🕷️ Crawler Inspector

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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 68 (from laksa128)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa068.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://seaborn.pydata.org/generated/seaborn.lineplot.html\')), getAhrefsUnparsedNoserviceFromURL(\'https://seaborn.pydata.org/generated/seaborn.lineplot.html\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html","crawl_time":1775094957,"first_indexed_time":1531880230,"http_code":200,"src_unparsed":"org,pydata!seaborn,\/generated\/seaborn.lineplot.html s443","src_root_hash":"10355827761381095868","history_drop_reason":null,"meta_title":"seaborn.lineplot — seaborn 0.13.2 documentation","meta_descriptions":[],"attrs_boilerpipe_text":"seaborn.\nlineplot\n(\ndata\n=\nNone\n,\n*\n,\nx\n=\nNone\n,\ny\n=\nNone\n,\nhue\n=\nNone\n,\nsize\n=\nNone\n,\nstyle\n=\nNone\n,\nunits\n=\nNone\n,\nweights\n=\nNone\n,\npalette\n=\nNone\n,\nhue_order\n=\nNone\n,\nhue_norm\n=\nNone\n,\nsizes\n=\nNone\n,\nsize_order\n=\nNone\n,\nsize_norm\n=\nNone\n,\ndashes\n=\nTrue\n,\nmarkers\n=\nNone\n,\nstyle_order\n=\nNone\n,\nestimator\n=\n'mean'\n,\nerrorbar\n=\n('ci',\n95)\n,\nn_boot\n=\n1000\n,\nseed\n=\nNone\n,\norient\n=\n'x'\n,\nsort\n=\nTrue\n,\nerr_style\n=\n'band'\n,\nerr_kws\n=\nNone\n,\nlegend\n=\n'auto'\n,\nci\n=\n'deprecated'\n,\nax\n=\nNone\n,\n**\nkwargs\n)\n#\nDraw a line plot with possibility of several semantic groupings.\nThe relationship between\nx\nand\ny\ncan be shown for different subsets\nof the data using the\nhue\n,\nsize\n, and\nstyle\nparameters. These\nparameters control what visual semantics are used to identify the different\nsubsets. It is possible to show up to three dimensions independently by\nusing all three semantic types, but this style of plot can be hard to\ninterpret and is often ineffective. Using redundant semantics (i.e. both\nhue\nand\nstyle\nfor the same variable) can be helpful for making\ngraphics more accessible.\nSee the\ntutorial\nfor more information.\nThe default treatment of the\nhue\n(and to a lesser extent,\nsize\n)\nsemantic, if present, depends on whether the variable is inferred to\nrepresent “numeric” or “categorical” data. In particular, numeric variables\nare represented with a sequential colormap by default, and the legend\nentries show regular “ticks” with values that may or may not exist in the\ndata. This behavior can be controlled through various parameters, as\ndescribed and illustrated below.\nBy default, the plot aggregates over multiple\ny\nvalues at each value of\nx\nand shows an estimate of the central tendency and a confidence\ninterval for that estimate.\nParameters\n:\ndata\npandas.DataFrame\n,\nnumpy.ndarray\n, mapping, or sequence\nInput data structure. Either a long-form collection of vectors that can be\nassigned to named variables or a wide-form dataset that will be internally\nreshaped.\nx, y\nvectors or keys in\ndata\nVariables that specify positions on the x and y axes.\nhue\nvector or key in\ndata\nGrouping variable that will produce lines with different colors.\nCan be either categorical or numeric, although color mapping will\nbehave differently in latter case.\nsize\nvector or key in\ndata\nGrouping variable that will produce lines with different widths.\nCan be either categorical or numeric, although size mapping will\nbehave differently in latter case.\nstyle\nvector or key in\ndata\nGrouping variable that will produce lines with different dashes\nand\/or markers. Can have a numeric dtype but will always be treated\nas categorical.\nunits\nvector or key in\ndata\nGrouping variable identifying sampling units. When used, a separate\nline will be drawn for each unit with appropriate semantics, but no\nlegend entry will be added. Useful for showing distribution of\nexperimental replicates when exact identities are not needed.\nweights\nvector or key in\ndata\nData values or column used to compute weighted estimation.\nNote that use of weights currently limits the choice of statistics\nto a ‘mean’ estimator and ‘ci’ errorbar.\npalette\nstring, list, dict, or\nmatplotlib.colors.Colormap\nMethod for choosing the colors to use when mapping the\nhue\nsemantic.\nString values are passed to\ncolor_palette()\n. List or dict values\nimply categorical mapping, while a colormap object implies numeric mapping.\nhue_order\nvector of strings\nSpecify the order of processing and plotting for categorical levels of the\nhue\nsemantic.\nhue_norm\ntuple or\nmatplotlib.colors.Normalize\nEither a pair of values that set the normalization range in data units\nor an object that will map from data units into a [0, 1] interval. Usage\nimplies numeric mapping.\nsizes\nlist, dict, or tuple\nAn object that determines how sizes are chosen when\nsize\nis used.\nList or dict arguments should provide a size for each unique data value,\nwhich forces a categorical interpretation. The argument may also be a\nmin, max tuple.\nsize_order\nlist\nSpecified order for appearance of the\nsize\nvariable levels,\notherwise they are determined from the data. Not relevant when the\nsize\nvariable is numeric.\nsize_norm\ntuple or Normalize object\nNormalization in data units for scaling plot objects when the\nsize\nvariable is numeric.\ndashes\nboolean, list, or dictionary\nObject determining how to draw the lines for different levels of the\nstyle\nvariable. Setting to\nTrue\nwill use default dash codes, or\nyou can pass a list of dash codes or a dictionary mapping levels of the\nstyle\nvariable to dash codes. Setting to\nFalse\nwill use solid\nlines for all subsets. Dashes are specified as in matplotlib: a tuple\nof\n(segment,\ngap)\nlengths, or an empty string to draw a solid line.\nmarkers\nboolean, list, or dictionary\nObject determining how to draw the markers for different levels of the\nstyle\nvariable. Setting to\nTrue\nwill use default markers, or\nyou can pass a list of markers or a dictionary mapping levels of the\nstyle\nvariable to markers. Setting to\nFalse\nwill draw\nmarker-less lines.  Markers are specified as in matplotlib.\nstyle_order\nlist\nSpecified order for appearance of the\nstyle\nvariable levels\notherwise they are determined from the data. Not relevant when the\nstyle\nvariable is numeric.\nestimator\nname of pandas method or callable or None\nMethod for aggregating across multiple observations of the\ny\nvariable at the same\nx\nlevel. If\nNone\n, all observations will\nbe drawn.\nerrorbar\nstring, (string, number) tuple, or callable\nName of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple\nwith a method name and a level parameter, or a function that maps from a\nvector to a (min, max) interval, or None to hide errorbar. See the\nerrorbar tutorial\nfor more information.\nn_boot\nint\nNumber of bootstraps to use for computing the confidence interval.\nseed\nint, numpy.random.Generator, or numpy.random.RandomState\nSeed or random number generator for reproducible bootstrapping.\norient\n“x” or “y”\nDimension along which the data are sorted \/ aggregated. Equivalently,\nthe “independent variable” of the resulting function.\nsort\nboolean\nIf True, the data will be sorted by the x and y variables, otherwise\nlines will connect points in the order they appear in the dataset.\nerr_style\n“band” or “bars”\nWhether to draw the confidence intervals with translucent error bands\nor discrete error bars.\nerr_kws\ndict of keyword arguments\nAdditional parameters to control the aesthetics of the error bars. The\nkwargs are passed either to\nmatplotlib.axes.Axes.fill_between()\nor\nmatplotlib.axes.Axes.errorbar()\n, depending on\nerr_style\n.\nlegend\n“auto”, “brief”, “full”, or False\nHow to draw the legend. If “brief”, numeric\nhue\nand\nsize\nvariables will be represented with a sample of evenly spaced values.\nIf “full”, every group will get an entry in the legend. If “auto”,\nchoose between brief or full representation based on number of levels.\nIf\nFalse\n, no legend data is added and no legend is drawn.\nci\nint or “sd” or None\nSize of the confidence interval to draw when aggregating.\nDeprecated since version 0.12.0:\nUse the new\nerrorbar\nparameter for more flexibility.\nax\nmatplotlib.axes.Axes\nPre-existing axes for the plot. Otherwise, call\nmatplotlib.pyplot.gca()\ninternally.\nkwargs\nkey, value mappings\nOther keyword arguments are passed down to\nmatplotlib.axes.Axes.plot()\n.\nReturns\n:\nmatplotlib.axes.Axes\nThe matplotlib axes containing the plot.\nSee also\nscatterplot\nPlot data using points.\npointplot\nPlot point estimates and CIs using markers and lines.\nExamples\nThe\nflights\ndataset has 10 years of monthly airline passenger data:\nflights\n=\nsns\n.\nload_dataset\n(\n\"flights\"\n)\nflights\n.\nhead\n()\nyear\nmonth\npassengers\n0\n1949\nJan\n112\n1\n1949\nFeb\n118\n2\n1949\nMar\n132\n3\n1949\nApr\n129\n4\n1949\nMay\n121\nTo draw a line plot using long-form data, assign the\nx\nand\ny\nvariables:\nmay_flights\n=\nflights\n.\nquery\n(\n\"month == 'May'\"\n)\nsns\n.\nlineplot\n(\ndata\n=\nmay_flights\n,\nx\n=\n\"year\"\n,\ny\n=\n\"passengers\"\n)\nPivot the dataframe to a wide-form representation:\nflights_wide\n=\nflights\n.\npivot\n(\nindex\n=\n\"year\"\n,\ncolumns\n=\n\"month\"\n,\nvalues\n=\n\"passengers\"\n)\nflights_wide\n.\nhead\n()\nmonth\nJan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug\nSep\nOct\nNov\nDec\nyear\n1949\n112\n118\n132\n129\n121\n135\n148\n148\n136\n119\n104\n118\n1950\n115\n126\n141\n135\n125\n149\n170\n170\n158\n133\n114\n140\n1951\n145\n150\n178\n163\n172\n178\n199\n199\n184\n162\n146\n166\n1952\n171\n180\n193\n181\n183\n218\n230\n242\n209\n191\n172\n194\n1953\n196\n196\n236\n235\n229\n243\n264\n272\n237\n211\n180\n201\nTo plot a single vector, pass it to\ndata\n. If the vector is a\npandas.Series\n, it will be plotted against its index:\nsns\n.\nlineplot\n(\ndata\n=\nflights_wide\n[\n\"May\"\n])\nPassing the entire wide-form dataset to\ndata\nplots a separate line for each column:\nsns\n.\nlineplot\n(\ndata\n=\nflights_wide\n)\nPassing the entire dataset in long-form mode will aggregate over repeated values (each year) to show the mean and 95% confidence interval:\nsns\n.\nlineplot\n(\ndata\n=\nflights\n,\nx\n=\n\"year\"\n,\ny\n=\n\"passengers\"\n)\nAssign a grouping semantic (\nhue\n,\nsize\n, or\nstyle\n) to plot separate lines\nsns\n.\nlineplot\n(\ndata\n=\nflights\n,\nx\n=\n\"year\"\n,\ny\n=\n\"passengers\"\n,\nhue\n=\n\"month\"\n)\nThe same column can be assigned to multiple semantic variables, which can increase the accessibility of the plot:\nsns\n.\nlineplot\n(\ndata\n=\nflights\n,\nx\n=\n\"year\"\n,\ny\n=\n\"passengers\"\n,\nhue\n=\n\"month\"\n,\nstyle\n=\n\"month\"\n)\nUse the\norient\nparameter to aggregate and sort along the vertical dimension of the plot:\nsns\n.\nlineplot\n(\ndata\n=\nflights\n,\nx\n=\n\"passengers\"\n,\ny\n=\n\"year\"\n,\norient\n=\n\"y\"\n)\nEach semantic variable can also represent a different column. For that, we’ll need a more complex dataset:\nfmri\n=\nsns\n.\nload_dataset\n(\n\"fmri\"\n)\nfmri\n.\nhead\n()\nsubject\ntimepoint\nevent\nregion\nsignal\n0\ns13\n18\nstim\nparietal\n-0.017552\n1\ns5\n14\nstim\nparietal\n-0.080883\n2\ns12\n18\nstim\nparietal\n-0.081033\n3\ns11\n18\nstim\nparietal\n-0.046134\n4\ns10\n18\nstim\nparietal\n-0.037970\nRepeated observations are aggregated even when semantic grouping is used:\nsns\n.\nlineplot\n(\ndata\n=\nfmri\n,\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\nhue\n=\n\"event\"\n)\nAssign both\nhue\nand\nstyle\nto represent two different grouping variables:\nsns\n.\nlineplot\n(\ndata\n=\nfmri\n,\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\nhue\n=\n\"region\"\n,\nstyle\n=\n\"event\"\n)\nWhen assigning a\nstyle\nvariable, markers can be used instead of (or along with) dashes to distinguish the groups:\nsns\n.\nlineplot\n(\ndata\n=\nfmri\n,\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\nhue\n=\n\"event\"\n,\nstyle\n=\n\"event\"\n,\nmarkers\n=\nTrue\n,\ndashes\n=\nFalse\n)\nShow error bars instead of error bands and extend them to two standard error widths:\nsns\n.\nlineplot\n(\ndata\n=\nfmri\n,\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\nhue\n=\n\"event\"\n,\nerr_style\n=\n\"bars\"\n,\nerrorbar\n=\n(\n\"se\"\n,\n2\n),\n)\nAssigning the\nunits\nvariable will plot multiple lines without applying a semantic mapping:\nsns\n.\nlineplot\n(\ndata\n=\nfmri\n.\nquery\n(\n\"region == 'frontal'\"\n),\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\nhue\n=\n\"event\"\n,\nunits\n=\n\"subject\"\n,\nestimator\n=\nNone\n,\nlw\n=\n1\n,\n)\nLoad another dataset with a numeric grouping variable:\ndots\n=\nsns\n.\nload_dataset\n(\n\"dots\"\n)\n.\nquery\n(\n\"align == 'dots'\"\n)\ndots\n.\nhead\n()\nalign\nchoice\ntime\ncoherence\nfiring_rate\n0\ndots\nT1\n-80\n0.0\n33.189967\n1\ndots\nT1\n-80\n3.2\n31.691726\n2\ndots\nT1\n-80\n6.4\n34.279840\n3\ndots\nT1\n-80\n12.8\n32.631874\n4\ndots\nT1\n-80\n25.6\n35.060487\nAssigning a numeric variable to\nhue\nmaps it differently, using a different default palette and a quantitative color mapping:\nsns\n.\nlineplot\n(\ndata\n=\ndots\n,\nx\n=\n\"time\"\n,\ny\n=\n\"firing_rate\"\n,\nhue\n=\n\"coherence\"\n,\nstyle\n=\n\"choice\"\n,\n)\nControl the color mapping by setting the\npalette\nand passing a\nmatplotlib.colors.Normalize\nobject:\nsns\n.\nlineplot\n(\ndata\n=\ndots\n.\nquery\n(\n\"coherence > 0\"\n),\nx\n=\n\"time\"\n,\ny\n=\n\"firing_rate\"\n,\nhue\n=\n\"coherence\"\n,\nstyle\n=\n\"choice\"\n,\npalette\n=\n\"flare\"\n,\nhue_norm\n=\nmpl\n.\ncolors\n.\nLogNorm\n(),\n)\nOr pass specific colors, either as a Python list or dictionary:\npalette\n=\nsns\n.\ncolor_palette\n(\n\"mako_r\"\n,\n6\n)\nsns\n.\nlineplot\n(\ndata\n=\ndots\n,\nx\n=\n\"time\"\n,\ny\n=\n\"firing_rate\"\n,\nhue\n=\n\"coherence\"\n,\nstyle\n=\n\"choice\"\n,\npalette\n=\npalette\n)\nAssign the\nsize\nsemantic to map the width of the lines with a numeric variable:\nsns\n.\nlineplot\n(\ndata\n=\ndots\n,\nx\n=\n\"time\"\n,\ny\n=\n\"firing_rate\"\n,\nsize\n=\n\"coherence\"\n,\nhue\n=\n\"choice\"\n,\nlegend\n=\n\"full\"\n)\nPass a a tuple,\nsizes=(smallest,\nlargest)\n, to control the range of linewidths used to map the\nsize\nsemantic:\nsns\n.\nlineplot\n(\ndata\n=\ndots\n,\nx\n=\n\"time\"\n,\ny\n=\n\"firing_rate\"\n,\nsize\n=\n\"coherence\"\n,\nhue\n=\n\"choice\"\n,\nsizes\n=\n(\n.25\n,\n2.5\n)\n)\nBy default, the observations are sorted by\nx\n. Disable this to plot a line with the order that observations appear in the dataset:\nx\n,\ny\n=\nnp\n.\nrandom\n.\nnormal\n(\nsize\n=\n(\n2\n,\n5000\n))\n.\ncumsum\n(\naxis\n=\n1\n)\nsns\n.\nlineplot\n(\nx\n=\nx\n,\ny\n=\ny\n,\nsort\n=\nFalse\n,\nlw\n=\n1\n)\nUse\nrelplot()\nto combine\nlineplot()\nand\nFacetGrid\n. This allows grouping within additional categorical variables. Using\nrelplot()\nis safer than using\nFacetGrid\ndirectly, as it ensures synchronization of the semantic mappings across facets:\nsns\n.\nrelplot\n(\ndata\n=\nfmri\n,\nx\n=\n\"timepoint\"\n,\ny\n=\n\"signal\"\n,\ncol\n=\n\"region\"\n,\nhue\n=\n\"event\"\n,\nstyle\n=\n\"event\"\n,\nkind\n=\n\"line\"\n)","attrs_markdown":"[![Logo image](https:\/\/seaborn.pydata.org\/_static\/logo-wide-lightbg.svg) ![Logo image](https:\/\/seaborn.pydata.org\/_static\/logo-wide-lightbg.svg)](https:\/\/seaborn.pydata.org\/index.html)\n\n- [Installing](https:\/\/seaborn.pydata.org\/installing.html)\n- [Gallery](https:\/\/seaborn.pydata.org\/examples\/index.html)\n- [Tutorial](https:\/\/seaborn.pydata.org\/tutorial.html)\n- [API](https:\/\/seaborn.pydata.org\/api.html)\n- [Releases](https:\/\/seaborn.pydata.org\/whatsnew\/index.html)\n- [Citing](https:\/\/seaborn.pydata.org\/citing.html)\n- [FAQ](https:\/\/seaborn.pydata.org\/faq.html)\n\n- [GitHub](https:\/\/github.com\/mwaskom\/seaborn \"GitHub\")\n- 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[seaborn.objects.Range](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Range.html)\n\n- [seaborn.objects.Bar](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Bar.html)\n- [seaborn.objects.Bars](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Bars.html)\n\n- [seaborn.objects.Area](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Area.html)\n- [seaborn.objects.Band](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Band.html)\n\n- [seaborn.objects.Text](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Text.html)\n\n- [seaborn.objects.Agg](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Agg.html)\n- [seaborn.objects.Est](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Est.html)\n- [seaborn.objects.Count](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Count.html)\n- [seaborn.objects.Hist](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Hist.html)\n- [seaborn.objects.KDE](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.KDE.html)\n- [seaborn.objects.Perc](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Perc.html)\n- [seaborn.objects.PolyFit](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.PolyFit.html)\n\n- [seaborn.objects.Dodge](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Dodge.html)\n- [seaborn.objects.Jitter](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Jitter.html)\n- [seaborn.objects.Norm](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Norm.html)\n- [seaborn.objects.Stack](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Stack.html)\n- [seaborn.objects.Shift](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Shift.html)\n\n- [seaborn.objects.Boolean](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Boolean.html)\n- [seaborn.objects.Continuous](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Continuous.html)\n- [seaborn.objects.Nominal](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Nominal.html)\n- [seaborn.objects.Temporal](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Temporal.html)\n\n- [seaborn.objects.Mark](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Mark.html)\n- [seaborn.objects.Stat](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Stat.html)\n- [seaborn.objects.Move](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Move.html)\n- [seaborn.objects.Scale](https:\/\/seaborn.pydata.org\/generated\/seaborn.objects.Scale.html)\n\n- [seaborn.relplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.relplot.html)\n- [seaborn.scatterplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.scatterplot.html)\n- [seaborn.lineplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html)\n\n- [seaborn.displot](https:\/\/seaborn.pydata.org\/generated\/seaborn.displot.html)\n- [seaborn.histplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.histplot.html)\n- [seaborn.kdeplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.kdeplot.html)\n- [seaborn.ecdfplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.ecdfplot.html)\n- [seaborn.rugplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.rugplot.html)\n- [seaborn.distplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.distplot.html)\n\n- [seaborn.catplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.catplot.html)\n- [seaborn.stripplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.stripplot.html)\n- [seaborn.swarmplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.swarmplot.html)\n- [seaborn.boxplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.boxplot.html)\n- [seaborn.violinplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.violinplot.html)\n- [seaborn.boxenplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.boxenplot.html)\n- [seaborn.pointplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.pointplot.html)\n- [seaborn.barplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.barplot.html)\n- [seaborn.countplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.countplot.html)\n\n- [seaborn.lmplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.lmplot.html)\n- [seaborn.regplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.regplot.html)\n- [seaborn.residplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.residplot.html)\n\n- [seaborn.heatmap](https:\/\/seaborn.pydata.org\/generated\/seaborn.heatmap.html)\n- [seaborn.clustermap](https:\/\/seaborn.pydata.org\/generated\/seaborn.clustermap.html)\n\n- [seaborn.FacetGrid](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.html)\n  - [seaborn.FacetGrid.\\_\\_init\\_\\_](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.__init__.html)\n  - [seaborn.FacetGrid.add\\_legend](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.add_legend.html)\n  - [seaborn.FacetGrid.apply](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.apply.html)\n  - [seaborn.FacetGrid.despine](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.despine.html)\n  - [seaborn.FacetGrid.facet\\_axis](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.facet_axis.html)\n  - [seaborn.FacetGrid.facet\\_data](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.facet_data.html)\n  - [seaborn.FacetGrid.map](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.map.html)\n  - [seaborn.FacetGrid.map\\_dataframe](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.map_dataframe.html)\n  - [seaborn.FacetGrid.pipe](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.pipe.html)\n  - [seaborn.FacetGrid.refline](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.refline.html)\n  - [seaborn.FacetGrid.savefig](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.savefig.html)\n  - [seaborn.FacetGrid.set](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set.html)\n  - [seaborn.FacetGrid.set\\_axis\\_labels](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_axis_labels.html)\n  - [seaborn.FacetGrid.set\\_titles](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_titles.html)\n  - [seaborn.FacetGrid.set\\_xlabels](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_xlabels.html)\n  - [seaborn.FacetGrid.set\\_xticklabels](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_xticklabels.html)\n  - [seaborn.FacetGrid.set\\_ylabels](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_ylabels.html)\n  - [seaborn.FacetGrid.set\\_yticklabels](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.set_yticklabels.html)\n  - [seaborn.FacetGrid.tick\\_params](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.tick_params.html)\n  - [seaborn.FacetGrid.tight\\_layout](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.tight_layout.html)\n\n- [seaborn.pairplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.pairplot.html)\n- [seaborn.PairGrid](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.html)\n  - [seaborn.PairGrid.\\_\\_init\\_\\_](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.__init__.html)\n  - [seaborn.PairGrid.add\\_legend](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.add_legend.html)\n  - [seaborn.PairGrid.apply](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.apply.html)\n  - [seaborn.PairGrid.map](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.map.html)\n  - [seaborn.PairGrid.map\\_diag](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.map_diag.html)\n  - [seaborn.PairGrid.map\\_lower](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.map_lower.html)\n  - [seaborn.PairGrid.map\\_offdiag](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.map_offdiag.html)\n  - [seaborn.PairGrid.map\\_upper](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.map_upper.html)\n  - [seaborn.PairGrid.pipe](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.pipe.html)\n  - [seaborn.PairGrid.savefig](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.savefig.html)\n  - [seaborn.PairGrid.set](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.set.html)\n  - [seaborn.PairGrid.tick\\_params](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.tick_params.html)\n  - [seaborn.PairGrid.tight\\_layout](https:\/\/seaborn.pydata.org\/generated\/seaborn.PairGrid.tight_layout.html)\n\n- [seaborn.jointplot](https:\/\/seaborn.pydata.org\/generated\/seaborn.jointplot.html)\n- [seaborn.JointGrid](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.html)\n  - [seaborn.JointGrid.\\_\\_init\\_\\_](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.__init__.html)\n  - [seaborn.JointGrid.apply](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.apply.html)\n  - [seaborn.JointGrid.pipe](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.pipe.html)\n  - [seaborn.JointGrid.plot](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.plot.html)\n  - [seaborn.JointGrid.plot\\_joint](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.plot_joint.html)\n  - [seaborn.JointGrid.plot\\_marginals](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.plot_marginals.html)\n  - [seaborn.JointGrid.refline](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.refline.html)\n  - [seaborn.JointGrid.savefig](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.savefig.html)\n  - [seaborn.JointGrid.set](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.set.html)\n  - [seaborn.JointGrid.set\\_axis\\_labels](https:\/\/seaborn.pydata.org\/generated\/seaborn.JointGrid.set_axis_labels.html)\n\n- [seaborn.set\\_theme](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_theme.html)\n- [seaborn.axes\\_style](https:\/\/seaborn.pydata.org\/generated\/seaborn.axes_style.html)\n- [seaborn.set\\_style](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_style.html)\n- [seaborn.plotting\\_context](https:\/\/seaborn.pydata.org\/generated\/seaborn.plotting_context.html)\n- [seaborn.set\\_context](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_context.html)\n- [seaborn.set\\_color\\_codes](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_color_codes.html)\n- [seaborn.reset\\_defaults](https:\/\/seaborn.pydata.org\/generated\/seaborn.reset_defaults.html)\n- [seaborn.reset\\_orig](https:\/\/seaborn.pydata.org\/generated\/seaborn.reset_orig.html)\n- [seaborn.set](https:\/\/seaborn.pydata.org\/generated\/seaborn.set.html)\n\n- [seaborn.set\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_palette.html)\n- [seaborn.color\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.color_palette.html)\n- [seaborn.husl\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.husl_palette.html)\n- [seaborn.hls\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.hls_palette.html)\n- [seaborn.cubehelix\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.cubehelix_palette.html)\n- [seaborn.dark\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.dark_palette.html)\n- [seaborn.light\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.light_palette.html)\n- [seaborn.diverging\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.diverging_palette.html)\n- [seaborn.blend\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.blend_palette.html)\n- [seaborn.xkcd\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.xkcd_palette.html)\n- [seaborn.crayon\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.crayon_palette.html)\n- [seaborn.mpl\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.mpl_palette.html)\n\n- [seaborn.choose\\_colorbrewer\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.choose_colorbrewer_palette.html)\n- [seaborn.choose\\_cubehelix\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.choose_cubehelix_palette.html)\n- [seaborn.choose\\_light\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.choose_light_palette.html)\n- [seaborn.choose\\_dark\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.choose_dark_palette.html)\n- [seaborn.choose\\_diverging\\_palette](https:\/\/seaborn.pydata.org\/generated\/seaborn.choose_diverging_palette.html)\n\n- [seaborn.despine](https:\/\/seaborn.pydata.org\/generated\/seaborn.despine.html)\n- [seaborn.move\\_legend](https:\/\/seaborn.pydata.org\/generated\/seaborn.move_legend.html)\n- [seaborn.saturate](https:\/\/seaborn.pydata.org\/generated\/seaborn.saturate.html)\n- [seaborn.desaturate](https:\/\/seaborn.pydata.org\/generated\/seaborn.desaturate.html)\n- [seaborn.set\\_hls\\_values](https:\/\/seaborn.pydata.org\/generated\/seaborn.set_hls_values.html)\n- [seaborn.load\\_dataset](https:\/\/seaborn.pydata.org\/generated\/seaborn.load_dataset.html)\n- [seaborn.get\\_dataset\\_names](https:\/\/seaborn.pydata.org\/generated\/seaborn.get_dataset_names.html)\n- [seaborn.get\\_data\\_home](https:\/\/seaborn.pydata.org\/generated\/seaborn.get_data_home.html)\n\nOn this page\n\n# seaborn.lineplot[\\#](https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html#seaborn-lineplot \"Permalink to this heading\")\nseaborn.lineplot(*data\\=None*, *\\**, *x\\=None*, *y\\=None*, *hue\\=None*, *size\\=None*, *style\\=None*, *units\\=None*, *weights\\=None*, *palette\\=None*, *hue\\_order\\=None*, *hue\\_norm\\=None*, *sizes\\=None*, *size\\_order\\=None*, *size\\_norm\\=None*, *dashes\\=True*, *markers\\=None*, *style\\_order\\=None*, *estimator\\='mean'*, *errorbar\\=('ci', 95\\)*, *n\\_boot\\=1000*, *seed\\=None*, *orient\\='x'*, *sort\\=True*, *err\\_style\\='band'*, *err\\_kws\\=None*, *legend\\='auto'*, *ci\\='deprecated'*, *ax\\=None*, *\\*\\*kwargs*)[\\#](https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html#seaborn.lineplot \"Permalink to this definition\")\n\nDraw a line plot with possibility of several semantic groupings.\n\nThe relationship between `x` and `y` can be shown for different subsets of the data using the `hue`, `size`, and `style` parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both `hue` and `style` for the same variable) can be helpful for making graphics more accessible.\n\nSee the [tutorial](https:\/\/seaborn.pydata.org\/tutorial\/relational.html#relational-tutorial) for more information.\n\nThe default treatment of the `hue` (and to a lesser extent, `size`) semantic, if present, depends on whether the variable is inferred to represent “numeric” or “categorical” data. In particular, numeric variables are represented with a sequential colormap by default, and the legend entries show regular “ticks” with values that may or may not exist in the data. This behavior can be controlled through various parameters, as described and illustrated below.\n\nBy default, the plot aggregates over multiple `y` values at each value of `x` and shows an estimate of the central tendency and a confidence interval for that estimate.\n\nParameters:\n\n**data**[`pandas.DataFrame`](https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.html#pandas.DataFrame \"(in pandas v2.2.0)\"), [`numpy.ndarray`](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.ndarray.html#numpy.ndarray \"(in NumPy v1.26)\"), mapping, or sequence\n\nInput data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.\n\n**x, y**vectors or keys in `data`\n\nVariables that specify positions on the x and y axes.\n\n**hue**vector or key in `data`\n\nGrouping variable that will produce lines with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case.\n\n**size**vector or key in `data`\n\nGrouping variable that will produce lines with different widths. Can be either categorical or numeric, although size mapping will behave differently in latter case.\n\n**style**vector or key in `data`\n\nGrouping variable that will produce lines with different dashes and\/or markers. Can have a numeric dtype but will always be treated as categorical.\n\n**units**vector or key in `data`\n\nGrouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed.\n\n**weights**vector or key in `data`\n\nData values or column used to compute weighted estimation. Note that use of weights currently limits the choice of statistics to a ‘mean’ estimator and ‘ci’ errorbar.\n\n**palette**string, list, dict, or [`matplotlib.colors.Colormap`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap \"(in Matplotlib v3.8.2)\")\n\nMethod for choosing the colors to use when mapping the `hue` semantic. String values are passed to [`color_palette()`](https:\/\/seaborn.pydata.org\/generated\/seaborn.color_palette.html#seaborn.color_palette \"seaborn.color_palette\"). List or dict values imply categorical mapping, while a colormap object implies numeric mapping.\n\n**hue\\_order**vector of strings\n\nSpecify the order of processing and plotting for categorical levels of the `hue` semantic.\n\n**hue\\_norm**tuple or [`matplotlib.colors.Normalize`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize \"(in Matplotlib v3.8.2)\")\n\nEither a pair of values that set the normalization range in data units or an object that will map from data units into a \\[0, 1\\] interval. Usage implies numeric mapping.\n\n**sizes**list, dict, or tuple\n\nAn object that determines how sizes are chosen when `size` is used. List or dict arguments should provide a size for each unique data value, which forces a categorical interpretation. The argument may also be a min, max tuple.\n\n**size\\_order**list\n\nSpecified order for appearance of the `size` variable levels, otherwise they are determined from the data. Not relevant when the `size` variable is numeric.\n\n**size\\_norm**tuple or Normalize object\n\nNormalization in data units for scaling plot objects when the `size` variable is numeric.\n\n**dashes**boolean, list, or dictionary\n\nObject determining how to draw the lines for different levels of the `style` variable. Setting to `True` will use default dash codes, or you can pass a list of dash codes or a dictionary mapping levels of the `style` variable to dash codes. Setting to `False` will use solid lines for all subsets. Dashes are specified as in matplotlib: a tuple of `(segment, gap)` lengths, or an empty string to draw a solid line.\n\n**markers**boolean, list, or dictionary\n\nObject determining how to draw the markers for different levels of the `style` variable. Setting to `True` will use default markers, or you can pass a list of markers or a dictionary mapping levels of the `style` variable to markers. Setting to `False` will draw marker-less lines. Markers are specified as in matplotlib.\n\n**style\\_order**list\n\nSpecified order for appearance of the `style` variable levels otherwise they are determined from the data. Not relevant when the `style` variable is numeric.\n\n**estimator**name of pandas method or callable or None\n\nMethod for aggregating across multiple observations of the `y` variable at the same `x` level. If `None`, all observations will be drawn.\n\n**errorbar**string, (string, number) tuple, or callable\n\nName of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar. See the [errorbar tutorial](https:\/\/seaborn.pydata.org\/tutorial\/error_bars.html) for more information.\n\n**n\\_boot**int\n\nNumber of bootstraps to use for computing the confidence interval.\n\n**seed**int, numpy.random.Generator, or numpy.random.RandomState\n\nSeed or random number generator for reproducible bootstrapping.\n\n**orient**“x” or “y”\n\nDimension along which the data are sorted \/ aggregated. Equivalently, the “independent variable” of the resulting function.\n\n**sort**boolean\n\nIf True, the data will be sorted by the x and y variables, otherwise lines will connect points in the order they appear in the dataset.\n\n**err\\_style**“band” or “bars”\n\nWhether to draw the confidence intervals with translucent error bands or discrete error bars.\n\n**err\\_kws**dict of keyword arguments\n\nAdditional parameters to control the aesthetics of the error bars. The kwargs are passed either to [`matplotlib.axes.Axes.fill_between()`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.axes.Axes.fill_between.html#matplotlib.axes.Axes.fill_between \"(in Matplotlib v3.8.2)\") or [`matplotlib.axes.Axes.errorbar()`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.axes.Axes.errorbar.html#matplotlib.axes.Axes.errorbar \"(in Matplotlib v3.8.2)\"), depending on `err_style`.\n\n**legend**“auto”, “brief”, “full”, or False\n\nHow to draw the legend. If “brief”, numeric `hue` and `size` variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If `False`, no legend data is added and no legend is drawn.\n\n**ci**int or “sd” or None\n\nSize of the confidence interval to draw when aggregating.\n\nDeprecated since version 0.12.0: Use the new `errorbar` parameter for more flexibility.\n\n**ax**[`matplotlib.axes.Axes`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.axes.Axes.html#matplotlib.axes.Axes \"(in Matplotlib v3.8.2)\")\n\nPre-existing axes for the plot. Otherwise, call [`matplotlib.pyplot.gca()`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca \"(in Matplotlib v3.8.2)\") internally.\n\n**kwargs**key, value mappings\n\nOther keyword arguments are passed down to [`matplotlib.axes.Axes.plot()`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot \"(in Matplotlib v3.8.2)\").\n\nReturns:\n\n[`matplotlib.axes.Axes`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.axes.Axes.html#matplotlib.axes.Axes \"(in Matplotlib v3.8.2)\")\n\nThe matplotlib axes containing the plot.\n\nSee also\n\n[`scatterplot`](https:\/\/seaborn.pydata.org\/generated\/seaborn.scatterplot.html#seaborn.scatterplot \"seaborn.scatterplot\")\n\nPlot data using points.\n\n[`pointplot`](https:\/\/seaborn.pydata.org\/generated\/seaborn.pointplot.html#seaborn.pointplot \"seaborn.pointplot\")\n\nPlot point estimates and CIs using markers and lines.\n\nExamples\n\nThe `flights` dataset has 10 years of monthly airline passenger data:\n```\nflights = sns.load_dataset(\"flights\")\nflights.head()\n```\n|   | year | month | passengers |\n|---|---|---|---|\n| 0 | 1949 | Jan | 112 |\n| 1 | 1949 | Feb | 118 |\n| 2 | 1949 | Mar | 132 |\n| 3 | 1949 | Apr | 129 |\n| 4 | 1949 | May | 121 |\n\nTo draw a line plot using long-form data, assign the `x` and `y` variables:\n```\nmay_flights = flights.query(\"month == 'May'\")\nsns.lineplot(data=may_flights, x=\"year\", y=\"passengers\")\n```\n![..\/\\_images\/lineplot\\_3\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_3_0.png)\n\nPivot the dataframe to a wide-form representation:\n```\nflights_wide = flights.pivot(index=\"year\", columns=\"month\", values=\"passengers\")\nflights_wide.head()\n```\n| month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| year |   |   |   |   |   |   |   |   |   |   |   |   |\n| 1949 | 112 | 118 | 132 | 129 | 121 | 135 | 148 | 148 | 136 | 119 | 104 | 118 |\n| 1950 | 115 | 126 | 141 | 135 | 125 | 149 | 170 | 170 | 158 | 133 | 114 | 140 |\n| 1951 | 145 | 150 | 178 | 163 | 172 | 178 | 199 | 199 | 184 | 162 | 146 | 166 |\n| 1952 | 171 | 180 | 193 | 181 | 183 | 218 | 230 | 242 | 209 | 191 | 172 | 194 |\n| 1953 | 196 | 196 | 236 | 235 | 229 | 243 | 264 | 272 | 237 | 211 | 180 | 201 |\n\nTo plot a single vector, pass it to `data`. If the vector is a [`pandas.Series`](https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.Series.html#pandas.Series \"(in pandas v2.2.0)\"), it will be plotted against its index:\n```\nsns.lineplot(data=flights_wide[\"May\"])\n```\n![..\/\\_images\/lineplot\\_7\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_7_0.png)\n\nPassing the entire wide-form dataset to `data` plots a separate line for each column:\n```\nsns.lineplot(data=flights_wide)\n```\n![..\/\\_images\/lineplot\\_9\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_9_0.png)\n\nPassing the entire dataset in long-form mode will aggregate over repeated values (each year) to show the mean and 95% confidence interval:\n```\nsns.lineplot(data=flights, x=\"year\", y=\"passengers\")\n```\n![..\/\\_images\/lineplot\\_11\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_11_0.png)\n\nAssign a grouping semantic (`hue`, `size`, or `style`) to plot separate lines\n```\nsns.lineplot(data=flights, x=\"year\", y=\"passengers\", hue=\"month\")\n```\n![..\/\\_images\/lineplot\\_13\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_13_0.png)\n\nThe same column can be assigned to multiple semantic variables, which can increase the accessibility of the plot:\n```\nsns.lineplot(data=flights, x=\"year\", y=\"passengers\", hue=\"month\", style=\"month\")\n```\n![..\/\\_images\/lineplot\\_15\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_15_0.png)\n\nUse the `orient` parameter to aggregate and sort along the vertical dimension of the plot:\n```\nsns.lineplot(data=flights, x=\"passengers\", y=\"year\", orient=\"y\")\n```\n![..\/\\_images\/lineplot\\_17\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_17_0.png)\n\nEach semantic variable can also represent a different column. For that, we’ll need a more complex dataset:\n```\nfmri = sns.load_dataset(\"fmri\")\nfmri.head()\n```\n|   | subject | timepoint | event | region | signal |\n|---|---|---|---|---|---|\n| 0 | s13 | 18 | stim | parietal | \\-0.017552 |\n| 1 | s5 | 14 | stim | parietal | \\-0.080883 |\n| 2 | s12 | 18 | stim | parietal | \\-0.081033 |\n| 3 | s11 | 18 | stim | parietal | \\-0.046134 |\n| 4 | s10 | 18 | stim | parietal | \\-0.037970 |\n\nRepeated observations are aggregated even when semantic grouping is used:\n```\nsns.lineplot(data=fmri, x=\"timepoint\", y=\"signal\", hue=\"event\")\n```\n![..\/\\_images\/lineplot\\_21\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_21_0.png)\n\nAssign both `hue` and `style` to represent two different grouping variables:\n```\nsns.lineplot(data=fmri, x=\"timepoint\", y=\"signal\", hue=\"region\", style=\"event\")\n```\n![..\/\\_images\/lineplot\\_23\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_23_0.png)\n\nWhen assigning a `style` variable, markers can be used instead of (or along with) dashes to distinguish the groups:\n```\nsns.lineplot(\n    data=fmri,\n    x=\"timepoint\", y=\"signal\", hue=\"event\", style=\"event\",\n    markers=True, dashes=False\n)\n```\n![..\/\\_images\/lineplot\\_25\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_25_0.png)\n\nShow error bars instead of error bands and extend them to two standard error widths:\n```\nsns.lineplot(\n    data=fmri, x=\"timepoint\", y=\"signal\", hue=\"event\", err_style=\"bars\", errorbar=(\"se\", 2),\n)\n```\n![..\/\\_images\/lineplot\\_27\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_27_0.png)\n\nAssigning the `units` variable will plot multiple lines without applying a semantic mapping:\n```\nsns.lineplot(\n    data=fmri.query(\"region == 'frontal'\"),\n    x=\"timepoint\", y=\"signal\", hue=\"event\", units=\"subject\",\n    estimator=None, lw=1,\n)\n```\n![..\/\\_images\/lineplot\\_29\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_29_0.png)\n\nLoad another dataset with a numeric grouping variable:\n```\ndots = sns.load_dataset(\"dots\").query(\"align == 'dots'\")\ndots.head()\n```\n|   | align | choice | time | coherence | firing\\_rate |\n|---|---|---|---|---|---|\n| 0 | dots | T1 | \\-80 | 0\\.0 | 33\\.189967 |\n| 1 | dots | T1 | \\-80 | 3\\.2 | 31\\.691726 |\n| 2 | dots | T1 | \\-80 | 6\\.4 | 34\\.279840 |\n| 3 | dots | T1 | \\-80 | 12\\.8 | 32\\.631874 |\n| 4 | dots | T1 | \\-80 | 25\\.6 | 35\\.060487 |\n\nAssigning a numeric variable to `hue` maps it differently, using a different default palette and a quantitative color mapping:\n```\nsns.lineplot(\n    data=dots, x=\"time\", y=\"firing_rate\", hue=\"coherence\", style=\"choice\",\n)\n```\n![..\/\\_images\/lineplot\\_33\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_33_0.png)\n\nControl the color mapping by setting the `palette` and passing a [`matplotlib.colors.Normalize`](https:\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize \"(in Matplotlib v3.8.2)\") object:\n```\nsns.lineplot(\n    data=dots.query(\"coherence > 0\"),\n    x=\"time\", y=\"firing_rate\", hue=\"coherence\", style=\"choice\",\n     palette=\"flare\", hue_norm=mpl.colors.LogNorm(),\n)\n```\n![..\/\\_images\/lineplot\\_35\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_35_0.png)\n\nOr pass specific colors, either as a Python list or dictionary:\n```\npalette = sns.color_palette(\"mako_r\", 6)\nsns.lineplot(\n    data=dots, x=\"time\", y=\"firing_rate\",\n    hue=\"coherence\", style=\"choice\",\n    palette=palette\n)\n```\n![..\/\\_images\/lineplot\\_37\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_37_0.png)\n\nAssign the `size` semantic to map the width of the lines with a numeric variable:\n```\nsns.lineplot(\n    data=dots, x=\"time\", y=\"firing_rate\",\n    size=\"coherence\", hue=\"choice\",\n    legend=\"full\"\n)\n```\n![..\/\\_images\/lineplot\\_39\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_39_0.png)\n\nPass a a tuple, `sizes=(smallest, largest)`, to control the range of linewidths used to map the `size` semantic:\n```\nsns.lineplot(\n    data=dots, x=\"time\", y=\"firing_rate\",\n    size=\"coherence\", hue=\"choice\",\n    sizes=(.25, 2.5)\n)\n```\n![..\/\\_images\/lineplot\\_41\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_41_0.png)\n\nBy default, the observations are sorted by `x`. Disable this to plot a line with the order that observations appear in the dataset:\n```\nx, y = np.random.normal(size=(2, 5000)).cumsum(axis=1)\nsns.lineplot(x=x, y=y, sort=False, lw=1)\n```\n![..\/\\_images\/lineplot\\_43\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_43_0.png)\n\nUse [`relplot()`](https:\/\/seaborn.pydata.org\/generated\/seaborn.relplot.html#seaborn.relplot \"seaborn.relplot\") to combine [`lineplot()`](https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html#seaborn.lineplot \"seaborn.lineplot\") and [`FacetGrid`](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.html#seaborn.FacetGrid \"seaborn.FacetGrid\"). This allows grouping within additional categorical variables. Using [`relplot()`](https:\/\/seaborn.pydata.org\/generated\/seaborn.relplot.html#seaborn.relplot \"seaborn.relplot\") is safer than using [`FacetGrid`](https:\/\/seaborn.pydata.org\/generated\/seaborn.FacetGrid.html#seaborn.FacetGrid \"seaborn.FacetGrid\") directly, as it ensures synchronization of the semantic mappings across facets:\n```\nsns.relplot(\n    data=fmri, x=\"timepoint\", y=\"signal\",\n    col=\"region\", hue=\"event\", style=\"event\",\n    kind=\"line\"\n)\n```\n![..\/\\_images\/lineplot\\_45\\_0.png](https:\/\/seaborn.pydata.org\/_images\/lineplot_45_0.png)\n\n© Copyright 2012-2024, [Michael Waskom](https:\/\/mwaskom.github.io\/).  \n Created using [Sphinx](https:\/\/www.sphinx-doc.org\/) and the [PyData Theme](https:\/\/pydata-sphinx-theme.readthedocs.io\/en\/stable\/).\n\n[Archive](https:\/\/seaborn.pydata.org\/generated\/seaborn.lineplot.html)\n\n[v0.12](https:\/\/seaborn.pydata.org\/archive\/0.12\/index.html) [v0.11](https:\/\/seaborn.pydata.org\/archive\/0.11\/index.html) [v0.10](https:\/\/seaborn.pydata.org\/archive\/0.10\/index.html) [v0.9](https:\/\/seaborn.pydata.org\/archive\/0.9\/index.html)\n\nv0.13.2","attrs_readable_markdown":null,"meta_canonical":null}

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Boilerpipe Text	seaborn. lineplot ( data = None , * , x = None , y = None , hue = None , size = None , style = None , units = None , weights = None , palette = None , hue_order = None , hue_norm = None , sizes = None , size_order = None , size_norm = None , dashes = True , markers = None , style_order = None , estimator = 'mean' , errorbar = ('ci', 95) , n_boot = 1000 , seed = None , orient = 'x' , sort = True , err_style = 'band' , err_kws = None , legend = 'auto' , ci = 'deprecated' , ax = None , ** kwargs ) # Draw a line plot with possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue , size , and style parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both hue and style for the same variable) can be helpful for making graphics more accessible. See the tutorial for more information. The default treatment of the hue (and to a lesser extent, size ) semantic, if present, depends on whether the variable is inferred to represent “numeric” or “categorical” data. In particular, numeric variables are represented with a sequential colormap by default, and the legend entries show regular “ticks” with values that may or may not exist in the data. This behavior can be controlled through various parameters, as described and illustrated below. By default, the plot aggregates over multiple y values at each value of x and shows an estimate of the central tendency and a confidence interval for that estimate. Parameters : data pandas.DataFrame , numpy.ndarray , mapping, or sequence Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped. x, y vectors or keys in data Variables that specify positions on the x and y axes. hue vector or key in data Grouping variable that will produce lines with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case. size vector or key in data Grouping variable that will produce lines with different widths. Can be either categorical or numeric, although size mapping will behave differently in latter case. style vector or key in data Grouping variable that will produce lines with different dashes and/or markers. Can have a numeric dtype but will always be treated as categorical. units vector or key in data Grouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed. weights vector or key in data Data values or column used to compute weighted estimation. Note that use of weights currently limits the choice of statistics to a ‘mean’ estimator and ‘ci’ errorbar. palette string, list, dict, or matplotlib.colors.Colormap Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette() . List or dict values imply categorical mapping, while a colormap object implies numeric mapping. hue_order vector of strings Specify the order of processing and plotting for categorical levels of the hue semantic. hue_norm tuple or matplotlib.colors.Normalize Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping. sizes list, dict, or tuple An object that determines how sizes are chosen when size is used. List or dict arguments should provide a size for each unique data value, which forces a categorical interpretation. The argument may also be a min, max tuple. size_order list Specified order for appearance of the size variable levels, otherwise they are determined from the data. Not relevant when the size variable is numeric. size_norm tuple or Normalize object Normalization in data units for scaling plot objects when the size variable is numeric. dashes boolean, list, or dictionary Object determining how to draw the lines for different levels of the style variable. Setting to True will use default dash codes, or you can pass a list of dash codes or a dictionary mapping levels of the style variable to dash codes. Setting to False will use solid lines for all subsets. Dashes are specified as in matplotlib: a tuple of (segment, gap) lengths, or an empty string to draw a solid line. markers boolean, list, or dictionary Object determining how to draw the markers for different levels of the style variable. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. Setting to False will draw marker-less lines. Markers are specified as in matplotlib. style_order list Specified order for appearance of the style variable levels otherwise they are determined from the data. Not relevant when the style variable is numeric. estimator name of pandas method or callable or None Method for aggregating across multiple observations of the y variable at the same x level. If None , all observations will be drawn. errorbar string, (string, number) tuple, or callable Name of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar. See the errorbar tutorial for more information. n_boot int Number of bootstraps to use for computing the confidence interval. seed int, numpy.random.Generator, or numpy.random.RandomState Seed or random number generator for reproducible bootstrapping. orient “x” or “y” Dimension along which the data are sorted / aggregated. Equivalently, the “independent variable” of the resulting function. sort boolean If True, the data will be sorted by the x and y variables, otherwise lines will connect points in the order they appear in the dataset. err_style “band” or “bars” Whether to draw the confidence intervals with translucent error bands or discrete error bars. err_kws dict of keyword arguments Additional parameters to control the aesthetics of the error bars. The kwargs are passed either to matplotlib.axes.Axes.fill_between() or matplotlib.axes.Axes.errorbar() , depending on err_style . legend “auto”, “brief”, “full”, or False How to draw the legend. If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If False , no legend data is added and no legend is drawn. ci int or “sd” or None Size of the confidence interval to draw when aggregating. Deprecated since version 0.12.0: Use the new errorbar parameter for more flexibility. ax matplotlib.axes.Axes Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca() internally. kwargs key, value mappings Other keyword arguments are passed down to matplotlib.axes.Axes.plot() . Returns : matplotlib.axes.Axes The matplotlib axes containing the plot. See also scatterplot Plot data using points. pointplot Plot point estimates and CIs using markers and lines. Examples The flights dataset has 10 years of monthly airline passenger data: flights = sns . load_dataset ( "flights" ) flights . head () year month passengers 0 1949 Jan 112 1 1949 Feb 118 2 1949 Mar 132 3 1949 Apr 129 4 1949 May 121 To draw a line plot using long-form data, assign the x and y variables: may_flights = flights . query ( "month == 'May'" ) sns . lineplot ( data = may_flights , x = "year" , y = "passengers" ) Pivot the dataframe to a wide-form representation: flights_wide = flights . pivot ( index = "year" , columns = "month" , values = "passengers" ) flights_wide . head () month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year 1949 112 118 132 129 121 135 148 148 136 119 104 118 1950 115 126 141 135 125 149 170 170 158 133 114 140 1951 145 150 178 163 172 178 199 199 184 162 146 166 1952 171 180 193 181 183 218 230 242 209 191 172 194 1953 196 196 236 235 229 243 264 272 237 211 180 201 To plot a single vector, pass it to data . If the vector is a pandas.Series , it will be plotted against its index: sns . lineplot ( data = flights_wide [ "May" ]) Passing the entire wide-form dataset to data plots a separate line for each column: sns . lineplot ( data = flights_wide ) Passing the entire dataset in long-form mode will aggregate over repeated values (each year) to show the mean and 95% confidence interval: sns . lineplot ( data = flights , x = "year" , y = "passengers" ) Assign a grouping semantic ( hue , size , or style ) to plot separate lines sns . lineplot ( data = flights , x = "year" , y = "passengers" , hue = "month" ) The same column can be assigned to multiple semantic variables, which can increase the accessibility of the plot: sns . lineplot ( data = flights , x = "year" , y = "passengers" , hue = "month" , style = "month" ) Use the orient parameter to aggregate and sort along the vertical dimension of the plot: sns . lineplot ( data = flights , x = "passengers" , y = "year" , orient = "y" ) Each semantic variable can also represent a different column. For that, we’ll need a more complex dataset: fmri = sns . load_dataset ( "fmri" ) fmri . head () subject timepoint event region signal 0 s13 18 stim parietal -0.017552 1 s5 14 stim parietal -0.080883 2 s12 18 stim parietal -0.081033 3 s11 18 stim parietal -0.046134 4 s10 18 stim parietal -0.037970 Repeated observations are aggregated even when semantic grouping is used: sns . lineplot ( data = fmri , x = "timepoint" , y = "signal" , hue = "event" ) Assign both hue and style to represent two different grouping variables: sns . lineplot ( data = fmri , x = "timepoint" , y = "signal" , hue = "region" , style = "event" ) When assigning a style variable, markers can be used instead of (or along with) dashes to distinguish the groups: sns . lineplot ( data = fmri , x = "timepoint" , y = "signal" , hue = "event" , style = "event" , markers = True , dashes = False ) Show error bars instead of error bands and extend them to two standard error widths: sns . lineplot ( data = fmri , x = "timepoint" , y = "signal" , hue = "event" , err_style = "bars" , errorbar = ( "se" , 2 ), ) Assigning the units variable will plot multiple lines without applying a semantic mapping: sns . lineplot ( data = fmri . query ( "region == 'frontal'" ), x = "timepoint" , y = "signal" , hue = "event" , units = "subject" , estimator = None , lw = 1 , ) Load another dataset with a numeric grouping variable: dots = sns . load_dataset ( "dots" ) . query ( "align == 'dots'" ) dots . head () align choice time coherence firing_rate 0 dots T1 -80 0.0 33.189967 1 dots T1 -80 3.2 31.691726 2 dots T1 -80 6.4 34.279840 3 dots T1 -80 12.8 32.631874 4 dots T1 -80 25.6 35.060487 Assigning a numeric variable to hue maps it differently, using a different default palette and a quantitative color mapping: sns . lineplot ( data = dots , x = "time" , y = "firing_rate" , hue = "coherence" , style = "choice" , ) Control the color mapping by setting the palette and passing a matplotlib.colors.Normalize object: sns . lineplot ( data = dots . query ( "coherence > 0" ), x = "time" , y = "firing_rate" , hue = "coherence" , style = "choice" , palette = "flare" , hue_norm = mpl . colors . LogNorm (), ) Or pass specific colors, either as a Python list or dictionary: palette = sns . color_palette ( "mako_r" , 6 ) sns . lineplot ( data = dots , x = "time" , y = "firing_rate" , hue = "coherence" , style = "choice" , palette = palette ) Assign the size semantic to map the width of the lines with a numeric variable: sns . lineplot ( data = dots , x = "time" , y = "firing_rate" , size = "coherence" , hue = "choice" , legend = "full" ) Pass a a tuple, sizes=(smallest, largest) , to control the range of linewidths used to map the size semantic: sns . lineplot ( data = dots , x = "time" , y = "firing_rate" , size = "coherence" , hue = "choice" , sizes = ( .25 , 2.5 ) ) By default, the observations are sorted by x . Disable this to plot a line with the order that observations appear in the dataset: x , y = np . random . normal ( size = ( 2 , 5000 )) . cumsum ( axis = 1 ) sns . lineplot ( x = x , y = y , sort = False , lw = 1 ) Use relplot() to combine lineplot() and FacetGrid . This allows grouping within additional categorical variables. Using relplot() is safer than using FacetGrid directly, as it ensures synchronization of the semantic mappings across facets: sns . relplot ( data = fmri , x = "timepoint" , y = "signal" , col = "region" , hue = "event" , style = "event" , kind = "line" )
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[seaborn.FacetGrid.map\_dataframe](https://seaborn.pydata.org/generated/seaborn.FacetGrid.map_dataframe.html) - [seaborn.FacetGrid.pipe](https://seaborn.pydata.org/generated/seaborn.FacetGrid.pipe.html) - [seaborn.FacetGrid.refline](https://seaborn.pydata.org/generated/seaborn.FacetGrid.refline.html) - [seaborn.FacetGrid.savefig](https://seaborn.pydata.org/generated/seaborn.FacetGrid.savefig.html) - [seaborn.FacetGrid.set](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set.html) - [seaborn.FacetGrid.set\_axis\_labels](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_axis_labels.html) - [seaborn.FacetGrid.set\_titles](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_titles.html) - [seaborn.FacetGrid.set\_xlabels](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_xlabels.html) - [seaborn.FacetGrid.set\_xticklabels](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_xticklabels.html) - [seaborn.FacetGrid.set\_ylabels](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_ylabels.html) - [seaborn.FacetGrid.set\_yticklabels](https://seaborn.pydata.org/generated/seaborn.FacetGrid.set_yticklabels.html) - [seaborn.FacetGrid.tick\_params](https://seaborn.pydata.org/generated/seaborn.FacetGrid.tick_params.html) - [seaborn.FacetGrid.tight\_layout](https://seaborn.pydata.org/generated/seaborn.FacetGrid.tight_layout.html) - [seaborn.pairplot](https://seaborn.pydata.org/generated/seaborn.pairplot.html) - [seaborn.PairGrid](https://seaborn.pydata.org/generated/seaborn.PairGrid.html) - [seaborn.PairGrid.\_\_init\_\_](https://seaborn.pydata.org/generated/seaborn.PairGrid.__init__.html) - [seaborn.PairGrid.add\_legend](https://seaborn.pydata.org/generated/seaborn.PairGrid.add_legend.html) - [seaborn.PairGrid.apply](https://seaborn.pydata.org/generated/seaborn.PairGrid.apply.html) - [seaborn.PairGrid.map](https://seaborn.pydata.org/generated/seaborn.PairGrid.map.html) - [seaborn.PairGrid.map\_diag](https://seaborn.pydata.org/generated/seaborn.PairGrid.map_diag.html) - [seaborn.PairGrid.map\_lower](https://seaborn.pydata.org/generated/seaborn.PairGrid.map_lower.html) - [seaborn.PairGrid.map\_offdiag](https://seaborn.pydata.org/generated/seaborn.PairGrid.map_offdiag.html) - [seaborn.PairGrid.map\_upper](https://seaborn.pydata.org/generated/seaborn.PairGrid.map_upper.html) - [seaborn.PairGrid.pipe](https://seaborn.pydata.org/generated/seaborn.PairGrid.pipe.html) - [seaborn.PairGrid.savefig](https://seaborn.pydata.org/generated/seaborn.PairGrid.savefig.html) - [seaborn.PairGrid.set](https://seaborn.pydata.org/generated/seaborn.PairGrid.set.html) - [seaborn.PairGrid.tick\_params](https://seaborn.pydata.org/generated/seaborn.PairGrid.tick_params.html) - [seaborn.PairGrid.tight\_layout](https://seaborn.pydata.org/generated/seaborn.PairGrid.tight_layout.html) - [seaborn.jointplot](https://seaborn.pydata.org/generated/seaborn.jointplot.html) - [seaborn.JointGrid](https://seaborn.pydata.org/generated/seaborn.JointGrid.html) - [seaborn.JointGrid.\_\_init\_\_](https://seaborn.pydata.org/generated/seaborn.JointGrid.__init__.html) - [seaborn.JointGrid.apply](https://seaborn.pydata.org/generated/seaborn.JointGrid.apply.html) - [seaborn.JointGrid.pipe](https://seaborn.pydata.org/generated/seaborn.JointGrid.pipe.html) - [seaborn.JointGrid.plot](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot.html) - [seaborn.JointGrid.plot\_joint](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot_joint.html) - [seaborn.JointGrid.plot\_marginals](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot_marginals.html) - [seaborn.JointGrid.refline](https://seaborn.pydata.org/generated/seaborn.JointGrid.refline.html) - [seaborn.JointGrid.savefig](https://seaborn.pydata.org/generated/seaborn.JointGrid.savefig.html) - [seaborn.JointGrid.set](https://seaborn.pydata.org/generated/seaborn.JointGrid.set.html) - [seaborn.JointGrid.set\_axis\_labels](https://seaborn.pydata.org/generated/seaborn.JointGrid.set_axis_labels.html) - [seaborn.set\_theme](https://seaborn.pydata.org/generated/seaborn.set_theme.html) - [seaborn.axes\_style](https://seaborn.pydata.org/generated/seaborn.axes_style.html) - [seaborn.set\_style](https://seaborn.pydata.org/generated/seaborn.set_style.html) - [seaborn.plotting\_context](https://seaborn.pydata.org/generated/seaborn.plotting_context.html) - [seaborn.set\_context](https://seaborn.pydata.org/generated/seaborn.set_context.html) - [seaborn.set\_color\_codes](https://seaborn.pydata.org/generated/seaborn.set_color_codes.html) - [seaborn.reset\_defaults](https://seaborn.pydata.org/generated/seaborn.reset_defaults.html) - [seaborn.reset\_orig](https://seaborn.pydata.org/generated/seaborn.reset_orig.html) - [seaborn.set](https://seaborn.pydata.org/generated/seaborn.set.html) - [seaborn.set\_palette](https://seaborn.pydata.org/generated/seaborn.set_palette.html) - [seaborn.color\_palette](https://seaborn.pydata.org/generated/seaborn.color_palette.html) - [seaborn.husl\_palette](https://seaborn.pydata.org/generated/seaborn.husl_palette.html) - [seaborn.hls\_palette](https://seaborn.pydata.org/generated/seaborn.hls_palette.html) - [seaborn.cubehelix\_palette](https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html) - [seaborn.dark\_palette](https://seaborn.pydata.org/generated/seaborn.dark_palette.html) - [seaborn.light\_palette](https://seaborn.pydata.org/generated/seaborn.light_palette.html) - [seaborn.diverging\_palette](https://seaborn.pydata.org/generated/seaborn.diverging_palette.html) - [seaborn.blend\_palette](https://seaborn.pydata.org/generated/seaborn.blend_palette.html) - [seaborn.xkcd\_palette](https://seaborn.pydata.org/generated/seaborn.xkcd_palette.html) - [seaborn.crayon\_palette](https://seaborn.pydata.org/generated/seaborn.crayon_palette.html) - [seaborn.mpl\_palette](https://seaborn.pydata.org/generated/seaborn.mpl_palette.html) - [seaborn.choose\_colorbrewer\_palette](https://seaborn.pydata.org/generated/seaborn.choose_colorbrewer_palette.html) - [seaborn.choose\_cubehelix\_palette](https://seaborn.pydata.org/generated/seaborn.choose_cubehelix_palette.html) - [seaborn.choose\_light\_palette](https://seaborn.pydata.org/generated/seaborn.choose_light_palette.html) - [seaborn.choose\_dark\_palette](https://seaborn.pydata.org/generated/seaborn.choose_dark_palette.html) - [seaborn.choose\_diverging\_palette](https://seaborn.pydata.org/generated/seaborn.choose_diverging_palette.html) - [seaborn.despine](https://seaborn.pydata.org/generated/seaborn.despine.html) - [seaborn.move\_legend](https://seaborn.pydata.org/generated/seaborn.move_legend.html) - [seaborn.saturate](https://seaborn.pydata.org/generated/seaborn.saturate.html) - [seaborn.desaturate](https://seaborn.pydata.org/generated/seaborn.desaturate.html) - [seaborn.set\_hls\_values](https://seaborn.pydata.org/generated/seaborn.set_hls_values.html) - [seaborn.load\_dataset](https://seaborn.pydata.org/generated/seaborn.load_dataset.html) - [seaborn.get\_dataset\_names](https://seaborn.pydata.org/generated/seaborn.get_dataset_names.html) - [seaborn.get\_data\_home](https://seaborn.pydata.org/generated/seaborn.get_data_home.html) On this page # seaborn.lineplot[\#](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn-lineplot "Permalink to this heading") seaborn.lineplot(data\=None, \, x\=None, y\=None, hue\=None, size\=None, style\=None, units\=None, weights\=None, palette\=None, hue\_order\=None, hue\_norm\=None, sizes\=None, size\_order\=None, size\_norm\=None, dashes\=True, markers\=None, style\_order\=None, estimator\='mean', errorbar\=('ci', 95\), n\_boot\=1000, seed\=None, orient\='x', sort\=True, err\_style\='band', err\_kws\=None, legend\='auto', ci\='deprecated', ax\=None, \\kwargs)[\#](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn.lineplot "Permalink to this definition") Draw a line plot with possibility of several semantic groupings. The relationship between `x` and `y` can be shown for different subsets of the data using the `hue`, `size`, and `style` parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both `hue` and `style` for the same variable) can be helpful for making graphics more accessible. See the [tutorial](https://seaborn.pydata.org/tutorial/relational.html#relational-tutorial) for more information. The default treatment of the `hue` (and to a lesser extent, `size`) semantic, if present, depends on whether the variable is inferred to represent “numeric” or “categorical” data. In particular, numeric variables are represented with a sequential colormap by default, and the legend entries show regular “ticks” with values that may or may not exist in the data. This behavior can be controlled through various parameters, as described and illustrated below. By default, the plot aggregates over multiple `y` values at each value of `x` and shows an estimate of the central tendency and a confidence interval for that estimate. Parameters: data[`pandas.DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "(in pandas v2.2.0)"), [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.26)"), mapping, or sequence Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped. x, yvectors or keys in `data` Variables that specify positions on the x and y axes. huevector or key in `data` Grouping variable that will produce lines with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case. sizevector or key in `data` Grouping variable that will produce lines with different widths. Can be either categorical or numeric, although size mapping will behave differently in latter case. stylevector or key in `data` Grouping variable that will produce lines with different dashes and/or markers. Can have a numeric dtype but will always be treated as categorical. unitsvector or key in `data` Grouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed. weightsvector or key in `data` Data values or column used to compute weighted estimation. Note that use of weights currently limits the choice of statistics to a ‘mean’ estimator and ‘ci’ errorbar. palettestring, list, dict, or [`matplotlib.colors.Colormap`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap "(in Matplotlib v3.8.2)") Method for choosing the colors to use when mapping the `hue` semantic. String values are passed to [`color_palette()`](https://seaborn.pydata.org/generated/seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette"). List or dict values imply categorical mapping, while a colormap object implies numeric mapping. hue\_ordervector of strings Specify the order of processing and plotting for categorical levels of the `hue` semantic. hue\_normtuple or [`matplotlib.colors.Normalize`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize "(in Matplotlib v3.8.2)") Either a pair of values that set the normalization range in data units or an object that will map from data units into a \[0, 1\] interval. Usage implies numeric mapping. sizeslist, dict, or tuple An object that determines how sizes are chosen when `size` is used. List or dict arguments should provide a size for each unique data value, which forces a categorical interpretation. The argument may also be a min, max tuple. size\_orderlist Specified order for appearance of the `size` variable levels, otherwise they are determined from the data. Not relevant when the `size` variable is numeric. size\_normtuple or Normalize object Normalization in data units for scaling plot objects when the `size` variable is numeric. dashesboolean, list, or dictionary Object determining how to draw the lines for different levels of the `style` variable. Setting to `True` will use default dash codes, or you can pass a list of dash codes or a dictionary mapping levels of the `style` variable to dash codes. Setting to `False` will use solid lines for all subsets. Dashes are specified as in matplotlib: a tuple of `(segment, gap)` lengths, or an empty string to draw a solid line. markersboolean, list, or dictionary Object determining how to draw the markers for different levels of the `style` variable. Setting to `True` will use default markers, or you can pass a list of markers or a dictionary mapping levels of the `style` variable to markers. Setting to `False` will draw marker-less lines. Markers are specified as in matplotlib. style\_orderlist Specified order for appearance of the `style` variable levels otherwise they are determined from the data. Not relevant when the `style` variable is numeric. estimatorname of pandas method or callable or None Method for aggregating across multiple observations of the `y` variable at the same `x` level. If `None`, all observations will be drawn. errorbarstring, (string, number) tuple, or callable Name of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar. See the [errorbar tutorial](https://seaborn.pydata.org/tutorial/error_bars.html) for more information. n\_bootint Number of bootstraps to use for computing the confidence interval. seedint, numpy.random.Generator, or numpy.random.RandomState Seed or random number generator for reproducible bootstrapping. orient“x” or “y” Dimension along which the data are sorted / aggregated. Equivalently, the “independent variable” of the resulting function. sortboolean If True, the data will be sorted by the x and y variables, otherwise lines will connect points in the order they appear in the dataset. err\_style“band” or “bars” Whether to draw the confidence intervals with translucent error bands or discrete error bars. err\_kwsdict of keyword arguments Additional parameters to control the aesthetics of the error bars. The kwargs are passed either to [`matplotlib.axes.Axes.fill_between()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.fill_between.html#matplotlib.axes.Axes.fill_between "(in Matplotlib v3.8.2)") or [`matplotlib.axes.Axes.errorbar()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.errorbar.html#matplotlib.axes.Axes.errorbar "(in Matplotlib v3.8.2)"), depending on `err_style`. legend“auto”, “brief”, “full”, or False How to draw the legend. If “brief”, numeric `hue` and `size` variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If `False`, no legend data is added and no legend is drawn. ciint or “sd” or None Size of the confidence interval to draw when aggregating. Deprecated since version 0.12.0: Use the new `errorbar` parameter for more flexibility. ax[`matplotlib.axes.Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.8.2)") Pre-existing axes for the plot. Otherwise, call [`matplotlib.pyplot.gca()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca "(in Matplotlib v3.8.2)") internally. kwargs*key, value mappings Other keyword arguments are passed down to [`matplotlib.axes.Axes.plot()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot "(in Matplotlib v3.8.2)"). Returns: [`matplotlib.axes.Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.8.2)") The matplotlib axes containing the plot. See also [`scatterplot`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html#seaborn.scatterplot "seaborn.scatterplot") Plot data using points. [`pointplot`](https://seaborn.pydata.org/generated/seaborn.pointplot.html#seaborn.pointplot "seaborn.pointplot") Plot point estimates and CIs using markers and lines. Examples The `flights` dataset has 10 years of monthly airline passenger data: ``` flights = sns.load_dataset("flights") flights.head() ``` \| \| year \| month \| passengers \| \|---\|---\|---\|---\| \| 0 \| 1949 \| Jan \| 112 \| \| 1 \| 1949 \| Feb \| 118 \| \| 2 \| 1949 \| Mar \| 132 \| \| 3 \| 1949 \| Apr \| 129 \| \| 4 \| 1949 \| May \| 121 \| To draw a line plot using long-form data, assign the `x` and `y` variables: ``` may_flights = flights.query("month == 'May'") sns.lineplot(data=may_flights, x="year", y="passengers") ``` ![../\_images/lineplot\_3\_0.png](https://seaborn.pydata.org/_images/lineplot_3_0.png) Pivot the dataframe to a wide-form representation: ``` flights_wide = flights.pivot(index="year", columns="month", values="passengers") flights_wide.head() ``` \| month \| Jan \| Feb \| Mar \| Apr \| May \| Jun \| Jul \| Aug \| Sep \| Oct \| Nov \| Dec \| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| year \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1949 \| 112 \| 118 \| 132 \| 129 \| 121 \| 135 \| 148 \| 148 \| 136 \| 119 \| 104 \| 118 \| \| 1950 \| 115 \| 126 \| 141 \| 135 \| 125 \| 149 \| 170 \| 170 \| 158 \| 133 \| 114 \| 140 \| \| 1951 \| 145 \| 150 \| 178 \| 163 \| 172 \| 178 \| 199 \| 199 \| 184 \| 162 \| 146 \| 166 \| \| 1952 \| 171 \| 180 \| 193 \| 181 \| 183 \| 218 \| 230 \| 242 \| 209 \| 191 \| 172 \| 194 \| \| 1953 \| 196 \| 196 \| 236 \| 235 \| 229 \| 243 \| 264 \| 272 \| 237 \| 211 \| 180 \| 201 \| To plot a single vector, pass it to `data`. If the vector is a [`pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series "(in pandas v2.2.0)"), it will be plotted against its index: ``` sns.lineplot(data=flights_wide["May"]) ``` ![../\_images/lineplot\_7\_0.png](https://seaborn.pydata.org/_images/lineplot_7_0.png) Passing the entire wide-form dataset to `data` plots a separate line for each column: ``` sns.lineplot(data=flights_wide) ``` ![../\_images/lineplot\_9\_0.png](https://seaborn.pydata.org/_images/lineplot_9_0.png) Passing the entire dataset in long-form mode will aggregate over repeated values (each year) to show the mean and 95% confidence interval: ``` sns.lineplot(data=flights, x="year", y="passengers") ``` ![../\_images/lineplot\_11\_0.png](https://seaborn.pydata.org/_images/lineplot_11_0.png) Assign a grouping semantic (`hue`, `size`, or `style`) to plot separate lines ``` sns.lineplot(data=flights, x="year", y="passengers", hue="month") ``` ![../\_images/lineplot\_13\_0.png](https://seaborn.pydata.org/_images/lineplot_13_0.png) The same column can be assigned to multiple semantic variables, which can increase the accessibility of the plot: ``` sns.lineplot(data=flights, x="year", y="passengers", hue="month", style="month") ``` ![../\_images/lineplot\_15\_0.png](https://seaborn.pydata.org/_images/lineplot_15_0.png) Use the `orient` parameter to aggregate and sort along the vertical dimension of the plot: ``` sns.lineplot(data=flights, x="passengers", y="year", orient="y") ``` ![../\_images/lineplot\_17\_0.png](https://seaborn.pydata.org/_images/lineplot_17_0.png) Each semantic variable can also represent a different column. For that, we’ll need a more complex dataset: ``` fmri = sns.load_dataset("fmri") fmri.head() ``` \| \| subject \| timepoint \| event \| region \| signal \| \|---\|---\|---\|---\|---\|---\| \| 0 \| s13 \| 18 \| stim \| parietal \| \-0.017552 \| \| 1 \| s5 \| 14 \| stim \| parietal \| \-0.080883 \| \| 2 \| s12 \| 18 \| stim \| parietal \| \-0.081033 \| \| 3 \| s11 \| 18 \| stim \| parietal \| \-0.046134 \| \| 4 \| s10 \| 18 \| stim \| parietal \| \-0.037970 \| Repeated observations are aggregated even when semantic grouping is used: ``` sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event") ``` ![../\_images/lineplot\_21\_0.png](https://seaborn.pydata.org/_images/lineplot_21_0.png) Assign both `hue` and `style` to represent two different grouping variables: ``` sns.lineplot(data=fmri, x="timepoint", y="signal", hue="region", style="event") ``` ![../\_images/lineplot\_23\_0.png](https://seaborn.pydata.org/_images/lineplot_23_0.png) When assigning a `style` variable, markers can be used instead of (or along with) dashes to distinguish the groups: ``` sns.lineplot( data=fmri, x="timepoint", y="signal", hue="event", style="event", markers=True, dashes=False ) ``` ![../\_images/lineplot\_25\_0.png](https://seaborn.pydata.org/_images/lineplot_25_0.png) Show error bars instead of error bands and extend them to two standard error widths: ``` sns.lineplot( data=fmri, x="timepoint", y="signal", hue="event", err_style="bars", errorbar=("se", 2), ) ``` ![../\_images/lineplot\_27\_0.png](https://seaborn.pydata.org/_images/lineplot_27_0.png) Assigning the `units` variable will plot multiple lines without applying a semantic mapping: ``` sns.lineplot( data=fmri.query("region == 'frontal'"), x="timepoint", y="signal", hue="event", units="subject", estimator=None, lw=1, ) ``` ![../\_images/lineplot\_29\_0.png](https://seaborn.pydata.org/_images/lineplot_29_0.png) Load another dataset with a numeric grouping variable: ``` dots = sns.load_dataset("dots").query("align == 'dots'") dots.head() ``` \| \| align \| choice \| time \| coherence \| firing\_rate \| \|---\|---\|---\|---\|---\|---\| \| 0 \| dots \| T1 \| \-80 \| 0\.0 \| 33\.189967 \| \| 1 \| dots \| T1 \| \-80 \| 3\.2 \| 31\.691726 \| \| 2 \| dots \| T1 \| \-80 \| 6\.4 \| 34\.279840 \| \| 3 \| dots \| T1 \| \-80 \| 12\.8 \| 32\.631874 \| \| 4 \| dots \| T1 \| \-80 \| 25\.6 \| 35\.060487 \| Assigning a numeric variable to `hue` maps it differently, using a different default palette and a quantitative color mapping: ``` sns.lineplot( data=dots, x="time", y="firing_rate", hue="coherence", style="choice", ) ``` ![../\_images/lineplot\_33\_0.png](https://seaborn.pydata.org/_images/lineplot_33_0.png) Control the color mapping by setting the `palette` and passing a [`matplotlib.colors.Normalize`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize "(in Matplotlib v3.8.2)") object: ``` sns.lineplot( data=dots.query("coherence > 0"), x="time", y="firing_rate", hue="coherence", style="choice", palette="flare", hue_norm=mpl.colors.LogNorm(), ) ``` ![../\_images/lineplot\_35\_0.png](https://seaborn.pydata.org/_images/lineplot_35_0.png) Or pass specific colors, either as a Python list or dictionary: ``` palette = sns.color_palette("mako_r", 6) sns.lineplot( data=dots, x="time", y="firing_rate", hue="coherence", style="choice", palette=palette ) ``` ![../\_images/lineplot\_37\_0.png](https://seaborn.pydata.org/_images/lineplot_37_0.png) Assign the `size` semantic to map the width of the lines with a numeric variable: ``` sns.lineplot( data=dots, x="time", y="firing_rate", size="coherence", hue="choice", legend="full" ) ``` ![../\_images/lineplot\_39\_0.png](https://seaborn.pydata.org/_images/lineplot_39_0.png) Pass a a tuple, `sizes=(smallest, largest)`, to control the range of linewidths used to map the `size` semantic: ``` sns.lineplot( data=dots, x="time", y="firing_rate", size="coherence", hue="choice", sizes=(.25, 2.5) ) ``` ![../\_images/lineplot\_41\_0.png](https://seaborn.pydata.org/_images/lineplot_41_0.png) By default, the observations are sorted by `x`. Disable this to plot a line with the order that observations appear in the dataset: ``` x, y = np.random.normal(size=(2, 5000)).cumsum(axis=1) sns.lineplot(x=x, y=y, sort=False, lw=1) ``` ![../\_images/lineplot\_43\_0.png](https://seaborn.pydata.org/_images/lineplot_43_0.png) Use [`relplot()`](https://seaborn.pydata.org/generated/seaborn.relplot.html#seaborn.relplot "seaborn.relplot") to combine [`lineplot()`](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn.lineplot "seaborn.lineplot") and [`FacetGrid`](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html#seaborn.FacetGrid "seaborn.FacetGrid"). This allows grouping within additional categorical variables. Using [`relplot()`](https://seaborn.pydata.org/generated/seaborn.relplot.html#seaborn.relplot "seaborn.relplot") is safer than using [`FacetGrid`](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html#seaborn.FacetGrid "seaborn.FacetGrid") directly, as it ensures synchronization of the semantic mappings across facets: ``` sns.relplot( data=fmri, x="timepoint", y="signal", col="region", hue="event", style="event", kind="line" ) ``` ![../\_images/lineplot\_45\_0.png](https://seaborn.pydata.org/_images/lineplot_45_0.png) © Copyright 2012-2024, [Michael Waskom](https://mwaskom.github.io/). Created using [Sphinx](https://www.sphinx-doc.org/) and the [PyData Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/). 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