If False, then don’t infer dtypes at all, applies only to the data.įor all orient values except 'table', default is True. If True, infer dtypes if a dict of column to dtype, then use those The DataFrame columns must be unique for orients 'index', The DataFrame index must be unique for orients 'index' and Indication of expected JSON string format.Ĭompatible JSON strings can be produced by to_json() with a If you want to pass in a path object, pandas accepts anyīy file-like object, we refer to objects with a read() method,
URL schemes include http, ftp, s3, and file. Parameters path_or_buf a valid JSON str, path object or file-like objectĪny valid string path is acceptable. read_json ( path_or_buf, *, orient = None, typ = 'frame', dtype = None, convert_axes = None, convert_dates = True, keep_default_dates = True, precise_float = False, date_unit = None, encoding = None, encoding_errors = 'strict', lines = False, chunksize = None, compression = 'infer', nrows = None, storage_options = None, dtype_backend = _NoDefault.no_default, engine = 'ujson' ) #Ĭonvert a JSON string to pandas object. Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine _json # pandas. I recommend you to check out the documentation for read_json() and json_normalize() APIs, and to know about other things you can do. I hope this article will help you to save time in converting JSON data into a DataFrame. When dealing with nested JSON, we can use the Pandas built-in json_normalize() function. Pandas read_json() function is a quick and convenient way for converting simple flattened JSON into a Pandas DataFrame. notation to access property from a deeply nested object. Glom is a Python library that allows us to use.
from glom import glom df = pd.read_json('data/nested_deep.json') df.apply( lambda row: glom(row, 'grade.math')) 0 60 1 89 2 79 Name: students, dtype: int64 How can we do that more effectively? The answer is using read_json with glom. What about JSON with a nested list? Let’s see how to convert the following JSON into a DataFrame: Pandas read_json() works great for flattened JSON like we have in the previous example. Same as reading from a local file, it returns a DataFrame, and columns that are numerical are cast to numeric types by default. Image by author > df.info() RangeIndex: 3 entries, 0 to 2 Data columns (total 5 columns): # Column Non-Null Count Dtype - 0 id 3 non-null object 1 name 3 non-null object 2 math 3 non-null int64 3 physics 3 non-null int64 4 chemistry 3 non-null int64 dtypes: int64(3), object(2) memory usage: 248.0+ bytes
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