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Cast from maptype to stringtype loses json formatting, pyspark

i have a .geojson that i converted to a dataframe using
df = geojson_to_dataframe(raw, glob='*.geojson', batch_size=100000)
Once that is set up, i need to convert the first column "properties" into a stringtype since pyspark labels it as a map type. To cast it as a string i run this code below.
df = df.select(df.properties.cast(StringType()).alias("properties"))
The problem i am having is the new column is stripped of all of the quotation marks and the colon is now a "->" and it loses its json formatting. Does anyone know how to convert a map type to string type without losing the json formatting?
The column as a map type before the cast
{
"BFE_LN_ID":"01001C_722",
"DFIRM_ID":"01001C",
"NAME":"Alabama",
"ALAND":"131185042550",
}
...
The column as a string after the cast
[
BFE_LN_ID->01001C_722,
DFIRM_ID->01001C,
NAME->Alabama,
ALAND->131185042550,
]
Thank you
Update:
Source geojson data that i converted using the above geojson_to_dataframe() code
properties
geometry
{"BFE_LN_ID":"01001C_722","DFIRM_ID":"01001C","NAME":"Alabama","ALAND":"131185042550","Shape_Length":"0.00010778212891927651","AWATER":"4582333181","LEN_UNIT":"Feet","VERSION_ID":"1.1.1.0","GFID":"20140910","INTPTLAT":"+32.7395785","STATENS":"01779775","REGION":"3","FUNCSTAT":"A","DIVISION":"6","GEOID":"01","INTPTLON":"-086.8434469","STATEFP":"01","SOURCE_CIT":"01001C_STUDY1","ELEV":"376.0","V_DATUM":"NAVD88","LSAD":"00","STUSPS":"AL","MTFCC":"G4000"}
{"coordinates": [[-86.46788227456062, 32.487228761833364], [-86.46796895264879, 32.48717204906342], [-86.46797248221748, 32.48716977416143]], "type": "LineString"}
Code used to get incorrect result
def nfhl_string(nfhl):
df = nfhl
df = df.select(df.properties.cast(StringType()).alias("properties"))
return df

pandas to json, don't put double quotes on geometry field

I have this dataframe with two fields coordinates and status
using pandas to_json, I get this
[{"coordinates":"[143.4865219,-34.7560602]","status":"not started"},
the correct format should be
[{"coordinates":[143.4865219,-34.7560602],"status":"not started"},
how to tell pandas not to put double quotes on the values of coordinates.

you can try explicitly convert string list to list by using ast module
code
import ast
s = [{"coordinates":"[143.4865219,-34.7560602]","status":"not started"},{"coordinates":"[143.4865241,-34.7561332]","status":"not started"}]
s = list(map(lambda x : {"coordinates": ast.literal_eval(x['coordinates'].strip('"')), "status": x['status']}, s))

pandas read_json: "If using all scalar values, you must pass an index"

I have some difficulty in importing a JSON file with pandas.
import pandas as pd
map_index_to_word = pd.read_json('people_wiki_map_index_to_word.json')
This is the error that I get:
ValueError: If using all scalar values, you must pass an index
The file structure is simplified like this:
{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}
It is from the machine learning course of University of Washington on Coursera. You can find the file here.

Try
ser = pd.read_json('people_wiki_map_index_to_word.json', typ='series')
That file only contains key value pairs where values are scalars. You can convert it to a dataframe with ser.to_frame('count').
You can also do something like this:
import json
with open('people_wiki_map_index_to_word.json', 'r') as f:
data = json.load(f)
Now data is a dictionary. You can pass it to a dataframe constructor like this:
df = pd.DataFrame({'count': data})

You can do as #ayhan mention which will give you a column base format
Or you can enclose the object in [ ] (source) as shown below to give you a row format that will be convenient if you are loading multiple values and planing on using matrix for your machine learning models.
df = pd.DataFrame([data])

I think what is happening is that the data in
map_index_to_word = pd.read_json('people_wiki_map_index_to_word.json')
is being read as a string instead of a json
{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}
is actually
'{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}'
Since a string is a scalar, it wants you to load it as a json, you have to convert it to a dict which is exactly what the other response is doing
The best way is to do a json loads on the string to convert it to a dict and load it into pandas
myfile=f.read()
jsonData=json.loads(myfile)
df=pd.DataFrame(data)

{
"biennials": 522004,
"lb915": 116290
}
df = pd.read_json('values.json')
As pd.read_json expects a list
{
"biennials": [522004],
"lb915": [116290]
}
for a particular key, it returns an error saying
If using all scalar values, you must pass an index.
So you can resolve this by specifying 'typ' arg in pd.read_json
map_index_to_word = pd.read_json('Datasets/people_wiki_map_index_to_word.json', typ='dictionary')

For newer pandas, 0.19.0 and later, use the lines parameter, set it to True.
The file is read as a json object per line.
import pandas as pd
map_index_to_word = pd.read_json('people_wiki_map_index_to_word.json', lines=True)
If fixed the following errors I encountered especially when some of the json files have only one value:
ValueError: If using all scalar values, you must pass an index
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
ValueError: Trailing data

For example
cat values.json
{
name: "Snow",
age: "31"
}
df = pd.read_json('values.json')
Chances are you might end up with this
Error: if using all scalar values, you must pass an index
Pandas looks up for a list or dictionary in the value. Something like
cat values.json
{
name: ["Snow"],
age: ["31"]
}
So try doing this. Later on to convert to html tohtml()
df = pd.DataFrame([pd.read_json(report_file, typ='series')])
result = df.to_html()

I solved this by converting it into an array like so
[{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}]

Python coinbase API prices as float

I am trying to work with Coinbase's API, and would like to use their prices as a float, but the object returns an API object and I don't know how to convert it.
For example if I call client.get_spot_price() it will return this:
{
"amount": "316.08",
"currency": "USD"
}
And I just want the 316.08. How can I solve it?

data = {
"amount": "316.08",
"currency": "USD"
}
price = float(text['amount'])
With API use JSON parser
import json
data = client.get_spot_price()
price = float(json.loads(data)['amount'])
print price

It looks like a json output. You could import json library in python and read it using the loads method, for sample:
import json
# get data from the API's method
response = client.get_spot_price()
# parse the content of the response using json format
data = json.loads(response )
# get the amount and convert to float
amount = float(data['amount'])
print(amount)

At first you put your returned object into a variable and check the type of the returned value.
just like this:
print type(your_returned object/variable)
If this is dictionary You can access data from a dictionary via dictionary key. A dictionary structure is:
dict = {key_1 : value_1 , key_2 : value_2, ......key_n : value_n}
1.You can access all values of dictionary.
like below:
print dict[key_1] #output will be returned value_1
Then you can convert returned data to integer or float.
For converting to integer:
int(your_data)
convert to float:
float(your_data)
If it is not a dictionary you need to convert it to a dictionary or json via :
json.loads(your returned object)
In your case you can do:
variable = client.get_spot_price()
print type(variable) #if it is dictionary or not
print float(variable["amount"]) #will return your price in float

Importing single record using read_json in pandas

I am trying to import a json file using the function:
sku = pandas.read_json('https://cws01.worldstores.co.uk/api/product.php?product_sku=125T:FT0111')
However, i keep getting the following error
ValueError: arrays must all be same length
What should I do to import it correctly into a dataframe?
this is the structure of the json:
{
"id": "5",
"sku": "JOSH:BECO-BRN",
"last_updated": "2013-06-10 15:46:22",
...
"propertyType1": [
"manufacturer_colour"
],
"category": [
{
"category_id": "10",
"category_name": "All Products"
},
...
{
"category_id": "238",
"category_name": "All Sofas"
}
],
"root_categories": [
"516"
],
"url": "/p/Beco Suede Sofa Bed?product_id=5",
"item": [
"2"
],
"image_names": "[\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/L\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/P\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/SP\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/SS\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/ST\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/WP\\/19\\/Beco_Suede_Sofa_Bed-1.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/L\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/P\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/SP\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\",\"https:\\/\\/cdn.worldstores.co.uk \\/images\\/products\\/SS\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/ST\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\",\"https:\\/\\/cdn.worldstores.co.uk\\/images\\/products\\/WP\\/19\\/Beco_Suede_Sofa_Bed-2.jpg\"]"
}

The pandas.read_json function takes multiple formats.
Since you did not specify which format your json file is in (orient= attribute), pandas will default to believing your data is columnar. The different formats pandas expects are discussed below.
The data that you are trying to parse from https://cws01.worldstores.co.uk/api/product.php?product_sku=125T:FT0111
Does not seem to conform to any of the supported formats as it seems to be only a single "record". Pandas expects some kind of collection.
You probably should try to collect multiple entries into a single file, then parse it with the read_json function.
EDIT:
Simple way of getting multiple rows and parsing it with the pandas.read_json function:
import urllib2
import pandas as pd
url_base = "https://cws01.worldstores.co.uk/api/product.php?product_sku={}"
products = ["125T:FT0111", "125T:FT0111", "125T:FT0111"]
raw_data_list = []
for sku in products:
url = url_base.format(sku)
raw_data_list.append(urllib2.urlopen(url).read())
data = "[" + (",".join(raw_data_list)) + "]"
data = pd.read_json(data, orient='records')
data
/EDIT
My take on the pandas.read_json function formats.
The pandas.read_json function is yet another shining example of pandas trying to jam as much functionality as possible into a single function. This leads of course to a very very complicated function.
Series
If your data is a Series, pandas.read_json(orient=) defaults to 'index'
The values allowed for orient while parsing a Series are: {'split','records','index'}
Note that the Series index must be unique for orient='index'.
DataFrame
If your data is a DataFrame, pandas.read_json(orient=) defaults to 'columns'
The values allowed for orient while parsing a DataFrame are:
{'split','records','index','columns','values'}
Note that the Series index must be unique for orient='index' and orient='columns', and the DataFrame columns must be unique for orient='index', orient='columns', and orient='records'.
Format
No matter if your data is a DataFrame or a Series, the orient= will expect data in the same format:
Split
Expects a string representation of a dict like what the DataFrame constructor takes:
{"index":[1,2,3,4], "columns":["col1","col2"], "data":[[8,7,6,5], [5,6,7,8]]}
Records
Expects a string representation of a list of dicts like:
[{"col1":8,"col2":5},{"col1":7,"col2":6},{"col1":6,"col2":7},{"col1":5,"col2":8}]
Note there is no index set here.
Index
Expects a string representation of a nested dict dict like:
{"1":{"col1":8,"col2":5},"2":{"col1":7,"col2":6},"3":{"col1":6,"col2":7},"4":{"col1":5,"col2":8}}
Good to note is that it won't accept indicies of other types than strings. May be fixed in later versions.
Columns
Expects a string representation of a nested dict like:
{"col1":{"1":8,"2":7,"3":6,"4":5},"col2":{"1":5,"2":6,"3":7,"4":8}}
Values
Expects a string representation of a list like:
[[8, 5],[7, 6],[6, 7],[5, 8]]
Resulting dataframe
In most cases, the dataframe you get will look like this, with the json strings above:
col1 col2
1 8 5
2 7 6
3 6 7
4 5 8

Maybe this is not the most elegant solution however gives me back what I want, or at least I believe so, feel free to warn if something is wrong
url = "https://cws01.worldstores.co.uk/api/product.php?product_sku=125T:FT0111"
data = urllib2.urlopen(url).read()
data = json.loads(data)
data = pd.DataFrame(data.items())
data = data.transpose()

Another solution is to use a try except.
json_path='https://cws01.worldstores.co.uk/api/product.php?product_sku=125T:FT0111'
try: a=pd.read_json(json_path)
except ValueError: a=pd.read_json("["+json_path+"]")

Iterating on #firelynx's answer:
#! /usr/bin/env python3
from urllib.request import urlopen
import pandas as pd
products = ["125T:FT0111", "125T:FT0111", "125T:FT0111"]
raw_lines = ""
for sku in products:
url = f"https://cws01.worldstores.co.uk/api/product.php?product_sku={sku}"
raw_lines += urlopen(url).read() + "\n"
data = pd.read_json(raw_lines, lines=True)
This would support any source returning a single JSON object or a bunch of newline ('\n') separated ones.
Or this one-liner(ish) should work the same:
#! /usr/bin/env python3
import pandas as pd
products = ["125T:FT0111", "125T:FT0111", "125T:FT0111"]
data = pd.concat(
pd.read_json(
f"https://cws01.worldstores.co.uk/api/product.php?product_sku={sku}",
lines=True
) for sku in products
)
PS: python3 is only for fstring support here, so you should use str.format for python2 compatibility.