plot_univariate_datetime_summary.rst

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        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_univariate_summaries_plot_univariate_datetime_summary.py>`
        to download the full example code

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Univariate Datetime Summary

Example of univariate eda summary for a datetime variable. Here we look at posting times for TidyTuesday tweets.

The datetime summary computes the following:

• A time seriesplot aggregated according to the ts_freq parameter
• Barplots showing counts by day of week, month, hour of day, day of month

import pandas as pd
import plotly

import intedact

data = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/tidytuesday_tweets/data.csv"
)
data["created_at"] = pd.to_datetime(data.created_at)
fig = intedact.datetime_summary(data, "created_at", fig_width=700)
plotly.io.show(fig)

By default, the summary tries to infer reasonable units for the time series. We can change these by using time unit strings for the ts_freq parameter.

fig = intedact.datetime_summary(data, "created_at", ts_freq="1 day", fig_width=700)
plotly.io.show(fig)

Example of changing plot type, removing trend line, and removing outliers.

fig = intedact.datetime_summary(
    data,
    "created_at",
    ts_type="markers",
    trend_line="none",
    upper_quantile=0.99,
    fig_width=700,
)
plotly.io.show(fig)

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   **Total running time of the script:** ( 0 minutes  4.192 seconds)

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     :download:`Download Python source code: plot_univariate_datetime_summary.py <plot_univariate_datetime_summary.py>`



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     :download:`Download Jupyter notebook: plot_univariate_datetime_summary.ipynb <plot_univariate_datetime_summary.ipynb>`

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