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I am writing a python program that load a json string and decode from a .csv file. The .csv file includs the title and one entry below for reference.
key,labels,raw_tweet
2017_Q3_270,"[0, 0]","{'in_reply_to_screen_name': None, 'user': {'profile_banner_url': 'https://pbs.twimg.com/profile_banners/148491006/1494299074', 'follow_request_sent': None, 'name': 'Vanessa', 'verified': False, 'profile_sidebar_fill_color': 'FFFFFF', 'profile_background_color': '352726', 'is_translator': False, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/578700342637895680/j-o_FCwY.png', 'id': 148491006, 'geo_enabled': True, 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/578700342637895680/j-o_FCwY.png', 'default_profile': False, 'contributors_enabled': False, 'default_profile_image': False, 'location': 'everywhere', 'profile_background_tile': True, 'notifications': None, 'listed_count': 9, 'profile_link_color': '7FDBB6', 'protected': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/891824958225215488/h__HMMlC_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/891824958225215488/h__HMMlC_normal.jpg', 'following': None, 'time_zone': 'Eastern Time (US & Canada)', 'friends_count': 588, 'url': 'https://Instagram.com/vmanks/', 'profile_text_color': '333333', 'followers_count': 541, 'utc_offset': -14400, 'id_str': '148491006', 'description': 'from the bronx, studying at cornell, slowly but surely finding solace', 'created_at': 'Wed May 26 21:01:46 +0000 2010', 'screen_name': 'vmankss', 'favourites_count': 19781, 'profile_use_background_image': True, 'profile_sidebar_border_color': 'FFFFFF', 'statuses_count': 50506, 'lang': 'en'}, 'retweet_count': 0, 'is_quote_status': False, 'in_reply_to_user_id': None, 'id': 901132409508421632, 'coordinates': None, 'entities': {'symbols': [], 'urls': [], 'user_mentions': [], 'hashtags': []}, 'text': ""I basically just go to financial aid to take candy from the candy bowl, y'all are unhelpful"", 'in_reply_to_status_id_str': None, 'in_reply_to_status_id': None, 'geo': None, 'favorited': False, 'place': {'country_code': 'US', 'bounding_box': {'type': 'Polygon', 'coordinates': [[[-76.547738, 42.41815], [-76.547738, 42.480827], [-76.469987, 42.480827], [-76.469987, 42.41815]]]}, 'attributes': {}, 'country': 'United States', 'url': 'https://api.twitter.com/1.1/geo/id/ae76bffcaf2bf545.json', 'full_name': 'Ithaca, NY', 'name': 'Ithaca', 'id': 'ae76bffcaf2bf545', 'place_type': 'city'}, 'favorite_count': 0, 'retweeted': False, 'timestamp_ms': '1503681683314', 'truncated': False, 'id_str': '901132409508421632', 'created_at': 'Fri Aug 25 17:21:23 +0000 2017', 'in_reply_to_user_id_str': None, 'contributors': None, 'source': 'Twitter for iPhone', 'lang': 'en', 'filter_level': 'low'}"
2015_Q1_494,"[0, 0]","{'in_reply_to_user_id_str': None, 'id_str': '577090329658175488', 'timestamp_ms': '1426424031067', 'in_reply_to_status_id_str': None, 'lang': 'en', 'favorited': False, 'retweeted': False, 'in_reply_to_status_id': None, 'id': 577090329658175488, 'filter_level': 'low', 'created_at': 'Sun Mar 15 12:53:51 +0000 2015', 'in_reply_to_user_id': None, 'place': {'country': 'United States', 'url': 'https://api.twitter.com/1.1/geo/id/a307591cd0413588.json', 'id': 'a307591cd0413588', 'country_code': 'US', 'place_type': 'city', 'attributes': {}, 'full_name': 'Buffalo, NY', 'bounding_box': {'type': 'Polygon', 'coordinates': [[[-78.912276, 42.826008], [-78.912276, 42.966451], [-78.79485, 42.966451], [-78.79485, 42.826008]]]}, 'name': 'Buffalo'}, 'truncated': False, 'entities': {'user_mentions': [], 'hashtags': [], 'symbols': [], 'trends': [], 'urls': []}, 'text': '""He licked coke off an encyclopedia"" only in south buffalo', 'retweet_count': 0, 'source': 'Twitter for iPhone', 'in_reply_to_screen_name': None, 'user': {'id_str': '480575646', 'friends_count': 367, 'profile_image_url': 'http://pbs.twimg.com/profile_images/571759767896629250/C-94okMM_normal.jpeg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/480575646/1402863912', 'listed_count': 2, 'screen_name': 'MichaelaFeeney', 'lang': 'en', 'notifications': None, 'profile_text_color': '333333', 'verified': False, 'favourites_count': 3995, 'name': 'Michæla...', 'protected': False, 'statuses_count': 2666, 'id': 480575646, 'profile_sidebar_border_color': 'C0DEED', 'profile_use_background_image': True, 'profile_sidebar_fill_color': 'DDEEF6', 'is_translator': False, 'time_zone': None, 'profile_link_color': '0084B4', 'created_at': 'Wed Feb 01 17:11:27 +0000 2012', 'geo_enabled': True, 'url': None, 'contributors_enabled': False, 'following': None, 'default_profile_image': False, 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'description': 'They call me Lông Isländ. Brockport2018✌', 'utc_offset': None, 'location': '', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/571759767896629250/C-94okMM_normal.jpeg', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'default_profile': True, 'followers_count': 221, 'follow_request_sent': None, 'profile_background_color': 'C0DEED'}, 'coordinates': {'type': 'Point', 'coordinates': [-78.805803, 42.869134]}, 'possibly_sensitive': False, 'geo': {'type': 'Point', 'coordinates': [42.869134, -78.805803]}, 'favorite_count': 0, 'contributors': None}"
2017_Q4_280,"[0, 0]","{'in_reply_to_screen_name': None, 'user': {'profile_banner_url': 'https://pbs.twimg.com/profile_banners/2812396208/1425183203', 'follow_request_sent': None, 'name': 'HunnyBon', 'verified': False, 'profile_sidebar_fill_color': '000000', 'profile_background_color': '000000', 'notifications': None, 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'id': 2812396208, 'geo_enabled': True, 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'default_profile': False, 'contributors_enabled': False, 'default_profile_image': False, 'location': 'New York, NY', 'profile_background_tile': False, 'translator_type': 'none', 'listed_count': 5, 'profile_link_color': '666666', 'protected': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/572570217272713216/rzw1Bbqs_normal.png', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/572570217272713216/rzw1Bbqs_normal.png', 'following': None, 'time_zone': None, 'friends_count': 68, 'url': 'http://www.hunnybon.com', 'profile_text_color': '000000', 'followers_count': 66, 'utc_offset': None, 'id_str': '2812396208', 'description': ""A Healthier Candy Store..organic, vegan, and nonGMO. Indulge your sweet tooth without the guilt. Chocolates, gummies, caramels...what's your indulgence?"", 'created_at': 'Tue Sep 16 03:56:36 +0000 2014', 'screen_name': 'HunnyBonSweets', 'favourites_count': 53, 'profile_use_background_image': False, 'profile_sidebar_border_color': '000000', 'lang': 'en', 'statuses_count': 252, 'is_translator': False}, 'retweet_count': 0, 'is_quote_status': False, 'in_reply_to_user_id': None, 'id': 925755798147313664, 'coordinates': {'type': 'Point', 'coordinates': [-74.0064, 40.7142]}, 'entities': {'symbols': [], 'urls': [{'expanded_url': '', 'display_url': 'instagram.com/p/Ba9WuoQlYuk/', 'url': '', 'indices': [98, 121]}], 'user_mentions': [], 'hashtags': []}, 'text': '🍫Hello November, and hello to our new Chocolate Matcha Truffles! 🍫RAW dark chocolate, CREAMY NUT… ', 'in_reply_to_status_id_str': None, 'in_reply_to_status_id': None, 'geo': {'type': 'Point', 'coordinates': [40.7142, -74.0064]}, 'favorited': False, 'reply_count': 0, 'place': {'country_code': 'US', 'bounding_box': {'type': 'Polygon', 'coordinates': [[[-74.026675, 40.683935], [-74.026675, 40.877483], [-73.910408, 40.877483], [-73.910408, 40.683935]]]}, 'attributes': {}, 'country': 'United States', 'url': '', 'full_name': 'Manhattan, NY', 'name': 'Manhattan', 'id': '01a9a39529b27f36', 'place_type': 'city'}, 'favorite_count': 0, 'retweeted': False, 'timestamp_ms': '1509552356646', 'possibly_sensitive': False, 'truncated': False, 'id_str': '925755798147313664', 'created_at': 'Wed Nov 01 16:05:56 +0000 2017', 'quote_count': 0, 'in_reply_to_user_id_str': None, 'contributors': None, 'source': '', 'lang': 'en', 'filter_level': 'low'}"
I am trying to load raw_tweet, which is a json object as a string and decode it into a json object. I keep getting errors regardless of how I decode the string.
import csv
import json
with open('testfile.csv','r', encoding='utf-8', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
jobj = row['raw_tweet'].replace("\'", "\"")
jobj = jobj.replace("None", "\"\"")
json.loads(jobj)
How I load the csv file. When I run the program, I get the following error. I also trying using panda dataframe to load and decode it into json object. I failed. Please suggest where I did wrong.
Traceback (most recent call last):
File "/Sandbox/csvfile.py", line 9, in <module>
json.loads(jobj)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 184 (char 183)
So in your csv file within the column raw tweet there are instances of False without any quotes. Also replacing the single quotes to double quotes has major break condition like your json already has strings like y'all which inherently uses single quote. So we only need to replace quotes for the keys and actual values and not quotes that occur within the string. So there are a lot of conditions to be replaced.
So I would rather suggest a different way of evaluating the csv and dumping jsons of the raw_tweet column.
import pandas as pd
data = pd.read_csv("test.csv").to_dict('records')
for d in data:
raw_tweet_dict = eval(d['raw_tweet'])
with open("json_dump.json", "w") as fp:
json.dump(raw_tweet_dict, fp)
You can use the raw_tweet_dict as a dictionary if this needs further transformation.
Alternatively you can also use your approach but you have add a lot of condition which I have added for now, it should work on your csv sample.
with open("test.csv", "r") as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
jobj = row['raw_tweet'].replace('"', "'")
jobj = jobj.replace("None", "''")
jobj = jobj.replace("False", "'False'").replace("True", "'True'")
jobj = jobj.replace("':", '\":').replace(": '", ': \"').replace("',", '\",').replace(", '", ', \"').replace("{'", '{\"').replace("'}", '\"}')
json.loads(jobj)
I have a nested json with an arbitrary depth level :
json_list = [
{
'class': 'Year 1',
'room': 'Yellow',
'students': [
{'name': 'James', 'sex': 'M', 'grades': {}},
]
},
{
'class': 'Year 2',
'info': {
'teachers': {
'math': 'Alan Turing',
'physics': []
}
},
'students': [
{ 'name': 'Tony', 'sex': 'M', 'age': ''},
{ 'name': 'Jacqueline', 'sex': 'F' },
],
'other': []
}
]
I want to remove any element that its value meet certain criteria.
For example:
values_to_drop = ({}, (), [], '', ' ')
filtered_json = clean_json(json_list, values_to_drop)
filtered_json
Expected Output of clean_json:
[
{
'class': 'Year 1',
'room': 'Yellow',
'students': [
{'name': 'James', 'sex': 'M'},
]
},
{
'class': 'Year 2',
'info': {
'teachers': {
'math': 'Alan Turing',
}
},
'students': [
{ 'name': 'Tony', 'sex': 'M'},
{ 'name': 'Jacqueline', 'sex': 'F'},
]
}
]
I thought of something like first converting the object to string using json.dumps and then looking in the string and replacing each value that meets the criteria with some kind of flag to filter it after before reading it again with json.loads but I couldn't figure it out and I don't know if this is the way to go
I managed to get the desired output by tweaking this answer a bit:
def clean_json(json_obj, values_to_drop):
if isinstance(json_obj, dict):
json_obj = {
key: clean_json(value, values_to_drop)
for key, value in json_obj.items()
if value not in values_to_drop}
elif isinstance(json_obj, list):
json_obj = [clean_json(item, values_to_drop)
for item in json_obj
if item not in values_to_drop]
return json_obj
I am trying to parse a json and insert the results in pandas dataframe.
My json looks like
{'result': {'data': [{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-01', 'name': ''}],
'metrics': [41, 4945]},
{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-02', 'name': ''}],
'metrics': [31, 2645]},
{'dimensions': [{'id': '219876166',
'name': 'Our awesome product'},
{'id': '2021-03-01', 'name': ''}], ....
So far, I've managed to get to this point:
[{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-01', 'name': ''}],
'metrics': [41, 4945]},
{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-02', 'name': ''}],
'metrics': [31, 2645]},
However, when I place it in Pandas I get
dimensions metrics
0 [{'id': '219876173', 'name': 'Our great product... [41, 4945]
1 [{'id': '219876173', 'name': 'Our great product... [31, 2645]
2 [{'id': '219876166', 'name': 'Our awesome product... [27, 2475]
I can now manually split the results in columns using some lambdas
df = pd.io.json.json_normalize(r.json().get('result').get('data'))
df['delivered_units'] = df['metrics'].apply(lambda x: x[0])
df['revenue'] = df['metrics'].apply(lambda x: x[1])
df['name'] = df['dimensions'].apply(lambda x: x[0])
df['sku'] = df['name'].apply(lambda x: x['name'])
Is there a better way to parse json directly without lambdas?
Look into flatten_json:
data = {'result': {'data': [{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-01', 'name': ''}],
'metrics': [41, 4945]},
{'dimensions': [{'id': '219876173',
'name': 'Our great product'},
{'id': '2021-03-02', 'name': ''}],
'metrics': [31, 2645]},
{'dimensions': [{'id': '219876166',
'name': 'Our awesome product'},
{'id': '2021-03-01', 'name': ''}]}]}}
from flatten_json import flatten
dic_flattened = (flatten(d, '.') for d in data['result']['data'])
df = pd.DataFrame(dic_flattened)
dimensions.0.id dimensions.0.name dimensions.1.id dimensions.1.name metrics.0 metrics.1
0 219876173 Our great product 2021-03-01 41.0 4945.0
1 219876173 Our great product 2021-03-02 31.0 2645.0
2 219876166 Our awesome product 2021-03-01 NaN NaN
I want to break down a column in a dataframe into multiple columns.
I have a dataframe with the following configuration:
GroupId,SubGroups,Type,Name
-4781505553015217258,"{'GroupId': -732592932641342965, 'SubGroups': [], 'Type': 'DefaultSite', 'Name': 'Default Site'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': 8123255835936628631, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'MERCEDES BENZ'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': -1785570219922840611, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'VOLVO'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': -3670461095557699088, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'SCANIA'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': 8683757391859854416, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'DRIVERS'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': -8066654520755643389, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'X - DECOMMISSION'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': 4177323092254043025, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'X-INSTALLATION'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': -6088426161802844604, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'FORD'}",OrganisationGroup,CompanyXYZ
-4781505553015217258,"{'GroupId': 8512440039365422841, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'HEAVY VEHICLES'}",OrganisationGroup,CompanyXYZ
I want to create a new dataframe where the SubGroups column is broken into its components. Note that the names inside SubGroups column are prefixed with SubGroups_
GroupId, SubGroup_GroupId, SubGroup_SubGroups, SubGroup_Type, SubGroup_Name, Type, Name
-4781505553015217258, -732592932641342965, [], 'DefaultSite', 'Default Site', OrganisationGroup, CompanyXYZ
-4781505553015217258, 8123255835936628631, [], 'SiteGroup', 'MERCEDES BENZ', OrganisationGroup, CompanyXYZ
I have tried the following code:
for row in AllSubGroupsDF.itertuples():
newDF= newDF.append((pd.io.json.json_normalize(row.SubGroups)))
But it returns
GroupId,SubGroups,Type,Name
-732592932641342965,[],DefaultSite,Default Site
8123255835936628631,[],SiteGroup,MERCEDES BENZ
-1785570219922840611,[],SiteGroup,VOLVO
-3670461095557699088,[],SiteGroup,SCANIA
8683757391859854416,[],SiteGroup,DRIVERS
-8066654520755643389,[],SiteGroup,X - DECOMMISSION
4177323092254043025,[],SiteGroup,X-INSTALLATION
-6088426161802844604,[],SiteGroup,FORD
8512440039365422841,[],SiteGroup,HEAVY VEHICLES
I would like to have it all end up in one dataframe but I'm not sure how. Please help?
You can try using ast package:-
import pandas as pd
import ast
data = [[-4781505553015217258,"{'GroupId': -732592932641342965, 'SubGroups': [], 'Type': 'DefaultSite', 'Name': 'Default Site'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': 8123255835936628631, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'MERCEDES BENZ'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': -1785570219922840611, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'VOLVO'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': -3670461095557699088, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'SCANIA'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': 8683757391859854416, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'DRIVERS'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': -8066654520755643389, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'X - DECOMMISSION'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': 4177323092254043025, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'X-INSTALLATION'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': -6088426161802844604, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'FORD'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': 8512440039365422841, 'SubGroups': [], 'Type': 'SiteGroup', 'Name': 'HEAVY VEHICLES'}","OrganisationGroup","CompanyXYZ"]]
df = pd.DataFrame(data,columns=["GroupId","SubGroups","Type","Name"])
df["SubGroup_GroupId"] = df["SubGroups"].map(lambda x: ast.literal_eval(x)["GroupId"])
df["SubGroup_SubGroups"] = df["SubGroups"].map(lambda x: ast.literal_eval(x)["SubGroups"])
df["SubGroup_Type"] = df["SubGroups"].map(lambda x: ast.literal_eval(x)["Type"])
df["SubGroup_Name"] = df["SubGroups"].map(lambda x: ast.literal_eval(x)["Name"])
df
Hope this helps!!
I am trying to write RDD which is structure like
(int , ListofList , ListofListofList)
Something like this
(49807360, [[111206019,'ABC','XYZ:RDC' , 'RDC' , 123] , [111206019,'ABC','XYZ:RDC' , 'RDC' , 123]] , [[[111206019,'ABC','XYZ:RDC' , 'RDC' , 123] , 111206019,'ABC','XYZ:RDC' , 'RDC' , 123]] , [[111206019,'ABC','XYZ:RDC' , 'RDC' , 123],[111206019,'ABC','XYZ:RDC' , 'RDC' , 123]])
When I print this is RDD form I see the data correctly. When I used inbuilt library to write it in JSON format I am getting null values in place of strings.
{"user":49807360,"history":[[111206019,null,null,null,123], [111206019,null,null,null,123]],"collection":...}
The line of code I am using to serialize RDD to JSON is
rdd.toDF().toJSON().saveAsTextFile(ouput_file_path)
I have also tried
rdd.toDF().write.json(ouput_file_path,"overwrite","gzip")
Above code was run in spark version 2.0.0
This happens because you use DataFrame as an intermediate step. Spark SQL doesn't support heterogeneous arrays, so values which don't match inferred type (array<bigint>) are replaced by NULL.
If you really want to go this way, and support heterogeneous structures, you should use tuples which should be mapped to Spark SQL structs, or don't depend on schema inference, and provide desired schema explicitly:
schema = ... # type: StructType
spark.createDataFrame(rdd, schema)
with schema (JSON representation) similar to:
{'fields': [{'metadata': {}, 'name': '_1', 'nullable': True, 'type': 'long'},
{'metadata': {},
'name': '_2',
'nullable': True,
'type': {'containsNull': True,
'elementType': {'fields': [{'metadata': {},
'name': '_1',
'nullable': True,
'type': 'long'},
{'metadata': {}, 'name': '_2', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_3', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_4', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_5', 'nullable': True, 'type': 'long'}],
'type': 'struct'},
'type': 'array'}},
{'metadata': {},
'name': '_3',
'nullable': True,
'type': {'fields': [{'metadata': {},
'name': '_1',
'nullable': True,
'type': {'fields': [{'metadata': {},
'name': '_1',
'nullable': True,
'type': 'long'},
{'metadata': {}, 'name': '_2', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_3', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_4', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_5', 'nullable': True, 'type': 'long'}],
'type': 'struct'}},
{'metadata': {}, 'name': '_2', 'nullable': True, 'type': 'long'},
{'metadata': {}, 'name': '_3', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_4', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_5', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_6', 'nullable': True, 'type': 'long'}],
'type': 'struct'}},
{'metadata': {},
'name': '_4',
'nullable': True,
'type': {'containsNull': True,
'elementType': {'fields': [{'metadata': {},
'name': '_1',
'nullable': True,
'type': 'long'},
{'metadata': {}, 'name': '_2', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_3', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_4', 'nullable': True, 'type': 'string'},
{'metadata': {}, 'name': '_5', 'nullable': True, 'type': 'long'}],
'type': 'struct'},
'type': 'array'}}],
'type': 'struct'}