# data[].attributes.body
탐색적 데이터분석과 활용을 위해 파이썬코드 기본과정과 바이브코딩을 통해 자신만의 웹/앱을 개발한다.

# data[].attributes.body
AI 기술 및 도구를 활용하여 문제 해결 능력과 비판적 사고 능력을 개발한다.

# data[].attributes.body
학생들의 그룹 토론 참여와 문제풀이 결과를 관찰하여 개별 피드백 제공.

# data[].attributes.body
시험과 유사한 문제를 사용한 소규모 퀴즈를 통해 진도를 점검.

# data[].attributes.body
학생별로 도전하는 문제 해결 접근 방식을 리뷰하고 지도.

# data[].attributes.body
학생들이 개인적으로 그날 배운 주요 개념을 작성해 제출.

# data[].attributes.body
주제별로 AI 기술을 활용한 간단한 프로젝트를 기획 및 실행.

# data[].attributes.attachment_link.title
초보자를 위한 20가지 인공지능 프로젝트 아이.. : 네이버블로그

# data[].attributes.body
1. 설치페이지가 이메일과 연동되어 업데이트 되었음

2. 설치시 PATH environment variable 반드시 체크할것

# data[].attributes.attachment_link.title
Anaconda | The World's Most Popular Data Science Platform

# data[].attributes.attachment_link.description
Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities.

# data[].attributes.body
np는 numpy의 약칭이다.

# data[].attributes.attachment_link.readable_text
This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation .

Acknowledgements

Large parts of this manual originate from Travis E. Oliphant's book Guide to NumPy (which generously entered Public Domain in August 2008). The reference documentation for many of the functions are written by numerous contributors and developers of NumPy.

# data[].attributes.attachment_link.description
This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation . Large parts of this manual originate from Travis E. Oliphant's book Guide to NumPy (which generously entered Public Domain in August 2008).

# data[].attributes.attachment_link.title
pandas documentation - pandas 1.1.4 documentation

# data[].attributes.attachment_link.description
Saw a typo in the documentation? Want to improve existing functionalities? The contributing guidelines will guide you through the process of improving pandas.

# data[].attributes.headline
01.파이선데이터분석 라이브러리활용

# data[].attributes.subject
01.파이선데이터분석 라이브러리활용

# data[].attributes.attachment_link.readable_text
pandas. read_csv( filepath_or_buffer, sep=<object object>, delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)

Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the file into chunks.

Additional help can be found in the online docs for IO Tools.

Parameters

filepath_or_bufferstr, path object or file-like object

Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv.

If you want to pass in a path object, pandas accepts any os.PathLike .

By file-like object, we refer to objects with a method, such as a file handle (e.g. via builtin function) or .

Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python's builtin sniffer tool, csv.Sniffer . In addition, separators longer than 1 character and different from will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: .

Alias for sep.

headerint, list of int, default 'infer'

Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to header=None . Explicitly pass to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True , so denotes the first line of data rather than the first line of the file.

namesarray-like, optional

List of column names to use. If the file contains a header row, then you should explicitly pass to override the column names. Duplicates in this list are not allowed.

index_colint, str, sequence of int / str, or False, default

Column(s) to use as the row labels of the , either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used.

Note: index_col=False can be used to force pandas to not use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line.

usecolslist-like or callable, optional

Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid list-like usecols parameter would be or . Element order is ignored, so is the same as . To instantiate a DataFrame from with element order preserved use for columns in order or for order.

If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be . Using this parameter results in much faster parsing time and lower memory usage.

squeezebool, default False

If the parsed data only contains one column then return a Series.

Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...

mangle_dupe_colsbool, default True

Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

dtypeType name or dict of column -> type, optional

Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32, 'c': 'Int64'} Use str or object together with suitable na_values settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.

engine{'c', 'python'}, optional

Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

convertersdict, optional

Dict of functions for converting values in certain columns. Keys can either be integers or column labels.

true_valueslist, optional

Values to consider as True.

false_valueslist, optional

Values to consider as False.

skipinitialspacebool, default False

Skip spaces after delimiter.

skiprowslist-like, int or callable, optional

Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.

If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be .

skipfooterint, default 0

Number of lines at bottom of file to skip (Unsupported with engine='c').

Number of rows of file to read. Useful for reading pieces of large files.

na_valuesscalar, str, list-like, or dict, optional

Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan', '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan', 'null'.

keep_default_nabool, default True

Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:

If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.

If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.

If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.

If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.

na_filterbool, default True

Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.

verbosebool, default False

Indicate number of NA values placed in non-numeric columns.

skip_blank_linesbool, default True

If True, skip over blank lines rather than interpreting as NaN values.

parse_datesbool or list of int or names or list of lists or dict, default False

The behavior is as follows:

boolean. If True -> try parsing the index.

list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.

list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.

dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'

If a column or index cannot be represented as an array of datetimes, say because of an unparsable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use pd.to_datetime after . To parse an index or column with a mixture of timezones, specify date_parser to be a partially-applied with . See Parsing a CSV with mixed timezones for more.

Note: A fast-path exists for iso8601-formatted dates.

infer_datetime_formatbool, default False

If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x.

keep_date_colbool, default False

If True and parse_dates specifies combining multiple columns then keep the original columns.

date_parserfunction, optional

Function to use for converting a sequence of string columns to an array of datetime instances. The default uses dateutil.parser.parser to do the conversion. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one or more strings (corresponding to the columns defined by parse_dates) as arguments.

dayfirstbool, default False

DD/MM format dates, international and European format.

cache_datesbool, default True

If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets.

iteratorbool, default False

Return TextFileReader object for iteration or getting chunks with .

Changed in version 1.2: TextFileReader is a context manager.

chunksizeint, optional

Return TextFileReader object for iteration. See the IO Tools docs for more information on and .

Changed in version 1.2: TextFileReader is a context manager.

compression{'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'

For on-the-fly decompression of on-disk data. If 'infer' and filepath_or_buffer is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no decompression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression.

thousandsstr, optional

Thousands separator.

Character to recognize as decimal point (e.g. use ',' for European data).

lineterminatorstr (length 1), optional

Character to break file into lines. Only valid with C parser.

quotecharstr (length 1), optional

The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.

quotingint or csv.QUOTE_* instance, default 0

Control field quoting behavior per constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).

When quotechar is specified and quoting is not , indicate whether or not to interpret two consecutive quotechar elements INSIDE a field as a single element.

escapecharstr (length 1), optional

One-character string used to escape other characters.

Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines (as long as skip_blank_lines=True ), fully commented lines are ignored by the parameter header but not by skiprows. For example, if , parsing#empty\na,b,c\n1,2,3 with will result in 'a,b,c' being treated as the header.

Encoding to use for UTF when reading/writing (ex. 'utf-8'). List of Python standard encodings . .. versionchanged:: 1.2

dialectstr or csv.Dialect, optional

If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details.

error_bad_linesbool, default True

Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will dropped from the DataFrame that is returned.

warn_bad_linesbool, default True

If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output.

delim_whitespacebool, default False

Specifies whether or not whitespace (e.g. or ) will be used as the sep. Equivalent to setting . If this option is set to True, nothing should be passed in for the parameter.

low_memorybool, default True

Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the dtype parameter. Note that the entire file is read into a single DataFrame regardless, use the chunksize or iterator parameter to return the data in chunks. (Only valid with C parser).

memory_mapbool, default False

If a filepath is provided for filepath_or_buffer, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.

float_precisionstr, optional

Specifies which converter the C engine should use for floating-point values. The options are or 'high' for the ordinary converter, 'legacy' for the original lower precision pandas converter, and 'round_trip' for the round-trip converter.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by , e.g., starting "s3://", "gcs://". An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.

Returns

DataFrame or TextParser

A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes.

Examples

# data[].attributes.attachment_link.title
pandas.read_csv - pandas 1.2.2 documentation

# data[].attributes.attachment_link.readable_text
Python is a programming language that lets you work quickly and integrate systems more effectively. Learn More

Fundraiser deal on now: Get a discount on PyCharm + all proceeds go to the Python Software Foundation. Ending soon! GET THE DEAL

# data[].attributes.attachment_link.keywords[]
python programming language object oriented web free open source software license documentation download community

# data[].attributes.attachment_link.description
The official home of the Python Programming Language

# data[].attributes.headline
데이터불러오기 및 저장하기

# data[].attributes.subject
데이터불러오기 및 저장하기

# data[].attributes.attachment_link.title
4. 데이터불러오기 및 저장하기

# data[].attributes.attachment_link.description
# 📌 Pandas read_csv() 풀옵션 예제 및 설명import pandas as pd# 1️⃣ 기본적으로 CSV 파일 불러오기df = pd.read_csv(“datasets/winequality-white.csv”)print(df.head())# 2️⃣ 다양한 옵션 사용 예제## ✅ 구분자 지정 (sep)df = pd.read_csv(“datasets/winequality-white.csv”, sep=”;”) # 세미콜론(;)으로 구분된 데이터df = pd.read_csv(“datasets/winequality-white.csv”, sep=”\t”) # 탭(\t)으로 구분된 데이터## ✅ 인코딩 방식 지정 (encoding)df = pd.read_csv(“datasets/winequalit…

# data[].attributes.body
머신러닝과 딥러닝

# data[].attributes.attachment_link.description
경영지도사, 경영컨설턴트. 데이터분석가. 기획자. 프리랜서
공부하는 걸 좋아하고 일하는 것을 좋아하는 워커홀릭
비즈니스기획자

# data[].attributes.attachment_link.title
AI 라이브러리란? | 퓨어스토리지 | 퓨어스토리지

# data[].attributes.attachment_link.description
AI 라이브러리는 AI 앱 구축을 위한 필수 기능을 제공하는 사전 작성된 코드 모음입니다.

# data[].attributes.attachment_link.title
The AI workspace that works for you. | Notion

# data[].attributes.attachment_link.description
A tool that connects everyday work into one space. It gives you and your teams AI tools—search, writing, note-taking—inside an all-in-one, flexible workspace.

# data[].attributes.body
바이브코딩으로 통해 자신만의 웹/앱을 개발하여 프로토콜 버전으로 제출할것, saas모델들을 확인하여 자신의 프로젝트를 완성하세요.

데이터 모델링을 하실 분은 아래 있는 AI응용프로젝트를 참고하세요.

# data[].attributes.attachment_link.description
Not your computer? Use Private Browsing windows to sign in. Learn more about using Guest mode

# data[].attributes.headline
강의영상(녹화후 공유)

# data[].attributes.subject
강의영상(녹화후 공유)

# data[].attributes.body
강의는 zoom으로 하지 않고 녹화해서 공유하겠습니다. 참고해주세요.

# data[].attributes.attachment_link.title
딥러닝 한번에 끝내기 (텐서플로우, 케라스) Deep Learning Full Tutorial Course using TensorFlow and Keras

# data[].attributes.headline
머신러닝과 딥러닝 개념

# data[].attributes.subject
머신러닝과 딥러닝 개념

# data[].attributes.attachment_link.title
이 영상 하나면 '인공지능', '머신러닝', '딥러닝' 이해가 됩니다ㅣ서울대 AI박사 6분 개념정리

# data[].attributes.attachment_link.title
[ML/DL] Lecture 2. Introduction to Supervised Learning

# data[].attributes.body
선형회기1

# data[].attributes.body
선형회기2

# data[].attributes.body
분류

# data[].attributes.attachment_link.title
[ML/DL] Lecture 5. Classification I (Logistic Regression)

# data[].attributes.body
분류2

# data[].attributes.attachment_link.title
[ML/DL] Lecture 6. Classification II (Bayes Classifiers)

# data[].attributes.body
Overfitting & Regularization

# data[].attributes.attachment_link.title
[ML/DL] Lecture 7. Overfitting & Regularization

# data[].attributes.body
결정나무

# data[].attributes.body
아상블과 부스팅

# data[].attributes.attachment_link.title
[ML/DL] Lecture 9. Ensemble Methods and Boosting

# data[].attributes.attachment_link.title
[ML/DL] Lecture 10. Support Vector Machines

# data[].attributes.attachment_link.title
[ML/DL] Lecture 11. Unsupervised Learning

# data[].attributes.attachment_link.title
[ML/DL] Lecture 12. Nearest Neighbors & Softmax Classifiers

# data[].attributes.attachment_link.title
[ML/DL] Lecture 13. Neural Networks & Backpropagation

# data[].attributes.body
구글 AI스튜디오

# data[].attributes.attachment_link.title
초보도 쉽게 따라하는 재미나이 앱 개발 꿀팁! | 바이브코딩 1편

# data[].attributes.headline
배혜숙- 과제(하루 15분 글쓰기)

# data[].attributes.subject
배혜숙- 과제(하루 15분 글쓰기)

# data[].attributes.attachment_link.description
매일 15분, 꾸준한 글쓰기 훈련으로서술형·요약문 실력을 키워보세요.

# data[].attributes.body
안녕하세요~다니엘님

# data[].attributes.attachment_link.title
슈퍼바이저 | AI 실습일지 코멘트 생성기

# data[].attributes.headline
진로로드맵-만다르타계획_손재영

# data[].attributes.subject
진로로드맵-만다르타계획_손재영

# data[].attributes.attachment_link.description
AI가 당신의 꿈들을 분석하여 실행 가능한 프로젝트로 만들어 드립니다.

# data[].attributes.headline
부동산 양도소득세 계산해 보기 APP (김재성)

# data[].attributes.subject
부동산 양도소득세 계산해 보기 APP (김재성)

# data[].attributes.body
영단어 학습 앱