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testing indexing methods
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search_index.pickle
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search_index.pickle
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searchindexer.ipynb
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{
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"metadata": {
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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": "3.9.0-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.9.0 64-bit",
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"metadata": {
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"interpreter": {
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"hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930"
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}
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: tqdm in /home/anson/.local/lib/python3.8/site-packages (4.59.0)\n"
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]
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}
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],
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"source": [
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"!pip install tqdm"
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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": 64,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests as r\n",
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"import pandas as pd\n",
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"from fuzzywuzzy import fuzz\n",
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"from functools import cache\n",
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"from tqdm import tqdm"
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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": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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" def stocks():\n",
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"\n",
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" raw_symbols = r.get(\n",
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" f\"https://cloud.iexapis.com/stable/ref-data/symbols?token=sk_b3323ec3072e44c5acc414868bdd40ce\"\n",
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" ).json()\n",
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" symbols = pd.DataFrame(data=raw_symbols)\n",
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"\n",
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" symbols[\"description\"] = \"$\" + symbols[\"symbol\"] + \": \" + symbols[\"name\"]\n",
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" symbols[\"id\"] = symbols[\"symbol\"]\n",
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"\n",
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" symbols = symbols[[\"id\", \"symbol\", \"name\", \"description\"]]\n",
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"\n",
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" return symbols\n",
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"\n",
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"\n",
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"\n",
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" def coins():\n",
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"\n",
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" raw_symbols = r.get(\"https://api.coingecko.com/api/v3/coins/list\").json()\n",
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" symbols = pd.DataFrame(data=raw_symbols)\n",
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"\n",
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" symbols[\"description\"] = \"$$\" + symbols[\"symbol\"] + \": \" + symbols[\"name\"]\n",
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" symbols = symbols[[\"id\", \"symbol\", \"name\", \"description\"]]\n",
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"\n",
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" return symbols"
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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": 51,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" id symbol \\\n",
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"0 A A \n",
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"1 AA AA \n",
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"2 AAA AAA \n",
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"3 AAAU AAAU \n",
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"4 AAC AAC \n",
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"... ... ... \n",
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"6565 zyro zyro \n",
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"6566 zytara-dollar zusd \n",
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"6567 zyx zyx \n",
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"6568 zzz-finance zzz \n",
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"6569 zzz-finance-v2 zzzv2 \n",
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"\n",
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" name \\\n",
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"0 Agilent Technologies Inc. \n",
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"1 Alcoa Corp \n",
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"2 Listed Funds Trust - AAF First Priority CLO Bo... \n",
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"3 Goldman Sachs Physical Gold ETF Shares - Goldm... \n",
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"4 Ares Acquisition Corporation - Class A \n",
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"... ... \n",
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"6565 Zyro \n",
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"6566 Zytara Dollar \n",
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"6567 ZYX \n",
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"6568 zzz.finance \n",
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"6569 zzz.finance v2 \n",
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"\n",
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" description \n",
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"0 $A: Agilent Technologies Inc. \n",
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"1 $AA: Alcoa Corp \n",
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"2 $AAA: Listed Funds Trust - AAF First Priority ... \n",
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"3 $AAAU: Goldman Sachs Physical Gold ETF Shares ... \n",
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"4 $AAC: Ares Acquisition Corporation - Class A \n",
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"... ... \n",
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"6565 $$zyro: Zyro \n",
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"6566 $$zusd: Zytara Dollar \n",
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"6567 $$zyx: ZYX \n",
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"6568 $$zzz: zzz.finance \n",
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"6569 $$zzzv2: zzz.finance v2 \n",
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"\n",
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"[16946 rows x 4 columns]"
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],
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"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>id</th>\n <th>symbol</th>\n <th>name</th>\n <th>description</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>A</td>\n <td>A</td>\n <td>Agilent Technologies Inc.</td>\n <td>$A: Agilent Technologies Inc.</td>\n </tr>\n <tr>\n <th>1</th>\n <td>AA</td>\n <td>AA</td>\n <td>Alcoa Corp</td>\n <td>$AA: Alcoa Corp</td>\n </tr>\n <tr>\n <th>2</th>\n <td>AAA</td>\n <td>AAA</td>\n <td>Listed Funds Trust - AAF First Priority CLO Bo...</td>\n <td>$AAA: Listed Funds Trust - AAF First Priority ...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>AAAU</td>\n <td>AAAU</td>\n <td>Goldman Sachs Physical Gold ETF Shares - Goldm...</td>\n <td>$AAAU: Goldman Sachs Physical Gold ETF Shares ...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>AAC</td>\n <td>AAC</td>\n <td>Ares Acquisition Corporation - Class A</td>\n <td>$AAC: Ares Acquisition Corporation - Class A</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>6565</th>\n <td>zyro</td>\n <td>zyro</td>\n <td>Zyro</td>\n <td>$$zyro: Zyro</td>\n </tr>\n <tr>\n <th>6566</th>\n <td>zytara-dollar</td>\n <td>zusd</td>\n <td>Zytara Dollar</td>\n <td>$$zusd: Zytara Dollar</td>\n </tr>\n <tr>\n <th>6567</th>\n <td>zyx</td>\n <td>zyx</td>\n <td>ZYX</td>\n <td>$$zyx: ZYX</td>\n </tr>\n <tr>\n <th>6568</th>\n <td>zzz-finance</td>\n <td>zzz</td>\n <td>zzz.finance</td>\n <td>$$zzz: zzz.finance</td>\n </tr>\n <tr>\n <th>6569</th>\n <td>zzz-finance-v2</td>\n <td>zzzv2</td>\n <td>zzz.finance v2</td>\n <td>$$zzzv2: zzz.finance v2</td>\n </tr>\n </tbody>\n</table>\n<p>16946 rows × 4 columns</p>\n</div>"
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},
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"metadata": {},
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"execution_count": 51
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}
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],
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"source": [
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"df = pd.concat([stocks(), coins()])\n",
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"df"
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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": 79,
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"metadata": {},
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"outputs": [],
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"source": [
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" def search_symbols(search: str):\n",
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" \"\"\"Performs a fuzzy search to find stock symbols closest to a search term.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" search : str\n",
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" String used to search, could be a company name or something close to the companies stock ticker.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" List[tuple[str, str]]\n",
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" A list tuples of every stock sorted in order of how well they match. Each tuple contains: (Symbol, Issue Name).\n",
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" \"\"\"\n",
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"\n",
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" try:\n",
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" if search_index[search]: return search_index[search]\n",
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" except KeyError:\n",
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" pass\n",
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"\n",
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"\n",
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"\n",
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" search = search.lower()\n",
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"\n",
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" df[\"Match\"] = df.apply(\n",
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" lambda x: fuzz.ratio(search, f\"{x['symbol']}\".lower()),\n",
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" axis=1,\n",
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" )\n",
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"\n",
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" df.sort_values(by=\"Match\", ascending=False, inplace=True)\n",
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" if df[\"Match\"].head().sum() < 300:\n",
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" df[\"Match\"] = df.apply(\n",
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" lambda x: fuzz.partial_ratio(search, x[\"name\"].lower()),\n",
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" axis=1,\n",
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" )\n",
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"\n",
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" df.sort_values(by=\"Match\", ascending=False, inplace=True)\n",
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"\n",
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" symbols = df.head(20)\n",
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" symbol_list = list(zip(list(symbols[\"symbol\"]), list(symbols[\"description\"])))\n",
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" \n",
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" return symbol_list"
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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": 91,
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"metadata": {},
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"outputs": [],
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"source": [
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"search_list = df['id'].to_list() + df['description'].to_list()\n",
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"search_index = {}"
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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": 92,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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" 5%|▍ | 1545/33892 [06:51<2:23:40, 3.75it/s]\n"
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]
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},
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{
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"output_type": "error",
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-92-559f57361529>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msearch_index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch_symbols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m<ipython-input-79-2cfb15c9428a>\u001b[0m in \u001b[0;36msearch_symbols\u001b[0;34m(search)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0msearch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m df[\"Match\"] = df.apply(\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfuzz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mratio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{x['symbol']}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m 7763\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7764\u001b[0m )\n\u001b[0;32m-> 7765\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7767\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.chained_assignment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 289\u001b[0m \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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|
"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mseries_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;31m# GH#35462 re-pin mgr in case setitem changed it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 411\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 5473\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5474\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5475\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5476\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5477\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"\n",
|
||||||
|
"for i in tqdm(search_list):\n",
|
||||||
|
" search_index[i] = search_symbols(i)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 89,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import pickle\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"with open('search_index.pickle', 'wb') as handle:\n",
|
||||||
|
" pickle.dump(search_index, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
|
||||||
|
"\n",
|
||||||
|
"# with open('filename.pickle', 'rb') as handle:\n",
|
||||||
|
"# b = pickle.load(handle)\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
Loading…
x
Reference in New Issue
Block a user