<jupyter_start><jupyter_text>Distribution of `MATH_AVG` appears to be bimodal.<jupyter_code>pisa_salary_ratio.READING_AVG.plot.hist(bins = 15)


pisa_salary_ratio.SCIENCE_AVG.plot.hist()<jupyter_output><empty_output><jupyter_text>Bivariate<jupyter_code>sns.pairplot(pisa_salary_ratio)
plt.show()
df_cleaned = pisa_salary_ratio.dropna(axis=0)
df_cleaned.shape
df_cleaned.head(10)
sns.pairplot(df_cleaned)
plt.show()
corrs = df_cleaned.corr(method ='pearson')
sns.heatmap(corrs, xticklabels=corrs.columns, yticklabels=corrs.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))
corrs<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12559618441971382, 'fraction_numerical': 0.01589825119236884, 'mean_word_length': 11.352941176470589, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '60969', 'license': 'MIT', 'n_tokens_mistral': 293, 'n_tokens_neox': 272, 'n_words': 27, 'path': 'EDA/EDA_example.ipynb', 'repo_name': 'One-O-Nine/data-cleaning', 'size': '629'}