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Daniel Langbein 2023-08-13 16:40:48 +02:00
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@ -187,6 +187,18 @@ source ~/de-p1st-monitor.venv/bin/activate
pip install graph-cli pip install graph-cli
``` ```
3) Prepare data (WIP)
```shell
qsv=/home/yoda/Downloads/qsv-0.108.0-x86_64-unknown-linux-gnu/qsv
file=foo.csv
# TODO: Divide net_* total sent bytes column by 1.000.000 to make numbers smaller
"${qsv}" headers --just-names "${file}"
"${qsv}" apply dynfmt --formatstr '{FOO} €' -c 'Total Price' "${file}"
"${qsv}" apply calcconv --formatstr '{col1} + 10' --new-column BAR "${file}"
```
3) Create plots 3) Create plots
Create one plot for each .csv file with different resampling methods (https://pandas.pydata.org/pandas-docs/stable/reference/resampling.html#computations-descriptive-stats): Create one plot for each .csv file with different resampling methods (https://pandas.pydata.org/pandas-docs/stable/reference/resampling.html#computations-descriptive-stats):