plot_anomalies

plot_anomalies(
    data,
    date_column,
    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,
    anom_color='#E31A1C',
    anom_alpha=1.0,
    anom_size=None,
    ribbon_fill='#646464',
    ribbon_alpha=0.2,
    y_intercept=None,
    y_intercept_color='#2c3e50',
    x_intercept=None,
    x_intercept_color='#2c3e50',
    legend_show=True,
    title='Anomaly 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,
)

Creates plot of anomalies in time series data using Plotly, Matplotlib, or Plotnine. See the anomalize() function required to prepare the data for plotting.

Parameters

Name	Type	Description	Default
data	Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]	The input data for the plot. It can be either a pandas DataFrame or a pandas DataFrameGroupBy object.	required
date_column	str	The `date_column` parameter is a string that specifies the name of the column in the dataframe that contains the dates for the plot.	required
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`
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`
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'`
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'`
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'`
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`
line_type	str	The `line_type` parameter is used to specify the type of line to be used in the time series plot.	`'solid'`
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`
anom_color	str	The `anom_color` parameter is used to specify the color of the anomalies in the plot. It accepts a string value representing a color code or name. The default value is `#E31A1C`, which corresponds to a shade of red.	`'#E31A1C'`
anom_alpha	float	The `anom_alpha` parameter controls the transparency (alpha) of the anomaly points in the plot. It accepts a float value between 0 and 1, where 0 means completely transparent and 1 means completely opaque.	`1.0`
anom_size	Optional[float]	The `anom_size` parameter is used to specify the size of the markers used to represent anomalies in the plot. It is an optional parameter, and if not provided, a default value will be used.	`None`
ribbon_fill	str	The `ribbon_fill` parameter is used to specify the fill color of the ribbon that represents the range of anomalies in the plot. It accepts a string value representing a color code or name.	`'#646464'`
ribbon_alpha	float	The parameter `ribbon_alpha` controls the transparency of the ribbon fill in the plot. It accepts a float value between 0 and 1, where 0 means completely transparent and 1 means completely opaque. A higher value will make the ribbon fill more visible, while a lower value will make it	`0.2`
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`
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'`
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`
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'`
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`
title	str	The title of the plot.	`'Anomaly Plot'`
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.	`''`
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.	`''`
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'`
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'`
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`
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`
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`
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'`
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`
plotly_dropdown_x	float	The x-axis location of the dropdown. Default: 0.	`0`
plotly_dropdown_y	float	The y-axis location of the dropdown. Default: 1.	`1`

Returns

Name	Type	Description
	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.

Examples

# EXAMPLE 1: SINGLE TIME SERIES
import pytimetk as tk
import pandas as pd
import numpy as np

# Create a date range
date_rng = pd.date_range(start='2021-01-01', end='2024-01-01', freq='MS')

# Generate some random data with a few outliers
np.random.seed(42)
data = np.random.randn(len(date_rng)) * 10 + 25  
data[3] = 100  # outlier

# Create a DataFrame
df = pd.DataFrame(date_rng, columns=['date'])
df['value'] = data

# Anomalize the data
anomalize_df = tk.anomalize(
    df, "date", "value",
    method = "twitter", 
    iqr_alpha = 0.10, 
    clean_alpha = 0.75,
    clean = "min_max",
)

# Visualize the anomaly bands, plotly engine
(
     anomalize_df
        .plot_anomalies(
            date_column = "date",
            engine = "plotly",
        )
)

# Visualize the anomaly bands, plotly engine
(
     anomalize_df
        .plot_anomalies(
            date_column = "date",
            engine = "plotnine",
        )
)

<Figure Size: (700 x 500)>

# EXAMPLE 2: MULTIPLE TIME SERIES
import pytimetk as tk
import pandas as pd

df = tk.load_dataset("walmart_sales_weekly", parse_dates=["Date"])[["id", "Date", "Weekly_Sales"]]

anomalize_df = (
    df
        .groupby('id') 
        .anomalize(
            "Date", "Weekly_Sales", 
        ) 
)

# Visualize the anomaly bands, plotly engine
(
    anomalize_df 
        .groupby(["id"]) 
        .plot_anomalies(
            date_column = "Date", 
            facet_ncol = 2, 
            width = 800,
            height = 800,
            engine = "plotly",
        )
)

# Visualize the anomaly bands, plotly engine, plotly dropdown
(
    anomalize_df 
        .groupby(["id"]) 
        .plot_anomalies(
            date_column = "Date", 
            engine = "plotly",
            plotly_dropdown=True,
            plotly_dropdown_x=1.05,
            plotly_dropdown_y=1.15
        )
)

# Visualize the anomaly bands, matplotlib engine
(
    anomalize_df 
        .groupby(["id"]) 
        .plot_anomalies(
            date_column = "Date", 
            facet_ncol = 2, 
            width = 800,
            height = 800,
            engine = "matplotlib",
        )
)

Parameters

Returns

See Also

Examples