232 lines
4.7 KiB
Plaintext
232 lines
4.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Załaduj bibliotekę `pandas`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Wczytaj dane z pliku *mieszkania.csv* do zmiennej i wyświetl 5 pierwszych wierczy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ODPOWIEDZ:\n",
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"\n",
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"\n",
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"\n",
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"#df = pd.read_csv(\"./mieszkania.csv\")\n",
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"\n",
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"#df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"3. Znajdź informacje ilu pokojowe mieszkania są najpopularniejsze i ile ich jest."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ODPOWIEDZ:\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"#df['Rooms'].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"4. Znajdź 10 najtańszych mieszkań."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"5. Napisz funkcje ``find_borough(desc)``, która przyjmuje 1 argument typu *string* i zwróci jedną z dzielnic zdefiniowaną w liście ``dzielnice``. Funkcja ma zwrócić pierwszą (wzgledem kolejności) nazwę dzielnicy, która jest zawarta w ``desc``. Jeżeli żadna nazwa nie została odnaleziona, zwróć napis *Inne*."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ODPOWIEDZ:\n",
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"\n",
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"\n",
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"\n",
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"# def find_borough(desc):\n",
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"# dzielnice = ['Stare Miasto',\n",
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"# 'Wilda',\n",
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"# 'Jeżyce',\n",
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"# 'Rataje',\n",
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"# 'Piątkowo',\n",
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"# 'Winogrady',\n",
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"# 'Miłostowo',\n",
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"# 'Dębiec']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"6. Dodaj kolumnę ``Borough``, która będzie zawierać informacje o dzielnicach i powstanie z kolumny ``Localization``. Wykorzystaj do tego funkcję ``find_borough``."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"7. Wyświetl histogram przedstawiający liczbę ogłoszeń mieszkań z podziałem na dzielnice."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"8. Znajdź średnią cenę mieszkania n-pokojowego."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"303861.86277173914"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# ODPOWIEDZ:\n",
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"\n",
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"\n",
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"#df[df['Rooms'] == 2]['Expected'].mean()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"9. Znajdź dzielnice, które zawierają oferty mieszkań na 13 piętrze."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2195 Poznań Winogrady ul. Os. Wichrowe Wzgórze - Zm...\n",
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"2773 Poznań Stare Miasto Winogrady ul. Os. Zwycięstwa\n",
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"Name: Location, dtype: object"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# ODPOWIEDZ:\n",
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"\n",
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"\n",
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"\n",
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"#df.query('Floor == 13')['Location']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"10. Znajdź wszystkie ogłoszenia mieszkań, które znajdują się na Winogradach, mają 3 pokoje i są położone na 1 piętrze."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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