Time Series Visualization
Last Updated on November 5, 2023 by Editorial Team
Author(s): Andrea Ianni
Originally published on Towards AI.
Common mistakes
Suppose you have a time series representing free-lance working hours in a period of time:
import pandas as pdimport plotly.express as pximport numpy as npimport datetime link = 'https://raw.githubusercontent.com/ianni-phd/Datasets/main/Timeseries/working_hours.csv'df = pd.read_csv(link)# Visualizationfig = px.line(df, x='day', y='working_hours', title='Working hours')fig.show()
We know that freelancers do not have a 9-to-5.
They have a 24/7, where one day feels like a work marathon, and the next feels like a work siesta!
Nevertheless, the time series seems pretty strange… it’s because of a common mistake in time series representations.
Let us create a more representative plot:
import pandas as pdimport plotly.express as pximport plotly.graph_objects as go# Read datalink = 'https://raw.githubusercontent.com/ianni-phd/Datasets/main/Timeseries/working_hours.csv'df =… Read the full blog for free on Medium.
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