<jupyter_start><jupyter_text>Importing and Organizing Your Data in Python - Part III Save the data obtained in the previous exercise as a csv file. Then, save it in an excel format.  Do you remember which library can help you do that? Don’t forget to import it first!<jupyter_code>tickers = ['AAPL', 'MSFT', 'XOM', 'BP']
new_data = pd.DataFrame()
for t in tickers:
    new_data[t] = wb.DataReader(t, data_source='iex', start='2015-1-1')['close']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11789473684210526, 'fraction_numerical': 0.01263157894736842, 'mean_word_length': 5.4324324324324325, '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': '32780', 'license': 'MIT', 'n_tokens_mistral': 167, 'n_tokens_neox': 161, 'n_words': 58, 'path': 'Python for Finance - Code Files/57 Importing Data - Part III/CSV/Python 3 CSV/Importing and Organizing Your Data in Python - Part III - Exercise_CSV.ipynb', 'repo_name': 'siddharthjain1611/Python_for_Finance_Investment_Fundamentals-and-Data-Analytics', 'size': '475'}