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satan87/Medium Writing Earnings.ipynb

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Medium Writing Earnings.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### MONTHS LIST"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:17.974681Z",
"start_time": "2020-06-20T21:50:17.969584Z"
}
},
"outputs": [],
"source": [
"class Month(Enum):\n",
" January = 1\n",
" February = 2\n",
" March = 3\n",
" April = 4\n",
" May = 5\n",
" June = 6\n",
" July = 7\n",
" August = 8\n",
" September = 9\n",
" October = 10\n",
" November = 11\n",
" December = 12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### STORY CLASS"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:17.982271Z",
"start_time": "2020-06-20T21:50:17.977787Z"
}
},
"outputs": [],
"source": [
"class Story:\n",
" title = \"\"\n",
" words = 0\n",
" lectureTime = 0\n",
" \n",
" def __init__(self, title, words, time):\n",
" self.title = title\n",
" self.words = words\n",
" self.lectureTime = time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PLATFORM LIST"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:17.989742Z",
"start_time": "2020-06-20T21:50:17.985148Z"
}
},
"outputs": [],
"source": [
"class Platform(Enum):\n",
" Medium = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### GAIN CLASS"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:17.997663Z",
"start_time": "2020-06-20T21:50:17.992412Z"
}
},
"outputs": [],
"source": [
"class Gain:\n",
" story = \"\"\n",
" platform = Platform.Medium\n",
" month = Month.January\n",
" year = 2020\n",
" gain = 0\n",
" \n",
" def __init__(self, story, platform, month, year, gain):\n",
" self.story = story\n",
" self.platform = platform\n",
" self.month = month\n",
" self.year = year\n",
" self.gain = gain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## -- DATA --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TAX RATE"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.002704Z",
"start_time": "2020-06-20T21:50:17.999474Z"
}
},
"outputs": [],
"source": [
"Tax = 0.7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### YOUR STORIES"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.008660Z",
"start_time": "2020-06-20T21:50:18.004925Z"
}
},
"outputs": [],
"source": [
"class Stories(Enum):\n",
" STORY_1_TITLE = Story(\"My First Story\", 500, 3)\n",
" STORY_2_TITLE = Story(\"My Second Story\", 1000, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### YOUR GAINS"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.016326Z",
"start_time": "2020-06-20T21:50:18.010836Z"
}
},
"outputs": [],
"source": [
"gains = [\n",
" Gain(Stories.STORY_1_TITLE, Platform.Medium, Month.January, 2020, 8.00),\n",
" Gain(Stories.STORY_1_TITLE, Platform.Medium, Month.February, 2020, 9.00),\n",
" Gain(Stories.STORY_1_TITLE, Platform.Medium, Month.March, 2020, 7.00),\n",
" Gain(Stories.STORY_2_TITLE, Platform.Medium, Month.March, 2020, 3.00),\n",
" Gain(Stories.STORY_1_TITLE, Platform.Medium, Month.May, 2020, 9.00),\n",
" Gain(Stories.STORY_2_TITLE, Platform.Medium, Month.May, 2020, 10.00),\n",
" Gain(Stories.STORY_1_TITLE, Platform.Medium, Month.June, 2020, 11.00),\n",
" Gain(Stories.STORY_2_TITLE, Platform.Medium, Month.June, 2020, 7.00)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T20:14:20.363860Z",
"start_time": "2020-06-20T20:14:20.361076Z"
}
},
"source": [
"## -- LET THE MAGIC HAPPENED --"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.023527Z",
"start_time": "2020-06-20T21:50:18.018510Z"
}
},
"outputs": [],
"source": [
"# We put all the data into the same array\n",
"myData = []\n",
"for gain in gains:\n",
" ligne = []\n",
" ligne.append(gain.year) # We get the year\n",
" ligne.append(gain.month.name) # We get the month\n",
" ligne.append(gain.month.value) # We get the month index\n",
" ligne.append(gain.story.value.title) # We get the story\n",
" ligne.append(gain.story.value.words) #We get the word count\n",
" ligne.append(gain.story.value.lectureTime) #We get the length\n",
" ligne.append(gain.platform.name) #We get the platform\n",
" ligne.append(gain.gain) # Gain before tax\n",
" ligne.append(gain.gain * Tax) # Gain After tax\n",
" myData.append(ligne) "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.044880Z",
"start_time": "2020-06-20T21:50:18.025568Z"
},
"scrolled": true
},
"outputs": [
{
"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>YEAR</th>\n",
" <th>MONTH</th>\n",
" <th>MONTHINDEX</th>\n",
" <th>STORY</th>\n",
" <th>WORD</th>\n",
" <th>LECTURETIME</th>\n",
" <th>PLATFORM</th>\n",
" <th>GAIN BEF TAX</th>\n",
" <th>GAIN AFT TAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020</td>\n",
" <td>January</td>\n",
" <td>1</td>\n",
" <td>My First Story</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>Medium</td>\n",
" <td>8.0</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020</td>\n",
" <td>February</td>\n",
" <td>2</td>\n",
" <td>My First Story</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>Medium</td>\n",
" <td>9.0</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020</td>\n",
" <td>March</td>\n",
" <td>3</td>\n",
" <td>My First Story</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>Medium</td>\n",
" <td>7.0</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020</td>\n",
" <td>March</td>\n",
" <td>3</td>\n",
" <td>My Second Story</td>\n",
" <td>1000</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" <td>3.0</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020</td>\n",
" <td>May</td>\n",
" <td>5</td>\n",
" <td>My First Story</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>Medium</td>\n",
" <td>9.0</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2020</td>\n",
" <td>May</td>\n",
" <td>5</td>\n",
" <td>My Second Story</td>\n",
" <td>1000</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" <td>10.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2020</td>\n",
" <td>June</td>\n",
" <td>6</td>\n",
" <td>My First Story</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>Medium</td>\n",
" <td>11.0</td>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2020</td>\n",
" <td>June</td>\n",
" <td>6</td>\n",
" <td>My Second Story</td>\n",
" <td>1000</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" <td>7.0</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" YEAR MONTH MONTHINDEX STORY WORD LECTURETIME PLATFORM \\\n",
"0 2020 January 1 My First Story 500 3 Medium \n",
"1 2020 February 2 My First Story 500 3 Medium \n",
"2 2020 March 3 My First Story 500 3 Medium \n",
"3 2020 March 3 My Second Story 1000 5 Medium \n",
"4 2020 May 5 My First Story 500 3 Medium \n",
"5 2020 May 5 My Second Story 1000 5 Medium \n",
"6 2020 June 6 My First Story 500 3 Medium \n",
"7 2020 June 6 My Second Story 1000 5 Medium \n",
"\n",
" GAIN BEF TAX GAIN AFT TAX \n",
"0 8.0 5.6 \n",
"1 9.0 6.3 \n",
"2 7.0 4.9 \n",
"3 3.0 2.1 \n",
"4 9.0 6.3 \n",
"5 10.0 7.0 \n",
"6 11.0 7.7 \n",
"7 7.0 4.9 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We create the Panda Data Frame \n",
"dataFrame = pd.DataFrame(myData, columns=[\"YEAR\",\"MONTH\",\"MONTHINDEX\",\"STORY\",\"WORD\",\"LECTURETIME\",\"PLATFORM\",\"GAIN BEF TAX\",\"GAIN AFT TAX\"])\n",
"dataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T20:19:01.555703Z",
"start_time": "2020-06-20T20:19:01.552341Z"
}
},
"source": [
"## -- TOTAL GAIN PER YEAR --"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.340313Z",
"start_time": "2020-06-20T21:50:18.047023Z"
}
},
"outputs": [
{
"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>YEAR</th>\n",
" <th>GAIN AFT TAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020</td>\n",
" <td>44.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" YEAR GAIN AFT TAX\n",
"0 2020 44.8"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f61d4a2b610>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gainsPerYear = dataFrame.groupby([\"YEAR\"])[\"GAIN AFT TAX\"].sum()\n",
"gainsPerYear.to_frame().reset_index()\n",
"gainsPerYear.plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T20:24:28.500578Z",
"start_time": "2020-06-20T20:24:28.498276Z"
}
},
"source": [
"## -- TOTAL GAIN PER YEAR / PER MONTH --"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.463699Z",
"start_time": "2020-06-20T21:50:18.342053Z"
}
},
"outputs": [
{
"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>YEAR</th>\n",
" <th>MONTHINDEX</th>\n",
" <th>GAIN AFT TAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020</td>\n",
" <td>1</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020</td>\n",
" <td>2</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020</td>\n",
" <td>3</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020</td>\n",
" <td>5</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020</td>\n",
" <td>6</td>\n",
" <td>12.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" YEAR MONTHINDEX GAIN AFT TAX\n",
"0 2020 1 5.6\n",
"1 2020 2 6.3\n",
"2 2020 3 7.0\n",
"3 2020 5 13.3\n",
"4 2020 6 12.6"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f61cfc50890>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gainsPerYearPerMonth = dataFrame.groupby([\"YEAR\",\"MONTHINDEX\"])[\"GAIN AFT TAX\"].sum()\n",
"gainsPerYearPerMonth.to_frame().reset_index()\n",
"gainsPerYearPerMonth.plot(kind='line')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## -- TOTAL GAIN PER STORY --"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-20T21:50:18.563872Z",
"start_time": "2020-06-20T21:50:18.465496Z"
}
},
"outputs": [
{
"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>STORY</th>\n",
" <th>GAIN AFT TAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>My First Story</td>\n",
" <td>30.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>My Second Story</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STORY GAIN AFT TAX\n",
"0 My First Story 30.8\n",
"1 My Second Story 14.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f61cfbe18d0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gainsPerStory = dataFrame.groupby([\"STORY\"])[\"GAIN AFT TAX\"].sum()\n",
"gainsPerStory.to_frame().reset_index()\n",
"gainsPerStory.plot(kind='bar')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": false,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": false,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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