<jupyter_start><jupyter_text>검색어2 : 분석데이터 기간(18.04~18.09) 내 일별/검색어별 검색량<jupyter_code>sch2 = pd.read_csv("../../data/03_Search2.csv")
sch2.tail()
sch2.info()
# SEARCH_CNT의 데이터 타입 변경
# 데이터 내 ','제거 후 integer 변환
sch2['SEARCH_CNT_int'] = sch2['SEARCH_CNT'].map(lambda x : int("".join(x.split(','))) if ',' in x else int(x))
sch2.tail()<jupyter_output><empty_output><jupyter_text>궁금증- 4~9월 사이의 data인데, tsdjpi82010(롱패딩)은 왜 검색한거지? 세일했나? 역시즌 할인행사?<jupyter_code>sch2.info()
pd.options.display.float_format = '{:.2f}'.format
sch2['SEARCH_CNT_int'].describe()
sch2[sch2.SEARCH_CNT_int==5043]
for i in sch2.columns:
    print("colum : {}, unique count : {}".format(i,sch2[i].nunique()))
    print(sch2[i].unique())<jupyter_output>colum : SESS_DT, unique count : 183
[20180407 20180402 20180414 20180415 20180420 20180406 20180403 20180405
 20180721 20180410 20180417 20180426 20180430 20180421 20180411 20180412
 20180925 20180926 20180401 20180422 20180911 20180912 20180423 20180501
 20180408 20180419 20180928 20180416 20180409 20180929 20180927 20180913
 20180915 20180418 20180404 20180916 20180919 20180413 20180910 20180920
 20180921 20180427 20180716 20180722 20180717 20180718 20180428 20180424
 20180922 20180914 20180923 20180924 20180930 20180907 20180425 20180715
 20180918 20180917 20180723 20180719 20180429 20180531 20180705 20180720
 20180612 20180901 20180704 20180601 20180902 20180502 20180903 20180706
 20180613 20180905 20180904 20180906 20180908 20180909 20180707 20180725
 20180603 20180604 20180605 20180730 20180503 20180805 20180804 20180828
 20180808 20180504 20180826 20180606 20180629 20180630 20180823 20180824
 20180729 20180810 20180821 20180711 20180712 20180827 20180505 20180727
 20180728 20180[...]
{'fraction_non_alphanumeric': 0.08943089430894309, 'fraction_numerical': 0.5139372822299652, 'mean_word_length': 6.524017467248909, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '450190', 'license': 'MIT', 'n_tokens_mistral': 1374, 'n_tokens_neox': 785, 'n_words': 161, 'path': 'code/EDA/1.EDA_Search2.ipynb', 'repo_name': 'Junhojuno/L.Point-BigData-Competition', 'size': '1722'}