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Kilokem
2024-12-09 19:41:42 +01:00
parent b99346376f
commit 32d805ecf1
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120
SajatMegoldasok/vizsga_01_minta.ipynb
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@ -23,7 +23,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 57, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -68,7 +68,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -96,7 +96,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 97, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -145,6 +145,118 @@
"- Cégkategóriánként hány dollárt fektettek be összesen?" "- Cégkategóriánként hány dollárt fektettek be összesen?"
] ]
}, },
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 LifeLock\n",
"1 LifeLock\n",
"2 LifeLock\n",
"3 MyCityFaces\n",
"4 Flypaper\n",
"Name: company, dtype: object\n",
"The head: company numEmps category city state fundedDate raisedAmt \\\n",
"0 LifeLock NaN web Tempe AZ 1-May-07 6850000 \n",
"1 LifeLock NaN web Tempe AZ 1-Oct-06 6000000 \n",
"2 LifeLock NaN web Tempe AZ 1-Jan-08 25000000 \n",
"3 MyCityFaces 7.0 web Scottsdale AZ 1-Jan-08 50000 \n",
"4 Flypaper NaN web Phoenix AZ 1-Feb-08 3000000 \n",
"\n",
" raisedCurrency round \n",
"0 USD b \n",
"1 USD a \n",
"2 USD c \n",
"3 USD seed \n",
"4 USD a \n",
"1434\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"frame = pd.read_csv(\"investments.txt\", sep=\"|\")\n",
"\n",
"print(frame[\"company\"].iloc[:5])\n",
"\n",
"print(\"The head:\", frame.head())\n",
"\n",
"framecount = frame[\"company\"].count()\n",
"print(framecount)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"company\n",
"Facebook 7\n",
"Juice Wireless 5\n",
"Viv’simo 5\n",
"Glam Media 5\n",
"Brightcove 5\n",
" ..\n",
"AdReady 1\n",
"AdMob 1\n",
"Acquia 1\n",
"x+1 1\n",
"vbs tv 1\n",
"Name: category, Length: 891, dtype: int64\n"
]
}
],
"source": [
"grouped = frame.groupby(\"company\").count()\n",
"\n",
"grouped = frame[\"company\"].value_counts()\n",
"\n",
"grouped = frame.groupby(\"company\")[\"category\"].count().sort_values(ascending=False)\n",
"\n",
"\n",
"print(grouped)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"category\n",
"web 11753474750\n",
"software 1017942000\n",
"hardware 824500000\n",
"mobile 323020000\n",
"cleantech 258900000\n",
"other 119850000\n",
"biotech 77250000\n",
"consulting 32135000\n",
"Name: raisedAmt, dtype: int64\n"
]
}
],
"source": [
"categ = frame.groupby(\"category\")[\"raisedAmt\"].sum().sort_values(ascending=False)\n",
"\n",
"print(categ)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -169,7 +281,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.6" "version": "3.13.0"
} }
}, },
"nbformat": 4, "nbformat": 4,

99
SajatMegoldasok/vizsga_02_minta.ipynb
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@ -137,17 +137,108 @@
"- Hány ponttal magasabb a medencével (`Pool`) rendelkező szállodák átlagos értékelése (`Score`) a többi szálloda átlagos értékelésénél?" "- Hány ponttal magasabb a medencével (`Pool`) rendelkező szállodák átlagos értékelése (`Score`) a többi szálloda átlagos értékelésénél?"
] ]
}, },
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Hotel name Nr. rooms Pool Gym Tennis court \\\n",
"0 Circus Circus Hotel & Casino Las Vegas 3773 NO YES NO \n",
"1 Circus Circus Hotel & Casino Las Vegas 3773 NO YES NO \n",
"2 Circus Circus Hotel & Casino Las Vegas 3773 NO YES NO \n",
"3 Circus Circus Hotel & Casino Las Vegas 3773 NO YES NO \n",
"4 Circus Circus Hotel & Casino Las Vegas 3773 NO YES NO \n",
"\n",
" Spa Casino Traveler type Period of stay Score \n",
"0 NO YES Friends Dec-Feb 5 \n",
"1 NO YES Business Dec-Feb 3 \n",
"2 NO YES Families Mar-May 5 \n",
"3 NO YES Friends Mar-May 4 \n",
"4 NO YES Solo Mar-May 4 \n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"frame = pd.read_csv(\"hotels.txt\", sep=';')\n",
"print(frame[:5])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hotel name\n",
"The Venetian Las Vegas Hotel 96648\n",
"Excalibur Hotel & Casino 95544\n",
"Bellagio Las Vegas 94392\n",
"Circus Circus Hotel & Casino Las Vegas 90552\n",
"Caesars Palace 80352\n",
"Name: Nr. rooms, dtype: int64\n"
]
}
],
"source": [
"legtobb = frame.groupby(\"Hotel name\")[\"Nr. rooms\"].sum().sort_values(ascending=False)[:5]\n",
"\n",
"print(legtobb)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"name": "stdout",
"output_type": "stream",
"text": [
"Hotel name\n",
"Wynn Las Vegas 4.625\n",
"Name: Score, dtype: float64\n"
]
}
],
"source": [
"scored = frame.groupby(\"Hotel name\")[\"Score\"].mean().sort_values(ascending=False)[:1]\n",
"\n",
"print(scored)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9604166666666667\n"
]
}
],
"source": [
"withspas = frame[frame[\"Pool\"] == \"YES\"][\"Score\"].mean()\n",
"withoutspas = frame[frame[\"Pool\"] == \"NO\"][\"Score\"].mean()\n",
"\n",
"print(withspas - withoutspas)"
]
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -161,7 +252,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.5" "version": "3.13.0"
} }
}, },
"nbformat": 4, "nbformat": 4,

96
SajatMegoldasok/vizsga_03_minta.ipynb
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@ -168,16 +168,100 @@
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "source": [
"source": [] "### 3. feladat [10p]\n",
"\n",
"Az [unicef.txt](unicef.txt) szövegfájl a világ 5 év alatti népességének élelmezési helyzetéről tartalmaz adatokat. Az egyes sorok felméréseknek felelnek meg, a felmérések országonként időbeli sorrendben vannak felsorolva. Töltsük be az adatokat, határozzuk meg és írjuk ki az alábbi statisztikákat!\n",
"- Hány felmérés készült és hány országot érintett?\n",
"- Az alábbi statisztikákat csak azon felmérések alapján készítsük el, amelyeknél mind a három érintett indikátor (`Severe Wasting`, `Underweight`, `Overweight`) definiált (azaz ezek pozitív adatok). Ha egy országra több ilyen felmérés is van, akkor a legutóbbit vegyük figyelembe!\n",
" - Mely 5 országban a legmagasabb a `Severe Wasting` indikátor?\n",
" - Az országok hányadrészében magasabb az `Underweight` indikátor az `Overweight` indikátornál?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Country United Nations Region United Nations Sub-Region \\\n",
"0 AFGHANISTAN Asia Southern Asia \n",
"1 AFGHANISTAN Asia Southern Asia \n",
"2 AFGHANISTAN Asia Southern Asia \n",
"3 ALBANIA Europe Southern Europe \n",
"4 ALBANIA Europe Southern Europe \n",
"\n",
" World Bank Income Classification Survey Year Survey Sample (N) \\\n",
"0 Low Income 1997 4846.0 \n",
"1 Low Income 2004 946.0 \n",
"2 Low Income 2013 21922.0 \n",
"3 Upper Middle Income 1996-98 7642.0 \n",
"4 Upper Middle Income 2000 1382.0 \n",
"\n",
" Severe Wasting Wasting Stunting Underweight Overweight \\\n",
"0 NaN 18,2 53,2 44,9 6,5 \n",
"1 3,5 8,6 59,3 32,9 4,6 \n",
"2 4,0 9,5 40,9 25,0 5,4 \n",
"3 NaN 8,1 20,4 7,1 9,5 \n",
"4 6,2 12,2 39,2 17,0 30,0 \n",
"\n",
" Source Notes \\\n",
"0  Afghanistan 1997 multiple indicator baseline ... Converted estimates \n",
"1 Summary report of the national nutrition surve... NaN \n",
"2 Afghanistan National Nutrition Survey 2013. (pending reanalysis) \n",
"3 National study on nutrition in Albania. Instit... Converted estimates \n",
"4 Multiple indicator cluster survey report Alban... NaN \n",
"\n",
" U5 Population ('000s) \n",
"0 3637,632 \n",
"1 4667,487 \n",
"2 5235,867 \n",
"3 307,887 \n",
"4 278,753 \n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"frame = pd.read_csv(\"unicef.txt\", sep=\"|\")\n",
"print(frame[:5])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"keszult: 854\n",
"Orszag: 152\n"
]
}
],
"source": [
"orszag = frame[\"Country\"].count()\n",
"\n",
"print(\"keszult: \",orszag)\n",
"\n",
"city = frame.groupby(\"Country\")[\"Survey Year\"].count().count()\n",
"\n",
"\n",
"print(\"Orszag: \",city)"
]
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -191,7 +275,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.5" "version": "3.13.0"
} }
}, },
"nbformat": 4, "nbformat": 4,