<jupyter_start><jupyter_text>Data Structures Continued<jupyter_code>contacts = {'John': '312-555-1234', 'Paul': '312-555-3123', 'George': '312-555-3333', 'Ringo': '312-555-2222'}
type(contacts)
contacts['John']
contacts['Paul']
contacts['PAUL']
contacts.keys()
contacts.values()
contacts['Himanshu'] = '480-111-2222'
print(contacts)
student_names = {1: 'John', 2: 'Smith', 3: 'Matt', 4: 'Jimmy', 5: 'Sue'}
student_names[1]
student_names[2]
student_data = {
    'Name': 'Himanshu',
    'E-mail': '<EMAIL>',
    'Age': 28,
    'subjects': ['math','science','history', 'geography']
}
student_data.keys()
student_data['Name']
student_data['E-mail']
student_data:['Age']
student_data['subjects']
student_data['subjects'][1]<jupyter_output><empty_output><jupyter_text>Exercises  1.<jupyter_code>word_freq = {'love': 25, 'conversation': 1, 'every': 6, "we're": 1, 'plate': 1, 'sour': 1, 'jukebox': 1, 'now': 11, 'taxi': 1, 'fast': 1, 'bag': 1, 'man': 1, 'push': 3, 'baby': 14, 'going': 1, 'you': 16, "don't": 2, 'one': 1, 'mind': 2, 'backseat': 1, 'friends': 1, 'then': 3, 'know': 2}
word_freq.keys()
print(len(word_freq.keys()))<jupyter_output>23<jupyter_text>23 keys<jupyter_code>word_freq.keys()
word_freq['friends']
word_freq['taxi']
word_freq['jukebox']

word_freq['begin']
word_freq['begin'] = '1'
print(word_freq)
word_freq['start'] = '2'
word_freq['over'] = '1'
word_freq['body'] = '17'
print(word_freq)
list(word_freq.keys())
word = list(word_freq)
print(word)
word[0]
word[-1]<jupyter_output><empty_output><jupyter_text>1.2 No a key-value has to have a unique key.  1.3 Yes, as long as the keys are different.  Additional Content: Nested Disctionaries<jupyter_code>contacts = {1: {'Name' : 'John' , 'Phone':'312-555-1234'},
            2: {'Name' : 'Paul' , 'Phone':'312-555-3123'},
            3: {'Name': 'George' , 'Phone':'312-555-3333'},
            4: {'Name' : 'Ringo' , 'Phone': '312-555-2222'}}
type(contacts)
contacts
contacts.values()
print(contacts[1])
contacts[1].keys()
contacts[1].values()
contacts[1]["Name"]
contacts[1]['Phone']
my_data = [{'Mike': [25,23000],'Jane': [38, 40000],'Bill': [45,35000]},{'Developers': ['Mike','Bill']},{'HR': ['Jane']}]
my_data
type(my_data)
my_data[0]
type(my_data[0])
my_data[0]['Jane']
my_data[0]['Jane'][1]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2353711790393013, 'fraction_numerical': 0.0777292576419214, 'mean_word_length': 6.792517006802721, 'pattern_counts': {'":': 2, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '17099', 'license': 'MIT', 'n_tokens_mistral': 1069, 'n_tokens_neox': 938, 'n_words': 180, 'path': 'Jupyter/Python; Data Structures Continued.ipynb', 'repo_name': 'dchrobakjr/march_2022_test', 'size': '2304'}