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business-science · Nov 6, 2024 · 726e187 · 726e187
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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
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diff --git a/reference/plot_timeseries.html b/reference/plot_timeseries.html
diff --git a/search.json b/search.json
@@ -1922,7 +1922,7 @@
     "href": "reference/plot_timeseries.html#examples",
     "title": "plot_timeseries",
     "section": "Examples",
-    "text": "Examples\n\nimport pytimetk as tk\n\ndf = tk.load_dataset('m4_monthly', parse_dates = ['date'])\n\n# Plotly Object: Single Time Series\nfig = (\n    df\n        .query('id == \"M750\"')\n        .plot_timeseries(\n            'date', 'value', \n            facet_ncol = 1,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Grouped Time Series (Facets)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            facet_ncol = 2,\n            facet_scales = \"free_y\",\n            smooth_frac = 0.2,\n            smooth_size = 2.0,\n            y_intercept = None,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n            width = 600,\n            height = 500,\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Grouped Time Series (Plotly Dropdown)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            facet_scales = \"free_y\",\n            smooth_frac = 0.2,\n            smooth_size = 2.0,\n            y_intercept = None,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n            width = 600,\n            height = 500,\n            plotly_dropdown = True, # Plotly Dropdown\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Color Column\nfig = (\n    df\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            smooth = False,\n            y_intercept = 0,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotnine Object: Single Time Series\nfig = (\n    df\n        .query('id == \"M1\"')\n        .plot_timeseries(\n            'date', 'value', \n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine'\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Plotnine Object: Grouped Time Series\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value',\n            facet_ncol = 2,\n            facet_scales = \"free\",\n            line_size = 0.35,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine'\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Plotnine Object: Color Column\nfig = (\n    df\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            smooth = False,\n            y_intercept = 0,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine',\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Matplotlib object (same as plotnine, but converted to matplotlib object)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            facet_ncol = 2,\n            x_axis_date_labels = \"%Y\",\n            engine = 'matplotlib',\n        )\n)\nfig"
+    "text": "Examples\n\nimport pytimetk as tk\n\ndf = tk.load_dataset('m4_monthly', parse_dates = ['date'])\n\n# Plotly Object: Single Time Series\nfig = (\n    df\n        .query('id == \"M750\"')\n        .plot_timeseries(\n            'date', 'value', \n            facet_ncol = 1,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Grouped Time Series (Facets)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            facet_ncol = 2,\n            facet_scales = \"free_y\",\n            smooth_frac = 0.2,\n            smooth_size = 2.0,\n            y_intercept = None,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n            width = 600,\n            height = 500,\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Grouped Time Series (Plotly Dropdown)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            facet_scales = \"free_y\",\n            smooth_frac = 0.2,\n            smooth_size = 2.0,\n            y_intercept = None,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n            width = 600,\n            height = 500,\n            plotly_dropdown = True, # Plotly Dropdown\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotly Object: Color Column\nfig = (\n    df\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            smooth = False,\n            y_intercept = 0,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n        )\n)\nfig\n\n\n                                                \n\n\n\n# Plotnine Object: Single Time Series\nfig = (\n    df\n        .query('id == \"M1\"')\n        .plot_timeseries(\n            'date', 'value', \n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine'\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Plotnine Object: Grouped Time Series\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value',\n            facet_ncol = 2,\n            facet_scales = \"free\",\n            line_size = 0.35,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine'\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Plotnine Object: Color Column\nfig = (\n    df\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            smooth = False,\n            y_intercept = 0,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotnine',\n        )\n)\nfig\n\n\n\n\n&lt;Figure Size: (700 x 500)&gt;\n\n\n\n# Matplotlib object (same as plotnine, but converted to matplotlib object)\nfig = (\n    df\n        .groupby('id')\n        .plot_timeseries(\n            'date', 'value', \n            color_column = 'id',\n            facet_ncol = 2,\n            x_axis_date_labels = \"%Y\",\n            engine = 'matplotlib',\n        )\n)\nfig\n\n\n\n\n\n# Wide-Format Plotting\n\n# Imports\nimport pandas as pd\nimport numpy as np\nimport pytimetk as tk\n\n# Set a random seed for reproducibility\nnp.random.seed(42) \n\n# Create a date range\ndates = pd.date_range(start=\"2020-01-01\", periods=100, freq=\"D\")\n\n# Generate random sales data and compute expenses and profit\nsales = np.random.uniform(1000, 5000, len(dates))\nexpenses = sales * np.random.uniform(0.5, 0.8, len(dates))\nprofit = sales - expenses\n\n# Create the DataFrame\ndf = pd.DataFrame({\n    'date': dates,\n    'sales': sales,\n    'expenses': expenses,\n    'profit': profit\n})\n\n(\n    df\n        .plot_timeseries(\n            date_column = 'date', \n            value_column = ['sales', 'expenses', 'profit'],\n            color_column = ['sales', 'expenses', 'profit'], \n            smooth = True,\n            x_axis_date_labels = \"%Y\",\n            engine = 'plotly',\n            plotly_dropdown = True, # Plotly Dropdown\n        )\n)"
   },
   {
     "objectID": "reference/augment_diffs.html",