regr_slope(y, x)

Описание

Функция regr_slope() вычисляет наклон линии регрессии для пары числовых столбцов. Это агрегатная функция, которая должна использоваться с groupBy().

Параметры

• y: Column - зависимая переменная (ордината)
• x: Column - независимая переменная (абсцисса)

Возвращаемое значение

Double - наклон линии регрессии

Пример использования

from pyspark.sql.functions import regr_slope
from pyspark.sql import SparkSession

# Создаем SparkSession
spark = SparkSession.builder.appName("regr_slope_example").getOrCreate()

# Создаем DataFrame
data = [
    ("A", 1, 2),
    ("A", 2, 4),
    ("A", 3, 6),
    ("B", 1, 3),
    ("B", 2, 5),
    ("B", 3, 7)
]
df = spark.createDataFrame(data, ["group", "x", "y"])

# Вычисляем наклон линии регрессии для каждой группы
result = df.groupBy("group").agg(
    regr_slope("y", "x").alias("slope")
).show()

# Результат:
# +-----+-----+
# |group|slope|
# +-----+-----+
# |A    |2.0  |
# |B    |2.0  |
# +-----+-----+

Примечания

• NULL значения в любом из столбцов игнорируются
• Для вычисления коэффициента корреляции используйте corr()
• Для вычисления точки пересечения линии регрессии используйте regr_intercept()
• Для вычисления среднего значения используйте avg()

XML функции

• from_xml(col, schema)
• schema_of_xml(xml)
• to_xml(col)

aggregate functions

• any_value(col[, ignoreNulls])
• approxCountDistinct(col[, rsd])
• approx_percentile(col, percentage[, accuracy])
• array_agg(col)
• avg(col)
• bit_and(col)
• bit_or(col)
• bit_xor(col)
• bool_and(col)
• bool_or(col)
• bool_xor(col)
• collect_list(col)
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• corr(col1, col2)
• count(col)
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• count_min_sketch(col, eps, confidence, seed)
• covar_pop(col1, col2)
• covar_samp(col1, col2)
• every(col)
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• grouping(col)
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• histogram_numeric(col, nBins)
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• kurtosis(col)
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• max(col)
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• min_by(col, ord)
• mode(col)
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• percentile_approx(col, percentage, accuracy=10000)
• product(col)
• regr_avgx(y, x)
• regr_avgy(y, x)
• regr_count()
• regr_intercept(y, x)
• regr_r2()
• regr_syy()
• skewness(col)
• some(col)
• stddev(col)
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• stddev_samp(col)
• sum(col)
• sum_distinct(col)
• var_pop(col)
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• variance(col)

array functions pyspark

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• try_element_at()
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basic functions

• col(colName)
• column(colName)
• lit(value)

bitmap functions

• bitmap_construct_agg(col)
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• bitmap_or_agg(bitmap)
• bitwise_not(col)
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• bitwise_xor(col1, col2)

bitwise functions

• bit_count()
• bit_get()
• bit_set(column, pos)
• bitwiseAND(column1, column2)
• bitwiseNOT(column)
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• shiftleft(column, numBits)
• shiftright(column, numBits)
• shiftrightunsigned(column, numBits)

call functions

• call_function()
• call_udf()
• pandas_udf()
• udf()
• udtf()
• unwrap_udt()

comparison functions

• least(expr1, expr2, ...)

conditional functions

• when(condition, value)

conversion functions

• to_binary(expr, format)
• to_char(expr, format)
• to_number(expr, format)

csv functions

• from_csv(col, schema[, options])
• schema_of_csv(csv[, options])
• to_csv(col[, options])

datetime functions pyspark

• add_months(start_date, num_months)
• curdate()
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• current_time()
• current_timezone()
• date_add()
• date_diff(endDate, startDate)
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• extract(field FROM source)
• from_utc_timestamp(timestamp, timezone)
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• make_dt_interval(days, hours, minutes, seconds)
• make_timestamp(year, month, day, hour, min, sec)
• make_timestamp_ltz(year, month, day, hour, minute, second)
• make_ym_interval(years, months)
• minute(timestamp)
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• next_day(date, day_of_week)
• now()
• quarter(date)
• second(timestamp)
• session_window(timeColumn, gapDuration)
• timestamp_micros(microseconds)
• timestamp_millis(milliseconds)
• timestamp_seconds(seconds)
• timestampadd(unit, interval, timestamp)
• to_date(col, format=None)
• to_timestamp(col, format=None)
• to_timestamp_ltz(col, format=None)
• to_timestamp_ntz(col, format=None)
• to_unix_timestamp(col, format=None)
• to_utc_timestamp(timestamp, timezone)
• trunc(date, format)
• try_to_timestamp(col, format=None)
• unix_date(col)
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• weekday(date)
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encryption functions

• try_aes_decrypt(input, key, mode, padding)

expression functions

• expr(expr)

hash functions

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java functions

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json functions

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map functions

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• map_from_entries(array)
• map_keys(map)
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• str_to_map(text[, pairDelim, keyValueDelim])

mathematical functions pyspark

• abs(col)
• asin(col)
• atan(col)
• atan2(y, x)
• bin(col)
• bround(col, scale=0)
• cbrt(col)
• ceil(col)
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• exp()
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• floor(col)
• hypot(col1, col2)
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• pmod(dividend, divisor)
• positive(col)
• power(col1, col2)
• rand(seed=None)
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• rint(col)
• round(col, scale=0)
• sec(col)
• sign(col)
• signum(col)
• tan(col)
• tanh(col)
• toDegrees(col)
• try_add()
• try_avg()
• try_divide()
• try_multiply()
• try_subtract()
• try_sum()
• try_to_number()

normal functions

• greatest(*cols)

null functions

• coalesce(expr1, expr2, ...)
• equal_null(expr1, expr2)
• ifnull(expr1, expr2)
• isnotnull(expr)
• isnull(expr)
• nanvl(col1, col2)
• nullif(expr1, expr2)
• nvl(expr1, expr2)
• nvl2(expr1, expr2, expr3)

sketch functions

• hll_sketch_estimate(sketch)
• hll_union(sketch1, sketch2)

sort functions

• asc(col)
• asc_nulls_first(col)
• asc_nulls_last(col)
• desc(col)
• desc_nulls_first(col)
• desc_nulls_last(col)

string functions pyspark

• char_length(col)
• eval(expr)
• left(col, len)
• levenshtein(col1, col2)
• mask(col, upperChar, lowerChar, digitChar, otherChar)
• regexp(col, pattern)
• regexp_extract_all(col, pattern)
• regexp_instr(col, pattern, pos, occurrence)
• regexp_like(col, pattern)
• regexp_substr(col, pattern, pos, occurrence)
• repeat(col, n)
• replace(col, search, replace)
• right(col, len)
• rlike(col, pattern)
• rpad(col, len, pad)
• rtrim(col)
• sentences(str, lang, country)
• soundex(col)
• split(str, pattern)
• split_part(str, delimiter, partNum)
• sql(query)
• startswith(col, prefix)
• substr(col, pos, len)
• substring_index(col, delim, count)
• to_varchar(col)
• translate(col, matchingString, replaceString)
• trim(col)
• ucase(col)
• unbase64(col)
• unhex(col)
• upper(col)
• url_decode(str)
• url_encode(str)
• uuid()

struct functions

• named_struct(name1, val1, name2, val2, ...)

system functions

• current_catalog()
• current_database()
• current_schema()
• current_user()
• input_file_block_length()
• input_file_block_start()
• input_file_name()
• spark_partition_id()
• user()

type functions

• cast()
• typeof()

window functions

• cume_dist()
• dense_rank()
• lag(col, offset, default)
• lead(col, offset, default)
• monotonically_increasing_id()
• nth_value(col, n)
• ntile(n)
• percent_rank()
• rank()
• row_number()
• window(timeColumn, windowDuration, slideDuration, startTime)

xml functions

• xml_tuple(xml, *paths)
• xpath(xml, path)
• xpath_boolean(xml, path)
• xpath_double(xml, path)
• xpath_float(xml, path)
• xpath_int(xml, path)
• xpath_long(xml, path)
• xpath_number(xml, path)
• xpath_short(xml, path)
• xpath_string(xml, path)

агрегатные функции

• approx_count_distinct(col, rsd)
• std(col)
• sumDistinct(col)

битовые операции

• bitwise_and(col1, col2)

оконные функции

• diff(col)
• session_window(timeColumn, gapDuration)
• sliding_window(timeColumn, windowDuration, slideDuration)
• tumbling_window(timeColumn, windowDuration)

операторы сравнения

• <>(left, right)
• <=>(left, right)
• =(left, right)

предикатные функции

• isnotnan(col)

системные функции

• database()
• session_user()

условные функции

• case_when(condition1, value1, condition2, value2, ..., else_value)
• if(condition, value1, value2)

функции для работы с битовыми картами

• bitmap_bit_position(bitmap, bit)
• bitmap_bucket_number(bitmap)

функции преобразования типов

• to_decimal(col, scale=0)

функции шифрования

• aes_decrypt(col, key)