finished some jrnl analysis, not sure what more to do other than NLP stuff maybe
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@ -6,7 +6,7 @@ data from:
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* Web browser
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* Loop Habits
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* * Nextcloud backup
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* * Nextcloud backup -> rename 'Date' to 'date'
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* Taskwarrior
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* * Run 'task export'
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* Wallabag
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165
notebook.ipynb
165
notebook.ipynb
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@ -17,6 +17,151 @@
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" return df.filter(like='2019', axis=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Shaarli"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Journal"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### make a journal_fixer script \n",
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"\n",
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"### import journal into habits data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"According to habits I have 49 Journal entries\n",
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"\n",
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"\n",
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"Let's compare that with this data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 106,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tags 87\n",
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"text 88\n",
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"title 88\n",
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"dtype: int64"
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]
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},
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"execution_count": 106,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"with open('data/tiddlers.json' , 'r') as file:\n",
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" tiddly = json.load(file)\n",
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"\n",
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"jrnl = pd.DataFrame.from_dict(tiddly)\n",
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"jrnl.set_axis(jrnl['title'], axis='index', inplace=True)\n",
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"jrnl = only2019(jrnl.drop(['created', 'modified'], axis=1))\n",
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"jrnl.count()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tags 30\n",
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"text 30\n",
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"title 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 107,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sameDay = jrnl\n",
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"sameDay['title'] = sameDay.title.map(lambda x: x[:-8])\n",
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"sameDay[sameDay['title'].duplicated(keep=False)].count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Where are the [87-15=(73)] - 49 = 23 entries that exist but aren't in my loop Habits?\n",
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"\n",
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"23 Entries before March?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 119,
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"metadata": {},
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"outputs": [],
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"source": [
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"def beforeMarch(df):\n",
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" return df.filter(regex='(January|Febuary|March)', axis=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 121,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tags 23\n",
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"text 23\n",
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"title 23\n",
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"dtype: int64"
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]
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},
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"execution_count": 121,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"beforeMarch(jrnl).count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -185,6 +330,16 @@
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" print(\" \" + e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Most described task?\n",
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"## TODO"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -194,7 +349,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 100,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -206,7 +361,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 183,
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"execution_count": 105,
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"metadata": {},
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"outputs": [
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{
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@ -404,14 +559,14 @@
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"12 0.387097 0.548387 1.096774 0.709677 0.612903 0.000000 0.612903"
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]
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},
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"execution_count": 183,
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"execution_count": 105,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#colNames = ['date', 'Godmode', 'Meditate', 'Exercise', 'Piano','Read', 'Journal', 'Gratitude', 'devlog','Plants', 'Job', 'Draw']\n",
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"checks = pd.read_csv('LoopHabits/Checkmarks.csv', header=0)\n",
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"checks = pd.read_csv('data/LoopHabits/Checkmarks.csv', header=0)\n",
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"checks.set_axis(checks['date'], axis='index', inplace=True)\n",
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"checks = checks.drop(['Godmode', 'Piano', 'Gratitude', 'Job', 'date'], axis=1)\n",
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"df = MarchOnwards(only2019(checks))\n",
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@ -429,7 +584,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 102,
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"metadata": {},
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"outputs": [],
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"source": [
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