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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 How the Length of a Jeopardy Question Relates to its Value?
The American television game show Jeopardy is probably one of the most famous shows ever aired on TV. Few years ago IBM's Watson conquered the show, and now, it's time the conquer the dataset of all the questions that were asked in years and see if any interesting relations lie behind them.
Thanks to Reddit user trexmatt for providing CSV data of the questions which can be found here.
Structure of the Dataset
As explained on the Reddit post given above, each row of the dataset contains information on a particular question
Step1:  Apparently columns have a blank space in the beginning. Let's get rid of them
Step2:  Hypothesis - "Value of the question is related to its length."
Let's have a copy of dataframe so that changes we make doesn't disturb further analysis.
Step3:  There are some media-based questions, and also some questions with hyper-links. These can disturb our analysis so we should get rid of them.
Step4:  We can add a column to dataframe for lenght of questions.
Step5:  When we look at the "Value" column, we see they are not integers but strings, also there are some "None" values. We should clean those values.
Step6:  The "Value" column has 145 different values. For the sake of simplicity, let's keep the ones that are multiples of 100 and between 200 and 2500 (first round questions has range of 200-1000, second round questions has range of 500-2500).
Step7:  It looks like there isn't a correlation, but this graph isn't structured well enough to draw conclusions. Instead, let's calculate average question length for each value and plot average length vs value.
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Python Code:
# this line is required to see visualizations inline for Jupyter notebook
%matplotlib inline
# importing modules that we need for analysis
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import re
# read the data from file and print out first few rows
jeopardy = pd.read_csv("jeopardy.csv")
print(jeopardy.head(3))
print(jeopardy.columns)
Explanation: How the Length of a Jeopardy Question Relates to its Value?
The American television game show Jeopardy is probably one of the most famous shows ever aired on TV. Few years ago IBM's Watson conquered the show, and now, it's time the conquer the dataset of all the questions that were asked in years and see if any interesting relations lie behind them.
Thanks to Reddit user trexmatt for providing CSV data of the questions which can be found here.
Structure of the Dataset
As explained on the Reddit post given above, each row of the dataset contains information on a particular question:
* Category
* Value
* Question text
* Answer text
* Round of the game the question was asked
* Show number
* Date
Hypothesis
Before diving into analysis of the data let's come up with a relation between different columns:
Value of the question is related to its length.
Setting up Data
End of explanation
jeopardy.rename(columns = lambda x: x[1:] if x[0] == " " else x, inplace=True)
jeopardy.columns
Explanation: Apparently columns have a blank space in the beginning. Let's get rid of them:
End of explanation
data1 = jeopardy
data1["Question"].value_counts()[:10]
Explanation: Hypothesis - "Value of the question is related to its length."
Let's have a copy of dataframe so that changes we make doesn't disturb further analysis.
End of explanation
# regex pattern used to remove hyper-links
pattern = re.compile("^<a href")
# remove media clue questions
data1 = data1[data1["Question"].str.contains(pattern) == False]
data1 = data1[data1["Question"] != "[audio clue]"]
data1 = data1[data1["Question"] != "(audio clue)"]
data1 = data1[data1["Question"] != "[video clue]"]
data1 = data1[data1["Question"] != "[filler]"]
data1["Question"].value_counts()[:10]
Explanation: There are some media-based questions, and also some questions with hyper-links. These can disturb our analysis so we should get rid of them.
End of explanation
data1["Question Length"] = data1["Question"].apply(lambda x: len(x))
data1["Question Length"][:12]
Explanation: We can add a column to dataframe for lenght of questions.
End of explanation
data1["Value"].value_counts()[:15]
# get rid of None values
data1 = data1[data1["Value"] != "None"]
# parse integers from strings
pattern = "[0-9]"
data1["Value"] = data1["Value"].apply(lambda x: "".join(re.findall(pattern,x)))
data1["Value"] = data1["Value"].astype(int)
print(data1["Value"].value_counts()[:10])
print("Number of distinct values:" + str(len(data1["Value"].value_counts())))
Explanation: When we look at the "Value" column, we see they are not integers but strings, also there are some "None" values. We should clean those values.
End of explanation
data1 = data1[(data1["Value"]%100 == 0) & (data1["Value"]<= 2500)]
print(data1["Value"].value_counts())
print("Number of distinct values: " + str(len(data1["Value"].value_counts())))
# set up the figure and plot length vs value on ax1
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_subplot(1,1,1)
ax1.scatter(data1["Question Length"], data1["Value"])
ax1.set_xlim(0, 800)
ax1.set_ylim(0, 2700)
ax1.set_title("The Relation between Question Length and Value")
ax1.set_xlabel("Lenght of the Question")
ax1.set_ylabel("Value of the Question")
plt.show()
Explanation: The "Value" column has 145 different values. For the sake of simplicity, let's keep the ones that are multiples of 100 and between 200 and 2500 (first round questions has range of 200-1000, second round questions has range of 500-2500).
End of explanation
#find the average length for each value
average_lengths = []
values = data1["Value"].unique()
for value in values:
    rows = data1[data1["Value"] == value]
    average = rows["Question Length"].mean()
    average_lengths.append(average)
print(average_lengths)
print(values)
# set up the figure and plot average length vs value on ax1
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_subplot(1,1,1)
ax1.scatter(average_lengths, values)
ax1.set_title("The Relation between Average Question Length and Value")
ax1.set_xlabel("Average Question Length")
ax1.set_ylabel("Value")
ax1.set_xlim(70, 105)
ax1.set_ylim(0, 3000)
plt.show()
print("Correlation coefficient: " + str(np.corrcoef(average_lengths, values)[0,1]))
Explanation: It looks like there isn't a correlation, but this graph isn't structured well enough to draw conclusions. Instead, let's calculate average question length for each value and plot average length vs value.
End of explanation
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