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# Define the data for Curb Weight and 0-60 MPH Time
data_spearman = {
    "Curb Weight (lbs)": [
        3527, 3054, 3845, 3636, 2227, 3750, 3135, 3472, 4850, 2952, 3483, 3236, 3434,
        3883, 3535, 4500, 4586, 3400, 3594, 3990, 3306, 3373, 4107, 4134, 4766, 4409,
        4065, 3865, 3507, 3486, 4480, 3830, 4114, 4722, 3616, 3417, 3843, 3682, 3968
    ],
    "0-60 MPH Time (s)": [
        2.5, 2.7, 2.8, 2.6, 3.7, 2.5, 2.5, 2.8, 3.1, 2.7, 2.8, 2.5, 2.6, 3.5, 2.9, 3.4,
        3.6, 3.3, 3.1, 3.7, 3.6, 3.5, 3.2, 3.9, 1.99, 1.9, 3.1, 2.9, 4.3, 5.0, 2.9, 2.8,
        2.9, 2.6, 2.9, 3.5, 3.5, 3.2, 3.9
    ]
}

# Create a DataFrame
df_spearman = pd.DataFrame(data_spearman)

# Assign ranks to both columns
df_spearman["Rank (Curb Weight)"] = df_spearman["Curb Weight (lbs)"].rank()
df_spearman["Rank (0-60 MPH Time)"] = df_spearman["0-60 MPH Time (s)"].rank()

# Calculate rank differences and squared differences
df_spearman["d_i"] = df_spearman["Rank (Curb Weight)"] - df_spearman["Rank (0-60 MPH Time)"]
df_spearman["d_i^2"] = df_spearman["d_i"]**2

# Calculate Spearman's rank correlation coefficient
n_spearman = len(df_spearman)  # Number of data points
spearman_rank_corr = 1 - (6 * df_spearman["d_i^2"].sum()) / (n_spearman * (n_spearman**2 - 1))

# Output the dataframe and Spearman's rank correlation coefficient
df_spearman, spearman_rank_corr