Hướng dẫn dùng pd read_json python
Cách đọc file Json trong Python Pandas.Python Pandas có các hàm đọc và ghi file json rất đơn giản, tốc độ nhanh. Để đọc và load dữ liệu từ file Json trong python pandas bạn sử dụng hàm read_json. Ví dụimport pandas as pd data_json =
pd.read_json('D:\Ihoclaptrinh.com\Data_Json/data.json') Kết quả : MatHang Gia Soluong Python Pandas sử dụng hàm to_json để ghi dữ liệu vào file json. Ví dụimport
pandas as pd data_series = {"MatHang" : ['MH1','MH2','MH3'], "Gia" :[500, 600, 800],"Soluong": [10, 20, 30]} js_data = df_data.to_json('D:\Ihoclaptrinh.com\Data_Json/data.json') Kết quả: File data.json đc tạo ra trong thư mục D:\Ihoclaptrinh.com\Data_Json
Convert a JSON string to pandas object. Parameters path_or_bufa valid JSON str, path object or file-like objectAny valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: If you want to pass in a path object, pandas accepts any By file-like object, we refer to objects with a Indication of expected JSON string format. Compatible JSON strings can be produced by
The allowed and default values depend on the value of the typ parameter.
The type of object to recover. dtypebool or dict, default NoneIf True, infer dtypes; if a dict of column to dtype, then use those; if False, then don’t infer dtypes at all, applies only to the data. For all Changed in
version 0.25.0: Not applicable for Try to convert the axes to the proper dtypes. For all Changed in version 0.25.0: Not applicable for If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates). keep_default_datesbool, default TrueIf parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if
Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. Deprecated since version 1.0.0. precise_floatbool, default FalseSet to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality. date_unitstr, default NoneThe timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively. encodingstr, default is ‘utf-8’The encoding to use to decode py3 bytes. encoding_errorsstr, optional, default “strict”How encoding errors are treated. List of possible values . New in version 1.3.0. linesbool, default FalseRead the file as a json object per line. Return JsonReader object for iteration. See the line-delimited json docs for more information on Changed
in version 1.2: For on-the-fly decompression of on-disk data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, or ‘.zst’ (otherwise no compression). If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to Changed in version 1.4.0: Zstandard support. nrowsint, optionalThe number of lines from the line-delimited jsonfile that has to be read. This can only be passed if lines=True. If this is None, all the rows will be returned. New in version 1.1. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to New in version 1.2.0. ReturnsSeries or DataFrameThe type returned depends on the value of typ. Notes Specific to Examples >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) Encoding/decoding a Dataframe using >>> df.to_json(orient='split') '{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}' >>> pd.read_json(_, orient='split') col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using >>> df.to_json(orient='index') '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}' >>> pd.read_json(_, orient='index') col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using >>> df.to_json(orient='records') '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]' >>> pd.read_json(_, orient='records') col 1 col 2 0 a b 1 c d Encoding with Table Schema >>> df.to_json(orient='table') '{"schema":{"fields":[{"name":"index","type":"string"},{"name":"col 1","type":"string"},{"name":"col 2","type":"string"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}' |