Powerful visualizations, and an easy to use, interactive api for exploring and loading datasets
Project description
chart-tools
Install & Use
Must have python 3.9+
pip install chart-tools
import chart_tools as ct
ct.load_data() # outputs available pre-defined data sources
Data Interface
Easily load datasets and explore available sources with one line of code
- The
load_data()
function andDataSource
object use Github's API to explore file structures in repositories containing.csv
files, and easily load files into dataframes. Chart-tools has a pre-defined library (collection of repositories) for you to explore within your notebook and load data from.
Robust caching system designed for Python notebooks, performing great with large datasets.
- Any dataframe you load gets cached in memory, remembering which pandas keyword arguments you used when loading the file. Next time you load it, you'll get a copy of the cached dataframe, unless you pass different keyword arguments. Not only is this great for performance with large datasets, but it also eliminates the common need to declare a
df_raw = ...
and then usedf = df_raw.copy()
to get your original data again.
Has a pre-defined library of data sources to explore, and lets you easily define your own library
- Save an entire Github repository file structure (csv files only) to your desktop
Charts & Visualization
superheat
- A "super" correlation heatmap you can't find elsewhere, designed for speed and ease of use.
- Marks are sized dynamically based on correlation strength, drawing your eyes straight to the most important relationships.
- Easily filter out variables whose coefficients average below a threshold, or simply mask/hide marks below a threshold.
Examples
Project details
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