def main():
# Cargamos los ficheros csv directamente como dataframes
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
df1 = spark.read.format("csv").option(
"header", "true").load("simpsons_characters.csv")
df2 = spark.read.format("csv").option("header",
"true").load("simpsons_episodes.csv")
df3 = spark.read.format("csv").option(
"header", "true").load("simpsons_locations.csv")
df4 = spark.read.format("csv").option(
"header", "true").load("simpsons_script_lines.csv")
# Obtenemos un df por episode_id y el count de los distintos personajes.
dfe = df4.groupBy('episode_id').agg(
functions.approx_count_distinct(
df4.character_id).alias("character_count")).selectExpr(
"episode_id as id", "character_count")
# Obtenemos un df con id, imbd_rating y el count de personajes.
dfjoin1 = df2.join(dfe, on="id",
how='outer').select("id", "imdb_rating",
"character_count")
# Obtenemos (id, genero) e (id, episodio) y los unimos por los id's de los personajes.
dfc = df1.select('id', 'gender').selectExpr("id as character_id", "gender")
dfe = df4.select('character_id', 'episode_id')
dfjoin2 = dfc.join(dfe, on="character_id",
how='outer').select("episode_id", "character_id",
"gender")
# Para obtener el count de los generos, hacemos un filtro y agrupamos por id de episodio, para después hacer el count de cada episodio.
dfCharacMasc = dfjoin2.filter(
dfjoin2.gender == 'm').groupBy('episode_id').agg(
functions.approx_count_distinct(
dfjoin2.character_id).alias("masc_count")).selectExpr(
"episode_id as id", "masc_count")
dfCharacFem = dfjoin2.filter(
dfjoin2.gender == 'f').groupBy('episode_id').agg(
functions.approx_count_distinct(
dfjoin2.character_id).alias("fem_count")).selectExpr(
"episode_id as id", "fem_count")
# Por último se unen todos los campos en un mismo dataframe y se muestra.
dfjoin3 = dfjoin1.join(dfCharacMasc, on="id",
how='outer').select("id", "imdb_rating",
"character_count", "masc_count")
dfjoin4 = dfjoin3.join(dfCharacFem, on="id",
how='outer').select("id", "imdb_rating",
"character_count", "masc_count",
"fem_count")
dfjoin4.show()
sc.stop()
def join_to_weekly_budget(df, email_vols):
return (
df.join(email_vols, on="prim_party_id").withColumn(
"email_budget",
F.col("nonflight")).drop(*["flight", "nonflight"]).select(
"*",
F.rank().over(
Window.partitionBy(F.col("prim_party_id")).orderBy(
F.col("score").desc())).alias("rank")).withColumn(
"rank_adj",
F.when(
F.col("OFFER_TYPE") != "recommended",
F.col("rank") * 100).otherwise(
F.col("rank"))).drop(*["rank"]).
select(
"*",
F.rank().over(
Window.partitionBy(F.col("prim_party_id")).orderBy(
F.col("rank_adj").desc())).alias('rank')).withColumn(
"elig_for_week",
F.approx_count_distinct("action_id").over(
Window.partitionBy(
F.col("prim_party_id")))).withColumn(
"email_budget_week",
F.when(
F.col("elig_for_week") <
F.col("email_budget"),
F.col("elig_for_week")).otherwise(
F.col("email_budget"))).
filter(F.col("rank") <= F.col("email_budget_week")).drop(*[
"email_budget", "score", "rank_adj", "offer_send_time",
"send_date", "elig_for_week"
]))
def perform_eda(data_pipeline, target, feature, nonnull_cutoff,
except_for_these, save_fig):
spark = _loadSpark()
base = spark.sql(data_pipeline)
base.persist()
cols_drop = []
for col_name in base.columns:
if base.select(col_name).filter((F.col(col_name).isNotNull()) | (F.col(
col_name).cast('double') == 0.0)).count() <= nonnull_cutoff:
cols_drop.append(col_name)
base2 = base.drop(*cols_drop)
print(
'*** Dropped null-only or columns having non-null values less than or equal to '
+ str(nonnull_cutoff) + '...')
print(str(cols_drop))
base.unpersist()
base2.persist()
print('\n*** Excluded features including a target...')
print(str(except_for_these))
print('\n*** Excluded timestamp type and single value numeric features...')
num = []
cat = []
for c in base2.drop(*except_for_these).dtypes:
if c[1] == 'string':
cat += [c[0]]
base2 = base2.withColumn(
c[0],
F.when((F.col(c[0]) == '') |
(F.col(c[0]) == ' ') | (F.col(c[0]).isNull()),
F.lit('None')).otherwise(F.col(c[0])))
if c[1] not in ('string', 'timestamp') and base2.select(
F.approx_count_distinct(c[0])).collect()[0][0] > 1:
num += [c[0]]
base2 = (base2.withColumn(
c[0],
F.when((F.col(c[0]) == '') | (F.col(c[0]) == ' '),
F.lit(None)).otherwise(F.col(c[0]))).withColumn(
c[0],
F.col(c[0]).cast('double')))
print('*** Distribution by target value ***')
base2.groupBy(target).count().show()
print('*** Describe categorical variables in the data set ***')
for i in cat:
base2.groupBy(i).count().orderBy('count',
ascending=False).show(2000,
truncate=False)
print('*** Describe numeric variables in the data set ***')
for i in num:
base2.select(i).summary().show(truncate=False)
if save_fig:
tmp = base2.select(i).toPandas()
tmp.hist(figsize=(12, 10))
plt.savefig('./fig/' + i + '.png')
plt.close()
def main():
# Cargamos los ficheros csv directamente como dataframes
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
df1 = spark.read.format("csv").option(
"header", "true").load("simpsons_characters.csv")
df2 = spark.read.format("csv").option("header",
"true").load("simpsons_episodes.csv")
df3 = spark.read.format("csv").option(
"header", "true").load("simpsons_locations.csv")
df4 = spark.read.format("csv").option(
"header", "true").load("simpsons_script_lines.csv")
dfScriptLines = df4.groupBy('episode_id').agg(
functions.approx_count_distinct(
df4.location_id).alias("count")).selectExpr(
"episode_id as id", "count")
dfLocations = df2.join(dfScriptLines, on="id",
how='outer').select("id", "imdb_rating", "count")
dfLocations.show()
sc.stop()
def getColumnUniqueCounts(clickDF):
"""
Parameters
----------
clickDF : class Dataframe
"""
distvals = clickDF.agg(*(approx_count_distinct(col(c)).alias(c) for c in clickDF.columns if str.startswith(c,"c")))
print(type(distvals))
return distvals
def test_aggregator(self):
df = self.df
g = df.groupBy()
self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
from pyspark.sql import functions
self.assertEqual((0, u'99'),
tuple(g.agg(functions.first(df.key), functions.last(df.value)).first()))
self.assertTrue(95 < g.agg(functions.approx_count_distinct(df.key)).first()[0])
self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
def test_aggregator(self):
df = self.df
g = df.groupBy()
self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
from pyspark.sql import functions
self.assertEqual((0, u'99'),
tuple(g.agg(functions.first(df.key), functions.last(df.value)).first()))
self.assertTrue(95 < g.agg(functions.approx_count_distinct(df.key)).first()[0])
self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
def aggregate_job(self, df_news):
df_finalized = df_news.groupby('action_state', 'Year', 'Actor1Type1Code') \
.agg(
F.approx_count_distinct(
'GLOBALEVENTID').alias('events_count'),
F.sum('normg_scale').alias('norm_score_cale'))
# Calculate avg by dividing by event count
print df_finalized.show(df_finalized.count())
#print df_finalized.printSchema()
return df_finalized
def distinct_count(table, col_names, method="exact"):
try:
if method == "exact" or method == None:
from pyspark.sql.functions import col, countDistinct
uniques = table.agg(*(countDistinct(col(c)).alias(c) for c in col_names))
elif method == "approx":
from pyspark.sql.functions import col, approx_count_distinct
uniques = table.agg(*(approx_count_distinct(col(c)).alias(c) for c in col_names))
else:
raise ValueError("Unknown method {}, choose between ['exact', 'approx']".format(method))
except:
print("Cannot resolve column: {}".format(col_names))
return uniques
def approx_cardinalities(df, cols):
from functools import reduce
counts = df.groupBy(
F.lit(True).alias("drop_me")
).agg(
F.count('*').alias("total"),
*[F.approx_count_distinct(F.col(c)).alias(c) for c in cols]
).drop("drop_me").cache()
result = reduce(lambda l, r: l.unionAll(r), [counts.select(F.lit(c).alias("field"), F.col(c).alias("approx_count")) for c in counts.columns]).collect()
counts.unpersist()
return dict([(r[0],r[1]) for r in result])
def aggregate_job(self, df_joined):
'''
This function is used to perform group by and aggregation on joined data frame
:param df_joined: joined events and mentions data frame will be provided
:return: aggregated data frame will be returned.
'''
df_finalized = df_joined.groupby('state', 'Year', 'MonthYear', 'Actor1Type1Code') \
.agg(
functions.approx_count_distinct('GLOBALEVENTID').alias('events_count'),
functions.sum('GoldsteinScale_norm').alias('GoldsteinScale_norm_sum'),
functions.sum('Avg_Confidence').alias('confidence_sum')
)
df_finalized.show()
return df_finalized
def aggregate(uri, conf):
spark = SparkSession.builder.config(conf=conf).getOrCreate()
parsed_df = spark.read.format('csv').options(header=True,
inferschema=True).load(uri)
columns = parsed_df.columns
identify_aggregations(columns)
print("unique_params_indices: " + str(unique_params_indices))
print("numeric_params_indices: " + str(numeric_params_indices))
# initiate output DF with record count
cnt_col_name = spark.conf.get("output") + "_count"
output = parsed_df.groupBy([parsed_df.temp_res, parsed_df.spat_res]) \
.count()
output = output.withColumnRenamed("count", cnt_col_name)
# select and handle categorical attributes
cat_col_names = [columns[i] for i in unique_params_indices]
for name in cat_col_names:
col = parsed_df.groupBy([parsed_df.temp_res, parsed_df.spat_res]) \
.agg(F.approx_count_distinct(name).alias(name+"_uniq"))
output = output.join(col, ["temp_res", "spat_res"], 'left')
# select and handle numeric attributes
num_col_names = [columns[i] for i in numeric_params_indices]
# fill in NaN numeric values to avoid error
parsed_df = parsed_df.fillna(0, subset=num_col_names)
for name in num_col_names:
col = parsed_df.groupBy([parsed_df.temp_res, parsed_df.spat_res]) \
.agg(F.mean(name).alias(name+"_avg"))
output = output.join(col, ["temp_res", "spat_res"], 'left')
# write aggregated dataset
out_dir = spark.conf.get("output")
out_dir = "aggregates/" + out_dir
# output.printSchema()
# output.show(40)
# print(output.count())
print("output directory is: " + out_dir)
output.write.csv(out_dir, header=True)
spark.stop()
def group_distinct(self, df_, columns: list) -> dict:
"""
:param df_: pysaprk df
:param columns: columns of type str in the dataframe
:return: dict of columns and distinct values
"""
data = []
for c in columns:
cnt = df_.agg(approx_count_distinct(col(c)).alias(c)).collect()
data.append(cnt[0][0])
df_pd = pd.DataFrame(index=columns,
columns=["num_distinct"],
data=data)
return df_pd.sort_values("num_distinct", axis=0,
ascending=True).to_dict()["num_distinct"]
def process_measures_from_grouped_message(self):
try:
input_df = self.get_kafka_consumer()
flat_message_df = self.get_parsed_message(input_df)
unique_users_measure_df = flat_message_df.groupBy(
'time_window').agg(
F.approx_count_distinct("users").alias(
"unique_users")).orderBy('time_window', 'unique_users')
country_count_df1 = flat_message_df.groupBy(
'time_window',
'country').count().orderBy('time_window', 'count')
most_represented_countries_df = country_count_df1.withColumn(
"rank_max",
F.rank().over(
Window.partitionBy("time_window").orderBy(F.desc("count")))
).where(F.col("rank_max") == 1).orderBy("time_window").select(
F.col("time_window"),
F.col("country").alias("most_represented_country"),
F.col("count").alias("most_represented_country_count"))
least_represented_countries_df = country_count_df1.withColumn(
"rank_min",
F.rank().over(
Window.partitionBy("time_window").orderBy(F.col("count")))
).where(F.col("rank_min") == 1).orderBy("time_window").select(
F.col("time_window"),
F.col("country").alias("least_represented_country"),
F.col("count").alias("least_represented_country_count"))
most_represented_country_df = self.get_first_record_from_grouped_data(
most_represented_countries_df, "most_represented_country")
least_represented_country_df = self.get_first_record_from_grouped_data(
least_represented_countries_df, "least_represented_country")
measures_df = most_represented_country_df.join(least_represented_country_df,"time_window").join(unique_users_measure_df,"time_window")\
.select(most_represented_countries_df.time_window,F.col("most_represented_country")\
,F.col("most_represented_country_count"),F.col("least_represented_country")\
,F.col("least_represented_country_count"),F.col("unique_users"))
measures_df.write.format("csv").save(self.output_file_dir)
#self.logger.info(most_represented_country_df.collect())
#self.logger.info(least_represented_country_df.collect())
#self.logger.info(unique_users_measure_df.collect())
except Exception as e:
self.logger.error(e)
def removeInvalidCols(df):
'''
Remove columns with all Nans or all same values
Parameter:
- dataframe
Returns:
- dataframe (after removing cols)
'''
count_distinct = df.select([
F.approx_count_distinct(c).alias("{0}".format(c)) for c in df.columns
])
# consider only columns with >= 2 distincts
distinct = count_distinct.toPandas().to_dict(orient='list')
to_consider = [k for k in distinct if distinct[k][0] >= 2]
# return the cleaned dataframe
return df[to_consider]
def _deduplication_approximated_todo(columns, df):
"""
Returns what (columns, as in spark columns) to compute to get the results requested by
the parameters.
:param columns:
:type columns: list
:param df:
:type df: DataFrame
:return: Pyspark columns representing what to compute.
"""
if columns is None:
print(
"Approximated count distinct spanning over the whole row is currently not supported"
)
else:
# multiple count distinct, one column each
todo = [approx_count_distinct(col(c)).alias(c) for c in columns]
return todo
def main():
# base streams from kafka
submission_stream = stream_from_kafka('SubmissionProducer')
comment_stream = stream_from_kafka('CommentProducer')
# subreddit_stream = stream_from_kafka('SubredditProducer')
# redditor_stream = stream_from_kafka('RedditorProducer')
# persist base streams to hdfs for batch processing
write_to_hdfs(submission_stream, 'submission')
write_to_hdfs(comment_stream, 'comment')
# write_to_hdfs(subreddit_stream, 'subreddit')
# write_to_hdfs(redditor_stream, 'redditor')
# streaming stats
windowed_submissions = submission_stream \
.groupby(window('created_utc', '5 seconds', '5 seconds')) \
.count() \
.withColumn('window', col('window')['end']) \
.withColumn('date', to_date('window'))
windowed_comments = comment_stream \
.groupby(window('created_utc', '5 seconds', '5 seconds')) \
.count() \
.withColumn('window', col('window')['end']) \
.withColumn('date', to_date('window'))
windowed_unique_active_subreddits = submission_stream \
.select('created_utc', 'subreddit') \
.union(comment_stream.select('created_utc', 'subreddit')) \
.groupby(window('created_utc', '5 seconds', '5 seconds')) \
.agg(approx_count_distinct('subreddit'), count('subreddit')) \
.withColumn('window', col('window')['end']) \
.withColumn('date', to_date('window')) \
.withColumnRenamed('approx_count_distinct(subreddit)', 'active_subreddits') \
.withColumnRenamed('count(subreddit)', 'submissions_and_comments')
# persist streaming stats to cassandra
stream_to_cassandra(windowed_submissions, 'windowed_submissions')
stream_to_cassandra(windowed_comments, 'windowed_comments')
stream_to_cassandra(windowed_unique_active_subreddits,
'unique_active_subreddits')
spark_session.streams.awaitAnyTermination()
def profile(df):
x = spark.createDataFrame(sc.emptyRDD(), SCHEMA)
df.cache()
for field in df.schema.fields:
if isinstance(field.dataType,
(pst.StructField, pst.ArrayType, pst.MapType,
pst.StructType, pst.BooleanType)):
continue
else:
y = df.agg(
F.lit(field.name).alias("desc"),
F.count(field.name).alias('count'),
F.min(field.name).alias('min'),
F.max(field.name).alias('max'),
F.sum(field.name).alias('sum'),
F.stddev(field.name).alias('stddev'),
F.max(F.length(field.name)).alias('max_length'),
F.approx_count_distinct(field.name).alias('cardinality'))
x = x.union(y)
return x
def test_aggregator(self):
df = self.df
g = df.groupBy()
self.assertEqual([99, 100],
sorted(
g.agg({
"key": "max",
"value": "count"
}).collect()[0]))
self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
from pyspark.sql import functions
self.assertEqual((0, "99"),
tuple(
g.agg(functions.first(df.key),
functions.last(df.value)).first()))
self.assertTrue(
95 < g.agg(functions.approx_count_distinct(df.key)).first()[0])
# test deprecated countDistinct
self.assertEqual(100,
g.agg(functions.countDistinct(df.value)).first()[0])
def readFile(spark, input_file):
'''read file (clickstream.parquet) and add new features to dataframe'''
parquetFile = spark.read.parquet(input_file) # clickstream.parquet
parquetFile = parquetFile.distinct() # drop dublicates
w = Window.partitionBy('ad_id')
parquetFile = parquetFile.withColumn(
'day_count',
fn.approx_count_distinct('date').over(w))
parquetFile = parquetFile.withColumn(
'is_cpm', (fn.col('ad_cost_type') == 'CPM').cast('integer'))
parquetFile = parquetFile.withColumn(
'is_cpc', (fn.col('ad_cost_type') == 'CPC').cast('integer'))
parquetFile = parquetFile.withColumn(
'is_click', (fn.col('event') == 'click').cast('integer'))
parquetFile = parquetFile.withColumn(
'is_view', (fn.col('event') == 'view').cast('integer'))
parquetFile = parquetFile.withColumn(
'CTR',
fn.when(fn.sum('is_view').over(w) == 0, 0).otherwise(
fn.sum('is_click').over(w) / fn.sum('is_view').over(w)))
return parquetFile
("James", "Sales", 3000),
("Scott", "Finance", 3300),
("Jen", "Finance", 3900),
("Jeff", "Marketing", 3000),
("Kumar", "Marketing", 2000),
("Saif", "Sales", 4100)
]
schema = ["employee_name", "department", "salary"]
df = spark.createDataFrame(data=simpleData, schema = schema)
df.printSchema()
df.show(truncate=False)
print("approx_count_distinct: " + \
str(df.select(approx_count_distinct("salary")).collect()[0][0]))
print("avg: " + str(df.select(avg("salary")).collect()[0][0]))
df.select(collect_list("salary")).show(truncate=False)
df.select(collect_set("salary")).show(truncate=False)
df2 = df.select(countDistinct("department", "salary"))
df2.show(truncate=False)
print("Distinct Count of Department & Salary: "+str(df2.collect()[0][0]))
print("count: "+str(df.select(count("salary")).collect()[0]))
df.select(first("salary")).show(truncate=False)
df.select(last("salary")).show(truncate=False)
df.select(kurtosis("salary")).show(truncate=False)
def estimate_segments(
df: pyspark.sql.dataframe.DataFrame,
target_field: str = None,
max_segments: int = 30,
include_columns: List[str] = [],
unique_perc_bounds: Tuple[float, float] = [None, 0.8],
null_perc_bounds: Tuple[float, float] = [None, 0.2],
) -> Optional[Union[List[Dict], List[str]]]:
"""
Estimates the most important features and values on which to segment
data profiling using entropy-based methods.
If no target column provided, maximum entropy column is substituted.
:param df: the dataframe of data to profile
:param target_field: target field (optional)
:param max_segments: upper threshold for total combinations of segments,
default 30
:param include_columns: additional non-string columns to consider in automatic segmentation. Warning: high cardinality columns will degrade performance.
:param unique_perc_bounds: tuple of form [lower, upper] with bounds on the percentage of unique values (|unique| / |X|). Upper bound exclusive.
:param null_perc_bounds: tuple of form [lower, upper] with bounds on the percentage of null values. Upper bound exclusive.
:return: a list of segmentation feature names
"""
current_split_columns = []
segments = []
segments_used = 1
max_entropy_column = (float("-inf"), None)
if not unique_perc_bounds[0]:
unique_perc_bounds[0] = float("-inf")
if not unique_perc_bounds[1]:
unique_perc_bounds[1] = float("inf")
if not null_perc_bounds[0]:
null_perc_bounds[0] = float("-inf")
if not null_perc_bounds[1]:
null_perc_bounds[1] = float("inf")
valid_column_names = set()
count = df.count()
print("Limiting to categorical (string) data columns...")
valid_column_names = {col for col in df.columns if (df.select(col).dtypes[0][1] == "string" or col in include_columns)}
print("Gathering cardinality information...")
n_uniques = {col: df.agg(F.approx_count_distinct(col)).collect()[0][0] for col in valid_column_names}
print("Gathering missing value information...")
n_nulls = {col: df.filter(df[col].isNull()).count() for col in valid_column_names}
print("Finding valid columns for autosegmentation...")
for col in valid_column_names.copy():
null_perc = 0.0 if count == 0 else n_nulls[col] / count
unique_perc = 0.0 if count == 0 else n_uniques[col] / count
if (
col in segments
or n_uniques[col] <= 1
or null_perc < null_perc_bounds[0]
or null_perc >= null_perc_bounds[1]
or unique_perc < unique_perc_bounds[0]
or unique_perc >= unique_perc_bounds[1]
):
valid_column_names.remove(col)
if not valid_column_names:
return []
if not target_field:
print("Finding alternative target field since none were specified...")
for col in valid_column_names:
col_entropy = _simple_entropy(df, col)
if n_uniques[col] > 1:
col_entropy /= math.log(n_uniques[col])
if col_entropy > max_entropy_column[0]:
max_entropy_column = (col_entropy, col)
target_field = max_entropy_column[1]
print(f"Using {target_field} column as target field.")
assert target_field in df.columns
valid_column_names.add(target_field)
valid_column_names = list(valid_column_names)
countdf = df.select(valid_column_names).groupby(valid_column_names).count().cache()
print("Calculating segments...")
while segments_used < max_segments:
valid_column_names = {col for col in valid_column_names if (col not in segments and n_uniques[col] * segments_used <= (max_segments - segments_used))}
_, segment_column_name = _find_best_split(
countdf, current_split_columns, list(valid_column_names), target_column_name=target_field, normalization=n_uniques
)
if not segment_column_name:
break
segments.append(segment_column_name)
current_split_columns.append(segment_column_name)
segments_used *= n_uniques[segment_column_name]
return segments
def get_data(self):
"""
Returns statistics about attributes in a data frame
"""
from pyspark.sql import functions
# Correlation pairs
corr_pairs = list(
chunks(list(itertools.product(self.attrs, self.attrs)),
len(self.attrs)))
# Cache data
self.data.cache()
df_count = self.data.count()
# TODO: Implement median using df.approxQuantile('col', [.5], .25)
stats = []
for i, name in enumerate(self.attrs):
df_col = functions.col(name)
stats.append(functions.lit(name))
stats.append(functions.max(df_col).alias('max_{}'.format(name)))
stats.append(functions.min(df_col).alias('min_{}'.format(name)))
if name in self.numeric_attrs:
stats.append(
functions.round(functions.stddev(df_col),
4).alias('stddev_{}'.format(name)))
else:
stats.append(functions.lit('-'))
stats.append(
functions.count(df_col).alias('count_{}'.format(name)))
if name in self.numeric_attrs:
stats.append(
functions.round(functions.avg(df_col),
4).alias('avg_{}'.format(name)))
else:
stats.append(functions.lit('-'))
stats.append(
functions.approx_count_distinct(df_col).alias(
'distinct_{}'.format(name)))
stats.append((df_count - functions.count(df_col)).alias(
'missing_{}'.format(name)))
if name in self.numeric_attrs:
stats.append(
functions.round(functions.skewness(df_col),
2).alias('skewness_{}'.format(name)))
stats.append(
functions.round(functions.kurtosis(df_col),
2).alias('kurtosis_{}'.format(name)))
else:
stats.append(functions.lit('-'))
stats.append(functions.lit('-'))
if self.params['correlation']:
for pair in corr_pairs[i]:
if all([
pair[0] in self.numeric_attrs, pair[1]
in self.numeric_attrs
]):
stats.append(
functions.round(functions.corr(*pair),
4).alias('corr_{}'.format(i)))
else:
stats.append(functions.lit('-'))
self.data = self.data.agg(*stats)
aggregated = self.data.take(1)[0]
n = len(self.names)
rows = [aggregated[i:i + n] for i in range(0, len(aggregated), n)]
return {"rows": rows, "attributes": self.get_column_names().split(',')}
def _get_approx_distinct_count_for_col(self,
df: DataFrame,
_dcol: str,
_rsd=0.05):
return df.select(F.approx_count_distinct(col(_dcol), rsd=_rsd)) \
.rdd.map(lambda row: row[0]).collect()[0]
data = data.select(
col("data").getItem(0).alias("TS"),
col("data").getItem(1).alias("UID"),
col("data").getItem(2).alias("URL"),
col("data").getItem(3).alias("Title"),
col("data").getItem(4).alias("User-Agent"),
)
data = data.selectExpr("TS", "UID", 'parse_url(URL, "HOST") as domain')
data = data.withColumn("TS", from_unixtime(col("TS").cast("int")))
data = data.withColumn(
"zone",
when(col("domain").endswith(".ru"), "ru").otherwise("not ru"))
data = data.groupBy("zone", window("TS", "2 seconds", "1 second")).agg(
count("UID").alias("view"),
approx_count_distinct(col("UID")).alias("unique"))
data = data.sort(["window", "view", "zone"], ascending=[True, False,
True]).limit(20)
data = data.select(col("window"), col("zone"), col("view"), col("unique"))
query = (data.writeStream.outputMode("complete").format("console").option(
"truncate", "false").trigger(once=args.once,
processingTime=args.processing_time).start())
query.awaitTermination()
StructField("merchant_id", LongType(), True),
StructField("created_time_ist", StringType(), True)
])
txnFctDf = sparkSession.createDataFrame(txnFctRdd, txnFctSchema)
txnFctDf.printSchema()
txnFctDf.show(5, False)
# Applying tranformation on dataframe using DSL (Domain Specific Language)
txnFctDf = txnFctDf\
.withColumn("created_time_ist", unix_timestamp(txnFctDf["created_time_ist"], "yyyy-MM-dd HH:mm:ss").cast(TimestampType()))
txnFctDf.printSchema()
txnFctDf.show(5, False)
print("# of records = " + str(txnFctDf.count()))
print("# of merchants = " +
str(txnFctDf.select(txnFctDf["merchant_id"]).distinct().count()))
txnAggDf = txnFctDf\
.repartition(10, txnFctDf["merchant_id"])\
.groupBy("merchant_id")\
.agg(sum("amount"), approx_count_distinct("status"))
txnAggDf.show(5, False)
txnAggDf\
.withColumnRenamed("sum(amount)", "total_amount")\
.withColumnRenamed("approx_count_distinct(status)", "dist_status_count")\
txnAggDf.show(5, False)
df_news.show(10)
df_news = df_news.filter(df_news.ActionGeo_CountryCode == 'US')
df_news = df_news.filter(df_news.Actor1Code != 'null')
df_news = df_news.filter(df_news.Actor1Type1Code != 'null')
name = 'ActionGeo_ADM1Code'
udf = UserDefinedFunction(lambda x: x[:2]+'-'+x[2:], StringType())
df_news = df_news.select(
*[udf(column).alias(name) if column == name else column for column in df_news.columns])
split_col = F.split(df_news['ActionGeo_ADM1Code'], '-')
df_news = df_news.withColumn('action_state', split_col.getItem(1))
print df_news.show()
df_news = df_news.groupby('action_state', 'Year', 'Actor1Type1Code').agg(F.approx_count_distinct('GLOBALEVENTID').alias('event_code'),
# F.col('SQLDATE'),
# F.collect_list('EventCode'),
# F.col('Actor1Name'),
# F.col('Actor2Name'),
# F.collect_list('ActionGeo_ADM1Code'),
# F.collect_list('action_state'),
# F.collect_list('ActionGeo_Fullname'),
F.avg('GoldsteinScale').alias(
'goldstein_scale'),
F.avg('AvgTone').alias('avg_tone'))
print df_news.show(df_news.count())
print df_news.printSchema()
postgres_dump(config, df_news)
df.createOrReplaceTempView("dfTable")
# COMMAND ----------
from pyspark.sql.functions import count
df.select(count("StockCode")).show() # 541909
# COMMAND ----------
from pyspark.sql.functions import countDistinct
df.select(countDistinct("StockCode")).show() # 4070
# COMMAND ----------
from pyspark.sql.functions import approx_count_distinct
df.select(approx_count_distinct("StockCode", 0.1)).show() # 3364
# COMMAND ----------
from pyspark.sql.functions import first, last
df.select(first("StockCode"), last("StockCode")).show()
# COMMAND ----------
from pyspark.sql.functions import min, max
df.select(min("Quantity"), max("Quantity")).show()
# COMMAND ----------
from pyspark.sql.functions import sum
df.select(sum("Quantity")).show() # 5176450
def run_pipeline(self):
try:
logging.info(
"https://sparkbyexamples.com/pyspark/pyspark-aggregate-functions/"
)
# check collect_list and collect_set
#collect_set() function returns all values from an input column with duplicate values eliminated.
#collect_list() function returns all values from an input column with duplicates
logging.info(
'run_pipeline method started --> https://sparkbyexamples.com/pyspark/pyspark-explode-array-and-map-columns-to-rows/'
)
simpleData = [("James", "Sales", 3000), ("Michael", "Sales", 4600),
("Robert", "Sales", 4100),
("Maria", "Finance", 3000), ("James", "Sales", 3000),
("Scott", "Finance", 3300), ("Jen", "Finance", 3900),
("Jeff", "Marketing", 3000),
("Kumar", "Marketing", 2000),
("Saif", "Sales", 4100)]
schema = ["employee_name", "department", "salary"]
df = self.spark.createDataFrame(data=simpleData,
schema=schema).cache()
df.show(truncate=False)
from pyspark.sql.functions import approx_count_distinct, collect_list
from pyspark.sql.functions import collect_set, sum, avg, max, countDistinct, count
from pyspark.sql.functions import first, last, kurtosis, min, mean, skewness
from pyspark.sql.functions import stddev, stddev_samp, stddev_pop, sumDistinct
from pyspark.sql.functions import variance, var_samp, var_pop
df.printSchema()
df.show(truncate=False)
print("approx_count_distinct: " + \
str(df.select(approx_count_distinct("salary")).collect()[0][0]))
print("avg: " + str(df.select(avg("salary")).collect()[0][0]))
df.select(collect_list("salary")).show(truncate=False)
df.select(collect_set("salary")).show(truncate=False)
df2 = df.select(countDistinct("department", "salary"))
df2.show(truncate=False)
print("Distinct Count of Department & Salary: " +
str(df2.collect()[0][0]))
print("count: " + str(df.select(count("salary")).collect()[0]))
dffirst = df.select(first("salary"))
dffirst.show(truncate=False)
df.select(last("salary")).show(truncate=False)
df.select(kurtosis("salary")).show(truncate=False)
df.select(max("salary")).show(truncate=False)
df.select(min("salary")).show(truncate=False)
df.select(mean("salary")).show(truncate=False)
df.select(skewness("salary")).show(truncate=False)
df.select(stddev("salary"), stddev_samp("salary"), \
stddev_pop("salary")).show(truncate=False)
df.select(sum("salary")).show(truncate=False)
df.select(sumDistinct("salary")).show(truncate=False)
df.select(variance("salary"), var_samp("salary"), var_pop("salary")) \
.show(truncate=False)
logging.info('run_pipeline method ended')
except Exception as exp:
logging.error("An error occured while running the pipeline > " +
str(exp))
# send email notification
# log error to database
sys.exit(1)
return
def create_base(_undersample,undersample_rate, _save_fig,data_pipeline, target,except_for_these,nonnull_cutoff,unique_num_value,balThreshold,n_feat, use_wgt):
base = spark.sql(data_pipeline)
base.persist()
#Drop all Nulls or less than a threshold
cols_drop = []
for col_name in base.drop(*except_for_these).columns:
if base.select(col_name).filter((F.col(col_name).isNotNull()) | (F.col(col_name).cast('double') == 0.0)).count() <= nonnull_cutoff:
cols_drop.append(col_name)
base2 = base.drop(*cols_drop)
print('*** Dropped null-only or columns having non-null values less than or equal to ' + str(nonnull_cutoff) + '...')
print(str(cols_drop))
base.unpersist()
base2.persist()
print('\n*** Excluded features including a target...')
print(str(except_for_these))
num = []
cat = []
for c in base2.drop(*except_for_these).dtypes:
if c[1] == 'string':
cat += [c[0]]
base2 = base2.withColumn(c[0], F.when((F.col(c[0]) == '') | (F.col(c[0]) == ' ') | (F.col(c[0]).isNull()), F.lit('None')).otherwise(F.col(c[0])))
if c[1] not in ('string', 'timestamp') and base2.select(F.approx_count_distinct(c[0])).collect()[0][0] > unique_num_value:
num += [c[0]]
base2 = ( base2.withColumn(c[0], F.when((F.col(c[0]) == '') | (F.col(c[0]) == ' '), F.lit(None)).otherwise(F.col(c[0])))
.withColumn(c[0], F.col(c[0]).cast('double')))
print('\n*** Distribution by target value ***')
base2.groupBy(target).count().show()
# Create batches for faster processing
base = (df1.join(df_seg, ['party_id', 'cmdb_partition'], 'left').withColumn('rnum',F.row_number().over(W.orderBy('party_id'))))
# Replace empty strings to None string or null
n_batch = 20
batch_size = int(n_sample/n_batch)
total_nulls = {}
dfs = {}
print('\n*** Replace empty strings to None string or null...***')
for i in range(n_batch):
print('*** batch :' + str(i+1) + ' out of ' + str(n_batch) + ' with a size of ' + str(batch_size))
lb = i* batch_size + 1
ub = i * batch_size + batch_size
df_b = base.filter((F.col('rnum') >= lb) & (F.col('rnum') <= ub))
for c in df_b.drop(*except_for_these).columns:
if c in cat:
df_b = df_b.withColumn(c, F.when((F.col(c) == '') | (F.col(c) == ' ') | (F.col(c).isNull()), F.lit('None')).otherwise(F.col(c)))
if c in num:
df_b = (df_b.withColumn(c, F.when((F.col(c) == '') | (F.col(c) == ' '), F.lit(None)).otherwise(F.col(c)))
.withColumn(c, F.col(c).cast('double')))
nulls = df_b.select(c).filter((F.col(c).isNull()) | (F.col(c) == 'None')).count()
total_nulls.setdefault(c, []).append(nulls)
dfs[str(i)]=df_b
drop_list = []
for col, nulls in total_nulls.items():
print('Column : ' + col + ' - null count : ' + str(nulls))
if sum(nulls) > 0:
drop_list.append(col)
print('\n*** Drop list ***')
print(str(drop_list))
print('\n*** Merge batches into one data frame...')
from functools import reduce
def unionAll(dfs):
return reduce(lambda df1, df2: df1.unionByName(df2), dfs)
base2 = (unionAll(list(dfs.values())).drop(*drop_list))
print('*** Merged as base2 ...')
#Check imbalance - weighting for the minority class
total_size = float(base2.count())
tp_size = float(base2.select(target).filter(F.col(target) == 1).count())
tn_size = float(total_size - tp_size)
if tp_size/tn_size < balThreshold:
print("Imbalance issue exists....target vs non-target ratio : " + str(tp_size/tn_size))
if use_wgt:
class_weight = tn_size / total_size
base2 = base2.withColumn('classWeight', F.when(F.col(target) == 0, class_weight).otherwise(1-class_weight))
else:
pass
else:
pass
print('\n*** Describe categorical variables in the data set ***')
print(str(cat))
for i in cat:
base2.groupBy(i).count().orderBy('count', ascending=False).show(2000,truncate=False)
print('*** Describe numeric variables in the data set ***')
print(str(num))
for i in num:
base2.select(i).summary().show(truncate=False)
if _save_fig:
tmp = base2.select(i).toPandas()
tmp.hist(figsize=(12,10))
plt.savefig('./fig/'+i+'.png')
plt.close()
if _undersample:
print('*** Undersampling major class due to imbalanced data ***')
base_tp = base2.filter(F.col(target) == 1)
_count = base_tp.count()
print('*** Target size :' + str(_count*undersample_rate))
base_tn = (base2.filter(F.col(target) == 0)
.withColumn('rand_num', F.lit(F.rand(_seed)))
.withColumn('row_num', F.row_number().over(W.orderBy('rand_num')))
.filter(F.col('row_num') <= _count*undersample_rate)
.drop('rand_num', 'row_num'))
df = base_tp.unionByName(base_tn)
else:
df = base2
base2.unpersist()
return df, num, cat
def main():
# Cargamos los ficheros csv directamente como dataframes
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
df1 = spark.read.format("csv").option(
"header", "true").load("simpsons_characters.csv")
df2 = spark.read.format("csv").option("header",
"true").load("simpsons_episodes.csv")
df3 = spark.read.format("csv").option(
"header", "true").load("simpsons_locations.csv")
df4 = spark.read.format("csv").option(
"header", "true").load("simpsons_script_lines.csv")
#A) numero de ubicaciones diferentes que aparecen en cada episodio
dfScriptLines = df4.groupBy('episode_id').agg(
functions.approx_count_distinct(
df4.location_id).alias("count")).selectExpr(
"episode_id as id", "count")
dfLocations = df2.join(dfScriptLines, on="id",
how='outer').select("id", "imdb_rating", "count")
dfaux1 = dfLocations.withColumn(
"imdb_rating_double", dfLocations["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("count_double",
dfLocations["count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(dfaux2.imdb_rating_double,
dfaux2.count_double).alias('Pearson-Imdb-NumLocations'))
dfcof.show()
#B) numero de personajes que aparecen en cada episodio
dfe = df4.groupBy('episode_id').agg(
functions.approx_count_distinct(
df4.character_id).alias("character_count")).selectExpr(
"episode_id as id", "character_count")
dfjoin1 = df2.join(dfe, on="id",
how='outer').select("id", "imdb_rating",
"character_count")
dfaux1 = dfjoin1.withColumn("imdb_rating_double",
dfLocations["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("character_count_double",
dfaux1["character_count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(
dfaux2.imdb_rating_double,
dfaux2.character_count_double).alias('Pearson-Imdb-NumCharacters'))
dfcof.show()
#C y D) numero de personajes masculinos y femeninos que aparecen en cada episodio
dfc = df1.select('id', 'gender').selectExpr("id as character_id", "gender")
dfe = df4.select('character_id', 'episode_id')
dfjoin2 = dfc.join(dfe, on="character_id",
how='outer').select("episode_id", "character_id",
"gender")
dfCharacMasc = dfjoin2.filter(
dfjoin2.gender == 'm').groupBy('episode_id').agg(
functions.approx_count_distinct(
dfjoin2.character_id).alias("masc_count")).selectExpr(
"episode_id as id", "masc_count")
dfCharacFem = dfjoin2.filter(
dfjoin2.gender == 'f').groupBy('episode_id').agg(
functions.approx_count_distinct(
dfjoin2.character_id).alias("fem_count")).selectExpr(
"episode_id as id", "fem_count")
dfjoin3 = dfjoin1.join(dfCharacMasc, on="id",
how='outer').select("id", "imdb_rating",
"character_count", "masc_count")
dfjoin4 = dfjoin3.join(dfCharacFem, on="id",
how='outer').select("id", "imdb_rating",
"character_count", "masc_count",
"fem_count")
dfaux1 = dfjoin4.withColumn("imdb_rating_double",
dfjoin4["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("masc_count_double",
dfaux1["masc_count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(
dfaux2.imdb_rating_double,
dfaux2.masc_count_double).alias('Pearson-Imdb-NumCharactersMasc'))
dfcof.show()
dfaux1 = dfjoin4.withColumn("imdb_rating_double",
dfjoin4["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("fem_count_double",
dfaux1["fem_count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(
dfaux2.imdb_rating_double,
dfaux2.fem_count_double).alias('Pearson-Imdb-NumCharactersFem'))
dfcof.show()
#E) numero de palabras
dfe = df4.groupBy('episode_id').agg(
functions.sum(df4.word_count).alias("word_count")).selectExpr(
"episode_id as id", "word_count")
dfjoin1 = df2.join(dfe, on="id",
how='outer').select("id", "imdb_rating", "word_count")
dfaux1 = dfjoin1.withColumn("imdb_rating_double",
dfjoin1["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("word_count_double",
dfaux1["word_count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(
dfaux2.imdb_rating_double,
dfaux2.word_count_double).alias('Pearson-Imdb-NumWords'))
dfcof.show()
#F) cantidad total de diálogos
dfe2 = df4.filter(df4.speaking_line == True).groupBy('episode_id').agg(
functions.count(df4.raw_text).alias("raw_text_count")).selectExpr(
"episode_id as id", "raw_text_count")
dfjoin2 = dfjoin1.join(dfe2, on="id",
how='outer').select("id", "imdb_rating",
"word_count", "raw_text_count")
dfaux1 = dfjoin2.withColumn("imdb_rating_double",
dfjoin1["imdb_rating"].cast(DoubleType()))
dfaux2 = dfaux1.withColumn("raw_text_count_double",
dfaux1["raw_text_count"].cast(DoubleType()))
dfcof = dfaux2.agg(
functions.corr(
dfaux2.imdb_rating_double,
dfaux2.raw_text_count_double).alias('Pearson-Imdb-NumRawText'))
dfcof.show()
sc.stop()
# COMMAND ----------
from pyspark.sql.functions import count
df.select(count("StockCode")).show() # 541909
# COMMAND ----------
from pyspark.sql.functions import countDistinct
df.select(countDistinct("StockCode")).show() # 4070
# COMMAND ----------
from pyspark.sql.functions import approx_count_distinct
df.select(approx_count_distinct("StockCode", 0.1)).show() # 3364
# COMMAND ----------
from pyspark.sql.functions import first, last
df.select(first("StockCode"), last("StockCode")).show()
# COMMAND ----------
from pyspark.sql.functions import min, max
df.select(min("Quantity"), max("Quantity")).show()
# COMMAND ----------
