def fix_country(col: Column) -> Column:
"""
Use built-in Spark functions to clean-up the "Country" column
e.g. " cAnAdA " -> "Canada"
Don't forget about those annoying leading/trailing spaces.
"""
fixed_country = F.initcap(F.trim(col))
return fixed_country
def test_initcap():
# Because we don't use the same unicode version we need to limit
# the charicter set to something more reasonable
# upper and lower should cover the corner cases, this is mostly to
# see if there are issues with spaces
gen = mk_str_gen('([aAbB]{0,5}[ \r\n\t]{1,2}){1,5}')
assert_gpu_and_cpu_are_equal_collect(
lambda spark: unary_op_df(spark, gen).select(f.initcap(f.col('a'))))
def load_police_districts(spark, path, output_path):
"""
Get police districts from CSV and write to parquet files.
"""
print("-- police districts --")
df_sfpd_districts = spark.read.csv(path + "sfpd_districts.csv", header = 'true') \
.withColumn("district_id", monotonically_increasing_id()) \
.select("district_id", col("COMPANY").alias("company"), initcap(col("DISTRICT")).alias("district"))
df_sfpd_districts.show()
df_sfpd_districts.write.mode("overwrite").orc(output_path +
"police_districts")
def get_cleaned_message(self, flat_message_df):
cleaned_message_df = flat_message_df.select(
F.col("id"), \
F.when(F.col("first_name").isNull(), 'NULL').otherwise(F.col("first_name")).alias("first_name"), \
F.col("last_name"), \
F.when(F.col("email").isNull(), 'NULL').otherwise(F.col("email")).alias("email"), \
F.col("gender"), \
F.col("ip_address"),\
F.date_format(F.col("date"), 'dd/MM/yyyy').alias("date"), \
F.initcap(F.col("country")).alias("country"), \
F.col("timestamp") \
).withColumn("ip_addr",F.when(F.col("ip_address").rlike(IP_REGEX),F.col("ip_address")).otherwise(DEFAULT_IP)).drop(F.col("ip_address"))
return cleaned_message_df
def run():
# Build session
sparkql = SparkSession.builder.master('local[1]').getOrCreate()
# Load config informaiton if __name__ == '__main__':
people_path = config['defaults']['ch3']['ep1']['passenger_input'].get(str)
save_path = config['defaults']['ch3']['ep1']['passenger_output'].get(str)
bq_table = config['defaults']['ch3']['ep1']['passenger_table'].get(str)
logger.info(f"Loading passenger info from {people_path}")
# read csv file into spark dataframe
passengers_df = sparkql.read.csv(people_path, header=True)
logger.info(f"There are {passengers_df.count()} rows")
# Load the passenger data and make sure the names have initial capitalization
logger.info("Cleaning names and creating full name")
passengers_df = passengers_df.withColumn('first_name', initcap(col('first_name')))\
.withColumn('middle_name', initcap(col('middle_name')))\
.withColumn('last_name', initcap(col('last_name')))
# Create full_name column
passengers_df = passengers_df.withColumn(
'full_name',
concat_ws(" ", col('first_name'), col('middle_name'),
col('last_name')))
logger.info("Creating sha2 uid from email")
# Create a sha2 uid based on the email
passengers_df = passengers_df.withColumn('uid', sha2(col('email'), 256))
logger.info(f"Saving file to {save_path}")
# Save dataframe as a parquet file
passengers_df.write.parquet(save_path)
logger.info("Uploading file to BigQuery")
# Upload the file as an external table in BigQuery
gbq_load(bq_table, save_path)
def load_passengers(self, passenger_filename, passenger_output):
"""
Function to load the passenger data from csv in GCS, clean, add UID,
and upload to BigQuery
:param passenger_filename: str input file name
:param passenger_output: str of project.dataset.table to save passenger data
"""
self.passenger_filename = passenger_filename
self.passenger_output = passenger_output
people_path = 'gs://{}/{}'.format(self.bucket, passenger_filename)
logger.info(f"Loading passenger info from {self.bucket}.{passenger_filename}")
passengers_df = self.sparkql.read.csv(people_path, header=True)
# Use withColumn and initcap to standardize the names
passengers_df = passengers_df.withColumn('first_name',
initcap(col('first_name')))\
.withColumn('middle_name',
initcap(col('middle_name')))\
.withColumn('last_name',
initcap(col('last_name')))
# Create full_name column
passengers_df = passengers_df.withColumn('full_name',
concat_ws(" ",
col('first_name'),
col('middle_name'),
col('last_name')))
passengers_df = passengers_df.withColumn('uid', sha2(col('email'), 256))
# Write to BigQuery
logger.info(f"Writing file to {passenger_output}")
passengers_df.write.format('bigquery') \
.option('table', passenger_output) \
.save()
self.passengers_df = passengers_df
from pyspark.sql.functions import desc
df.orderBy(desc("salary")).show(5)
df.sort(desc("salary")).show(5)
"""操作Number"""
# 1. 取次方 透過pwd
from pyspark.sql.functions import expr, pow
expected_salary = df.select(expr("name"), pow(col("salary"),2)-10000)
# 2. 四捨五入 round()
df.describe().show()
"""操作String"""
# 1. 將首字母轉為大寫
from pyspark.sql.functions import initcap, lower,upper
df.select(initcap(col("name"))).show()
df.select(lower(col("name"))).show()
df.select(upper(col("name"))).show()
# 2. 刪除空格 :
from pyspark.sql.functions import lpad, ltrim, rtrim, rpad, trim
# 3. 模糊查詢：
df.filter(col('name').like('%nd%')).show()
# 4. 去重：
df.select('name').dropDuplicates().show()
# 5. 分割字符串 :
df.withColumn("splited_name",split(df_webpages["name"],",")[1]).show()
def test_initcap_space():
# we see a lot more space delim
gen = StringGen('([aAbB]{0,5}[ ]{1,2}){1,5}')
assert_gpu_and_cpu_are_equal_collect(
lambda spark: unary_op_df(spark, gen).select(f.initcap(f.col('a'))))
Person("Sidhartha", "Ray", 32, None, "Programmer"),
Person("Pratik", "Solanki", 22, 176.7, None),
Person("Ashok ", "Pradhan", 62, None, None),
Person(" ashok", "Pradhan", 42, 125.3, "Chemical Engineer"),
Person("Pratik", "Solanki", 22, 222.2, "Teacher")
])
people_df.show()
people_df.groupBy("firstName").agg(first("weightInLbs")).show()
people_df.groupBy(trim(lower(col('firstName')))).agg(first("weightInLbs")).show()
people_df.groupBy(trim(lower(col("firstName")))).agg(first("weightInLbs", True)).show()
people_df.sort(col("weightInLbs").desc()).groupBy(trim(lower(col("firstName")))).agg(first("weightInLbs", True)).show()
people_df.sort(col("weightInLbs").asc_nulls_last()).groupBy(trim(lower(col("firstName")))).agg(first("weightInLbs", True)).show()
corrected_people_df = people_df\
.withColumn("firstName", initcap("firstName"))\
.withColumn("firstName", ltrim(initcap("firstName")))\
.withColumn("firstName", trim(initcap("firstName")))\
corrected_people_df.groupBy("firstName").agg(first("weightInLbs")).show()
corrected_people_df = corrected_people_df\
.withColumn("fullName", format_string("%s %s", "firstName", "lastName"))\
corrected_people_df.show()
corrected_people_df = corrected_people_df\
.withColumn("weightInLbs", coalesce("weightInLbs", lit(0)))\
corrected_people_df.show()
df.stat.approxQuantile("UnitPrice", quantileProbs, relError)
df.stat.crosstab("StockCode", "Quantity").show()
df.stat.freqItems(["StockCode", "Quantity"]).show()
##creating a unique sequence id
from pyspark.sql.functions import monotonically_increasing_id
df.select(monotonically_increasing_id()).show(10)
#working with strings
#perform case converstions
from pyspark.sql.functions import initcap, lower, upper, ltrim, rtrim, trim, lpad, rpad
df.select(initcap(col("Description"))).show(5)
df.select(col("Description"), initcap(col("Description")),
lower(col("Description")), upper(col("Description"))).show(5)
string_with_space = " hello "
df.select(ltrim(lit(string_with_space)), rtrim(lit(string_with_space)),
trim(lit(string_with_space))).show()
#regular expressions
#working with dates, timestamps
from pyspark.sql.functions import current_date, current_timestamp, date_add, date_sub, datediff, months_between, to_date, to_timestamp
dateDF = spark.range(10).withColumn("today", current_date()).withColumn(
"now", current_timestamp())
#verificando la estructura del dataframe WordlCupMatches
df_wcm.printSchema()
print('El dataframe df_wc tiene ' + str(df_wcm.count()) + ' registros.')
print('El dataframe df_wc tiene ' + str(df_wcm.distinct().count()) +
' registros distintos.')
# COMMAND ----------
from pyspark.sql.functions import desc, asc, col, column, expr, instr, length, substring, regexp_replace, trim, lit, initcap, sum, concat
#
#Transformaciones al datraframe WordlCupPlayers
# El campo Event guarda un dato del tipo "G43' G87'", lo que significa que el jugador usar dos goles, por lo que necesitamos contar el número de "G"
# De igual manera podemos obtener el número de penales y tarjetas
df_wcp1=df_wcp.withColumn('POSICION_JUGADOR',expr("case when position='C' THEN 'Captain' WHEN position='GK' THEN 'Goalkeeper' ELSE 'Other' end "))\
.withColumn('NOMBRE_JUGADOR', initcap(regexp_replace('Player Name','�','u')))\
.withColumn('NUMERO_GOLES',length('Event')-length(trim(regexp_replace('Event','G',''))))\
.withColumn('NUMERO_PENALES',length('Event')-length(trim(regexp_replace('Event','P',''))))\
.withColumn('NUMERO_PENALES_FALLADOS',length('Event')-length(trim(regexp_replace('Event','MP',''))))\
.withColumn('NUMERO_TARJETAS_ROJAS',length('Event')-length(trim(regexp_replace('Event','R',''))))
#Reemplazo los valores nulos con 0
df_wcp1=df_wcp1.withColumn('NUMERO_GOLES',expr("case when NUMERO_GOLES is null then 0 else NUMERO_GOLES end "))\
.withColumn('NUMERO_PENALES',expr("case when NUMERO_PENALES is null then 0 else NUMERO_PENALES end "))\
.withColumn('NUMERO_PENALES_FALLADOS',expr("case when NUMERO_PENALES_FALLADOS is null then 0 else NUMERO_PENALES_FALLADOS end "))\
.withColumn('NUMERO_TARJETAS_ROJAS',expr("case when NUMERO_TARJETAS_ROJAS is null then 0 else NUMERO_TARJETAS_ROJAS end "))\
.withColumnRenamed('Team Initials','INICIALES_PAIS')\
.drop('Player Name','Position','Shirt Number')
#Sumarizo por nombre de jugador , posición e iniciales de país
df_wcp_rep=df_wcp1.select('NOMBRE_JUGADOR','POSICION_JUGADOR','INICIALES_PAIS','NUMERO_GOLES','NUMERO_PENALES','NUMERO_PENALES_FALLADOS','NUMERO_TARJETAS_ROJAS')\
.groupby('NOMBRE_JUGADOR','POSICION_JUGADOR','INICIALES_PAIS')\
df = spark.read.csv(path="transactions.csv",
header=True,
inferSchema=False,
sep=";")
df1 = lower_header(data_frame=df)
df2 = df1.select(
df1.id,
df1.created_at,
df1.merchant_id,
df1.valor,
df1.n_parcelas,
functions.round(df1.valor / df1.n_parcelas, 2),
functions.initcap(df1.nome_no_cartao),
functions.when(
functions.lower(df1.status) == "refunded", "refused"
).when(
functions.lower(df1.status) == "in process", "processing"
).otherwise(functions.lower(df1.status)),
df1.card_id,
df1.iso_id,
df1.card_brand,
functions.regexp_replace(functions.lower(df1.documento), pattern=r"(\D+)", replacement="")
)
df3 = casting_fields(data_frame=df2, json_schema=get_json_schema_mapping("types_mapping.json"))
df3.write.csv(path="output/sanitize_transactions", header=True, sep=";")
.format("jdbc") \
.option("url", "jdbc:postgresql://10.0.0.8:5432/my_db") \
.option("dbtable", "airbnb") \
.option("user", "test") \
.option("password", "test") \
.option("driver", "org.postgresql.Driver") \
.load()
df = df.withColumn('bedrooms', F.round(df['bedrooms'], 0))
df = df.filter(~F.col('city').contains("/"))
df = df.filter(~F.col('city').contains(","))
df = df.filter(~F.col('city').contains("-"))
df = df.filter(~F.col('city').contains("^[0-9]*$"))
df = df.filter(~F.col('city').contains("*"))
df = df.withColumn('city', F.ltrim(df.city))
df = df.withColumn("city", F.initcap(F.col("city")))
df = df.filter(~df.city.rlike("[ ,;{}()\n\t=]"))
df = df.filter(~df.city.rlike("[^0-9A-Za-z]"))
df = df.filter(~F.col('city').contains("("))
df = df.groupBy('city', 'bedrooms').agg(
F.avg('average').alias('average'), F.first('state'))
df = df.withColumnRenamed('first(state)', 'state')
df = df.sort('city')
df1 = df.withColumn('average', F.round(df['average'], 0))
df1.write \
.format("jdbc") \
.option("url", "jdbc:postgresql://10.0.0.8:5432/my_db") \
.option("dbtable", "bnbclean") \
.option("user", "test") \
.option("password", "test") \
def init_cap_and_trim_all(column_name: str) -> Column:
return initcap(trim_all(column_name))
from pyspark.sql.functions import initcap, concat_ws, col, sha2
# Build session
sparkql = SparkSession.builder.master('yarn').getOrCreate()
# Load passenger data
bucket = <your bucket>
sparkql.conf.set('temporaryGcsBucket', bucket) #this gives our job a temporary bucket to use when writint
bucket_path = 'gs://{}/'.format(bucket)
people_path = bucket_path + 'passengers_1k.csv'
passengers_df = sparkql.read.csv(people_path, header=True)
# Use withColumn and initcap to standardize the names
passengers_df = passengers_df.withColumn('first_name', initcap(col('first_name')))\
.withColumn('middle_name', initcap(col('middle_name')))\
.withColumn('last_name', initcap(col('last_name')))
# Create full_name column
passengers_df = passengers_df.withColumn('full_name',
concat_ws(" ",
col('first_name'),
col('middle_name'),
col('last_name')))
passengers_df = passengers_df.withColumn('uid', sha2(col('email'), 256))
bq_dataset = <your dataset>
bq_table = 'passengers'
passengers_df.write.format('bigquery') \
.option('table', '{}.{}'.format(bq_dataset, bq_table)) \
def test_initcap_special_chars():
gen = mk_str_gen('ʼn([aAbB13ȺéŸ]{0,5}){1,5}')
assert_gpu_and_cpu_are_equal_collect(
lambda spark: unary_op_df(spark, gen).select(
f.initcap(f.col('a'))))
df.selectExpr('round(2.5)', 'bround(2.5)').show(2)
# Unique ID
# We can add a unique ID to each row by using the function monotonically_increasing_id
# The function generates a unique value for each row, starting with 0
from pyspark.sql.functions import monotonically_increasing_id
df.select(monotonically_increasing_id()).show(2)
# Working with String
from pyspark.sql.functions import initcap, lower, upper
df.select(initcap(col('Description'))).show(2)
# select initcap(Description) from dfTable
df.select(col('Description'),
lower(col('Description')),
upper(col('Description'))).show(2)
df.selectExpr(
'Description',
'lower(Description)',
'upper(lower(Description))').show(2)
# select description, lower(Description), upper(lower(Description)) from dfTable
from pyspark.sql.functions import ltrim, rtrim, rpad, lpad, trim
def process_biomarkers(
self,
biomarkers_df: DataFrame,
source_df: DataFrame,
disease_df: DataFrame,
drugs_df: DataFrame
) -> DataFrame:
"""The diverse steps to prepare and enrich the input table"""
biomarkers_enriched = (
biomarkers_df
.select(
'Biomarker', 'IndividualMutation',
array_distinct(split(col('Alteration'), ';')).alias('alterations'),
array_distinct(split(col('Gene'), ';')).alias('gene'),
split(col('AlterationType'), ';').alias('alteration_types'),
array_distinct(split(col("PrimaryTumorTypeFullName"), ";")).alias('tumor_type_full_name'),
array_distinct(split(col('Drug'), ';|,')).alias('drug'),
'DrugFullName', 'Association', 'gDNA',
array_distinct(split(col('EvidenceLevel'), ',')).alias('confidence'),
array_distinct(split(col('Source'), ';')).alias('source')
)
.withColumn('confidence', explode(col('confidence')))
.withColumn('tumor_type_full_name', explode(col('tumor_type_full_name')))
.withColumn('tumor_type', translate(col('tumor_type_full_name'), ' -', ''))
.withColumn('drug', explode(col('drug')))
.withColumn('drug', translate(col('drug'), '[]', ''))
.withColumn('gene', explode(col('gene')))
.replace(to_replace=GENENAMESOVERRIDE, subset=['gene'])
.withColumn('gene', upper(col('gene')))
# At this stage alterations and alteration_types are both arrays
# Disambiguation when the biomarker consists of multiple alterations is needed
# This is solved by:
# 1. Zipping both fields - tmp consists of a list of alteration/type tuples
# 2. tmp is exploded - tmp consists of the alteration/type tuple
# 3. alteration & alteration_type columns are overwritten with the elements in the tuple
.withColumn(
'tmp',
self.zip_alterations_with_type_udf(col('alterations'), col('alteration_types')))
.withColumn('tmp', explode(col('tmp')))
.withColumn('alteration_type', element_at(col('tmp'), 2))
.withColumn(
'alteration',
when(
~col('IndividualMutation').isNull(),
col('IndividualMutation')
)
.otherwise(element_at(col('tmp'), 1))
)
.drop('tmp')
# Clean special cases on the alteration string
.withColumn(
'alteration',
when(
col('alteration') == 'NRAS:.12.,.13.,.59.,.61.,.117.,.146.',
col('Biomarker') # 'NRAS (12,13,59,61,117,146)'
)
.when(
# Cleans strings like 'ARAF:.'
col('alteration').contains(':.'),
translate(col('alteration'), ':.', '')
)
.when(
# Fusion genes are described with '__'
# biomarker is a cleaner representation when there's one alteration
(col('alteration').contains('__')) & (~col('Biomarker').contains('+')),
col('Biomarker')
)
.otherwise(col('alteration'))
)
# Split source into literature and urls
# literature contains PMIDs
# urls are enriched from the source table if not a CT
.withColumn('source', explode(col('source')))
.withColumn('source', trim(regexp_extract(col('source'), r'(PMID:\d+)|([\w ]+)', 0).alias('source')))
.join(source_df, on='source', how='left')
.withColumn(
'literature',
when(col('source').startswith('PMID'), regexp_extract(col('source'), r'(PMID:)(\d+)', 2))
)
.withColumn(
'urls',
when(
col('source').startswith('NCT'),
struct(
lit('Clinical Trials').alias('niceName'),
concat(lit('https://clinicaltrials.gov/ct2/show/'), col('source')).alias('url')
)
)
.when(
(~col('source').startswith('PMID')) | (~col('source').startswith('NCIT')),
struct(col('niceName'), col('url'))
)
)
# The previous conditional clause creates a struct regardless of
# whether any condition is met. The empty struct is replaced with null
.withColumn('urls', when(~col('urls.niceName').isNull(), col('urls')))
# Enrich data
.withColumn('functionalConsequenceId', col('alteration_type'))
.replace(to_replace=ALTERATIONTYPE2FUNCTIONCSQ, subset=['functionalConsequenceId'])
.replace(to_replace=DRUGRESPONSE2EFO, subset=['Association'])
.join(disease_df, on='tumor_type', how='left')
.withColumn('drug', upper(col('drug')))
.withColumn(
# drug class is coalesced when the precise name of the medicine is not provided
'drug',
when(col('drug') == '', col('DrugFullName')).otherwise(col('drug')))
.join(drugs_df, on='drug', how='left')
.withColumn('drug', initcap(col('drug')))
# Translate variantId
.withColumn(
'variantId',
when(~col('gDNA').isNull(), self.get_variantId_udf(col('gDNA')))
)
# Assign a GO ID when a gene expression data is reported
.withColumn(
'geneExpressionId',
when(
(col('alteration_type') == 'EXPR') & (col('alteration').contains('over')),
'GO_0010628'
)
.when(
(col('alteration_type') == 'EXPR') & (col('alteration').contains('under')),
'GO_0010629'
)
.when(
(col('alteration_type') == 'EXPR') & (col('alteration').contains('norm')),
'GO_0010467'
)
)
# Create variant struct
.withColumn(
'variant',
when(
col('alteration_type') != 'EXPR',
struct(
col('alteration').alias('name'),
col('variantId').alias('id'),
col('functionalConsequenceId')
)
)
)
# Create geneExpression struct
.withColumn(
'geneExpression',
when(
col('alteration_type') == 'EXPR',
struct(
col('alteration').alias('name'),
col('geneExpressionId').alias('id'))
)
)
)
pre_evidence = (
biomarkers_enriched
.withColumn('datasourceId', lit('cancer_biomarkers'))
.withColumn('datatypeId', lit('affected_pathway'))
.withColumnRenamed('tumor_type_full_name', 'diseaseFromSource')
.withColumnRenamed('drug', 'drugFromSource')
# diseaseFromSourceMappedId, drugId populated above
.withColumnRenamed('Association', 'drugResponse')
# confidence, literature and urls populated above
.withColumnRenamed('gene', 'targetFromSourceId')
.withColumnRenamed('Biomarker', 'biomarkerName')
# variant, geneExpression populated above
.drop(
'tumor_type', 'source', 'alteration', 'alteration_type', 'IndividualMutation', 'geneExpressionId',
'gDNA', 'functionalConsequenceId', 'variantId', 'DrugFullName', 'niceName', 'url')
)
# Group evidence
self.evidence = (
pre_evidence
.groupBy('datasourceId', 'datatypeId', 'drugFromSource', 'drugId',
'drugResponse', 'targetFromSourceId', 'diseaseFromSource',
'diseaseFromSourceMappedId', 'confidence', 'biomarkerName')
.agg(
collect_set('literature').alias('literature'),
collect_set('urls').alias('urls'),
collect_set('variant').alias('variant'),
collect_set('geneExpression').alias('geneExpression'),
)
# Replace empty lists with null values
.withColumn('literature', when(size(col('literature')) == 0, lit(None)).otherwise(col('literature')))
.withColumn('urls', when(size(col('urls')) == 0, lit(None)).otherwise(col('urls')))
.withColumn('variant', when(size(col('variant')) == 0, lit(None)).otherwise(col('variant')))
.withColumn(
'geneExpression',
when(size(col('geneExpression')) == 0, lit(None))
.otherwise(col('geneExpression')))
# Collect variant info into biomarkers struct
.withColumn(
'biomarkers',
struct(
'variant',
'geneExpression'
))
.drop('variant', 'geneExpression')
.distinct()
)
return self.evidence
# Databricks notebook source
##문자열 데이터 타입
from pyspark.sql.functions import initcap, col, lower, upper
#data load
df = spark.read.format("csv")\
.option("header","true")\
.option("inferSchema","true")\
.load('/databricks-datasets/definitive-guide/data/retail-data/by-day/2010-12-01.csv')
#data schema 확인
df.printSchema()
#initcap : 주어진 문자열에서 공백을 나눠 첫글자를 대문자로 반환
df.select(initcap(col("Description"))).show(2, False)
#lower // upper
df.select(lower(col("StockCode"))).show(2)
#공백 추가 및 제거 (lit,ltrim,rtrim,rpad,lpad,trim)
from pyspark.sql.functions import lit, ltrim, rtrim, rpad, lpad, trim
df.select(
ltrim(lit(" HELLO ")).alias("ltrim"),
rtrim(lit(" HELLO ")).alias("rtrim"),
trim(lit(" HELLO ")).alias("trim"),
lpad(lit("HELLO"), 3, " ").alias("lpad"),
rpad(lit("HELLP"), 10, " ").alias("rpad")).show(2)
##정규 표현식
#description컬럼의 값을 COLOR 값으로 치환
from pyspark.sql.functions import regexp_replace
regex_string = "BLACK|WHITE|RED|GREEN|BLUE"
df.select(
regexp_replace(col("Description"), regex_string,
"COLOR").alias("color_clean"), col("Description")).show(2)
def etl_world_temperature(spark, input_dir, output_dir):
"""Clean the temperature data"""
# load data
data_input_full_file_path = f'{input_dir}/GlobalLandTemperaturesByCity.csv'
world_temperature_spark_df = spark.read \
.format('csv') \
.options(header='true', inferSchema='true', encoding="ISO-8859-1") \
.load(data_input_full_file_path)
# just take temperature data after 2003-01-01 and only keep the US data
world_temperature_spark_df = world_temperature_spark_df \
.filter(F.col('dt') >= datetime(2003, 1, 1)) \
.filter(F.col('Country') == 'United States')
# parse month and day
us_temperature_spark_df = world_temperature_spark_df \
.withColumn('month', F.month(F.col('dt'))) \
.withColumn('day', F.dayofmonth(F.col('dt'))) \
.drop(F.col('dt'))
# groupby columns and get the new avg temperature
avg_us_temperature_spark_df = us_temperature_spark_df \
.groupBy(['month', 'day', 'City', 'Country', 'Latitude', 'Longitude']) \
.agg(F.mean('AverageTemperature')) \
.withColumnRenamed('avg(AverageTemperature)', 'AverageTemperature') \
.withColumn('month', F.col('month').cast('integer')) \
.withColumn('day', F.col('day').cast('integer'))
# covert DMS Lat and Lon to numeric format to get state info with an udf func
avg_us_temperature_spark_df = avg_us_temperature_spark_df \
.withColumn('Latitude', F.when(F.col('Latitude').rlike('N'), F.regexp_replace('Latitude', 'N', '').cast('double'))
.otherwise(-1*F.when(F.col('Latitude').rlike('N'), F.regexp_replace('Latitude', 'N', '').cast('double')))) \
.withColumn('Longitude', F.when(F.col('Longitude').rlike('W'), -1 * F.regexp_replace('Longitude', 'W', '').cast('double'))
.otherwise(F.when(F.col('Longitude').rlike('W'), F.regexp_replace('Longitude', 'W', '').cast('double'))))
# define a udf function to get state based on lat and lon by using reverse_geocoder library
# https://github.com/thampiman/reverse-geocoder
def _helper_get_state_(lat, lon):
coor = (lat, lon)
result = rg.search(coor)
return result[0].get('admin1')
_helper_get_state_udf = F.udf(lambda x, y: _helper_get_state_(x, y),
StringType())
avg_us_temperature_spark_df = avg_us_temperature_spark_df\
.withColumn('state', _helper_get_state_udf(F.col('Latitude'), F.col('Longitude')))
# load i94addr dictionary - map the i94addr values
i94addr_dictionary_input_full_file_path = f'{input_dir}/dictionary_data/i94addr_dictionary.csv'
i94addr_dictionary_spark_df = spark \
.read \
.format('csv') \
.options(header='true', inferSchema='true', encoding="ISO-8859-1") \
.load(i94addr_dictionary_input_full_file_path)
i94addr_dictionary_spark_df = i94addr_dictionary_spark_df \
.withColumn('init_cap_value', F.initcap(F.col('value')))
avg_us_temperature_spark_df = avg_us_temperature_spark_df \
.join(i94addr_dictionary_spark_df, avg_us_temperature_spark_df.state == i94addr_dictionary_spark_df.init_cap_value, 'left') \
.drop('init_cap_value') \
.drop('value') \
.withColumnRenamed('key', 'state_code')
avg_us_temperature_spark_df = avg_us_temperature_spark_df \
.withColumnRenamed("Country", "country") \
.withColumnRenamed("City", "city") \
.withColumnRenamed("Latitude", "latitude") \
.withColumnRenamed("Longitude", "longitude") \
.withColumnRenamed("AverageTemperature", "avg_temperature")
avg_us_temperature_spark_df = avg_us_temperature_spark_df \
.withColumn('city_state_code', F.concat_ws(', ', F.upper(F.col('city')), F.upper(F.col('state_code'))))
avg_us_temperature_spark_df = avg_us_temperature_spark_df.select(
'month', 'day', 'city', 'state', 'state_code', 'city_state_code',
'avg_temperature').distinct()
# output clean data
data_output_full_file_path = f'{output_dir}/USCitiesTemperaturesByMonth.parquet'
avg_us_temperature_spark_df \
.write \
.options(encoding="ISO-8859-1") \
.mode('overwrite') \
.parquet(data_output_full_file_path)
def process_immigration(spark, input_bucket, output_bucket):
'''
Processing Immigration Data
Arguments:
spark = Spark Cluster
input_bucket = S3 Bucket name - Input
output_bucket = S3 Bucket name - Output
'''
## Loading Data
df = spark.read.parquet('{}data/*.parquet'.format(input_bucket))
## Loading Dicts from Bucket
# Load Port Dict
df_port = spark.read.json(
'{}data/i94prtl.json'.format(input_bucket)).toPandas()
df_port=spark.createDataFrame(df_port.transpose().reset_index()\
.rename(columns={'index':'i94port',0:'Port'}))
# Load Origin Dict
df_country = spark.read.json(
'{}data/i94cntyl.json'.format(input_bucket)).toPandas()
df_country=spark.createDataFrame(df_country.transpose().reset_index().\
rename(columns={'index':'i94cit',0:'Origin_Country'}))
# Load Travelmode Dict
df_mode = spark.read.json(
'{}data/i94model.json'.format(input_bucket)).toPandas()
df_mode=spark.createDataFrame(df_mode.transpose().reset_index().\
rename(columns={'index':'i94mode',0:'Travelmode'}))
# Load Destination Dict
df_dest = spark.read.json(
'{}data/i94addrl.json'.format(input_bucket)).toPandas()
df_dest=spark.createDataFrame(df_dest.transpose().reset_index().\
rename(columns={'index':'i94addr',0:'Destination'}))
# Load Visa Dict
df_visa = spark.read.json(
'{}data/i94visa.json'.format(input_bucket)).toPandas()
df_visa=spark.createDataFrame(df_visa.transpose().reset_index().\
rename(columns={'index':'i94visa',0:'Visa'}))
## Joining Data
# Join Port
df = df.join(df_port, df.i94port == df_port.i94port,
how='left').drop(df_port.i94port)
# Join Country
df = df.join(df_country, df.i94cit == df_country.i94cit,
how='left').drop(df_country.i94cit)
# Join Travelmode
df = df.join(df_mode, df.i94mode == df_mode.i94mode,
how='left').drop(df_mode.i94mode)
# Join Destination
df = df.join(df_dest, df.i94addr == df_dest.i94addr,
how='left').drop(df_dest.i94addr)
# Join Visa Data
df = df.join(df_visa, df.i94visa == df_visa.i94visa,
how='left').drop(df_visa.i94visa)
## Cleaning Data
# Dropping Column
col_to_drop = [
'visapost', 'occup', 'entdepu', 'count', 'admnum', 'matflag',
'dtaddto', 'insnum', 'fltno', 'i94cit', 'i94mode', 'i94addr', 'i94visa'
]
df = df.drop(*col_to_drop)
# Strip Whitespaces
df_clean = df.withColumn('Port', trim(col('Port')))
# Clean Invalid Ports
df_port_clean=df_clean.filter(~col('Port').contains('No PORT Code'))\
.filter(~col('Port').contains('Collapsed'))\
.filter(col('Port').isNotNull())
# Invalid Country Codes
df_country_clean=df_port_clean.filter(~col('Origin_Country').contains('INVALID'))\
.filter(~col('Origin_Country').contains('Collapsed'))\
.filter(col('Origin_Country').isNotNull())
# Remove Null-Values in Travlemode
df_travelmode_clean = df_country_clean.filter(
col('Travelmode').isNotNull())
# Remove Null-Values in Destination
df_destination_clean = df_travelmode_clean.filter(
col('Destination').isNotNull())
# Get City Names #
df_city = df_destination_clean.withColumn(
"City", initcap(split(col("Port"), ',')[0])).drop('Port')
# Quality Check
if df_city.count() < 1:
raise AssertionError('Error in ETL; Immigration ROW-Count is to low!')
else:
logging.info('Quality Check succeded')
## Writing Data
df_city.write.parquet(
'{}processed/cleaned_immigration'.format(output_bucket),
mode='overwrite')
print('Data cleaned and saved')
# COMMAND ----------
df.stat.freqItems(["StockCode", "Quantity"]).show()
# COMMAND ----------
from pyspark.sql.functions import monotonically_increasing_id
df.select(monotonically_increasing_id()).show(2)
# COMMAND ----------
from pyspark.sql.functions import initcap
df.select(initcap(col("Description"))).show()
# COMMAND ----------
from pyspark.sql.functions import lower, upper
df.select(col("Description"),
lower(col("Description")),
upper(lower(col("Description")))).show(2)
# COMMAND ----------
from pyspark.sql.functions import lit, ltrim, rtrim, rpad, lpad, trim
df.select(
ltrim(lit(" HELLO ")).alias("ltrim"),
"frequent items"
print(retail_df.stat.freqItems(['Quantity', 'UnitPrice']).show())
"approx quantile"
colName = "UnitPrice"
quantileProbs = [0.5]
relError = 0.05
print(retail_df.stat.approxQuantile("UnitPrice", quantileProbs, relError))
#%%
" string manipulations "
"changing column case"
from pyspark.sql.functions import initcap, upper, lower, col
print(
retail_df.select(initcap(col("Description")), lower(col('Description')),
upper(col('Description'))).show(5, False))
#%%
"regex replace"
from pyspark.sql.functions import regexp_replace
regex_string = "BLACK|WHITE|RED|GREEN|BLUE"
print(
retail_df.select(
regexp_replace(col("Description"),
regex_string, "COLOR").alias("color_clean"),
col("Description")).show(2))
#%%
"regex translate"
from pyspark.sql.functions import translate, lit
print(
retail_df.select(translate(lit("Descreeption"), "leet", "1627"),
def compile_capitalize(t, expr, scope, **kwargs):
op = expr.op()
src_column = t.translate(op.arg, scope)
return F.initcap(src_column)
def process_i94_cit_res_data(spark: SparkSession,
df_right: DataFrame) -> DataFrame:
""" Processes mapping file between countries_of_the_world.csv and i94_data.parquet """
# Filename of input file
filename = 's3://data-eng-capstone-cf/staging/i94_cit_res_data.csv'
# Read into a spark dataframe
df = spark.read.csv(filename, header=True)
# Cast country_id as an IntegerType()
df = df.withColumn('country_id', df.country_id.cast(IntegerType()))
# Make country unique, by appending '(<country_id>)' to string name
# Only do when country equals INVALID: STATELESS or INVALID: UNITED STATES
df = df.withColumn(
'country',
F.when(df.country.isin('INVALID: STATELESS', 'INVALID: UNITED STATES'),
F.concat(df.country, F.lit(' ('), df.country_id,
F.lit(')'))).otherwise(df.country))
# Add foreign key column so can join to df_cow
df = df.withColumn('country_join', F.initcap('country'))
# Manual adjustments
df = df.withColumn(
'country_join',
F.when(
df.country ==
'MEXICO Air Sea, and Not Reported (I-94, no land arrivals)',
'Mexico').when(
df.country == 'ANTIGUA-BARBUDA', 'Antigua & Barbuda').when(
df.country == 'BAHAMAS', 'Bahamas, The').when(
df.country == 'BOSNIA-HERZEGOVINA',
'Bosnia & Herzegovina').when(
df.country == 'BRITISH VIRGIN ISLANDS',
'British Virgin Is.').when(
df.country == 'CENTRAL AFRICAN REPUBLIC',
'Central African Rep.').when(
df.country == 'GAMBIA',
'Gambia, The').when(
df.country == 'GUINEA-BISSAU',
'Guinea-Bissau').
when(df.country == 'MAYOTTE (AFRICA - FRENCH)', 'Mayotte').when(
df.country == 'MICRONESIA, FED. STATES OF',
'Micronesia, Fed. St.').when(
df.country == 'NORTH KOREA', 'Korea, North').when(
df.country == 'MICRONESIA, FED. STATES OF',
'Micronesia, Fed. St.').when(
df.country == 'MICRONESIA, FED. STATES OF',
'Micronesia, Fed. St.').when(
df.country == 'SOUTH KOREA',
'Korea, South').when(df.country == 'ST. HELENA',
'Saint Helena').
when(df.country == 'ST. KITTS-NEVIS',
'Saint Kitts & Nevis').when(
df.country == 'ST. LUCIA',
'Saint Lucia').when(
df.country == 'ST. PIERRE AND MIQUELON',
'St Pierre & Miquelon').when(
df.country == 'ST. VINCENT-GRENADINES',
'Saint Vincent and the Grenadines').when(
df.country == 'TRINIDAD AND TOBAGO',
'Trinidad & Tobago').when(
df.country == 'TURKS AND CAICOS ISLANDS',
'Turks & Caicos Is').when(
df.country == 'WALLIS AND FUTUNA ISLANDS',
'Wallis and Futuna').when(
df.country == 'CHINA, PRC',
'China').otherwise(df.country_join))
# Define country_fk via left outer join
df = df.join(df_right, df.country_join == df_right.country, how='left') \
.select('country_id', df.country, df_right.country.alias('country_fk'))
# Check schema and count
df.printSchema()
df.count()
# Return transformed dataframe
return df