[USER (data scientist)]: Great, We want to analyze how the impact of surface types on player performance varies across different player ranking groups (top-ranked, mid-ranked, and low-ranked players) in the atp_tennis dataset. To do this, we'll perform a Stratified Analysis, which includes charts, and statistical test results (p-values) comparing performance across ranking groups and surface types. [0, 50) refers to top-ranked players, [50, 200) refers to mid-ranked, while others are low-ranked.
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
from decision_company import read_csv_file, create_dataframe, df_copy, concatenate_objects, bind_dataframe, aggregate_grouped_data, positive_infinity, make_bins, join_dataframes, create_figure, create_barplot, set_plot_title, set_yaxis_label, show_plots, join_dataframes, anova_test, dropna

atp_tennis = read_csv_file('atp_tennis.csv')

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

# Print the statistical test results (P-values) for each ranking group
print("Statistical Test Results (P-values):")
for group, pvalue in anova_results.items():
    print(f"{group}: {pvalue}")

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, let's perform a Stratified Analysis for the atp_tennis dataset. Here's the code to generate charts, and statistical test results (p-values):

# MY SOLUTION BEGIN:
