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Replace pandas with Modin to exercise 2 #112

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Replace pandas with Modin to exercise 2 #112

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09f347d
Replace pandas with Modin to exercise 2
devin-petersohn Apr 11, 2019
8e1fc8b
Update exercise02-Task_Dependencies.ipynb
devin-petersohn Apr 15, 2019
bf1a349
Update exercise02-Task_Dependencies.ipynb
devin-petersohn Apr 15, 2019
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37 changes: 22 additions & 15 deletions exercises/exercise02-Task_Dependencies.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -227,7 +227,7 @@
"from bs4 import BeautifulSoup\n",
"import requests\n",
"\n",
"import pandas as pd"
"import modin.pandas as pd"
]
},
{
Expand All @@ -250,22 +250,21 @@
"\n",
"def parse_google_response(keyword, response):\n",
" soup = BeautifulSoup(response.text, 'lxml')\n",
" df = pd.DataFrame(columns=['title', 'link', 'description'])\n",
" data = {'title': [], 'link': [], 'description': [], 'library': []}\n",
" for g in soup.find_all(class_='g'):\n",
" entry = {}\n",
" headers = g.find_all(class_='r')\n",
" # Sometimes results have no headers\n",
" if len(headers) != 1:\n",
" continue\n",
" entry['title'] = headers[0].text\n",
" entry['link'] = headers[0].find('a').get('href')[7:]\n",
" data['title'].append(headers[0].text)\n",
" data['link'].append(headers[0].find('a').get('href')[7:])\n",
" description = g.find_all(class_='st')\n",
" entry['description'] = description[0].text if len(description) > 0 else \"\"\n",
" df = df.append(pd.DataFrame(entry, index=[0]))\n",
" \n",
" df['library'] = keyword\n",
" return df\n",
" data['description'].append(description[0].text if len(description) > 0 else \"\")\n",
" data['library'].append(keyword)\n",
" return data\n",
"\n",
"# @ray.remote\n",
"def get_results(keyword):\n",
" response = query_google(\"learn {}\".format(keyword))\n",
" return parse_google_response(keyword, response)"
Expand All @@ -285,13 +284,13 @@
"outputs": [],
"source": [
"start = time.time()\n",
"keywords = [\"ray\", \"rllib\", \"tune\", \"modin\", \"plasma\", \"arrow\"]\n",
"keywords = [\"ray\", \"rllib\", \"tune\", \"modin.pandas\", \"plasma\", \"arrow\"]\n",
"results = []\n",
"for keyword in keywords:\n",
" df = get_results(keyword)\n",
" results.append(df)\n",
" \n",
"df = pd.concat(results)\n",
" data = get_results(keyword)\n",
" results.append(data)\n",
"\n",
"df = pd.concat([pd.DataFrame(r) for r in results])\n",
"duration = time.time() - start\n",
"print(\"Constructing the dataframe took {} seconds.\".format(duration))"
]
Expand All @@ -311,6 +310,14 @@
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<center> The results of the queries are now stored in a Modin DataFrame (`df`), a DataFrame with an identical API to pandas.**\n",
"**<center> Learn more about using the Modin DataFrame in the `examples/accelerate_pandas.ipynb` tutorial**"
]
}
],
"metadata": {
Expand All @@ -329,7 +336,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.7"
},
"toc": {
"base_numbering": 1,
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