Python's equivalent for R's dput() function

for a pandas.DataFrame, print(df.to_dict()), as shown here and detailed in the manual.

And back again with df = pandas.DataFrame.from_dict(data_as_dict)

The default output style is 'orient=dict', but if you prefer 'orient=list', then:

print(df.to_dict('list')) 

There are several options for serializing Python objects to files:

• json.dump() stores the data in JSON format. It is very read- and editable, but can only store lists, dicts, strings, numbers, booleans, so no compound objects. You need to import json before to make the json module available.
• pickle.dump() can store most objects.

Less common:

• The shelve module stores multiple Python objects in a DBM database, mostly acting like a persistent dict.
• marshal.dump(): Not sure when you'd ever need that.

How no one has mentioned repr() yet is a mystery to me. repr() does almost exactly what R's dput() does. Here's a few examples:

>>> a = np.arange(10) >>> repr(a) 'array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])' >>> d = dict(x=1, y=2) >>> repr(d) "{'x': 1, 'y': 2}" >>> b = range(10) >>> repr(b) 'range(0, 10)' 

This answer focuses on json.dump() and json.dumps() and how to use them with numpy arrays. If you try, Python will hit you with an error saying that ndarrays are not JSON serializable:

import numpy as np import json a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) json.dumps(a) 

TypeError: Object of type 'ndarray' is not JSON serializable 

You can avoid this by translating it to a list first. See below for two working examples:

json.dumps()

json.dumps() seems to be the closest to R's dput() since it allows you to copy-paste the result straight from the console:

json.dumps(a.tolist()) # '[[1, 2, 3], [4, 5, 6], [7, 8, 9]]' 

json.dump()

json.dump() is not the same as dput() but it's still very useful. json.dump() will encode your object to a json file.

# Encode: savehere = open('file_location.json', 'w') json.dump(a.tolist(), savehere) 

which you can then decode elsewhere:

# Decode: b = open('file_location.json', 'r').read() # b is '[[1, 2, 3], [4, 5, 6], [7, 8, 9]]' c = json.loads(b) 

Then you can transform it back a numpy array again:

c = np.array(c) 

More information

on avoiding the 'not serializable' error see:

• numpy array is not json serializable

• how to make classes json serializable (kind of unrelated, but very interesting)

IMO, json.dumps() (note the s) is even better since it returns a string, as opposed to json.dump() which requires you to write to a file.