In this tutorial, we will take a look at 6 different types of visualizations that you can use on your own time series data. They are: Line Plots. Histograms and Density Plots. Box and Whisker Plots. Heat Maps. Lag Plots or Scatter Plots. Autocorrelation Plots.

Apr 7, 2023 · datetime-like data should directly be plotted using plot. If you need to plot plain numeric data as Matplotlib date format or need to set a timezone, call ax.xaxis.axis_date / ax.yaxis.axis_date before plot. See Axis.axis_date. Old, outdated answer: You must first convert your timestamps to Python datetime objects (use datetime.strptime).

Jan 22, 2025 · Plotting data in a continuous time series can be effectively represented graphically using line, area, or smooth plots, which offer insights into the dynamic behavior of the trends being studied. To show patterns and distributions within discrete time series data, bar charts, histograms, and stacked bar plots are frequently utilized.

# plot time-series plt.plot(xs, ys) # draw vertical lines xmin, xmax = map(mdates.num2date, plt.xlim()) # get dates on x-limits as dates for yr in range(xmin.year, xmax.year): # vertical line on Jan 1 midnight plt.axvline(datetime(yr + 1, 1, 1), color='#b0b0b0', linewidth=0.8) # show datetimes in a specific format pos = mdates.YearLocator ...

Apr 24, 2025 · Visualizations are vital in the process of obtaining insightful information from time series data and enable us to comprehend complex relationships and make intelligent decisions. This article covers several types of plots that will help you with time series analysis using Python, with detailed examples using a freely accessible dataset.

May 15, 2019 · Complete code for both seaborn and plotly: The following code sample will let you produce both plots. Another plotly option is: Considering a toy dataframe: Hey, I tried your suggestion with Plotly, It runs without error, but it gives no graph as output in spyder, but gives the output in Jupyter. Any idea why is it so?

Feb 28, 2021 · A simple tutorial on handling time series data in Python from extracting the dates and others to plotting them to charts.

Dec 14, 2024 · Visualizing time series data is a crucial step in understanding and analyzing the trends, patterns, and anomalies in data that changes over time. In this tutorial, we will explore how to visualize time series data using Python and the popular data visualization library, Matplotlib.

Jan 1, 2016 · In this article, you will learn how to explore time series data with Python using the following: Let’s get started! A line plot is commonly used for visualizing time series data. In a line plot, time is usually on the x-axis and the observation values are on the y-axis.

Apr 21, 2025 · Visualizing rolling statistics in a time series. It can be observed that in general, the larger the window size, the smoother the temporal behavior of the rolling average being analyzed. Wrapping Up. This article showed how to calculate rolling statistics on time series data in Python.