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Technocolabs Internship Week
  • Internship First Week Assignment
  • Introduction - Data Analysis with Python 3 and Pandas
  • Graphing/visualization - Data Analysis with Python 3 and Pandas
  • Groupby - Data Analysis with Python 3 and Pandas
  • Visualizing Correlation Table - Data Analysis with Python 3 and Pandas
  • Combining multiple datasets - Data Analysis with Python 3 and Pandas
  • Machine Learning with Scikit-learn - Data Analysis with Python 3 and Pandas
  • Data Analysis Project Task HandsOn
    • Line Charts
    • Multiple Plots
    • Bar Plots And Scatter Plots
    • Histograms And Box Plots
  • Final Project
  • Mini Project
    • Task 1
    • Task 2
    • Task 3
    • Task 4
    • Task5
    • Task 6
    • Task 7
    • Task 8
    • Task 9
    • Task 10
    • Task 11
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  • Am I progressing?
  • Create a plot with observed distance of runs and decomposed trend.
  • Helpful links:

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  1. Mini Project

Task 8

Am I progressing?

Let's dive a little deeper into the data to answer a tricky question: am I progressing in terms of my running skills?

To answer this question, we'll decompose my weekly distance run and visually compare it to the raw data. A red trend line will represent the weekly distance run.

We are going to use statsmodels library to decompose the weekly trend.

Create a plot with observed distance of runs and decomposed trend.

  • Import the statsmodels.api under the alias sm.

  • Subset df_run from 2013 through 2018, select Distance (km) column, resample weekly, and fill NaN values with the bfill() method. Assign to df_run_dist_wkly.

  • Create a plot with plt.figure(), defining plot size by setting figsize to (12,5).

Helpful links:

  • seasonal_decompose() using moving averages documentation

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Last updated 4 years ago

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