<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
df = pd.read_csv (r'sample_data/wine.csv')
print(df)

print("Rows, columns: " + str(df.shape))
print(df.head())
print(df.mean())

print(df.median())

print(df.mode())

print(df.std())
print(df.mean(axis=1))

print(df.median(axis=1))

print(df.mode(axis=1))

print(df.std(axis=1))
data["Wine"].mean()
#Pearson's correlation coefficient = covariance(X, Y) / (stdv(X) * stdv(Y))
import scipy.stats
Alcohol = df['Alcohol'] 
Proline= df['Proline']
corr, _ = pearsonr(Alcohol, Proline) 
print('Pearsons correlation: %.3f' % corr) 
np.cov((df[1:5]).to_numpy() )<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2039274924471299, 'fraction_numerical': 0.010574018126888218, 'mean_word_length': 5.835051546391752, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '213940', 'license': 'MIT', 'n_tokens_mistral': 290, 'n_tokens_neox': 282, 'n_words': 43, 'path': 'Assignment_02.ipynb', 'repo_name': 'bharathulaprasad/cs360ml', 'size': '662'}