I made a big mistake, when I choose the way of dumping data;
Now I have a text file, that consist of
{ "13234134": ["some", "strings", ...]}{"34545345": ["some", "strings", ...]} ..so on
How can I read it into python?
edit:
I have tried json,
when I add at begin and at end of file curly-braces manually, I have "ValueError: Expecting property name:", because "13234134" string maybi invalid for json, I do not know how to avoid it.
edit1
with open('new_file.txt', 'w') as outfile:
for index, user_id in enumerate(users):
json.dump(dict = get_user_tweets(user_id), outfile)
It looks like what you have is an undelimited stream of JSON objects. As if you'd called json.dump over and over on the same file, or ''.join(json.dumps(…) for …). And, in fact, the first one is exactly what you did. :)
So, you're in luck. JSON is a self-delimiting format, which means you can read up to the end of the first JSON object, then read from there up to the end of the next JSON object, and so on. The raw_decode method essentially does the hard part.
There's no stdlib function that wraps it up, and I don't know of any library that does it, but it's actually very easy to do yourself:
def loads_multiple(s):
decoder = json.JSONDecoder()
pos = 0
while pos < len(s):
pos, obj = decoder.raw_decode(s, pos)
yield obj
So, instead of doing this:
obj = json.loads(s)
do_stuff_with(obj)
… you do this:
for obj in loads_multi(s):
do_stuff_with(obj)
Or, if you want to combine all the objects into one big list:
objs = list(loads_multi(s))
Consider simply rewriting it to something that is valid json. If indeed your bad data only contains the format that you've shown (a series of json structures that are not comma-separated), then just add commas and square braces:
with open('/tmp/sto/junk.csv') as f:
data = f.read()
print(data)
s = "[ {} ]".format(data.strip().replace("}{", "},{"))
print(s)
import json
data = json.loads(s)
print(type(data))
Output:
{ "13234134": ["some", "strings"]}{"34545345": ["some", "strings", "like", "this"]}
[ { "13234134": ["some", "strings"]},{"34545345": ["some", "strings", "like", "this"]} ]
<class 'list'>
Related
Suppose I have a config file like this where I list a bunch of values. I am running a function in which I am checking that a set of strings will always begin with one of these defined values.
start_values = [
"cats",
"dogs",
"birds",
"horses"
]
And I also have a json file on which I want run unit tests on to make sure that my function is running properly, like this.
{
"sentence_tests": [
"horses eat grass.",
"birds fly high.",
"cats like to nap.",
"dogs are cool."
]
}
However, the problem I am facing is that if I want to change one of my start_values to be something else, I want to also update my json file for that specific value. For example, if I change "dogs" to "cows", I want that to update automatically in my json file instead of having to do that manually.
So this is how I would want it to be after I change the start_values:
Modified start_values:
start_values = [
"cats",
"cows",
"birds",
"horses"
]
Modified json file:
{
"sentence_tests": [
"horses eat grass.",
"birds fly high.",
"cats like to nap.",
"cows are cool."
]
}
Is there a way to do this in python?
import json
with open(filename, "rt") as f:
sentences = json.loads(f.read())
for i, value in enumerate(starts_values):
words = sentences["sentences_tests"][i].split()
if words[0] != value:
words[0] = value
words = " ".join(words)
sentences["sentences_tests"][i] = words
with open(filename, "wt") as f:
f.write(json.dumps(sentences, indent=4))
Yes,
you can easily load a JSON object using the json python library. It will transform into a python dictionary. All you have to do after this is rewrite the file.
I am using csv module to convert json to csv and store it in a file or print it to stdout.
def write_csv(data:list, header:list, path:str=None):
# data is json format data as list
output_file = open(path, 'w') if path else sys.stdout
out = csv.writer(output_file)
out.writerow(header)
for row in data:
out.writerow([row[attr] for attr in header])
if path: output_file.close()
I want to store the converted csv to a variable instead of sending it to a file or stdout.
say I want to create a function like this:
def json_to_csv(data:list, header:list):
# convert json data into csv string
return string_csv
NOTE: format of data is simple
data is list of dictionaries of string to string maping
[
{
"username":"srbcheema",
"name":"Sarbjit Singh"
},
{
"username":"testing",
"name":"Test, user"
}
]
I want csv output to look like:
username,name
srbcheema,Sarbjit Singh
testing,"Test, user"
Converting JSON to CSV is not a trivial operation. There is also no standardized way to translate between them...
For example
my_json = {
"one": 1,
"two": 2,
"three": {
"nested": "structure"
}
}
Could be represented in a number of ways...
These are all (to my knowledge) valid CSVs that contain all the information from the JSON structure.
data
'{"one": 1, "two": 2, "three": {"nested": "structure"}}'
one,two,three
1,2,'{"nested": "structure"}'
one,two,three__nested
1,2,structure
In essence, you will have to figure out the best translation between the two based on your knowledge of the data. There is no right answer on how to go about this.
I'm relatively knew to Python so there's probably a better way, but this works:
def get_safe_string(string):
return '"'+string+'"' if "," in string else string
def json_to_csv(data):
csv_keys = data[0].keys()
header = ",".join(csv_keys)
res = list(",".join(get_safe_string(row.get(k)) for k in csv_keys) for row in data)
res.insert(0,header)
return "\n".join(r for r in res)
I am trying to setup a simple data file format, and I am working with these files in Python for analysis. The format basically consists of header information, followed by the data. For syntax and future extensibility reasons, I want to use a JSON object for the header information. An example file looks like this:
{
"name": "my material",
"sample-id": null,
"description": "some material",
"funit": "MHz",
"filetype": "material_data"
}
18 6.269311533 0.128658208 0.962033017 0.566268827
18.10945274 6.268810641 0.128691962 0.961950095 0.565591807
18.21890547 6.268312637 0.128725463 0.961814928 0.564998228...
If the data length/structure is always the same, this is not hard to parse. However, it brought up in my mind a question about the most flexible way to parse out the JSON object, given an unknown number of lines, and an unknown number of nested curly braces, and potentially more than one JSON object in the file.
If there is only one JSON object in the file, one can use this regular expression:
with open(fname, 'r') as fp:
fstring = fp.read()
json_string = re.search('{.*}', fstring, flags=re.S)
However, if there is more than one JSON string, and I want to grab the first one, I need to use something like this:
def grab_json(mystring):
lbracket = 0
rbracket = 0
lbracket_pos = 0
rbracket_pos = 0
for i in range(len(mystring)):
if mystring[i] == '{':
lbracket = 1
lbracket_pos = i
break
for i in range(lbracket_pos+1, len(mystring)):
if mystring[i] == '}':
rbracket += 1
if rbracket == lbracket:
rbracket_pos = i
break
elif mystring[i] == '{':
lbracket += 1
json_string = mystring[lbracket_pos : rbracket_pos + 1]
return json_string, lbracket_pos, rbracket_pos
json_string, beg_pos, end_pos = grab_json(fstring)
I guess the question as always: is there a better way to do this? Better meaning simpler code, more flexible code, more robust code, or really anything?
The easiest solution, as Klaus suggested, is just to use JSON for the entire file. That makes your life much simpler because than writing is just json.dump and reading is just json.load.
A second solution is to put the metadata in a separate file, which keeps reading and writing simple at the expense of multiple files for each data set.
A third solution would be, when writing the file to disk, to prepend the length of the JSON data. So writing might look something like:
metadata_json = json.dumps(metadata)
myfile.write('%d\n' % len(metadata_json))
myfile.write(metadata_json)
myfile.write(data)
Then reading looks like:
with open('myfile') as fd:
len = fd.readline()
metadata_json = fd.read(int(len))
metadata = json.loads(metadata)
data = fd.read()
A fourth option is to adopt an existing storage format (maybe hdf?) that already has the features you are looking for in terms of storing both data and metadata in the same file.
I would store headers separately. It'll give you a possibility to use the same header file for multiple data files
Alternatively you may want to take a look at Apache Parquet Format especially if you want to process your data on distributed cluster(s) using Spark power
I've a text document that has several thousand jsons strings in the form of: "{...}{...}{...}". This is not a valid json it self but each {...} is.
I currently use the following a regular expression to split them:
fp = open('my_file.txt', 'r')
raw_dataset = (re.sub('}{', '}\n{', fp.read())).split('\n')
Which basically breaks every line where a curly bracket closes and other opens (}{ -> }\n{) so I can split them into different lines.
The problem is that few of them have a tags attribute written as "{tagName1}{tagName2}" which breaks my regular expression.
An example would be:
'{"name":\"Bob Dylan\", "tags":"{Artist}{Singer}"}{"name": "Michael Jackson"}'
Is parsed into
'{"name":"Bob Dylan", "tags":"{Artist}'
'{Singer}"}'
'{"name": "Michael Jackson"}'
instead of
'{"name":"Bob Dylan", "tags":"{Artist}{Singer}"}'
'{"name": "Michael Jackson"}'
What is the proper way of achieve this for further json parsing?
Use the raw_decode method of json.JSONDecoder
>>> import json
>>> d = json.JSONDecoder()
>>> x='{"name":\"Bob Dylan\", "tags":"{Artist}{Singer}"}{"name": "Michael Jackson"}'
>>> d.raw_decode(x)
({'tags': '{Artist}{Singer}', 'name': 'Bob Dylan'}, 47)
>>> x=x[47:]
>>> d.raw_decode(x)
({'name': 'Michael Jackson'}, 27)
raw_decode returns a 2-tuple, the first element being the decoded JSON and the second being the offset in the string of the next byte after the JSON ended.
To loop until the end or until an invalid JSON element is encountered:
>>> while True:
... try:
... j,n = d.raw_decode(x)
... except ValueError:
... break
... print(j)
... x=x[n:]
...
{'name': 'Bob Dylan', 'tags': '{Artist}{Singer}'}
{'name': 'Michael Jackson'}
When the loop breaks, inspection of x will reveal if it has processed the whole string or had encountered a JSON syntax error.
With a very long file of short elements you might read a chunk into a buffer and apply the above loop, concatenating anything that's left over with the next chunk after the loop breaks.
You can use the jq command line utility to transfer your input to json. Let's say you have the following input:
input.txt:
{"name":"Bob Dylan", "tags":"{Artist}{Singer}"}{"name": "Michael Jackson"}
You can use jq -s, which consumes multiple json documents from input and transfers them into a single output array:
jq -s . input.txt
Gives you:
[
{
"name": "Bob Dylan",
"tags": "{Artist}{Singer}"
},
{
"name": "Michael Jackson"
}
]
I've just realized that there are python bindings for libjq. Meaning you
don't need to use the command line, you can use jq directly in python.
https://github.com/mwilliamson/jq.py
However, I've not tried it so far. Let me give it a try :) ...
Update: The above library is nice, but it does not support the slurp mode so far.
you need to make a parser ... I dont think regex can help you for
data = ""
curlies = []
def get_dicts(file_text):
for letter in file_text:
data += letter
if letter == "{":
curlies.append(letter)
elif letter == "}":
curlies.pop() # remove last
if not curlies:
yield json.loads(data)
data = ""
note that this does not actually solve the problem that {name:"bob"} is not valid json ... {"name":"bob"} is
this will also break in the event you have weird unbalanced parenthesis inside of strings ie {"name":"{{}}}"} would break this
really your json is so broken based on your example your best bet is probably to edit it by hand and fix the code that is generating it ... if that is not feasible you may need to write a more complex parser using pylex or some other grammar library (effectively writing your own language parser)
So I have a bunch of line of codes like these in a row in my program:
str = str.replace('ten', '10s')
str = str.replace('twy', '20s')
str = str.replave('fy', '40s')
...
I want to make it such that I don't have to manually open my source file to add new cases. For example ('sy', '70'). I know I have to put all these in a function somehow, but I'd like to map cases that are not in my "mapper lib" from the command line. Configuration file maybe? how?
Thanks!
You could use a config file in json format like this:
[
["ten", "10s"],
["twy", "20s"],
["fy", "40s"]
]
Save it as 'replacements.json' and then use it this way:
import json
with open('replacements.json') as i:
replacements = json.load(i)
text = 'ten, twy, fy'
for r in replacements:
text = text.replace(r[0], r[1])
Then when you need to change the values just edit the replacements.json file without touching any Python code.
The format for you replacements file could be anything but json is easy to use and edit.
a simple solution could be to put those in a file, read them in your program and do your replaces in a loop..
Many ways to do this, if it's a rarely changing thing you could consider doing it with a Python dict:
mappings = {
'ten': '10s',
'twy': '20s',
'fy': '40s',
}
def replace(str_):
for s, r in mappings.iteritems():
str_.replace(s, r)
return str_
Alternatively in a Text file (make sure you use a safe delimiter which isn't used in any of the keys!)
mappings.txt
ten|10s
twy|20s
fy|40s
And the Python part:
mappings = {}
for line in open('mappings.txt'):
k, v = line.split('|', 1)
mappings[k] = v
And use the replace from above :)
You could use csv to store the replacements in a human-editable form in a file:
import csv
with open('replacements.csv', 'rb') as f:
replacements = list(csv.reader(f))
for old, new in replacements:
your_string = your_string.replace(old, new)
where replacements.csv:
ten,10s
twy,20s
fy,40s
It avoids unnecessary markup such as ", [] in the json format and allows a delimiter (,) to be present in a string itself unlike the plain text format from #WoLpH's answer.
(live example)