时间:2021-05-22
有时候需要读取一定格式的json文件为DataFrame,可以通过json来转换或者pandas中的read_json()。
import pandas as pdimport jsondata = pd.DataFrame(json.loads(open('jsonFile.txt','r+').read()))#方法一dataCopy = pd.read_json('jsonFile.txt',typ='frame') #方法二pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False)[source] Convert a JSON string to pandas object Parameters: path_or_buf : a valid JSON string or file-like, default: None The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/table.json orient : string, Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is: 'split' : dict like {index -> [index], columns -> [columns], data -> [values]} 'records' : list like [{column -> value}, ... , {column -> value}] 'index' : dict like {index -> {column -> value}} 'columns' : dict like {column -> {index -> value}} 'values' : just the values array The allowed and default values depend on the value of the typ parameter. when typ == 'series', allowed orients are {'split','records','index'} default is 'index' The Series index must be unique for orient 'index'. when typ == 'frame', allowed orients are {'split','records','index', 'columns','values'} default is 'columns' The DataFrame index must be unique for orients 'index' and 'columns'. The DataFrame columns must be unique for orients 'index', 'columns', and 'records'. typ : type of object to recover (series or frame), default ‘frame' dtype : boolean or dict, default True If 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. convert_axes : boolean, default True Try to convert the axes to the proper dtypes. convert_dates : boolean, default True List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if it ends with '_at', it ends with '_time', it begins with 'timestamp', it is 'modified', or it is 'date' keep_default_dates : boolean, default True If parsing dates, then parse the default datelike columns numpy : boolean, default False 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. precise_float : boolean, default False Set 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_unit : string, default None The 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. lines : boolean, default False Read the file as a json object per line. New in version 0.19.0. encoding : str, default is ‘utf-8' The encoding to use to decode py3 bytes. New in version 0.19.0.以上这篇读取json格式为DataFrame(可转为.csv)的实例讲解就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
声明:本页内容来源网络,仅供用户参考;我单位不保证亦不表示资料全面及准确无误,也不保证亦不表示这些资料为最新信息,如因任何原因,本网内容或者用户因倚赖本网内容造成任何损失或损害,我单位将不会负任何法律责任。如涉及版权问题,请提交至online#300.cn邮箱联系删除。
从json文件读取jsonstring或者自定义jsonstring,将其转为object。下面采用的object为map,根据map读取json的某个数据,可
在数据分析中经常需要从csv格式的文件中存取数据以及将数据写书到csv文件中。将csv文件中的数据直接读取为dict类型和DataFrame是非常方便也很省事的
在机器学习过程中,通常会通过pandas读取csv文件,保持成dadaframe格式,然而有时候需要对dataframe中的时间字段进行数据建模,比如时间格式为
数据加载、存储与文件格式pandas提供了一些用于将表格型数据读取为DataFrame对象的函数。其中read_csv和read_talbe用得最多pandas
在python处理数据时,经常用到DataFrame和set。train=pd.read_csv('XXX.csv')#读取文件train=train['ite