Davrot: /* The goal */

2025-10-17T16:56:47Z

The goal

← Older revision		Revision as of 16:56, 17 October 2025
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	~~== The goal ==~~
	Normally we can not use JSON with Numpy. However, if we use [https://pandas.pydata.org/ Pandas] as an intermediary then we can do it.		Normally we can not use JSON with Numpy. However, if we use [https://pandas.pydata.org/ Pandas] as an intermediary then we can do it.

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	# As string		# As string
	output = df.to_json(orient="index")		output = df.to_json(orient="index")
	print(output)</syntaxhighlight>Output (~~reformated~~):<syntaxhighlight lang="python">{		print(output)</syntaxhighlight>Output (reformatted):<syntaxhighlight lang="python">{
	"0": {		"0": {
	"0": 0.3145859169,		"0": 0.3145859169,

Davrot: Created page with "== The goal == Normally we can not use JSON with Numpy. However, if we use [https://pandas.pydata.org/ Pandas] as an intermediary then we can do it. Questions to [mailto:davrot@uni-bremen.de David Rotermund] '''Note: Pandas can also be used for many other formats beside JSON.''' == Writing JSON [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas-dataframe-to-json pandas.DataFrame.to_json] == DataFrame.to_js..."

2025-10-17T13:54:09Z

Created page with "== The goal == Normally we can not use JSON with Numpy. However, if we use [https://pandas.pydata.org/ Pandas] as an intermediary then we can do it. Questions to [mailto:davrot@uni-bremen.de David Rotermund] '''Note: Pandas can also be used for many other formats beside JSON.''' == Writing JSON [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas-dataframe-to-json pandas.DataFrame.to_json] == <syntaxhighlight lang="python">DataFrame.to_js..."

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== The goal ==
Normally we can not use JSON with Numpy. However, if we use [https://pandas.pydata.org/ Pandas] as an intermediary then we can do it.

Questions to [mailto:davrot@uni-bremen.de David Rotermund]

'''Note: Pandas can also be used for many other formats beside JSON.'''

== Writing JSON [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas-dataframe-to-json pandas.DataFrame.to_json] ==
<syntaxhighlight lang="python">DataFrame.to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=None, indent=None, storage_options=None, mode='w')[source]</syntaxhighlight><blockquote>Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.</blockquote><syntaxhighlight lang="python">import numpy as np
import pandas as pd

rng = np.random.default_rng()

some_data = rng.random((11, 3))

df = pd.DataFrame(some_data)
# As file
filename = "mynumpydata.json"
df.to_json(filename, orient="index")

# As string
output = df.to_json(orient="index")
print(output)</syntaxhighlight>Output (reformated):<syntaxhighlight lang="python">{
"0": {
"0": 0.3145859169,
"1": 0.2517001569,
"2": 0.6685086575
},
"1": {
"0": 0.7324177066,
"1": 0.6750562092,
"2": 0.0086333192
},
"2": {
"0": 0.7529914827,
"1": 0.3597052352,
"2": 0.2780062722
},
"3": {
"0": 0.2847410336,
"1": 0.5572451873,
"2": 0.5591149362
},
"4": {
"0": 0.4507115703,
"1": 0.9623511422,
"2": 0.7180796014
},
"5": {
"0": 0.5406601852,
"1": 0.9315847158,
"2": 0.2456480951
},
"6": {
"0": 0.3441382077,
"1": 0.4714817658,
"2": 0.1777388975
},
"7": {
"0": 0.6994839505,
"1": 0.6520935819,
"2": 0.9870686976
},
"8": {
"0": 0.187576403,
"1": 0.7466669157,
"2": 0.2952841542
},
"9": {
"0": 0.9140410582,
"1": 0.6828387334,
"2": 0.165762789
},
"10": {
"0": 0.644055269,
"1": 0.6122094952,
"2": 0.9695111468
}
}</syntaxhighlight>

== Read JSON [https://pandas.pydata.org/docs/reference/api/pandas.read_json.html#pandas-read-json pandas.read_json] ==
<syntaxhighlight lang="python">pandas.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')[source]</syntaxhighlight><blockquote>Convert a JSON string to pandas object.</blockquote><syntaxhighlight lang="python">import numpy as np
import pandas as pd

filename = "mynumpydata.json"
df = pd.read_json(filename, orient="index")
output_np = df.to_numpy()
print(type(output_np)) # -> <class 'numpy.ndarray'>
print(output_np)</syntaxhighlight>Output:<syntaxhighlight lang="python">[[0.31458592 0.25170016 0.66850866]
[0.73241771 0.67505621 0.00863332]
[0.75299148 0.35970524 0.27800627]
[0.28474103 0.55724519 0.55911494]
[0.45071157 0.96235114 0.7180796 ]
[0.54066019 0.93158472 0.2456481 ]
[0.34413821 0.47148177 0.1777389 ]
[0.69948395 0.65209358 0.9870687 ]
[0.1875764 0.74666692 0.29528415]
[0.91404106 0.68283873 0.16576279]
[0.64405527 0.6122095 0.96951115]]</syntaxhighlight>

Numpy and JSON over Pandas - Revision history

Davrot: /* The goal */