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Install *timewarrior-dataframe* using *pipx* for pivot tables of *timewarrior* summaries

Things to know:

  • Timewarrior (timew) is a command line tool for tracking time.
  • Timewarrior-Dataframe is an extension processes timew export output using the Pandas Python module.

Install #

Timewarrior can be installed on recent Ubuntu releases from the apt repositories. It can also be found in a number of other distro’s packaging systems.

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# Ubuntu (and/or Debian?)
sudo apt install timewarrior

pipx can be used to install the timewarrior-dataframe’s twdf command from it’s git repository.

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pipx install git+https://github.com/alexlyttle/timewarrior-dataframe.git

Export with “twdf” #

Export time tracking data from timewarrior with timew export [:sometimespec] to CSV with twdf as follows.

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timew export :lastmonth | twdf --format csv > last_month_hours.csv

Pivot CSV to another time reporting system using Pandas #

Assuming we already exported to CSV with twdf called “last_month_hours.csv”, use Python and Pandas to…

  • Pivot the table by tag/day
  • Add days not found in exported data to table
  • Round tags to nearest quarter-hour for each day
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import pandas as pd
tw = pd.read_csv("last_month_hours.csv")
output = "last_month_hours.pivot.csv" 

# Create index with all days in month 
month_days = pd.date_range(tw.Date.iloc[0], tw.Date.iloc[-1], freq="1D").strftime("%Y-%m-%d")

# Sum up by tag each day
tw = tw[["Date", "Tags", "Hours"]].groupby(["Date", "Tags"], as_index=False).sum()  

tw.index = tw.Date
tw = tw.pivot(index="Date", columns="Tags") # Pivot table to read by date and tag
tw = tw.reindex(month_days)                 # Reindex to include all days of month
tw = tw.T                                   # Transpose table to each date a column

tw = 0.25 * ((tw // 0.25) + 1)              # Round hour entries to nearest 15-min

tw.to_csv(output)

I really wish I had the time to contribute those bits of functionality back into the timewarrior-dataframe extension.

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