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extract_data.py
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extract_data.py
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import pandas as pd
from py2neo import Graph
#####################################################################
# Graph database config
#####################################################################
# Set up a link to the local graph database.
# Ideally get password from ENV variable
# graph = Graph(getenv("NEO4J_URL"), auth=(getenv("NEO4J_UID"), getenv("NEO4J_PASSWORD")))
graph = Graph("bolt://127.0.0.1:7687", auth=('neo4j', 'test'))
# Add uniqueness constraints.
graph.run("CREATE CONSTRAINT ON (p:Person) ASSERT p.uid IS UNIQUE;")
graph.run("CREATE CONSTRAINT ON (c:Country) ASSERT c.name IS UNIQUE;")
graph.run("CREATE CONSTRAINT ON (m:MajorStream) ASSERT m.name IS UNIQUE;")
graph.run("CREATE CONSTRAINT ON (w:WorkType) ASSERT w.name IS UNIQUE;")
def read_data():
data = pd.read_csv(
"./data/survey_results_public.csv",
low_memory=False)
print("Column name of data : ", data.columns)
return data
def process_user_data(data):
user_data = data[['Respondent','Hobby', 'OpenSource', 'Student', 'Employment', 'CompanySize', 'YearsCoding']]
user_data = user_data.dropna()
# Convert data frame to list of dictionaries
# Neo4j UNWIND query expects a list of dictionaries
# for bulk insertion
user_data = list(user_data.T.to_dict().values())
print(user_data)
query = """
UNWIND {rows} AS row
MERGE (person:Person {uid:row.Respondent})
ON CREATE SET
person.codes_as_hobby = row.Hobby,
person.contributes_to_open_source = row.OpenSource,
person.is_student = row.Student,
person.employment_status = row.Employment,
person.company_size = row.CompanySize,
person.total_years_of_coding_experience = row.YearsCoding
"""
run_neo_query(user_data,query)
def process_country_data(data):
country_data = data[['Respondent', 'Country']]
country_data = country_data.dropna()
country_data = list(country_data.T.to_dict().values())
query = """
UNWIND {rows} AS row
MERGE (person:Person {uid:row.Respondent})
MERGE (country:Country {name:row.Country})
MERGE (person)-[:LIVES_IN]->(country)
"""
run_neo_query(country_data,query)
def process_major_data(data):
major_data = data[['Respondent', 'UndergradMajor']]
major_data = major_data.dropna()
major_data = list(major_data.T.to_dict().values())
query = """
UNWIND {rows} AS row
MERGE (person:Person {uid:row.Respondent})
MERGE (major:MajorStream {name:row.UndergradMajor})
MERGE (person)-[:MAJORED_IN]->(major)
"""
run_neo_query(major_data,query)
def process_dev_data(data):
dev_data = data[['Respondent', 'DevType']]
dev_data = dev_data.dropna()
s = dev_data['DevType'].str.split(';').apply(pd.Series, 1).stack()
s.name = "DevType"
del dev_data["DevType"]
s = s.to_frame().reset_index()
dev_data = pd.merge(dev_data, s, right_on='level_0', left_index = True)
del dev_data["level_0"]
del dev_data["level_1"]
dev_data = list(dev_data.T.to_dict().values())
query = """
UNWIND {rows} AS row
MERGE (person:Person {uid:row.Respondent})
MERGE (work:WorkType {name:row.DevType})
MERGE (person)-[:WORKS_IN_INDUSTRY]->(work)
"""
run_neo_query(dev_data,query)
def run_neo_query(data, query):
batches = get_batches(data)
for index, batch in batches:
print('[Batch: %s] Will add %s node to Graph' % (index, len(batch)))
graph.run(query, rows=batch)
def get_batches(lst, batch_size=100):
return [(i, lst[i:i + batch_size]) for i in range(0, len(lst), batch_size)]
if __name__== "__main__":
data = read_data()
process_user_data(data)
process_country_data(data)
process_major_data(data)
process_dev_data(data)