From 52d5b40741ef5bccd0f2b374b060e0d41f07c123 Mon Sep 17 00:00:00 2001 From: Anish Lakhwara Date: Mon, 27 Jan 2020 13:14:55 +1100 Subject: [PATCH] task warrior update --- README.md | 2 ++ notebook.ipynb | 57 ++++++++++++++++++++++++++++++++++---------------- 2 files changed, 41 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index dd85aa8..2fee0f9 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,9 @@ data from: * Web browser * Loop Habits +* * Nextcloud backup * Taskwarrior +* * Run 'task export' * Wallabag * Journal * RSS stars diff --git a/notebook.ipynb b/notebook.ipynb index 458435b..69c86f2 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -9,14 +9,15 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "import json\n", "from tasklib import TaskWarrior\n", "import datetime\n", - "import pandas as pd" + "import pandas as pd\n", + "import matplotlib.pyplot as plt" ] }, { @@ -43,34 +44,54 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "with open('data/task.json', 'r') as myfile:\n", " task = json.load(myfile)\n", "\n", - "tasks = pd.DataFrame.from_dict(task)" + "tasks = pd.DataFrame.from_dict(task)\n", + "tasks.set_axis(tasks['entry'], axis='index', inplace=True)\n", + "tasks = tasks.drop(['annotations', 'depends', 'description', 'parent', 'uuid', 'entry'], axis=1)\n", + "only2019(tasks).count()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([164., 3., 27., 2., 11., 1., 2., 1., 2., 2.]),\n", + " array([ 0. , 1.5, 3. , 4.5, 6. , 7.5, 9. , 10.5, 12. , 13.5, 15. ]),\n", + " )" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# this was dumb, using a list\n", + "l = only2019(tasks).tags.dropna().to_list()\n", + "flat_list = [item for sublist in l for item in sublist]\n", "\n", - "tagslist = []\n", - "for e in task:\n", - " # filter based on date\n", - " ###\n", - " # only check tags where they exist\n", - " if 'tags' in e.keys():\n", - " count += 1\n", - " tagslist.append(e[\"tags\"])\n", - "\n", - "print(tagslist)" + "plt.hist(flat_list)" ] }, { @@ -317,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [