lontras

class lontras.DataFrame(data: Mapping[Hashable, Series] | Mapping[Hashable, Collection[int | float | complex | str | bool]] | Collection[Series] | Collection[Mapping[Hashable, int | float | complex | str | bool]] | Collection[int | float | complex | str | bool] | Collection[Collection[int | float | complex | str | bool]] | Iterator | None = None, index: Sequence[Hashable] | None = None, columns: Sequence[Hashable] | None = None)

Bases: UserDict

property T: DataFrame

abs() → DataFrame

Returns the absolute values for DataFrame

Returns:

DataFrame: Absolute values DataFrame

agg(method: Callable[[Collection[Any]], Any], axis: Literal[0, 1] = 0) → Series

all() → Series

all(axis: Literal[0, 1]) → Series

all(axis: None) → bool

any() → Series

any(axis: Literal[0, 1]) → Series

any(axis: None) → bool

apply(method: Callable[[Series], Any], axis: Literal[0, 1] = 0) → Series

astype(new_type: type) → DataFrame

Casts the DataFrame to a new type.

Args:

new_type (type): The type to cast to.

Returns:

DataFrame: A new DataFrame with the values cast to the new type.

columns: Sequence[Hashable]

idxmax() → Series

idxmax(axis: Literal[0, 1]) → Series

idxmax(axis: None) → bool

idxmin() → Series

idxmin(axis: Literal[0, 1]) → Series

idxmin(axis: None) → bool

iloc_indexer: IlocIndexer

index: Sequence[Hashable]

iterrows() → Generator[tuple[Hashable, Series]]

loc_indexer: LocIndexer

map(func: Callable) → DataFrame

Applies a function to each value in the DataFrame.

Args:

func (Callable): The function to apply.

Returns:

DataFrame: A new DataFrame with the results of the function applied.

max() → Series

max(axis: Literal[0, 1]) → Series

max(axis: None) → int | float | complex | str | bool

mean(axis: Literal[0, 1]) → Series

mean(axis: None) → int | float | complex | str | bool

Computes the mean of the Series.

Returns:

float: Series mean

median(axis: Literal[0, 1]) → Series

median(axis: None) → int | float | complex | str | bool

Return the median (middle value) of numeric data, using the common “mean of middle two” method. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

Returns:

float | int: Series median

min() → Series

min(axis: Literal[0, 1]) → Series

min(axis: None) → int | float | complex | str | bool

mode(axis: Literal[0, 1] = 0) → Series

Return the single most common data point from discrete or nominal data. The mode (when it exists) is the most typical value and serves as a measure of central location.

Returns:

Any: Series mode

name

op(op: str, other: DataFrame | Series | Mapping | Collection | int | float | complex | str | bool) → DataFrame

quantiles(*, n=4, method: Literal['exclusive', 'inclusive'] = 'exclusive', axis: Literal[0, 1] = 0) → Series

Divide data into n continuous intervals with equal probability. Returns a list of n - 1 cut points separating the intervals.

Returns:

list[float]: List containing quantiles

property shape: tuple[int, int]

std(xbar, axis: Literal[0, 1]) → Series

std(xbar, axis: None) → int | float | complex | str | bool

Return the sample standard deviation (the square root of the sample variance). See variance() for arguments and other details.

Returns:

float: Series standard deviation

sum() → Series

sum(axis: Literal[0, 1]) → Series

sum(axis: None) → int | float | complex | str | bool

to_dict() → dict[Hashable, dict[Hashable, Any]]

to_dict(orient: Literal['dict']) → dict[Hashable, dict[Hashable, Any]]

to_dict(orient: Literal['list']) → dict[Hashable, list[Any]]

to_dict(orient: Literal['records']) → list[dict[Hashable, Any]]

Converts the DataFrame to a dictionary.

Args:

orient str {dict, list, records}: Determines the type of the values of the

dictionary.

Returns:

dict[Hashable, Any]: A dictionary representation of the Series.

to_list() → list[list[Any]]

Converts the DataFrame to a list.

Returns:

list[list[Any]]: A list of the Series values.

property values: list[list[Any]]

Return a list representation of the DataFrame.

Returns:

list: The values of the DataFrame.

var(xbar, axis: Literal[0, 1]) → Series

var(xbar, axis: None) → int | float | complex | str | bool

Return the sample variance of data, an iterable of at least two real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean.

Returns:

float: Series variance

class lontras.Series(data: Mapping | Collection | int | float | complex | str | bool | None = None, index: Sequence[Hashable] | None = None, name: Hashable = None)

Bases: UserDict

Series class representing a one-dimensional labeled array with capabilities for data analysis.

Attributes:

name (Hashable): Name of the Series. loc_indexer (LocIndexer): Indexer for label-based location selection. iloc_indexer (ilocIndexer): Indexer for integer-based location selection.

abs() → Series

Returns the absolute values for Series

Returns:

Series: Absolute values Series

agg(func: Callable) → Any

Applies an aggregation function to the Series’ values.

This method applies a given function to all the values in the Series. It is intended for aggregation functions that operate on a collection of values and return a single result.

Args:

func (Callable): The aggregation function to apply. This function

should accept an iterable (like a list or NumPy array) and return a single value.

Returns:

Any: The result of applying the aggregation function to the Series’ values.

all() → bool

Returns True if all values in the Series are True.

Returns:

bool: True if all values are True, False otherwise.

any() → bool

Returns True if any value in the Series is True.

Returns:

bool: True if any value is True, False otherwise.

argmax() → int

Returns the index of the maximum value.

Returns:

int: The index of the maximum value.

argmin() → int

Returns the index of the minimum value.

Returns:

int: The index of the minimum value.

astype(new_type: type) → Series

Casts the Series to a new type.

Args:

new_type (type): The type to cast to.

Returns:

Series: A new Series with the values cast to the new type.

copy(*, deep: bool = True)

Creates a copy of the Series.

Args:

deep (bool, optional): If True, creates a deep copy. Otherwise, creates a shallow copy. Defaults to True.

Returns:

Series: A copy of the Series.

dot(other: Series | Collection | int | float | complex | str | bool) → int | float | complex | str | bool

Performs dot product with another Series, Collection or Scalar.

If other is a Series or a Collection, performs the dot product between the two. If other is a Scalar, multiplies all elements of the Series by the scalar and returns the sum.

Args:

other (Series | Collection | Scalar)

Returns:

Scalar: The dot product of the Series.

find(val: Any) → Hashable | None

Finds the first label (key) associated with a given value in the Series.

Args:

val (Any): The value to search for.

Returns:

Hashable | None: The label (key) of the first occurrence of the value,

or None if the value is not found.

head(n: int = 5) → Series

Returns the first n rows.

Args:

n (int, optional): Number of rows to return. Defaults to 5.

Returns:

Series: A new Series containing the first n rows.

idxmax() → Hashable | None

Returns the label of the maximum value.

Returns:

Hashable: The label of the maximum value.

idxmin() → Hashable | None

Returns the label of the minimum value.

Returns:

Hashable: The label of the minimum value.

ifind(val: Any) → int | None

Finds the first integer position (index) of a given value in the Series.

Args:

val (Any): The value to search for.

Returns:

int | None: The integer position (index) of the first occurrence of the value,

or None if the value is not found.

iloc_indexer: IlocIndexer

property index: Sequence[Hashable]

Returns the index of the Series.

Returns:

Index: The index of the Series.

loc_indexer: LocIndexer

map(func: Callable) → Series

Applies a function to each value in the Series.

Args:

func (Callable): The function to apply.

Returns:

Series: A new Series with the results of the function applied.

max() → int | float | complex | str | bool

Returns the maximum value in the Series.

Returns:

Any: The maximum value.

mean() → int | float | complex | str | bool

Computes the mean of the Series.

Returns:

float: Series mean

median() → int | float | complex | str | bool

Return the median (middle value) of numeric data, using the common “mean of middle two” method. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

Returns:

float | int: Series median

min() → int | float | complex | str | bool

Returns the minimum value in the Series.

Returns:

Any: The minimum value.

mode() → int | float | complex | str | bool

Return the single most common data point from discrete or nominal data. The mode (when it exists) is the most typical value and serves as a measure of central location.

Returns:

Any: Series mode

name: Hashable

quantiles(*, n=4, method: Literal['exclusive', 'inclusive'] = 'exclusive') → Collection[float]

Divide data into n continuous intervals with equal probability. Returns a list of n - 1 cut points separating the intervals.

Returns:

list[float]: List containing quantiles

reduce(func: Callable, initial: Any)

Reduces the Series using a function.

Args:

func (Callable): The function to apply for reduction. initial (Any): The initial value for the reduction.

Returns:

Any: The reduced value.

reindex(index: Sequence[Hashable]) → Series

Sets the index of the Series.

Args:

value (Index): The new index for the Series.

Raises:

ValueError: If the length of the new index does not match the length of the Series.

rename(name: Hashable) → Series

Renames the Series.

Args:

name (Hashable): The new name for the Series.

Returns:

Series: A new Series with the updated name (a copy).

std(xbar=None) → int | float | complex | str | bool

Return the sample standard deviation (the square root of the sample variance). See variance() for arguments and other details.

Returns:

float: Series standard deviation

sum() → int | float | complex | str | bool

Returns the sum of the values in the Series.

Returns:

Any: The sum of the values.

tail(n: int = 5) → Series

Returns the last n rows.

Args:

n (int, optional): Number of rows to return. Defaults to 5.

Returns:

Series: A new Series containing the last n rows.

to_dict() → dict[Hashable, Any]

Converts the Series to a dictionary.

Returns:

dict[Hashable, Any]: A dictionary representation of the Series.

to_list() → list[Any]

Converts the Series to a list.

Returns:

list[Any]: A list of the Series values.

property values: list[Any]

Return a list representation of the Series.

Returns:

list: The values of the Series.

var(xbar=None) → int | float | complex | str | bool

Return the sample variance of data, an iterable of at least two real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean.

Returns:

float: Series variance