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 dụ
import pandas as pd
data_json =
pd.read_json['D:\Ihoclaptrinh.com\Data_Json/data.json']
print[data_json.to_string[]]
Kết quả :
MatHang Gia Soluong
0 MH1 500 10
1 MH2 600 20
2 MH3 800 30
Python Pandas sử dụng hàm to_json để ghi dữ liệu vào file json.
Ví dụ
import pandas
as pd
data_series = {"MatHang" : ['MH1','MH2','MH3'], "Gia" :[500, 600, 800],"Soluong": [10, 20, 30]}
df_data = pd.DataFrame[data_series]
js_data = df_data.to_json['D:\Ihoclaptrinh.com\Data_Json/data.json']
print['Save json is successfully !']
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: file://localhost/path/to/table.json
.
If you want to
pass in a path object, pandas accepts any os.PathLike
.
By file-like object, we refer to objects with a read[]
method, such as a file handle [e.g. via builtin open
function] or StringIO
.
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', 'table'}
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'
.
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 orient
values except 'table'
, default is True.
Changed in version 0.25.0: Not applicable for orient='table'
.
Try to convert the axes to the proper dtypes.
For all orient
values except 'table'
, default is True.
Changed in version 0.25.0: Not applicable for orient='table'
.
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
it ends with
'_at'
,it ends with
'_time'
,it begins with
'timestamp'
,it is
'modified'
, orit is
'date'
.
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.
chunksizeint, optionalReturn JsonReader object
for iteration. See the line-delimited json docs for more information on chunksize
. This can only be passed if lines=True. If this is None, the file will be read into memory all at once.
Changed in version 1.2: JsonReader
is a context manager.
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 None
for no decompression. Can also be a dict with key 'method'
set to one of {'zip'
, 'gzip'
, 'bz2'
, 'zstd'
} and other key-value pairs are forwarded to zipfile.ZipFile
, gzip.GzipFile
, bz2.BZ2File
, or zstandard.ZstdDecompressor
, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: compression={'method': 'zstd', 'dict_data': my_compression_dict}
.
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 urllib
as header options. For other URLs [e.g. starting with “s3://”, and “gcs://”] the key-value pairs are forwarded to fsspec
. Please see fsspec
and urllib
for more details.
New in version 1.2.0.
ReturnsSeries or DataFrameThe type returned depends on the value of typ.
Notes
Specific to orient='table'
, if a DataFrame
with a literal Index
name of index gets written with to_json[]
, the subsequent read operation will incorrectly set
the Index
name to None
. This is because index is also used by DataFrame.to_json[]
to denote a missing Index
name, and the subsequent read_json[]
operation cannot distinguish between the two. The same limitation is encountered with a MultiIndex
and any names beginning with 'level_'
.
Examples
>>> df = pd.DataFrame[[['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']]
Encoding/decoding a Dataframe using 'split'
formatted JSON:
>>> 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 'index'
formatted JSON:
>>> 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
'records'
formatted JSON. Note that index labels are not preserved with this encoding.
>>> 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"}]}'