<jupyter_start><jupyter_text>Column Indexing<jupyter_code>data['Yearly Amount Spent']
data[['Time on App', 'Yearly Amount Spent']]
data.drop('Yearly Amount Spent', axis = 1)
data.drop(['Yearly Amount Spent','Email'], axis = 1)
data.drop('Email', axis = 1, inplace = True)
data<jupyter_output><empty_output><jupyter_text>Row Indexing  별도 데이터 프레임 준비<jupyter_code>new_data  = data.head(10)
new_data.index = ['a','b','c','d','e','f','g','h','i','j']
new_data
new_data.loc['d']
new_data.loc['d':'h']
new_data.iloc[3]
new_data.iloc[3:8]
new_data.iloc[3:]
new_data.iloc[:6]
new_data.iloc[:]
new_data.iloc[1:4, 0:3]
new_data.iloc[:, 1:4]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.212443095599393, 'fraction_numerical': 0.024279210925644917, 'mean_word_length': 8.705882352941176, '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': '583709', 'license': 'MIT', 'n_tokens_mistral': 310, 'n_tokens_neox': 313, 'n_words': 46, 'path': 'data/02.Python Tips #Pandas Indexing.ipynb', 'repo_name': 'goldang01/E-Commerce', 'size': '659'}