740 lines
32 KiB
Plaintext
740 lines
32 KiB
Plaintext
{
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"cells": [
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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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"# Common functions"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def only2019(df):\n",
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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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"source": [
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"# Taskwarrior"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"from tasklib import TaskWarrior\n",
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"import datetime\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"Year Month Added Completed Deleted Net\n",
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"---- --------- ----- --------- ------- ---\n",
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"2019 January 18 19 0 -1\n",
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" February 9 7 0 2\n",
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" March 27 23 0 4\n",
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" April 13 14 3 -4\n",
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" May 10 9 0 1\n",
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" June 17 13 0 4\n",
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" July 17 17 0 0\n",
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" August 35 37 0 -2\n",
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" September 22 24 1 -3\n",
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" October 19 15 2 2\n",
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" November 17 16 0 1\n",
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" December 37 42 1 -6\n",
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" \n",
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" Total 241 236 7 5 "
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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": 36,
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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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"description 241\n",
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"due 107\n",
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"end 241\n",
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"id 241\n",
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"imask 0\n",
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"mask 0\n",
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"modified 241\n",
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"priority 3\n",
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"project 188\n",
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"recur 0\n",
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"status 241\n",
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"tags 215\n",
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"until 0\n",
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"urgency 241\n",
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"dtype: int64"
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]
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},
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"execution_count": 36,
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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/task.json', 'r') as myfile:\n",
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" task = json.load(myfile)\n",
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"\n",
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"# Convert the data into a data frame\n",
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"# Some preliminary analysis\n",
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"tasks = pd.DataFrame.from_dict(task)\n",
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"tasks.set_axis(tasks['entry'], axis='index', inplace=True)\n",
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"tasks = tasks.drop(['annotations', 'depends', 'parent', 'uuid', 'entry'], axis=1)\n",
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"only2019(tasks).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": 35,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"16 unique tags\n",
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"215 tagged items\n",
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"The tags are: {'rocks', 'friends', 'd.tech', 'chores', 'job', 'prpj', 'fam', 'artifex', 'contact', 'd.infra', 'fun', 'piracy', 'work', 'uni', 'life', 'travel'}\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1008x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# How many of each tag did I do?\n",
|
|
"\n",
|
|
"l = only2019(tasks).tags.dropna().to_list()\n",
|
|
"flat_list = [item for sublist in l for item in sublist]\n",
|
|
"print(str(len(set(flat_list))) + \" unique tags\")\n",
|
|
"print(str(len(l)) + \" tagged items\")\n",
|
|
"print(\"The tags are: \" + str(set(flat_list)))\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(14,8))\n",
|
|
"plt.hist(flat_list, rwidth=1/3, align='left', bins=16)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"count 241\n",
|
|
"unique 216\n",
|
|
"top fold clothes\n",
|
|
"freq 7\n",
|
|
"Name: description, dtype: object\n",
|
|
"\n",
|
|
"Repeated descriptions: \n",
|
|
" laundry\n",
|
|
" haircut\n",
|
|
" fold clothes\n",
|
|
" recharge my way\n",
|
|
" change sheets\n",
|
|
" cut hair\n",
|
|
" buy condoms\n",
|
|
" schedule counsellor meeting\n",
|
|
" cut nails\n",
|
|
" recharge myway\n",
|
|
" book counsellor meeting\n",
|
|
" shave\n",
|
|
" do laundry\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Description mining\n",
|
|
"\n",
|
|
"l = only2019(tasks).description\n",
|
|
"print(l.describe())\n",
|
|
"print()\n",
|
|
"print(\"Repeated descriptions: \")\n",
|
|
"for e in l[l.duplicated(keep=False)].unique():\n",
|
|
" print(\" \" + e)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Most described task?\n",
|
|
"## TODO"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Habits"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 100,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import calendar\n",
|
|
"import matplotlib \n",
|
|
"import matplotlib.pyplot as plt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 105,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Days done: Meditate 64\n",
|
|
"Exercise 65\n",
|
|
"Read 69\n",
|
|
"Journal 49\n",
|
|
"devlog 32\n",
|
|
"Plants 38\n",
|
|
"Draw 12\n",
|
|
"dtype: int64\n",
|
|
"Non-streak days: Meditate 176\n",
|
|
"Exercise 179\n",
|
|
"Read 161\n",
|
|
"Journal 70\n",
|
|
"devlog 154\n",
|
|
"Plants 120\n",
|
|
"Draw 252\n",
|
|
"dtype: int64\n",
|
|
"Streak days: Meditate 35\n",
|
|
"Exercise 31\n",
|
|
"Read 45\n",
|
|
"Journal 156\n",
|
|
"devlog 89\n",
|
|
"Plants 117\n",
|
|
"Draw 11\n",
|
|
"dtype: int64\n",
|
|
"Longest streak: Meditate 17\n",
|
|
"Exercise 11\n",
|
|
"Read 12\n",
|
|
"Journal 55\n",
|
|
"devlog 29\n",
|
|
"Plants 87\n",
|
|
"Draw 17\n",
|
|
"dtype: int64\n",
|
|
"Longest zeros: Meditate 23\n",
|
|
"Exercise 26\n",
|
|
"Read 43\n",
|
|
"Journal 27\n",
|
|
"devlog 121\n",
|
|
"Plants 54\n",
|
|
"Draw 229\n",
|
|
"dtype: int64\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Meditate</th>\n",
|
|
" <th>Exercise</th>\n",
|
|
" <th>Read</th>\n",
|
|
" <th>Journal</th>\n",
|
|
" <th>devlog</th>\n",
|
|
" <th>Plants</th>\n",
|
|
" <th>Draw</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>month</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0.133333</td>\n",
|
|
" <td>0.266667</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.900000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1.133333</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>0.387097</td>\n",
|
|
" <td>0.193548</td>\n",
|
|
" <td>0.193548</td>\n",
|
|
" <td>0.645161</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.516129</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>0.266667</td>\n",
|
|
" <td>0.166667</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>0.516129</td>\n",
|
|
" <td>0.838710</td>\n",
|
|
" <td>0.967742</td>\n",
|
|
" <td>0.774194</td>\n",
|
|
" <td>0.064516</td>\n",
|
|
" <td>0.612903</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>0.806452</td>\n",
|
|
" <td>0.741935</td>\n",
|
|
" <td>0.935484</td>\n",
|
|
" <td>1.161290</td>\n",
|
|
" <td>1.225806</td>\n",
|
|
" <td>1.193548</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>1.133333</td>\n",
|
|
" <td>0.600000</td>\n",
|
|
" <td>1.233333</td>\n",
|
|
" <td>1.300000</td>\n",
|
|
" <td>1.066667</td>\n",
|
|
" <td>1.333333</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>0.774194</td>\n",
|
|
" <td>1.064516</td>\n",
|
|
" <td>0.580645</td>\n",
|
|
" <td>0.935484</td>\n",
|
|
" <td>1.032258</td>\n",
|
|
" <td>1.193548</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>0.700000</td>\n",
|
|
" <td>0.733333</td>\n",
|
|
" <td>0.800000</td>\n",
|
|
" <td>1.233333</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>0.533333</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>0.387097</td>\n",
|
|
" <td>0.548387</td>\n",
|
|
" <td>1.096774</td>\n",
|
|
" <td>0.709677</td>\n",
|
|
" <td>0.612903</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.612903</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Meditate Exercise Read Journal devlog Plants Draw\n",
|
|
"month \n",
|
|
"4 0.133333 0.266667 0.000000 0.900000 0.000000 1.133333 0.000000\n",
|
|
"5 0.387097 0.193548 0.193548 0.645161 0.000000 0.516129 0.000000\n",
|
|
"6 0.500000 0.266667 0.166667 0.666667 0.000000 0.000000 0.000000\n",
|
|
"7 0.516129 0.838710 0.967742 0.774194 0.064516 0.612903 0.000000\n",
|
|
"8 0.806452 0.741935 0.935484 1.161290 1.225806 1.193548 0.000000\n",
|
|
"9 1.133333 0.600000 1.233333 1.300000 1.066667 1.333333 0.000000\n",
|
|
"10 0.774194 1.064516 0.580645 0.935484 1.032258 1.193548 0.000000\n",
|
|
"11 0.700000 0.733333 0.800000 1.233333 1.000000 0.333333 0.533333\n",
|
|
"12 0.387097 0.548387 1.096774 0.709677 0.612903 0.000000 0.612903"
|
|
]
|
|
},
|
|
"execution_count": 105,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#colNames = ['date', 'Godmode', 'Meditate', 'Exercise', 'Piano','Read', 'Journal', 'Gratitude', 'devlog','Plants', 'Job', 'Draw']\n",
|
|
"checks = pd.read_csv('data/LoopHabits/Checkmarks.csv', header=0)\n",
|
|
"checks.set_axis(checks['date'], axis='index', inplace=True)\n",
|
|
"checks = checks.drop(['Godmode', 'Piano', 'Gratitude', 'Job', 'date'], axis=1)\n",
|
|
"df = MarchOnwards(only2019(checks))\n",
|
|
"print(\"Days done: \" + str(countValue(2, df)))\n",
|
|
"print(\"Non-streak days: \" + str(countZeros(df)))\n",
|
|
"print(\"Streak days: \" + str(countValue(1, df)))\n",
|
|
"print(\"Longest streak: \" + str(df.apply(longestStreak, axis=0)))\n",
|
|
"print(\"Longest zeros: \" + str(df.apply(longestZero, axis=0)))\n",
|
|
"#print(\"Best month: \" + str(sumMonth(df)))\n",
|
|
"sumMonth(df)\n",
|
|
"meanMonth(df)\n",
|
|
"#print(\"Worst month: \" + calendar.month_name[int(sumMonth(df).idxmin())])\n",
|
|
"#plotScore(df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 102,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def MarchOnwards(df):\n",
|
|
" # For habits only, lost my phone in late Febuary, didn't have a recent backup\n",
|
|
" return df.filter(regex='[0-9]{4}-(0?[4-9]|1?[0-2])-[0-9]{2}', axis=0)\n",
|
|
"\n",
|
|
"def longestZero(df):\n",
|
|
" # reverse the series so dates are ascending (increasing?)\n",
|
|
" # pad out the series with 0s, then diff it to track total, use that total to calculate highest streak\n",
|
|
" # streak increases when sum is 0\n",
|
|
" diffStreak = pd.concat([pd.Series([0]) , df[::-1], pd.Series([0])]).diff().tolist()\n",
|
|
" runningSum = 0\n",
|
|
" streak = 0\n",
|
|
" streakList = []\n",
|
|
" for e in diffStreak[1:-1]:\n",
|
|
" runningSum += e\n",
|
|
" if runningSum == 0:\n",
|
|
" streak += 1\n",
|
|
" else:\n",
|
|
" streakList.append(streak)\n",
|
|
" streak = 0\n",
|
|
" return max(streakList)\n",
|
|
"\n",
|
|
"def longestStreak(df):\n",
|
|
" # reverse the series so dates are ascending (increasing?)\n",
|
|
" # pad out the series with 0s, then diff it to track total, use that total to calculate highest streak\n",
|
|
" # streak increases when sum is non-zero\n",
|
|
" diffStreak = pd.concat([pd.Series([0]) , df[::-1], pd.Series([0])]).diff().tolist()\n",
|
|
" runningSum = 0\n",
|
|
" streak = 0\n",
|
|
" streakList = []\n",
|
|
" for e in diffStreak[1:-1]:\n",
|
|
" runningSum += e\n",
|
|
" if runningSum != 0:\n",
|
|
" streak += 1\n",
|
|
" else:\n",
|
|
" streakList.append(streak)\n",
|
|
" streak = 0\n",
|
|
" return max(streakList)\n",
|
|
"\n",
|
|
"def sumMonth(df):\n",
|
|
" df2 = df.reset_index()\n",
|
|
" df['month'] = pd.DatetimeIndex(df2['date']).month\n",
|
|
" return df.groupby(['month']).sum()\n",
|
|
"\n",
|
|
"def meanMonth(df):\n",
|
|
" df2 = df.reset_index()\n",
|
|
" df['month'] = pd.DatetimeIndex(df2['date']).month\n",
|
|
" return df.groupby(['month']).mean()\n",
|
|
"\n",
|
|
"def plotScore(df):\n",
|
|
" df = meanMonth(df)\n",
|
|
" plt.plot(df)\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"def countZeros(df):\n",
|
|
" return countValue(0, df)\n",
|
|
"\n",
|
|
"def countValue(countValue, df):\n",
|
|
" return (df == countValue).astype(int).sum()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# checking for CSV of simply one habit\n",
|
|
"\n",
|
|
"colNames = ['date', 'value']\n",
|
|
"meditateChecks = pd.read_csv('LoopHabits/002 Meditate/Checkmarks.csv', names=colNames, header=None)\n",
|
|
"meditateScore = pd.read_csv('LoopHabits/002 Meditate/Scores.csv', names=colNames, header=None)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# might be broken now\n",
|
|
"# need to deal with dates in columns\n",
|
|
"meditateChecks.set_axis(checks['date'], axis='index', inplace=True)\n",
|
|
"df = MarchOnwards(only2019(meditateChecks))\n",
|
|
"print(\"Days done: \" + str(countValue(2, df)))\n",
|
|
"print(\"Non-streak days: \" + str(countZeros(df)))\n",
|
|
"print(\"Streak days: \" + str(countValue(1, df)))\n",
|
|
"print(\"Longest streak: \" + str(longestStreak(df)))\n",
|
|
"print(\"Longest zeros: \" + str(longestZero(df)))\n",
|
|
"print(\"Best month: \" + calendar.month_name[int(sumMonth(df).idxmax())])\n",
|
|
"print(\"Worst month: \" + calendar.month_name[int(sumMonth(df).idxmin())])\n",
|
|
"plotScore(df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"306\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"299"
|
|
]
|
|
},
|
|
"execution_count": 71,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Total days for habits: 306\n",
|
|
"# How come when counting streaks I only get 299?\n",
|
|
"# missing 1 week??\n",
|
|
"print(72 + 195 + 39)\n",
|
|
"longestStreak(onlyMarch(only2019(meditateChecks))) + longestZero(onlyMarch(only2019(meditateChecks)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Why don't the above two numbers match??"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|