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adityantamarapu/TorontohoodsSeg.ipynb

Created June 4, 2020 12:50
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TorontohoodsSeg.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting the data and pouring it into a Dataframe\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# import html5lib\n",
"# from bs4 import BeautifulSoup\n",
"df=pd.read_html(\"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\")"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"df=df[0]"
]
},
{
"cell_type": "code",
"execution_count": 83,
"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>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1A</td>\n",
" <td>Not assigned</td>\n",
" <td>Not assigned</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2A</td>\n",
" <td>Not assigned</td>\n",
" <td>Not assigned</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M3A</td>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4A</td>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood\n",
"0 M1A Not assigned Not assigned\n",
"1 M2A Not assigned Not assigned\n",
"2 M3A North York Parkwoods\n",
"3 M4A North York Victoria Village\n",
"4 M5A Downtown Toronto Regent Park, Harbourfront"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # vc=df[\"Postal Code\"].value_counts()\n",
"# for i in vc:\n",
"# if vc[i]>1:\n",
" \n",
"# print( \"t\")\n",
"# else :\n",
"# print(\"no\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"df.drop(\"index\",axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"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>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M3A</td>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M4A</td>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M6A</td>\n",
" <td>North York</td>\n",
" <td>Lawrence Manor, Lawrence Heights</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M7A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood\n",
"0 M3A North York Parkwoods\n",
"1 M4A North York Victoria Village\n",
"2 M5A Downtown Toronto Regent Park, Harbourfront\n",
"3 M6A North York Lawrence Manor, Lawrence Heights\n",
"4 M7A Downtown Toronto Queen's Park, Ontario Provincial Government"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(180, 3)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"M4G 1\n",
"M4M 1\n",
"M1L 1\n",
"M1W 1\n",
"M1K 1\n",
" ..\n",
"M2L 1\n",
"M6H 1\n",
"M6N 1\n",
"M3L 1\n",
"M9A 1\n",
"Name: Postal Code, Length: 103, dtype: int64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Postal Code'].value_counts() "
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"df=df[df.Borough!='Not assigned']\n"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(103, 3)"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Postal Code 0\n",
"Borough 0\n",
"Neighborhood 0\n",
"dtype: int64"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.Neighborhood=='Not assigned'].count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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