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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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  • Detailed summary report
  • Create a summary report.
  • Helpful links:

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  1. Mini Project

Task 10

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Last updated 4 years ago

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Detailed summary report

With all this data cleaning, analysis, and visualization, let's create detailed summary tables of my training.

To do this, we'll create two tables. The first table will be a summary of the distance (km) and climb (m) variables for each training activity. The second table will list the summary statistics for the average speed (km/hr), climb (m), and distance (km) variables for each training activity.

Create a summary report.

  • Concatenate the df_run DataFrame with df_walk and df_cycle using append(), then sort based on the index in descending order. Assign the result to df_run_walk_cycle.

  • Group df_run_walk_cycle by activity type, then select the columns in dist_climb_cols. Sum the result using sum(). Assign the result to df_totals.

  • Use the stack() method on df_summary to show a compact reshaped form of the full summary report.

Helpful links:

  • describe() function

  • stack() method

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