<jupyter_start><jupyter_text>**Jeopardy** is a popular TV show in the US where participants answer questions to win money. It's been running for a few decades, and is a major force in popular culture.  The dataset is named [jeopardy.csv](https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file), and contains 20000 rows from the beginning of a full dataset of Jeopardy questions.  Data | Description  --- | --- Show Number | the Jeopardy episode number of the show this question was in.Air Date | the date the episode aired.Round | the round of Jeopardy that the question was asked in. Jeopardy has several rounds as each episode progresses.Category | the category of the question.Value | the number of dollars answering the question correctly is worth.Question | the text of the question.Answer | the text of the answer.  Importing data and libraries<jupyter_code>import numpy as np
import pandas as pd

import re

from bs4 import BeautifulSoup

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

%autosave 10
data = pd.read_csv("jeopardy.csv")
data.columns = [x.strip() for x in data.columns]
data.head(3)
data.info()
data['Air Date'] = pd.to_datetime(data['Air Date'])
data['Value'].unique()
def cleanValue(bad_price):
    bad_price = bad_price.replace("$","")
    good_price = bad_price.replace(",","")
    return(good_price)
data['Value'] = data['Value'].apply(cleanValue).replace("None",np.nan).fillna(0).astype(int)
data['Value'].value_counts(bins = 15,dropna=False, sort = True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10355781448538755, 'fraction_numerical': 0.012706480304955527, 'mean_word_length': 5.176470588235294, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '457019', 'license': 'MIT', 'n_tokens_mistral': 499, 'n_tokens_neox': 472, 'n_words': 171, 'path': 'Case Studies/Python/Winning Jeopardy/Basics.ipynb', 'repo_name': 'AnanduR32/Data_Science_R', 'size': '1574'}