Python: Read several json files from a folder

Question

I would like to know how to read several json files from a single folder (without specifying the files names, just that they are json files).

Also, it

Answer 1

Loads all files that end with * .json from a specific directory into a dict:

import os,json

path_to_json = '/lala/'

for file_name in [file for file in os.listdir(path_to_json) if file.endswith('.json')]:
  with open(path_to_json + file_name) as json_file:
    data = json.load(json_file)
    print(data)

Try it yourself: https://repl.it/@SmaMa/loadjsonfilesfromfolderintodict

Answer 2

One option is listing all files in a directory with os.listdir and then finding only those that end in '.json':

import os, json
import pandas as pd

path_to_json = 'somedir/'
json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]
print(json_files)  # for me this prints ['foo.json']

Now you can use pandas DataFrame.from_dict to read in the json (a python dictionary at this point) to a pandas dataframe:

montreal_json = pd.DataFrame.from_dict(many_jsons[0])
print montreal_json['features'][0]['geometry']

Prints:

{u'type': u'Point', u'coordinates': [-73.6051013, 45.5115944]}

In this case I had appended some jsons to a list many_jsons. The first json in my list is actually a geojson with some geo data on Montreal. I'm familiar with the content already so I print out the 'geometry' which gives me the lon/lat of Montreal.

The following code sums up everything above:

import os, json
import pandas as pd

# this finds our json files
path_to_json = 'json/'
json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]

# here I define my pandas Dataframe with the columns I want to get from the json
jsons_data = pd.DataFrame(columns=['country', 'city', 'long/lat'])

# we need both the json and an index number so use enumerate()
for index, js in enumerate(json_files):
    with open(os.path.join(path_to_json, js)) as json_file:
        json_text = json.load(json_file)

        # here you need to know the layout of your json and each json has to have
        # the same structure (obviously not the structure I have here)
        country = json_text['features'][0]['properties']['country']
        city = json_text['features'][0]['properties']['name']
        lonlat = json_text['features'][0]['geometry']['coordinates']
        # here I push a list of data into a pandas DataFrame at row given by 'index'
        jsons_data.loc[index] = [country, city, lonlat]

# now that we have the pertinent json data in our DataFrame let's look at it
print(jsons_data)

for me this prints:

  country           city                   long/lat
0  Canada  Montreal city  [-73.6051013, 45.5115944]
1  Canada        Toronto  [-79.3849008, 43.6529206]

It may be helpful to know that for this code I had two geojsons in a directory name 'json'. Each json had the following structure:

{"features":
[{"properties":
{"osm_key":"boundary","extent":
[-73.9729016,45.7047897,-73.4734865,45.4100756],
"name":"Montreal city","state":"Quebec","osm_id":1634158,
"osm_type":"R","osm_value":"administrative","country":"Canada"},
"type":"Feature","geometry":
{"type":"Point","coordinates":
[-73.6051013,45.5115944]}}],
"type":"FeatureCollection"}

Answer 3

If turning into a pandas dataframe, use the pandas API.

More generally, you can use a generator..

def data_generator(my_path_regex):
    for filename in glob.glob(my_path_regex):
        for json_line in open(filename, 'r'):
            yield json.loads(json_line)


my_arr = [_json for _json in data_generator(my_path_regex)]

Answer 4

To read the json files,

import os
import glob

contents = []
json_dir_name = '/path/to/json/dir'

json_pattern = os.path.join(json_dir_name, '*.json')
file_list = glob.glob(json_pattern)
for file in file_list:
  contents.append(read(file))

Answer 5

Iterating a (flat) directory is easy with the glob module

from glob import glob

for f_name in glob('foo/*.json'):
    ...

As for reading JSON directly into pandas, see here.