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authorEgor Tensin <Egor.Tensin@gmail.com>2016-06-18 06:22:54 +0300
committerEgor Tensin <Egor.Tensin@gmail.com>2016-06-18 06:22:54 +0300
commit5ec558eed235176be9a2e7b8ab5b56f54bc6263d (patch)
tree5f8af7b0e7de733be15d08ccf9b4aca6b4b56528
parentREADME update (diff)
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README update
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1 files changed, 20 insertions, 16 deletions
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@@ -89,22 +89,26 @@ You can change that by supplying either `date` (to group by dates) or `weekday`
For example (assuming that both Jane and Joe spent their time online on Friday,
June 17, 2016).
- > online_duration.py --output-format json --grouping date db.csv
- [
- {
- "date": "2016-06-17",
- "duration": "1:47:17"
- }
- ]
-
- > online_duration.py --output-format csv --grouping weekday db.csv
- Monday,0:00:00
- Tuesday,0:00:00
- Wednesday,0:00:00
- Thursday,0:00:00
- Friday,1:47:17
- Saturday,0:00:00
- Sunday,0:00:00
+```
+> online_duration.py --output-format json --grouping date db.csv
+[
+ {
+ "date": "2016-06-17",
+ "duration": "1:47:17"
+ }
+]
+```
+
+```
+> online_duration.py --output-format csv --grouping weekday db.csv
+Monday,0:00:00
+Tuesday,0:00:00
+Wednesday,0:00:00
+Thursday,0:00:00
+Friday,1:47:17
+Saturday,0:00:00
+Sunday,0:00:00
+```
In my opinion, the script's most useful feature is the ability to easily create
plots that represent the text data (like in the examples above).