<jupyter_start><jupyter_text>Importaciones<jupyter_code>fig, ax = plt.subplots(figsize=(14,8))
sns.heatmap(basededatosimportaciones, cmap='jet')
exportaciones=pd.read_excel("/content/export.xlsx")
basededatosexportaciones=exportaciones.set_index("paises")
basededatosexportaciones<jupyter_output><empty_output><jupyter_text>Exportaciones<jupyter_code>fig, ax = plt.subplots(figsize=(14,8))
sns.heatmap(basededatosexportaciones, cmap='cool')
balanza=pd.read_excel("/content/balanza.xlsx")
basededatosbalanza=balanza.set_index("paises")
basededatosbalanza<jupyter_output><empty_output><jupyter_text>Balanza Comercial<jupyter_code>fig, ax = plt.subplots(figsize=(14,8))
sns.heatmap(basededatosbalanza, cmap='inferno_r')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13941018766756033, 'fraction_numerical': 0.012064343163538873, 'mean_word_length': 18.153846153846153, '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': '425136', 'license': 'MIT', 'n_tokens_mistral': 303, 'n_tokens_neox': 294, 'n_words': 20, 'path': 'proyecto_6.ipynb', 'repo_name': 'DanielArth/proyecto66', 'size': '746'}