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)