/
dataframe_stat_functions.py
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/
dataframe_stat_functions.py
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#
# Copyright (c) 2012-2023 Snowflake Computing Inc. All rights reserved.
#
import sys
from functools import reduce
from typing import Dict, List, Optional, Union
import snowflake.snowpark
from snowflake.snowpark import Column
from snowflake.snowpark._internal.error_message import SnowparkClientExceptionMessages
from snowflake.snowpark._internal.telemetry import adjust_api_subcalls
from snowflake.snowpark._internal.type_utils import ColumnOrName, LiteralType
from snowflake.snowpark.functions import (
_to_col_if_str,
approx_percentile_accumulate,
approx_percentile_estimate,
corr as corr_func,
count,
count_distinct,
covar_samp,
)
# Python 3.8 needs to use typing.Iterable because collections.abc.Iterable is not subscriptable
# Python 3.9 can use both
# Python 3.10 needs to use collections.abc.Iterable because typing.Iterable is removed
if sys.version_info <= (3, 9):
from typing import Iterable
else:
from collections.abc import Iterable
_MAX_COLUMNS_PER_TABLE = 1000
class DataFrameStatFunctions:
"""Provides computed statistical functions for DataFrames.
To access an object of this class, use :attr:`DataFrame.stat`.
"""
def __init__(self, df: "snowflake.snowpark.DataFrame") -> None:
self._df = df
def approx_quantile(
self,
col: Union[ColumnOrName, Iterable[ColumnOrName]],
percentile: Iterable[float],
*,
statement_params: Optional[Dict[str, str]] = None,
) -> Union[List[float], List[List[float]]]:
"""For a specified numeric column and a list of desired quantiles, returns an approximate value for the column at each of the desired quantiles.
This function uses the t-Digest algorithm.
Examples::
>>> df = session.create_dataframe([1, 2, 3, 4, 5, 6, 7, 8, 9, 0], schema=["a"])
>>> df.stat.approx_quantile("a", [0, 0.1, 0.4, 0.6, 1]) # doctest: +SKIP
>>> df2 = session.create_dataframe([[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]], schema=["a", "b"])
>>> df2.stat.approx_quantile(["a", "b"], [0, 0.1, 0.6]) # doctest: +SKIP
Args:
col: The name of the numeric column.
percentile: A list of float values greater than or equal to 0.0 and less than 1.0.
statement_params: Dictionary of statement level parameters to be set while executing this action.
Returns:
A list of approximate percentile values if ``col`` is a single column name, or a matrix
with the dimensions ``(len(col) * len(percentile)`` containing the
approximate percentile values if ``col`` is a list of column names.
"""
temp_col_name = "t"
if not percentile or not col:
return []
if isinstance(col, (Column, str)):
df = self._df.select(
approx_percentile_accumulate(col).as_(temp_col_name)
).select([approx_percentile_estimate(temp_col_name, p) for p in percentile])
adjust_api_subcalls(
df, "DataFrameStatFunctions.approx_quantile", len_subcalls=2
)
res = df._internal_collect_with_tag(statement_params=statement_params)
return list(res[0])
elif isinstance(col, (list, tuple)):
accumate_cols = [
approx_percentile_accumulate(col_i).as_(f"{temp_col_name}_{i}")
for i, col_i in enumerate(col)
]
output_cols = [
approx_percentile_estimate(f"{temp_col_name}_{i}", p)
for i in range(len(accumate_cols))
for p in percentile
]
percentile_len = len(output_cols) // len(accumate_cols)
df = self._df.select(accumate_cols).select(output_cols)
adjust_api_subcalls(
df, "DataFrameStatFunctions.approx_quantile", len_subcalls=2
)
res = df._internal_collect_with_tag(statement_params=statement_params)
return [
[x for x in res[0][j * percentile_len : (j + 1) * percentile_len]]
for j in range(len(accumate_cols))
]
else:
raise TypeError( # pragma: no cover
"'col' must be a column name, a column object, or a list of them."
)
def corr(
self,
col1: ColumnOrName,
col2: ColumnOrName,
*,
statement_params: Optional[Dict[str, str]] = None,
) -> Optional[float]:
"""Calculates the correlation coefficient for non-null pairs in two numeric columns.
Example::
>>> df = session.create_dataframe([[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]], schema=["a", "b"])
>>> df.stat.corr("a", "b")
0.9999999999999991
Args:
col1: The name of the first numeric column to use.
col2: The name of the second numeric column to use.
statement_params: Dictionary of statement level parameters to be set while executing this action.
Return:
The correlation of the two numeric columns.
If there is not enough data to generate the correlation, the method returns ``None``.
statement_params: Dictionary of statement level parameters to be set while executing this action.
"""
df = self._df.select(corr_func(col1, col2))
adjust_api_subcalls(df, "DataFrameStatFunctions.corr", len_subcalls=1)
res = df._internal_collect_with_tag(statement_params=statement_params)
return res[0][0] if res[0] is not None else None
def cov(
self,
col1: ColumnOrName,
col2: ColumnOrName,
*,
statement_params: Optional[Dict[str, str]] = None,
) -> Optional[float]:
"""Calculates the sample covariance for non-null pairs in two numeric columns.
Example::
>>> df = session.create_dataframe([[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]], schema=["a", "b"])
>>> df.stat.cov("a", "b")
0.010000000000000037
Args:
col1: The name of the first numeric column to use.
col2: The name of the second numeric column to use.
statement_params: Dictionary of statement level parameters to be set while executing this action.
Return:
The sample covariance of the two numeric columns.
If there is not enough data to generate the covariance, the method returns None.
"""
df = self._df.select(covar_samp(col1, col2))
adjust_api_subcalls(df, "DataFrameStatFunctions.corr", len_subcalls=1)
res = df._internal_collect_with_tag(statement_params=statement_params)
return res[0][0] if res[0] is not None else None
def crosstab(
self,
col1: ColumnOrName,
col2: ColumnOrName,
*,
statement_params: Optional[Dict[str, str]] = None,
) -> "snowflake.snowpark.DataFrame":
"""Computes a pair-wise frequency table (a ``contingency table``) for the specified columns.
The method returns a DataFrame containing this table.
In the returned contingency table:
- The first column of each row contains the distinct values of ``col1``.
- The name of the first column is the name of ``col1``.
- The rest of the column names are the distinct values of ``col2``.
- For pairs that have no occurrences, the contingency table contains 0 as the count.
Note:
The number of distinct values in ``col2`` should not exceed 1000.
Example::
>>> df = session.create_dataframe([(1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)], schema=["key", "value"])
>>> ct = df.stat.crosstab("key", "value").sort(df["key"])
>>> ct.show()
---------------------------------------------------------------------------------------------
|"KEY" |"CAST(1 AS NUMBER(38,0))" |"CAST(2 AS NUMBER(38,0))" |"CAST(3 AS NUMBER(38,0))" |
---------------------------------------------------------------------------------------------
|1 |1 |1 |0 |
|2 |2 |0 |1 |
|3 |0 |1 |1 |
---------------------------------------------------------------------------------------------
<BLANKLINE>
Args:
col1: The name of the first column to use.
col2: The name of the second column to use.
statement_params: Dictionary of statement level parameters to be set while executing this action.
"""
row_count = self._df.select(count_distinct(col2))._internal_collect_with_tag(
statement_params=statement_params
)[0][0]
if row_count > _MAX_COLUMNS_PER_TABLE:
raise SnowparkClientExceptionMessages.DF_CROSS_TAB_COUNT_TOO_LARGE(
row_count, _MAX_COLUMNS_PER_TABLE
)
column_names = [
row[0]
for row in self._df.select(col2)
.distinct()
._internal_collect_with_tag(statement_params=statement_params)
]
df = self._df.select(col1, col2).pivot(col2, column_names).agg(count(col2))
adjust_api_subcalls(df, "DataFrameStatFunctions.crosstab", len_subcalls=3)
return df
def sample_by(
self, col: ColumnOrName, fractions: Dict[LiteralType, float]
) -> "snowflake.snowpark.DataFrame":
"""Returns a DataFrame containing a stratified sample without replacement, based on a ``dict`` that specifies the fraction for each stratum.
Example::
>>> df = session.create_dataframe([("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 12)], schema=["name", "age"])
>>> fractions = {"Bob": 0.5, "Nico": 1.0}
>>> sample_df = df.stat.sample_by("name", fractions) # non-deterministic result
Args:
col: The name of the column that defines the strata.
fractions: A ``dict`` that specifies the fraction to use for the sample for each stratum.
If a stratum is not specified in the ``dict``, the method uses 0 as the fraction.
"""
if not fractions:
res_df = self._df.limit(0)
adjust_api_subcalls(
res_df, "DataFrameStatFunctions.sample_by", len_subcalls=1
)
return res_df
col = _to_col_if_str(col, "sample_by")
res_df = reduce(
lambda x, y: x.union_all(y),
[self._df.filter(col == k).sample(v) for k, v in fractions.items()],
)
adjust_api_subcalls(
res_df,
"DataFrameStatFunctions.sample_by",
precalls=self._df._plan.api_calls,
subcalls=res_df._plan.api_calls.copy(),
)
return res_df
approxQuantile = approx_quantile
sampleBy = sample_by