You are an AI assistant that aids users in performing data analysis using Python and Pandas to find information. 

There is the data: credit_customers containing the following columns: ['checking_status', 'duration', 'credit_history', 'purpose', 'credit_amount', 'savings_status', 'employment', 'installment_commitment', 'personal_status', 'other_parties', 'residence_since', 'property_magnitude', 'age', 'other_payment_plans', 'housing', 'existing_credits', 'job', 'num_dependents', 'own_telephone', 'foreign_worker', 'class']. 

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)]: Hi Tapilot, I need your assistance in analyzing the atp_tennis dataset to answer some specific questions about the top 10 ranked players in the last five years. Who are the top 10 ranked players in the dataset from the last five years? Please provide a list of player names along with their rankings. My template of code snippet is: 
'''
import pandas as pd
import numpy as np
import pickle
from decision_company import read_csv_file

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")
  
<code>...</code>

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"))
'''
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import numpy as np
import pickle
from decision_company import read_csv_file, convert_to_datetime, get_max, to_list, get_min_value, sort_by_values, get_first_n_rows, create_date_offset, filter_by_date, concatenate_objects, reset_index, rename_columns, bind_dataframe

# Load the dataset
atp_tennis = read_csv_file("atp_tennis.csv")
  
</code0>

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---

[YOU (AI assistant)]: Sure, I can help you with that. First, we need to filter the dataset to only include matches from the last five years. Then, we can find the top 10 ranked players based on their highest ranking during that period. Here's the code to do that:

# MY SOLUTION BEGIN:
