isaricanalytics.analytics

isaricanalytics.analytics.convert_categorical_to_onehot(data: DataFrame, dictionary: DataFrame, categorical_columns: Iterable[str], sep: str = '___', missing_val: str = 'nan', drop_first: bool = False) → DataFrame

pandas.DataFrame : Returns the given dataframe with categorical variable columns converted to onehot-encoded variable columns.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

categorical_columnstyping.Iterable

An iterable of categorical column names.

sepstr, default=”___”

Field-value separator, defaults to "___"

missing_valstr, default=”nan”

Optional value with which to replace missing values, defaults to "nan".

drop_firstbool, default=False

Optional boolean to indicate how to drop categorical columns [?], defaults to False.

Returns:

pandas.DataFrame

The original dataframe with the categorical -> one-hot-encoded variable columns.

isaricanalytics.analytics.convert_onehot_to_categorical(data: DataFrame, dictionary: DataFrame, categorical_columns: Iterable[str], sep: str = '___', missing_val: str = 'nan') → DataFrame

pandas.DataFrame : Returns the given dataframe with onehot-encoded variable columns to categorical variable columns.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

Data dictionary.

categorical_columnstyping.Iterable

An iterable of categorical column names.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

missing_valstr, default=”nan”

Optional value with which to replace missing values, defaults to "nan".

Returns:

pandas.DataFrame

The original dataframe with the one-hot-encoded -> categorical variable columns.

isaricanalytics.analytics.create_grouped_results(selected_features: Iterable[str], feature_importance: dict[str, float], sep: str = '___') → tuple[DataFrame, Iterable[str], Iterable[str]]

tuple : Creates and returns grouped feature results.

The main dataFrame lists categories under their main fields, with main fields sorted by their maximum coefficient magnitude.

Parameters:

selected_featurestyping.Iterable

An iterable of selected feature names.

feature_importancedict

A dict of feature names and coefficient weights.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

tuple

Feature results.

isaricanalytics.analytics.descriptive_comparison_table(data: DataFrame, dictionary: DataFrame, by_column: str | None = None, include_totals: bool = True, column_reorder: Iterable[str] | None = None, sep: str = '___', pvalue_significance: dict[str, float] = {'*': 0.05, '**': 0.01}) → tuple[DataFrame, str]

tuple : Returns the descriptive comparison table and table key for binary (including onehot-encoded categorical) and numerical variables in data.

The descriptive table will have separate columns for each category that exists for the by_column variable, if this is provided.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

by_columnstr, default=None

Optional. No description available, defaults to None.

include_totalsbool, default=None

Optional. No description available, defaults to None.

column_reordertyping.Iterable, default=None

Optional iterable of names of columns to reorder by, defaults to None.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

pvalue_significancedict, default={“*”: 0.05, “**”: 0.01}

A dict of significance levels, defaults to {"*": 0.05, "**": 0.01}.

Returns:

tuple

Returns the descriptive comparison table and table key for binary and numerical variables in the data.

isaricanalytics.analytics.descriptive_table(data: DataFrame, dictionary: DataFrame, by_column: str | None = None, include_totals: bool = True, column_reorder: Iterable[str] | None = None, include_raw_variable_name: bool = False, sep: str = '___') → tuple[DataFrame, str]

tuple : Returns the descriptive table and table key for binary and numerical variables in the data.

The descriptive table will have separate columns for each category that exists for the by_column variable, if this is provided.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

by_columnstr, default=None

Optional. No description available, defaults to None.

include_totalsbool, default=None

Optional. No description available, defaults to None.

column_reordertyping.Iterable, default=None

Optional iterable of names of columns to reorder by, defaults to None.

include_raw_variable_namebool, default=False

Optional boolean indicating whether to include the raw variable name, defaults to False.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

tuple

Returns the descriptive table and table key for binary and numerical variables in the data.

isaricanalytics.analytics.execute_cox_model(data: DataFrame, duration_col: str, event_col: str, predictors: Iterable[str], labels: dict[str, str] | None = None) → DataFrame

pandas.DataFrame : Executes a Cox Proportional Hazards model without weights and returns a summary of the results.

Parameters:

datapandas.DataFrame

The incoming data.

duration_colstr

Name of the time variable.

event_colstr

Name of the outcome variable (binary event).

predictorstyping.Iterable

Names of predictor variables.

labelsdict, default=None

Dictionary mapping variable names to readable labels, default=``None``.

Returns:

pandas.DataFrame

The model results.

isaricanalytics.analytics.execute_glm_regression(elr_dataframe_df: DataFrame, elr_outcome: str, elr_predictors: Iterable, model_type: str = 'linear', print_results: bool = True, labels: dict[str, str] | None = None, reg_type: str = 'Multi') → DataFrame

pandas.DataFrame : Executes a GLM (Generalized Linear Model) for linear or logistic regression.

Parameters:

elr_dataframe_dfpandas.DataFrame

The incoming data.

elr_outcomestr

Name of the response variable.

elr_predictors: typing.Iterable

Iterable of predictor variable names.

model_typestr, default=”linear”

Optional indicator regression model type - use "linear" for linear regression (Gaussian) or "logistic" for logistic regression (Binomial); defaults to "linear".

print_resultsbool, default=True

Optional indicator of whether to print the results table, defaults to True.

labelsdict, default=None

Optional map of variable names to readable labels, defaults to None.`.

reg_typestr, default=”multi”

Optional regression type - "uni" for univariate, "multi" for multivariate. Defaults to "multi".

Returns:

pandas.DataFrame

The model results.

isaricanalytics.analytics.execute_glmm_regression(elr_dataframe_df: DataFrame, elr_outcome: str, elr_predictors: Iterable[str], elr_groups: str, model_type: str = 'linear', print_results: bool = True, labels: dict[str, str] | None = None, reg_type: str = 'multi') → DataFrame

pandas.DataFrame : Executes a mixed effects model for linear or logistic regression.

Parameters:

elr_dataframe_dfpandas.DataFrame

The incoming data.

elr_outcomestr

Name of the response variable.

elr_predictorstyping.Iterable

Iterable of predictor variable names.

elr_groupsstr

Name of the variable that defines the groups (random effect).

model_typestr, default=”linear”

Optional regression model type - use "linear" for linear regression or "logistic" for logistic regression; defaults to "linear".

print_resultsbool, default=True

Optional indicator of whether to print the results summary, defaults to True.

labelsdict, default=None

Optional map of variable names to readable labels, defaults to None.

reg_typestr, default=”multi”

Optional regression type - "uni" for univariate, "multi" for multivariate. Defaults to "multi".

Returns:

pandas.DataFrame

The model results.

isaricanalytics.analytics.execute_kaplan_meier(data: DataFrame, duration_col: str, event_col: str, group_col: str, alpha=0.05, n_times=5) → tuple[DataFrame, DataFrame, float]

tuple : Executes the Kaplan-Meier model and returns the results.

Parameters:

datapandas.DataFrame

The incoming data.

duration_colstr

Name of the time variable.

event_colstr

Name of the outcome variable (binary event).

group_colstr

Name of the grouping column.

alphafloat, default=0.05

Optional alpha, defaults to \(0.05\).

n_timesint, default=5

Optional. No description available, defaults to \(5\).

Returns:

pandas.DataFrame

A tuple consisting of the model results, risk table and the \(p\)-value.

isaricanalytics.analytics.extend_dictionary(dictionary: DataFrame, new_variable_dict: dict[str, Any], data: DataFrame, sep: str = '___') → DataFrame

pandas.DataFrame : Returns the VERTEX dictionary with new custom variables added.

Parameters:

dictionarypandas.DataFrame

VERTEX dictionary containing columns "field_name", "form_name", "field_type", "field_label", "parent", "branching_logic".

new_variable_dictdict

A dict with the same keys as the dictionary columns, the values for each item can be a string or a list.

datapandas.DataFrame

Pandas dataframe containing the data for the project. The columns of this dataframe must include the variables in new_variable_dict["field_type"].

sepstr

Separator for creating new one-hot-encoded variable names.

Returns:

pandas.DataFrame

VERTEX dictionary containing the original variables, plus the new variables and any one-hot-encoded variables derived from this.

isaricanalytics.analytics.format_descriptive_table_variables(dictionary: DataFrame, max_len: int = 100, add_key: bool = True, sep: str = '___', binary_symbol: str = '*', numeric_symbol: str = '+') → str

str : Returns a formatted string of the descriptive table variable field names.

Parameters:

dictionarypandas.DataFrame

The data dictionary.

max_lenint, default=100

Optional maximum length of field names, defaults to \(100\).

add_keybool, default=True

Optional. No description available, defaults to True.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

binary_symbolstr, default=”*”

Optional. No description available, defaults to "*".

numeric_symbolstr, default=”*”

Optional. No description available, defaults to "+".

Returns:

str

A formatted string of the descriptive table variable field names.

isaricanalytics.analytics.format_pvalue(pvalue: float, dp: int = 3, min_val: float = 0.001, significance: dict[str, float] = {'*': 0.05, '**': 0.01}) → str

str : Returns a formatted \(p\)-value string.

Parameters:

pvaluefloat

The \(p\)-value.

dpint, default=3

Optional. No description available, defaults to \(3\).

min_valfloat, default=0.001

Optional. No description available, defaults to \(0.001\).

significancedict, default={“*”: 0.05, “**”: 0.01}.

Dict of significance levels, defaults to {"*": 0.05, "**": 0.01}.

Returns:

str

The formatted \(p\)-value string.

isaricanalytics.analytics.format_variables(dictionary: DataFrame, max_len: int = 40, sep: str = '___') → str

str : Returns a formatted string of the descriptive table variable field names.

Parameters:

dictionarypandas.DataFrame

The data dictionary.

max_lenint, default=40

Optional maximum length of field names, defaults to \(40\).

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

str

A formatted string of the descriptive table variable field names.

isaricanalytics.analytics.from_timeA_to_timeB(data: DataFrame, dictionary: DataFrame, timeA_column: str, timeB_column: str, timediff_column: str, timediff_label: str, time_unit: str = 'days') → tuple[DataFrame]

tuple : Returns the data and data dictionary updated with time difference calculation between two given data columns.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

timeA_columnstr

The name of the first time column.

timeB_columnstr

The name of the second time column.

timediff_columnstr

The name of the column for storing the time difference of the two given time columns.

timediff_labelstr

A label to attach to the time difference column.

time_unitstr, default=”days”

An optional indicator of which time unit to use for the time difference calculation, defaults to "days".

Returns:

tuple

The data and data dictionary updated with time difference information.

isaricanalytics.analytics.get_chi2_pvalue(x: Series, y: Series, x_cat: Iterable[Any] = [True, False], y_cat: Iterable[Any] = [True, False]) → float

float : Returns the :math`p`-value for a Chi-squared test.

Parameters:

xpandas.Series

The first series/factor.

ypandas.Series

The second series/factor.

x_cattyping.Iterable

An iterable of categories by which to group the first series.

y_cattyping.Iterable

An interable of categories by which to group the second series.

Returns:

float

The \(p\)-value for the test.

isaricanalytics.analytics.get_counts(data: DataFrame, dictionary: DataFrame, max_n_variables: int = 10, sep: str = '___') → DataFrame

pandas.DataFrame : Returns a dataframe of variable column counts.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

max_n_variablesint, default=10

Optional number of variables for which to take counts, defaults to \(10\).

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

The variable column counts dataframe.

isaricanalytics.analytics.get_descriptive_data(data: DataFrame, dictionary: DataFrame, by_column: str | None = None, include_sections: Iterable[str] = ['demog'], include_types: Iterable[str] = ['binary', 'categorical', 'numeric'], exclude_suffix: Iterable[str] = ['_units', 'addi', 'otherl2', 'item', '_oth', '_unlisted', 'otherl3'], include_subjid: bool = False, exclude_negatives: bool = True, sep: str = '___') → DataFrame

pandas.DataFrame : Returns descriptive data.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

by_columnstr, default=None

Optional. No description available, defaults to None.

include_sectionstyping.Iterable, default=[“demog”]

Optional list of names of sections to include, defaults to ["demog"].

include_typestyping.Iterable, default=[“binary”, “categorical”, “numeric”]

Optional iterable of variable type names, defaults to ["binary", "categorical", "numeric"].

exclude_suffixtyping.Iterable, default=[“_units”, “addi”, “otherl2”, “item”, “_oth”, “_unlisted”, “otherl3”]

Optional iterable of suffixes to exclude, defaults to ["_units", "addi", "otherl2", "item", "_oth", "_unlisted", "otherl3"].

include_subjidbool, default=False

Optional boolean to indicate whether to include subject ID, defaults to False.

exclude_negativesbool, default=True

Optional boolean to indicate whether to drop negatives, defaults to True.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

pandas.DataFrame

Returns the descriptive data.

isaricanalytics.analytics.get_fisher_exact_pvalue(x: Series, y: Series, x_cat: Iterable[Any] = [True, False], y_cat: Iterable[Any] = [True, False])

float : Returns the \(p\)-value for a Fisher exact test.

Parameters:

xpandas.Series

The first series/factor.

ypandas.Series

The second series/factor.

x_cattyping.Iterable

An iterable of categories by which to group the first series.

y_cattyping.Iterable

An interable of categories by which to group the second series.

Returns:

float

The \(p\)-value for the test.

isaricanalytics.analytics.get_mean_and_stdev(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 3) → str

str : Returns the mean and standard deviation of a series of values as a formatted string.

Parameters:

seriespandas.Series

The input series for which to calculate the mean and st. dev.

add_spacesbool, default=False

Add spacing in the string.

dpint, default=1

No description available.

mfwint, default=4

No description available.

min_nint, default=3

No description available.

Returns:

str

The mean and st. dev. as a formatted string.

isaricanalytics.analytics.get_median_interquartile_range(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 3) → str

str : Returns the median interquartile range (IQR) of a given series of values as a string.

Parameters:

seriespandas.Series

The input series for which to calculate the IQR.

add_spacesbool, default=False

Add spacing in the string.

dpint, default=1

No description available.

mfwint, default=4

No description available.

min_nint, default=3

No description available.

Returns:

str

The IQR as a string.

isaricanalytics.analytics.get_modelling_data(data: DataFrame, dictionary: DataFrame, outcome_columns: str | Iterable[str], include_sections: Iterable[str] = ['demog', 'comor', 'adsym', 'vacci', 'vital', 'sympt', 'labs'], required_variables: Iterable[str] | None = None, include_types: Iterable[str] = ['binary', 'categorical', 'numeric'], exclude_suffix: Iterable[str] = ['_units', 'addi', 'otherl2', 'item', '_oth', '_unlisted', 'otherl3'], include_subjid: bool = False, exclude_negatives: bool = True, fillna: bool = True, drop_first: bool = False, sep: str = '___') → DataFrame

pandas.DataFrame : Returns modelling data.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

outcome_columnstyping.Iterable

Outcome columns.

include_sectionstyping.Iterable, default=[“demog”, “comor”, “adsym”, “vacci”, “vital”, “sympt”, “labs”]

Optional list of names of sections to include, defaults to ["demog", "comor", "adsym", "vacci", "vital", "sympt", "labs"].

required_variables: typing.Iterable, default=None

Required variable column names, defaults to None.

include_typestyping.Iterable, default=[“binary”, “categorical”, “numeric”]

Optional iterable of variable type names, defaults to ["binary", "categorical", "numeric"].

exclude_suffixtyping.Iterable, default=[“_units”, “addi”, “otherl2”, “item”, “_oth”, “_unlisted”, “otherl3”]

Optional iterable of suffixes to exclude, defaults to ["_units", "addi", "otherl2", "item", "_oth", "_unlisted", "otherl3"].

include_subjidbool, default=False

Optional boolean to indicate whether to include subject ID, defaults to False.

exclude_negativesbool, default=True

Optional boolean to indicate whether to drop negatives, defaults to True.

fillnabool, default=True

Optional boolean to fill nulls, defaults to True.

drop_firstbool, default=False

Optional boolean relating to dropping columns, defaults to False.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

pandas.DataFrame

Returns the modelling data.

isaricanalytics.analytics.get_n_percent_value(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 1) → str

str : Returns the n-percent value of a series as a string.

Parameters:

seriespandas.Series

The input series.

add_spacesbool, default=False

Add spacing around the string.

dpint, default=1

No description available.

mfwint, default=1

No description available.

min_nint, default=1

No description available.

Returns:

str

The n-percent value of the series as a string.

isaricanalytics.analytics.get_parameter_ranking(logistic: Any, n_top: int = 10, threshold: float = 0.001) → DataFrame

:py:class:pd.DataFrame : Returns a dataframe of rankings of parameter combinations using stored scores and coefficient paths.

Parameters:

logistictyping.Any

The logistic model.

n_topint, default=10

Optional number of top ranking features to select, defaults to \(10\).

thresholdfloat, default=1e-3

Optional ranking threshold, defaults to \(0.001\).

Returns:

pandas.DataFrame

The dataframe of parameter rankings.

isaricanalytics.analytics.get_proportions(data: DataFrame, dictionary: DataFrame, max_n_variables: int = 10, ignore_branching_logic: bool = False, branching_logic: str = '', sep: str = '___') → DataFrame

pandas.DataFrame : Returns a dataframe of proportional counts for variable columns.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

max_n_variablesint, default=10

Optional number of variables for which to take counts, defaults to \(10\).

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

pandas.DataFrame

A dataframe of proportional counts for variable columns.

isaricanalytics.analytics.get_pyramid_data(data: DataFrame, column_dict: dict[str, str], left_side: str = 'Female', right_side: str = 'Male') → DataFrame

pandas.DataFrame : Returns dual stack pyramid data.

Parameters:

datapandas.DataFrame

The incoming data.

column_dictdict

Dict of pyramid keys and data column names.

left_sidestr, default=”Female”

Optional label for the left side of the pyramid, defaults to "Female".

right_sidestr, default=”Male”

Optional label for the right side of the pyramid, defaults to "Male".

Returns:

pandas.DataFrame

Dual stack pyramid data.

isaricanalytics.analytics.get_upset_counts_intersections(data: DataFrame, dictionary: DataFrame, variables: list[str] | None = None, n_variables: int = 5, sep: str = '___') → tuple[DataFrame]

pandas.DataFrame : Returns a dataframe of upset counts intersections.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

variableslist, default=None

Optional list of names of variable columns for which to take counts, defaults to None.

n_variablesint, default=5

Optional limit for the number of variable columns, defaults to \(5\).

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

pandas.DataFrame

A dataframe of upset counts intersections.

isaricanalytics.analytics.get_variables_by_section_and_type(data: DataFrame, dictionary: DataFrame, required_variables: Iterable[str] | None = None, include_sections: Iterable[str] = ['demog'], include_types: Iterable[str] = ['binary', 'categorical', 'numeric'], exclude_suffixes: Iterable[str] = ['_units', 'addi', 'otherl2', 'item', '_oth', '_unlisted', 'otherl3'], include_subjid: bool = False) → list[str]

list : Returns a list of all variables in the dataframe from specified sections and types, plus any required variables.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

required_variablestyping.Iterable, default=None

Optional iterable of required variable names, defaults to None.

include_sectionstyping.Iterable, default=[“demog”]

Optional iterable of names of sections to include, defaults to ["demog"].

include_typestyping.Iterable, default=[“binary”, “categorical”, “numeric”]

Optional iterable of variable type names, defaults to ["binary", "categorical", "numeric"].

exclude_suffixestyping.Iterable, default=``[“_units”, “addi”, “otherl2”, “item”, “_oth”, “_unlisted”, “otherl3”]``

Optional iterable of suffixes to exclude, defaults to ["_units", "addi", "otherl2", "item", "_oth", "_unlisted", "otherl3"].

include_subjidbool, default=False

Optional boolean to indicate whether to include subject ID, defaults to False.

Returns:

list

A list of all variables in the dataframe from specified sections and types, plus any required variables.

isaricanalytics.analytics.impute_miss_val(data: DataFrame, dictionary: DataFrame, outcome_column: str = 'outco_binary_outcome', missing_threshold: float = 0.7, verbose: bool = False) → DataFrame

pandas.DataFrame : The data with missing values imputed.

Imputes missing values or drops columns based on missing value proportion and median.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

outcome_columnstr, default=”outco_binary_outcome”

Optional outcome column, defaults to "outco_binary_outcome".

missing_thresholdfloat, default=0.7

A proportional imputation threshold for missing values, defaults to \(0.7\).

verbose, bool=False

Optional indicator of whether to print imputations summary, defaults to False.

Returns:

pandas.DataFrame

Data with missing values imputed or columns dropped.

isaricanalytics.analytics.lasso_var_sel_binary(data: DataFrame, outcome_column: str = 'mapped_outcome', metric: str = 'balanced_accuracy', threshold: float = 0.001, gridsearch_params: dict[str, Iterable[float]] = {'Cs': [0.001, 0.00316, 0.01, 0.0316, 0.1, 0.316, 1, 3.16, 10], 'l1_ratios': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, random_state: int = 42, verbose: bool = False, sep: str = '___') → tuple[Any]

tuple : Prepares data and selects features using binary logistic regression with elastic net penalty.

Specifically designed for binary outcomes only.

Parameters:

datapandas.DataFrame

The incoming data.

outcome_columnstr, default=”mapped_outcome”

Optional outcome column, defaults to "mapped_outcome".

metricstr, default=”balanced_accuracy”

Optional metric, defaults to "balanced_accuracy".

thresholdfloat, default=1e-3

Optional threshold, defaults to \(0.001\).

gridsearch_paramsdict, default={“l1_ratios”: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], “Cs”: [1e-3, 3.16e-3, 1e-2, 3.16e-2, 1e-1, 3.16e-1, 1, 3.16, 10]}

Optional grid search params, defaults to:

{
    "l1_ratios": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
    "Cs": [1e-3, 3.16e-3, 1e-2, 3.16e-2, 1e-1, 3.16e-1, 1, 3.16, 10]
}

random_stateint, default=42

Optional random state, defaults to \(42\).

verbosebool, default=False

Optional indicator of whether to print the analysis, defaults to False.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

tuple

Results tuple.

isaricanalytics.analytics.mean_std_str(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 3) → str

str : Returns the mean and standard deviation of a series of values as a formatted string.

Warning

DEPRECATED. Please use get_mean_and_stdev() instead.

Parameters:

seriespandas.Series

The input series for which to calculate the mean and st. dev.

add_spacesbool, default=False

Add spacing in the string.

dpint, default=1

No description available.

mfwint, default=4

No description available.

min_nint, default=3

No description available.

Returns:

str

The mean and st. dev. as a formatted string.

isaricanalytics.analytics.median_iqr_str(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 3) → str

str : Returns the median interquartile range (IQR) of a given series of values as a formatted string.

Warning

DEPRECATED. Please use get_median_interquartile_range() instead.

Parameters:

seriespandas.Series

The input series for which to calculate the IQR.

add_spacesbool, default=False

Add spacing in the string.

dpint, default=1

No description available.

mfwint, default=4

No description available.

min_nint, default=3

No description available.

Returns:

str

The IQR as a string.

isaricanalytics.analytics.n_percent_str(series: Series, add_spaces: bool = False, dp: int = 1, mfw: int = 4, min_n: int = 1) → str

str : Returns the n-percent value of a series as a string.

Warning

DEPRECATED. Please use get_n_percent_value() instead.

Parameters:

seriespandas.Series

The input series.

add_spacesbool, default=False

Add spacing around the string.

dpint, default=1

No description available.

mfwint, default=1

No description available.

min_nint, default=1

No description available.

Returns:

str

The n-percent value of the series as a string.

isaricanalytics.analytics.regression_summary_table(table: DataFrame, dictionary: DataFrame, highlight_predictors: dict[str, Iterable[str]] | None = None, pvalue_significance: float | None = None, result_type: str = 'OddsRatio', sep: str = '___') → DataFrame

pandas.DataFrame : Returns a regression summary table.

Parameters:

tablepandas.DataFrame

The incoming table.

dictionarypandas.DataFrame

The data dictionary.

highlight_predictorsdict, default=None

Optional. No description available, defaults to None.

pvalue_significanceint, default=5

Optional \(p\)-value significance level, defaults to None.

result_typestr, default=”OddsRatio”

Optional. No description avaiable, defaults to "OddsRatio".

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

pandas.DataFrame

Regression summary table.

isaricanalytics.analytics.remove_single_binary_outcome_predictors(data: DataFrame, dictionary: DataFrame, predictors: Iterable[str], outcome: str) → Iterable[str]

typing.Iterable : Returns a list of retained columns in the data after removing single binary outcome predictor columns.

Removes binary predictors that are associated with only one outcome, e.g. if all patients with some_variable=1 have outcome=1.

Parameters:

data: pandas.DataFrame

The incoming data.

dictionary: pandas.DataFrame

The data dictionary.

predictors: typing.Iterable

Iterable of predictor variable column names.

outcomestr

The outcome string.

Returns:

typing.Iterable:

List of predictor variable column names excluding any that can’t be used in the logistic regression model.

isaricanalytics.analytics.rmv_high_corr(data: DataFrame, dictionary: DataFrame, outcome_column: str = 'outco_binary_outcome', correlation_threshold: float = 0.5, verbose: bool = False) → DataFrame

pandas.DataFrame : Removes variables in the data with high multicollinearity.

Arbitrarily selecting one variable to remove if the correlation between two variables is above a threshold.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

outcome_columnstr, default=``”outco_binary_outcome”``

Optional outcome column, defaults to "outco_binary_outcome".

correlation_thresholdfloat, default=0.5

Optional correlation threshold, defaults to \(0.5\).

verbosebool, default=False

Optional indicator of whether to print correlation summary.

Returns:

pandas.DataFrame

The data with high correlation variables removed.

isaricanalytics.analytics.rmv_low_var(data: DataFrame, dictionary: DataFrame, mad_threshold: float = 0.1, freq_threshold: float = 0.05, outcome_column: str = 'outco_binary_outcome', verbose: bool = False) → DataFrame

pandas.DataFrame : Removes numerical variables from the data with Median Absolute Deviation (MAD) below a given threshold.

Excludes binary columns from MAD calculation. Removes binary columns with very low frequencies.

Parameters:

datapandas.DataFrame

The incoming data.

dictionarypandas.DataFrame

The data dictionary.

mad_thresholdfloat, default=0.1

Optional MAD threshold, defaults to \(0.1\).

freq_thresholdfloat, default=0.5

Optional frequency threshold, defaults to \(0.05\).

outcome_columnstr, default=``”outco_binary_outcome”``

Optional outcome column, defaults to "outco_binary_outcome".

verbosebool, default=False

Optional indicator of whether to print MAD analysis summary.

Returns:

pandas.DataFrame

The data with low MAD columns removed.

isaricanalytics.analytics.trim_field_label(x: str, max_len: int = 40) → str

str : Trims field label using an optional max. length parameter that defaults to 40 characters.

Parameters:

xstr

The input field label.

max_lenint, default=40

An optional maximum length parameter to use for trimming, defaults to

:math:`40`.

Returns:

str

The trimmed field label.

isaricanalytics.analytics.variance_influence_factor_backwards_elimination(data: DataFrame, dictionary: DataFrame, predictors_list: Iterable[str], sep: str = '___') → tuple[Iterable[str], DataFrame]

tuple : Returns an iterable of retained columns and the VIF backwards elimination data.

Parameters:

datapandas.DataFrame

Incoming data.

dictionarypandas.DataFrame

Data dictionary.

predictors_listtyping.Iterable

Iterable of predictor variable column names.

sepstr, default=”___”

Optional field-value separator, defaults to "___".

Returns:

tuple

An iterable of retained columns and the VIF backwards elimination data.