
There is the dataset: 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 dataset 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")
---

You are a data scientist with an impressive array of skills including data analysis, statistics, machine learning, and proficiency in Pandas. The plot mentioned in the question below has been transformed into a Filtered Dataframe, and your task is to answer the following question from User based on this Filtered Dataframe below.

--- Filtered Dataframe: ---
TITLE | Win/Loss Ratios by Surface Type and Year
Year | Surface | Hard | Clay | Grass
2012 | 1.47 | 1.47 | 1.06 | 1.00
2014 | 1.30 | 1.30 | 1.09 | 1.14
2016 | 1.27 | 1.26 | 1.16 | 1.17
2018 | 1.29 | 1.15 | 1.15 | 1.27
2020 | 1.29 | 1.29 | 1.19 | 1.23
2022 | 1.24 | 1.23 | 1.24 | 1.24

--- Filtered Dataframe ---

[USER (data scientist)]: The win/loss ratio of which is increased that implies players' adaptation and improved performance on these surfaces?
A. Hard and Clay
B. Hard and Grass
C. Clay and Carpet
D. Grass and Clay
E. None of above

[YOU (AI assistant)]:
