<jupyter_start><jupyter_text>Imports<jupyter_code>import os
import pdb
from nl4dv import NL4DV<jupyter_output><empty_output><jupyter_text>Initialize NL4DV<jupyter_code>nl4dv_instance = NL4DV(verbose=False,data_url=os.path.join("..","..","assets","data","movies-w-year.csv"))
nl4dv_instance.debug<jupyter_output><empty_output><jupyter_text>Set dependency parser<jupyter_code>dependency_parser_config = {'name': 'corenlp','model': os.path.join("..","..","assets","jars","stanford-english-corenlp-2018-10-05-models.jar"),'parser': os.path.join("..","..","assets","jars","stanford-parser.jar")}
nl4dv_instance.set_dependency_parser(config=dependency_parser_config)<jupyter_output>Dependency Parser: corenlp<jupyter_text>Execute Query<jupyter_code>nl4dv_instance.analyze_query("Create a histogram showing distribution of IMDB ratings")
# pdb.set_trace()
nl4dv_instance.render_vis("Create a histogram showing distribution of IMDB ratings")
nl4dv_instance.render_vis("gross as a stripplot")
nl4dv_instance.render_vis("budget as a boxplot")
nl4dv_instance.render_vis("Show average gross across genres for science fiction and fantasy movies")
nl4dv_instance.render_vis("Visualize rating and budget")
nl4dv_instance.render_vis("show the relationship between budget and rating for action and adventure movies that grossed over 100M")<jupyter_output><empty_output>
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