accessors module¶
Custom pandas accessors for generic data.
Methods can be accessed as follows:
- GenericSRAccessor ->
pd.Series.vbt.*
- GenericDFAccessor ->
pd.DataFrame.vbt.*
>>> import pandas as pd
>>> import vectorbt as vbt
>>> # vectorbt.generic.accessors.GenericAccessor.rolling_mean
>>> pd.Series([1, 2, 3, 4]).vbt.rolling_mean(2)
0 NaN
1 1.5
2 2.5
3 3.5
dtype: float64
The accessors inherit vectorbt.base.accessors and are inherited by more specialized accessors, such as vectorbt.signals.accessors and vectorbt.returns.accessors.
Note
Grouping is only supported by the methods that accept the group_by
argument.
Accessors do not utilize caching.
Run for the examples below
>>> import vectorbt as vbt
>>> import numpy as np
>>> import pandas as pd
>>> from numba import njit
>>> from datetime import datetime, timedelta
>>> df = pd.DataFrame({
... 'a': [1, 2, 3, 4, 5],
... 'b': [5, 4, 3, 2, 1],
... 'c': [1, 2, 3, 2, 1]
... }, index=pd.Index([
... datetime(2020, 1, 1),
... datetime(2020, 1, 2),
... datetime(2020, 1, 3),
... datetime(2020, 1, 4),
... datetime(2020, 1, 5)
... ]))
>>> df
a b c
2020-01-01 1 5 1
2020-01-02 2 4 2
2020-01-03 3 3 3
2020-01-04 4 2 2
2020-01-05 5 1 1
>>> index = [datetime(2020, 1, 1) + timedelta(days=i) for i in range(10)]
>>> sr = pd.Series(np.arange(len(index)), index=index)
>>> sr
2020-01-01 0
2020-01-02 1
2020-01-03 2
2020-01-04 3
2020-01-05 4
2020-01-06 5
2020-01-07 6
2020-01-08 7
2020-01-09 8
2020-01-10 9
dtype: int64
Stats¶
Hint
>>> df2 = pd.DataFrame({
... 'a': [np.nan, 2, 3],
... 'b': [4, np.nan, 5],
... 'c': [6, 7, np.nan]
... }, index=['x', 'y', 'z'])
>>> df2.vbt(freq='d').stats(column='a')
Start x
End z
Period 3 days 00:00:00
Count 2
Mean 2.5
Std 0.707107
Min 2.0
Median 2.5
Max 3.0
Min Index y
Max Index z
Name: a, dtype: object
Mapping¶
Mapping can be set both in GenericAccessor (preferred) and StatsBuilderMixin.stats():
>>> mapping = {x: 'test_' + str(x) for x in pd.unique(df2.values.flatten())}
>>> df2.vbt(freq='d', mapping=mapping).stats(column='a')
Start x
End z
Period 3 days 00:00:00
Count 2
Value Counts: test_2.0 1
Value Counts: test_3.0 1
Value Counts: test_4.0 0
Value Counts: test_5.0 0
Value Counts: test_6.0 0
Value Counts: test_7.0 0
Value Counts: test_nan 1
Name: a, dtype: object
>>> df2.vbt(freq='d').stats(column='a', settings=dict(mapping=mapping))
UserWarning: Changing the mapping will create a copy of this object.
Consider setting it upon object creation to re-use existing cache.
Start x
End z
Period 3 days 00:00:00
Count 2
Value Counts: test_2.0 1
Value Counts: test_3.0 1
Value Counts: test_4.0 0
Value Counts: test_5.0 0
Value Counts: test_6.0 0
Value Counts: test_7.0 0
Value Counts: test_nan 1
Name: a, dtype: object
Selecting a column before calling stats
will consider uniques from this column only:
>>> df2['a'].vbt(freq='d', mapping=mapping).stats()
Start x
End z
Period 3 days 00:00:00
Count 2
Value Counts: test_2.0 1
Value Counts: test_3.0 1
Value Counts: test_nan 1
Name: a, dtype: object
To include all keys from mapping
, pass incl_all_keys=True
:
df2['a'].vbt(freq='d', mapping=mapping).stats(settings=dict(incl_all_keys=True)) Start x End z Period 3 days 00:00:00 Count 2 Value Counts: test_2.0 1 Value Counts: test_3.0 1 Value Counts: test_4.0 0 Value Counts: test_5.0 0 Value Counts: test_6.0 0 Value Counts: test_7.0 0 Value Counts: test_nan 1 Name: a, dtype: object
`GenericAccessor.stats` also supports (re-)grouping: ```pycon >>> df2.vbt(freq='d').stats(column=0, group_by=[0, 0, 1]) Start x End z Period 3 days 00:00:00 Count 4 Mean 3.5 Std 1.290994 Min 2.0 Median 3.5 Max 5.0 Min Index y Max Index z Name: 0, dtype: object
Plots¶
Hint
GenericAccessor class has a single subplot based on GenericAccessor.plot():
>>> df2.vbt.plots()
nb_config Config¶
Config of Numba methods to be added to GenericAccessor.
Config({
"shuffle": {
"func": "CPUDispatcher(<function shuffle_nb at 0x1268bdee0>)",
"path": "vectorbt.generic.nb.shuffle_nb"
},
"fillna": {
"func": "CPUDispatcher(<function fillna_nb at 0x1268bf880>)",
"path": "vectorbt.generic.nb.fillna_nb"
},
"bshift": {
"func": "CPUDispatcher(<function bshift_nb at 0x1268bdc60>)",
"path": "vectorbt.generic.nb.bshift_nb"
},
"fshift": {
"func": "CPUDispatcher(<function fshift_nb at 0x1268bce00>)",
"path": "vectorbt.generic.nb.fshift_nb"
},
"diff": {
"func": "CPUDispatcher(<function diff_nb at 0x1268b02c0>)",
"path": "vectorbt.generic.nb.diff_nb"
},
"pct_change": {
"func": "CPUDispatcher(<function pct_change_nb at 0x1268b0900>)",
"path": "vectorbt.generic.nb.pct_change_nb"
},
"bfill": {
"func": "CPUDispatcher(<function bfill_nb at 0x1268b0f40>)",
"path": "vectorbt.generic.nb.bfill_nb"
},
"ffill": {
"func": "CPUDispatcher(<function ffill_nb at 0x1268b1580>)",
"path": "vectorbt.generic.nb.ffill_nb"
},
"cumsum": {
"func": "CPUDispatcher(<function nancumsum_nb at 0x1268b1e40>)",
"path": "vectorbt.generic.nb.nancumsum_nb"
},
"cumprod": {
"func": "CPUDispatcher(<function nancumprod_nb at 0x1268b2200>)",
"path": "vectorbt.generic.nb.nancumprod_nb"
},
"rolling_min": {
"func": "CPUDispatcher(<function rolling_min_nb at 0x1268940e0>)",
"path": "vectorbt.generic.nb.rolling_min_nb"
},
"rolling_max": {
"func": "CPUDispatcher(<function rolling_max_nb at 0x126894720>)",
"path": "vectorbt.generic.nb.rolling_max_nb"
},
"rolling_mean": {
"func": "CPUDispatcher(<function rolling_mean_nb at 0x126894d60>)",
"path": "vectorbt.generic.nb.rolling_mean_nb"
},
"expanding_min": {
"func": "CPUDispatcher(<function expanding_min_nb at 0x126896660>)",
"path": "vectorbt.generic.nb.expanding_min_nb"
},
"expanding_max": {
"func": "CPUDispatcher(<function expanding_max_nb at 0x126896ca0>)",
"path": "vectorbt.generic.nb.expanding_max_nb"
},
"expanding_mean": {
"func": "CPUDispatcher(<function expanding_mean_nb at 0x126897240>)",
"path": "vectorbt.generic.nb.expanding_mean_nb"
},
"product": {
"func": "CPUDispatcher(<function nanprod_nb at 0x1268b1a80>)",
"is_reducing": true,
"path": "vectorbt.generic.nb.nanprod_nb"
}
})
transform_config Config¶
Config of transform methods to be added to GenericAccessor.
Config({
"binarize": {
"transformer": "<class 'sklearn.preprocessing._data.Binarizer'>",
"docstring": "See `sklearn.preprocessing.Binarizer`."
},
"minmax_scale": {
"transformer": "<class 'sklearn.preprocessing._data.MinMaxScaler'>",
"docstring": "See `sklearn.preprocessing.MinMaxScaler`."
},
"maxabs_scale": {
"transformer": "<class 'sklearn.preprocessing._data.MaxAbsScaler'>",
"docstring": "See `sklearn.preprocessing.MaxAbsScaler`."
},
"normalize": {
"transformer": "<class 'sklearn.preprocessing._data.Normalizer'>",
"docstring": "See `sklearn.preprocessing.Normalizer`."
},
"robust_scale": {
"transformer": "<class 'sklearn.preprocessing._data.RobustScaler'>",
"docstring": "See `sklearn.preprocessing.RobustScaler`."
},
"scale": {
"transformer": "<class 'sklearn.preprocessing._data.StandardScaler'>",
"docstring": "See `sklearn.preprocessing.StandardScaler`."
},
"quantile_transform": {
"transformer": "<class 'sklearn.preprocessing._data.QuantileTransformer'>",
"docstring": "See `sklearn.preprocessing.QuantileTransformer`."
},
"power_transform": {
"transformer": "<class 'sklearn.preprocessing._data.PowerTransformer'>",
"docstring": "See `sklearn.preprocessing.PowerTransformer`."
}
})
GenericAccessor class¶
GenericAccessor(
obj,
mapping=None,
**kwargs
)
Accessor on top of data of any type. For both, Series and DataFrames.
Accessible through pd.Series.vbt
and pd.DataFrame.vbt
.
Superclasses
- AttrResolver
- BaseAccessor
- Configured
- Documented
- IndexingBase
- PandasIndexer
- Pickleable
- PlotsBuilderMixin
- StatsBuilderMixin
- Wrapping
Inherited members
- AttrResolver.deep_getattr()
- AttrResolver.post_resolve_attr()
- AttrResolver.pre_resolve_attr()
- AttrResolver.resolve_attr()
- BaseAccessor.align_to()
- BaseAccessor.apply()
- BaseAccessor.apply_and_concat()
- BaseAccessor.apply_on_index()
- BaseAccessor.broadcast()
- BaseAccessor.broadcast_to()
- BaseAccessor.combine()
- BaseAccessor.concat()
- BaseAccessor.config
- BaseAccessor.df_accessor_cls
- BaseAccessor.drop_duplicate_levels()
- BaseAccessor.drop_levels()
- BaseAccessor.drop_redundant_levels()
- BaseAccessor.empty()
- BaseAccessor.empty_like()
- BaseAccessor.iloc
- BaseAccessor.indexing_func()
- BaseAccessor.indexing_kwargs
- BaseAccessor.loc
- BaseAccessor.make_symmetric()
- BaseAccessor.obj
- BaseAccessor.rename_levels()
- BaseAccessor.repeat()
- BaseAccessor.select_levels()
- BaseAccessor.self_aliases
- BaseAccessor.sr_accessor_cls
- BaseAccessor.stack_index()
- BaseAccessor.tile()
- BaseAccessor.to_1d_array()
- BaseAccessor.to_2d_array()
- BaseAccessor.to_dict()
- BaseAccessor.unstack_to_array()
- BaseAccessor.unstack_to_df()
- BaseAccessor.wrapper
- BaseAccessor.writeable_attrs
- Configured.copy()
- Configured.dumps()
- Configured.loads()
- Configured.replace()
- Configured.to_doc()
- Configured.update_config()
- PandasIndexer.xs()
- Pickleable.load()
- Pickleable.save()
- PlotsBuilderMixin.build_subplots_doc()
- PlotsBuilderMixin.override_subplots_doc()
- PlotsBuilderMixin.plots()
- StatsBuilderMixin.build_metrics_doc()
- StatsBuilderMixin.override_metrics_doc()
- StatsBuilderMixin.stats()
- Wrapping.regroup()
- Wrapping.select_one()
- Wrapping.select_one_from_obj()
Subclasses
apply_along_axis method¶
GenericAccessor.apply_along_axis(
apply_func_nb,
*args,
axis=0,
wrap_kwargs=None
)
Apply a function apply_func_nb
along an axis.
apply_and_reduce method¶
GenericAccessor.apply_and_reduce(
apply_func_nb,
reduce_func_nb,
apply_args=None,
reduce_args=None,
wrap_kwargs=None
)
Usage
>>> greater_nb = njit(lambda col, a: a[a > 2])
>>> mean_nb = njit(lambda col, a: np.nanmean(a))
>>> df.vbt.apply_and_reduce(greater_nb, mean_nb)
a 4.0
b 4.0
c 3.0
dtype: float64
apply_mapping method¶
GenericAccessor.apply_mapping(
**kwargs
)
See apply_mapping().
applymap method¶
GenericAccessor.applymap(
apply_func_nb,
*args,
wrap_kwargs=None
)
See applymap_nb().
Usage
>>> multiply_nb = njit(lambda i, col, a: a ** 2)
>>> df.vbt.applymap(multiply_nb)
a b c
2020-01-01 1.0 25.0 1.0
2020-01-02 4.0 16.0 4.0
2020-01-03 9.0 9.0 9.0
2020-01-04 16.0 4.0 4.0
2020-01-05 25.0 1.0 1.0
barplot method¶
GenericAccessor.barplot(
trace_names=None,
x_labels=None,
return_fig=True,
**kwargs
)
Create Bar and return the figure.
Usage
>>> df.vbt.barplot()
bfill method¶
GenericAccessor.bfill(
*,
wrap_kwargs=None
)
See bfill_nb().
binarize method¶
GenericAccessor.binarize(
*,
threshold=0.0,
copy=True,
**kwargs
)
See sklearn.preprocessing.Binarizer
.
boxplot method¶
GenericAccessor.boxplot(
trace_names=None,
group_by=None,
return_fig=True,
**kwargs
)
Create Box and return the figure.
Usage
>>> df.vbt.boxplot()
bshift method¶
GenericAccessor.bshift(
n=1,
fill_value=nan,
*,
wrap_kwargs=None
)
See bshift_nb().
count method¶
GenericAccessor.count(
group_by=None,
wrap_kwargs=None
)
Return count of non-NaN elements.
crossed_above method¶
GenericAccessor.crossed_above(
other,
wait=0,
broadcast_kwargs=None,
wrap_kwargs=None
)
Generate crossover above another array.
See crossed_above_nb().
Usage
>>> df['b'].vbt.crossed_above(df['c'])
2020-01-01 False
2020-01-02 False
2020-01-03 False
2020-01-04 False
2020-01-05 False
dtype: bool
>>> df['a'].vbt.crossed_above(df['b'])
2020-01-01 False
2020-01-02 False
2020-01-03 False
2020-01-04 True
2020-01-05 False
dtype: bool
>>> df['a'].vbt.crossed_above(df['b'], wait=1)
2020-01-01 False
2020-01-02 False
2020-01-03 False
2020-01-04 False
2020-01-05 True
dtype: bool
crossed_below method¶
GenericAccessor.crossed_below(
other,
wait=0,
broadcast_kwargs=None,
wrap_kwargs=None
)
Generate crossover below another array.
See crossed_above_nb() but in reversed order.
cumprod method¶
GenericAccessor.cumprod(
*,
wrap_kwargs=None
)
See nancumprod_nb().
cumsum method¶
GenericAccessor.cumsum(
*,
wrap_kwargs=None
)
See nancumsum_nb().
describe method¶
GenericAccessor.describe(
percentiles=None,
ddof=1,
group_by=None,
wrap_kwargs=None
)
See describe_reduce_nb().
For percentiles
, see pd.DataFrame.describe
.
Usage
>>> df.vbt.describe()
a b c
count 5.000000 5.000000 5.00000
mean 3.000000 3.000000 1.80000
std 1.581139 1.581139 0.83666
min 1.000000 1.000000 1.00000
25% 2.000000 2.000000 1.00000
50% 3.000000 3.000000 2.00000
75% 4.000000 4.000000 2.00000
max 5.000000 5.000000 3.00000
diff method¶
GenericAccessor.diff(
n=1,
*,
wrap_kwargs=None
)
See diff_nb().
drawdown method¶
GenericAccessor.drawdown(
wrap_kwargs=None
)
Drawdown series.
drawdowns property¶
GenericAccessor.get_drawdowns() with default arguments.
ewm_mean method¶
GenericAccessor.ewm_mean(
span,
minp=0,
adjust=True,
wrap_kwargs=None
)
See ewm_mean_nb().
ewm_std method¶
GenericAccessor.ewm_std(
span,
minp=0,
adjust=True,
ddof=1,
wrap_kwargs=None
)
See ewm_std_nb().
expanding_apply method¶
GenericAccessor.expanding_apply(
apply_func_nb,
*args,
minp=1,
on_matrix=False,
wrap_kwargs=None
)
See expanding_apply_nb() and expanding_matrix_apply_nb() for on_matrix=True
.
Usage
>>> mean_nb = njit(lambda i, col, a: np.nanmean(a))
>>> df.vbt.expanding_apply(mean_nb)
a b c
2020-01-01 1.0 5.0 1.0
2020-01-02 1.5 4.5 1.5
2020-01-03 2.0 4.0 2.0
2020-01-04 2.5 3.5 2.0
2020-01-05 3.0 3.0 1.8
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> df.vbt.expanding_apply(mean_matrix_nb, on_matrix=True)
a b c
2020-01-01 2.333333 2.333333 2.333333
2020-01-02 2.500000 2.500000 2.500000
2020-01-03 2.666667 2.666667 2.666667
2020-01-04 2.666667 2.666667 2.666667
2020-01-05 2.600000 2.600000 2.600000
expanding_max method¶
GenericAccessor.expanding_max(
minp=1,
*,
wrap_kwargs=None
)
See expanding_max_nb().
expanding_mean method¶
GenericAccessor.expanding_mean(
minp=1,
*,
wrap_kwargs=None
)
See expanding_mean_nb().
expanding_min method¶
GenericAccessor.expanding_min(
minp=1,
*,
wrap_kwargs=None
)
See expanding_min_nb().
expanding_split method¶
GenericAccessor.expanding_split(
**kwargs
)
Split using GenericAccessor.split() on ExpandingSplitter.
Usage
>>> train_set, valid_set, test_set = sr.vbt.expanding_split(
... n=5, set_lens=(1, 1), min_len=3, left_to_right=False)
>>> train_set[0]
split_idx 0 1 2 3 4 5 6 7
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
1 NaN 1.0 1.0 1.0 1.0 1.0 1.0 1
2 NaN NaN 2.0 2.0 2.0 2.0 2.0 2
3 NaN NaN NaN 3.0 3.0 3.0 3.0 3
4 NaN NaN NaN NaN 4.0 4.0 4.0 4
5 NaN NaN NaN NaN NaN 5.0 5.0 5
6 NaN NaN NaN NaN NaN NaN 6.0 6
7 NaN NaN NaN NaN NaN NaN NaN 7
>>> valid_set[0]
split_idx 0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7 8
>>> test_set[0]
split_idx 0 1 2 3 4 5 6 7
0 2 3 4 5 6 7 8 9
>>> sr.vbt.expanding_split(
... set_lens=(1, 1), min_len=3, left_to_right=False,
... plot=True, trace_names=['train', 'valid', 'test'])
expanding_std method¶
GenericAccessor.expanding_std(
minp=1,
ddof=1,
wrap_kwargs=None
)
See expanding_std_nb().
ffill method¶
GenericAccessor.ffill(
*,
wrap_kwargs=None
)
See ffill_nb().
fillna method¶
GenericAccessor.fillna(
value,
*,
wrap_kwargs=None
)
See fillna_nb().
filter method¶
GenericAccessor.filter(
filter_func_nb,
*args,
wrap_kwargs=None
)
See filter_nb().
Usage
>>> greater_nb = njit(lambda i, col, a: a > 2)
>>> df.vbt.filter(greater_nb)
a b c
2020-01-01 NaN 5.0 NaN
2020-01-02 NaN 4.0 NaN
2020-01-03 3.0 3.0 3.0
2020-01-04 4.0 NaN NaN
2020-01-05 5.0 NaN NaN
fshift method¶
GenericAccessor.fshift(
n=1,
fill_value=nan,
*,
wrap_kwargs=None
)
See fshift_nb().
get_drawdowns method¶
GenericAccessor.get_drawdowns(
wrapper_kwargs=None,
**kwargs
)
Generate drawdown records.
See Drawdowns.
get_ranges method¶
GenericAccessor.get_ranges(
wrapper_kwargs=None,
**kwargs
)
Generate range records.
See Ranges.
groupby_apply method¶
GenericAccessor.groupby_apply(
by,
apply_func_nb,
*args,
on_matrix=False,
wrap_kwargs=None,
**kwargs
)
See groupby_apply_nb() and groupby_matrix_apply_nb() for on_matrix=True
.
For by
, see pd.DataFrame.groupby
.
Usage
>>> mean_nb = njit(lambda i, col, a: np.nanmean(a))
>>> df.vbt.groupby_apply([1, 1, 2, 2, 3], mean_nb)
a b c
1 1.5 4.5 1.5
2 3.5 2.5 2.5
3 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> df.vbt.groupby_apply([1, 1, 2, 2, 3], mean_matrix_nb, on_matrix=True)
a b c
1 2.500000 2.500000 2.500000
2 2.833333 2.833333 2.833333
3 2.333333 2.333333 2.333333
histplot method¶
GenericAccessor.histplot(
trace_names=None,
group_by=None,
return_fig=True,
**kwargs
)
Create Histogram and return the figure.
Usage
>>> df.vbt.histplot()
idxmax method¶
GenericAccessor.idxmax(
group_by=None,
order='C',
wrap_kwargs=None
)
Return labeled index of max of non-NaN elements.
idxmin method¶
GenericAccessor.idxmin(
group_by=None,
order='C',
wrap_kwargs=None
)
Return labeled index of min of non-NaN elements.
lineplot method¶
GenericAccessor.lineplot(
**kwargs
)
GenericAccessor.plot() with 'lines' mode.
Usage
>>> df.vbt.lineplot()
mapping property¶
Mapping.
max method¶
GenericAccessor.max(
group_by=None,
wrap_kwargs=None
)
Return max of non-NaN elements.
maxabs_scale method¶
GenericAccessor.maxabs_scale(
*,
copy=True,
**kwargs
)
See sklearn.preprocessing.MaxAbsScaler
.
mean method¶
GenericAccessor.mean(
group_by=None,
wrap_kwargs=None
)
Return mean of non-NaN elements.
median method¶
GenericAccessor.median(
group_by=None,
wrap_kwargs=None
)
Return median of non-NaN elements.
metrics class variable¶
Metrics supported by GenericAccessor.
Config({
"start": {
"title": "Start",
"calc_func": "<function GenericAccessor.<lambda> at 0x1562347c0>",
"agg_func": null,
"tags": "wrapper"
},
"end": {
"title": "End",
"calc_func": "<function GenericAccessor.<lambda> at 0x156234860>",
"agg_func": null,
"tags": "wrapper"
},
"period": {
"title": "Period",
"calc_func": "<function GenericAccessor.<lambda> at 0x156234900>",
"apply_to_timedelta": true,
"agg_func": null,
"tags": "wrapper"
},
"count": {
"title": "Count",
"calc_func": "count",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"mean": {
"title": "Mean",
"calc_func": "mean",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"std": {
"title": "Std",
"calc_func": "std",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"min": {
"title": "Min",
"calc_func": "min",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"median": {
"title": "Median",
"calc_func": "median",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"max": {
"title": "Max",
"calc_func": "max",
"inv_check_has_mapping": true,
"tags": [
"generic",
"describe"
]
},
"idx_min": {
"title": "Min Index",
"calc_func": "idxmin",
"agg_func": null,
"inv_check_has_mapping": true,
"tags": [
"generic",
"index"
]
},
"idx_max": {
"title": "Max Index",
"calc_func": "idxmax",
"agg_func": null,
"inv_check_has_mapping": true,
"tags": [
"generic",
"index"
]
},
"value_counts": {
"title": "Value Counts",
"calc_func": "<function GenericAccessor.<lambda> at 0x1562349a0>",
"resolve_value_counts": true,
"check_has_mapping": true,
"tags": [
"generic",
"value_counts"
]
}
})
Returns GenericAccessor._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 GenericAccessor._metrics
.
min method¶
GenericAccessor.min(
group_by=None,
wrap_kwargs=None
)
Return min of non-NaN elements.
minmax_scale method¶
GenericAccessor.minmax_scale(
feature_range=(0, 1),
*,
copy=True,
clip=False,
**kwargs
)
See sklearn.preprocessing.MinMaxScaler
.
normalize method¶
GenericAccessor.normalize(
norm='l2',
*,
copy=True,
**kwargs
)
See sklearn.preprocessing.Normalizer
.
pct_change method¶
GenericAccessor.pct_change(
n=1,
*,
wrap_kwargs=None
)
See pct_change_nb().
plot method¶
GenericAccessor.plot(
trace_names=None,
x_labels=None,
return_fig=True,
**kwargs
)
Create Scatter and return the figure.
Usage
>>> df.vbt.plot()
plots_defaults property¶
Defaults for PlotsBuilderMixin.plots().
Merges PlotsBuilderMixin.plots_defaults and generic.plots
from settings.
power_transform method¶
GenericAccessor.power_transform(
method='yeo-johnson',
*,
standardize=True,
copy=True,
**kwargs
)
See sklearn.preprocessing.PowerTransformer
.
product method¶
GenericAccessor.product(
*,
wrap_kwargs=None
)
See nanprod_nb().
quantile_transform method¶
GenericAccessor.quantile_transform(
*,
n_quantiles=1000,
output_distribution='uniform',
ignore_implicit_zeros=False,
subsample=10000,
random_state=None,
copy=True,
**kwargs
)
See sklearn.preprocessing.QuantileTransformer
.
range_split method¶
GenericAccessor.range_split(
**kwargs
)
Split using GenericAccessor.split() on RangeSplitter.
Usage
>>> range_df, range_indexes = sr.vbt.range_split(n=2)
>>> range_df
split_idx 0 1
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> range_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-05'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-06', ..., '2020-01-10'], dtype='datetime64[ns]', name='split_1')]
>>> range_df, range_indexes = sr.vbt.range_split(range_len=4)
>>> range_df
split_idx 0 1 2 3 4 5 6
0 0 1 2 3 4 5 6
1 1 2 3 4 5 6 7
2 2 3 4 5 6 7 8
3 3 4 5 6 7 8 9
>>> range_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-04'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-02', ..., '2020-01-05'], dtype='datetime64[ns]', name='split_1'),
DatetimeIndex(['2020-01-03', ..., '2020-01-06'], dtype='datetime64[ns]', name='split_2'),
DatetimeIndex(['2020-01-04', ..., '2020-01-07'], dtype='datetime64[ns]', name='split_3'),
DatetimeIndex(['2020-01-05', ..., '2020-01-08'], dtype='datetime64[ns]', name='split_4'),
DatetimeIndex(['2020-01-06', ..., '2020-01-09'], dtype='datetime64[ns]', name='split_5'),
DatetimeIndex(['2020-01-07', ..., '2020-01-10'], dtype='datetime64[ns]', name='split_6')]
>>> range_df, range_indexes = sr.vbt.range_split(start_idxs=[0, 2], end_idxs=[5, 7])
>>> range_df
split_idx 0 1
0 0 2
1 1 3
2 2 4
3 3 5
4 4 6
5 5 7
>>> range_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-06'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-03', ..., '2020-01-08'], dtype='datetime64[ns]', name='split_1')]
>>> range_df, range_indexes = sr.vbt.range_split(start_idxs=[0], end_idxs=[2, 3, 4])
>>> range_df
split_idx 0 1 2
0 0.0 0.0 0
1 1.0 1.0 1
2 2.0 2.0 2
3 NaN 3.0 3
4 NaN NaN 4
>>> range_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-03'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-01', ..., '2020-01-04'], dtype='datetime64[ns]', name='split_1'),
DatetimeIndex(['2020-01-01', ..., '2020-01-05'], dtype='datetime64[ns]', name='split_2')]
>>> range_df, range_indexes = sr.vbt.range_split(
... start_idxs=pd.Index(['2020-01-01', '2020-01-02']),
... end_idxs=pd.Index(['2020-01-04', '2020-01-05'])
... )
>>> range_df
split_idx 0 1
0 0 1
1 1 2
2 2 3
3 3 4
>>> range_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-04'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-02', ..., '2020-01-05'], dtype='datetime64[ns]', name='split_1')]
>>> sr.vbt.range_split(
... start_idxs=pd.Index(['2020-01-01', '2020-01-02', '2020-01-01']),
... end_idxs=pd.Index(['2020-01-08', '2020-01-04', '2020-01-07']),
... plot=True
... )
ranges property¶
GenericAccessor.get_ranges() with default arguments.
rebase method¶
GenericAccessor.rebase(
base,
wrap_kwargs=None
)
Rebase all series to a given intial base.
This makes comparing/plotting different series together easier. Will forward and backward fill NaN values.
reduce method¶
GenericAccessor.reduce(
reduce_func_nb,
*args,
returns_array=False,
returns_idx=False,
flatten=False,
order='C',
to_index=True,
group_by=None,
wrap_kwargs=None
)
Reduce by column.
See flat_reduce_grouped_to_array_nb() if grouped, returns_array
is True and flatten
is True. See flat_reduce_grouped_nb() if grouped, returns_array
is False and flatten
is True. See reduce_grouped_to_array_nb() if grouped, returns_array
is True and flatten
is False. See reduce_grouped_nb() if grouped, returns_array
is False and flatten
is False. See reduce_to_array_nb() if not grouped and returns_array
is True. See reduce_nb() if not grouped and returns_array
is False.
Set returns_idx
to True if values returned by reduce_func_nb
are indices/positions. Set to_index
to False to return raw positions instead of labels.
Usage
>>> mean_nb = njit(lambda col, a: np.nanmean(a))
>>> df.vbt.reduce(mean_nb)
a 3.0
b 3.0
c 1.8
dtype: float64
>>> argmax_nb = njit(lambda col, a: np.argmax(a))
>>> df.vbt.reduce(argmax_nb, returns_idx=True)
a 2020-01-05
b 2020-01-01
c 2020-01-03
dtype: datetime64[ns]
>>> argmax_nb = njit(lambda col, a: np.argmax(a))
>>> df.vbt.reduce(argmax_nb, returns_idx=True, to_index=False)
a 4
b 0
c 2
dtype: int64
>>> min_max_nb = njit(lambda col, a: np.array([np.nanmin(a), np.nanmax(a)]))
>>> df.vbt.reduce(min_max_nb, returns_array=True, wrap_kwargs=dict(name_or_index=['min', 'max']))
a b c
min 1.0 1.0 1.0
max 5.0 5.0 3.0
>>> group_by = pd.Series(['first', 'first', 'second'], name='group')
>>> df.vbt.reduce(mean_nb, group_by=group_by)
group
first 3.0
second 1.8
dtype: float64
>>> df.vbt.reduce(min_max_nb, name_or_index=['min', 'max'],
... returns_array=True, group_by=group_by)
group first second
min 1.0 1.0
max 5.0 3.0
resample_apply method¶
GenericAccessor.resample_apply(
freq,
apply_func_nb,
*args,
on_matrix=False,
wrap_kwargs=None,
**kwargs
)
See groupby_apply_nb() and groupby_matrix_apply_nb() for on_matrix=True
.
For freq
, see pd.DataFrame.resample
.
Usage
>>> mean_nb = njit(lambda i, col, a: np.nanmean(a))
>>> df.vbt.resample_apply('2d', mean_nb)
a b c
2020-01-01 1.5 4.5 1.5
2020-01-03 3.5 2.5 2.5
2020-01-05 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> df.vbt.resample_apply('2d', mean_matrix_nb, on_matrix=True)
a b c
2020-01-01 2.500000 2.500000 2.500000
2020-01-03 2.833333 2.833333 2.833333
2020-01-05 2.333333 2.333333 2.333333
resolve_self method¶
GenericAccessor.resolve_self(
cond_kwargs=None,
custom_arg_names=None,
impacts_caching=True,
silence_warnings=False
)
Resolve self.
Creates a copy of this instance mapping
is different in cond_kwargs
.
robust_scale method¶
GenericAccessor.robust_scale(
*,
with_centering=True,
with_scaling=True,
quantile_range=(25.0, 75.0),
copy=True,
unit_variance=False,
**kwargs
)
See sklearn.preprocessing.RobustScaler
.
rolling_apply method¶
GenericAccessor.rolling_apply(
window,
apply_func_nb,
*args,
minp=None,
on_matrix=False,
wrap_kwargs=None
)
See rolling_apply_nb() and rolling_matrix_apply_nb() for on_matrix=True
.
Usage
>>> mean_nb = njit(lambda i, col, a: np.nanmean(a))
>>> df.vbt.rolling_apply(3, mean_nb)
a b c
2020-01-01 1.0 5.0 1.000000
2020-01-02 1.5 4.5 1.500000
2020-01-03 2.0 4.0 2.000000
2020-01-04 3.0 3.0 2.333333
2020-01-05 4.0 2.0 2.000000
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> df.vbt.rolling_apply(3, mean_matrix_nb, on_matrix=True)
a b c
2020-01-01 2.333333 2.333333 2.333333
2020-01-02 2.500000 2.500000 2.500000
2020-01-03 2.666667 2.666667 2.666667
2020-01-04 2.777778 2.777778 2.777778
2020-01-05 2.666667 2.666667 2.666667
rolling_max method¶
GenericAccessor.rolling_max(
window,
minp=None,
*,
wrap_kwargs=None
)
See rolling_max_nb().
rolling_mean method¶
GenericAccessor.rolling_mean(
window,
minp=None,
*,
wrap_kwargs=None
)
See rolling_mean_nb().
rolling_min method¶
GenericAccessor.rolling_min(
window,
minp=None,
*,
wrap_kwargs=None
)
See rolling_min_nb().
rolling_split method¶
GenericAccessor.rolling_split(
**kwargs
)
Split using GenericAccessor.split() on RollingSplitter.
Usage
>>> train_set, valid_set, test_set = sr.vbt.rolling_split(
... window_len=5, set_lens=(1, 1), left_to_right=False)
>>> train_set[0]
split_idx 0 1 2 3 4 5
0 0 1 2 3 4 5
1 1 2 3 4 5 6
2 2 3 4 5 6 7
>>> valid_set[0]
split_idx 0 1 2 3 4 5
0 3 4 5 6 7 8
>>> test_set[0]
split_idx 0 1 2 3 4 5
0 4 5 6 7 8 9
>>> sr.vbt.rolling_split(
... window_len=5, set_lens=(1, 1), left_to_right=False,
... plot=True, trace_names=['train', 'valid', 'test'])
rolling_std method¶
GenericAccessor.rolling_std(
window,
minp=None,
ddof=1,
wrap_kwargs=None
)
See rolling_std_nb().
scale method¶
GenericAccessor.scale(
*,
copy=True,
with_mean=True,
with_std=True,
**kwargs
)
See sklearn.preprocessing.StandardScaler
.
scatterplot method¶
GenericAccessor.scatterplot(
**kwargs
)
GenericAccessor.plot() with 'markers' mode.
Usage
>>> df.vbt.scatterplot()
shuffle method¶
GenericAccessor.shuffle(
seed=None,
*,
wrap_kwargs=None
)
See shuffle_nb().
split method¶
GenericAccessor.split(
splitter,
stack_kwargs=None,
keys=None,
plot=False,
trace_names=None,
heatmap_kwargs=None,
**kwargs
)
Split using a splitter.
Returns a tuple of tuples, each corresponding to a set and composed of a dataframe and split indexes.
A splitter can be any class instance that has split
method, ideally subclassing sklearn.model_selection.BaseCrossValidator
or BaseSplitter.
heatmap_kwargs
are passed to Heatmap if plot
is True, can be a dictionary or a list per set, for example, to set trace name for each set ('train', 'test', etc.).
**kwargs
are passed to the split
method.
Note
The datetime-like format of the index will be lost as result of this operation. Make sure to store the index metadata such as frequency information beforehand.
Usage
>>> from sklearn.model_selection import TimeSeriesSplit
>>> splitter = TimeSeriesSplit(n_splits=3)
>>> (train_df, train_indexes), (test_df, test_indexes) = sr.vbt.split(splitter)
>>> train_df
split_idx 0 1 2
0 0.0 0.0 0
1 1.0 1.0 1
2 2.0 2.0 2
3 3.0 3.0 3
4 NaN 4.0 4
5 NaN 5.0 5
6 NaN NaN 6
7 NaN NaN 7
>>> train_indexes
[DatetimeIndex(['2020-01-01', ..., '2020-01-04'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-01', ..., '2020-01-06'], dtype='datetime64[ns]', name='split_1'),
DatetimeIndex(['2020-01-01', ..., '2020-01-08'], dtype='datetime64[ns]', name='split_2')]
>>> test_df
split_idx 0 1 2
0 4 6 8
1 5 7 9
>>> test_indexes
[DatetimeIndex(['2020-01-05', '2020-01-06'], dtype='datetime64[ns]', name='split_0'),
DatetimeIndex(['2020-01-07', '2020-01-08'], dtype='datetime64[ns]', name='split_1'),
DatetimeIndex(['2020-01-09', '2020-01-10'], dtype='datetime64[ns]', name='split_2')]
>>> sr.vbt.split(splitter, plot=True, trace_names=['train', 'test'])
stats_defaults property¶
Defaults for StatsBuilderMixin.stats().
Merges StatsBuilderMixin.stats_defaults and generic.stats
from settings.
std method¶
GenericAccessor.std(
ddof=1,
group_by=None,
wrap_kwargs=None
)
Return standard deviation of non-NaN elements.
subplots class variable¶
Subplots supported by GenericAccessor.
Config({
"plot": {
"check_is_not_grouped": true,
"plot_func": "plot",
"pass_trace_names": false,
"tags": "generic"
}
})
Returns GenericAccessor._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 GenericAccessor._subplots
.
sum method¶
GenericAccessor.sum(
group_by=None,
wrap_kwargs=None
)
Return sum of non-NaN elements.
to_mapped method¶
GenericAccessor.to_mapped(
dropna=True,
dtype=None,
group_by=None,
**kwargs
)
Convert this object into an instance of MappedArray.
to_returns method¶
GenericAccessor.to_returns(
**kwargs
)
Get returns of this object.
transform method¶
GenericAccessor.transform(
transformer,
wrap_kwargs=None,
**kwargs
)
Transform using a transformer.
A transformer can be any class instance that has transform
and fit_transform
methods, ideally subclassing sklearn.base.TransformerMixin
and sklearn.base.BaseEstimator
.
Will fit transformer
if not fitted.
**kwargs
are passed to the transform
or fit_transform
method.
Usage
>>> from sklearn.preprocessing import MinMaxScaler
>>> df.vbt.transform(MinMaxScaler((-1, 1)))
a b c
2020-01-01 -1.0 1.0 -1.0
2020-01-02 -0.5 0.5 0.0
2020-01-03 0.0 0.0 1.0
2020-01-04 0.5 -0.5 0.0
2020-01-05 1.0 -1.0 -1.0
>>> fitted_scaler = MinMaxScaler((-1, 1)).fit(np.array([[2], [4]]))
>>> df.vbt.transform(fitted_scaler)
a b c
2020-01-01 -2.0 2.0 -2.0
2020-01-02 -1.0 1.0 -1.0
2020-01-03 0.0 0.0 0.0
2020-01-04 1.0 -1.0 -1.0
2020-01-05 2.0 -2.0 -2.0
value_counts method¶
GenericAccessor.value_counts(
normalize=False,
sort_uniques=True,
sort=False,
ascending=False,
dropna=False,
group_by=None,
mapping=None,
incl_all_keys=False,
wrap_kwargs=None,
**kwargs
)
Return a Series/DataFrame containing counts of unique values.
- Enable
normalize
flag to return the relative frequencies of the unique values. - Enable
sort_uniques
flag to sort uniques. - Enable
sort
flag to sort by frequencies. - Enable
ascending
flag to sort in ascending order. - Enable
dropna
flag to exclude counts of NaN. - Enable
incl_all_keys
to include all mapping keys, no only those that are present in the array.
Mapping will be applied using apply_mapping() with **kwargs
.
zscore method¶
GenericAccessor.zscore(
**kwargs
)
Compute z-score using sklearn.preprocessing.StandardScaler
.
GenericDFAccessor class¶
GenericDFAccessor(
obj,
mapping=None,
**kwargs
)
Accessor on top of data of any type. For DataFrames only.
Accessible through pd.DataFrame.vbt
.
Superclasses
- AttrResolver
- BaseAccessor
- BaseDFAccessor
- Configured
- Documented
- GenericAccessor
- IndexingBase
- PandasIndexer
- Pickleable
- PlotsBuilderMixin
- StatsBuilderMixin
- Wrapping
Inherited members
- AttrResolver.deep_getattr()
- AttrResolver.post_resolve_attr()
- AttrResolver.pre_resolve_attr()
- AttrResolver.resolve_attr()
- BaseAccessor.align_to()
- BaseAccessor.apply()
- BaseAccessor.apply_and_concat()
- BaseAccessor.apply_on_index()
- BaseAccessor.broadcast()
- BaseAccessor.broadcast_to()
- BaseAccessor.combine()
- BaseAccessor.concat()
- BaseAccessor.drop_duplicate_levels()
- BaseAccessor.drop_levels()
- BaseAccessor.drop_redundant_levels()
- BaseAccessor.empty()
- BaseAccessor.empty_like()
- BaseAccessor.indexing_func()
- BaseAccessor.make_symmetric()
- BaseAccessor.rename_levels()
- BaseAccessor.repeat()
- BaseAccessor.select_levels()
- BaseAccessor.stack_index()
- BaseAccessor.tile()
- BaseAccessor.to_1d_array()
- BaseAccessor.to_2d_array()
- BaseAccessor.to_dict()
- BaseAccessor.unstack_to_array()
- BaseAccessor.unstack_to_df()
- Configured.copy()
- Configured.dumps()
- Configured.loads()
- Configured.replace()
- Configured.to_doc()
- Configured.update_config()
- GenericAccessor.apply_along_axis()
- GenericAccessor.apply_and_reduce()
- GenericAccessor.apply_mapping()
- GenericAccessor.applymap()
- GenericAccessor.barplot()
- GenericAccessor.bfill()
- GenericAccessor.binarize()
- GenericAccessor.boxplot()
- GenericAccessor.bshift()
- GenericAccessor.config
- GenericAccessor.count()
- GenericAccessor.crossed_above()
- GenericAccessor.crossed_below()
- GenericAccessor.cumprod()
- GenericAccessor.cumsum()
- GenericAccessor.describe()
- GenericAccessor.df_accessor_cls
- GenericAccessor.diff()
- GenericAccessor.drawdown()
- GenericAccessor.drawdowns
- GenericAccessor.ewm_mean()
- GenericAccessor.ewm_std()
- GenericAccessor.expanding_apply()
- GenericAccessor.expanding_max()
- GenericAccessor.expanding_mean()
- GenericAccessor.expanding_min()
- GenericAccessor.expanding_split()
- GenericAccessor.expanding_std()
- GenericAccessor.ffill()
- GenericAccessor.fillna()
- GenericAccessor.filter()
- GenericAccessor.fshift()
- GenericAccessor.get_drawdowns()
- GenericAccessor.get_ranges()
- GenericAccessor.groupby_apply()
- GenericAccessor.histplot()
- GenericAccessor.idxmax()
- GenericAccessor.idxmin()
- GenericAccessor.iloc
- GenericAccessor.indexing_kwargs
- GenericAccessor.lineplot()
- GenericAccessor.loc
- GenericAccessor.mapping
- GenericAccessor.max()
- GenericAccessor.maxabs_scale()
- GenericAccessor.mean()
- GenericAccessor.median()
- GenericAccessor.min()
- GenericAccessor.minmax_scale()
- GenericAccessor.normalize()
- GenericAccessor.obj
- GenericAccessor.pct_change()
- GenericAccessor.plot()
- GenericAccessor.plots_defaults
- GenericAccessor.power_transform()
- GenericAccessor.product()
- GenericAccessor.quantile_transform()
- GenericAccessor.range_split()
- GenericAccessor.ranges
- GenericAccessor.rebase()
- GenericAccessor.reduce()
- GenericAccessor.resample_apply()
- GenericAccessor.resolve_self()
- GenericAccessor.robust_scale()
- GenericAccessor.rolling_apply()
- GenericAccessor.rolling_max()
- GenericAccessor.rolling_mean()
- GenericAccessor.rolling_min()
- GenericAccessor.rolling_split()
- GenericAccessor.rolling_std()
- GenericAccessor.scale()
- GenericAccessor.scatterplot()
- GenericAccessor.self_aliases
- GenericAccessor.shuffle()
- GenericAccessor.split()
- GenericAccessor.sr_accessor_cls
- GenericAccessor.stats_defaults
- GenericAccessor.std()
- GenericAccessor.sum()
- GenericAccessor.to_mapped()
- GenericAccessor.to_returns()
- GenericAccessor.transform()
- GenericAccessor.value_counts()
- GenericAccessor.wrapper
- GenericAccessor.writeable_attrs
- GenericAccessor.zscore()
- PandasIndexer.xs()
- Pickleable.load()
- Pickleable.save()
- PlotsBuilderMixin.build_subplots_doc()
- PlotsBuilderMixin.override_subplots_doc()
- PlotsBuilderMixin.plots()
- StatsBuilderMixin.build_metrics_doc()
- StatsBuilderMixin.override_metrics_doc()
- StatsBuilderMixin.stats()
- Wrapping.regroup()
- Wrapping.select_one()
- Wrapping.select_one_from_obj()
Subclasses
flatten_grouped method¶
GenericDFAccessor.flatten_grouped(
group_by=None,
order='C',
wrap_kwargs=None
)
Flatten each group of columns.
See flatten_grouped_nb(). If all groups have the same length, see flatten_uniform_grouped_nb().
Warning
Make sure that the distribution of group lengths is close to uniform, otherwise groups with less columns will be filled with NaN and needlessly occupy memory.
Usage
>>> group_by = pd.Series(['first', 'first', 'second'], name='group')
>>> df.vbt.flatten_grouped(group_by=group_by, order='C')
group first second
2020-01-01 1.0 1.0
2020-01-01 5.0 NaN
2020-01-02 2.0 2.0
2020-01-02 4.0 NaN
2020-01-03 3.0 3.0
2020-01-03 3.0 NaN
2020-01-04 4.0 2.0
2020-01-04 2.0 NaN
2020-01-05 5.0 1.0
2020-01-05 1.0 NaN
>>> df.vbt.flatten_grouped(group_by=group_by, order='F')
group first second
2020-01-01 1.0 1.0
2020-01-02 2.0 2.0
2020-01-03 3.0 3.0
2020-01-04 4.0 2.0
2020-01-05 5.0 1.0
2020-01-01 5.0 NaN
2020-01-02 4.0 NaN
2020-01-03 3.0 NaN
2020-01-04 2.0 NaN
2020-01-05 1.0 NaN
heatmap method¶
GenericDFAccessor.heatmap(
x_labels=None,
y_labels=None,
return_fig=True,
**kwargs
)
Create Heatmap and return the figure.
Usage
>>> df = pd.DataFrame([
... [0, np.nan, np.nan],
... [np.nan, 1, np.nan],
... [np.nan, np.nan, 2]
... ])
>>> df.vbt.heatmap()
squeeze_grouped method¶
GenericDFAccessor.squeeze_grouped(
squeeze_func_nb,
*args,
group_by=None,
wrap_kwargs=None
)
Squeeze each group of columns into a single column.
See squeeze_grouped_nb().
Usage
>>> group_by = pd.Series(['first', 'first', 'second'], name='group')
>>> mean_squeeze_nb = njit(lambda i, group, a: np.nanmean(a))
>>> df.vbt.squeeze_grouped(mean_squeeze_nb, group_by=group_by)
group first second
2020-01-01 3.0 1.0
2020-01-02 3.0 2.0
2020-01-03 3.0 3.0
2020-01-04 3.0 2.0
2020-01-05 3.0 1.0
ts_heatmap method¶
GenericDFAccessor.ts_heatmap(
is_y_category=True,
**kwargs
)
Heatmap of time-series data.
GenericSRAccessor class¶
GenericSRAccessor(
obj,
mapping=None,
**kwargs
)
Accessor on top of data of any type. For Series only.
Accessible through pd.Series.vbt
.
Superclasses
- AttrResolver
- BaseAccessor
- BaseSRAccessor
- Configured
- Documented
- GenericAccessor
- IndexingBase
- PandasIndexer
- Pickleable
- PlotsBuilderMixin
- StatsBuilderMixin
- Wrapping
Inherited members
- AttrResolver.deep_getattr()
- AttrResolver.post_resolve_attr()
- AttrResolver.pre_resolve_attr()
- AttrResolver.resolve_attr()
- BaseAccessor.align_to()
- BaseAccessor.apply()
- BaseAccessor.apply_and_concat()
- BaseAccessor.apply_on_index()
- BaseAccessor.broadcast()
- BaseAccessor.broadcast_to()
- BaseAccessor.combine()
- BaseAccessor.concat()
- BaseAccessor.drop_duplicate_levels()
- BaseAccessor.drop_levels()
- BaseAccessor.drop_redundant_levels()
- BaseAccessor.empty()
- BaseAccessor.empty_like()
- BaseAccessor.indexing_func()
- BaseAccessor.make_symmetric()
- BaseAccessor.rename_levels()
- BaseAccessor.repeat()
- BaseAccessor.select_levels()
- BaseAccessor.stack_index()
- BaseAccessor.tile()
- BaseAccessor.to_1d_array()
- BaseAccessor.to_2d_array()
- BaseAccessor.to_dict()
- BaseAccessor.unstack_to_array()
- BaseAccessor.unstack_to_df()
- Configured.copy()
- Configured.dumps()
- Configured.loads()
- Configured.replace()
- Configured.to_doc()
- Configured.update_config()
- GenericAccessor.apply_along_axis()
- GenericAccessor.apply_and_reduce()
- GenericAccessor.apply_mapping()
- GenericAccessor.applymap()
- GenericAccessor.barplot()
- GenericAccessor.bfill()
- GenericAccessor.binarize()
- GenericAccessor.boxplot()
- GenericAccessor.bshift()
- GenericAccessor.config
- GenericAccessor.count()
- GenericAccessor.crossed_above()
- GenericAccessor.crossed_below()
- GenericAccessor.cumprod()
- GenericAccessor.cumsum()
- GenericAccessor.describe()
- GenericAccessor.df_accessor_cls
- GenericAccessor.diff()
- GenericAccessor.drawdown()
- GenericAccessor.drawdowns
- GenericAccessor.ewm_mean()
- GenericAccessor.ewm_std()
- GenericAccessor.expanding_apply()
- GenericAccessor.expanding_max()
- GenericAccessor.expanding_mean()
- GenericAccessor.expanding_min()
- GenericAccessor.expanding_split()
- GenericAccessor.expanding_std()
- GenericAccessor.ffill()
- GenericAccessor.fillna()
- GenericAccessor.filter()
- GenericAccessor.fshift()
- GenericAccessor.get_drawdowns()
- GenericAccessor.get_ranges()
- GenericAccessor.groupby_apply()
- GenericAccessor.histplot()
- GenericAccessor.idxmax()
- GenericAccessor.idxmin()
- GenericAccessor.iloc
- GenericAccessor.indexing_kwargs
- GenericAccessor.lineplot()
- GenericAccessor.loc
- GenericAccessor.mapping
- GenericAccessor.max()
- GenericAccessor.maxabs_scale()
- GenericAccessor.mean()
- GenericAccessor.median()
- GenericAccessor.min()
- GenericAccessor.minmax_scale()
- GenericAccessor.normalize()
- GenericAccessor.obj
- GenericAccessor.pct_change()
- GenericAccessor.plot()
- GenericAccessor.plots_defaults
- GenericAccessor.power_transform()
- GenericAccessor.product()
- GenericAccessor.quantile_transform()
- GenericAccessor.range_split()
- GenericAccessor.ranges
- GenericAccessor.rebase()
- GenericAccessor.reduce()
- GenericAccessor.resample_apply()
- GenericAccessor.resolve_self()
- GenericAccessor.robust_scale()
- GenericAccessor.rolling_apply()
- GenericAccessor.rolling_max()
- GenericAccessor.rolling_mean()
- GenericAccessor.rolling_min()
- GenericAccessor.rolling_split()
- GenericAccessor.rolling_std()
- GenericAccessor.scale()
- GenericAccessor.scatterplot()
- GenericAccessor.self_aliases
- GenericAccessor.shuffle()
- GenericAccessor.split()
- GenericAccessor.sr_accessor_cls
- GenericAccessor.stats_defaults
- GenericAccessor.std()
- GenericAccessor.sum()
- GenericAccessor.to_mapped()
- GenericAccessor.to_returns()
- GenericAccessor.transform()
- GenericAccessor.value_counts()
- GenericAccessor.wrapper
- GenericAccessor.writeable_attrs
- GenericAccessor.zscore()
- PandasIndexer.xs()
- Pickleable.load()
- Pickleable.save()
- PlotsBuilderMixin.build_subplots_doc()
- PlotsBuilderMixin.override_subplots_doc()
- PlotsBuilderMixin.plots()
- StatsBuilderMixin.build_metrics_doc()
- StatsBuilderMixin.override_metrics_doc()
- StatsBuilderMixin.stats()
- Wrapping.regroup()
- Wrapping.select_one()
- Wrapping.select_one_from_obj()
Subclasses
flatten_grouped method¶
GenericSRAccessor.flatten_grouped(
group_by=None,
order='C',
wrap_kwargs=None
)
Flatten each group of elements.
Based on GenericDFAccessor.flatten_grouped().
heatmap method¶
GenericSRAccessor.heatmap(
x_level=None,
y_level=None,
symmetric=False,
sort=True,
x_labels=None,
y_labels=None,
slider_level=None,
active=0,
slider_labels=None,
return_fig=True,
fig=None,
**kwargs
)
Create a heatmap figure based on object's multi-index and values.
If index is not a multi-index, converts Series into a DataFrame and calls GenericDFAccessor.heatmap().
If multi-index contains more than two levels or you want them in specific order, pass x_level
and y_level
, each (int
if index or str
if name) corresponding to an axis of the heatmap. Optionally, pass slider_level
to use a level as a slider.
Creates Heatmap and returns the figure.
Usage
>>> multi_index = pd.MultiIndex.from_tuples([
... (1, 1),
... (2, 2),
... (3, 3)
... ])
>>> sr = pd.Series(np.arange(len(multi_index)), index=multi_index)
>>> sr
1 1 0
2 2 1
3 3 2
dtype: int64
>>> sr.vbt.heatmap()
- Using one level as a slider:
>>> multi_index = pd.MultiIndex.from_tuples([
... (1, 1, 1),
... (1, 2, 2),
... (1, 3, 3),
... (2, 3, 3),
... (2, 2, 2),
... (2, 1, 1)
... ])
>>> sr = pd.Series(np.arange(len(multi_index)), index=multi_index)
>>> sr
1 1 1 0
2 2 1
3 3 2
2 3 3 3
2 2 4
1 1 5
dtype: int64
>>> sr.vbt.heatmap(slider_level=0)
overlay_with_heatmap method¶
GenericSRAccessor.overlay_with_heatmap(
other,
trace_kwargs=None,
heatmap_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs
)
Plot Series as a line and overlays it with a heatmap.
Args
other
:array_like
- Second array. Will broadcast.
trace_kwargs
:dict
- Keyword arguments passed to
plotly.graph_objects.Scatter
. heatmap_kwargs
:dict
- Keyword arguments passed to GenericDFAccessor.heatmap().
add_trace_kwargs
:dict
- Keyword arguments passed to
add_trace
. fig
:Figure
orFigureWidget
- Figure to add traces to.
**layout_kwargs
- Keyword arguments for layout.
Usage
>>> df['a'].vbt.overlay_with_heatmap(df['b'])
plot_against method¶
GenericSRAccessor.plot_against(
other,
trace_kwargs=None,
other_trace_kwargs=None,
pos_trace_kwargs=None,
neg_trace_kwargs=None,
hidden_trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs
)
Plot Series as a line against another line.
Args
other
:array_like
- Second array. Will broadcast.
trace_kwargs
:dict
- Keyword arguments passed to
plotly.graph_objects.Scatter
. other_trace_kwargs
:dict
-
Keyword arguments passed to
plotly.graph_objects.Scatter
forother
.Set to 'hidden' to hide.
pos_trace_kwargs
:dict
- Keyword arguments passed to
plotly.graph_objects.Scatter
for positive line. neg_trace_kwargs
:dict
- Keyword arguments passed to
plotly.graph_objects.Scatter
for negative line. hidden_trace_kwargs
:dict
- Keyword arguments passed to
plotly.graph_objects.Scatter
for hidden lines. add_trace_kwargs
:dict
- Keyword arguments passed to
add_trace
. fig
:Figure
orFigureWidget
- Figure to add traces to.
**layout_kwargs
- Keyword arguments for layout.
Usage
>>> df['a'].vbt.plot_against(df['b'])
qqplot method¶
GenericSRAccessor.qqplot(
sparams=(),
dist='norm',
plot_line=True,
line_shape_kwargs=None,
xref='x',
yref='y',
fig=None,
**kwargs
)
Plot probability plot using scipy.stats.probplot
.
**kwargs
are passed to GenericAccessor.scatterplot().
Usage
>>> pd.Series(np.random.standard_normal(100)).vbt.qqplot()
squeeze_grouped method¶
GenericSRAccessor.squeeze_grouped(
squeeze_func_nb,
*args,
group_by=None,
wrap_kwargs=None
)
Squeeze each group of elements into a single element.
Based on GenericDFAccessor.squeeze_grouped().
ts_heatmap method¶
GenericSRAccessor.ts_heatmap(
**kwargs
)
Heatmap of time-series data.
volume method¶
GenericSRAccessor.volume(
x_level=None,
y_level=None,
z_level=None,
x_labels=None,
y_labels=None,
z_labels=None,
slider_level=None,
slider_labels=None,
active=0,
scene_name='scene',
fillna=None,
fig=None,
return_fig=True,
**kwargs
)
Create a 3D volume figure based on object's multi-index and values.
If multi-index contains more than three levels or you want them in specific order, pass x_level
, y_level
, and z_level
, each (int
if index or str
if name) corresponding to an axis of the volume. Optionally, pass slider_level
to use a level as a slider.
Creates Volume and returns the figure.
Usage
>>> multi_index = pd.MultiIndex.from_tuples([
... (1, 1, 1),
... (2, 2, 2),
... (3, 3, 3)
... ])
>>> sr = pd.Series(np.arange(len(multi_index)), index=multi_index)
>>> sr
1 1 1 0
2 2 2 1
3 3 3 2
dtype: int64
>>> sr.vbt.volume().show()
MetaGenericAccessor class¶
MetaGenericAccessor(
*args,
**kwargs
)
Meta class that exposes a read-only class property StatsBuilderMixin.metrics
.
Superclasses
- MetaPlotsBuilderMixin
- MetaStatsBuilderMixin
builtins.type
Inherited members
TransformerT class¶
TransformerT(
**kwargs
)
Base class for protocol classes.
Protocol classes are defined as::
class Proto(Protocol):
def meth(self) -> int:
...
Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing).
For example::
__ class C__
def meth(self) -> int:
return 0
def func(x: Proto) -> int: return x.meth()
func(C()) # Passes static type check
See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as::
class GenProto(Protocol[T]):
def meth(self) -> T:
...
Superclasses
typing.Generic
typing.Protocol
fit_transform method¶
TransformerT.fit_transform(
*args,
**kwargs
)
transform method¶
TransformerT.transform(
*args,
**kwargs
)