D Array
View in nbviewer: Numpy 1D Array
2020-08-31
Numpy Getting Started
View the summary in nbviewer: Numpy basics Universial function Aggregations Broadcasting Boolean masking Fancy indexing Sorting Structured array
2020-08-31
Python
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small- and large-scale projects.
2020-08-30
Getting Started
Source This is the summary of the book “A Whirlwind Tour of Python” by Jake VanderPlas. You can view it in nbviewer: A whirlwind Tour of Python, or Github: A whirlwind Tour of Python import this The Zen of Python, by Tim Peters Beautiful is better than ugly.
2020-08-30
Stack and Concatenate
TL;DR Stack of Numpy array Prepare Data import numpy as np x1 = np.array([[[9, 3, 7, 3], [2, 1, 1, 2], [1, 4, 2, 5]], [[5, 5, 2, 5], [7, 7, 6, 1], [6, 7, 2, 3]]]) x1 array([[[9, 3, 7, 3], [2, 1, 1, 2], [1, 4, 2, 5]], [[5, 5, 2, 5], [7, 7, 6, 1], [6, 7, 2, 3]]]) x2 = np.
2020-08-16
[Issues] Dictionary
Get Dictionary Items with Specified Initialization dict.get(key, default = None)) returns the value of the item with the specified key. Parameters key − This is the Key to be searched in the dictionary.
2020-07-08