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sh.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-2019.xlsx curr.xlsx sh.ipynb\r\n"
]
}
],
"source": [
"!ls"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# https://www.odata.org.il/dataset/de74c280-e91f-4771-8c23-eb8bfe885093\n",
"df = pd.read_excel(\"-2019.xlsx\", skiprows=15, usecols=list(range(8)))\n",
"df = df.drop(df.index[0])"
]
},
{
"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>ממוצע ציון סופי</th>\n",
" <th>מספר נבחנים</th>\n",
" <th>י\"ל</th>\n",
" <th>מחזור סיום</th>\n",
" <th>מקצוע</th>\n",
" <th>ישוב</th>\n",
" <th>שם מוסד</th>\n",
" <th>סמל מוסד</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>72.38</td>\n",
" <td>66.0</td>\n",
" <td>2.0</td>\n",
" <td>2019.0</td>\n",
" <td>אזרחות</td>\n",
" <td>אבו גוש</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>148080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>75.00</td>\n",
" <td>32.0</td>\n",
" <td>3.0</td>\n",
" <td>2019.0</td>\n",
" <td>אנגלית</td>\n",
" <td>אבו גוש</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>148080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>74.44</td>\n",
" <td>16.0</td>\n",
" <td>4.0</td>\n",
" <td>2019.0</td>\n",
" <td>אנגלית</td>\n",
" <td>אבו גוש</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>148080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>81.81</td>\n",
" <td>21.0</td>\n",
" <td>5.0</td>\n",
" <td>2019.0</td>\n",
" <td>אנגלית</td>\n",
" <td>אבו גוש</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>148080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>77.19</td>\n",
" <td>32.0</td>\n",
" <td>5.0</td>\n",
" <td>2019.0</td>\n",
" <td>ביולוגיה</td>\n",
" <td>אבו גוש</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>148080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15188</th>\n",
" <td>82.67</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>2019.0</td>\n",
" <td>יהדות</td>\n",
" <td>תל אביב - יפו</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>714857.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15189</th>\n",
" <td>75.54</td>\n",
" <td>13.0</td>\n",
" <td>5.0</td>\n",
" <td>2019.0</td>\n",
" <td>מדע וטכנולוגיה לכל</td>\n",
" <td>תל אביב - יפו</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>714857.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15190</th>\n",
" <td>67.18</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>2019.0</td>\n",
" <td>ספרות</td>\n",
" <td>תל אביב - יפו</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>714857.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15191</th>\n",
" <td>80.33</td>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>2019.0</td>\n",
" <td>פסיכולוגיה התפתחותית</td>\n",
" <td>תל אביב - יפו</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>714857.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15192</th>\n",
" <td>81.17</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>2019.0</td>\n",
" <td>תנ'ך</td>\n",
" <td>תל אביב - יפו</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>714857.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15192 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" ממוצע ציון סופי מספר נבחנים י\"ל מחזור סיום מקצוע \n",
"1 72.38 66.0 2.0 2019.0 אזרחות \\\n",
"2 75.00 32.0 3.0 2019.0 אנגלית \n",
"3 74.44 16.0 4.0 2019.0 אנגלית \n",
"4 81.81 21.0 5.0 2019.0 אנגלית \n",
"5 77.19 32.0 5.0 2019.0 ביולוגיה \n",
"... ... ... ... ... ... \n",
"15188 82.67 12.0 3.0 2019.0 יהדות \n",
"15189 75.54 13.0 5.0 2019.0 מדע וטכנולוגיה לכל \n",
"15190 67.18 11.0 2.0 2019.0 ספרות \n",
"15191 80.33 12.0 5.0 2019.0 פסיכולוגיה התפתחותית \n",
"15192 81.17 12.0 3.0 2019.0 תנ'ך \n",
"\n",
" ישוב שם מוסד סמל מוסד \n",
"1 אבו גוש מקיף אבו גוש 148080.0 \n",
"2 אבו גוש מקיף אבו גוש 148080.0 \n",
"3 אבו גוש מקיף אבו גוש 148080.0 \n",
"4 אבו גוש מקיף אבו גוש 148080.0 \n",
"5 אבו גוש מקיף אבו גוש 148080.0 \n",
"... ... ... ... \n",
"15188 תל אביב - יפו תיכון בית יעקב תל אב 714857.0 \n",
"15189 תל אביב - יפו תיכון בית יעקב תל אב 714857.0 \n",
"15190 תל אביב - יפו תיכון בית יעקב תל אב 714857.0 \n",
"15191 תל אביב - יפו תיכון בית יעקב תל אב 714857.0 \n",
"15192 תל אביב - יפו תיכון בית יעקב תל אב 714857.0 \n",
"\n",
"[15192 rows x 8 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>city</th>\n",
" <th>school_id</th>\n",
" <th>school_name</th>\n",
" <th>year</th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" <th>sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>אבו גוש</td>\n",
" <td>148080</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>2019</td>\n",
" <td>אזרחות</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>72.38</td>\n",
" <td>4777.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>אבו גוש</td>\n",
" <td>148080</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>2019</td>\n",
" <td>אנגלית</td>\n",
" <td>3</td>\n",
" <td>32</td>\n",
" <td>75.00</td>\n",
" <td>2400.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>אבו גוש</td>\n",
" <td>148080</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>2019</td>\n",
" <td>אנגלית</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>74.44</td>\n",
" <td>1191.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>אבו גוש</td>\n",
" <td>148080</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>2019</td>\n",
" <td>אנגלית</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>81.81</td>\n",
" <td>1718.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>אבו גוש</td>\n",
" <td>148080</td>\n",
" <td>מקיף אבו גוש</td>\n",
" <td>2019</td>\n",
" <td>ביולוגיה</td>\n",
" <td>5</td>\n",
" <td>32</td>\n",
" <td>77.19</td>\n",
" <td>2470.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15188</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>714857</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>2019</td>\n",
" <td>יהדות</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>82.67</td>\n",
" <td>992.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15189</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>714857</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>2019</td>\n",
" <td>מדע וטכנולוגיה לכל</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>75.54</td>\n",
" <td>982.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15190</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>714857</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>2019</td>\n",
" <td>ספרות</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>67.18</td>\n",
" <td>738.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15191</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>714857</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>2019</td>\n",
" <td>פסיכולוגיה התפתחותית</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>80.33</td>\n",
" <td>963.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15192</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>714857</td>\n",
" <td>תיכון בית יעקב תל אב</td>\n",
" <td>2019</td>\n",
" <td>תנ'ך</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>81.17</td>\n",
" <td>974.04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15192 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" city school_id school_name year \n",
"1 אבו גוש 148080 מקיף אבו גוש 2019 \\\n",
"2 אבו גוש 148080 מקיף אבו גוש 2019 \n",
"3 אבו גוש 148080 מקיף אבו גוש 2019 \n",
"4 אבו גוש 148080 מקיף אבו גוש 2019 \n",
"5 אבו גוש 148080 מקיף אבו גוש 2019 \n",
"... ... ... ... ... \n",
"15188 תל אביב - יפו 714857 תיכון בית יעקב תל אב 2019 \n",
"15189 תל אביב - יפו 714857 תיכון בית יעקב תל אב 2019 \n",
"15190 תל אביב - יפו 714857 תיכון בית יעקב תל אב 2019 \n",
"15191 תל אביב - יפו 714857 תיכון בית יעקב תל אב 2019 \n",
"15192 תל אביב - יפו 714857 תיכון בית יעקב תל אב 2019 \n",
"\n",
" subject credits num_of_students average sum \n",
"1 אזרחות 2 66 72.38 4777.08 \n",
"2 אנגלית 3 32 75.00 2400.00 \n",
"3 אנגלית 4 16 74.44 1191.04 \n",
"4 אנגלית 5 21 81.81 1718.01 \n",
"5 ביולוגיה 5 32 77.19 2470.08 \n",
"... ... ... ... ... ... \n",
"15188 יהדות 3 12 82.67 992.04 \n",
"15189 מדע וטכנולוגיה לכל 5 13 75.54 982.02 \n",
"15190 ספרות 2 11 67.18 738.98 \n",
"15191 פסיכולוגיה התפתחותית 5 12 80.33 963.96 \n",
"15192 תנ'ך 3 12 81.17 974.04 \n",
"\n",
"[15192 rows x 9 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"city\"] = df[\"ישוב\"].apply(lambda x: x.strip())\n",
"df[\"school_id\"] = df[\"סמל מוסד\"].astype(int)\n",
"df[\"school_name\"] = df[\"שם מוסד\"].apply(lambda x: x.strip())\n",
"df[\"year\"] = df[\"מחזור סיום\"].astype(int)\n",
"df[\"subject\"] = df[\"מקצוע\"].apply(lambda x: x.strip())\n",
"df[\"credits\"] = df['י\"ל'].astype(int)\n",
"df[\"num_of_students\"] = df[\"מספר נבחנים\"].astype(int)\n",
"df[\"average\"] = df[\"ממוצע ציון סופי\"]\n",
"df = df.drop(\n",
" [\n",
" \"ישוב\",\n",
" \"סמל מוסד\",\n",
" \"שם מוסד\",\n",
" \"מחזור סיום\",\n",
" \"מקצוע\",\n",
" 'י\"ל',\n",
" \"מספר נבחנים\",\n",
" \"ממוצע ציון סופי\",\n",
" ],\n",
" axis=1,\n",
")\n",
"df[\"sum\"] = df[\"average\"] * df[\"num_of_students\"]\n",
"df = df.dropna()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"city 15192\n",
"school_id 15192\n",
"school_name 15192\n",
"year 15192\n",
"subject 15192\n",
"credits 15192\n",
"num_of_students 15192\n",
"average 15192\n",
"sum 15192\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.count()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1060"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.school_id.unique())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"759247"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.num_of_students.sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2019])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.year.unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['אזרחות', 'אנגלית', 'ביולוגיה', \"הסטוריה לבי'ס ערבי\", 'כימיה',\n",
" 'מדעי לימודי הסביבה', 'מערכות תקשוב', 'מתמטיקה',\n",
" \"עברית לבי'ס ערבי\", 'ערבית לערבים', 'הבעה עברית', 'הסטוריה',\n",
" 'מחשבת ישראל וספרות', \"תושבע'פ ותלמוד\", \"תנ'ך\",\n",
" 'למודי ארץ ישראל וארכ', 'תשתיות מחשוב ותקשוב', 'חקלאות',\n",
" 'מדע וטכנולוגיה לכל', 'מחשבת ישראל לבי\"ס דת', 'ספרות', 'עצוב',\n",
" 'פסיכולוגיה התפתחותית', 'מדעי החברה', 'אלקטרוניקה ומחשבים',\n",
" 'מערכות אלקטרוניות', 'גאוגרפיה', 'מדעי המחשב',\n",
" 'מחשבת ישראל לבי\"ס כל', 'מנהל תיירותי', 'ניהול משאבי אנוש',\n",
" \"ערבית לבי'ס יהודי\", 'פיסיקה', 'תאטרון', 'תיירות', 'אמנות שימושית',\n",
" 'אמנות )הקולנוע(', 'ישומים בביוטכנולוגיה', 'מערכות ביוטכנולוגיה',\n",
" 'אומנות', 'תכנון ותכנות מערכות', 'מוסיקה', 'דת האסלם', 'ספרדית',\n",
" 'פילוסופיה', 'תקשורת )בנתיב העיוני', 'בקרת מכונות',\n",
" 'תולדות הערבים והאיסל', 'תקשורת בינלאומית )אנ', 'הפקות בתקשורת',\n",
" 'חנוך גופני', 'תקשורת וחברה', 'מחול', 'מנהל וכלכלה',\n",
" 'טכנולוגיה מוכללת', 'טכנולוגית בנייה', 'מדעי ההנדסה',\n",
" 'ניהול הייצור', 'ניהול ותפעול', 'מערכות חשמל', 'מכניקה הנדסית',\n",
" 'מערכות מכונאות רכב', 'רישומי פעילויות בחינ', 'מערכות פקוד ובקרה',\n",
" 'מדעי הים', 'ניהול מלונאי', 'שווק וקדום מכירות',\n",
" 'תחזוקת מערכות מכניות', 'מכטרוניקה', 'רוסית', \"תושבע'פ לבי'ס כללי\",\n",
" 'מדעי הבריאות', 'מערכות ניהול מידע וי', 'מערכות רפואיות',\n",
" 'אופטיקה יישומית', 'מחשבים ומערכות', 'אדריכלות ועצוב פנים',\n",
" 'צרפתית', 'מערכות תיב\"מ', 'מערכות רכב', 'תכנון הנדסי של מבנים',\n",
" 'אוטו-טק מערכות ממוחש', 'אומניות הבשול המלונא', 'מדעי התזונה',\n",
" 'עיצוב וטיפוח החן', 'עצוב אופנה', 'מערכות תעופה',\n",
" 'טלוויזיה וקולנוע', 'מדעי כדור הארץ', 'יהדות', 'חשבונאות', 'אידיש',\n",
" 'צלום', 'פרסית', 'מידע וידע באינטרנט', 'ניתוח ואיתור מידע די',\n",
" 'גרמנית', 'מדע חישובי', \"הסטוריה לבי'ס דרוזי\", 'עברית לבי\"ס דרוזי',\n",
" 'ערבית לדרוזים', 'ימאות וספינות', 'מורשת דרוזית',\n",
" 'תחזוקת מערכות סלולרי', 'אמהרית', 'איטלקית', 'מחשוב ובקרה',\n",
" 'תרמודינמיקה טכנית'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.subject.unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"int_subjects = [\n",
" \"אזרחות\",\n",
" \"אנגלית\",\n",
" \"ביולוגיה\",\n",
" \"כימיה\",\n",
" \"מתמטיקה\",\n",
" \"פיסיקה\",\n",
" \"מדעי המחשב\",\n",
" \"גאוגרפיה\",\n",
" \"הסטוריה\",\n",
" \"חקלאות\",\n",
" \"תנ'ך\",\n",
" \"אומנות\",\n",
" \"פסיכולוגיה\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city</th>\n",
" <th>school_id</th>\n",
" <th>school_name</th>\n",
" <th>year</th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" <th>sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8156</th>\n",
" <td>פוריידיס</td>\n",
" <td>360800</td>\n",
" <td>מקיף גבעת פרדיס</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>92.64</td>\n",
" <td>1019.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4877</th>\n",
" <td>גבעת שמואל</td>\n",
" <td>444620</td>\n",
" <td>אולפנה לבנות</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>31</td>\n",
" <td>92.58</td>\n",
" <td>2869.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>526</th>\n",
" <td>מודיעין-מכבים-רעות</td>\n",
" <td>165852</td>\n",
" <td>ישיבת בנ\"ע לפיד</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>43</td>\n",
" <td>92.37</td>\n",
" <td>3971.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10658</th>\n",
" <td>עראבה</td>\n",
" <td>249284</td>\n",
" <td>מקיף אלבטוף עראבה</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>91.40</td>\n",
" <td>3656.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>מעלה עירון</td>\n",
" <td>348359</td>\n",
" <td>מקיף מושריפה</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>91.27</td>\n",
" <td>1003.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5650</th>\n",
" <td>לוד</td>\n",
" <td>441196</td>\n",
" <td>עתיד למדעים לוד</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>109</td>\n",
" <td>90.67</td>\n",
" <td>9883.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8189</th>\n",
" <td>פרדס חנה-כרכור</td>\n",
" <td>380030</td>\n",
" <td>חקלאי פרדס-חנה</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>29</td>\n",
" <td>90.66</td>\n",
" <td>2629.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614</th>\n",
" <td>מודיעין-מכבים-רעות</td>\n",
" <td>450270</td>\n",
" <td>אמי\"ת בנים מודיעין</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>24</td>\n",
" <td>90.50</td>\n",
" <td>2172.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9286</th>\n",
" <td>כאוכב אבו אל-היג'א</td>\n",
" <td>372243</td>\n",
" <td>מקיף כאוכב</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>90.31</td>\n",
" <td>1174.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9722</th>\n",
" <td>מעיליא</td>\n",
" <td>248823</td>\n",
" <td>נוטרדאם</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>35</td>\n",
" <td>90.09</td>\n",
" <td>3153.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15141</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>540476</td>\n",
" <td>ישיבת הרב עמיאל</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>89.90</td>\n",
" <td>1887.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14929</th>\n",
" <td>פתח תקווה</td>\n",
" <td>441204</td>\n",
" <td>ישיבת דרכי נעם</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>17</td>\n",
" <td>89.88</td>\n",
" <td>1527.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10318</th>\n",
" <td>סח'נין</td>\n",
" <td>248278</td>\n",
" <td>בית ספר אלבשאאר</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>30</td>\n",
" <td>89.43</td>\n",
" <td>2682.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11479</th>\n",
" <td>גדרה</td>\n",
" <td>440834</td>\n",
" <td>אולפנא בהר\"ן</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>28</td>\n",
" <td>89.36</td>\n",
" <td>2502.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7278</th>\n",
" <td>ג'ת</td>\n",
" <td>348185</td>\n",
" <td>ביה\"ס גת המשולש</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>38</td>\n",
" <td>89.21</td>\n",
" <td>3389.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8802</th>\n",
" <td>דייר אל-אסד</td>\n",
" <td>248302</td>\n",
" <td>תיכון דיר אל-אסד</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" <td>88.88</td>\n",
" <td>2222.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4862</th>\n",
" <td>גבעת שמואל</td>\n",
" <td>441634</td>\n",
" <td>ישיבת בנ\"ע ג. שמואל</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>53</td>\n",
" <td>88.85</td>\n",
" <td>4709.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11807</th>\n",
" <td>כברי</td>\n",
" <td>260240</td>\n",
" <td>כברי חט\"ע</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>33</td>\n",
" <td>88.70</td>\n",
" <td>2927.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>807</th>\n",
" <td>עופרה</td>\n",
" <td>140970</td>\n",
" <td>אולפנא עופרה</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>20</td>\n",
" <td>88.65</td>\n",
" <td>1773.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12841</th>\n",
" <td>צפריה</td>\n",
" <td>440479</td>\n",
" <td>אולפנה בנ\"ע צפירה</td>\n",
" <td>2019</td>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>36</td>\n",
" <td>88.56</td>\n",
" <td>3188.16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city school_id school_name year subject \n",
"8156 פוריידיס 360800 מקיף גבעת פרדיס 2019 מתמטיקה \\\n",
"4877 גבעת שמואל 444620 אולפנה לבנות 2019 מתמטיקה \n",
"526 מודיעין-מכבים-רעות 165852 ישיבת בנ\"ע לפיד 2019 מתמטיקה \n",
"10658 עראבה 249284 מקיף אלבטוף עראבה 2019 מתמטיקה \n",
"8031 מעלה עירון 348359 מקיף מושריפה 2019 מתמטיקה \n",
"5650 לוד 441196 עתיד למדעים לוד 2019 מתמטיקה \n",
"8189 פרדס חנה-כרכור 380030 חקלאי פרדס-חנה 2019 מתמטיקה \n",
"614 מודיעין-מכבים-רעות 450270 אמי\"ת בנים מודיעין 2019 מתמטיקה \n",
"9286 כאוכב אבו אל-היג'א 372243 מקיף כאוכב 2019 מתמטיקה \n",
"9722 מעיליא 248823 נוטרדאם 2019 מתמטיקה \n",
"15141 תל אביב - יפו 540476 ישיבת הרב עמיאל 2019 מתמטיקה \n",
"14929 פתח תקווה 441204 ישיבת דרכי נעם 2019 מתמטיקה \n",
"10318 סח'נין 248278 בית ספר אלבשאאר 2019 מתמטיקה \n",
"11479 גדרה 440834 אולפנא בהר\"ן 2019 מתמטיקה \n",
"7278 ג'ת 348185 ביה\"ס גת המשולש 2019 מתמטיקה \n",
"8802 דייר אל-אסד 248302 תיכון דיר אל-אסד 2019 מתמטיקה \n",
"4862 גבעת שמואל 441634 ישיבת בנ\"ע ג. שמואל 2019 מתמטיקה \n",
"11807 כברי 260240 כברי חט\"ע 2019 מתמטיקה \n",
"807 עופרה 140970 אולפנא עופרה 2019 מתמטיקה \n",
"12841 צפריה 440479 אולפנה בנ\"ע צפירה 2019 מתמטיקה \n",
"\n",
" credits num_of_students average sum \n",
"8156 5 11 92.64 1019.04 \n",
"4877 5 31 92.58 2869.98 \n",
"526 5 43 92.37 3971.91 \n",
"10658 5 40 91.40 3656.00 \n",
"8031 5 11 91.27 1003.97 \n",
"5650 5 109 90.67 9883.03 \n",
"8189 5 29 90.66 2629.14 \n",
"614 5 24 90.50 2172.00 \n",
"9286 5 13 90.31 1174.03 \n",
"9722 5 35 90.09 3153.15 \n",
"15141 5 21 89.90 1887.90 \n",
"14929 5 17 89.88 1527.96 \n",
"10318 5 30 89.43 2682.90 \n",
"11479 5 28 89.36 2502.08 \n",
"7278 5 38 89.21 3389.98 \n",
"8802 5 25 88.88 2222.00 \n",
"4862 5 53 88.85 4709.05 \n",
"11807 5 33 88.70 2927.10 \n",
"807 5 20 88.65 1773.00 \n",
"12841 5 36 88.56 3188.16 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c1 = df[\"subject\"] == \"מתמטיקה\"\n",
"c2 = df[\"credits\"] == 5\n",
"df[c1 & c2].nlargest(20, \"average\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019 16323\n"
]
}
],
"source": [
"for y in df.year.unique():\n",
" c3 = df[\"year\"] == y\n",
" print(y, df[c1 & c2 & c3].num_of_students.sum())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>sum</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>9290</th>\n",
" <td>כאוכב אבו אל-היג'א</td>\n",
" <td>372243</td>\n",
" <td>מקיף כאוכב</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
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" <td>94.47</td>\n",
" <td>1417.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5562</th>\n",
" <td>כפר קאסם</td>\n",
" <td>448167</td>\n",
" <td>חט\"ב אבן סינא</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
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" <td>94.08</td>\n",
" <td>3386.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13225</th>\n",
" <td>ירושלים</td>\n",
" <td>140061</td>\n",
" <td>תכון ליד האוניברסיטה</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>29</td>\n",
" <td>93.90</td>\n",
" <td>2723.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5964</th>\n",
" <td>קלנסווה</td>\n",
" <td>448076</td>\n",
" <td>תיכון מקיף קלנסווה</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>24</td>\n",
" <td>93.83</td>\n",
" <td>2251.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8677</th>\n",
" <td>בענה</td>\n",
" <td>248260</td>\n",
" <td>מכללת אלביאן</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>93.45</td>\n",
" <td>1027.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3703</th>\n",
" <td>נתניה</td>\n",
" <td>470187</td>\n",
" <td>תיכון תמר אריאל</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>93.33</td>\n",
" <td>1119.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4878</th>\n",
" <td>גבעת שמואל</td>\n",
" <td>444620</td>\n",
" <td>אולפנה לבנות</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>28</td>\n",
" <td>93.32</td>\n",
" <td>2612.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11709</th>\n",
" <td>יגור</td>\n",
" <td>360222</td>\n",
" <td>מקיף כרמל זבולון</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>18</td>\n",
" <td>93.00</td>\n",
" <td>1674.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4456</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>540153</td>\n",
" <td>עירוני ה'</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>20</td>\n",
" <td>92.95</td>\n",
" <td>1859.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14930</th>\n",
" <td>פתח תקווה</td>\n",
" <td>441204</td>\n",
" <td>ישיבת דרכי נעם</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" <td>92.93</td>\n",
" <td>1393.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4863</th>\n",
" <td>גבעת שמואל</td>\n",
" <td>441634</td>\n",
" <td>ישיבת בנ\"ע ג. שמואל</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>28</td>\n",
" <td>92.89</td>\n",
" <td>2600.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5600</th>\n",
" <td>כפר קאסם</td>\n",
" <td>800086</td>\n",
" <td>מקיף כפר קאסם</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>33</td>\n",
" <td>92.67</td>\n",
" <td>3058.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7674</th>\n",
" <td>חיפה</td>\n",
" <td>340216</td>\n",
" <td>הריאלי העברי חיפה</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>115</td>\n",
" <td>92.46</td>\n",
" <td>10632.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4303</th>\n",
" <td>תל אביב - יפו</td>\n",
" <td>515502</td>\n",
" <td>תיכונ\"ט ע\"ש אלתרמן</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>50</td>\n",
" <td>92.32</td>\n",
" <td>4616.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13568</th>\n",
" <td>ירושלים</td>\n",
" <td>144097</td>\n",
" <td>התיכון הישראלי למדעי</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>23</td>\n",
" <td>92.30</td>\n",
" <td>2122.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>602</th>\n",
" <td>מודיעין-מכבים-רעות</td>\n",
" <td>442780</td>\n",
" <td>אמי\"ת בנות מודיעין</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>92.25</td>\n",
" <td>1107.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5500</th>\n",
" <td>כפר סבא</td>\n",
" <td>444117</td>\n",
" <td>תיכון ע\"ש חיים הרצוג</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>30</td>\n",
" <td>92.20</td>\n",
" <td>2766.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10116</th>\n",
" <td>נצרת</td>\n",
" <td>247056</td>\n",
" <td>נזירות סנט גוזף</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>92.17</td>\n",
" <td>3686.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5078</th>\n",
" <td>הוד השרון</td>\n",
" <td>441410</td>\n",
" <td>תיכון ע\"ש אילן רמון</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>45</td>\n",
" <td>92.13</td>\n",
" <td>4145.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>440</th>\n",
" <td>מודיעין-מכבים-רעות</td>\n",
" <td>144675</td>\n",
" <td>ע\"ש יצחק רבין עי\"ס</td>\n",
" <td>2019</td>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>23</td>\n",
" <td>92.04</td>\n",
" <td>2116.92</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city school_id school_name year subject \n",
"9290 כאוכב אבו אל-היג'א 372243 מקיף כאוכב 2019 פיסיקה \\\n",
"5562 כפר קאסם 448167 חט\"ב אבן סינא 2019 פיסיקה \n",
"13225 ירושלים 140061 תכון ליד האוניברסיטה 2019 פיסיקה \n",
"5964 קלנסווה 448076 תיכון מקיף קלנסווה 2019 פיסיקה \n",
"8677 בענה 248260 מכללת אלביאן 2019 פיסיקה \n",
"3703 נתניה 470187 תיכון תמר אריאל 2019 פיסיקה \n",
"4878 גבעת שמואל 444620 אולפנה לבנות 2019 פיסיקה \n",
"11709 יגור 360222 מקיף כרמל זבולון 2019 פיסיקה \n",
"4456 תל אביב - יפו 540153 עירוני ה' 2019 פיסיקה \n",
"14930 פתח תקווה 441204 ישיבת דרכי נעם 2019 פיסיקה \n",
"4863 גבעת שמואל 441634 ישיבת בנ\"ע ג. שמואל 2019 פיסיקה \n",
"5600 כפר קאסם 800086 מקיף כפר קאסם 2019 פיסיקה \n",
"7674 חיפה 340216 הריאלי העברי חיפה 2019 פיסיקה \n",
"4303 תל אביב - יפו 515502 תיכונ\"ט ע\"ש אלתרמן 2019 פיסיקה \n",
"13568 ירושלים 144097 התיכון הישראלי למדעי 2019 פיסיקה \n",
"602 מודיעין-מכבים-רעות 442780 אמי\"ת בנות מודיעין 2019 פיסיקה \n",
"5500 כפר סבא 444117 תיכון ע\"ש חיים הרצוג 2019 פיסיקה \n",
"10116 נצרת 247056 נזירות סנט גוזף 2019 פיסיקה \n",
"5078 הוד השרון 441410 תיכון ע\"ש אילן רמון 2019 פיסיקה \n",
"440 מודיעין-מכבים-רעות 144675 ע\"ש יצחק רבין עי\"ס 2019 פיסיקה \n",
"\n",
" credits num_of_students average sum \n",
"9290 5 15 94.47 1417.05 \n",
"5562 5 36 94.08 3386.88 \n",
"13225 5 29 93.90 2723.10 \n",
"5964 5 24 93.83 2251.92 \n",
"8677 5 11 93.45 1027.95 \n",
"3703 5 12 93.33 1119.96 \n",
"4878 5 28 93.32 2612.96 \n",
"11709 5 18 93.00 1674.00 \n",
"4456 5 20 92.95 1859.00 \n",
"14930 5 15 92.93 1393.95 \n",
"4863 5 28 92.89 2600.92 \n",
"5600 5 33 92.67 3058.11 \n",
"7674 5 115 92.46 10632.90 \n",
"4303 5 50 92.32 4616.00 \n",
"13568 5 23 92.30 2122.90 \n",
"602 5 12 92.25 1107.00 \n",
"5500 5 30 92.20 2766.00 \n",
"10116 5 40 92.17 3686.80 \n",
"5078 5 45 92.13 4145.85 \n",
"440 5 23 92.04 2116.92 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c1 = df[\"subject\"] == \"פיסיקה\"\n",
"df[c1 & c2].nlargest(20, \"average\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019 11344\n"
]
}
],
"source": [
"for y in df.year.unique():\n",
" c3 = df[\"year\"] == y\n",
" print(y, df[c1 & c2 & c3].num_of_students.sum())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>city</th>\n",
" <th>school_id</th>\n",
" <th>school_name</th>\n",
" <th>year</th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" <th>sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7274</th>\n",
" <td>ג'ת</td>\n",
" <td>348185</td>\n",
" <td>ביה\"ס גת המשולש</td>\n",
" <td>2019</td>\n",
" <td>מערכות אלקטרוניות</td>\n",
" <td>5</td>\n",
" <td>36</td>\n",
" <td>100.0</td>\n",
" <td>3600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6899</th>\n",
" <td>תל מונד</td>\n",
" <td>441279</td>\n",
" <td>בית חינוך ע\"ש רבין</td>\n",
" <td>2019</td>\n",
" <td>טכנולוגיה מוכללת</td>\n",
" <td>5</td>\n",
" <td>33</td>\n",
" <td>100.0</td>\n",
" <td>3300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7173</th>\n",
" <td>באקה אל-גרביה</td>\n",
" <td>348342</td>\n",
" <td>עי\"ס אלקאסמי</td>\n",
" <td>2019</td>\n",
" <td>מערכות אלקטרוניות</td>\n",
" <td>5</td>\n",
" <td>31</td>\n",
" <td>100.0</td>\n",
" <td>3100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10324</th>\n",
" <td>סח'נין</td>\n",
" <td>248278</td>\n",
" <td>בית ספר אלבשאאר</td>\n",
" <td>2019</td>\n",
" <td>תכנון ותכנות מערכות</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>100.0</td>\n",
" <td>2700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10117</th>\n",
" <td>נצרת</td>\n",
" <td>247056</td>\n",
" <td>נזירות סנט גוזף</td>\n",
" <td>2019</td>\n",
" <td>תכנון ותכנות מערכות</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" <td>100.0</td>\n",
" <td>2500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13015</th>\n",
" <td>רמת השרון</td>\n",
" <td>580019</td>\n",
" <td>חקלאי הכפר הירוק</td>\n",
" <td>2019</td>\n",
" <td>מערכות אלקטרוניות</td>\n",
" <td>5</td>\n",
" <td>17</td>\n",
" <td>100.0</td>\n",
" <td>1700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4084</th>\n",
" <td>רמת גן</td>\n",
" <td>540211</td>\n",
" <td>עירוני ע\"ש בליך</td>\n",
" <td>2019</td>\n",
" <td>מחול</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>100.0</td>\n",
" <td>1100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6779</th>\n",
" <td>רעננה</td>\n",
" <td>441808</td>\n",
" <td>תיכון אביב</td>\n",
" <td>2019</td>\n",
" <td>תכנון ותכנות מערכות</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>100.0</td>\n",
" <td>1100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14326</th>\n",
" <td>ביתר עילית</td>\n",
" <td>722066</td>\n",
" <td>סמינר מורשת ירושלים</td>\n",
" <td>2019</td>\n",
" <td>רישומי פעילויות בחינ</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>100.0</td>\n",
" <td>1100.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city school_id school_name year \n",
"7274 ג'ת 348185 ביה\"ס גת המשולש 2019 \\\n",
"6899 תל מונד 441279 בית חינוך ע\"ש רבין 2019 \n",
"7173 באקה אל-גרביה 348342 עי\"ס אלקאסמי 2019 \n",
"10324 סח'נין 248278 בית ספר אלבשאאר 2019 \n",
"10117 נצרת 247056 נזירות סנט גוזף 2019 \n",
"13015 רמת השרון 580019 חקלאי הכפר הירוק 2019 \n",
"4084 רמת גן 540211 עירוני ע\"ש בליך 2019 \n",
"6779 רעננה 441808 תיכון אביב 2019 \n",
"14326 ביתר עילית 722066 סמינר מורשת ירושלים 2019 \n",
"\n",
" subject credits num_of_students average sum \n",
"7274 מערכות אלקטרוניות 5 36 100.0 3600.0 \n",
"6899 טכנולוגיה מוכללת 5 33 100.0 3300.0 \n",
"7173 מערכות אלקטרוניות 5 31 100.0 3100.0 \n",
"10324 תכנון ותכנות מערכות 5 27 100.0 2700.0 \n",
"10117 תכנון ותכנות מערכות 5 25 100.0 2500.0 \n",
"13015 מערכות אלקטרוניות 5 17 100.0 1700.0 \n",
"4084 מחול 5 11 100.0 1100.0 \n",
"6779 תכנון ותכנות מערכות 5 11 100.0 1100.0 \n",
"14326 רישומי פעילויות בחינ 3 11 100.0 1100.0 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"average\"] == 100].sort_values(by=[\"num_of_students\"], ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"city 77\n",
"school_id 77\n",
"school_name 77\n",
"year 77\n",
"subject 77\n",
"credits 77\n",
"num_of_students 77\n",
"average 77\n",
"sum 77\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"average\"] < 56].count()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>מערכות תעופה</td>\n",
" <td>3</td>\n",
" <td>28</td>\n",
" <td>60.820000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>עברית לבי\"ס דרוזי</td>\n",
" <td>3</td>\n",
" <td>1213</td>\n",
" <td>61.006892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>מערכות מכונאות רכב</td>\n",
" <td>3</td>\n",
" <td>161</td>\n",
" <td>63.757826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>מערכות רכב</td>\n",
" <td>3</td>\n",
" <td>43</td>\n",
" <td>64.580930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>מדעי התזונה</td>\n",
" <td>5</td>\n",
" <td>83</td>\n",
" <td>66.506024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>מחשבים ומערכות</td>\n",
" <td>5</td>\n",
" <td>143</td>\n",
" <td>93.888531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>מחשוב ובקרה</td>\n",
" <td>5</td>\n",
" <td>62</td>\n",
" <td>93.934839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>תכנון ותכנות מערכות</td>\n",
" <td>5</td>\n",
" <td>4413</td>\n",
" <td>94.551634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>מחול</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>95.716720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>איטלקית</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>99.290000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>151 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" subject credits num_of_students average\n",
"122 מערכות תעופה 3 28 60.820000\n",
"140 עברית לבי\"ס דרוזי 3 1213 61.006892\n",
"84 מערכות מכונאות רכב 3 161 63.757826\n",
"110 מערכות רכב 3 43 64.580930\n",
"118 מדעי התזונה 5 83 66.506024\n",
".. ... ... ... ...\n",
"104 מחשבים ומערכות 5 143 93.888531\n",
"149 מחשוב ובקרה 5 62 93.934839\n",
"61 תכנון ותכנות מערכות 5 4413 94.551634\n",
"74 מחול 5 311 95.716720\n",
"148 איטלקית 5 14 99.290000\n",
"\n",
"[151 rows x 4 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dict = {\"subject\": [], \"credits\": [], \"num_of_students\": [], \"average\": []}\n",
"for sub in df.subject.unique():\n",
" c1 = df[\"subject\"] == sub\n",
" for cr in sorted(df.loc[c1, \"credits\"].unique()):\n",
" c2 = df[\"credits\"] == cr\n",
" tot = float(df.loc[c1 & c2, \"sum\"].sum())\n",
" st = int(df.loc[c1 & c2, \"num_of_students\"].sum())\n",
"\n",
" df_dict[\"subject\"].append(sub)\n",
" df_dict[\"credits\"].append(cr)\n",
" df_dict[\"num_of_students\"].append(st)\n",
" df_dict[\"average\"].append(tot / st)\n",
"\n",
"df_subjects = pd.DataFrame(df_dict)\n",
"df_subjects = df_subjects[df_subjects[\"average\"] > 0].sort_values(by=[\"average\"])\n",
"df_subjects[\"num_of_students\"] = df_subjects[\"num_of_students\"].astype(int)\n",
"df_subjects"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>אזרחות</td>\n",
" <td>2</td>\n",
" <td>90225</td>\n",
" <td>74.539032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>הבעה עברית</td>\n",
" <td>2</td>\n",
" <td>68930</td>\n",
" <td>75.284605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>הסטוריה</td>\n",
" <td>2</td>\n",
" <td>68014</td>\n",
" <td>77.931136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>ספרות</td>\n",
" <td>2</td>\n",
" <td>53268</td>\n",
" <td>77.328742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>תנ'ך</td>\n",
" <td>2</td>\n",
" <td>50240</td>\n",
" <td>78.425645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>מתמטיקה</td>\n",
" <td>3</td>\n",
" <td>47043</td>\n",
" <td>77.158361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>אנגלית</td>\n",
" <td>5</td>\n",
" <td>45869</td>\n",
" <td>87.609294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>אנגלית</td>\n",
" <td>4</td>\n",
" <td>28004</td>\n",
" <td>77.512400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>הסטוריה לבי'ס ערבי</td>\n",
" <td>2</td>\n",
" <td>20411</td>\n",
" <td>84.703571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>מתמטיקה</td>\n",
" <td>4</td>\n",
" <td>20308</td>\n",
" <td>81.188945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ערבית לערבים</td>\n",
" <td>3</td>\n",
" <td>18046</td>\n",
" <td>74.953832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ביולוגיה</td>\n",
" <td>5</td>\n",
" <td>17443</td>\n",
" <td>79.553223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>16323</td>\n",
" <td>81.697470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>עברית לבי'ס ערבי</td>\n",
" <td>3</td>\n",
" <td>15124</td>\n",
" <td>69.769589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>אנגלית</td>\n",
" <td>3</td>\n",
" <td>14650</td>\n",
" <td>73.856224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>11344</td>\n",
" <td>85.682271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>מחשבת ישראל וספרות</td>\n",
" <td>2</td>\n",
" <td>11278</td>\n",
" <td>81.830137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>תנ'ך</td>\n",
" <td>5</td>\n",
" <td>9810</td>\n",
" <td>83.693271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>כימיה</td>\n",
" <td>5</td>\n",
" <td>9634</td>\n",
" <td>81.825381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>תושבע'פ ותלמוד</td>\n",
" <td>5</td>\n",
" <td>9579</td>\n",
" <td>81.523992</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject credits num_of_students average\n",
"0 אזרחות 2 90225 74.539032\n",
"21 הבעה עברית 2 68930 75.284605\n",
"22 הסטוריה 2 68014 77.931136\n",
"36 ספרות 2 53268 77.328742\n",
"27 תנ'ך 2 50240 78.425645\n",
"14 מתמטיקה 3 47043 77.158361\n",
"4 אנגלית 5 45869 87.609294\n",
"3 אנגלית 4 28004 77.512400\n",
"7 הסטוריה לבי'ס ערבי 2 20411 84.703571\n",
"15 מתמטיקה 4 20308 81.188945\n",
"19 ערבית לערבים 3 18046 74.953832\n",
"6 ביולוגיה 5 17443 79.553223\n",
"16 מתמטיקה 5 16323 81.697470\n",
"17 עברית לבי'ס ערבי 3 15124 69.769589\n",
"2 אנגלית 3 14650 73.856224\n",
"52 פיסיקה 5 11344 85.682271\n",
"24 מחשבת ישראל וספרות 2 11278 81.830137\n",
"29 תנ'ך 5 9810 83.693271\n",
"10 כימיה 5 9634 81.825381\n",
"26 תושבע'פ ותלמוד 5 9579 81.523992"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_subjects.nlargest(20, \"num_of_students\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
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" <th>average</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>אנגלית</td>\n",
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" <th>0</th>\n",
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" <td>90225</td>\n",
" <td>74.539032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>גאוגרפיה</td>\n",
" <td>5</td>\n",
" <td>5606</td>\n",
" <td>75.351561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>מתמטיקה</td>\n",
" <td>3</td>\n",
" <td>47043</td>\n",
" <td>77.158361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>אנגלית</td>\n",
" <td>4</td>\n",
" <td>28004</td>\n",
" <td>77.512400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>הסטוריה</td>\n",
" <td>2</td>\n",
" <td>68014</td>\n",
" <td>77.931136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>תנ'ך</td>\n",
" <td>2</td>\n",
" <td>50240</td>\n",
" <td>78.425645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ביולוגיה</td>\n",
" <td>5</td>\n",
" <td>17443</td>\n",
" <td>79.553223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>כימיה</td>\n",
" <td>3</td>\n",
" <td>26</td>\n",
" <td>79.620000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>תנ'ך</td>\n",
" <td>3</td>\n",
" <td>7526</td>\n",
" <td>79.867222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>חקלאות</td>\n",
" <td>5</td>\n",
" <td>1932</td>\n",
" <td>81.096599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>מתמטיקה</td>\n",
" <td>4</td>\n",
" <td>20308</td>\n",
" <td>81.188945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>מתמטיקה</td>\n",
" <td>5</td>\n",
" <td>16323</td>\n",
" <td>81.697470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>כימיה</td>\n",
" <td>5</td>\n",
" <td>9634</td>\n",
" <td>81.825381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ביולוגיה</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>83.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>תנ'ך</td>\n",
" <td>5</td>\n",
" <td>9810</td>\n",
" <td>83.693271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>אזרחות</td>\n",
" <td>5</td>\n",
" <td>368</td>\n",
" <td>84.531223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>הסטוריה</td>\n",
" <td>5</td>\n",
" <td>570</td>\n",
" <td>84.853035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>פיסיקה</td>\n",
" <td>5</td>\n",
" <td>11344</td>\n",
" <td>85.682271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>מדעי המחשב</td>\n",
" <td>5</td>\n",
" <td>7838</td>\n",
" <td>86.369865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>אומנות</td>\n",
" <td>5</td>\n",
" <td>642</td>\n",
" <td>86.867508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>אנגלית</td>\n",
" <td>5</td>\n",
" <td>45869</td>\n",
" <td>87.609294</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject credits num_of_students average\n",
"2 אנגלית 3 14650 73.856224\n",
"0 אזרחות 2 90225 74.539032\n",
"45 גאוגרפיה 5 5606 75.351561\n",
"14 מתמטיקה 3 47043 77.158361\n",
"3 אנגלית 4 28004 77.512400\n",
"22 הסטוריה 2 68014 77.931136\n",
"27 תנ'ך 2 50240 78.425645\n",
"6 ביולוגיה 5 17443 79.553223\n",
"9 כימיה 3 26 79.620000\n",
"28 תנ'ך 3 7526 79.867222\n",
"33 חקלאות 5 1932 81.096599\n",
"15 מתמטיקה 4 20308 81.188945\n",
"16 מתמטיקה 5 16323 81.697470\n",
"10 כימיה 5 9634 81.825381\n",
"5 ביולוגיה 3 13 83.000000\n",
"29 תנ'ך 5 9810 83.693271\n",
"1 אזרחות 5 368 84.531223\n",
"23 הסטוריה 5 570 84.853035\n",
"52 פיסיקה 5 11344 85.682271\n",
"46 מדעי המחשב 5 7838 86.369865\n",
"60 אומנות 5 642 86.867508\n",
"4 אנגלית 5 45869 87.609294"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_subjects[df_subjects[\"subject\"].isin(int_subjects)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>subject</th>\n",
" <th>credits</th>\n",
" <th>num_of_students</th>\n",
" <th>average</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>מדעי התזונה</td>\n",
" <td>5</td>\n",
" <td>83</td>\n",
" <td>66.506024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>מערכות מכונאות רכב</td>\n",
" <td>5</td>\n",
" <td>69</td>\n",
" <td>69.854783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>תיירות</td>\n",
" <td>5</td>\n",
" <td>608</td>\n",
" <td>69.924720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>מערכות חשמל</td>\n",
" <td>5</td>\n",
" <td>932</td>\n",
" <td>70.149989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>אומניות הבשול המלונא</td>\n",
" <td>5</td>\n",
" <td>82</td>\n",
" <td>72.572805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>מחשבים ומערכות</td>\n",
" <td>5</td>\n",
" <td>143</td>\n",
" <td>93.888531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>מחשוב ובקרה</td>\n",
" <td>5</td>\n",
" <td>62</td>\n",
" <td>93.934839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>תכנון ותכנות מערכות</td>\n",
" <td>5</td>\n",
" <td>4413</td>\n",
" <td>94.551634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>מחול</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>95.716720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>איטלקית</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>99.290000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>103 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" subject credits num_of_students average\n",
"118 מדעי התזונה 5 83 66.506024\n",
"85 מערכות מכונאות רכב 5 69 69.854783\n",
"54 תיירות 5 608 69.924720\n",
"82 מערכות חשמל 5 932 70.149989\n",
"117 אומניות הבשול המלונא 5 82 72.572805\n",
".. ... ... ... ...\n",
"104 מחשבים ומערכות 5 143 93.888531\n",
"149 מחשוב ובקרה 5 62 93.934839\n",
"61 תכנון ותכנות מערכות 5 4413 94.551634\n",
"74 מחול 5 311 95.716720\n",
"148 איטלקית 5 14 99.290000\n",
"\n",
"[103 rows x 4 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_subjects[df_subjects[\"credits\"] == 5]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"average\"].plot.hist(bins=int(df[\"average\"].max() - df[\"average\"].min()))\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"79.83"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"average\"].median()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kSYmJiWrQoIG2bdumoUOHKicnR/369ZPD4fDUJCcna+bMmfrxxx/VrFmzSu9ZWlqq0tJSz3JxcbEkye12y+12V2ncFXVVqQ8JtCq9DtVTnX7DP+i5/ei5vei3/fzd8+rsp04HooEDB2rYsGGKj4/XoUOH9Kc//UmDBg1STk6OAgMDVVhYqMjISK/XBAUFqXnz5iosLJQkFRYWKj4+3qsmKirKs+1ygSgjI0PTp0+vtD4zM1OhoaHVOoasrKxfrZnV61/PV69eXa39w1tV+g3/ouf2o+f2ot/281fPz549W+VanwLR119/rRtvvNGXl1bLvffe63neuXNndenSRTfddJM2btyohISEGnvfyZMnKz093bNcXFys2NhYJSUlKSwsrEr7cLvdysrK0oABAxQcHHzF2k7T1nqe75mW7NugDVedfsM/6Ln96Lm96Lf9/N3zijM8VeFTIGrdurV++9vfKjU1Vf/xH/+hhg0b+rKbarvxxhvVokULHTx4UAkJCXI6nTp27JhXzYULF3TixAnPdUdOp1NFRUVeNRXLv3RtUkhIiEJCQiqtDw4OrvYHVJXXlJYFeNXDd758Rrg69Nx+9Nxe9Nt+/up5dfbh011mn332mbp06aL09HQ5nU794Q9/0Pbt233ZVbUcOXJEx48fV3R0tCTJ5XLp5MmTys3N9dSsX79e5eXl6t27t6dm8+bNXucRs7Ky1K5du8ueLgMAAObxKRB169ZNL730ko4ePaoFCxaooKBAffv2VadOnTR37lz98MMPVdpPSUmJ8vLylJeXJ0k6fPiw8vLylJ+fr5KSEj3++OPaunWrvvnmG2VnZ+uuu+5S69atlZx88bRShw4dNHDgQD3yyCPavn27Pv74Y40fP1733nuvYmJiJEkjRoyQw+FQamqq9u7dq2XLlumll17yOiUGAADMdlXfQxQUFKRhw4Zp+fLlmjlzpg4ePKjHHntMsbGxGjVqlAoKCq74+h07dqh79+7q3r27JCk9PV3du3fXlClTFBgYqF27dul3v/ud2rZtq9TUVPXo0UMfffSR1+msxYsXq3379kpISNDgwYPVt29fr+8YCg8PV2Zmpg4fPqwePXpo0qRJmjJlCrfcAwAAj6u6y2zHjh1asGCBli5dqsaNG+uxxx5Tamqqjhw5ounTp+uuu+664qm0/v37y7KsX9y+du3aX9xWoXnz5lqyZMkVa7p06aKPPvroV/cFAADM5FMgmjt3rhYuXKj9+/dr8ODBeuuttzR48GA1aHBxwik+Pl6LFi1Sq1at/DlWAACAGuFTIHrttdf08MMP68EHH/Rc4PxzkZGReuONN65qcAAAAHbwKRAdOHDgV2scDodGjx7ty+4BAABs5dNF1QsXLtTy5csrrV++fLnefPPNqx4UAACAnXwKRBkZGWrRokWl9ZGRkXruueeuelAma/XUKs8DAADYw6dAlJ+fX+n3wSQpLi5O+fn5Vz0oAAAAO/kUiCIjI7Vr165K6z///HPPL9EDAADUFz4Fovvuu0//9V//pQ0bNqisrExlZWVav369JkyY4PWDrAAAAPWBT3eZzZgxQ998840SEhIUFHRxF+Xl5Ro1ahTXEAEAgHrHp0DkcDi0bNkyzZgxQ59//rkaNWqkzp07Ky4uzt/jAwAAqHFX9dMdbdu2Vdu2bf01FgAAgFrhUyAqKyvTokWLlJ2drWPHjqm8vNxr+/r16/0yONNdeuv9N8+n1OJIAAC4tvkUiCZMmKBFixYpJSVFnTp1UkBAgL/HBQAAYBufAtHSpUv1zjvvaPDgwf4eDwAAgO18uu3e4XCodevW/h4LAABArfApEE2aNEkvvfSSLMvy93gAAABs59Mpsy1btmjDhg368MMPdfPNNys4ONhr+4oVK/wyOAAAADv4FIgiIiI0dOhQf48FAACgVvgUiBYuXOjvcQAAANQan64hkqQLFy5o3bp1+u///m+dPn1aknT06FGVlJT4bXAAAAB28GmG6Ntvv9XAgQOVn5+v0tJSDRgwQE2bNtXMmTNVWlqq+fPn+3ucAAAANcanGaIJEyaoZ8+e+vHHH9WoUSPP+qFDhyo7O9tvgwMAALCDTzNEH330kT755BM5HA6v9a1atdL333/vl4EBAADYxacZovLycpWVlVVaf+TIETVt2vSqBwUAAGAnnwJRUlKSXnzxRc9yQECASkpKNHXqVH7OAwAA1Ds+nTKbM2eOkpOT1bFjR507d04jRozQgQMH1KJFC7399tv+HiMAAECN8ikQ3XDDDfr888+1dOlS7dq1SyUlJUpNTdXIkSO9LrIGAACoD3wKRJIUFBSk+++/359jAQAAqBU+BaK33nrrittHjRrl02AAAABqg0+BaMKECV7LbrdbZ8+elcPhUGhoKIEIAADUKz7dZfbjjz96PUpKSrR//3717duXi6oBAEC94/Nvmf1cmzZt9Pzzz1eaPQIAAKjr/BaIpIsXWh89etSfuwQAAKhxPl1D9P7773stW5algoIC/e1vf9Ntt93ml4EBAADYxadAdPfdd3stBwQE6Prrr9edd96pOXPm+GNcAAAAtvEpEJWXl/t7HKiGVk+t8jz/5vmUWhwJAADXBr9eQwQAAFAf+TRDlJ6eXuXauXPn+vIWAAAAtvEpEO3cuVM7d+6U2+1Wu3btJElfffWVAgMDdcstt3jqAgIC/DNKAACAGuRTIBoyZIiaNm2qN998U82aNZN08csaH3roId1+++2aNGmSXwcJAABQk3y6hmjOnDnKyMjwhCFJatasmZ599lnuMgMAAPWOT4GouLhYP/zwQ6X1P/zwg06fPn3VgwIAALCTT4Fo6NCheuihh7RixQodOXJER44c0f/+7/8qNTVVw4YN8/cYAQAAapRP1xDNnz9fjz32mEaMGCG3231xR0FBSk1N1ezZs/06QAAAgJrmUyAKDQ3Vq6++qtmzZ+vQoUOSpJtuukmNGzf26+AAAADscFVfzFhQUKCCggK1adNGjRs3lmVZ/hoXAACAbXwKRMePH1dCQoLatm2rwYMHq6CgQJKUmprKLfcAAKDe8SkQPfroowoODlZ+fr5CQ0M96++55x6tWbPGb4MDAACwg0/XEGVmZmrt2rW64YYbvNa3adNG3377rV8GBgAAYBefZojOnDnjNTNU4cSJEwoJCbnqQQEAANjJp0B0++2366233vIsBwQEqLy8XLNmzdIdd9zht8EBAADYwadTZrNmzVJCQoJ27Nih8+fP64knntDevXt14sQJffzxx/4eIwAAQI3yaYaoU6dO+uqrr9S3b1/dddddOnPmjIYNG6adO3fqpptu8vcYAQAAalS1Z4jcbrcGDhyo+fPn689//nNNjAmX0eqpVbU9BAAArlnVniEKDg7Wrl27amIsAAAAtcKnU2b333+/3njjDX+PBQAAoFb4dFH1hQsXtGDBAq1bt049evSo9Btmc+fO9cvgAAAA7FCtQPT111+rVatW2rNnj2655RZJ0ldffeVVExAQ4L/RAQAA2KBagahNmzYqKCjQhg0bJF38qY6XX35ZUVFRNTI4AAAAO1TrGqKf/5r9hx9+qDNnzvh1QAAAAHbz6aLqCj8PSAAAAPVRtQJRQEBApWuEuGYIAADUd9U+Zfbggw9q2LBhGjZsmM6dO6exY8d6liseVbV582YNGTJEMTExCggI0HvvvVfp/aZMmaLo6Gg1atRIiYmJOnDggFfNiRMnNHLkSIWFhSkiIkKpqakqKSnxqtm1a5duv/12NWzYULGxsZo1a1Z1DhsAAFzjqhWIRo8ercjISIWHhys8PFz333+/YmJiPMsVj6o6c+aMunbtqnnz5l12+6xZs/Tyyy9r/vz52rZtmxo3bqzk5GSdO3fOUzNy5Ejt3btXWVlZWrlypTZv3qwxY8Z4thcXFyspKUlxcXHKzc3V7NmzNW3aNP3973+vzqEDAIBrWLXuMlu4cKFf33zQoEEaNGjQZbdZlqUXX3xRTz/9tO666y5J0ltvvaWoqCi99957uvfee/XFF19ozZo1+vTTT9WzZ09J0iuvvKLBgwfrr3/9q2JiYrR48WKdP39eCxYskMPh0M0336y8vDzNnTvXKzgBAABz+fTFjHY4fPiwCgsLlZiY6FkXHh6u3r17KycnR/fee69ycnIUERHhCUOSlJiYqAYNGmjbtm0aOnSocnJy1K9fPzkcDk9NcnKyZs6cqR9//FHNmjWr9N6lpaUqLS31LBcXF0u6+Dtubre7SuOvqKtKfUig7xenV3U817rq9Bv+Qc/tR8/tRb/t5++eV2c/dTYQFRYWSlKl7ziKiorybCssLFRkZKTX9qCgIDVv3tyrJj4+vtI+KrZdLhBlZGRo+vTpldZnZmYqNDS0WseRlZX1qzWzelVrl15Wr17t+4uvQVXpN/yLntuPntuLftvPXz0/e/ZslWvrbCCqTZMnT1Z6erpnubi4WLGxsUpKSlJYWFiV9uF2u5WVlaUBAwYoODj4irWdpq31eax7piX7/NprSXX6Df+g5/aj5/ai3/bzd88rzvBURZ0NRE6nU5JUVFSk6Ohoz/qioiJ169bNU3Ps2DGv1124cEEnTpzwvN7pdKqoqMirpmK5oubnQkJCFBISUml9cHBwtT+gqrymtMz3ry7gL6k3Xz4jXB16bj96bi/6bT9/9bw6+7iqL2asSfHx8XI6ncrOzvasKy4u1rZt2+RyuSRJLpdLJ0+eVG5urqdm/fr1Ki8vV+/evT01mzdv9jqPmJWVpXbt2l32dBkAADBPrQaikpIS5eXlKS8vT9LFC6nz8vKUn5+vgIAATZw4Uc8++6zef/997d69W6NGjVJMTIzuvvtuSVKHDh00cOBAPfLII9q+fbs+/vhjjR8/Xvfee69iYmIkSSNGjJDD4VBqaqr27t2rZcuW6aWXXvI6JQYAAMxWq6fMduzYoTvuuMOzXBFSRo8erUWLFumJJ57QmTNnNGbMGJ08eVJ9+/bVmjVr1LBhQ89rFi9erPHjxyshIUENGjTQ8OHD9fLLL3u2h4eHKzMzU2lpaerRo4datGihKVOmcMs9AADwqNVA1L9//yv+HlpAQICeeeYZPfPMM79Y07x5cy1ZsuSK79OlSxd99NFHPo8TAABc2+rsNUQAAAB2IRABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGC+otgeAq9PqqVWe5988n1KLIwEAoP5ihggAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4/HjrtcQfugVAADfMEMEAACMxwyRAZg5AgDgypghAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwXp0ORNOmTVNAQIDXo3379p7t586dU1pamq677jo1adJEw4cPV1FRkdc+8vPzlZKSotDQUEVGRurxxx/XhQsX7D4UAABQh9X57yG6+eabtW7dOs9yUNC/hvzoo49q1apVWr58ucLDwzV+/HgNGzZMH3/8sSSprKxMKSkpcjqd+uSTT1RQUKBRo0YpODhYzz33nO3HAgAA6qY6H4iCgoLkdDorrT916pTeeOMNLVmyRHfeeackaeHCherQoYO2bt2qPn36KDMzU/v27dO6desUFRWlbt26acaMGXryySc1bdo0ORwOuw8HAADUQXU+EB04cEAxMTFq2LChXC6XMjIy1LJlS+Xm5srtdisxMdFT2759e7Vs2VI5OTnq06ePcnJy1LlzZ0VFRXlqkpOTNW7cOO3du1fdu3e/7HuWlpaqtLTUs1xcXCxJcrvdcrvdVRp3RV1V6kMCrSrtszoufd9L91/V8dc31ek3/IOe24+e24t+28/fPa/OfgIsy/L/f4395MMPP1RJSYnatWungoICTZ8+Xd9//7327NmjDz74QA899JBXcJGkXr166Y477tDMmTM1ZswYffvtt1q7dq1n+9mzZ9W4cWOtXr1agwYNuuz7Tps2TdOnT6+0fsmSJQoNDfXvQQIAgBpx9uxZjRgxQqdOnVJYWNgVa+v0DNGlgaVLly7q3bu34uLi9M4776hRo0Y19r6TJ09Wenq6Z7m4uFixsbFKSkr61YZWcLvdysrK0oABAxQcHHzF2k7T1l5xuy/2TEu+7P4vXX8tqU6/4R/03H703F70237+7nnFGZ6qqNOB6OciIiLUtm1bHTx4UAMGDND58+d18uRJRUREeGqKioo81xw5nU5t377dax8Vd6Fd7rqkCiEhIQoJCam0Pjg4uNofUFVeU1oWUK19VvV9L7f/a/0vtS+fEa4OPbcfPbcX/bafv3penX3U6dvuf66kpESHDh1SdHS0evTooeDgYGVnZ3u279+/X/n5+XK5XJIkl8ul3bt369ixY56arKwshYWFqWPHjraPHwAA1E11eoboscce05AhQxQXF6ejR49q6tSpCgwM1H333afw8HClpqYqPT1dzZs3V1hYmP74xz/K5XKpT58+kqSkpCR17NhRDzzwgGbNmqXCwkI9/fTTSktLu+wMEAAAMFOdDkRHjhzRfffdp+PHj+v6669X3759tXXrVl1//fWSpBdeeEENGjTQ8OHDVVpaquTkZL366que1wcGBmrlypUaN26cXC6XGjdurNGjR+uZZ56prUMCAAB1UJ0OREuXLr3i9oYNG2revHmaN2/eL9bExcVp9erV/h4aAAC4htSra4gAAABqQp2eIYLvWj21qraHAABAvcEMEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8bjt3jCX3o7/zfMptTgSAADqDmaIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj7vMUAl3ogEATMMMEQAAMB6BCAAAGI9ABAAAjMc1RAbjWiEAAC5ihggAABiPQAQAAIxHIAIAAMbjGiJI8r6eCAAA0zBDBAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYjy9mxBXxA7AAABMwQwQAAIxHIAIAAMbjlBmqjNNnAIBrFTNEAADAeAQiAABgPAIRAAAwHtcQ4apxbREAoL5jhggAABiPQAQAAIzHKTP4FafPAAD1ETNEAADAeMwQwSeXzgRdzWuZRQIA1AXMEAEAAOMxQ4Qaw0wQAKC+YIYIAAAYjxki2OJqrjkCAKCmEYhQ53HqDQBQ0zhlBgAAjEcgAgAAxiMQAQAA43ENEa45XHMEAKguAhHqjOreiXZpfUigpVm9pE7T1koK8PPIAADXOgIRahW34wMA6gICEXAJTrcBgJkIRKhXmFECANQEAhGM4a8wxSwSAFx7CES4pl1NCKpu8PFXUCJwAYD9CERAFVxLp+pqOnAR6ADURwQi4CpUJShdTUCobhC7mv0TXgCYjEAE1IBfCjI1PdP0SwHHzlOHAFAfGRWI5s2bp9mzZ6uwsFBdu3bVK6+8ol69etX2sIAacTVfdFmVmgMzknx+r6q8L+ELgJ2M+S2zZcuWKT09XVOnTtVnn32mrl27Kjk5WceOHavtoQH10sVvBf/XPy+n1VOrPA8AqMuMmSGaO3euHnnkET300EOSpPnz52vVqlVasGCBnnrqqVoeHXDtq4lQVNN3AnK60P/oKeoqIwLR+fPnlZubq8mTJ3vWNWjQQImJicrJyalUX1paqtLSUs/yqVOnJEknTpyQ2+2u0nu63W6dPXtWx48fV3Bw8BVrgy6cqdI+8cuCyi2dPVuuIHcDlZXzW2Z2qOmet37snV8fw1XUHz9+3PO8d0a2z/vfNjnhV/fzSy59bVX0m7lOT3cvV7c/r1DpFXpe3f3a6dJ/3136GdRF1fn3OPzD3z0/ffq0JMmyrF8vtgzw/fffW5KsTz75xGv9448/bvXq1atS/dSpUy1JPHjw4MGDB49r4PHdd9/9alYwYoaouiZPnqz09HTPcnl5uU6cOKHrrrtOAQFV+z/h4uJixcbG6rvvvlNYWFhNDRX/H/22Hz23Hz23F/22n797blmWTp8+rZiYmF+tNSIQtWjRQoGBgSoqKvJaX1RUJKfTWak+JCREISEhXusiIiJ8eu+wsDD+ItmIftuPntuPntuLftvPnz0PDw+vUp0Rd5k5HA716NFD2dn/Or9fXl6u7OxsuVyuWhwZAACoC4yYIZKk9PR0jR49Wj179lSvXr304osv6syZM567zgAAgLmMCUT33HOPfvjhB02ZMkWFhYXq1q2b1qxZo6ioqBp5v5CQEE2dOrXSqTfUDPptP3puP3puL/ptv9rseYBlVeVeNAAAgGuXEdcQAQAAXAmBCAAAGI9ABAAAjEcgAgAAxiMQ1YB58+apVatWatiwoXr37q3t27fX9pDqrc2bN2vIkCGKiYlRQECA3nvvPa/tlmVpypQpio6OVqNGjZSYmKgDBw541Zw4cUIjR45UWFiYIiIilJqaqpKSEhuPov7IyMjQrbfeqqZNmyoyMlJ333239u/f71Vz7tw5paWl6brrrlOTJk00fPjwSl96mp+fr5SUFIWGhioyMlKPP/64Lly4YOeh1AuvvfaaunTp4vkSOpfLpQ8//NCznV7XvOeff14BAQGaOHGiZx19969p06YpICDA69G+fXvP9jrTb7/8WBg8li5dajkcDmvBggXW3r17rUceecSKiIiwioqKanto9dLq1autP//5z9aKFSssSda7777rtf3555+3wsPDrffee8/6/PPPrd/97ndWfHy89dNPP3lqBg4caHXt2tXaunWr9dFHH1mtW7e27rvvPpuPpH5ITk62Fi5caO3Zs8fKy8uzBg8ebLVs2dIqKSnx1IwdO9aKjY21srOzrR07dlh9+vSxfvOb33i2X7hwwerUqZOVmJho7dy501q9erXVokULa/LkybVxSHXa+++/b61atcr66quvrP3791t/+tOfrODgYGvPnj2WZdHrmrZ9+3arVatWVpcuXawJEyZ41tN3/5o6dap18803WwUFBZ7HDz/84NleV/pNIPKzXr16WWlpaZ7lsrIyKyYmxsrIyKjFUV0bfh6IysvLLafTac2ePduz7uTJk1ZISIj19ttvW5ZlWfv27bMkWZ9++qmn5sMPP7QCAgKs77//3rax11fHjh2zJFmbNm2yLOtif4ODg63ly5d7ar744gtLkpWTk2NZ1sUQ26BBA6uwsNBT89prr1lhYWFWaWmpvQdQDzVr1sx6/fXX6XUNO336tNWmTRsrKyvL+u1vf+sJRPTd/6ZOnWp17dr1stvqUr85ZeZH58+fV25urhITEz3rGjRooMTEROXk5NTiyK5Nhw8fVmFhoVe/w8PD1bt3b0+/c3JyFBERoZ49e3pqEhMT1aBBA23bts32Mdc3p06dkiQ1b95ckpSbmyu32+3V8/bt26tly5ZePe/cubPXl54mJyeruLhYe/futXH09UtZWZmWLl2qM2fOyOVy0esalpaWppSUFK/+SvwZrykHDhxQTEyMbrzxRo0cOVL5+fmS6la/jfmmajv83//9n8rKyip9+3VUVJS+/PLLWhrVtauwsFCSLtvvim2FhYWKjIz02h4UFKTmzZt7anB55eXlmjhxom677TZ16tRJ0sV+OhyOSj92/POeX+4zqdgGb7t375bL5dK5c+fUpEkTvfvuu+rYsaPy8vLodQ1ZunSpPvvsM3366aeVtvFn3P969+6tRYsWqV27diooKND06dN1++23a8+ePXWq3wQiAJeVlpamPXv2aMuWLbU9lGtau3btlJeXp1OnTul//ud/NHr0aG3atKm2h3XN+u677zRhwgRlZWWpYcOGtT0cIwwaNMjzvEuXLurdu7fi4uL0zjvvqFGjRrU4Mm+cMvOjFi1aKDAwsNLV8UVFRXI6nbU0qmtXRU+v1G+n06ljx455bb9w4YJOnDjBZ3IF48eP18qVK7VhwwbdcMMNnvVOp1Pnz5/XyZMnvep/3vPLfSYV2+DN4XCodevW6tGjhzIyMtS1a1e99NJL9LqG5Obm6tixY7rlllsUFBSkoKAgbdq0SS+//LKCgoIUFRVF32tYRESE2rZtq4MHD9apP+cEIj9yOBzq0aOHsrOzPevKy8uVnZ0tl8tViyO7NsXHx8vpdHr1u7i4WNu2bfP02+Vy6eTJk8rNzfXUrF+/XuXl5erdu7ftY67rLMvS+PHj9e6772r9+vWKj4/32t6jRw8FBwd79Xz//v3Kz8/36vnu3bu9gmhWVpbCwsLUsWNHew6kHisvL1dpaSm9riEJCQnavXu38vLyPI+ePXtq5MiRnuf0vWaVlJTo0KFDio6Orlt/zv12eTYsy7p4231ISIi1aNEia9++fdaYMWOsiIgIr6vjUXWnT5+2du7cae3cudOSZM2dO9fauXOn9e2331qWdfG2+4iICOuf//yntWvXLuuuu+667G333bt3t7Zt22Zt2bLFatOmDbfd/4Jx48ZZ4eHh1saNG71ukT179qynZuzYsVbLli2t9evXWzt27LBcLpflcrk82ytukU1KSrLy8vKsNWvWWNdffz23JF/GU089ZW3atMk6fPiwtWvXLuupp56yAgICrMzMTMuy6LVdLr3LzLLou79NmjTJ2rhxo3X48GHr448/thITE60WLVpYx44dsyyr7vSbQFQDXnnlFatly5aWw+GwevXqZW3durW2h1RvbdiwwZJU6TF69GjLsi7eev+Xv/zFioqKskJCQqyEhARr//79Xvs4fvy4dd9991lNmjSxwsLCrIceesg6ffp0LRxN3Xe5XkuyFi5c6Kn56aefrP/8z/+0mjVrZoWGhlpDhw61CgoKvPbzzTffWIMGDbIaNWpktWjRwpo0aZLldrttPpq67+GHH7bi4uIsh8NhXX/99VZCQoInDFkWvbbLzwMRffeve+65x4qOjrYcDof1b//2b9Y999xjHTx40LO9rvQ7wLIsy3/zTQAAAPUP1xABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYLz/B40tVEx8ng0fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"num_of_students\"].plot.hist(bins=150)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"28.0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"num_of_students\"].median()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"498"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"num_of_students\"].max()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_subjects[\"average\"].plot.hist(\n",
" bins=int(df_subjects[\"average\"].max() - df_subjects[\"average\"].min())\n",
")\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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