Seaborn is a data visualisation library based upon matplotlib. It has an application interface which simplifies the creation of complex but common visualisation plots from data contained within a pandas DataFrame. In other words seaborn can be thought of as a library that bridges between pandas and matplotlib.
The library name seaborn is derived from the two words sea and born. In this context the sea refers to a body of water i.e. a collection of data and born refers to the creation of the visualisation for that data. It is common to import seaborn using a 3 letter abbreviation. The abbreviation sns is standard:
In [1]: import seaborn as snssns comes from the fictional character Samual Norman Seaborn from the West Wing. His initials were selected as the abbreviation by developers of the seaborn library as he has the sir name Seaborn. The library version can be determined using the data model attribute __version__:
In [2]: sns.__version__
Out[2]: '0.13.2'The version of a Python library has the form:
major.minor.patch
Normally when the major version of a library is 0, it is regarded as unstable and as the library is developed, a new major version is created as depreciations are made and the application interface is cleared up as previously mentioned when examining other libraries. seaborn remains at a major version of 0 however should be regarded as a stable library.
seaborn is part of the "numpy-stack" and typically the following imports are made together:
In [3]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: import pandas as pd
: import seaborn as snsWhen an import sorter is used, these third-party libraries are typically grouped alphabetically (by name of the library, not the alias, the library is imported as):
In [4]: import matplotlib as mpl
: import matplotlib.pyplot as plt
: import numpy as np
: import pandas as pd
: import seaborn as snsThe identifiers for sns can be examined:
In [5]: sns.
# -------------------------------------
# Available Identifiers for Seaborn:
# -------------------------------------
# 🔧 Version Information:
# - __version__ : The version number of the currently installed Seaborn library.
# 🔬 Seaborn Styling and Theming Modules:
# - sns.colors : Color conversion and manipulation utilities.
# - sns.palettes : Functions for color palette management.
# - sns.rcmod : Theme and style configuration.
# 🎨 Styling and Theming (sns.rcmod & sns.palettes):
# - sns.set_theme : Sets the Seaborn theme for plots.
# - sns.set_style : Configures the aesthetic style of plots.
# - sns.set_context : Adjusts plot elements for different presentation settings.
# - sns.set_palette : Sets the color palette for plots.
# - sns.color_palette : Retrieves a color palette.
# - sns.cubehelix_palette : Generates a cubehelix color palette.
# - sns.dark_palette : Creates a darkened version of a color palette.
# - sns.light_palette : Creates a lightened version of a color palette.
# - sns.diverging_palette : Generates a diverging color palette.
# 🔬 Seaborn Utility Modules:
# - sns.utils : Internal utility functions.
# - sns.widgets : Widgets for interactive visualization.
# - sns.external : External dependencies and extensions.
# 📌 Standard Datasets:
# - sns.load_dataset : Loads example datasets from Seaborn.
# - sns.get_dataset_names : Returns available built-in datasets.
# 📊📈📉 Faceted and Multi-Plot Classes:
# - sns.FacetGrid : 📊📈📉 Class for creating faceted plots across multiple subplots.
# - sns.PairGrid : 📊📈📉 Class for pairwise relationships between variables across multiple subplots.
# - sns.JointGrid : 📊📈📉 Class for bivariate plots with marginal distributions across multiple subplots.
# 🔬 Seaborn Plotting Modules:
# - sns.relational : Relational plotting functions.
# - sns.regression : Regression-based plots.
# - sns.distributions : Distribution plots (histograms, KDE, ECDF).
# - sns.axisgrid : Grid-based plotting utilities (FacetGrid, PairGrid).
# - sns.categorical : Categorical plotting functions.
# - sns.matrix : Functions for heatmaps and clustering.
# 📈 Relational Plotting Functions (sns.relational):
# - sns.relplot : 📊📈📉 FacetGrid-based regressional plot.
# - sns.lineplot : 📉 Axes-level line plot with optional confidence intervals.
# - sns.scatterplot : 📉 Axes-level scatter plot with additional styling options.
# 📈 Regression and Statistical Plots (sns.regression):
# - sns.lmplot : 📊📈📉 FacetGrid-based linear regression plot.
# - sns.regplot : 📉 Axes-level plot. Plots a scatter plot with a regression line.
# - sns.residplot : 📉 Axes-level plot. Residuals plot for regression analysis.
# 📊 Distribution Plots (sns.distributions):
# - sns.displot : 📊📈📉 FacetGrid-based distribution plot.
# - sns.histplot : 📉 Axes-level histogram with flexible binning.
# - sns.kdeplot : 📉 Axes-level kernel density estimation plot.
# - sns.ecdfplot : 📉 Axes-level empirical cumulative distribution function.
# 📊 Axis Grid Plots (sns.axisgrid):
# - sns.pairplot : 📊📈📉 FacetGrid-based pairwise scatter plots of numerical variables.
# 📊 Categorical Plotting Functions (sns.categorical):
# - sns.catplot : 📊📈📉 FacetGrid-based categorical plot (bar, box, violin, strip, swarm).
# - sns.swarmplot : 📉 Axes-level categorical scatter plot with non-overlapping points.
# - sns.stripplot : 📉 Axes-level categorical scatter plot (jittered points).
# - sns.barplot : 📉 Axes-level categorical bar plot with grouped data.
# - sns.countplot : 📉 Axes-level count of categorical observations.
# - sns.boxplot : 📉 Axes-level box-and-whisker plot.
# - sns.violinplot : 📉 Axes-level violin plot for visualizing distribution.
# 📊 Matrix Plots (sns.matrix):
# - sns.heatmap : 📉 Axes-level matrix visualization using color encoding.
# 🔧 Utility Functions (sns.utils):
# - sns.despine : Removes axis spines for a cleaner plot.
# - sns.move_legend : Moves the legend within a plot.
# - sns.axis_ticklabels_overlap : Checks for overlapping tick labels.
# - sns.relative_luminance : Computes the luminance of a color.
# - sns.ci_to_errsize : Converts confidence intervals into error bar sizes.In the above styling and theming, utility functions and plotting functions are listed. Organisationally sns is compartmentalised into modules, and a function can normally be found within its expected module. The functions that are most commonly used are also found in the sns namespace directly:
In [5]: sns.
# -------------------------------------
# Available Modules for Seaborn:
# -------------------------------------
# 🔬 Seaborn Styling and Theming Modules:
# - sns.colors : Color conversion and manipulation utilities.
# - sns.palettes : Functions for color palette management.
# - sns.rcmod : Theme and style configuration.
# 🔬 Seaborn Plotting Modules:
# - sns.regression : Regression-based plots.
# - sns.categorical : Categorical plotting functions.
# - sns.distributions : Distribution plots (histograms, KDE, ECDF).
# - sns.matrix : Functions for heatmaps and clustering.
# - sns.axisgrid : Grid-based plotting utilities (FacetGrid, PairGrid).
# - sns.relational : Relational plotting functions.
# 🔬 Seaborn Utility Modules:
# - sns.utils : Internal utility functions.
# - sns.widgets : Widgets for interactive visualization.
# - sns.external : External dependencies and extensions.Recall mpl.colors module contains color dict instances which map an English color str to a harder to remember normalised color tuple of the form (r, g, b) or because 8 bits are used per color a hexadecimal str of the form '#rrggbb'. The most common color dict instances are:
In [5]: mpl.colors.BASE_COLORS
Out[5]:
{'b': (0, 0, 1),
'g': (0, 0.5, 0),
'r': (1, 0, 0),
'c': (0, 0.75, 0.75),
'm': (0.75, 0, 0.75),
'y': (0.75, 0.75, 0),
'k': (0, 0, 0),
'w': (1, 1, 1)}And:
In [6]: mpl.colors.CSS4_COLORS
Out[6]:
{'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkgrey': '#A9A9A9',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkslategrey': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dimgrey': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'grey': '#808080',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgray': '#D3D3D3',
'lightgreen': '#90EE90',
'lightgrey': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightslategrey': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'rebeccapurple': '#663399',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#F4A460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'slategrey': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}seaborn supplements these dict instances in sns with colors of Crayola crayon colours and kxcd colours:
In [8]: sns.colors.crayons
Out[8]:
{'Almond': '#EFDECD',
'Antique Brass': '#CD9575',
'Apricot': '#FDD9B5',
'Aquamarine': '#78DBE2',
'Asparagus': '#87A96B',
'Atomic Tangerine': '#FFA474',
'Banana Mania': '#FAE7B5',
'Beaver': '#9F8170',
'Bittersweet': '#FD7C6E',
'Black': '#000000',
'Blue': '#1F75FE',
'Blue Bell': '#A2A2D0',
'Blue Green': '#0D98BA',
'Blue Violet': '#7366BD',
'Blush': '#DE5D83',
'Brick Red': '#CB4154',
'Brown': '#B4674D',
'Burnt Orange': '#FF7F49',
'Burnt Sienna': '#EA7E5D',
'Cadet Blue': '#B0B7C6',
'Canary': '#FFFF99',
'Caribbean Green': '#00CC99',
'Carnation Pink': '#FFAACC',
'Cerise': '#DD4492',
'Cerulean': '#1DACD6',
'Chestnut': '#BC5D58',
'Copper': '#DD9475',
'Cornflower': '#9ACEEB',
'Cotton Candy': '#FFBCD9',
'Dandelion': '#FDDB6D',
'Denim': '#2B6CC4',
'Desert Sand': '#EFCDB8',
'Eggplant': '#6E5160',
'Electric Lime': '#CEFF1D',
'Fern': '#71BC78',
'Forest Green': '#6DAE81',
'Fuchsia': '#C364C5',
'Fuzzy Wuzzy': '#CC6666',
'Gold': '#E7C697',
'Goldenrod': '#FCD975',
'Granny Smith Apple': '#A8E4A0',
'Gray': '#95918C',
'Green': '#1CAC78',
'Green Yellow': '#F0E891',
'Hot Magenta': '#FF1DCE',
'Inchworm': '#B2EC5D',
'Indigo': '#5D76CB',
'Jazzberry Jam': '#CA3767',
'Jungle Green': '#3BB08F',
'Laser Lemon': '#FEFE22',
'Lavender': '#FCB4D5',
'Macaroni and Cheese': '#FFBD88',
'Magenta': '#F664AF',
'Mahogany': '#CD4A4C',
'Manatee': '#979AAA',
'Mango Tango': '#FF8243',
'Maroon': '#C8385A',
'Mauvelous': '#EF98AA',
'Melon': '#FDBCB4',
'Midnight Blue': '#1A4876',
'Mountain Meadow': '#30BA8F',
'Navy Blue': '#1974D2',
'Neon Carrot': '#FFA343',
'Olive Green': '#BAB86C',
'Orange': '#FF7538',
'Orchid': '#E6A8D7',
'Outer Space': '#414A4C',
'Outrageous Orange': '#FF6E4A',
'Pacific Blue': '#1CA9C9',
'Peach': '#FFCFAB',
'Periwinkle': '#C5D0E6',
'Piggy Pink': '#FDDDE6',
'Pine Green': '#158078',
'Pink Flamingo': '#FC74FD',
'Pink Sherbert': '#F78FA7',
'Plum': '#8E4585',
'Purple Heart': '#7442C8',
"Purple Mountains' Majesty": '#9D81BA',
'Purple Pizzazz': '#FE4EDA',
'Radical Red': '#FF496C',
'Raw Sienna': '#D68A59',
'Razzle Dazzle Rose': '#FF48D0',
'Razzmatazz': '#E3256B',
'Red': '#EE204D',
'Red Orange': '#FF5349',
'Red Violet': '#C0448F',
"Robin's Egg Blue": '#1FCECB',
'Royal Purple': '#7851A9',
'Salmon': '#FF9BAA',
'Scarlet': '#FC2847',
"Screamin' Green": '#76FF7A',
'Sea Green': '#93DFB8',
'Sepia': '#A5694F',
'Shadow': '#8A795D',
'Shamrock': '#45CEA2',
'Shocking Pink': '#FB7EFD',
'Silver': '#CDC5C2',
'Sky Blue': '#80DAEB',
'Spring Green': '#ECEABE',
'Sunglow': '#FFCF48',
'Sunset Orange': '#FD5E53',
'Tan': '#FAA76C',
'Tickle Me Pink': '#FC89AC',
'Timberwolf': '#DBD7D2',
'Tropical Rain Forest': '#17806D',
'Tumbleweed': '#DEAA88',
'Turquoise Blue': '#77DDE7',
'Unmellow Yellow': '#FFFF66',
'Violet (Purple)': '#926EAE',
'Violet Red': '#F75394',
'Vivid Tangerine': '#FFA089',
'Vivid Violet': '#8F509D',
'White': '#FFFFFF',
'Wild Blue Yonder': '#A2ADD0',
'Wild Strawberry': '#FF43A4',
'Wild Watermelon': '#FC6C85',
'Wisteria': '#CDA4DE',
'Yellow': '#FCE883',
'Yellow Green': '#C5E384',
'Yellow Orange': '#FFAE42'}In [9]: sns.colors.xkcd_rgb
Out[9]:
{'acid green': '#8ffe09',
'adobe': '#bd6c48',
'algae': '#54ac68',
'algae green': '#21c36f',
'almost black': '#070d0d',
'amber': '#feb308',
'amethyst': '#9b5fc0',
'apple': '#6ecb3c',
'apple green': '#76cd26',
'apricot': '#ffb16d',
'aqua': '#13eac9',
'aqua blue': '#02d8e9',
'aqua green': '#12e193',
'aqua marine': '#2ee8bb',
'aquamarine': '#04d8b2',
'army green': '#4b5d16',
'asparagus': '#77ab56',
'aubergine': '#3d0734',
'auburn': '#9a3001',
'avocado': '#90b134',
'avocado green': '#87a922',
'azul': '#1d5dec',
'azure': '#069af3',
'baby blue': '#a2cffe',
'baby green': '#8cff9e',
'baby pink': '#ffb7ce',
'baby poo': '#ab9004',
'baby poop': '#937c00',
'baby poop green': '#8f9805',
'baby puke green': '#b6c406',
'baby purple': '#ca9bf7',
'baby shit brown': '#ad900d',
'baby shit green': '#889717',
'banana': '#ffff7e',
'banana yellow': '#fafe4b',
'barbie pink': '#fe46a5',
'barf green': '#94ac02',
'barney': '#ac1db8',
'barney purple': '#a00498',
'battleship grey': '#6b7c85',
'beige': '#e6daa6',
'berry': '#990f4b',
'bile': '#b5c306',
'black': '#000000',
'bland': '#afa88b',
'blood': '#770001',
'blood orange': '#fe4b03',
'blood red': '#980002',
'blue': '#0343df',
'blue blue': '#2242c7',
'blue green': '#137e6d',
'blue grey': '#607c8e',
'blue purple': '#5729ce',
'blue violet': '#5d06e9',
'blue with a hint of purple': '#533cc6',
'blue/green': '#0f9b8e',
'blue/grey': '#758da3',
'blue/purple': '#5a06ef',
'blueberry': '#464196',
'bluegreen': '#017a79',
'bluegrey': '#85a3b2',
'bluey green': '#2bb179',
'bluey grey': '#89a0b0',
'bluey purple': '#6241c7',
'bluish': '#2976bb',
'bluish green': '#10a674',
'bluish grey': '#748b97',
'bluish purple': '#703be7',
'blurple': '#5539cc',
'blush': '#f29e8e',
'blush pink': '#fe828c',
'booger': '#9bb53c',
'booger green': '#96b403',
'bordeaux': '#7b002c',
'boring green': '#63b365',
'bottle green': '#044a05',
'brick': '#a03623',
'brick orange': '#c14a09',
'brick red': '#8f1402',
'bright aqua': '#0bf9ea',
'bright blue': '#0165fc',
'bright cyan': '#41fdfe',
'bright green': '#01ff07',
'bright lavender': '#c760ff',
'bright light blue': '#26f7fd',
'bright light green': '#2dfe54',
'bright lilac': '#c95efb',
'bright lime': '#87fd05',
'bright lime green': '#65fe08',
'bright magenta': '#ff08e8',
'bright olive': '#9cbb04',
'bright orange': '#ff5b00',
'bright pink': '#fe01b1',
'bright purple': '#be03fd',
'bright red': '#ff000d',
'bright sea green': '#05ffa6',
'bright sky blue': '#02ccfe',
'bright teal': '#01f9c6',
'bright turquoise': '#0ffef9',
'bright violet': '#ad0afd',
'bright yellow': '#fffd01',
'bright yellow green': '#9dff00',
'british racing green': '#05480d',
'bronze': '#a87900',
'brown': '#653700',
'brown green': '#706c11',
'brown grey': '#8d8468',
'brown orange': '#b96902',
'brown red': '#922b05',
'brown yellow': '#b29705',
'brownish': '#9c6d57',
'brownish green': '#6a6e09',
'brownish grey': '#86775f',
'brownish orange': '#cb7723',
'brownish pink': '#c27e79',
'brownish purple': '#76424e',
'brownish red': '#9e3623',
'brownish yellow': '#c9b003',
'browny green': '#6f6c0a',
'browny orange': '#ca6b02',
'bruise': '#7e4071',
'bubble gum pink': '#ff69af',
'bubblegum': '#ff6cb5',
'bubblegum pink': '#fe83cc',
'buff': '#fef69e',
'burgundy': '#610023',
'burnt orange': '#c04e01',
'burnt red': '#9f2305',
'burnt siena': '#b75203',
'burnt sienna': '#b04e0f',
'burnt umber': '#a0450e',
'burnt yellow': '#d5ab09',
'burple': '#6832e3',
'butter': '#ffff81',
'butter yellow': '#fffd74',
'butterscotch': '#fdb147',
'cadet blue': '#4e7496',
'camel': '#c69f59',
'camo': '#7f8f4e',
'camo green': '#526525',
'camouflage green': '#4b6113',
'canary': '#fdff63',
'canary yellow': '#fffe40',
'candy pink': '#ff63e9',
'caramel': '#af6f09',
'carmine': '#9d0216',
'carnation': '#fd798f',
'carnation pink': '#ff7fa7',
'carolina blue': '#8ab8fe',
'celadon': '#befdb7',
'celery': '#c1fd95',
'cement': '#a5a391',
'cerise': '#de0c62',
'cerulean': '#0485d1',
'cerulean blue': '#056eee',
'charcoal': '#343837',
'charcoal grey': '#3c4142',
'chartreuse': '#c1f80a',
'cherry': '#cf0234',
'cherry red': '#f7022a',
'chestnut': '#742802',
'chocolate': '#3d1c02',
'chocolate brown': '#411900',
'cinnamon': '#ac4f06',
'claret': '#680018',
'clay': '#b66a50',
'clay brown': '#b2713d',
'clear blue': '#247afd',
'cloudy blue': '#acc2d9',
'cobalt': '#1e488f',
'cobalt blue': '#030aa7',
'cocoa': '#875f42',
'coffee': '#a6814c',
'cool blue': '#4984b8',
'cool green': '#33b864',
'cool grey': '#95a3a6',
'copper': '#b66325',
'coral': '#fc5a50',
'coral pink': '#ff6163',
'cornflower': '#6a79f7',
'cornflower blue': '#5170d7',
'cranberry': '#9e003a',
'cream': '#ffffc2',
'creme': '#ffffb6',
'crimson': '#8c000f',
'custard': '#fffd78',
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'dark': '#1b2431',
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'dark beige': '#ac9362',
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'dark brown': '#341c02',
'dark coral': '#cf524e',
'dark cream': '#fff39a',
'dark cyan': '#0a888a',
'dark forest green': '#002d04',
'dark fuchsia': '#9d0759',
'dark gold': '#b59410',
'dark grass green': '#388004',
'dark green': '#033500',
'dark green blue': '#1f6357',
'dark grey': '#363737',
'dark grey blue': '#29465b',
'dark hot pink': '#d90166',
'dark indigo': '#1f0954',
'dark khaki': '#9b8f55',
'dark lavender': '#856798',
'dark lilac': '#9c6da5',
'dark lime': '#84b701',
'dark lime green': '#7ebd01',
'dark magenta': '#960056',
'dark maroon': '#3c0008',
'dark mauve': '#874c62',
'dark mint': '#48c072',
'dark mint green': '#20c073',
'dark mustard': '#a88905',
'dark navy': '#000435',
'dark navy blue': '#00022e',
'dark olive': '#373e02',
'dark olive green': '#3c4d03',
'dark orange': '#c65102',
'dark pastel green': '#56ae57',
'dark peach': '#de7e5d',
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'dark pink': '#cb416b',
'dark plum': '#3f012c',
'dark purple': '#35063e',
'dark red': '#840000',
'dark rose': '#b5485d',
'dark royal blue': '#02066f',
'dark sage': '#598556',
'dark salmon': '#c85a53',
'dark sand': '#a88f59',
'dark sea green': '#11875d',
'dark seafoam': '#1fb57a',
'dark seafoam green': '#3eaf76',
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'dark teal': '#014d4e',
'dark turquoise': '#045c5a',
'dark violet': '#34013f',
'dark yellow': '#d5b60a',
'dark yellow green': '#728f02',
'darkblue': '#030764',
'darkgreen': '#054907',
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'deep green': '#02590f',
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'deep pink': '#cb0162',
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'deep red': '#9a0200',
'deep rose': '#c74767',
'deep sea blue': '#015482',
'deep sky blue': '#0d75f8',
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'deep turquoise': '#017374',
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'denim': '#3b638c',
'denim blue': '#3b5b92',
'desert': '#ccad60',
'diarrhea': '#9f8303',
'dirt': '#8a6e45',
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'dust': '#b2996e',
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'dusty rose': '#c0737a',
'dusty teal': '#4c9085',
'earth': '#a2653e',
'easter green': '#8cfd7e',
'easter purple': '#c071fe',
'ecru': '#feffca',
'egg shell': '#fffcc4',
'eggplant': '#380835',
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'fawn': '#cfaf7b',
'fern': '#63a950',
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'gold': '#dbb40c',
'golden': '#f5bf03',
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'golden rod': '#f9bc08',
'golden yellow': '#fec615',
'goldenrod': '#fac205',
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'grape purple': '#5d1451',
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'leaf': '#71aa34',
'leaf green': '#5ca904',
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'magenta': '#c20078',
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'maize': '#f4d054',
'mango': '#ffa62b',
'manilla': '#fffa86',
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'marine': '#042e60',
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'mauve': '#ae7181',
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'mint': '#9ffeb0',
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'muted green': '#5fa052',
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'muted purple': '#805b87',
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'ocher': '#bf9b0c',
'ochre': '#bf9005',
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'sage': '#87ae73',
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'stone': '#ada587',
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'strong pink': '#ff0789',
'sun yellow': '#ffdf22',
'sunflower': '#ffc512',
'sunflower yellow': '#ffda03',
'sunny yellow': '#fff917',
'sunshine yellow': '#fffd37',
'swamp': '#698339',
'swamp green': '#748500',
'tan': '#d1b26f',
'tan brown': '#ab7e4c',
'tan green': '#a9be70',
'tangerine': '#ff9408',
'taupe': '#b9a281',
'tea': '#65ab7c',
'tea green': '#bdf8a3',
'teal': '#029386',
'teal blue': '#01889f',
'teal green': '#25a36f',
'tealish': '#24bca8',
'tealish green': '#0cdc73',
'terra cotta': '#c9643b',
'terracota': '#cb6843',
'terracotta': '#ca6641',
'tiffany blue': '#7bf2da',
'tomato': '#ef4026',
'tomato red': '#ec2d01',
'topaz': '#13bbaf',
'toupe': '#c7ac7d',
'toxic green': '#61de2a',
'tree green': '#2a7e19',
'true blue': '#010fcc',
'true green': '#089404',
'turquoise': '#06c2ac',
'turquoise blue': '#06b1c4',
'turquoise green': '#04f489',
'turtle green': '#75b84f',
'twilight': '#4e518b',
'twilight blue': '#0a437a',
'ugly blue': '#31668a',
'ugly brown': '#7d7103',
'ugly green': '#7a9703',
'ugly pink': '#cd7584',
'ugly purple': '#a442a0',
'ugly yellow': '#d0c101',
'ultramarine': '#2000b1',
'ultramarine blue': '#1805db',
'umber': '#b26400',
'velvet': '#750851',
'vermillion': '#f4320c',
'very dark blue': '#000133',
'very dark brown': '#1d0200',
'very dark green': '#062e03',
'very dark purple': '#2a0134',
'very light blue': '#d5ffff',
'very light brown': '#d3b683',
'very light green': '#d1ffbd',
'very light pink': '#fff4f2',
'very light purple': '#f6cefc',
'very pale blue': '#d6fffe',
'very pale green': '#cffdbc',
'vibrant blue': '#0339f8',
'vibrant green': '#0add08',
'vibrant purple': '#ad03de',
'violet': '#9a0eea',
'violet blue': '#510ac9',
'violet pink': '#fb5ffc',
'violet red': '#a50055',
'viridian': '#1e9167',
'vivid blue': '#152eff',
'vivid green': '#2fef10',
'vivid purple': '#9900fa',
'vomit': '#a2a415',
'vomit green': '#89a203',
'vomit yellow': '#c7c10c',
'warm blue': '#4b57db',
'warm brown': '#964e02',
'warm grey': '#978a84',
'warm pink': '#fb5581',
'warm purple': '#952e8f',
'washed out green': '#bcf5a6',
'water blue': '#0e87cc',
'watermelon': '#fd4659',
'weird green': '#3ae57f',
'wheat': '#fbdd7e',
'white': '#ffffff',
'windows blue': '#3778bf',
'wine': '#80013f',
'wine red': '#7b0323',
'wintergreen': '#20f986',
'wisteria': '#a87dc2',
'yellow': '#ffff14',
'yellow brown': '#b79400',
'yellow green': '#c0fb2d',
'yellow ochre': '#cb9d06',
'yellow orange': '#fcb001',
'yellow tan': '#ffe36e',
'yellow/green': '#c8fd3d',
'yellowgreen': '#bbf90f',
'yellowish': '#faee66',
'yellowish brown': '#9b7a01',
'yellowish green': '#b0dd16',
'yellowish orange': '#ffab0f',
'yellowish tan': '#fcfc81',
'yellowy brown': '#ae8b0c',
'yellowy green': '#bff128'}Plotting can be carried out using any of these colors:
In [10]: fig, ax = plt.subplots()
: # color value retrieved using key in color dict
: plt.plot(x,
: color=mpl.colors.CSS4_COLORS['royalblue'],
: linewidth=5, ls=':')
: plt.plot(x+1,
: color=sns.colors.crayons['Wild Strawberry'],
: linewidth=5, ls=':')
: # color value mapped to str
: plt.plot(x+5,
: color='royalblue',
: linewidth=5, ls=':')
Out[10]: [<matplotlib.lines.Line2D at 0x222131babd0>] The CSS4_COLORS is inbuilt into mpl and therefore each key in the color dict can be used can be supplied as a str directly. This cannot be done with the supplementary color dict instances included in seaborn:
seaborn includes a number of styles, which aesthetically improve the visual layout used for a mpl Figure. Under the hood these styles are a set of options to change in mpl recall parameters. seaborn also includes functions that under the hood modify mpl recall parameters to apply the style for the current context.
The function set_style which will change the style used for a matplotlib plot and by extension a seaborn plot. There are four styles 'dark', 'darkgrid, 'white' and 'whitegrid'. Normally the style is set after importing the libraries and made consistent for all the plots in a script file or notebook file:
In [11]: sns.set_style('dark')
: fig, ax = plt.subplots()
: ax.plot(x, color='royalblue', linewidth=5)
Out[11]: [<matplotlib.lines.Line2D at 0x2221324eae0>]
In [12]: sns.set_style('darkgrid')
: fig, ax = plt.subplots()
: ax.plot(x, color='royalblue', linewidth=5)
Out[12]: [<matplotlib.lines.Line2D at 0x22213266fc0>]
In [13]: sns.set_style('white')
: fig, ax = plt.subplots()
: ax.plot(x, color='royalblue', linewidth=5)
Out[13]: [<matplotlib.lines.Line2D at 0x22216fec890>]
In [14]: sns.set_style('whitegrid')
: fig, ax = plt.subplots()
: ax.plot(x, color='royalblue', linewidth=5)
Out[14]: [<matplotlib.lines.Line2D at 0x2221326eb10>]
Instead of manually selecting the color of each line individually, a color palette is typically used. mpl uses the default discrete color palette:
In [15]: mpl.colors.TABLEAU_COLORS
Out[15]:
{'tab:blue': '#1f77b4',
'tab:orange': '#ff7f0e',
'tab:green': '#2ca02c',
'tab:red': '#d62728',
'tab:purple': '#9467bd',
'tab:brown': '#8c564b',
'tab:pink': '#e377c2',
'tab:gray': '#7f7f7f',
'tab:olive': '#bcbd22',
'tab:cyan': '#17becf'}This is also known as 'tab10' and can be retrieved using the function color_palette:
In [16]: sns.color_palette('tab10')
Out[16]:
[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
(1.0, 0.4980392156862745, 0.054901960784313725),
(0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
(0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
(0.5803921568627451, 0.403921568627451, 0.7411764705882353),
(0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
(0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
(0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
(0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
(0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]seaborn tweaks this palette slightly and the tweaked palette is called 'deep':
In [17]: sns.color_palette('deep')
Out[17]:
[(0.2980392156862745, 0.4470588235294118, 0.6901960784313725),
(0.8666666666666667, 0.5176470588235295, 0.3215686274509804),
(0.3333333333333333, 0.6588235294117647, 0.40784313725490196),
(0.7686274509803922, 0.3058823529411765, 0.3215686274509804),
(0.5058823529411764, 0.4470588235294118, 0.7019607843137254),
(0.5764705882352941, 0.47058823529411764, 0.3764705882352941),
(0.8549019607843137, 0.5450980392156862, 0.7647058823529411),
(0.5490196078431373, 0.5490196078431373, 0.5490196078431373),
(0.8, 0.7254901960784313, 0.4549019607843137),
(0.39215686274509803, 0.7098039215686275, 0.803921568627451)]In Spyder the color palette displays as a list where each element is a 3-element color tuple consisting of r, g, b normalised values. In JupyterLab, this displays visually:
Visually, it can be seen that a discrete color palette is used to clearly distinguish each line. The deep palette is applied by default when a plot is created:
In [18]: fig, ax = plt.subplots()
: for num in range(20):
: plt.plot(x+num, linewidth=5)
:
There are additional variations of this color palette:
In [19]: sns.color_palette('tab10')
In [20]: sns.color_palette('deep')
In [21]: sns.color_palette('pastel')
In [22]: sns.color_palette('muted')
In [23]: sns.color_palette('dark')
In [24]: sns.color_palette('bright')
In [25]: sns.color_palette('colorblind')
Notice that each of these color palettes based on 'tab10' has 10 discrete values, as a consequence in the plot above with 15 lines the last 5 lines use repeat colors.
The color palette 'tab20' as the name suggests has 20 values, it essentially takes a color from 'tab10' and pairs it with a complementary color from 'pastel' (the complementary color is tweaked slightly to make it visually more discrete):
In [26]: sns.color_palette('tab10')
In [27]: sns.color_palette('pastel')
In [28]: sns.color_palette('tab20')
The set_palette function can be used to change the color palette:
In [29]: sns.set_palette('tab20')
: fig, ax = plt.subplots()
: for num in range(15):
: ax.plot(x, x+num, lw=5)
A custom palette can also be made using the function blend_palette and supplying the input argument colors which is usually a list of color str instances, where each str is a CSS4 color natively supported by matplotlib and n_colors is an int instance corresponding to the number of colors to be added to the palette:
In [30]: sns.blend_palette(colors=['royalblue',
'darkseagreen',
'yellow',
'tomato'],
n_colors=15)
When only 2 colors are specified in the blend palette, the pattern can often be more sequential:
In [31]: sns.blend_palette(colors=['royalblue',
: 'tomato'],
: n_colors=15)
When this sequential palette is used for the previous plot, notice that it visually complements the +1 upward trend of each line:
In [32]: blend_palette15 = sns.blend_palette(colors=['royalblue',
: 'tomato'],
: n_colors=15)
:
: sns.set_palette(palette=blend_palette15)
: fig, ax = plt.subplots()
: for num in range(20):
: ax.plot(x, x+num, lw=5)
:
Once again blend_palette15 only has 15 colors and therefore the last 5 lines repeat the last 5 colors. The xkcd_palette function can also be used to create a color palette from a list of strings where each string corresponds to a key in the dict instance sns.colors.xkcd_rgb:
In [33]: sns.xkcd_palette(colors=['tomato red',
'coral',
'light plum',
'cocoa',
'yellowish',
'cool green',
'faded green',
'light teal',
'faded blue',
'cornflower blue'])
The names of colormaps, used in mpl can be accessed using:
In [34]: mpl.colormaps()
Out[34]:
['magma',
'inferno',
'plasma',
'viridis',
'cividis',
'twilight',
'twilight_shifted',
'turbo',
'berlin',
'managua',
'vanimo',
'Blues',
'BrBG',
'BuGn',
'BuPu',
'CMRmap',
'GnBu',
'Greens',
'Greys',
'OrRd',
'Oranges',
'PRGn',
'PiYG',
'PuBu',
'PuBuGn',
'PuOr',
'PuRd',
'Purples',
'RdBu',
'RdGy',
'RdPu',
'RdYlBu',
'RdYlGn',
'Reds',
'Spectral',
'Wistia',
'YlGn',
'YlGnBu',
'YlOrBr',
'YlOrRd',
'afmhot',
'autumn',
'binary',
'bone',
'brg',
'bwr',
'cool',
'coolwarm',
'copper',
'cubehelix',
'flag',
'gist_earth',
'gist_gray',
'gist_heat',
'gist_ncar',
'gist_rainbow',
'gist_stern',
'gist_yarg',
'gnuplot',
'gnuplot2',
'gray',
'hot',
'hsv',
'jet',
'nipy_spectral',
'ocean',
'pink',
'prism',
'rainbow',
'seismic',
'spring',
'summer',
'terrain',
'winter',
'Accent',
'Dark2',
'Paired',
'Pastel1',
'Pastel2',
'Set1',
'Set2',
'Set3',
'tab10',
'tab20',
'tab20b',
'tab20c',
'grey',
'gist_grey',
'gist_yerg',
'Grays',
'magma_r',
'inferno_r',
'plasma_r',
'viridis_r',
'cividis_r',
'twilight_r',
'twilight_shifted_r',
'turbo_r',
'berlin_r',
'managua_r',
'vanimo_r',
'Blues_r',
'BrBG_r',
'BuGn_r',
'BuPu_r',
'CMRmap_r',
'GnBu_r',
'Greens_r',
'Greys_r',
'OrRd_r',
'Oranges_r',
'PRGn_r',
'PiYG_r',
'PuBu_r',
'PuBuGn_r',
'PuOr_r',
'PuRd_r',
'Purples_r',
'RdBu_r',
'RdGy_r',
'RdPu_r',
'RdYlBu_r',
'RdYlGn_r',
'Reds_r',
'Spectral_r',
'Wistia_r',
'YlGn_r',
'YlGnBu_r',
'YlOrBr_r',
'YlOrRd_r',
'afmhot_r',
'autumn_r',
'binary_r',
'bone_r',
'brg_r',
'bwr_r',
'cool_r',
'coolwarm_r',
'copper_r',
'cubehelix_r',
'flag_r',
'gist_earth_r',
'gist_gray_r',
'gist_heat_r',
'gist_ncar_r',
'gist_rainbow_r',
'gist_stern_r',
'gist_yarg_r',
'gnuplot_r',
'gnuplot2_r',
'gray_r',
'hot_r',
'hsv_r',
'jet_r',
'nipy_spectral_r',
'ocean_r',
'pink_r',
'prism_r',
'rainbow_r',
'seismic_r',
'spring_r',
'summer_r',
'terrain_r',
'winter_r',
'Accent_r',
'Dark2_r',
'Paired_r',
'Pastel1_r',
'Pastel2_r',
'Set1_r',
'Set2_r',
'Set3_r',
'tab10_r',
'tab20_r',
'tab20b_r',
'tab20c_r',
'grey_r',
'gist_grey_r',
'gist_yerg_r',
'Grays_r',
'rocket',
'rocket_r',
'mako',
'mako_r',
'icefire',
'icefire_r',
'vlag',
'vlag_r',
'flare',
'flare_r',
'crest',
'crest_r']The additional color palettes in seaborn are in the SEABORN_PALETTES dict. In this dict the keys correspond to the colormap names and the values is a list of colors in the form of hexadecimal strings:
In [35]: list(sns.palettes.SEABORN_PALETTES.keys())
Out[35]:
['deep',
'deep6',
'muted',
'muted6',
'pastel',
'pastel6',
'bright',
'bright6',
'dark',
'dark6',
'colorblind',
'colorblind6']All these keys can be used in the set_palette function. The choice between a discrete (categorical, qualitative) and sequential palette
depends on the data being visualised and the trend being highlighted visually. The colormaps are typically grouped accordingly:
| Category | Palettes |
|---|---|
| Seaborn Categorical | 'deep', 'muted', 'bright', 'pastel', 'dark', 'colorblind' |
| Matplotlib Qualitative | 'tab10', 'tab20', 'tab20b', 'tab20c', 'Set1', 'Set2', 'Set3', 'Paired', 'Pastel1', 'Pastel2', 'Dark2', 'Accent' |
| Sequential | 'viridis', 'plasma', 'inferno', 'magma', 'cividis', 'Blues', 'BuGn', 'BuPu', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd' |
| Diverging | 'coolwarm', 'Spectral', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'BrBG', 'PiYG', 'PRGn', 'PuOr' |
| Cyclic | 'twilight', 'twilight_shifted', 'hsv' |
This was discussed in the previous tutorial on matplotlib. More details about the CSS4 colors are in the documentation Matplotlib: colors. For the XKCD colors see the documentation xkcd: colors and for the Crayola crayons see crayon collection.
The set_style and set_palette functions are often placed at the top of a script file under library imports:
In [36]: sns.set_style('darkgrid')
: sns.set_palette('Set1')
:
: fig, ax = plt.subplots()
: for num in range(20):
: ax.plot(x, x+num, lw=5)
They are also often combined using the function set_theme:
In [37]: sns.set_theme(style='dark', palette='Set2')
:
: fig, ax = plt.subplots()
: for num in range(20):
: ax.plot(x, x+num, lw=5)
In [37]: sns.set_theme(style='white', palette='Set3')
:
: fig, ax = plt.subplots()
: for num in range(20):
: ax.plot(x, x+num, lw=5)
The functions choose_diverging_palette, choose_light_palette, choose_dark_palette, choose_colorbrewer_palette, choose_cube_helix_palette, and choose_cubehelix_palette use interactive Python widgets for a customisable color palette. This is typically used in an interactive Python notebook and the sliders in the cell can be modified to get a custom palette:
In [38]: sns.choose_diverging_palette()
Typically this is assigned to an instance name so it can be used in a plotting function:
In [39]: diverging = sns.choose_diverging_palette()
In [40]: sns.set_palette(diverging)
: fig, ax = plt.subplots()
: for num in range(20):
: plt.plot(x+num, linewidth=5)
ipywidgets uses nodejs which displays visual elements in a browser in an interactive Python notebook based and is unavailable for a Python script or the ipython console in Spyder.
The style can be returned to 'whitegrid' with a default palette of 'deep':
In [41]: sns.set_theme(style='whitegrid', palette='deep')mpl plotting functions are generally configured for data in the form of a ndarray:
In [42]: x = np.array([0, 1, 2, 3, 4])
: y = np.array([0, 2, 4, 6, 8])| Variable Explorer | |||
|---|---|---|---|
| x | Array of int64 | (5,) | [ 0 1 2 3 4] |
| y | Array of int64 | (5,) | [ 0 2 4 6 8] |
| x - numpy int64 array | |
|---|---|
| 0 | 0 |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| y - numpy int64 array | |
|---|---|
| 0 | 0 |
| 1 | 2 |
| 2 | 4 |
| 3 | 6 |
| 4 | 8 |
In [43]: plt.plot(x, y, linewidth=5);
Recall that a Series is essentially a 1d ndarray with a name:
In [44]: x = pd.Series([0, 1, 2, 3, 4], name='x')
: y = pd.Series([0, 2, 4, 6, 8], name='y')| Variable Explorer | |||
|---|---|---|---|
| x | Series of int64 | (5,) | index=[ 0 1 2 3 4], data=[ 0 1 2 3 4], name=x |
| y | Series of int64 | (5,) | index=[ 0 1 2 3 4], data=[ 0 2 4 6 8], name=y |
| x - numpy int64 array | |
|---|---|
| x | |
| 0 | 0 |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| y - numpy int64 array | |
|---|---|
| y | |
| 0 | 0 |
| 1 | 2 |
| 2 | 4 |
| 3 | 6 |
| 4 | 8 |
And a DataFrame is essentially a Collection of Series:
In [45]: df = pd.DataFrame({'x': [0, 1, 2, 3, 4],
: 'y': [0, 2, 4, 6, 8]})| Variable Explorer | |||
|---|---|---|---|
| df | DataFrame | (5, 2) | Column names: x, y |
| df - DataFrame | ||
|---|---|---|
| Index | x | y |
| 0 | 0 | 0 |
| 1 | 1 | 2 |
| 2 | 2 | 4 |
| 3 | 4 | 6 |
| 4 | 6 | 8 |
In [46]: plt.plot(df['x'], df['y'], linewidth=5);
The Series name, can be used for the xlabel and ylabel:
In [47]: plt.xlabel('x')
Out[47]: Text(0.5, 25.52222222222222, 'x')
In [48]: plt.ylabel('y')
Out[48]: Text(56.972222222222214, 0.5, 'y')
sns plotting functions on the other hand are generally configured with the input argument data which is provided as a DataFrame instance and each axis is assigned to the respective Series name. These names will also automatically be used for the perspective axis label when not otherwise specified:
In [49]: sns.lineplot(data=df, x='x', y='y');
Because the plotting function above is essentially a wrapper around plt.plot, it can accept many of the same optional parameters:
In [50]: sns.lineplot(data=df, x='x', y='y', linewidth=5);
If a categorical Series is added:
In [51]: df['group'] = np.array(['one', 'two', 'one', 'two', 'one'])
In [52]: df['group'] = df['group'].astype('category')| Variable Explorer | |||
|---|---|---|---|
| df | DataFrame | (5, 3) | Column names: x, y, group |
| df - DataFrame | |||
|---|---|---|---|
| Index | x | y | group |
| 0 | 0 | 0 | one |
| 1 | 1 | 2 | two |
| 2 | 2 | 4 | one |
| 3 | 4 | 6 | two |
| 4 | 6 | 8 | one |
df can be split by each category in `df['group']:
In [53]: df[df['group'] == 'one']
Out[53]:
x y group
0 0 0 one
2 2 4 one
4 4 8 oneIn [54]: df[df['group'] == 'two']
Out[54]:
x y group
1 1 2 two
3 3 6 twoThese two groups can be plotted using mpl directly but the mpl syntax is somewhat cumbersome:
In [55]: plt.scatter(df['x'][df['group'] == 'one'], df['y'][df['group'] == 'one'])
: plt.scatter(df['x'][df['group'] == 'two'], df['y'][df['group'] == 'two'])
: plt.xlabel('x')
: plt.xlabel('y');
sns syntax is much simpler and uses the hue parameter, to seperate out each category.Notice also that a legend is also automatically generated because multiple traces are present on the plot:
In [56]: sns.scatterplot(data=df, x='x', y='y', hue='group');
seaborn includes a number of practice datasets which are useful for exploring data visualisation principles. These are in the DataFrame format and the function get_dataset_names lists the dataset names:
In [57]: sns.get_dataset_names()
Out[57]:
['anagrams',
'anscombe',
'attention',
'brain_networks',
'car_crashes',
'diamonds',
'dots',
'dowjones',
'exercise',
'flights',
'fmri',
'geyser',
'glue',
'healthexp',
'iris',
'mpg',
'penguins',
'planets',
'seaice',
'taxis',
'tips',
'titanic']A dataset can be loaded using the function sns.load_dataset and providing the name of the dataset. For example the 'flights' dataset which shows the number of passengers each month for the years 1949-1960:
In [58]: flights = sns.load_dataset(name='flights')The DataFrame instance flights can be explored in the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| flights | DataFrame | (144, 3) | Column names: year, month, passengers |
The head values are:
| flights - DataFrame | |||
|---|---|---|---|
| Index | year | month | passengers |
| 0 | 1949 | Jan | 112 |
| 1 | 1949 | Feb | 118 |
| 2 | 1949 | Mar | 132 |
| 3 | 1949 | Apr | 129 |
| 4 | 1949 | May | 121 |
And the tail values are:
| flights - DataFrame | |||
|---|---|---|---|
| Index | year | month | passengers |
| 0 | 1960 | Aug | 606 |
| 1 | 1960 | Sep | 508 |
| 2 | 1960 | Oct | 461 |
| 3 | 1960 | Nov | 390 |
| 4 | 1960 | Dec | 432 |
The DataFrame method info can be used to summerise each Series:
In [59]: flights.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 144 entries, 0 to 143
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 year 144 non-null int64
1 month 144 non-null category
2 passengers 144 non-null int64
dtypes: category(1), int64(2)
memory usage: 2.9 KBAnd the DataFrame method describe will give numeric statistics for each numeric Series:
In [60]: flights.describe()
Out[60]:
year passengers
count 144.000000 144.000000
mean 1954.500000 280.298611
std 3.464102 119.966317
min 1949.000000 104.000000
25% 1951.750000 180.000000
50% 1954.500000 265.500000
75% 1957.250000 360.500000
max 1960.000000 622.000000If the plotting related functions are examined. Notice that there are three new classes FacetGrid, PairGrid and JointGrid. When one of these classes is used, the data being visualised is normally spanned along a grid of subplots. In mpl a grid of subplots is essentially a Figure level operation. These classes can conceptually be thought of as child class of mpl.Figure and essentially return a new Figure.
Many plotting functions return one of these three classes which means they are Figure-level plotting functions. Most Figure-level plotting function has an Axes-level plotting function. The Axes-level plotting functions, like pyplot plotting functions operate on the currently selected Axes:
In [61]: sns.
# 📊📈📉 Faceted and Multi-Plot Classes:
# - sns.FacetGrid : 📊📈📉 Class for creating faceted plots across multiple subplots.
# - sns.PairGrid : 📊📈📉 Class for pairwise relationships between variables across multiple subplots.
# - sns.JointGrid : 📊📈📉 Class for bivariate plots with marginal distributions across multiple subplots.
# 🔬 Seaborn Plotting Modules:
# - sns.relational : Relational plotting functions.
# - sns.regression : Regression-based plots.
# - sns.distributions : Distribution plots (histograms, KDE, ECDF).
# - sns.axisgrid : Grid-based plotting utilities (FacetGrid, PairGrid).
# - sns.categorical : Categorical plotting functions.
# - sns.matrix : Functions for heatmaps and clustering.
# 📈 Relational Plotting Functions (sns.relational):
# - sns.relplot : 📊📈📉 FacetGrid-based regressional plot.
# - sns.lineplot : 📉 Axes-level line plot with optional confidence intervals.
# - sns.scatterplot : 📉 Axes-level scatter plot with additional styling options.
# 📈 Regression and Statistical Plots (sns.regression):
# - sns.lmplot : 📊📈📉 FacetGrid-based linear regression plot.
# - sns.regplot : 📉 Axes-level plot. Plots a scatter plot with a regression line.
# - sns.residplot : 📉 Axes-level plot. Residuals plot for regression analysis.
# 📊 Distribution Plots (sns.distributions):
# - sns.displot : 📊📈📉 FacetGrid-based distribution plot.
# - sns.histplot : 📉 Axes-level histogram with flexible binning.
# - sns.kdeplot : 📉 Axes-level kernel density estimation plot.
# - sns.ecdfplot : 📉 Axes-level empirical cumulative distribution function.
# 📊 Axis Grid Plots (sns.axisgrid):
# - sns.pairplot : 📊📈📉 FacetGrid-based pairwise scatter plots of numerical variables.
# 📊 Categorical Plotting Functions (sns.categorical):
# - sns.catplot : 📊📈📉 FacetGrid-based categorical plot (bar, box, violin, strip, swarm).
# - sns.swarmplot : 📉 Axes-level categorical scatter plot with non-overlapping points.
# - sns.stripplot : 📉 Axes-level categorical scatter plot (jittered points).
# - sns.barplot : 📉 Axes-level categorical bar plot with grouped data.
# - sns.countplot : 📉 Axes-level count of categorical observations.
# - sns.boxplot : 📉 Axes-level box-and-whisker plot.
# - sns.violinplot : 📉 Axes-level violin plot for visualizing distribution.
# 📊 Matrix Plots (sns.matrix):
# - sns.heatmap : 📉 Axes-level matrix visualization using color encoding. A relational plots is used to plot the relationship usually between an independent x variable and depenendent y variable. For example the lineplot plotting function can be used. Notice that the return value is a new Axes as this plot operates on the Axes level and no Figure or Axes previously existed:
In [62]: sns.lineplot(data=flights, x='year', y='passengers')
Out[62]: <Axes: xlabel='year', ylabel='passengers'>
If the scatterplot function is now used, notice that the return value is an Axes however notice that the plotting function operates on the currently selected Axes which updates figure(1) in place:
In [63]: sns.scatterplot(data=flights, x='year', y='passengers')
Out[63]: <Axes: xlabel='year', ylabel='passengers'>
If the hue parameter is used and assigned to the Series 'month', the 12 months can be visualised and once again the Axes is modified in place:
In [64]: sns.scatterplot(data=flights, x='year', y='passengers', hue='month')
Out[64]: <Axes: xlabel='year', ylabel='passengers'>Having a plot operate on the Axes level allows interoperability with other plt plotting functions. For example:
In [65]: fig, ax = plt.subplots(2, 2)
Recall that ax is essentially an ndarray:
| Variable Explorer | |||
|---|---|---|---|
| fig | Figure | 1 | <Figure size 640x480 with 6 Axes> |
| ax | Array of Axes | (2, 2) | [[ <Axes: > <Axes: >] [ <Axes: > <Axes: >]] |
| ax - numpy Axes array | ||
|---|---|---|
| Index ▲ | 0 | 1 |
| 0 | <Axes: > | <Axes: > |
| 1 | <Axes: > | <Axes: > |
In the form above, a plt plotting function is typically called as a method from the Axes:
In [66]: ax[0, 0].plot(flights['year'], flights['passengers'])
Out[66]: [<matplotlib.lines.Line2D at 0x1f4a9795810>]
sns is a separate library and as a consequence the plotting functions do not show as Axes methods. Therefore the set current Axes function sca is used before calling the sns plotting function can be used:
In [67]: plt.sca(ax[0, 1])
In [68]: sns.lineplot(data=flights, x='year', y='passengers')
Out[68]: <Axes: xlabel='year', ylabel='passengers'>
Notice this is the same format as:
In [69]: plt.sca(ax[1, 0])
In [70]: plt.scatter(flights['year'], flights['passengers'])
Out[70]: <matplotlib.collections.PathCollection at 0x1f4aa3c4190>
In [71]: plt.sca(ax[1, 1])
In [72]: sns.scatterplot(data=flights, x='year', y='passengers')
Out[72]: <Axes: xlabel='year', ylabel='passengers'>
In the subplots above, the similarities and differences between the plt plotting functions plot, scatter and sns plotting functions lineplot and scatterplot can be seen. Notice that the syntax used between the two sns plotting functions is identical.
The sns plotting function returns a FacetGrid which is on the Figure-level, this means any sca will be overridden when the new Figure and Axes :
In [73]: plt.sca(ax[1, 1])
In [74]: sns.relplot(data=flights, x='year', y='passengers')
Out[74]: <seaborn.axisgrid.FacetGrid at 0x1f4a13f7380>
Notice the plot above is figure (3). The current Figure can be selected using the pyplot function gcf:
In [75]: fig = plt.gcf()Or by specifying the figure number:
In [76]: fig = plt.figure(3)The axes attribute can be used to access each Axes:
In [77]: fig.axes
Out[77]: [<Axes: xlabel='year', ylabel='passengers'>]Because there is only a single Axes, it can be retrieved using the pyplot function gca:
In [78]: ax = plt.gca()Once the Figure and Axes are instantiated to the instance names fig and ax, Figure and Axes methods can be used to get and set properties:
In [79]: ax.get_xlabel()
Out[79]: 'year'
In [80]: ax.get_ylabel()
Out[80]: 'passengers'
In [81]: ax.set_ylabel('number of passengers')
Out[81]: Text(33.0, 0.5, 'number of passengers')
The relplot outputs a FacetGrid which can be instantiated when the sns plotting function relplot is used. The hue parameter previously seen in the other sns plotting functions can be used:
In [82]: fg = sns.relplot(data=flights, x='year', y='passengers', hue='month')
In [83]: fg.
# 🔗 Data and Layout
# - fig # The Matplotlib figure containing the facets.
# - axes # 2D NumPy array of subplot axes.
# - ax # The primary plotting axis of the grid.
# - legend # Returns the legend instance.
# - data # The dataset used to create the FacetGrid.
# - hue_names # Names of the hue variable categories.
# - hue_kws # Dictionary of keyword arguments for the hue variable.
# - col_names # List of column facet variable names.
# - row_names # List of row facet variable names.
# - col_var # Column variable name.
# - row_var # Row variable name.
# - col_wrap # Number of columns to wrap facets into.
# - axes_dict # Dictionary mapping facet positions to axes.
# 🔗 Plotting Methods
# - add_legend # Adds a legend.
# - set # Sets axis labels and limits.
# - set_axis_labels # Sets labels for the x and y axes.
# - set_titles # Sets titles for the facets.
# - set_xlabels # Sets x-axis labels.
# - set_ylabels # Sets y-axis labels.
# - set_xticklabels # Sets x-axis tick labels.
# - set_yticklabels # Sets y-axis tick labels.
# - add_legend # Adds a legend to the plot.
# - savefig # Saves the figure to a file.
# - map # Maps a plotting function to the grid.
# - map_dataframe # Maps a function using a DataFrame format.
# - facet_data # Generates subsets of data for each facet.
# - facet_axis # Returns the axis for a specific facet.
# - despine # Removes the top and right spines from the plot.
# - refline # Adds reference lines to the plot.
# 🔗 Utility Methods
# - apply # Applies a function to each facet.
# - pipe # Allows method chaining using a function.
# - tick_params # Modifies tick properties.
# - tight_layout # Adjusts subplot parameters for better layout.Notice that the fg has the attributes fig and ax which makes it easier to retrieve the Figure and Axes:
In [84]: fig = fg.fig
In [85]: fg.axes
Out[85]: array([[<Axes: xlabel='year', ylabel='passengers'>]], dtype=object)Notice that the axes is a 2d ndarray, despite in this case only containing only a scalar Axes. This Axes can be retrieved by indexing:
In [86]: fg.axes[0, 0]
Out[86]: <Axes: xlabel='year', ylabel='passengers'>
In [87]: ax = fg.axes[0, 0]Notice there is col_names, row_names and col_wrap and the plotting methods are plural equivalents to what is found in an Axes. If the categorical Series 'month' is assigned to the parameter col:
In [88]: fg = sns.relplot(data=flights, x='year', y='passengers', hue='month', col='month')
Notice that each 'month' is now a new column in the FacetGrid. The plural plotting methods can be used to update the labels of all the subplots in the FacetGrid. If a scalar is supplied, all the labels will be updated:
In [89]: fg.set_xlabels('date')
Out[89]: <seaborn.axisgrid.FacetGrid at 0x1f4ae5b3250>
Note if a list of labels is provided, this list is cast to a str and each individual str is not applied to the respective column:
In [90]: fg.set_titles(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
Out[90]: <seaborn.axisgrid.FacetGrid at 0x1f4ae5b3250>
An ndarray of the correct dimensions also gives this behaviour:
In [91]: fg.set_titles(np.array([['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']]))
Out[91]: <seaborn.axisgrid.FacetGrid at 0x1f4ae5b3250>
To perform this operation the axes attribute can be looped over. Normally this ndarray is flattened making it easier to apply the loop:
In [92]: titles = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
In [93]: for ax, title in zip(fg.axes.flat, titles):
ax.set_title(title)
Another categorical Series could be assigned to the parameter row however this dataset only has one categorical Series and it is more appropriate to use col_wrap to wrap the columns:
In [94]: fg = sns.relplot(data=flights, x='year', y='passengers', hue='month', col='month', col_wrap=4)
The same code as before can be sued to rename each title:
In [95]: titles = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
In [96]: for ax, title in zip(fg.axes.flat, titles):
ax.set_title(title)
The data above is only split into columns, therefore only the attribute col_names is populated and row_names and hue_names are unpopulated:
In [97]: fg.col_names
Out[97]:
['Jan',
'Feb',
'Mar',
'Apr',
'May',
'Jun',
'Jul',
'Aug',
'Sep',
'Oct',
'Nov',
'Dec']
In [98]: fg.row_names
Out[98]: []
In [99]: fg.hue_names
Out[99]The method facet_data is a generator. When next is called on the generator a 2-element tuple is shown. The first element has the form (row_index, col_index, hue_index) and the second is the DataFrame used to plot the data. In this case, the DataFrame is only split over a col_index by use of the categorical Series 'month':
In [100]: data = fg.facet_data()
In [101]: next(data)
Out[101]:
((0, 0, 0),
year month passengers
0 1949 Jan 112
12 1950 Jan 115
24 1951 Jan 145
36 1952 Jan 171
48 1953 Jan 196
60 1954 Jan 204
72 1955 Jan 242
84 1956 Jan 284
96 1957 Jan 315
108 1958 Jan 340
120 1959 Jan 360
132 1960 Jan 417)
In [102]: next(data)
Out[102]:
((0, 1, 0),
year month passengers
1 1949 Feb 118
13 1950 Feb 126
25 1951 Feb 150
37 1952 Feb 180
49 1953 Feb 196
61 1954 Feb 188
73 1955 Feb 233
85 1956 Feb 277
97 1957 Feb 301
109 1958 Feb 318
121 1959 Feb 342
133 1960 Feb 391)
In [103]: next(data)
Out[103]:
((0, 2, 0),
year month passengers
2 1949 Mar 132
14 1950 Mar 141
26 1951 Mar 178
38 1952 Mar 193
50 1953 Mar 236
62 1954 Mar 235
74 1955 Mar 267
86 1956 Mar 317
98 1957 Mar 356
110 1958 Mar 362
122 1959 Mar 406
134 1960 Mar 419)
In [104]: next(data)
Out[104]:
((0, 3, 0),
year month passengers
3 1949 Apr 129
15 1950 Apr 135
27 1951 Apr 163
39 1952 Apr 181
51 1953 Apr 235
63 1954 Apr 227
75 1955 Apr 269
87 1956 Apr 313
99 1957 Apr 348
111 1958 Apr 348
123 1959 Apr 396
135 1960 Apr 461)
In [105]: next(data)
Out[105]:
((0, 4, 0),
year month passengers
4 1949 May 121
16 1950 May 125
28 1951 May 172
40 1952 May 183
52 1953 May 229
64 1954 May 234
76 1955 May 270
88 1956 May 318
100 1957 May 355
112 1958 May 363
124 1959 May 420
136 1960 May 472)
In [106]: next(data)
Out[106]:
((0, 5, 0),
year month passengers
5 1949 Jun 135
17 1950 Jun 149
29 1951 Jun 178
41 1952 Jun 218
53 1953 Jun 243
65 1954 Jun 264
77 1955 Jun 315
89 1956 Jun 374
101 1957 Jun 422
113 1958 Jun 435
125 1959 Jun 472
137 1960 Jun 535)If only the flights in April are examined:
In [107]: april_flights = flights[flights['month'] == 'Apr']The DataFrame instance flights can be explored in the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| april_flights | DataFrame | (12, 3) | Column names: year, month, passengers |
| flights - DataFrame | |||
|---|---|---|---|
| Index | year | month | passengers |
| 3 | 1949 | Apr | 129 |
| 15 | 1950 | Apr | 135 |
| 27 | 1951 | Apr | 163 |
| 39 | 1952 | Apr | 181 |
| 51 | 1953 | Apr | 235 |
| 63 | 1954 | Apr | 227 |
| 75 | 1955 | Apr | 269 |
| 87 | 1956 | Apr | 313 |
| 99 | 1957 | Apr | 348 |
| 111 | 1958 | Apr | 348 |
| 123 | 1959 | Apr | 396 |
| 135 | 1960 | Apr | 461 |
Visually, a linear trend can be seen by comparing the color maps applied to each numeric Series by the Spyder IDE. This can also be seen in a scatter plot:
In [108]: fig, ax = plt.subplots()
: sns.scatterplot(data=april_flights, x='year', y='passengers');
A regression plot is essentially a scatter plot with a line or curve of best fit. sns plotting functions regplot, residplot occur on the Axes-level and lmplot occurs on the Figure-level (outputting a FacetGrid).
The Axes-level regplot is more powerful than the Figure-level lmplot and allows a 1st, 2nd and 3rd order polynomial fit. It is commonly used with the Axes-level residplot to plot the residuals:
In [109]: fig, ax = plt.subplots(2, 1)
: plt.sca(ax[0])
: sns.regplot(data=april_flights, x='year', y='passengers')
: plt.sca(ax[1])
: sns.residplot(data=april_flights, x='year', y='passengers');
Visually a linear fit appears to be good for this data and the residuals are randomly distributed around 0.
In the above the plt function set current Axes sca was used to select the Axes to apply each plot to. sns, Axes-level plorring functions have the parameter ax which can be used to simplify the code:
In [110]: fig, ax = plt.subplots(2, 1)
: sns.regplot(data=april_flights, x='year', y='passengers', ax=ax[0])
: sns.residplot(data=april_flights, x='year', y='passengers', ax=ax[1]);
Higher order models are not required for this dataset, however they can be compared using the regplot and residplot specific parameter order:
In [111]: sns.regplot(data=april_flights, x='year', y='passengers', ax=ax[0], order=2)
: sns.residplot(data=april_flights, x='year', y='passengers', ax=ax[1], order=2);
There is a slight difference in the 2nd order and 1st order fit and the residuals are marginally better but it is likely the data is overfitted as a 1st order model is sufficient.
In [112]: sns.regplot(data=april_flights, x='year', y='passengers', ax=ax[0], order=3)
: sns.residplot(data=april_flights, x='year', y='passengers', ax=ax[1], order=3);
Notice the 3rd order fit overlaps completely with the 2nd order fit and the residuals do not change between the 2nd and 3rd order fit because the data is already overfitted.
The sns plotting function linear model plot lmplot gives a linear fit consistent with that of regplot (when the default parameters are used for regplot) returning a FacetGrid:
In [113]: fg = sns.lmplot(data=april_flights, x='year', y='passengers')
The optional parameters hue, col and row can be set to a categorical Series. The behaviour is consistent to the behaviour previously seen when relplot was used as both these plotting functions output a FacetGrid:
In [113]: fg = sns.lmplot(data=flights, x='year', y='passengers', hue='month', col='month', col_wrap=4)
The 'iris' dataset measures physical features for sepals and petals for 3 species of iris plant and is an example of a distribution dataset. The load_dataset function can be used this dataset:
In [114]: iris = sns.load_dataset('iris')| Variable Explorer | |||
|---|---|---|---|
| iris | DataFrame | (150, 5) | Column names: sepal_length, sepal_width, petal_length, petal_height, species |
| flights - DataFrame | |||||
|---|---|---|---|---|---|
| Index | sepal_length | sepal_width | petal_length | petal_height | species |
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
The DataFrame methods info and describe can be used to get an overview of the DataFrame. All of the Series in the DataFrame have 150 entries are there are no NaN values:
In [115]: iris.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 sepal_length 150 non-null float64
1 sepal_width 150 non-null float64
2 petal_length 150 non-null float64
3 petal_width 150 non-null float64
4 species 150 non-null object
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
In [116]: iris.describe()
Out[116]:
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000The sns plotting functions histplot, rugplot, kdeplot and ecdfplot are Axes-level distribution functions. These plotting functions have the parameters x and y which can be assigned to a numeric Series. If only x is assigned, the plot will be 1d:
In [117]: fig, ax = plt.subplots(2, 2)
: sns.histplot(data=iris, x='sepal_length', hue='species', ax=ax[0, 0])
: sns.histplot(data=iris, x='sepal_width', hue='species', ax=ax[0, 1])
: sns.histplot(data=iris, x='petal_length', hue='species', ax=ax[1, 0])
: sns.histplot(data=iris, x='petal_width', hue='species', ax=ax[1, 1]);
If the third subplot is examined with x='petal_length', notice that the blue category 'setosa' is visually distinct from the orange category 'versicolor' and green category 'virginica'. The orange category 'versicolor' and green category 'virginica' can be seen to have different histograms but these histograms overlap. The other three categories can be set to y outputting the following 2d plots:
In [117]: fig, ax = plt.subplots(3, 1)
: sns.histplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[0])
: sns.histplot(data=iris, x='petal_length', y='sepal_width', hue='species', ax=ax[1])
: sns.histplot(data=iris, x='petal_length', y='petal_width', hue='species', ax=ax[2]);
Of the following 2d plots, y='petal_width' gives the best distinction between the orange and green categories. The legend in the above plot becomes a hinderance when it comes to visually the data on such small subplots and it can be disabled by assigning the optional boolean parameter legend to False. The number of bins used for the histogram can be controlled using the optional parameter bins. For a 1d plot, this is assigned to a scalar and for a 2d plot it is assigned to a 2-element tuple which has the form (nbin_x, nbin_y):
In [118]: fig, ax = plt.subplots(3, 2)
: sns.histplot(data=iris, x='petal_length', hue='species', ax=ax[0, 0], legend=False, bins=5)
: sns.histplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[0, 1], legend=False, bins=(5, 5))
: sns.histplot(data=iris, x='petal_length', hue='species', ax=ax[1, 0], legend=False, bins=50)
: sns.histplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[1, 1], legend=False, bins=(5, 10))
: sns.histplot(data=iris, x='petal_length', hue='species', ax=ax[2, 0], legend=False, bins=200)
: sns.histplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[2, 1], legend=False, bins=(50, 50));
When the number of bins is too small, like in the first row, the overall trend may be obscured. The last row gives enough bins to visually distinguish each category. A larger number of bins would make the datapoints too small to visually examine.
The rugplot plotting function displays each data point as a discrete transparent line and therefore the density of the lines around a specific value depict the frequency of that value. The kdeplot plotting function essentially sums each of the lines in the rugplotm then normalised the area under the curve to 1 to display the probability distribution:
In [119]: fig, ax = plt.subplots(3, 1)
: sns.histplot(data=iris, x='petal_length', hue='species', ax=ax[0], legend=False, bins=50)
: sns.rugplot(data=iris, x='petal_length', hue='species', ax=ax[1], legend=False)
: sns.kdeplot(data=iris, x='petal_length', hue='species', ax=ax[2], legend=False);
These can also be examined in 2d:
In [120]: fig, ax = plt.subplots(3, 1)
sns.histplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[0], legend=False, bins=(50, 50))
sns.rugplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[1], legend=False)
sns.kdeplot(data=iris, x='petal_length', y='sepal_length', hue='species', ax=ax[2], legend=False);
The emperical cumlative distribution function ecdf plotting function plots out the proportion of datapoints at and below a specified value. This is again normalised to 1 and therefore starts at 0 and ends at 1. It is 1d only and can again be used to visually distinguish the three distributions:
In [121]: fig, ax = plt.subplots()
: sns.ecdfplot(data=iris, x='petal_length', hue='species');
In 2d, the rugplot is often used as a companion to the kdeplot:
In [122]: fig, ax = plt.subplots()
: sns.rugplot(data=iris, x='petal_length', y='sepal_length', hue='species')
: sns.kdeplot(data=iris, x='petal_length', y='sepal_length', hue='species');
The sns Axes-level distribution plotting functions histplot, rugplot, kdeplot and ecdfplot are complemented by displot which is a Figure-level function that outputs a FacetGrid:
In [123]: fg = sns.displot(data=iris, x='petal_length', y='sepal_length', hue='species')
Notice that displot displays a histogram by default and because this is a Figure-level FacetGrid, the legend is more appropriately handled. displot has a parameter kind which defaults to 'hist' but can be assigned to another plot type such as 'kde' or 'ecfd' (recalling that 'ecfd' only works in 1d). rug is a boolean parameter and can be enabled:
In [124]: fg = sns.displot(data=iris, x='petal_length', y='sepal_length', hue='species', kind='kde', rug=True)
Because this is a FacetGrid, the optional parameters hue, col and row can be set to a categorical Series. The behaviour is consistent to the behaviour previously seen when relplot and lmplot.
When the 'iris' dataset was explored, a 1d kdeplot was used to explore a numeric Series and categorical hue and a 2d histplot was used to explore the relationship between two numeric Series and a categorical 'hue'. When the number of numeric Series is low and there is a single categorical hue, the plotting function pairplot can be used to view all the possible combinations as a subplot within an AxisGrid:
In [125]: sns.pairplot(data=iris, hue='species')
Out[125]: <seaborn.axisgrid.PairGrid at 0x1f4bfe75810>
Notice the return value is a PairGrid instance instead of a FacetGrid instance. Notice in all the FacetGrid instances previously explorered each subplot in the FacetGrid was the same plot type. In a PairGrid there is a difference in the plot used in diagonal Axes instances which are 1d plots and in the non-diagonal instances which are 2d plots. The PairGrid class essentially allows grouping of these diagonal plots. Notice in the PairGrid above the diagonal and below the diagonal display essentially the same data, where the xaxis and yaxis are essentially transposed. It is therefore common to just display one of the corners:
In [125]: pg = sns.pairplot(data=iris, hue='species', corner=True) 
The identifiers of the PairGrid instance pg can be viewed. Notice the inclusion of attributes related specifically related to the diagonal:
In [126]: pg.
# 🔗 Data and Layout
# - fig # The Matplotlib figure containing the subplots.
# - axes # 2D NumPy array of subplot axes.
# - legend # Returns the legend instance.
# - palette # The color palette used for the plot.
# - data # The dataset used to create the grid.
# - hue_names # Names of the hue variable categories.
# - hue_vals # Unique values of the hue variable.
# - hue_kws # Dictionary of keyword arguments for the hue variable.
# - x_vars # List of variables plotted along the x-axis.
# - y_vars # List of variables plotted along the y-axis.
# - diag_vars # List of variables plotted on the diagonal (for pair plots).
# - diag_axes # Axes corresponding to the diagonal subplots.
# - diag_sharey # Boolean indicating whether diagonal subplots share the y-axis.
# - square_grid # Boolean indicating if the grid is square.
# 🔗 Plotting Methods
# - add_legend # Adds a legend.
# - set # Sets axis labels and limits.
# - savefig # Saves the figure to a file.
# - map # Maps a plotting function to all subplots.
# - map_diag # Maps a function to the diagonal subplots.
# - map_lower # Maps a function to the lower triangle of the grid.
# - map_upper # Maps a function to the upper triangle of the grid.
# - map_offdiag # Maps a function to the off-diagonal subplots.
# 🔗 Utility Methods
# - apply # Applies a function to each subplot.
# - pipe # Allows method chaining using a function.
# - tick_params # Modifies tick properties.
# - tight_layout # Adjusts subplot parameters for better layout.The pairplot has the optional parameters kind and diag_kind which can be used to change the kind of 2d plot used on the non-diagonal and kind of 1d plot used on the diagonal respectively. The default for the non-diagonal is 'scatter' and the diagonal is 'kde' respectively. Both of these can be changed to 'hist':
In [127]: pg = sns.pairplot(data=iris, hue='species', corner=True, kind='hist', diag_kind='hist')
Both the non-diagonal and diagonal plots are histograms. However the non-diagonal plot is 2d and the diagonal plot is 1d. There is therefore some ambiguity when the optional parameter bins is used. plot_kws and diag_kws are dict instances used to compartmentalise parameters for the non-diagonal and diagonal plots respectively. This allows setting the parameter bins to be set for the non-diagonal (2d plot, so 2-element tuple of integer bins) and setting the parameter bins to the diagonal (1d plot so scalar of integer bins) respectively:
In [128]: pg = sns.pairplot(data=iris, hue='species', corner=True, kind='hist', diag_kind='hist',
plot_kws={'bins': (50, 50)},
diag_kws={'bins': 50})Instantiation of the dict class using the dict class directly opposed to {} is preferred in this use case because syntax highlighting of the input parameters is consistent:
In [128]: pg = sns.pairplot(data=iris, hue='species', corner=True, kind='hist', diag_kind='hist',
plot_kws=dict(bins=(50, 50)),
diag_kws=dict(bins=50))
Another distribution dataset is the penguins dataset, which measures physical features of 3 species of penguin on 3 different islands of differing sex:
In [129]: penguins = sns.load_dataset('penguins')| Variable Explorer | |||
|---|---|---|---|
| penguins | DataFrame | (344, 7) | Column names: 'species', 'island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex' |
| penguins - DataFrame | |||||||
|---|---|---|---|---|---|---|---|
| Index | species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | Male |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | Female |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 196 | 3250 | Female |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | Female |
Note this dataset has 340 rows in the Index but each Series contains null values:
In [130]: penguins.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 species 344 non-null object
1 island 344 non-null object
2 bill_length_mm 342 non-null float64
3 bill_depth_mm 342 non-null float64
4 flipper_length_mm 342 non-null float64
5 body_mass_g 342 non-null float64
6 sex 333 non-null object
dtypes: float64(4), object(3)
memory usage: 18.9+ KBThe DataFrame method dropna can be used to drop any row with null values outputing a new DataFrame and the DataFrame method info can in turn be called on this:
In [131]: penguins.dropna().info()
<class 'pandas.core.frame.DataFrame'>
Index: 333 entries, 0 to 343
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 species 333 non-null object
1 island 333 non-null object
2 bill_length_mm 333 non-null float64
3 bill_depth_mm 333 non-null float64
4 flipper_length_mm 333 non-null float64
5 body_mass_g 333 non-null float64
6 sex 333 non-null object
dtypes: float64(4), object(3)
memory usage: 20.8+ KBThe number of rows is still sufficient, so penguins can be updated in place:
In [132]: penguins = penguins.dropna()| Variable Explorer | |||
|---|---|---|---|
| penguins | DataFrame | (333, 7) | Column names: 'species', 'island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex' |
| penguins - DataFrame | |||||||
|---|---|---|---|---|---|---|---|
| Index | species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | Male |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | Female |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 196 | 3250 | Female |
| 3 | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | Female |
| 4 | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | Male |
In [133]: penguins.info()
<class 'pandas.core.frame.DataFrame'>
Index: 333 entries, 0 to 343
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 species 333 non-null object
1 island 333 non-null object
2 bill_length_mm 333 non-null float64
3 bill_depth_mm 333 non-null float64
4 flipper_length_mm 333 non-null float64
5 body_mass_g 333 non-null float64
6 sex 333 non-null object
dtypes: float64(4), object(3)
memory usage: 20.8+ KBIn [134]: penguins.describe()
Out[134]:
bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
count 333.000000 333.000000 333.000000 333.000000
mean 43.992793 17.164865 200.966967 4207.057057
std 5.468668 1.969235 14.015765 805.215802
min 32.100000 13.100000 172.000000 2700.000000
25% 39.500000 15.600000 190.000000 3550.000000
50% 44.500000 17.300000 197.000000 4050.000000
75% 48.600000 18.700000 213.000000 4775.000000
max 59.600000 21.500000 231.000000 6300.000000In [135]: pg = sns.pairplot(data=penguins, hue='species', corner=True) 
The scatterplot with the two numeric Series x='bill_length_mm' and y='flipper_length_mm' shows the clearest distinction between the three species. A displot can be used with these two numeric Series and the col and row can be set to the other categorical Series, 'island' and 'sex':
In [136]: fg = sns.displot(data=penguins, x='bill_length_mm', y='flipper_length_mm', hue='species', col='island', row='sex')
Here it is clear to visualise the additional information such as the 'Adelie' species (blue category) residing on the three islands, the 'Gentoo' species (green category) only being on the first island 'Biscoe' and the 'Chinstrap' species (red category) only being on the second island 'Dream'.
This means the use of the island can clearly separate out the 'Gentoo' species (green category) and the 'Chinstrap' species (red category).
The exercise dataset compares the performance of 2 groups of people that are either on a low fat diet or a no fat diet. Their pulse is measured at 3 times 1 min, 15 min and 30 min after a different level of exercise rest, walking or running. It can be imported using:
In [137]: exercise = sns.load_dataset('exercise')| Variable Explorer | |||
|---|---|---|---|
| exercise | DataFrame | (90, 6) | Column names: 'Unnamed: 0', 'id', 'diet', 'pulse', 'time', 'kind'], dtype='object' |
| exercise - DataFrame | ||||||
|---|---|---|---|---|---|---|
| Index | Unnamed: 0 | id | diet | pulse | time | kind |
| 0 | 0 | 1 | low fat | 85 | 1 min | rest |
| 1 | 1 | 1 | low fat | 85 | 15 min | rest |
| 2 | 2 | 1 | low fat | 88 | 30 min | rest |
| 3 | 3 | 2 | low fat | 90 | 1 min | rest |
| 4 | 4 | 3 | low fat | 902 | 30 min | rest |
The Series 'Unnamed: 0' is a copy of the Index column (likely the file was saved to a 'csv' with the Index column and this was read in as a new Series) and can be dropped. The DataFrame drop method acts on the Index by default. The axis parameter therefore needs to be changed from the default value 'index' (alias 'rows') to 'columns':
In [138]: exercise.drop('Unnamed: 0', axis='columns')
Out[138]:
id diet pulse time kind
0 1 low fat 85 1 min rest
1 1 low fat 85 15 min rest
2 1 low fat 88 30 min rest
3 2 low fat 90 1 min rest
4 2 low fat 92 15 min rest
.. .. ... ... ... ...
85 29 no fat 135 15 min running
86 29 no fat 130 30 min running
87 30 no fat 99 1 min running
88 30 no fat 111 15 min running
89 30 no fat 150 30 min running
[90 rows x 5 columns]The DataFrame can be updated in place:
In [139]: exercise = exercise.drop('Unnamed: 0', axis='columns')| Variable Explorer | |||
|---|---|---|---|
| exercise | DataFrame | (90, 5) | Column names: 'id', 'diet', 'pulse', 'time', 'kind'], dtype='object' |
| exercise - DataFrame | |||||
|---|---|---|---|---|---|
| Index | id | diet | pulse | time | kind |
| 0 | 1 | low fat | 85 | 1 min | rest |
| 1 | 1 | low fat | 85 | 15 min | rest |
| 2 | 1 | low fat | 88 | 30 min | rest |
| 3 | 2 | low fat | 90 | 1 min | rest |
| 4 | 3 | low fat | 902 | 30 min | rest |
Note this dataset has 90 rows in the Index and each Series contains no null values:
In [140]: exercise.info()
Out[140]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 90 entries, 0 to 89
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 90 non-null int64
1 diet 90 non-null category
2 pulse 90 non-null int64
3 time 90 non-null category
4 kind 90 non-null category
dtypes: category(3), int64(2)
memory usage: 2.2 KBTwo of the rows are numeric and three are categorical and descriptive statistics can be applied to the numeric Series:
In [141]: exercise.describe()
Out[141]:
id pulse
count 90.000000 90.000000
mean 15.500000 99.700000
std 8.703932 14.858471
min 1.000000 80.000000
25% 8.000000 90.250000
50% 15.500000 96.000000
75% 23.000000 103.000000
max 30.000000 150.000000A categorical plot typically examines the distribution of a numeric Series x, in this case the 'pulse' measured and compares the distribution in response to a categorical Series y, in this case, the 'kind' of exercise. The hue parameter can be used to compare the distribution of some categorical Series, in this case the 'diet'. The plotting functions stripplot, swarmplot, boxplot and violinplot are Axes-level distribution functions. The stripplot and swarmplot are both categorical scatter plots, however in the swarmplot there is a slight difference in the way the datapoints are laid out in order to make the trend more visible:
In [142]: fig, ax = plt.subplots(1, 2)
: sns.stripplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[0])
: sns.swarmplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[1]);
The boxplot visually summarises the physical distribution using a mean value, enclosed in an inner quartile range box that has whiskers which span out to the minimum and maximum values (excluding outliers). The inner quartile range contains the closest 50 % of values to the mean (1 standard deviation). The violinplot is similar but makes a KDE instead of a box. Normally the KDE is mirrored around the top and bottom of the box however when there are only two categories it can be split as shown:
In [143]: fig, ax = plt.subplots(2, 2)
: sns.boxplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[0, 0])
: sns.violinplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[0, 1])
: sns.swarmplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[1, 0])
: sns.violinplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[1, 1], split=True);
The barplot and pointplot are other simple representations of the distribution. The barplot is typically projected from a mean value to the origin. It also has an errorbar of 1 standard deviation. The pointplot essentially just plots the mean value as a point and shows the standard deviation around it:
In [144]: fig, ax = plt.subplots(1, 2)
: sns.barplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[0])
: sns.pointplot(data=exercise, x='pulse', y='kind', hue='diet', ax=ax[1])
The Axes-level stripplot, swarmplot, boxplot, violinplot, pointplot and barplot and complemented by the Figure-level catplot which returns a FacetGrid:
In [145]: fg = sns.catplot(data=exercise, x='pulse', y='kind', hue='diet', col='time')
In the plot above, the greatest comparison between the hue parameter 'diet' is seen when 'running' at a 'time' of 30 minutes. The catplot defaults to a strip plot and the
'kind' parameter can be used to change this plot to a different plot type, for example to 'swarm', 'box', 'violin':
In [146]: fg = sns.catplot(data=exercise, x='pulse', y='kind', hue='diet', col='time', kind='swarm')
In [147]: fg = sns.catplot(data=exercise, x='pulse', y='kind', hue='diet', col='time', kind='box')
In [148]: fg = sns.catplot(data=exercise, x='pulse', y='kind', hue='diet', col='time', kind='violin')
The other kind of plots are 'boxen', 'bar', 'count', and 'point'.
If the 'iris' data is re-examined:
| Variable Explorer | |||
|---|---|---|---|
| iris | DataFrame | (150, 5) | Column names: sepal_length, sepal_width, petal_length, petal_height, species |
| flights - DataFrame | |||||
|---|---|---|---|---|---|
| Index | sepal_length | sepal_width | petal_length | petal_height | species |
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
Notice that it has 4 numeric Series and one categorical Series, the categorical Series 'species' can be dropped and this allows the correlation of each numeric Series to be examined:
In [149]: iris.drop('species', axis='columns').corr()
Out[149]:
sepal_length sepal_width petal_length petal_width
sepal_length 1.000000 -0.117570 0.871754 0.817941
sepal_width -0.117570 1.000000 -0.428440 -0.366126
petal_length 0.871754 -0.428440 1.000000 0.962865
petal_width 0.817941 -0.366126 0.962865 1.000000A perfect correlation of 1 or -1 and essentially means the two Series display the same information and if the correlation is 0, then the 2 Series are completely at random.
A Series correlated with itself is always 1. Note that 'petal_width' and 'petal_length' have a high correlation of 0.96. If the pairplot is re-examined:
In [150]: pg = sns.pairplot(data=iris, hue='species', corner=True) 
Notice the data shown in red, is largely replicated. While these two parameters may at first appear to be the best when it comes to distinguishing the three species, the essential overlap of the 1d plots and the high correlation essentially means the two features being used to make the 2d plot is pretty much a duplication. Having x='sepal_width' and y='petal_width' with a correlation of -0.367 is a better choice as the features are not highly correlated and therefore give supplementary information in 2 dimensions. When x='sepal_length' and y='sepal_width', the correlation -0.118 is very low however the 'sepal_length' does not seem to clearly distinguish each of the three Species.
The correlation is often viewed as a heatmap:
In [151]: fig, ax = plt.subplots()
: sns.heatmap(iris.drop('species', axis='columns').corr());
This can optionally be annotated:
In [152]: fig, ax = plt.subplots()
: sns.heatmap(iris.drop('species', axis='columns').corr(), annot=True);
If the following 2d scatterplot is made x='sepal_width' and y='petal_width'
In [153]: fig, ax = plt.subplots()
: sns.scatterplot(data=iris, x='sepal_width', hue='species');
It can be supplemented with the following 1d histplot where x='sepal_width':
In [154]: fig, ax = plt.subplots()
: sns.histplot(data=iris, x='sepal_width', hue='species');
And another 1d histplot where y='petal_width':
In [155]: fig, ax = plt.subplots()
: sns.histplot(data=iris, y='petal_width', hue='species');
These can be plotted together using:
In [156]: fig, ax = plt.subplots(2, 2)
: sns.histplot(data=iris, x='sepal_width', hue='species', ax=ax[0, 0], legend=False)
: # None
: sns.scatterplot(data=iris, x='sepal_width', y='petal_width', hue='species', ax=ax[1, 0], legend=False)
: sns.histplot(data=iris, y='petal_width', hue='species', ax=ax[1, 1], legend=False);
The JointGrid class is a better way of implementing the plot above. The JoinGrid class is typically instantiated directly, supplying the DataFrame to data and the Series to 'x' and 'y':
In [157]: jg = sns.JointGrid(data=penguins, x='bill_length_mm', y='bill_depth_mm', hue='species')
The identifiers for the JointGGrid class are:
In [157]: jg.
# 🔗 Data and Layout
# - fig # The Matplotlib figure containing the plot.
# - ax_joint # The main plotting axis for the joint plot.
# - ax_marg_x # The axis for the x-axis marginal plot.
# - ax_marg_y # The axis for the y-axis marginal plot.
# - data # The dataset used to create the JointGrid.
# - hue_names # Names of the hue variable categories.
# - hue_kws # Dictionary of keyword arguments for the hue variable.
# - legend # Returns the legend instance.
# 🔗 Plotting Methods
# - plot # Generic method for mapping functions to the grid.
# - plot_joint # Maps a function to the main joint plot.
# - plot_marginals # Maps a function to the marginal plots.
# - refline # Adds reference lines to the plot.
# - set # Sets axis labels and limits.
# - set_axis_labels # Sets labels for the x and y axes.
# - set_xlabels # Sets x-axis labels.
# - set_ylabels # Sets y-axis labels.
# - set_xticklabels # Sets x-axis tick labels.
# - set_yticklabels # Sets y-axis tick labels.
# - add_legend # Adds a legend to the plot.
# - savefig # Saves the figure to a file.
# 🔗 Utility Methods
# - apply # Applies a function to the entire grid.
# - pipe # Allows method chaining using a function.
# - tick_params # Modifies tick properties.
# - tight_layout # Adjusts subplot parameters for better layout.The plot_joint can be used to select a 2d plot type to use for the main plot:
In [157]: jg.plot_joint(sns.scatterplot);
The plot_marginals can be used to select a 1d plot type for the marginal plots:
In [158]: jg.plot_marginals(sns.histplot);
The plot function combines these together:
In [159]: jg = sns.JointGrid(data=penguins, x='bill_length_mm', y='bill_depth_mm', hue='species')
: jg.plot(sns.kdeplot, sns.kdeplot);
Each Axes in the JointGrid can be selected:
In [160]: jg.ax_joint
Out[160]: <Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'>
In [161]: jg.ax_marg_x
Out[161]: <Axes: xlabel='bill_length_mm', ylabel='Density'>
In [162]: jg.ax_marg_y
Out[162]: <Axes: xlabel='Density', ylabel='bill_depth_mm'>The method move_legned can be used to select the legend from the Axes instance ax_joint and move the legend to the 'lower left' of the Axes:
In [163]: sns.move_legend(jg.ax_joint, 'lower left')