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Numeric updates (#1103)
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* update profiler utils

* finish updates

* finish int updates

* update float precision

* finish float col profile updates

* update text_col_profile

* update float col profiler completely

* finish int col tests

* update text profiler tests

* fully finished

* fix pandas df in update
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@atl1502
atl1502 committed Apr 15, 2024
1 parent 61b8737 commit 23443d6
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Showing 8 changed files with 500 additions and 500 deletions.
66 changes: 36 additions & 30 deletions dataprofiler/profilers/float_column_profile.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,10 @@
from __future__ import annotations

import copy
import re

import numpy as np
import pandas as pd
import polars as pl

from . import profiler_utils
from .base_column_profilers import BaseColumnPrimitiveTypeProfiler, BaseColumnProfiler
Expand Down Expand Up @@ -241,7 +241,6 @@ def precision(self) -> dict[str, float | None]:
var = self._correct_bias_variance(
self._precision["sample_size"], self._precision["biased_var"]
)

std = np.sqrt(var)
margin_of_error = (
None
Expand Down Expand Up @@ -275,14 +274,14 @@ def data_type_ratio(self) -> float | None:

@classmethod
def _get_float_precision(
cls, df_series_clean: pd.Series, sample_ratio: float = None
cls, df_series_clean: pl.Series, sample_ratio: float = None
) -> dict | None:
"""
Determine the precision of the numeric value.
:param df_series_clean: df series with nulls removed, assumes all values
are floats as well
:type df_series_clean: pandas.core.series.Series
:type df_series_clean: polars.series.series.Series
:param sample_ratio: Ratio of samples used for float precision
:type sample_ratio: float (between 0 and 1)
:return: string representing its precision print format
Expand All @@ -294,7 +293,7 @@ def _get_float_precision(

# Lead zeros: ^[+-.0\s]+ End zeros: \.?0+(\s|$)
# Scientific Notation: (?<=[e])(.*) Any non-digits: \D
r = re.compile(r"^[+-.0\s]+|\.?0+(\s|$)|(?<=[e])(.*)|\D")
r = r"^[+-.0\s]+|\.?0+(\s|$)|([e].*)|\D"

# DEFAULT: Sample the dataset. If small use full dataset,
# OR 20k samples or 5% of the dataset which ever is larger.
Expand All @@ -305,15 +304,17 @@ def _get_float_precision(

# length of sampled cells after all punctuation removed
len_per_float = (
df_series_clean.sample(sample_size).replace(to_replace=r, value="").map(len)
).astype(float)
df_series_clean.sample(sample_size)
.str.replace_all(pattern=r, value="")
.map_elements(len)
)

# Determine statistics precision
precision_sum = len_per_float.sum()
precision_sum = sum(len_per_float)
subset_precision = {
"min": np.float64(len_per_float.min()),
"max": np.float64(len_per_float.max()),
"biased_var": np.var(len_per_float),
"min": np.float64(min(len_per_float)),
"max": np.float64(max(len_per_float)),
"biased_var": np.var([len_per_float]),
"sum": np.float64(precision_sum),
"mean": np.float64(precision_sum / sample_size),
"sample_size": sample_size,
Expand All @@ -322,7 +323,7 @@ def _get_float_precision(
return subset_precision

@classmethod
def _is_each_row_float(cls, df_series: pd.Series) -> list[bool] | pd.Series[bool]:
def _is_each_row_float(cls, df_series: pl.Series) -> pl.Series:
"""
Determine if each value in a dataframe is a float.
Expand All @@ -332,18 +333,22 @@ def _is_each_row_float(cls, df_series: pd.Series) -> list[bool] | pd.Series[bool
For column [1.0, np.NaN, 1.0] returns [True, True, True]
For column [1.0, "a", "b"] returns [True, False, False]
:param df_series: series of values to evaluate
:type df_series: pandas.core.series.Series
:type df_series: polars.series.series.Series
:return: is_float_col
:rtype: Union[List[bool], pandas.Series[bool]]
:rtype: pl.Series
"""
if len(df_series) == 0:
return list()
return df_series.map(NumericStatsMixin.is_float).astype("bool")
return pl.Series()
if sum(df_series.is_null()) == len(df_series):
return df_series
df_series = df_series.map_elements(NumericStatsMixin.is_float)
df_series = df_series.cast(bool)
return df_series

@BaseColumnProfiler._timeit(name="precision")
def _update_precision(
self,
df_series: pd.DataFrame,
df_series: pl.Series,
prev_dependent_properties: dict,
subset_properties: dict,
) -> None:
Expand All @@ -357,7 +362,7 @@ def _update_precision(
subset before they are merged into the main data profile.
:type subset_properties: dict
:param df_series: Data to be profiled
:type df_series: pandas.DataFrame
:type df_series: polars.DataFrame
:return: None
"""
sample_ratio = None
Expand Down Expand Up @@ -394,12 +399,12 @@ def _update_precision(
self._precision["sum"] / self._precision["sample_size"]
)

def _update_helper(self, df_series_clean: pd.Series, profile: dict) -> None:
def _update_helper(self, df_series_clean: pl.Series, profile: dict) -> None:
"""
Update column profile properties with cleaned dataset and its known profile.
:param df_series_clean: df series with nulls removed
:type df_series_clean: pandas.core.series.Series
:type df_series_clean: polars.series.series.Series
:param profile: float profile dictionary
:type profile: dict
:return: None
Expand All @@ -410,7 +415,7 @@ def _update_helper(self, df_series_clean: pd.Series, profile: dict) -> None:

def _update_numeric_stats(
self,
df_series: pd.DataFrame,
df_series: pl.Series,
prev_dependent_properties: dict,
subset_properties: dict,
) -> None:
Expand All @@ -425,38 +430,39 @@ def _update_numeric_stats(
subset before they are merged into the main data profile.
:type subset_properties: Dict
:param df_series: Data to be profiled
:type df_series: Pandas Dataframe
:type df_series: Polars Dataframe
:return: None
"""
super()._update_helper(df_series, subset_properties)

def update(self, df_series: pd.Series) -> FloatColumn:
def update(self, df_series: pl.Series) -> FloatColumn:
"""
Update the column profile.
:param df_series: df series
:type df_series: pandas.core.series.Series
:type df_series: polars.series.series.Series
:return: updated FloatColumn
:rtype: FloatColumn
"""
# TODO remove onces profiler builder is updated
if type(df_series) == pd.Series:
df_series = pl.from_pandas(df_series) # type: ignore
if len(df_series) == 0:
return self

is_each_row_float = self._is_each_row_float(df_series)
is_each_row_float = self._is_each_row_float(df_series).replace(None, False)
sample_size = len(is_each_row_float)
float_count = np.sum(is_each_row_float)
float_count = np.sum([is_each_row_float])
profile = dict(match_count=float_count, sample_size=sample_size)

BaseColumnProfiler._perform_property_calcs(
self,
self.__calculations,
df_series=df_series[is_each_row_float],
df_series=df_series.filter(is_each_row_float),
prev_dependent_properties={},
subset_properties=profile,
)

self._update_helper(
df_series_clean=df_series[is_each_row_float], profile=profile
df_series_clean=df_series.filter(is_each_row_float), profile=profile
)

return self
26 changes: 16 additions & 10 deletions dataprofiler/profilers/int_column_profile.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@

import numpy as np
import pandas as pd
import polars as pl

from .base_column_profilers import BaseColumnPrimitiveTypeProfiler, BaseColumnProfiler
from .numerical_column_stats import NumericStatsMixin
Expand Down Expand Up @@ -113,7 +114,7 @@ def data_type_ratio(self) -> float | None:
return None

@classmethod
def _is_each_row_int(cls, df_series: pd.Series) -> list[bool]:
def _is_each_row_int(cls, df_series: pl.Series) -> list[bool]:
"""
Return true if given is numerical and int values.
Expand All @@ -124,7 +125,7 @@ def _is_each_row_int(cls, df_series: pd.Series) -> list[bool]:
For column [1.1 1.1 1.1] returns False
:param df_series: series of values to evaluate
:type df_series: pandas.core.series.Series
:type df_series: polars.series.series.Series
:return: is_int_col
:rtype: list
"""
Expand All @@ -134,12 +135,12 @@ def _is_each_row_int(cls, df_series: pd.Series) -> list[bool]:

return [NumericStatsMixin.is_int(x) for x in df_series]

def _update_helper(self, df_series_clean: pd.Series, profile: dict) -> None:
def _update_helper(self, df_series_clean: pl.Series, profile: dict) -> None:
"""
Update col profile properties with clean dataset and its known null params.
:param df_series_clean: df series with nulls removed
:type df_series_clean: pandas.core.series.Series
:type df_series_clean: polars.series.series.Series
:param profile: int profile dictionary
:type profile: dict
:return: None
Expand All @@ -148,32 +149,37 @@ def _update_helper(self, df_series_clean: pd.Series, profile: dict) -> None:
NumericStatsMixin._update_helper(self, df_series_clean, profile)
self._update_column_base_properties(profile)

def update(self, df_series: pd.Series) -> IntColumn:
def update(self, df_series: pl.Series) -> IntColumn:
"""
Update the column profile.
:param df_series: df series
:type df_series: pandas.core.series.Series
:type df_series: polars.series.series.Series
:return: updated IntColumn
:rtype: IntColumn
"""
# TODO remove onces profiler builder is updated
if type(df_series) == pd.Series:
df_series = pl.from_pandas(df_series) # type: ignore
self._greater_than_64_bit = df_series.dtype == pl.Object
if len(df_series) == 0:
return self

df_series = df_series.reset_index(drop=True)
is_each_row_int = self._is_each_row_int(df_series)
sample_size = len(is_each_row_int)
match_int_count = np.sum(is_each_row_int)
match_int_count = np.sum([is_each_row_int])
profile = dict(match_count=match_int_count, sample_size=sample_size)

BaseColumnProfiler._perform_property_calcs(
self,
self.__calculations,
df_series=df_series[is_each_row_int],
df_series=df_series.filter(is_each_row_int),
prev_dependent_properties={},
subset_properties=profile,
)

self._update_helper(df_series_clean=df_series[is_each_row_int], profile=profile)
self._update_helper(
df_series_clean=df_series.filter(is_each_row_int), profile=profile
)

return self
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