plot_anomalies_cleaned

plot_anomalies_cleaned(data, date_column, facet_ncol=1, line_color='#2c3e50', line_color_cleaned='#e31a1c', line_size=None, line_type='solid', line_alpha=1.0, y_intercept=None, y_intercept_color='#2c3e50', x_intercept=None, x_intercept_color='#2c3e50', title='Anomalies Cleaned Plot', x_lab='', y_lab='', x_axis_date_labels='%b %Y', base_size=11, width=None, height=None, engine='plotly')

The plot_anomalies_cleaned function takes in data from the anomalize() function, and returns a plot of the anomalies cleaned.

Parameters

Name	Type	Description	Default
`data`	Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]	The input data for the plot from `anomalize`. It can be either a pandas DataFrame or a pandas DataFrameGroupBy object.	required
`date_column`	str	The name of the column in the data that contains the dates.	required
`facet_ncol`	int	The number of columns in the facet grid. It is an optional parameter,	`1`
`line_color`	str	The color of the line in the plot. It is specified as a hexadecimal color code. The default value is “#2c3e50”.	`'#2c3e50'`
`line_color_cleaned`	str	The color of the line in the plot. It is specified as a hexadecimal or a matplotlib color name. The default value is “#e31a1c”.	`'#e31a1c'`
`line_size`	Optional[float]	The line_size parameter determines the thickness of the lines in the plot. It is an optional parameter, so if you don’t specify a value, the default line size will be used.	`None`
`line_type`	str	The `line_type` parameter specifies the type of line to be used in the plot. It can take the following values: - “solid” (default): a solid line - “dashed”: a dashed line	`'solid'`
`line_alpha`	float	The `line_alpha` parameter controls the transparency of the lines in the plot. It accepts a float value between 0 and 1, where 0 means completely transparent and 1 means completely opaque.	`1.0`
`y_intercept`	Optional[float]	The `y_intercept` parameter is an optional float value that specifies the y-coordinate of a horizontal line to be plotted on the graph. This line can be used to indicate a specific threshold or reference value. If not specified, no horizontal line will be plotted.	`None`
`y_intercept_color`	str	The `y_intercept_color` parameter is used to specify the color of the y-intercept line on the plot. By default, it is set to `"#2c3e50"`, which is a dark blue color. You can change this parameter to any valid color code or name to change the color of the line.	`'#2c3e50'`
`x_intercept`	Optional[str]	The `x_intercept` parameter is used to specify the value on the x-axis where you want to draw a vertical line. This can be useful for highlighting a specific point or event in the data.	`None`
`x_intercept_color`	str	The `x_intercept_color` parameter is used to specify the color of the vertical line representing the x-intercept on the plot. By default, it is set to “#2c3e50”, which is a dark blue color. You can change this parameter to any valid color code or name to change the color of the line.	`'#2c3e50'`
`title`	str	The title of the plot. It is set to “Anomalies Cleaned Plot” by default.	`'Anomalies Cleaned Plot'`
`x_lab`	str	The x_lab parameter is used to specify the label for the x-axis of 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 of the plot. It is a string that represents the label text.	`''`
`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 2019).	`'%b %Y'`
`base_size`	float	The `base_size` parameter determines the base font size for the plot. It is used to control the size of the text elements in the plot, such as axis labels, titles, and tick labels. The default value is 11, but you can adjust it to make the text larger or smaller	`11`
`width`	Optional[int]	The width parameter determines the width of the plot in pixels. It is an optional parameter, so if you don’t specify a value, the plot will be displayed with the default width.	`None`
`height`	Optional[int]	The height parameter determines the height of the plot in pixels. It is an optional parameter, so if you don’t specify a value, the plot will be displayed with a default height.	`None`
`engine`	str	The `engine` parameter specifies the plotting engine to use. It can be set to either “plotly”, “plotnine”, or “matplotlib”.	`'plotly'`

Returns

Type	Description
A plotly, plotnine, or matplotlib plot.

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",
    verbose = True,
)

# Visualize the results
anomalize_df.plot_anomalies_cleaned("date")

Using seasonal frequency of 12 observations
Using trend frequency of 37 observations

# 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", 
            period = 52, 
            trend = 52, 
            threads = 1
        ) 
)

# Visualize the decomposition results

(
    anomalize_df
        .groupby("id")
        .plot_anomalies_cleaned(
            date_column = "Date",
            line_color = "steelblue",
            width = 600,
            height = 1000,
            x_axis_date_labels = "%y",
            engine = 'plotly',                
        )
)

Parameters

Returns

See Also

Examples