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1 | 1 | #! /usr/bin/env python
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2 | 2 | #
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3 |
| -# Copyright (C) 2012-2020 Michael Waskom |
| 3 | +# Copyright (C) 2012-2022 Michael Waskom |
4 | 4 |
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5 | 5 | DESCRIPTION = "seaborn: statistical data visualization"
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6 | 6 | LONG_DESCRIPTION = """\
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7 |
| -Seaborn is a library for making statistical graphics in Python. It is built on top of `matplotlib <https://matplotlib.org/>`_ and closely integrated with `pandas <https://pandas.pydata.org/>`_ data structures. |
| 7 | +
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| 8 | +Seaborn is a library for making statistical graphics in Python. It is built on |
| 9 | +top of `matplotlib <https://matplotlib.org/>`_ and closely integrated with |
| 10 | +`pandas <https://pandas.pydata.org/>`_ data structures. |
8 | 11 |
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9 | 12 | Here is some of the functionality that seaborn offers:
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10 | 13 |
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11 | 14 | - A dataset-oriented API for examining relationships between multiple variables
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12 |
| -- Specialized support for using categorical variables to show observations or aggregate statistics |
13 |
| -- Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data |
14 |
| -- Automatic estimation and plotting of linear regression models for different kinds dependent variables |
15 |
| -- Convenient views onto the overall structure of complex datasets |
16 |
| -- High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations |
| 15 | +- Flexible data aggregation with automatic estimation and plotting of error bars |
| 16 | +- Multiple options for visualizing univariate or bivariate distributions |
| 17 | +- Estimation and plotting of linear regression models to reveal trends |
| 18 | +- Tools for building figures with multidimensional views onto complex dataset structure |
17 | 19 | - Concise control over matplotlib figure styling with several built-in themes
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18 | 20 | - Tools for choosing color palettes that faithfully reveal patterns in your data
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19 | 21 |
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20 |
| -Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. |
| 22 | +Seaborn aims to make visualization a central part of exploring and understanding |
| 23 | +data. Its dataset-oriented plotting functions operate on dataframes and arrays |
| 24 | +containing whole datasets and internally perform the necessary semantic mapping |
| 25 | +and statistical aggregation to produce informative plots. |
| 26 | +
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21 | 27 | """
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22 | 28 |
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23 | 29 | DISTNAME = 'seaborn'
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46 | 52 |
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47 | 53 | PACKAGES = [
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48 | 54 | 'seaborn',
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49 |
| - 'seaborn.colors', |
50 |
| - 'seaborn.external', |
51 | 55 | 'seaborn._core',
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52 | 56 | 'seaborn._marks',
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53 | 57 | 'seaborn._stats',
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| 58 | + 'seaborn.colors', |
| 59 | + 'seaborn.external', |
54 | 60 | ]
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55 | 61 |
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56 | 62 | CLASSIFIERS = [
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57 | 63 | 'Intended Audience :: Science/Research',
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58 | 64 | 'Programming Language :: Python :: 3.7',
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59 | 65 | 'Programming Language :: Python :: 3.8',
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60 | 66 | 'Programming Language :: Python :: 3.9',
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| 67 | + 'Programming Language :: Python :: 3.10', |
61 | 68 | 'License :: OSI Approved :: BSD License',
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62 | 69 | 'Topic :: Scientific/Engineering :: Visualization',
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63 | 70 | 'Topic :: Multimedia :: Graphics',
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