A quick reference guide to the most commonly used patterns and functions in PySpark SQL.
- Common Patterns
- String Operations
- Number Operations
- Date & Timestamp Operations
- Array Operations
- Aggregation Operations
- Advanced Operations
If you can't find what you're looking for, check out the PySpark Official Documentation and add it here!
# Easily reference these as F.my_function() and T.my_type() below
from pyspark.sql import functions as F, types as T# Filter on equals condition
df = df.filter(df.is_adult == 'Y')
# Filter on >, <, >=, <= condition
df = df.filter(df.age > 25)
# Multiple conditions require parentheses around each condition
df = df.filter((df.age > 25) & (df.is_adult == 'Y'))
# Compare against a list of allowed values
df = df.filter(col('first_name').isin([3, 4, 7]))
# Sort results
df = df.orderBy(df.age.asc()))
df = df.orderBy(df.age.desc()))# Left join in another dataset
df = df.join(person_lookup_table, 'person_id', 'left')
# Match on different columns in left & right datasets
df = df.join(other_table, df.id == other_table.person_id, 'left')
# Match on multiple columns
df = df.join(other_table, ['first_name', 'last_name'], 'left')
# Useful for one-liner lookup code joins if you have a bunch
def lookup_and_replace(df1, df2, df1_key, df2_key, df2_value):
return (
df1
.join(df2[df2_key, df2_value], df1[df1_key] == df2[df2_key], 'left')
.withColumn(df1_key, F.coalesce(F.col(df2_value), F.col(df1_key)))
.drop(df2_key)
.drop(df2_value)
)
df = lookup_and_replace(people, pay_codes, id, pay_code_id, pay_code_desc)# Add a new static column
df = df.withColumn('status', F.lit('PASS'))
# Construct a new dynamic column
df = df.withColumn('full_name', F.when(
(df.fname.isNotNull() & df.lname.isNotNull()), F.concat(df.fname, df.lname)
).otherwise(F.lit('N/A'))
# Pick which columns to keep, optionally rename some
df = df.select(
'name',
'age',
F.col('dob').alias('date_of_birth'),
)
# Remove columns
df = df.drop('mod_dt', 'mod_username')
# Rename a column
df = df.withColumnRenamed('dob', 'date_of_birth')
# Keep all the columns which also occur in another dataset
df = df.select(*(F.col(c) for c in df2.columns))
# Batch Rename/Clean Columns
for col in df.columns:
df = df.withColumnRenamed(col, col.lower().replace(' ', '_').replace('-', '_'))# Cast a column to a different type
df = df.withColumn('price', df.price.cast(T.DoubleType()))
# Replace all nulls with a specific value
df = df.fillna({
'first_name': 'Tom',
'age': 0,
})
# Take the first value that is not null
df = df.withColumn('last_name', F.coalesce(df.last_name, df.surname, F.lit('N/A')))
# Drop duplicate rows in a dataset (distinct)
df = df.dropDuplicates()
# Drop duplicate rows, but consider only specific columns
df = df.dropDuplicates(['name', 'height'])
# Replace empty strings with null (leave out subset keyword arg to replace in all columns)
df = df.replace({"": None}, subset=["name"])
# Convert Python/PySpark/NumPy NaN operator to null
df = df.replace(float("nan"), None)# Contains - col.contains(string)
df = df.filter(df.name.contains('o'))
# Starts With - col.startswith(string)
df = df.filter(df.name.startswith('Al'))
# Ends With - col.endswith(string)
df = df.filter(df.name.endswith('ice'))
# Is Null - col.isNull()
df = df.filter(df.is_adult.isNull())
# Is Not Null - col.isNotNull()
df = df.filter(df.first_name.isNotNull())
# Like - col.like(string_with_sql_wildcards)
df = df.filter(df.name.like('Al%'))
# Regex Like - col.rlike(regex)
df = df.filter(df.name.rlike('[A-Z]*ice$'))
# Is In List - col.isin(*cols)
df = df.filter(df.name.isin('Bob', 'Mike'))# Substring - col.substr(startPos, length)
df = df.withColumn('short_id', df.id.substr(0, 10))
# Trim - F.trim(col)
df = df.withColumn('name', F.trim(df.name))
# Left Pad - F.lpad(col, len, pad)
# Right Pad - F.rpad(col, len, pad)
df = df.withColumn('id', F.lpad('id', 4, '0'))
# Left Trim - F.ltrim(col)
# Right Trim - F.rtrim(col)
df = df.withColumn('id', F.ltrim('id'))
# Concatenate - F.concat(*cols)
df = df.withColumn('full_name', F.concat('fname', F.lit(' '), 'lname'))
# Concatenate with Separator/Delimiter - F.concat_ws(delimiter, *cols)
df = df.withColumn('full_name', F.concat_ws('-', 'fname', 'lname'))
# Regex Replace - F.regexp_replace(str, pattern, replacement)[source]
df = df.withColumn('id', F.regexp_replace(id, '0F1(.*)', '1F1-$1'))
# Regex Extract - F.regexp_extract(str, pattern, idx)
df = df.withColumn('id', F.regexp_extract(id, '[0-9]*', 0))# Round - F.round(col, scale=0)
df = df.withColumn('price', F.round('price', 0))
# Floor - F.floor(col)
df = df.withColumn('price', F.floor('price'))
# Ceiling - F.ceil(col)
df = df.withColumn('price', F.ceil('price'))
# Absolute Value - F.abs(col)
df = df.withColumn('price', F.abs('price'))
# X raised to power Y – F.pow(x, y)
df = df.withColumn('exponential_growth', F.pow('x', 'y'))
# Select smallest value out of multiple columns – F.least(*cols)
df = df.withColumn('least', F.least('subtotal', 'total'))
# Select largest value out of multiple columns – F.greatest(*cols)
df = df.withColumn('greatest', F.greatest('subtotal', 'total'))# Convert a string of known format to a date (excludes time information)
df = df.withColumn('date_of_birth', F.to_date('date_of_birth', 'yyyy-MM-dd'))
# Convert a string of known format to a timestamp (includes time information)
df = df.withColumn('time_of_birth', F.to_timestamp('time_of_birth', 'yyyy-MM-dd HH:mm:ss'))
# Get year from date: F.year(col)
# Get month from date: F.month(col)
# Get day from date: F.dayofmonth(col)
# Get hour from date: F.hour(col)
# Get minute from date: F.minute(col)
# Get second from date: F.second(col)
df = df.filter(F.year('date_of_birth') == F.lit('2017'))
# Add & subtract days
df = df.withColumn('three_days_after', F.date_add('date_of_birth', 3))
df = df.withColumn('three_days_before', F.date_sub('date_of_birth', 3))
# Add & Subtract months
df = df.withColumn('next_month', F.add_month('date_of_birth', 1))
# Get number of days between two dates
df = df.withColumn('days_between', F.datediff('start', 'end'))
# Get number of months between two dates
df = df.withColumn('months_between', F.months_between('start', 'end'))
# Keep only rows where date_of_birth is between 2017-05-10 and 2018-07-21
df = df.filter(
(F.col('date_of_birth') >= F.lit('2017-05-10')) &
(F.col('date_of_birth') <= F.lit('2018-07-21'))
)# Column Array - F.array(*cols)
df = df.withColumn('full_name', F.array('fname', 'lname'))
# Empty Array - F.array(*cols)
df = df.withColumn('empty_array_column', F.array([]))
# Array Size/Length – F.size(col)
df = df.withColumn('array_length', F.size('my_array'))
# Flatten Array – F.flatten(col)
df = df.withColumn('flattened', F.flatten('my_array'))
# Unique/Distinct Elements – F.array_distinct(col)
df = df.withColumn('unique_elements', F.array_distinct('my_array'))# Row Count: F.count()
# Sum of Rows in Group: F.sum(*cols)
# Mean of Rows in Group: F.mean(*cols)
# Max of Rows in Group: F.max(*cols)
# Min of Rows in Group: F.min(*cols)
# First Row in Group: F.alias(*cols)
df = df.groupBy('gender').agg(F.max('age').alias('max_age_by_gender'))
# Collect a Set of all Rows in Group: F.collect_set(col)
# Collect a List of all Rows in Group: F.collect_list(col)
df = df.groupBy('age').agg(F.collect_set('name').alias('person_names'))
# Just take the lastest row for each combination (Window Functions)
from pyspark.sql import Window as W
window = W.partitionBy("first_name", "last_name").orderBy(F.desc("date"))
df = df.withColumn("row_number", F.row_number().over(window))
df = df.filter(F.col("row_number") == 1)
df = df.drop("row_number")# Repartition – df.repartition(num_output_partitions)
df = df.repartition(1)# Multiply each row's age column by two
times_two_udf = F.udf(lambda x: x * 2)
df = df.withColumn('age', times_two_udf(df.age))
# Randomly choose a value to use as a row's name
import random
random_name_udf = F.udf(lambda: random.choice(['Bob', 'Tom', 'Amy', 'Jenna']))
df = df.withColumn('name', random_name_udf())