<jupyter_start><jupyter_text>If you haven't installed dataprep, run command `pip install dataprep` or execute the following cell<jupyter_code># Run me if you'd like to install
!pip install dataprep
from dataprep.data_connector import Connector<jupyter_output><empty_output><jupyter_text>Download restaurant data from YelpImaging you want to download the restaurant data from Yelp into a pandas Dataframe. With data connector, you can easily accomplish the task with two lines of code. 1. Create a Connector, provide the data source and the access token.   In this case, the data source is "yelp". You can get a token by following the [yelp document](https://www.yelp.com/developers/documentation/v3/authentication)<jupyter_code>dc = Connector("yelp", auth_params={"access_token":"<Your yelp access token>"})<jupyter_output><empty_output><jupyter_text>2. Get the data from the "restaurant" table, providing the term "ramen" and location "vancouver".<jupyter_code>df = dc.query("businesses", term="ramen", location="vancouver")<jupyter_output><empty_output><jupyter_text>3. Tada, you get the datafram<jupyter_code>df.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0947002606429192, 'fraction_numerical': 0.0034752389226759338, 'mean_word_length': 7.228571428571429, 'pattern_counts': {'":': 1, '<': 18, '<?xml version=': 0, '>': 18, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '226558', 'license': 'MIT', 'n_tokens_mistral': 371, 'n_tokens_neox': 353, 'n_words': 117, 'path': 'examples/DataConnector.ipynb', 'repo_name': 'Sanjana12111994/dataprep', 'size': '1151'}