From 68b2fcec03a36bea121404ac14b82ef5281dd069 Mon Sep 17 00:00:00 2001 From: Matt Dancho Date: Fri, 1 Nov 2024 11:52:27 -0400 Subject: [PATCH] update docs --- .../execute-results/html.json | 8 +- .../plot_timeseries/execute-results/html.json | 6 +- .../figure-html/cell-6-output-1.png | Bin 228639 -> 162403 bytes .../figure-html/cell-7-output-1.png | Bin 229981 -> 228639 bytes .../figure-html/cell-8-output-1.png | Bin 251014 -> 229981 bytes docs/_site/changelog-news.html | 23 +- docs/_site/guides/01_visualization.html | 106 +++-- .../figure-html/cell-5-output-1.png | Bin 320752 -> 320752 bytes docs/_site/reference/plot_anomalies.html | 97 ++++- .../figure-html/cell-4-output-1.png | Bin 95437 -> 95696 bytes .../figure-html/cell-8-output-1.png | Bin 0 -> 448480 bytes docs/_site/reference/plot_timeseries.html | 214 ++++++---- .../figure-html/cell-6-output-1.png | Bin 228639 -> 162403 bytes .../figure-html/cell-7-output-1.png | Bin 229981 -> 228639 bytes .../figure-html/cell-8-output-1.png | Bin 251014 -> 229981 bytes .../figure-html/cell-9-output-1.png | Bin 0 -> 251014 bytes docs/_site/search.json | 36 +- docs/_site/sitemap.xml | 170 ++++---- docs/changelog-news.qmd | 3 +- docs/guides/01_visualization.qmd | 25 +- docs/guides/06_anomalize.ipynb | 383 ++++++++++++++++++ docs/guides/06_anomalize.qmd | 22 +- .../execute-results/html.json | 15 + docs/reference/plot_anomalies.qmd | 23 +- docs/reference/plot_timeseries.qmd | 28 +- 25 files changed, 929 insertions(+), 230 deletions(-) create mode 100644 docs/_site/reference/plot_anomalies_files/figure-html/cell-8-output-1.png create mode 100644 docs/_site/reference/plot_timeseries_files/figure-html/cell-9-output-1.png create mode 100644 docs/guides/06_anomalize.ipynb create mode 100644 docs/guides/06_anomalize_files/execute-results/html.json diff --git a/docs/_freeze/guides/01_visualization/execute-results/html.json b/docs/_freeze/guides/01_visualization/execute-results/html.json index 189e784f..fed36cf1 100644 --- a/docs/_freeze/guides/01_visualization/execute-results/html.json +++ b/docs/_freeze/guides/01_visualization/execute-results/html.json @@ -1,14 +1,14 @@ { - "hash": "4ceecc636a4701bb12225859730a4790", + "hash": "d4946eeacd612fd73cd275c51ddd2a18", "result": { - "markdown": "---\ntitle: Data Visualization\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\ncode-fold: show\ncode-tools:\n source: false\n toggle: true\n---\n\n::: {.callout-note collapse=\"false\"}\n## How this guide benefits you\n\nThis guide covers how to use the `plot_timeseries()` for data visualization. Once you understand how it works, you can apply explore time series data easier than ever. \n:::\n\nThis tutorial focuses on, [plot_timeseries()](https://business-science.github.io/pytimetk/reference/plot_timeseries.html#timetk.plot_timeseries), a workhorse time-series plotting function that:\n\n* Generates interactive plotly plots (great for exploring & streamlit/shiny apps)\n* Consolidates 20+ lines of plotnine/matpotlib & plotly code\n* Scales well to many time series\n* Can be converted from interactive plotly to static plotnine/matplotlib plots\n\n# Libraries\n\nRun the following code to setup for this tutorial.\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\n# Import packages\nimport pytimetk as tk\nimport pandas as pd\n```\n:::\n\n\n# Plotting Time Series\n\nLet’s start with a popular time series, `taylor_30_min`, which includes energy demand in megawatts at a sampling interval of 30-minutes. This is a single time series.\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Import a Time Series Data Set\ntaylor_30_min = tk.load_dataset(\"taylor_30_min\", parse_dates = ['date'])\ntaylor_30_min\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datevalue
02000-06-05 00:00:00+00:0022262
12000-06-05 00:30:00+00:0021756
22000-06-05 01:00:00+00:0022247
32000-06-05 01:30:00+00:0022759
42000-06-05 02:00:00+00:0022549
.........
40272000-08-27 21:30:00+00:0027946
40282000-08-27 22:00:00+00:0027133
40292000-08-27 22:30:00+00:0025996
40302000-08-27 23:00:00+00:0024610
40312000-08-27 23:30:00+00:0023132
\n

4032 rows × 2 columns

\n
\n```\n:::\n:::\n\n\nThe [plot_timeseries()](https://business-science.github.io/pytimetk/reference/plot_timeseries.html#timetk.plot_timeseries) function generates an interactive plotly chart by default.\n\n* Simply provide the date variable (time-based column, date_column) and the numeric variable (value_column) that changes over time as the first 2 arguments.\n* By default, the plotting engine is plotly, which is interactive and excellent for data exploration and apps. However, if you require static plots for reports, you can set the engine to engine = 'plotnine' or engine = 'matplotlib'.\n\nInteractive plot\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\ntaylor_30_min.plot_timeseries('date', 'value')\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\nStatic plot\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\ntaylor_30_min.plot_timeseries(\n 'date', 'value',\n engine = 'plotnine'\n)\n```\n\n::: {.cell-output .cell-output-display}\n![](01_visualization_files/figure-html/cell-5-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n
\n```\n:::\n:::\n\n\n## Plotting Groups\n\nNext, let’s move on to a dataset with time series groups, m4_monthly, which is a sample of 4 time series from the M4 competition that are sampled at a monthly frequency.\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# Import a Time Series Data Set\nm4_monthly = tk.load_dataset(\"m4_monthly\", parse_dates = ['date'])\nm4_monthly\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
iddatevalue
0M11976-06-018000
1M11976-07-018350
2M11976-08-018570
3M11976-09-017700
4M11976-10-017080
............
1569M10002015-02-01880
1570M10002015-03-01800
1571M10002015-04-011140
1572M10002015-05-01970
1573M10002015-06-011430
\n

1574 rows × 3 columns

\n
\n```\n:::\n:::\n\n\nVisualizing grouped data is as simple as grouping the data set with `groupby()` before run it into the `plot_timeseries()` function. Here are the key points:\n\n* Groups can be added using the pandas `groupby()`.\n* These groups are then converted into facets.\n* Using `facet_ncol = 2` returns a 2-column faceted plot.\n* Setting `facet_scales = \"free\"` allows the x and y-axes of each plot to scale independently of the other plots.\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\nm4_monthly.groupby('id').plot_timeseries(\n 'date', 'value', \n facet_ncol = 2, \n facet_scales = \"free\"\n)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\nThe groups can also be vizualized in the same plot using `color_column` paramenter. Let's come back to `taylor_30_min` dataframe.\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\n# load data\ntaylor_30_min = tk.load_dataset(\"taylor_30_min\", parse_dates = ['date'])\n\n# extract the month using pandas\ntaylor_30_min['month'] = pd.to_datetime(taylor_30_min['date']).dt.month\n\n# plot groups\ntaylor_30_min.plot_timeseries(\n 'date', 'value', \n color_column = 'month'\n)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n# Next steps\n\nCheck out the [Pytimetk Basics Guide next.](/guides/02_timetk_concepts.html)\n\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `pytimetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n", + "markdown": "---\ntitle: Data Visualization\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\ncode-fold: show\ncode-tools:\n source: false\n toggle: true\n---\n\n::: {.callout-note collapse=\"false\"}\n## How this guide benefits you\n\nThis guide covers how to use the `plot_timeseries()` for data visualization. Once you understand how it works, you can apply explore time series data easier than ever. \n:::\n\nThis tutorial focuses on, [plot_timeseries()](https://business-science.github.io/pytimetk/reference/plot_timeseries.html#timetk.plot_timeseries), a workhorse time-series plotting function that:\n\n* Generates interactive plotly plots (great for exploring & streamlit/shiny apps)\n* Consolidates 20+ lines of plotnine/matpotlib & plotly code\n* Scales well to many time series\n* Can be converted from interactive plotly to static plotnine/matplotlib plots\n\n# Libraries\n\nRun the following code to setup for this tutorial.\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\n# Import packages\nimport pytimetk as tk\nimport pandas as pd\n```\n:::\n\n\n# Plotting Time Series\n\nThe main function is `plot_timeseries()`. We'll cover some key functionality for easy time series visualization for single and grouped time series. \n\n## Plotting a Single Time Series\n\nLet’s start with a popular time series, `taylor_30_min`, which includes energy demand in megawatts at a sampling interval of 30-minutes. This is a single time series.\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Import a Time Series Data Set\ntaylor_30_min = tk.load_dataset(\"taylor_30_min\", parse_dates = ['date'])\ntaylor_30_min\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datevalue
02000-06-05 00:00:00+00:0022262
12000-06-05 00:30:00+00:0021756
22000-06-05 01:00:00+00:0022247
32000-06-05 01:30:00+00:0022759
42000-06-05 02:00:00+00:0022549
.........
40272000-08-27 21:30:00+00:0027946
40282000-08-27 22:00:00+00:0027133
40292000-08-27 22:30:00+00:0025996
40302000-08-27 23:00:00+00:0024610
40312000-08-27 23:30:00+00:0023132
\n

4032 rows × 2 columns

\n
\n```\n:::\n:::\n\n\nThe [plot_timeseries()](https://business-science.github.io/pytimetk/reference/plot_timeseries.html#timetk.plot_timeseries) function generates an interactive plotly chart by default.\n\n* Simply provide the date variable (time-based column, date_column) and the numeric variable (value_column) that changes over time as the first 2 arguments.\n* By default, the plotting engine is plotly, which is interactive and excellent for data exploration and apps. However, if you require static plots for reports, you can set the engine to engine = 'plotnine' or engine = 'matplotlib'.\n\nInteractive plot\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\ntaylor_30_min.plot_timeseries('date', 'value')\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\nStatic plot\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\ntaylor_30_min.plot_timeseries(\n 'date', 'value',\n engine = 'plotnine'\n)\n```\n\n::: {.cell-output .cell-output-display}\n![](01_visualization_files/figure-html/cell-5-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n
\n```\n:::\n:::\n\n\n## Plotting Groups\n\nNext, let’s move on to a dataset with time series groups, m4_monthly, which is a sample of 4 time series from the M4 competition that are sampled at a monthly frequency.\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# Import a Time Series Data Set\nm4_monthly = tk.load_dataset(\"m4_monthly\", parse_dates = ['date'])\nm4_monthly\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
iddatevalue
0M11976-06-018000
1M11976-07-018350
2M11976-08-018570
3M11976-09-017700
4M11976-10-017080
............
1569M10002015-02-01880
1570M10002015-03-01800
1571M10002015-04-011140
1572M10002015-05-01970
1573M10002015-06-011430
\n

1574 rows × 3 columns

\n
\n```\n:::\n:::\n\n\nVisualizing grouped data is as simple as grouping the data set with `groupby()` before run it into the `plot_timeseries()` function. There are 2 methods:\n\n1. Facets\n2. Plotly Dropdown\n\n### Facets (Subgroups on one plot)\n\nThis is great to see all time series in one plot. Here are the key points:\n\n* Groups can be added using the pandas `groupby()`.\n* These groups are then converted into facets.\n* Using `facet_ncol = 2` returns a 2-column faceted plot.\n* Setting `facet_scales = \"free\"` allows the x and y-axes of each plot to scale independently of the other plots.\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\nm4_monthly.groupby('id').plot_timeseries(\n 'date', 'value', \n facet_ncol = 2, \n facet_scales = \"free\"\n)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n### Plotly Dropdown\n\nSometimes you have many groups and would prefer to see one plot per group. This can be accomplished with `plotly_dropdown`. You can adjust the x and y position as follows: \n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\nm4_monthly.groupby('id').plot_timeseries(\n 'date', 'value', \n plotly_dropdown=True,\n plotly_dropdown_x=0,\n plotly_dropdown_y=1\n)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\nThe groups can also be vizualized in the same plot using `color_column` paramenter. Let's come back to `taylor_30_min` dataframe.\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\n# load data\ntaylor_30_min = tk.load_dataset(\"taylor_30_min\", parse_dates = ['date'])\n\n# extract the month using pandas\ntaylor_30_min['month'] = pd.to_datetime(taylor_30_min['date']).dt.month\n\n# plot groups\ntaylor_30_min.plot_timeseries(\n 'date', 'value', \n color_column = 'month'\n)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n# Next steps\n\nCheck out the [Pytimetk Basics Guide next.](/guides/02_timetk_concepts.html)\n\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `pytimetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n", "supporting": [ - "01_visualization_files" + "01_visualization_files/figure-html" ], "filters": [], "includes": { "include-in-header": [ - "\n\n\n\n\n" + "\n\n\n\n\n" ] } } diff --git a/docs/_freeze/reference/plot_timeseries/execute-results/html.json b/docs/_freeze/reference/plot_timeseries/execute-results/html.json index 7af233bd..e61fef7e 100644 --- a/docs/_freeze/reference/plot_timeseries/execute-results/html.json +++ b/docs/_freeze/reference/plot_timeseries/execute-results/html.json @@ -1,14 +1,14 @@ { - "hash": "c09f83ebd7b644b2f1319aab7632bbac", + "hash": "f5b7ed4ca7720a9d0e3d5c2a6727a0f5", "result": { - "markdown": "---\ntitle: plot_timeseries\n---\n\n\n\n`plot_timeseries(data, date_column, value_column, color_column=None, color_palette=None, facet_ncol=1, facet_nrow=None, facet_scales='free_y', facet_dir='h', line_color='#2c3e50', line_size=None, line_type='solid', line_alpha=1.0, y_intercept=None, y_intercept_color='#2c3e50', x_intercept=None, x_intercept_color='#2c3e50', smooth=True, smooth_color='#3366FF', smooth_frac=0.2, smooth_size=1.0, smooth_alpha=1.0, legend_show=True, title='Time Series Plot', x_lab='', y_lab='', color_lab='Legend', x_axis_date_labels='%b %Y', base_size=11, width=None, height=None, engine='plotly')`\n\nCreates time series plots using different plotting engines such as Plotnine, \nMatplotlib, and Plotly.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------------|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|\n| `data` | pd.DataFrame or pd.core.groupby.generic.DataFrameGroupBy | The input data for the plot. It can be either a Pandas DataFrame or a Pandas DataFrameGroupBy object. | _required_ |\n| `date_column` | str | The name of the column in the DataFrame that contains the dates for the time series data. | _required_ |\n| `value_column` | str | The `value_column` parameter is used to specify the name of the column in the DataFrame that contains the values for the time series data. This column will be plotted on the y-axis of the time series plot. | _required_ |\n| `color_column` | str | The `color_column` parameter is an optional parameter that specifies the column in the DataFrame that will be used to assign colors to the different time series. If this parameter is not provided, all time series will have the same color. | `None` |\n| `color_palette` | list | The `color_palette` parameter is used to specify the colors to be used for the different time series. It accepts a list of color codes or names. If the `color_column` parameter is not provided, the `tk.palette_timetk()` color palette will be used. | `None` |\n| `facet_ncol` | int | The `facet_ncol` parameter determines the number of columns in the facet grid. It specifies how many subplots will be arranged horizontally in the plot. | `1` |\n| `facet_nrow` | int | The `facet_nrow` parameter determines the number of rows in the facet grid. It specifies how many subplots will be arranged vertically in the grid. | `None` |\n| `facet_scales` | str | The `facet_scales` parameter determines the scaling of the y-axis in the facetted plots. It can take the following values: - \"free_y\": The y-axis scale will be free for each facet, but the x-axis scale will be fixed for all facets. This is the default value. - \"free_x\": The y-axis scale will be free for each facet, but the x-axis scale will be fixed for all facets. - \"free\": The y-axis scale will be free for each facet (subplot). This is the default value. | `'free_y'` |\n| `facet_dir` | str | The `facet_dir` parameter determines the direction in which the facets (subplots) are arranged. It can take two possible values: - \"h\": The facets will be arranged horizontally (in rows). This is the default value. - \"v\": The facets will be arranged vertically (in columns). | `'h'` |\n| `line_color` | str | The `line_color` parameter is used to specify the color of the lines in the time series plot. It accepts a string value representing a color code or name. The default value is \"#2c3e50\", which corresponds to a dark blue color. | `'#2c3e50'` |\n| `line_size` | float | The `line_size` parameter is used to specify the size of the lines in the time series plot. It determines the thickness of the lines. | `None` |\n| `line_type` | str | The `line_type` parameter is used to specify the type of line to be used in the time series plot. | `'solid'` |\n| `line_alpha` | float | The `line_alpha` parameter controls the transparency of the lines in the time series plot. It accepts a value between 0 and 1, where 0 means completely transparent (invisible) and 1 means completely opaque (solid). | `1.0` |\n| `y_intercept` | float | The `y_intercept` parameter is used to add a horizontal line to the plot at a specific y-value. It can be set to a numeric value to specify the y-value of the intercept. If set to `None` (default), no y-intercept line will be added to the plot | `None` |\n| `y_intercept_color` | str | The `y_intercept_color` parameter is used to specify the color of the y-intercept line in the plot. It accepts a string value representing a color code or name. The default value is \"#2c3e50\", which corresponds to a dark blue color. You can change this value. | `'#2c3e50'` |\n| `x_intercept` | str | The `x_intercept` parameter is used to add a vertical line at a specific x-axis value on the plot. It is used to highlight a specific point or event in the time series data. - By default, it is set to `None`, which means no vertical line will be added. - You can use a date string to specify the x-axis value of the intercept. For example, \"2020-01-01\" would add a vertical line at the beginning of the year 2020. | `None` |\n| `x_intercept_color` | str | The `x_intercept_color` parameter is used to specify the color of the vertical line that represents the x-intercept in the plot. By default, it is set to \"#2c3e50\", which is a dark blue color. You can change this value to any valid color code. | `'#2c3e50'` |\n| `smooth` | bool | The `smooth` parameter is a boolean indicating whether or not to apply smoothing to the time eries data. If set to True, the time series will be smoothed using the lowess algorithm. The default value is True. | `True` |\n| `smooth_color` | str | The `smooth_color` parameter is used to specify the color of the smoothed line in the time series plot. It accepts a string value representing a color code or name. The default value is `#3366FF`, which corresponds to a shade of blue. You can change this value to any valid color code. | `'#3366FF'` |\n| `smooth_frac` | float | The `smooth_frac` parameter is used to control the fraction of data points used for smoothing the time series. It determines the degree of smoothing applied to the data. A smaller value of `smooth_frac` will result in more smoothing, while a larger value will result in less smoothing. The default value is 0.2. | `0.2` |\n| `smooth_size` | float | The `smooth_size` parameter is used to specify the size of the line used to plot the smoothed values in the time series plot. It is a numeric value that controls the thickness of the line. A larger value will result in a thicker line, while a smaller value will result in a thinner line | `1.0` |\n| `smooth_alpha` | float | The `smooth_alpha` parameter controls the transparency of the smoothed line in the plot. It accepts a value between 0 and 1, where 0 means completely transparent and 1 means completely opaque. | `1.0` |\n| `legend_show` | bool | The `legend_show` parameter is a boolean indicating whether or not to show the legend in the plot. If set to True, the legend will be displayed. The default value is True. | `True` |\n| `title` | str | The title of the plot. | `'Time Series Plot'` |\n| `x_lab` | str | The `x_lab` parameter is used to specify the label for the x-axis in the plot. It is a string that represents the label text. | `''` |\n| `y_lab` | str | The `y_lab` parameter is used to specify the label for the y-axis in the plot. It is a string that represents the label for the y-axis. | `''` |\n| `color_lab` | str | The `color_lab` parameter is used to specify the label for the legend or color scale in the plot. It is used to provide a description of the colors used in the plot, typically when a color column is specified. | `'Legend'` |\n| `x_axis_date_labels` | str | The `x_axis_date_labels` parameter is used to specify the format of the date labels on the x-axis of the plot. It accepts a string representing the format of the date labels. For example, \"%b %Y\" would display the month abbreviation and year (e.g., Jan 2020). | `'%b %Y'` |\n| `base_size` | float | The `base_size` parameter is used to set the base font size for the plot. It determines the size of the text elements such as axis labels, titles, and legends. | `11` |\n| `width` | int | The `width` parameter is used to specify the width of the plot. It determines the horizontal size of the plot in pixels. | `None` |\n| `height` | int | The `height` parameter is used to specify the height of the plot in pixels. It determines the vertical size of the plot when it is rendered. | `None` |\n| `engine` | str | The `engine` parameter specifies the plotting library to use for creating the time series plot. It can take one of the following values: - \"plotly\" (interactive): Use the plotly library to create the plot. This is the default value. - \"plotnine\" (static): Use the plotnine library to create the plot. This is the default value. - \"matplotlib\" (static): Use the matplotlib library to create the plot. | `'plotly'` |\n\n## Returns\n\n| Type | Description |\n|------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| The function `plot_timeseries` returns a plot object, depending on the | specified `engine` parameter. - If `engine` is set to 'plotnine' or 'matplotlib', the function returns a plot object that can be further customized or displayed. - If `engine` is set to 'plotly', the function returns a plotly figure object. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Plotly Object: Grouped Time Series\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-5-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-6-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-7-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display execution_count=7}\n![](plot_timeseries_files/figure-html/cell-8-output-1.png){}\n:::\n:::\n\n\n", + "markdown": "---\ntitle: plot_timeseries\n---\n\n\n\n`plot_timeseries(data, date_column, value_column, color_column=None, color_palette=None, facet_ncol=1, facet_nrow=None, facet_scales='free_y', facet_dir='h', line_color='#2c3e50', line_size=None, line_type='solid', line_alpha=1.0, y_intercept=None, y_intercept_color='#2c3e50', x_intercept=None, x_intercept_color='#2c3e50', smooth=True, smooth_color='#3366FF', smooth_frac=0.2, smooth_size=1.0, smooth_alpha=1.0, legend_show=True, title='Time Series Plot', x_lab='', y_lab='', color_lab='Legend', x_axis_date_labels='%b %Y', base_size=11, width=None, height=None, engine='plotly', plotly_dropdown=False, plotly_dropdown_x=0, plotly_dropdown_y=1)`\n\nCreates time series plots using different plotting engines such as Plotnine, \nMatplotlib, and Plotly.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------------|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|\n| `data` | pd.DataFrame or pd.core.groupby.generic.DataFrameGroupBy | The input data for the plot. It can be either a Pandas DataFrame or a Pandas DataFrameGroupBy object. | _required_ |\n| `date_column` | str | The name of the column in the DataFrame that contains the dates for the time series data. | _required_ |\n| `value_column` | str | The `value_column` parameter is used to specify the name of the column in the DataFrame that contains the values for the time series data. This column will be plotted on the y-axis of the time series plot. | _required_ |\n| `color_column` | str | The `color_column` parameter is an optional parameter that specifies the column in the DataFrame that will be used to assign colors to the different time series. If this parameter is not provided, all time series will have the same color. | `None` |\n| `color_palette` | list | The `color_palette` parameter is used to specify the colors to be used for the different time series. It accepts a list of color codes or names. If the `color_column` parameter is not provided, the `tk.palette_timetk()` color palette will be used. | `None` |\n| `facet_ncol` | int | The `facet_ncol` parameter determines the number of columns in the facet grid. It specifies how many subplots will be arranged horizontally in the plot. | `1` |\n| `facet_nrow` | int | The `facet_nrow` parameter determines the number of rows in the facet grid. It specifies how many subplots will be arranged vertically in the grid. | `None` |\n| `facet_scales` | str | The `facet_scales` parameter determines the scaling of the y-axis in the facetted plots. It can take the following values: - \"free_y\": The y-axis scale will be free for each facet, but the x-axis scale will be fixed for all facets. This is the default value. - \"free_x\": The y-axis scale will be free for each facet, but the x-axis scale will be fixed for all facets. - \"free\": The y-axis scale will be free for each facet (subplot). This is the default value. | `'free_y'` |\n| `facet_dir` | str | The `facet_dir` parameter determines the direction in which the facets (subplots) are arranged. It can take two possible values: - \"h\": The facets will be arranged horizontally (in rows). This is the default value. - \"v\": The facets will be arranged vertically (in columns). | `'h'` |\n| `line_color` | str | The `line_color` parameter is used to specify the color of the lines in the time series plot. It accepts a string value representing a color code or name. The default value is \"#2c3e50\", which corresponds to a dark blue color. | `'#2c3e50'` |\n| `line_size` | float | The `line_size` parameter is used to specify the size of the lines in the time series plot. It determines the thickness of the lines. | `None` |\n| `line_type` | str | The `line_type` parameter is used to specify the type of line to be used in the time series plot. | `'solid'` |\n| `line_alpha` | float | The `line_alpha` parameter controls the transparency of the lines in the time series plot. It accepts a value between 0 and 1, where 0 means completely transparent (invisible) and 1 means completely opaque (solid). | `1.0` |\n| `y_intercept` | float | The `y_intercept` parameter is used to add a horizontal line to the plot at a specific y-value. It can be set to a numeric value to specify the y-value of the intercept. If set to `None` (default), no y-intercept line will be added to the plot | `None` |\n| `y_intercept_color` | str | The `y_intercept_color` parameter is used to specify the color of the y-intercept line in the plot. It accepts a string value representing a color code or name. The default value is \"#2c3e50\", which corresponds to a dark blue color. You can change this value. | `'#2c3e50'` |\n| `x_intercept` | str | The `x_intercept` parameter is used to add a vertical line at a specific x-axis value on the plot. It is used to highlight a specific point or event in the time series data. - By default, it is set to `None`, which means no vertical line will be added. - You can use a date string to specify the x-axis value of the intercept. For example, \"2020-01-01\" would add a vertical line at the beginning of the year 2020. | `None` |\n| `x_intercept_color` | str | The `x_intercept_color` parameter is used to specify the color of the vertical line that represents the x-intercept in the plot. By default, it is set to \"#2c3e50\", which is a dark blue color. You can change this value to any valid color code. | `'#2c3e50'` |\n| `smooth` | bool | The `smooth` parameter is a boolean indicating whether or not to apply smoothing to the time eries data. If set to True, the time series will be smoothed using the lowess algorithm. The default value is True. | `True` |\n| `smooth_color` | str | The `smooth_color` parameter is used to specify the color of the smoothed line in the time series plot. It accepts a string value representing a color code or name. The default value is `#3366FF`, which corresponds to a shade of blue. You can change this value to any valid color code. | `'#3366FF'` |\n| `smooth_frac` | float | The `smooth_frac` parameter is used to control the fraction of data points used for smoothing the time series. It determines the degree of smoothing applied to the data. A smaller value of `smooth_frac` will result in more smoothing, while a larger value will result in less smoothing. The default value is 0.2. | `0.2` |\n| `smooth_size` | float | The `smooth_size` parameter is used to specify the size of the line used to plot the smoothed values in the time series plot. It is a numeric value that controls the thickness of the line. A larger value will result in a thicker line, while a smaller value will result in a thinner line | `1.0` |\n| `smooth_alpha` | float | The `smooth_alpha` parameter controls the transparency of the smoothed line in the plot. It accepts a value between 0 and 1, where 0 means completely transparent and 1 means completely opaque. | `1.0` |\n| `legend_show` | bool | The `legend_show` parameter is a boolean indicating whether or not to show the legend in the plot. If set to True, the legend will be displayed. The default value is True. | `True` |\n| `title` | str | The title of the plot. | `'Time Series Plot'` |\n| `x_lab` | str | The `x_lab` parameter is used to specify the label for the x-axis in the plot. It is a string that represents the label text. | `''` |\n| `y_lab` | str | The `y_lab` parameter is used to specify the label for the y-axis in the plot. It is a string that represents the label for the y-axis. | `''` |\n| `color_lab` | str | The `color_lab` parameter is used to specify the label for the legend or color scale in the plot. It is used to provide a description of the colors used in the plot, typically when a color column is specified. | `'Legend'` |\n| `x_axis_date_labels` | str | The `x_axis_date_labels` parameter is used to specify the format of the date labels on the x-axis of the plot. It accepts a string representing the format of the date labels. For example, \"%b %Y\" would display the month abbreviation and year (e.g., Jan 2020). | `'%b %Y'` |\n| `base_size` | float | The `base_size` parameter is used to set the base font size for the plot. It determines the size of the text elements such as axis labels, titles, and legends. | `11` |\n| `width` | int | The `width` parameter is used to specify the width of the plot. It determines the horizontal size of the plot in pixels. | `None` |\n| `height` | int | The `height` parameter is used to specify the height of the plot in pixels. It determines the vertical size of the plot when it is rendered. | `None` |\n| `engine` | str | The `engine` parameter specifies the plotting library to use for creating the time series plot. It can take one of the following values: - \"plotly\" (interactive): Use the plotly library to create the plot. This is the default value. - \"plotnine\" (static): Use the plotnine library to create the plot. This is the default value. - \"matplotlib\" (static): Use the matplotlib library to create the plot. | `'plotly'` |\n| `plotly_dropdown` | bool | For analyzing many plots. When set to True and groups are provided, the function switches from faceting to create a dropdown menu to switch between different groups. Default: `False`. | `False` |\n| `plotly_dropdown_x` | float | The x-axis location of the dropdown. Default: 0. | `0` |\n| `plotly_dropdown_y` | float | The y-axis location of the dropdown. Default: 1. | `1` |\n\n## Returns\n\n| Type | Description |\n|------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| The function `plot_timeseries` returns a plot object, depending on the | specified `engine` parameter. - If `engine` is set to 'plotnine' or 'matplotlib', the function returns a plot object that can be further customized or displayed. - If `engine` is set to 'plotly', the function returns a plotly figure object. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-6-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-7-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display}\n![](plot_timeseries_files/figure-html/cell-8-output-1.png){}\n:::\n\n::: {.cell-output .cell-output-display execution_count=7}\n```\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\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::: {.cell-output .cell-output-display execution_count=8}\n![](plot_timeseries_files/figure-html/cell-9-output-1.png){}\n:::\n:::\n\n\n", "supporting": [ "plot_timeseries_files/figure-html" ], "filters": [], "includes": { "include-in-header": [ - "\n\n\n\n\n" + "\n\n\n\n\n" ] } } diff --git a/docs/_freeze/reference/plot_timeseries/figure-html/cell-6-output-1.png b/docs/_freeze/reference/plot_timeseries/figure-html/cell-6-output-1.png index c0ef28891fd747eb295edf82014c2c85ca15224e..20e701769dc92342d6892725b562ca6362020fc8 100644 GIT binary patch literal 162403 zcmd?Rc|6o@_&=J6BvitawZ)R%Q>3hwkX^{Wq%0%LkZr6ZiYG$GPRO2Jwy});u_gO5 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