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SqrtMinusOne/analyze.ipynb

Created October 31, 2019 09:19
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analyze.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Diplomas 2019\n",
"An analysis of the bachelor's and master's graduation work subjects in Saint-Petersburg State Electrotechnical University, Faculty of Computer Technologies and Information"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"from IPython.display import display\n",
"from IPython.core.debugger import set_trace\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"plt.style.use('dark_background')\n",
"pd.options.mode.chained_assignment = None\n",
"\n",
"def display_full(x):\n",
" pd.set_option('display.max_rows', len(x))\n",
" display(x)\n",
" pd.reset_option('display.max_rows')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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>Professor</th>\n",
" <th>Subject</th>\n",
" <th>Number</th>\n",
" <th>Comment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Беляев Сергей Алексеевич</td>\n",
" <td>Разработка программы оценки ситуации в многоаг...</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Беляев Сергей Алексеевич</td>\n",
" <td>Разработка программы планирования действий в м...</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Берленко Татьяна Андреевна</td>\n",
" <td>Разработка системы электронной очереди для лаб...</td>\n",
" <td>1</td>\n",
" <td>http://se.moevm.info/doku.php/staff:courses:th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Берленко Татьяна Андреевна</td>\n",
" <td>Разработка системы проверки студенческих решен...</td>\n",
" <td>1</td>\n",
" <td>http://se.moevm.info/doku.php/staff:courses:th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Бушмакин А.Л.</td>\n",
" <td>Машинный разбор формализованных сообщений упра...</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Professor \\\n",
"0 Беляев Сергей Алексеевич \n",
"1 Беляев Сергей Алексеевич \n",
"2 Берленко Татьяна Андреевна \n",
"3 Берленко Татьяна Андреевна \n",
"4 Бушмакин А.Л. \n",
"\n",
" Subject Number \\\n",
"0 Разработка программы оценки ситуации в многоаг... 1 \n",
"1 Разработка программы планирования действий в м... 1 \n",
"2 Разработка системы электронной очереди для лаб... 1 \n",
"3 Разработка системы проверки студенческих решен... 1 \n",
"4 Машинный разбор формализованных сообщений упра... 1 \n",
"\n",
" Comment \n",
"0 NaN \n",
"1 NaN \n",
"2 http://se.moevm.info/doku.php/staff:courses:th... \n",
"3 http://se.moevm.info/doku.php/staff:courses:th... \n",
"4 NaN "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_t = pd.read_csv('topics.csv')\n",
"df_t.columns = '№', 'Professor', 'Subject', 'Number', 'Comment'\n",
"df_t = df_t.drop('№',axis=1)\n",
"\n",
"display(df_t.head())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"professors = np.append(df_t['Professor'].unique(), [\n",
" 'Филатов Артём Юрьевич',\n",
" 'Филатов Антон Юрьевич',\n",
" 'Лисс Анна Александровна',\n",
" 'Фомичёва Татьяна Генриховна',\n",
" 'Романенко Сергей Александрович',\n",
" 'Борисенко Константин Алексеевич',\n",
" 'Спицын Александр Валентинович',\n",
" 'Калишенко Евгений Леонидович',\n",
" 'Чайка Константин Владимирович',\n",
" 'Кухарев Г.А.'\n",
"])\n",
"\n",
"professors = [p.replace('ё', 'е') for p in professors]\n",
"professors = [p.replace('\t', '') for p in professors]\n",
"map_professors = {\n",
" 'Борисенко К.А.': 'Борисенко Константин Алексеевич',\n",
" \"Размочаева Наталья 'Владимировна\": 'Размочаева Наталья Владимировна',\n",
" 'Размочаева Н.В.': 'Размочаева Наталья Владимировна',\n",
" 'Кринкин К.В.': 'Кринкин Кирилл Владимирович',\n",
"}\n",
"\n",
"# display(professors)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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>Time</th>\n",
" <th>Email</th>\n",
" <th>Student</th>\n",
" <th>Subject</th>\n",
" <th>Comment</th>\n",
" <th>Professor</th>\n",
" <th>Type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-10-29 19:53:57</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>Разработка компьютерной игры Сапер от первого ...</td>\n",
" <td></td>\n",
" <td>Герасимова Тамара Владимировна</td>\n",
" <td>Chosen now</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-10-25 13:10:32</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>Разработка системы электронной очереди для лаб...</td>\n",
" <td></td>\n",
" <td>Берленко Татьяна Андреевна</td>\n",
" <td>Chosen now</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-10-25 13:10:35</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>Разработка программы планирования действий в м...</td>\n",
" <td></td>\n",
" <td>Беляев Сергей Алексеевич</td>\n",
" <td>Chosen now</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-10-25 13:10:52</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>Автоматизация проверки студенческих работ в ра...</td>\n",
" <td></td>\n",
" <td>Заславский Марк Маркович</td>\n",
" <td>Chosen now</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-10-25 13:32:42</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>&lt;PERSONAL DATA&gt;</td>\n",
" <td>Разработка и программная реализация методов ап...</td>\n",
" <td></td>\n",
" <td>Середа Альгирдас-Владимир Игнатьевич</td>\n",
" <td>Chosen now</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Email Student \\\n",
"0 2019-10-29 19:53:57 <PERSONAL DATA> <PERSONAL DATA> \n",
"1 2019-10-25 13:10:32 <PERSONAL DATA> <PERSONAL DATA> \n",
"2 2019-10-25 13:10:35 <PERSONAL DATA> <PERSONAL DATA> \n",
"3 2019-10-25 13:10:52 <PERSONAL DATA> <PERSONAL DATA> \n",
"4 2019-10-25 13:32:42 <PERSONAL DATA> <PERSONAL DATA> \n",
"\n",
" Subject Comment \\\n",
"0 Разработка компьютерной игры Сапер от первого ... \n",
"1 Разработка системы электронной очереди для лаб... \n",
"2 Разработка программы планирования действий в м... \n",
"3 Автоматизация проверки студенческих работ в ра... \n",
"4 Разработка и программная реализация методов ап... \n",
"\n",
" Professor Type \n",
"0 Герасимова Тамара Владимировна Chosen now \n",
"1 Берленко Татьяна Андреевна Chosen now \n",
"2 Беляев Сергей Алексеевич Chosen now \n",
"3 Заславский Марк Маркович Chosen now \n",
"4 Середа Альгирдас-Владимир Игнатьевич Chosen now "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_c = pd.read_csv('choices.csv')\n",
"df_c = df_c[df_c.columns[0:-1]]\n",
"df_c.columns = 'Time', 'Email', 'Student', 'Subject', 'Comment'\n",
"df_c = df_c.fillna('')\n",
"df_c['Comment'] = df_c['Comment'].apply(lambda c: c.replace('ё', 'е'))\n",
"df_c['Subject'] = df_c['Subject'].apply(lambda c: c.replace(' ', ' '))\n",
"df_c['Time'] = pd.to_datetime(df_c['Time'])\n",
"df_c['Email'] = '<PERSONAL DATA>'\n",
"df_c['Student'] = '<PERSONAL DATA>'\n",
"\n",
"prof_chosen = []\n",
"types = []\n",
"\n",
"for index, row in df_c.iterrows():\n",
" if not row['Subject'].startswith('!!!'):\n",
" prof_chosen.append(' '.join(row['Subject'].split(' ')[:3]))\n",
" df_c.at[index, 'Subject'] = ' '.join(row['Subject'].split(' ')[3:])\n",
" types.append('Chosen now')\n",
" elif row['Subject'].startswith('!!!У меня уже есть руководитель'):\n",
" prof = next((p for p in professors if p in row['Comment']), np.nan)\n",
" if not prof:\n",
" prof = next((p for p in map_professors.keys() if p in row['Comment']), np.nan)\n",
" if prof:\n",
" prof = map_professors[prof]\n",
" if not prof:\n",
" print('Not found:')\n",
" print(row)\n",
" types.append('Chosen preemtively')\n",
" prof_chosen.append(prof)\n",
" else:\n",
" prof_chosen.append(np.nan)\n",
" types.append('From a job')\n",
" \n",
"df_c = df_c.copy()\n",
"df_c['Professor'] = prof_chosen\n",
"df_c['Type'] = types\n",
"# display_full(df_c)\n",
"df_c.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6, 6), facecolor='0.1')\n",
"df_ = df_c[['Type', 'Time']].groupby('Type').count()\n",
"_, _, autotexts = ax.pie(df_.Time, labels=df_.index, autopct='%1.1f%%')\n",
"ax.set_ylabel('')\n",
"[a.set_color('0.0') for a in autotexts]\n",
"ax.set_title('Choice types breakdown')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"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>Chosen now</th>\n",
" <th>Chosen preemtively</th>\n",
" <th>Not selected</th>\n",
" <th>Proposed</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Professor</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Беляев Сергей Алексеевич</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Берленко Татьяна Андреевна</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Борисенко Константин Алексеевич</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Бушмакин А.Л.</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>В процессе уточнения</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Герасимова Тамара Владимировна</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Жангиров Тимур Рафаилович</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Заславский Марк Маркович</th>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Калишенко Евгений Леонидович</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Кирьянчиков Владимир Андреевич</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Кринкин Кирилл Владимирович</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>13</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Кухарев Г.А.</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Лавров Андрей Александрович</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Лисс Анна Александровна</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Попова Елена Владимировна</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Размочаева Наталья Владимировна</th>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Романенко Сергей Александрович</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Середа Альгирдас-Владимир Игнатьевич</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Спицын Александр Валентинович</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Филатов Антон Юрьевич</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Филатов Артем Юрьевич</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Фомичева Татьяна Генриховна</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Чайка Константин Владимирович</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Черниченко Дмитрий Александрович</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Яновский В.В.</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chosen now Chosen preemtively \\\n",
"Professor \n",
"Беляев Сергей Алексеевич 1 6 \n",
"Берленко Татьяна Андреевна 5 0 \n",
"Борисенко Константин Алексеевич 0 4 \n",
"Бушмакин А.Л. 0 0 \n",
"В процессе уточнения 4 0 \n",
"Герасимова Тамара Владимировна 5 1 \n",
"Жангиров Тимур Рафаилович 4 0 \n",
"Заславский Марк Маркович 6 4 \n",
"Калишенко Евгений Леонидович 0 1 \n",
"Кирьянчиков Владимир Андреевич 0 0 \n",
"Кринкин Кирилл Владимирович 2 1 \n",
"Кухарев Г.А. 0 1 \n",
"Лавров Андрей Александрович 1 0 \n",
"Лисс Анна Александровна 0 1 \n",
"Попова Елена Владимировна 1 0 \n",
"Размочаева Наталья Владимировна 0 5 \n",
"Романенко Сергей Александрович 0 1 \n",
"Середа Альгирдас-Владимир Игнатьевич 1 0 \n",
"Спицын Александр Валентинович 0 1 \n",
"Филатов Антон Юрьевич 0 3 \n",
"Филатов Артем Юрьевич 0 4 \n",
"Фомичева Татьяна Генриховна 0 1 \n",
"Чайка Константин Владимирович 0 1 \n",
"Черниченко Дмитрий Александрович 2 0 \n",
"Яновский В.В. 0 0 \n",
"\n",
" Not selected Proposed \n",
"Professor \n",
"Беляев Сергей Алексеевич 1 2 \n",
"Берленко Татьяна Андреевна 0 5 \n",
"Борисенко Константин Алексеевич 0 0 \n",
"Бушмакин А.Л. 1 1 \n",
"В процессе уточнения 1 5 \n",
"Герасимова Тамара Владимировна 1 6 \n",
"Жангиров Тимур Рафаилович 1 5 \n",
"Заславский Марк Маркович 4 10 \n",
"Калишенко Евгений Леонидович 0 0 \n",
"Кирьянчиков Владимир Андреевич 2 2 \n",
"Кринкин Кирилл Владимирович 13 15 \n",
"Кухарев Г.А. 0 0 \n",
"Лавров Андрей Александрович 2 3 \n",
"Лисс Анна Александровна 0 0 \n",
"Попова Елена Владимировна 1 2 \n",
"Размочаева Наталья Владимировна 1 1 \n",
"Романенко Сергей Александрович 0 0 \n",
"Середа Альгирдас-Владимир Игнатьевич 4 5 \n",
"Спицын Александр Валентинович 0 0 \n",
"Филатов Антон Юрьевич 0 0 \n",
"Филатов Артем Юрьевич 0 0 \n",
"Фомичева Татьяна Генриховна 0 0 \n",
"Чайка Константин Владимирович 0 0 \n",
"Черниченко Дмитрий Александрович 1 3 \n",
"Яновский В.В. 2 2 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_ = df_c.dropna()[['Professor', 'Type', 'Time']]\n",
"\n",
"res_df = pd.DataFrame({'Professor': professors}).set_index('Professor')\n",
"for type_, group in df_.groupby('Type'):\n",
" group = group[['Professor', 'Time']].groupby('Professor').count()\n",
" group.columns = type_,\n",
" res_df = res_df.join(group, how='outer')\n",
" \n",
"\n",
"df_j = df_t[['Subject', 'Professor']].join(\n",
" df_c[['Student', 'Type', 'Subject']].set_index('Subject'),\n",
" on='Subject')\n",
"df_r = df_j[df_j['Student'].isna()][['Professor', 'Subject']].groupby('Professor').count()\n",
"df_p = df_j[['Professor', 'Subject']].groupby('Professor').count()\n",
"df_r.columns = 'Not selected', \n",
"df_p.columns = 'Proposed',\n",
"res_df = res_df.join(df_r, how='outer')\n",
"res_df = res_df.join(df_p, how='outer')\n",
"res_df = res_df.fillna(0).astype(int)\n",
"\n",
"display_full(res_df)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6, 10), facecolor='0.1')\n",
"df_ = res_df[['Chosen now', 'Chosen preemtively']]\n",
"df_['Sum'] = df_.sum(axis=1)\n",
"df_ = df_.sort_values(by='Sum').drop('Sum', axis=1)\n",
"df_.plot(ax=ax, kind='barh', stacked=True, colormap='summer')\n",
"ax.grid(alpha=0.25)\n",
"ax.set_axisbelow(True)\n",
"ax.set_title('Number of students per professor')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(6, 6), facecolor='0.1')\n",
"df_ = res_df[['Proposed', 'Not selected']]\n",
"df_ = df_[df_['Proposed'] > 0]\n",
"df_ = df_.sort_values(by='Proposed')\n",
"df_['Selected'] = df_['Proposed'] - df_['Not selected']\n",
"df_ = df_.drop('Proposed', axis=1)\n",
"df_.plot(ax=ax, kind='barh', stacked=True, colormap='summer')\n",
"ax.grid(alpha=0.25)\n",
"ax.set_axisbelow(True)\n",
"ax.set_title('Subjects proposed & selected per professor')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_ = df_c.copy().sort_values(by='Time')\n",
"df_['Time'] = df_['Time'].apply(lambda t: f\"{t.day}/{t.month}\")\n",
"res_df = pd.DataFrame({'Time': df_['Time'].unique()}).set_index('Time')\n",
"for type_, group in df_.groupby('Type'):\n",
" group = group[['Student', 'Time']].groupby('Time').count()\n",
" group.columns = type_,\n",
" res_df = res_df.join(group, how='outer')\n",
" \n",
"res_df = res_df.fillna(0).astype(int)\n",
"res_df['sum'] = res_df.sum(axis=1)\n",
"sum_ = res_df['sum'].sum()\n",
"res_df = res_df.sort_values(by='sum', ascending=False)\n",
"res_df = res_df.drop('sum', axis=1)\n",
"res_df['cumsum'] = res_df.cumsum().sum(axis=1) / sum_ * 100\n",
"fig, ax = plt.subplots(figsize=(10, 6), facecolor='0.1')\n",
"ax2 = ax.twinx()\n",
"\n",
"ORD = 'From a job', 'Chosen preemtively', 'Chosen now'\n",
"\n",
"ax.bar(res_df.index, res_df[ORD[0]], color='C4', label=ORD[0])\n",
"ax.bar(res_df.index, res_df[ORD[1]], bottom=res_df[ORD[0]], color='C1', label=ORD[1])\n",
"ax.bar(res_df.index, res_df[ORD[2]],\n",
" bottom=res_df[ORD[1]] + res_df[ORD[0]], color='C3', label=ORD[2])\n",
"ax2.plot(res_df.index, res_df['cumsum'], color='C7', marker='D')\n",
"ax.legend(loc='center right')\n",
"ax2.set_ylim(0, 100)\n",
"ax.grid(alpha=0.25)\n",
"ax.set_axisbelow(True)\n",
"ax.set_title('Submissions per day')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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