<jupyter_start><jupyter_text>Top Countries in Terms on GDP<jupyter_code>temp = df.sort_values(by = 'GDP', ascending = False).head(5)
temp
plt.plot(temp['Country'].values ,temp['GDP'].values)

plt.xlabel("Counties")
plt.ylabel("GDP in Trillion Dollars")
plt.title("Top 5 Countries in terms of GDP")

plt.show()<jupyter_output><empty_output><jupyter_text>Top Countries in Terms on Population<jupyter_code>temp = df.sort_values(by = 'Population', ascending = False).head(5)
temp
plt.plot(temp['Country'].values ,temp['Population'].values)

plt.xlabel("Counties")
plt.ylabel("Population in Billions")
plt.title("Top 5 Countries in terms of Population")

plt.show()<jupyter_output><empty_output><jupyter_text>Top Countries in Terms of GDP per Capita<jupyter_code>temp = df.sort_values(by = 'GDP per Capita', ascending = False).head(5)
temp
plt.plot(temp['Country'].values ,temp['GDP per Capita'].values)

plt.xlabel("Counties")
plt.ylabel("GDP Per Capita")
plt.title("Top 5 Countries in terms of GDP per Capita")

plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15061963775023832, 'fraction_numerical': 0.005719733079122974, 'mean_word_length': 7.467741935483871, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '154214', 'license': 'Apache-2.0', 'n_tokens_mistral': 405, 'n_tokens_neox': 373, 'n_words': 82, 'path': 'GDP Analysis/GDP Analysis.ipynb', 'repo_name': 'teenage-coder/Data-Analysis-with-Python', 'size': '1049'}