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)]: Can you help me change the 'Date' column to a datetime format? Please generate a Series by converting the 'Date' column to datetime format in the ATP tennis dataset.

[YOU (AI assistant)]: Sure! You can use this code to change the 'Date' column to datetime format :
'''
import pandas as pd
import pickle
from decision_company import read_csv_file, convert_to_datetime

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")

# Convert the 'Date' column to datetime format
atp_tennis['Date'] = convert_to_datetime(atp_tennis['Date'])

print(atp_tennis['Date'])

'''

[USER (data scientist)]: What's the most recent date in the dataset? Please generate a value of the most recent date in the ATP tennis dataset.

[YOU (AI assistant)]: To find the most recent date, you can use this code :
'''
import pandas as pd
import pickle
from decision_company import read_csv_file, get_max

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")

# Find the last date in the dataset
last_date = get_max(atp_tennis['Date'])

print(last_date)

'''

[USER (data scientist)]: I'd like to see only the matches from the last five years. Can you help me with that? Please generate and display a DataFrame containing matches from the last five years in the ATP tennis dataset.

[YOU (AI assistant)]: Absolutely! You can use this code to filter the dataset for matches from the last five years :
'''
import pandas as pd
import pickle
from decision_company import read_csv_file, create_date_offset, filter_by_date

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")

# Filter the dataset to only include matches from the last five years
five_years_ago = last_date - create_date_offset(years=5)
recent_matches = filter_by_date(atp_tennis, 'Date', five_years_ago)

print(recent_matches)

'''

[USER (data scientist)]: Can you show me the top 10 ranked players from the filtered data? Please generate a DataFrame listing the top 10 ranked players based on their minimum ranking in recent matches from the ATP tennis dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to find the top 10 ranked players from the filtered data :
'''
import pandas as pd
import pickle
from decision_company import read_csv_file, get_min_value, sort_by_values, get_first_n_rows, concatenate_objects, rename_columns, bind_dataframe

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")

concatenated_data = concatenate_objects(
    recent_matches[['Player_1', 'Rank_1']],
    rename_columns(recent_matches[['Player_2', 'Rank_2']], {'Player_2': 'Player_1', 'Rank_2': 'Rank_1'})
)
grouped_data = bind_dataframe(concatenated_data, 'Player_1')
min_values = get_min_value(grouped_data)
sorted_data = sort_by_values(min_values, 'Rank_1')
top_10_ranked_players = get_first_n_rows(sorted_data, 10)

print(top_10_ranked_players)

'''

[USER (data scientist)]: Please convert these to python list. Please generate a list of the top 10 ranked players and their rankings from the ATP tennis dataset.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print(top_10_ranked_players_list)

# save data
pickle.dump(top_10_ranked_players_list,open("./pred_result/top_10_ranked_players_list.pkl","wb"))

---END CODE TEMPLATE---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: here's the code to convert format
'''
import pandas as pd
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

# YOUR SOLUTION BEGIN:
