accessors module ¶

Custom pandas accessors for returns data.

Methods can be accessed as follows:

ReturnsSRAccessor -> pd.Series.vbt.returns.*
ReturnsDFAccessor -> pd.DataFrame.vbt.returns.*

Note

The underlying Series/DataFrame must already be a return series. To convert price to returns, use ReturnsAccessor.from_value().

Grouping is only supported by the methods that accept the group_by argument.

Accessors do not utilize caching.

There are three options to compute returns and get the accessor:

>>> import numpy as np
>>> import pandas as pd
>>> import vectorbt as vbt

>>> price = pd.Series([1.1, 1.2, 1.3, 1.2, 1.1])

>>> # 1. pd.Series.pct_change
>>> rets = price.pct_change()
>>> ret_acc = rets.vbt.returns(freq='d')

>>> # 2. vectorbt.generic.accessors.GenericAccessor.to_returns
>>> rets = price.vbt.to_returns()
>>> ret_acc = rets.vbt.returns(freq='d')

>>> # 3. vectorbt.returns.accessors.ReturnsAccessor.from_value
>>> ret_acc = pd.Series.vbt.returns.from_value(price, freq='d')

>>> # vectorbt.returns.accessors.ReturnsAccessor.total
>>> ret_acc.total()
0.0

The accessors extend vectorbt.generic.accessors.

>>> # inherited from GenericAccessor
>>> ret_acc.max()
0.09090909090909083

Defaults¶

ReturnsAccessor accepts defaults dictionary where you can pass defaults for arguments used throughout the accessor, such as

start_value: The starting value.
window: Window length.
minp: Minimum number of observations in a window required to have a value.
ddof: Delta Degrees of Freedom.
risk_free: Constant risk-free return throughout the period.
levy_alpha: Scaling relation (Levy stability exponent).
required_return: Minimum acceptance return of the investor.
cutoff: Decimal representing the percentage cutoff for the bottom percentile of returns.

Stats¶

Hint

See StatsBuilderMixin.stats() and ReturnsAccessor.metrics.

>>> ret_acc.stats()
UserWarning: Metric 'benchmark_return' requires benchmark_rets to be set
UserWarning: Metric 'alpha' requires benchmark_rets to be set
UserWarning: Metric 'beta' requires benchmark_rets to be set

Start                                      0
End                                        4
Duration                     5 days 00:00:00
Total Return [%]                           0
Annualized Return [%]                      0
Annualized Volatility [%]            184.643
Sharpe Ratio                        0.691185
Calmar Ratio                               0
Max Drawdown [%]                     15.3846
Omega Ratio                          1.08727
Sortino Ratio                        1.17805
Skew                              0.00151002
Kurtosis                            -5.94737
Tail Ratio                           1.08985
Common Sense Ratio                   1.08985
Value at Risk                     -0.0823718
dtype: object

The missing benchmark_rets can be either passed to the contrustor of the accessor or as a setting to StatsBuilderMixin.stats():

>>> benchmark = pd.Series([1.05, 1.1, 1.15, 1.1, 1.05])
>>> benchmark_rets = benchmark.vbt.to_returns()

>>> ret_acc.stats(settings=dict(benchmark_rets=benchmark_rets))
Start                                      0
End                                        4
Duration                     5 days 00:00:00
Total Return [%]                           0
Benchmark Return [%]                       0
Annualized Return [%]                      0
Annualized Volatility [%]            184.643
Sharpe Ratio                        0.691185
Calmar Ratio                               0
Max Drawdown [%]                     15.3846
Omega Ratio                          1.08727
Sortino Ratio                        1.17805
Skew                              0.00151002
Kurtosis                            -5.94737
Tail Ratio                           1.08985
Common Sense Ratio                   1.08985
Value at Risk                     -0.0823718
Alpha                                0.78789
Beta                                 1.83864
dtype: object

Note

StatsBuilderMixin.stats() does not support grouping.

Plots¶

Hint

See PlotsBuilderMixin.plots() and ReturnsAccessor.subplots.

This class inherits subplots from GenericAccessor.

ReturnsAccessor class ¶

ReturnsAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For both, Series and DataFrames.

Accessible through pd.Series.vbt.returns and pd.DataFrame.vbt.returns.

Args

obj : pd.Series or pd.DataFrame: Pandas object representing returns.
benchmark_rets : array_like: Pandas object representing benchmark returns.
year_freq : any: Year frequency for annualization purposes.
defaults : dict: Defaults that override returns.defaults in settings.
**kwargs: Keyword arguments that are passed down to GenericAccessor.

Superclasses

Inherited members

Subclasses

alpha method ¶

ReturnsAccessor.alpha(
    benchmark_rets=None,
    risk_free=None,
    wrap_kwargs=None
)

See alpha_nb().

ann_factor property ¶

Get annualization factor.

annual method ¶

ReturnsAccessor.annual(
    **kwargs
)

Annual returns.

annualized method ¶

ReturnsAccessor.annualized(
    wrap_kwargs=None
)

See annualized_return_nb().

annualized_volatility method ¶

ReturnsAccessor.annualized_volatility(
    levy_alpha=None,
    ddof=None,
    wrap_kwargs=None
)

See annualized_volatility_nb().

benchmark_rets property ¶

Benchmark returns.

beta method ¶

ReturnsAccessor.beta(
    benchmark_rets=None,
    wrap_kwargs=None
)

See beta_nb().

calmar_ratio method ¶

ReturnsAccessor.calmar_ratio(
    wrap_kwargs=None
)

See calmar_ratio_nb().

capture method ¶

ReturnsAccessor.capture(
    benchmark_rets=None,
    wrap_kwargs=None
)

See capture_nb().

common_sense_ratio method ¶

ReturnsAccessor.common_sense_ratio(
    wrap_kwargs=None
)

Common Sense Ratio.

cond_value_at_risk method ¶

ReturnsAccessor.cond_value_at_risk(
    cutoff=None,
    wrap_kwargs=None
)

See cond_value_at_risk_nb().

cumulative method ¶

ReturnsAccessor.cumulative(
    start_value=None,
    wrap_kwargs=None
)

See cum_returns_nb().

daily method ¶

ReturnsAccessor.daily(
    **kwargs
)

Daily returns.

defaults property ¶

Defaults for ReturnsAccessor.

Merges returns.defaults from settings with defaults from ReturnsAccessor.

deflated_sharpe_ratio method ¶

ReturnsAccessor.deflated_sharpe_ratio(
    risk_free=None,
    ddof=None,
    var_sharpe=None,
    nb_trials=None,
    bias=True,
    wrap_kwargs=None
)

Deflated Sharpe Ratio (DSR).

Expresses the chance that the advertised strategy has a positive Sharpe ratio.

If var_sharpe is None, is calculated based on all columns. If nb_trials is None, is set to the number of columns.

down_capture method ¶

ReturnsAccessor.down_capture(
    benchmark_rets=None,
    wrap_kwargs=None
)

See down_capture_nb().

downside_risk method ¶

ReturnsAccessor.downside_risk(
    required_return=None,
    wrap_kwargs=None
)

See downside_risk_nb().

drawdown method ¶

ReturnsAccessor.drawdown(
    wrap_kwargs=None
)

Relative decline from a peak.

drawdowns property ¶

ReturnsAccessor.get_drawdowns() with default arguments.

from_value class method ¶

ReturnsAccessor.from_value(
    value,
    init_value=nan,
    broadcast_kwargs=None,
    wrap_kwargs=None,
    **kwargs
)

Returns a new ReturnsAccessor instance with returns calculated from value.

get_drawdowns method ¶

ReturnsAccessor.get_drawdowns(
    wrapper_kwargs=None,
    **kwargs
)

Generate drawdown records of cumulative returns.

See Drawdowns.

indexing_func method ¶

ReturnsAccessor.indexing_func(
    pd_indexing_func,
    **kwargs
)

Perform indexing on ReturnsAccessor.

information_ratio method ¶

ReturnsAccessor.information_ratio(
    benchmark_rets=None,
    ddof=None,
    wrap_kwargs=None
)

See information_ratio_nb().

max_drawdown method ¶

ReturnsAccessor.max_drawdown(
    wrap_kwargs=None
)

See max_drawdown_nb().

Yields the same result as max_drawdown of ReturnsAccessor.drawdowns.

metrics class variable ¶

Metrics supported by ReturnsAccessor.

Config({
    "start": {
        "title": "Start",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f2e0>",
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "end": {
        "title": "End",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f380>",
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "period": {
        "title": "Period",
        "calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f420>",
        "apply_to_timedelta": true,
        "agg_func": null,
        "check_is_not_grouped": false,
        "tags": "wrapper"
    },
    "total_return": {
        "title": "Total Return [%]",
        "calc_func": "total",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f4c0>",
        "tags": "returns"
    },
    "benchmark_return": {
        "title": "Benchmark Return [%]",
        "calc_func": "benchmark_rets.vbt.returns.total",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f560>",
        "check_has_benchmark_rets": true,
        "tags": "returns"
    },
    "ann_return": {
        "title": "Annualized Return [%]",
        "calc_func": "annualized",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f600>",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "ann_volatility": {
        "title": "Annualized Volatility [%]",
        "calc_func": "annualized_volatility",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f6a0>",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "max_dd": {
        "title": "Max Drawdown [%]",
        "calc_func": "drawdowns.max_drawdown",
        "post_calc_func": "<function ReturnsAccessor.<lambda> at 0x12d42f740>",
        "tags": [
            "returns",
            "drawdowns"
        ]
    },
    "max_dd_duration": {
        "title": "Max Drawdown Duration",
        "calc_func": "drawdowns.max_duration",
        "fill_wrap_kwargs": true,
        "tags": [
            "returns",
            "drawdowns",
            "duration"
        ]
    },
    "sharpe_ratio": {
        "title": "Sharpe Ratio",
        "calc_func": "sharpe_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "calmar_ratio": {
        "title": "Calmar Ratio",
        "calc_func": "calmar_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "omega_ratio": {
        "title": "Omega Ratio",
        "calc_func": "omega_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "sortino_ratio": {
        "title": "Sortino Ratio",
        "calc_func": "sortino_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "skew": {
        "title": "Skew",
        "calc_func": "obj.skew",
        "tags": "returns"
    },
    "kurtosis": {
        "title": "Kurtosis",
        "calc_func": "obj.kurtosis",
        "tags": "returns"
    },
    "tail_ratio": {
        "title": "Tail Ratio",
        "calc_func": "tail_ratio",
        "tags": "returns"
    },
    "common_sense_ratio": {
        "title": "Common Sense Ratio",
        "calc_func": "common_sense_ratio",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "tags": "returns"
    },
    "value_at_risk": {
        "title": "Value at Risk",
        "calc_func": "value_at_risk",
        "tags": "returns"
    },
    "alpha": {
        "title": "Alpha",
        "calc_func": "alpha",
        "check_has_freq": true,
        "check_has_year_freq": true,
        "check_has_benchmark_rets": true,
        "tags": "returns"
    },
    "beta": {
        "title": "Beta",
        "calc_func": "beta",
        "check_has_benchmark_rets": true,
        "tags": "returns"
    }
})

Returns ReturnsAccessor._metrics, which gets (deep) copied upon creation of each instance. Thus, changing this config won't affect the class.

To change metrics, you can either change the config in-place, override this property, or overwrite the instance variable ReturnsAccessor._metrics.

omega_ratio method ¶

ReturnsAccessor.omega_ratio(
    risk_free=None,
    required_return=None,
    wrap_kwargs=None
)

See omega_ratio_nb().

plots_defaults property ¶

Defaults for PlotsBuilderMixin.plots().

Merges GenericAccessor.plots_defaults, defaults from ReturnsAccessor.defaults (acting as settings), and returns.plots from settings

qs property ¶

Quantstats adapter.

resolve_self method ¶

ReturnsAccessor.resolve_self(
    cond_kwargs=None,
    custom_arg_names=None,
    impacts_caching=True,
    silence_warnings=False
)

Resolve self.

See Wrapping.resolve_self().

Creates a copy of this instance year_freq is different in cond_kwargs.

rolling_alpha method ¶

ReturnsAccessor.rolling_alpha(
    benchmark_rets=None,
    window=None,
    minp=None,
    risk_free=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.alpha().

rolling_annualized method ¶

ReturnsAccessor.rolling_annualized(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.annualized().

rolling_annualized_volatility method ¶

ReturnsAccessor.rolling_annualized_volatility(
    window=None,
    minp=None,
    levy_alpha=None,
    ddof=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.annualized_volatility().

rolling_beta method ¶

ReturnsAccessor.rolling_beta(
    benchmark_rets=None,
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.beta().

rolling_calmar_ratio method ¶

ReturnsAccessor.rolling_calmar_ratio(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.calmar_ratio().

rolling_capture method ¶

ReturnsAccessor.rolling_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.capture().

rolling_common_sense_ratio method ¶

ReturnsAccessor.rolling_common_sense_ratio(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.common_sense_ratio().

rolling_cond_value_at_risk method ¶

ReturnsAccessor.rolling_cond_value_at_risk(
    window=None,
    minp=None,
    cutoff=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.cond_value_at_risk().

rolling_down_capture method ¶

ReturnsAccessor.rolling_down_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.down_capture().

rolling_downside_risk method ¶

ReturnsAccessor.rolling_downside_risk(
    window=None,
    minp=None,
    required_return=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.downside_risk().

rolling_information_ratio method ¶

ReturnsAccessor.rolling_information_ratio(
    benchmark_rets=None,
    window=None,
    minp=None,
    ddof=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.information_ratio().

rolling_max_drawdown method ¶

ReturnsAccessor.rolling_max_drawdown(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.max_drawdown().

rolling_omega_ratio method ¶

ReturnsAccessor.rolling_omega_ratio(
    window=None,
    minp=None,
    risk_free=None,
    required_return=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.omega_ratio().

rolling_sharpe_ratio method ¶

ReturnsAccessor.rolling_sharpe_ratio(
    window=None,
    minp=None,
    risk_free=None,
    ddof=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.sharpe_ratio().

rolling_sortino_ratio method ¶

ReturnsAccessor.rolling_sortino_ratio(
    window=None,
    minp=None,
    required_return=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.sortino_ratio().

rolling_tail_ratio method ¶

ReturnsAccessor.rolling_tail_ratio(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.tail_ratio().

rolling_total method ¶

ReturnsAccessor.rolling_total(
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.total().

rolling_up_capture method ¶

ReturnsAccessor.rolling_up_capture(
    benchmark_rets=None,
    window=None,
    minp=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.up_capture().

rolling_value_at_risk method ¶

ReturnsAccessor.rolling_value_at_risk(
    window=None,
    minp=None,
    cutoff=None,
    wrap_kwargs=None
)

Rolling version of ReturnsAccessor.value_at_risk().

sharpe_ratio method ¶

ReturnsAccessor.sharpe_ratio(
    risk_free=None,
    ddof=None,
    wrap_kwargs=None
)

See sharpe_ratio_nb().

sortino_ratio method ¶

ReturnsAccessor.sortino_ratio(
    required_return=None,
    wrap_kwargs=None
)

See sortino_ratio_nb().

stats_defaults property ¶

Defaults for StatsBuilderMixin.stats().

Merges GenericAccessor.stats_defaults, defaults from ReturnsAccessor.defaults (acting as settings), and returns.stats from settings

subplots class variable ¶

Subplots supported by ReturnsAccessor.

Config({
    "plot": {
        "check_is_not_grouped": true,
        "plot_func": "plot",
        "pass_trace_names": false,
        "tags": "generic"
    }
})

Returns ReturnsAccessor._subplots, which gets (deep) copied upon creation of each instance. Thus, changing this config won't affect the class.

To change subplots, you can either change the config in-place, override this property, or overwrite the instance variable ReturnsAccessor._subplots.

tail_ratio method ¶

ReturnsAccessor.tail_ratio(
    wrap_kwargs=None
)

See tail_ratio_nb().

total method ¶

ReturnsAccessor.total(
    wrap_kwargs=None
)

See cum_returns_final_nb().

up_capture method ¶

ReturnsAccessor.up_capture(
    benchmark_rets=None,
    wrap_kwargs=None
)

See up_capture_nb().

value_at_risk method ¶

ReturnsAccessor.value_at_risk(
    cutoff=None,
    wrap_kwargs=None
)

See value_at_risk_nb().

year_freq property ¶

Year frequency for annualization purposes.

ReturnsDFAccessor class ¶

ReturnsDFAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For DataFrames only.

Accessible through pd.DataFrame.vbt.returns.

Superclasses

Inherited members

ReturnsSRAccessor class ¶

ReturnsSRAccessor(
    obj,
    benchmark_rets=None,
    year_freq=None,
    defaults=None,
    **kwargs
)

Accessor on top of return series. For Series only.

Accessible through pd.Series.vbt.returns.

Superclasses

Inherited members

plot_cumulative method ¶

ReturnsSRAccessor.plot_cumulative(
    benchmark_rets=None,
    start_value=1,
    fill_to_benchmark=False,
    main_kwargs=None,
    benchmark_kwargs=None,
    hline_shape_kwargs=None,
    add_trace_kwargs=None,
    xref='x',
    yref='y',
    fig=None,
    **layout_kwargs
)

Plot cumulative returns.

Args

benchmark_rets : array_like: Benchmark return to compare returns against. Will broadcast per element.
start_value : float: The starting returns.
fill_to_benchmark : bool: Whether to fill between main and benchmark, or between main and start_value.
main_kwargs : dict: Keyword arguments passed to GenericAccessor.plot() for main.
benchmark_kwargs : dict: Keyword arguments passed to GenericAccessor.plot() for benchmark.
hline_shape_kwargs : dict: Keyword arguments passed to plotly.graph_objects.Figure.add_shape for start_value line.
add_trace_kwargs : dict: Keyword arguments passed to add_trace.
xref : str: X coordinate axis.
yref : str: Y coordinate axis.
fig : Figure or FigureWidget: Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.

Usage

>>> import pandas as pd
>>> import numpy as np

>>> np.random.seed(0)
>>> rets = pd.Series(np.random.uniform(-0.05, 0.05, size=100))
>>> benchmark_rets = pd.Series(np.random.uniform(-0.05, 0.05, size=100))
>>> rets.vbt.returns.plot_cumulative(benchmark_rets=benchmark_rets)

accessors module¶

Defaults¶

Stats¶

Plots¶

ReturnsAccessor class¶

alpha method¶

ann_factor property¶

annual method¶

annualized method¶

annualized_volatility method¶

benchmark_rets property¶

beta method¶

calmar_ratio method¶

capture method¶

common_sense_ratio method¶

cond_value_at_risk method¶

cumulative method¶

daily method¶

defaults property¶

deflated_sharpe_ratio method¶

down_capture method¶

downside_risk method¶

drawdown method¶

drawdowns property¶

from_value class method¶

get_drawdowns method¶

indexing_func method¶

information_ratio method¶

max_drawdown method¶

metrics class variable¶

omega_ratio method¶

plots_defaults property¶

qs property¶

resolve_self method¶

rolling_alpha method¶

rolling_annualized method¶

rolling_annualized_volatility method¶

rolling_beta method¶

rolling_calmar_ratio method¶

rolling_capture method¶

rolling_common_sense_ratio method¶

rolling_cond_value_at_risk method¶

rolling_down_capture method¶

rolling_downside_risk method¶

rolling_information_ratio method¶

rolling_max_drawdown method¶

rolling_omega_ratio method¶

rolling_sharpe_ratio method¶

rolling_sortino_ratio method¶

rolling_tail_ratio method¶

rolling_total method¶

rolling_up_capture method¶

rolling_value_at_risk method¶

sharpe_ratio method¶

sortino_ratio method¶

stats_defaults property¶

subplots class variable¶

tail_ratio method¶

total method¶

up_capture method¶

value_at_risk method¶

year_freq property¶

ReturnsDFAccessor class¶

ReturnsSRAccessor class¶

plot_cumulative method¶

accessors module ¶

ReturnsAccessor class ¶

alpha method ¶

ann_factor property ¶

annual method ¶

annualized method ¶

annualized_volatility method ¶

benchmark_rets property ¶

beta method ¶

calmar_ratio method ¶

capture method ¶

common_sense_ratio method ¶

cond_value_at_risk method ¶

cumulative method ¶

daily method ¶

defaults property ¶

deflated_sharpe_ratio method ¶

down_capture method ¶

downside_risk method ¶

drawdown method ¶

drawdowns property ¶

from_value class method ¶

get_drawdowns method ¶

indexing_func method ¶

information_ratio method ¶

max_drawdown method ¶

metrics class variable ¶

omega_ratio method ¶

plots_defaults property ¶

qs property ¶

resolve_self method ¶

rolling_alpha method ¶

rolling_annualized method ¶

rolling_annualized_volatility method ¶

rolling_beta method ¶

rolling_calmar_ratio method ¶

rolling_capture method ¶

rolling_common_sense_ratio method ¶

rolling_cond_value_at_risk method ¶

rolling_down_capture method ¶

rolling_downside_risk method ¶

rolling_information_ratio method ¶

rolling_max_drawdown method ¶

rolling_omega_ratio method ¶

rolling_sharpe_ratio method ¶

rolling_sortino_ratio method ¶

rolling_tail_ratio method ¶

rolling_total method ¶

rolling_up_capture method ¶

rolling_value_at_risk method ¶

sharpe_ratio method ¶

sortino_ratio method ¶

stats_defaults property ¶

subplots class variable ¶

tail_ratio method ¶

total method ¶

up_capture method ¶

value_at_risk method ¶

year_freq property ¶

ReturnsDFAccessor class ¶

ReturnsSRAccessor class ¶

plot_cumulative method ¶