[USER (data scientist)]: Thank you, Tapilot. We want to analyze any noticeable trends or changes in the relationship between court surfaces and player performance in the atp_tennis dataset over years. To do this, we'll perform a Time Series Analysis, which includes line charts, trend analysis, and statistical test results.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import pickle

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

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

# Print the trend analysis results for each surface type
print("Trend Analysis Results:")
for surface, result in trend_results.items():
    print(f"{surface}: Slope = {result['Slope']}, P-value = {result['P-value']}")

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, let's perform a Time Series Analysis for the atp_tennis dataset. Here's the code to generate line charts, trend analysis, and statistical test results:
'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import pickle

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

# YOUR SOLUTION BEGIN:
