There is the data: atp_tennis containing the following columns: ['Tournament', 'Date', 'Series', 'Court', 'Surface', 'Round', 'Best of', 'Player_1', 'Player_2', 'Winner', 'Rank_1', 'Rank_2', 'Pts_1', 'Pts_2', 'Odd_1', 'Odd_2', 'score'].  
--- The description for each column this data is:
Tournament: Name of the tennis tournament (Brisbane International, Chennai Open, Qatar Exxon Mobil Open ...etc)
Date: Date the match was played (year-month-day)
Series: Category or level of the tennis tournament (ATP250, ATP500, Masters1000 and Grand Slams offer 250, 500, 1000, and 2000 ranking points to the winner seperately.)
Court: Place the match was held (Indoors or Outdoors)
Surface: Type of court surface (Hard, Grass and Clay)
Round: Stage of the tournament (1st Round, 2nd Round, Quarterfinals, Semifinal and The Final)
Best of: Tourament systems ("best of 3" or "best of 5")
Player_1: Name of the first competitor in each match
Player_2: Name of the second competitor in each match
Winner: Name of the Player_1 or Player_2 who won the match 
Rank_1: World rankings of the Player_1 at the time of the match
Rank_2: World rankings of the Player_2 at the time of the match
Pts_1: Ranking points held by Player_1 before the match (accumulated over the season and higher points typically indicate a more successful player)
Pts_2: Ranking points held by Player_2 before the match
Odd_1: Betting odds for Player_1 (reflecting their perceived chances of winning)
Odd_2: Betting odds for Player_2 
Score: Final score of the match ("score of Player_1" - "score of Player_2")
---
Considering contents from the dataset and requirements from user. Please note DO NOT CHANGE FILE AND VARIABLE NAMES THAT I HAVE SET!
Interactions begin:
[USER (data scientist)]: Hey Tapilot, I'm working on this atp_tennis dataset and I could use a hand. Can you help me figure out if there are any missing values or weird data stuff going on? Please generate a value to show the number of missing values in this dataset.

[YOU (AI assistant)]: No problem, happy to help! Let's start by finding any missing values in the dataset. Here's some code for that :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Calculate the sum of missing (null) values in each column
missing_values = atp_tennis.isnull().sum()

print(missing_values)

'''

[USER (data scientist)]: Awesome, thanks! Now, I wanna know how many unique values there are for a few columns like Tournament, Series, Court, Surface, and Round. Can you help me out with that? Please generate a Series summarizing the unique value counts for selected columns in this dataset.

[YOU (AI assistant)]: You got it! Here's the code to find the unique values for those columns :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Calculate the number of unique values for specific columns
# Columns considered are 'Tournament', 'Series', 'Court', 'Surface', 'Round'
unique_values = atp_tennis[['Tournament', 'Series', 'Court', 'Surface', 'Round']].nunique() 

print(unique_values)

'''

[USER (data scientist)]: Sweet! Now, I wanna make a data quality report that shows both the missing values and unique values for each column. Can you help me whip that up? Please generate a dataframe called data quality report showing the number of missing values and unique values in each column of the dataset.

[YOU (AI assistant)]: Absolutely! Let's create a DataFrame to show the data quality report. Here's the code for that :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Create a DataFrame to store the data quality report, with columns for missing values and unique values
data_quality_report = pd.DataFrame({'Missing Values': missing_values, 'Unique Values': unique_values})    

print(data_quality_report)

'''

[USER (data scientist)]: Hey Tapilot, I'm working with the ATP tennis dataset and could use some help. Can you give me a hand? 

[YOU (AI assistant)]: Absolutely! I'd be happy to help. What do you need assistance with? 

[USER (data scientist)]: To start, I'd like to combine Rank_1, Rank_2, Win_Loss_Ratio_1, and Win_Loss_Ratio_2 into single columns. Please generate a dataframe called combined data report showing the win/loss ratios and ranks of players in this dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to merge those columns :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Count the number of wins for each player
player_wins = atp_tennis['Winner'].value_counts()
  
# Count the total number of matches played by each player
player_matches = atp_tennis['Player_1'].value_counts() + atp_tennis['Player_2'].value_counts()
  
# Calculate win/loss ratio for each player
win_loss_ratios = player_wins / (player_matches - player_wins)
  
# Add win/loss ratios to the dataset
atp_tennis['Win_Loss_Ratio_1'] = atp_tennis['Player_1'].map(win_loss_ratios)
atp_tennis['Win_Loss_Ratio_2'] = atp_tennis['Player_2'].map(win_loss_ratios)

# Combine Rank_1, Rank_2, Win_Loss_Ratio_1, and Win_Loss_Ratio_2 into single columns   
combined_ranks = pd.concat([atp_tennis['Rank_1'], atp_tennis['Rank_2']]).reset_index(drop=True)   
combined_win_loss_ratios = pd.concat([atp_tennis['Win_Loss_Ratio_1'], atp_tennis['Win_Loss_Ratio_2']]).reset_index(drop=True)   

# Create a DataFrame with combined ranks and win/loss ratios   
combined_data = pd.DataFrame({'Rank': combined_ranks, 'Win_Loss_Ratio': combined_win_loss_ratios})  

print(combined_data)

'''

[USER (data scientist)]: Awesome! Now, can you show me some basic stats for this combined data? Please generate a DataFrame containing descriptive statistics for the ATP tennis dataset.

[YOU (AI assistant)]: You got it! Here's the code to display the descriptive statistics :
'''
import pandas as pd
import numpy as np
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Calculate and store descriptive statistics for the DataFrame
desc_stats = combined_data.describe()

# Print the descriptive statistics to the console
print(desc_stats)

'''

[USER (data scientist)]: Thanks a bunch! How about a histogram for player rankings distribution? Please generate and display a histogram showing the distribution of player rankings in the ATP tennis dataset.

[YOU (AI assistant)]: No problem! Here's the code to create a histogram using matplotlib :
'''
import pandas as pd
import matplotlib.pyplot as plt 
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

# Histogram for Player Rankings Distribution
fig1, ax1 = plt.subplots()
ax1.hist(combined_ranks, bins=50, alpha=0.5)
ax1.set_title('Player Rankings Distribution')
ax1.set_xlabel('Ranking')
plt.savefig('./ref_result/hist_chart.png')
plt.show()
'''

[USER (data scientist)]: That's cool. Now, I'd like to see a scatter plot of player rankings vs win/loss ratios. Please generate and display a scatter plot that compares player rankings with their win/loss ratios in the ATP tennis dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to create a scatter plot using matplotlib :
'''
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

atp_tennis = pd.read_csv('atp_tennis.csv')

# Scatter Plot for Player Rankings vs Win/Loss Ratios
fig2, ax2 = plt.subplots()
ax2.scatter(combined_data['Rank'], combined_data['Win_Loss_Ratio'], alpha=0.5)
ax2.set_title('Player Rankings vs Win/Loss Ratios')
ax2.set_xlabel('Ranking')
ax2.set_ylabel('Win/Loss Ratio')
plt.savefig('./ref_result/scatter_chart.png')
plt.show()

'''

[USER (data scientist)]: Lastly, can you whip up a bar chart showing the number of matches played on different surface types? Please generate and display a bar chart showing the number of matches played on different surface types in the ATP tennis dataset.

[YOU (AI assistant)]: Of course! Here's the code to create a bar chart using pandas and matplotlib :
'''
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

atp_tennis = pd.read_csv('atp_tennis.csv')

# Bar Chart for Surface Types
fig3, ax3 = plt.subplots()
surface_counts = atp_tennis['Surface'].value_counts()
surface_counts.plot.bar(ax=ax3)
ax3.set_title('Matches by Surface Type')
ax3.set_xlabel('Surface')
ax3.set_ylabel('Number of Matches')
plt.savefig('./ref_result/bar_chart.png')
plt.show()

'''

[USER (data scientist)]: Hey, I've got this ATP tennis dataset, and I'm curious if there's any connection between the court surfaces (hard, clay, grass) and how well the players perform (like their win/loss ratios and rankings). Can you help me out? Please generate a DataFrame that includes selected columns related to surface type and player rankings from the ATP tennis dataset.

[YOU (AI assistant)]: Absolutely! Let's start by making a DataFrame with the court surfaces, player rankings, and win/loss ratios. Here's the code for that :
'''
import pandas as pd
import numpy as np
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

surface_data = atp_tennis[['Surface', 'Rank_1', 'Rank_2', 'Win_Loss_Ratio_1', 'Win_Loss_Ratio_2']].copy()   

print(surface_data)

'''

[USER (data scientist)]: Cool, now let's clean up the data by getting rid of any rows with missing or wonky data in the Win_Loss_Ratio columns. Please generate a DataFrame that removes rows with missing values in 'Win_Loss_Ratio_1' and 'Win_Loss_Ratio_2' from a subset of the ATP tennis dataset.

[YOU (AI assistant)]: You got it! Here's the code to clean up the data :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

surface_data_clean = surface_data.dropna(subset=['Win_Loss_Ratio_1', 'Win_Loss_Ratio_2']).copy()

print(surface_data_clean)

'''

[USER (data scientist)]: Great, now let's do some one-hot encoding for the court surfaces. Please generate a DataFrame with one-hot encoded values for the 'Surface' column in a subset of the ATP tennis dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to do one-hot encoding for the court surfaces :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

surface_data_clean = pd.get_dummies(surface_data_clean, columns=['Surface'], prefix='', prefix_sep='')   

print(surface_data_clean)

'''

[USER (data scientist)]: Now, let's find the average player ranking and win/loss ratio for each match. Please generate a DataFrame that calculates and includes the average rank and average win/loss ratio for each row in a subset of the ATP tennis dataset.

[YOU (AI assistant)]: You got it! Here's the code to calculate those averages :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

surface_data_clean.loc[:, 'Avg_Rank'] = (surface_data_clean['Rank_1'] + surface_data_clean['Rank_2']) / 2   
surface_data_clean.loc[:, 'Avg_Win_Loss_Ratio'] = (surface_data_clean['Win_Loss_Ratio_1'] + surface_data_clean['Win_Loss_Ratio_2']) / 2   

print(surface_data_clean)

'''

[USER (data scientist)]: Next up, let's make a correlation matrix for the court surfaces, average player rankings, and average win/loss ratios. Please generate a DataFrame that displays the correlation matrix for selected columns, including surface types and average statistics, in a subset of the ATP tennis dataset.

[YOU (AI assistant)]: Here's the code to create that correlation matrix :
'''
import pandas as pd
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

corr_matrix = surface_data_clean[['Hard', 'Clay', 'Grass', 'Avg_Rank', 'Avg_Win_Loss_Ratio']].corr()   

print(corr_matrix)

'''

[USER (data scientist)]: Now, let's do an ANOVA test to see if there are any big differences in the average win/loss ratios between the court surfaces. Please conduct an ANOVA test to compare the average win/loss ratios across different surface types in the ATP tennis dataset.

[YOU (AI assistant)]: You got it! Here's the code to run the ANOVA test :
'''
import pandas as pd
import scipy.stats as stats
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

anova_result_clean = stats.f_oneway(surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Hard'] == 1],   
                                    surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Clay'] == 1],   
                                    surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Grass'] == 1])   

print(anova_result_clean)

'''

[USER (data scientist)]: Lastly, let's find confidence intervals for the average win/loss ratio for each court surface, so at first calculate the mean and standard error of the mean for the average win/loss ratio for each surface type. Please generate and display a series of values representing the mean and standard error of the mean (SEM) for average win/loss ratios on hard, clay, and grass surfaces in the ATP tennis dataset.

[YOU (AI assistant)]: Here's the code to get those stats for each court surface :
'''
import pandas as pd
import scipy.stats as stats
import pickle

atp_tennis = pd.read_csv('atp_tennis.csv')

hard_mean = surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Hard'] == 1].mean()

print(hard_mean)

hard_sem = stats.sem(surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Hard'] == 1])   

print(hard_sem)

clay_mean = surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Clay'] == 1].mean()   

print(clay_mean)

clay_sem = stats.sem(surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Clay'] == 1])   

print(clay_sem)

grass_mean = surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Grass'] == 1].mean() 

print(grass_mean)

grass_sem = stats.sem(surface_data_clean['Avg_Win_Loss_Ratio'][surface_data_clean['Grass'] == 1]) 

print(grass_sem)

'''

[USER (data scientist)]: Awesome! Now, how about finding the confidence intervals for the average win/loss ratio of each surface type? Please choose the most suitable confidence level for hard, clay, grass.

A. (1.2654959718677968, 1.3054959718677968); (1.165532187121182, 1.215532187121182); (1.1889036322379533, 1.2559036322379533)
B. (1.2604959718677968, 1.3004959718677968); (1.159532187121182, 1.210532187121182); (1.1839036322379533, 1.2509036322379533)
C. (1.2604959718677968, 1.3104959718677968); (1.159532187121182, 1.220532187121182); (1.1839036322379533, 1.2609036322379533)
D. (1.2851298832875326, 1.2604959718677968); (1.192818903559593, 1.159532187121182); (1.2383437238191124, 1.1839036322379533)
E. None of above

[YOU (AI assistant)]: 