From 9e0bbef1f158ac76dda5c74050967974e669d410 Mon Sep 17 00:00:00 2001 From: Matt Dancho Date: Thu, 18 Jul 2024 10:51:58 -0400 Subject: [PATCH] update dist and docs --- dist/pytimetk-0.4.0.9000-py3-none-any.whl | Bin 4284678 -> 4286545 bytes dist/pytimetk-0.4.0.9000.tar.gz | Bin 4122624 -> 4123481 bytes .../execute-results/html.json | 6 +- .../augment_rolling/execute-results/html.json | 8 +- .../future_frame/execute-results/html.json | 8 +- .../execute-results/html.json | 6 +- .../is_holiday/execute-results/html.json | 6 +- docs/_site/changelog-news.html | 38 +++- docs/_site/reference/anomalize.html | 40 ++--- docs/_site/reference/augment_expanding.html | 28 +-- .../reference/augment_expanding_apply.html | 6 +- docs/_site/reference/augment_ppo.html | 16 +- docs/_site/reference/augment_rolling.html | 30 ++-- docs/_site/reference/future_frame.html | 156 ++++++---------- docs/_site/reference/index.html | 4 +- .../timeseries_unit_frequency_table.html | 4 +- docs/_site/search.json | 48 +++-- docs/_site/sitemap.xml | 170 +++++++++--------- docs/reference/anomalize.qmd | 4 - docs/reference/apply_by_time.qmd | 19 +- docs/reference/augment_atr.qmd | 2 +- docs/reference/augment_expanding.qmd | 25 ++- docs/reference/augment_expanding_apply.qmd | 1 - docs/reference/augment_holiday_signature.qmd | 2 - docs/reference/augment_macd.qmd | 2 - docs/reference/augment_ppo.qmd | 2 - docs/reference/augment_rolling.qmd | 26 +-- docs/reference/augment_wavelet.qmd | 6 +- docs/reference/future_frame.qmd | 3 - docs/reference/get_frequency_summary.qmd | 1 - docs/reference/get_holiday_signature.qmd | 2 - docs/reference/index.qmd | 2 +- docs/reference/is_holiday.qmd | 4 - docs/reference/palette_timetk.qmd | 2 +- .../timeseries_unit_frequency_table.qmd | 8 +- 35 files changed, 324 insertions(+), 361 deletions(-) diff --git a/dist/pytimetk-0.4.0.9000-py3-none-any.whl b/dist/pytimetk-0.4.0.9000-py3-none-any.whl index 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zFaP615l;!Bm850fd%Fas4mP)9&3m=N?fl!n+phS9B!V?yrXQsim(jWo6}>mb+y_e za0;?#hPTi|NC`E#bI0rY-m5euy(o)7($s1%Ffu^~D=-Ph%iz7TRqXo@P@6%CoXQHZ zpCYXkY{&4KZqJu(@VJ6jEBKMta?nOVQzs#_0GV2te7de-WbwRzBa$MQ;^6NAp*=}w zPtj?4`a1CWO4PuI2~8?ERsf{BsQkX~J)UBa3c7Ks?7Fp@erk+EsuHla&-N~3xd2Y^ zMwD|ayD*C!sLp}z%O?c-4(SkTy@q$3xgoetnCsU(7`?+|6w+ByB{TAw3mIn?Ik`j_ z;hgdrfL@s{mZ&IyzUTQR^07v2u9{};a)o4wQ{^o24sN;rEAItr6^|1-2YQphwD(~+ zep1j{SU`xI1(diKtWX-uwC_CvH9SJwOLG1`N|s`qkf1#sISP$l#;CGS+bh7(}M!vqR+>>khUhU(1Y4L=PG1NVaN)WIAwsnW-y)wbMM>l zyyqD0$h*_qu2%ObNK$sM2POY3hS(|_dn^s9T0e=HC(0Nd{*8C?MpVhPP%P7K0O0`B3fIN} diff --git a/docs/_freeze/reference/augment_holiday_signature/execute-results/html.json b/docs/_freeze/reference/augment_holiday_signature/execute-results/html.json index 6a9c8c56..bb08450c 100644 --- a/docs/_freeze/reference/augment_holiday_signature/execute-results/html.json +++ b/docs/_freeze/reference/augment_holiday_signature/execute-results/html.json @@ -1,9 +1,9 @@ { - "hash": "230580482b92e6e76fd26ac0963eb152", + "hash": "ee70a64b9aabf17673ffaca5ccda2516", "result": { - "markdown": "---\ntitle: augment_holiday_signature\n---\n\n\n\n`augment_holiday_signature(data, date_column, country_name='UnitedStates', reduce_memory=False, engine='pandas')`\n\nEngineers 4 different holiday features from a single datetime for 137 countries \nand 2 financial markets.\n\nNote: Requires the `holidays` package to be installed. See \n https://pypi.org/project/holidays/ for more information.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|-----------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `data` | Union\\[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy\\] | The input DataFrame. | _required_ |\n| `date_column` | str | The name of the datetime-like column in the DataFrame. | _required_ |\n| `country_name` | str | The name of the country for which to generate holiday features. Defaults to United States holidays, but the following countries are currently available and accessible by the full name or ISO code: See NOTES. | `'UnitedStates'` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is True. | `False` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for augmenting holidays. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for augmenting holidays. This can be faster than using \"pandas\" for large datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame: | A pandas DataFrame with three holiday-specific features: - is_holiday: (0, 1) indicator for holiday - before_holiday: (0, 1) indicator for day before holiday - after_holiday: (0, 1) indicator for day after holiday - holiday_name: name of the holiday |\n\n## Notes\n\n\nAny of the following are acceptable keys for `country_name`:\n\n| Available Countries | Full Country | Code |\n|:-----------------------------------------:|:--------------------------------:|:----:|\n| Albania | Albania | AL |\n| Algeria | Algeria | DZ |\n| American Samoa | AmericanSamoa | AS |\n| Andorra | Andorra | AD |\n| Angola | Angola | AO |\n| Argentina | Argentina | AR |\n| Armenia | Armenia | AM |\n| Aruba | Aruba | AW |\n| Australia | Australia | AU |\n| Austria | Austria | AT |\n| Azerbaijan | Azerbaijan | AZ |\n| Bahrain | Bahrain | BH |\n| Bangladesh | Bangladesh | BD |\n| Barbados | Barbados | BB |\n| Belarus | Belarus | BY |\n| Belgium | Belgium | BE |\n| Belize | Belize | BZ |\n| Bolivia | Bolivia | BO |\n| Bosnia and Herzegovina | BosniaandHerzegovina | BA |\n| Botswana | Botswana | BW |\n| Brazil | Brazil | BR |\n| Brunei | Brunei | BN |\n| Bulgaria | Bulgaria | BG |\n| Burkina Faso | BurkinaFaso | BF |\n| Burundi | Burundi | BI |\n| Laos | Laos | LA |\n| Latvia | Latvia | LV |\n| Lesotho | Lesotho | LS |\n| Liechtenstein | Liechtenstein | LI |\n| Lithuania | Lithuania | LT |\n| Luxembourg | Luxembourg | LU |\n| Madagascar | Madagascar | MG |\n| Malawi | Malawi | MW |\n| Malaysia | Malaysia | MY |\n| Maldives | Maldives | MV |\n| Malta | Malta | MT |\n| Marshall Islands | MarshallIslands | MH |\n| Mexico | Mexico | MX |\n| Moldova | Moldova | MD |\n| Monaco | Monaco | MC |\n| Montenegro | Montenegro | ME |\n| Morocco | Morocco | MA |\n| Mozambique | Mozambique | MZ |\n| Namibia | Namibia | NA |\n| Netherlands | Netherlands | NL |\n| New Zealand | NewZealand | NZ |\n| Nicaragua | Nicaragua | NI |\n| Nigeria | Nigeria | NG |\n| Northern Mariana Islands | NorthernMarianaIslands | MP |\n| North Macedonia | NorthMacedonia | MK |\n| Norway | Norway | NO |\n| Pakistan | Pakistan | PK |\n| Panama | Panama | PA |\n| Paraguay | Paraguay | PY |\n| Peru | Peru | PE |\n| Philippines | Philippines | PH |\n| Poland | Poland | PL |\n| Portugal | Portugal | PT |\n| Puerto Rico | PuertoRico | PR |\n| Romania | Romania | RO |\n| Russia | Russia | RU |\n| San Marino | SanMarino | SM |\n| Saudi Arabia | SaudiArabia | SA |\n| Serbia | Serbia | RS |\n| Singapore | Singapore | SG |\n| Slovakia | Slovakia | SK |\n| Slovenia | Slovenia | SI |\n| South Africa | SouthAfrica | ZA |\n| South Korea | SouthKorea | KR |\n| Spain | Spain | ES |\n| Sweden | Sweden | SE |\n| Switzerland | Switzerland | CH |\n| Taiwan | Taiwan | TW |\n| Tanzania | Tanzania | TZ |\n| Thailand | Thailand | TH |\n| Tunisia | Tunisia | TN |\n| Turkey | Turkey | TR |\n| Ukraine | Ukraine | UA |\n| United Arab Emirates | UnitedArabEmirates | AE |\n| United Kingdom | UnitedKingdom | GB |\n| United States Minor Outlying Islands | UnitedStatesMinorOutlyingIslands | UM |\n| United States of America | UnitedStatesofAmerica | US |\n| United States Virgin Islands | UnitedStatesVirginIslands | VI |\n| Uruguay | Uruguay | UY |\n| Uzbekistan | Uzbekistan | UZ |\n| Vanuatu | Vanuatu | VU |\n| Vatican City | VaticanCity | VA |\n| Venezuela | Venezuela | VE |\n| Vietnam | Vietnam | VN |\n| Virgin Islands (U.S.) | VirginIslandsUS | VI |\n| Zambia | Zambia | ZM |\n| Zimbabwe | Zimbabwe | ZW |\n\n\nThese are the Available Financial Markets:\n\n| Available Financial Markets | Full Country | Code |\n|:----------------------------:|:----------------------:|:----:|\n| European Central Bank | EuropeanCentralBank | ECB |\n| New York Stock Exchange | NewYorkStockExchange | XNYS |\n\n\n\n## Example\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\n# Make a DataFrame with a date column\nstart_date = '2023-01-01'\nend_date = '2023-01-10'\ndf = pd.DataFrame(pd.date_range(start=start_date, end=end_date), columns=['date'])\n\n# Add holiday features for US\ntk.augment_holiday_signature(df, 'date', 'UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n

    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.augment_holiday_signature(df, 'date', 'France')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.augment_holiday_signature(df, 'date', 'France', engine='polars')\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n", + "markdown": "---\ntitle: augment_holiday_signature\n---\n\n\n\n`augment_holiday_signature(data, date_column, country_name='UnitedStates', reduce_memory=False, engine='pandas')`\n\nEngineers 4 different holiday features from a single datetime for 137 countries \nand 2 financial markets.\n\nNote: Requires the `holidays` package to be installed. See \n https://pypi.org/project/holidays/ for more information.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|-----------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `data` | Union\\[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy\\] | The input DataFrame. | _required_ |\n| `date_column` | str | The name of the datetime-like column in the DataFrame. | _required_ |\n| `country_name` | str | The name of the country for which to generate holiday features. Defaults to United States holidays, but the following countries are currently available and accessible by the full name or ISO code: See NOTES. | `'UnitedStates'` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is True. | `False` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for augmenting holidays. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for augmenting holidays. This can be faster than using \"pandas\" for large datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame: | A pandas DataFrame with three holiday-specific features: - is_holiday: (0, 1) indicator for holiday - before_holiday: (0, 1) indicator for day before holiday - after_holiday: (0, 1) indicator for day after holiday - holiday_name: name of the holiday |\n\n## Notes\n\n\nAny of the following are acceptable keys for `country_name`:\n\n| Available Countries | Full Country | Code |\n|:-----------------------------------------:|:--------------------------------:|:----:|\n| Albania | Albania | AL |\n| Algeria | Algeria | DZ |\n| American Samoa | AmericanSamoa | AS |\n| Andorra | Andorra | AD |\n| Angola | Angola | AO |\n| Argentina | Argentina | AR |\n| Armenia | Armenia | AM |\n| Aruba | Aruba | AW |\n| Australia | Australia | AU |\n| Austria | Austria | AT |\n| Azerbaijan | Azerbaijan | AZ |\n| Bahrain | Bahrain | BH |\n| Bangladesh | Bangladesh | BD |\n| Barbados | Barbados | BB |\n| Belarus | Belarus | BY |\n| Belgium | Belgium | BE |\n| Belize | Belize | BZ |\n| Bolivia | Bolivia | BO |\n| Bosnia and Herzegovina | BosniaandHerzegovina | BA |\n| Botswana | Botswana | BW |\n| Brazil | Brazil | BR |\n| Brunei | Brunei | BN |\n| Bulgaria | Bulgaria | BG |\n| Burkina Faso | BurkinaFaso | BF |\n| Burundi | Burundi | BI |\n| Laos | Laos | LA |\n| Latvia | Latvia | LV |\n| Lesotho | Lesotho | LS |\n| Liechtenstein | Liechtenstein | LI |\n| Lithuania | Lithuania | LT |\n| Luxembourg | Luxembourg | LU |\n| Madagascar | Madagascar | MG |\n| Malawi | Malawi | MW |\n| Malaysia | Malaysia | MY |\n| Maldives | Maldives | MV |\n| Malta | Malta | MT |\n| Marshall Islands | MarshallIslands | MH |\n| Mexico | Mexico | MX |\n| Moldova | Moldova | MD |\n| Monaco | Monaco | MC |\n| Montenegro | Montenegro | ME |\n| Morocco | Morocco | MA |\n| Mozambique | Mozambique | MZ |\n| Namibia | Namibia | NA |\n| Netherlands | Netherlands | NL |\n| New Zealand | NewZealand | NZ |\n| Nicaragua | Nicaragua | NI |\n| Nigeria | Nigeria | NG |\n| Northern Mariana Islands | NorthernMarianaIslands | MP |\n| North Macedonia | NorthMacedonia | MK |\n| Norway | Norway | NO |\n| Pakistan | Pakistan | PK |\n| Panama | Panama | PA |\n| Paraguay | Paraguay | PY |\n| Peru | Peru | PE |\n| Philippines | Philippines | PH |\n| Poland | Poland | PL |\n| Portugal | Portugal | PT |\n| Puerto Rico | PuertoRico | PR |\n| Romania | Romania | RO |\n| Russia | Russia | RU |\n| San Marino | SanMarino | SM |\n| Saudi Arabia | SaudiArabia | SA |\n| Serbia | Serbia | RS |\n| Singapore | Singapore | SG |\n| Slovakia | Slovakia | SK |\n| Slovenia | Slovenia | SI |\n| South Africa | SouthAfrica | ZA |\n| South Korea | SouthKorea | KR |\n| Spain | Spain | ES |\n| Sweden | Sweden | SE |\n| Switzerland | Switzerland | CH |\n| Taiwan | Taiwan | TW |\n| Tanzania | Tanzania | TZ |\n| Thailand | Thailand | TH |\n| Tunisia | Tunisia | TN |\n| Turkey | Turkey | TR |\n| Ukraine | Ukraine | UA |\n| United Arab Emirates | UnitedArabEmirates | AE |\n| United Kingdom | UnitedKingdom | GB |\n| United States Minor Outlying Islands | UnitedStatesMinorOutlyingIslands | UM |\n| United States of America | UnitedStatesofAmerica | US |\n| United States Virgin Islands | UnitedStatesVirginIslands | VI |\n| Uruguay | Uruguay | UY |\n| Uzbekistan | Uzbekistan | UZ |\n| Vanuatu | Vanuatu | VU |\n| Vatican City | VaticanCity | VA |\n| Venezuela | Venezuela | VE |\n| Vietnam | Vietnam | VN |\n| Virgin Islands (U.S.) | VirginIslandsUS | VI |\n| Zambia | Zambia | ZM |\n| Zimbabwe | Zimbabwe | ZW |\n\n\nThese are the Available Financial Markets:\n\n| Available Financial Markets | Full Country | Code |\n|:----------------------------:|:----------------------:|:----:|\n| European Central Bank | EuropeanCentralBank | ECB |\n| New York Stock Exchange | NewYorkStockExchange | XNYS |\n\n## Example\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\n# Make a DataFrame with a date column\nstart_date = '2023-01-01'\nend_date = '2023-01-10'\ndf = pd.DataFrame(pd.date_range(start=start_date, end=end_date), columns=['date'])\n\n# Add holiday features for US\ntk.augment_holiday_signature(df, 'date', 'UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01110New Year's Day
    12023-01-02101New Year's Day (Observed)
    22023-01-03001NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.augment_holiday_signature(df, 'date', 'France')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.augment_holiday_signature(df, 'date', 'France', engine='polars')\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n", "supporting": [ - "augment_holiday_signature_files/figure-html" + "augment_holiday_signature_files" ], "filters": [], "includes": { diff --git a/docs/_freeze/reference/augment_rolling/execute-results/html.json b/docs/_freeze/reference/augment_rolling/execute-results/html.json index 8a546bad..63ee76b8 100644 --- a/docs/_freeze/reference/augment_rolling/execute-results/html.json +++ b/docs/_freeze/reference/augment_rolling/execute-results/html.json @@ -1,9 +1,9 @@ { - "hash": "2111c58a593416e6a024f5436378a1ad", + "hash": "5276d9008435f6da5516f2195b4efdb8", "result": { - "markdown": "---\ntitle: augment_rolling\n---\n\n\n\n`augment_rolling(data, date_column, value_column, window_func='mean', window=2, min_periods=None, engine='pandas', center=False, threads=1, show_progress=True, reduce_memory=False, **kwargs)`\n\nApply one or more Series-based rolling functions and window sizes to one or more columns of a DataFrame.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|-----------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `data` | Union\\[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy\\] | Input data to be processed. Can be a Pandas DataFrame or a GroupBy object. | _required_ |\n| `date_column` | str | Name of the datetime column. Data is sorted by this column within each group. | _required_ |\n| `value_column` | Union\\[str, list\\] | Column(s) to which the rolling window functions should be applied. Can be a single column name or a list. | _required_ |\n| `window_func` | Union\\[str, list, Tuple\\[str, Callable\\]\\] | The `window_func` parameter in the `augment_rolling` function specifies the function(s) to be applied to the rolling windows of the value column(s). 1. It can be either: - A string representing the name of a standard function (e.g., 'mean', 'sum'). 2. For custom functions: - Provide a list of tuples. Each tuple should contain a custom name for the function and the function itself. - Each custom function should accept a Pandas Series as its input and operate on that series. Example: (\"range\", lambda x: x.max() - x.min()) (See more Examples below.) Note: If your function needs to operate on multiple columns (i.e., it requires access to a DataFrame rather than just a Series), consider using the `augment_rolling_apply` function in this library. | `'mean'` |\n| `window` | Union\\[int, tuple, list\\] | Specifies the size of the rolling windows. - An integer applies the same window size to all columns in `value_column`. - A tuple generates windows from the first to the second value (inclusive). - A list of integers designates multiple window sizes for each respective column. | `2` |\n| `min_periods` | int | Minimum observations in the window to have a value. Defaults to the window size. If set, a value will be produced even if fewer observations are present than the window size. | `None` |\n| `center` | bool | If `True`, the rolling window will be centered on the current value. For even-sized windows, the window will be left-biased. Otherwise, it uses a trailing window. | `False` |\n| `threads` | int | Number of threads to use for parallel processing. If `threads` is set to 1, parallel processing will be disabled. Set to -1 to use all available CPU cores. | `1` |\n| `show_progress` | bool | If `True`, a progress bar will be displayed during parallel processing. | `True` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is False. | `False` |\n| `engine` | str | Specifies the backend computation library for augmenting expanding window functions. The options are: - \"pandas\" (default): Uses the `pandas` library. - \"polars\": Uses the `polars` library, which may offer performance benefits for larger datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|--------------|--------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame | The `augment_rolling` function returns a DataFrame with new columns for each applied function, window size, and value column. |\n\n## Notes\n\n## Performance\n\nThis function uses parallel processing to speed up computation for large \ndatasets with many time series groups: \n\nParallel processing has overhead and may not be faster on small datasets.\n\nTo use parallel processing, set `threads = -1` to use all available processors.\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pytimetk as tk\nimport pandas as pd\nimport numpy as np\n\ndf = tk.load_dataset(\"m4_daily\", parse_dates = ['date'])\n```\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Example 1 - Using a single window size and a single function name, pandas engine\n# This example demonstrates the use of both string-named functions and lambda \n# functions on a rolling window. We specify a list of window sizes: [2,7]. \n# As a result, the output will have computations for both window sizes 2 and 7.\n# Note - It's preferred to use built-in or configurable functions instead of \n# lambda functions for performance reasons.\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = [2,7], # Specifying multiple window sizes\n window_func = [\n 'mean', # Built-in mean function\n ('std', lambda x: x.std()) # Lambda function to compute standard deviation\n ],\n threads = 1, # Disabling parallel processing\n engine = 'pandas' # Using pandas engine\n )\n)\ndisplay(rolled_df)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_7value_rolling_std_win_7
    0D102014-07-032076.2NaNNaNNaNNaN
    1D102014-07-042073.42074.801.402074.8000001.400000
    2D102014-07-052048.72061.0512.352066.10000012.356645
    3D102014-07-062048.92048.800.102061.80000013.037830
    4D102014-07-072006.42027.6521.252050.72000025.041038
    ........................
    9738D5002012-09-199418.89425.356.559382.07142974.335988
    9739D5002012-09-209365.79392.2526.559396.40000058.431303
    9740D5002012-09-219445.99405.8040.109419.11428639.184451
    9741D5002012-09-229497.99471.9026.009438.92857138.945336
    9742D5002012-09-239545.39521.6023.709449.02857153.379416
    \n

    9743 rows × 7 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Example 2 - Multiple groups, pandas engine\n# Example showcasing the use of string function names and lambda functions \n# applied on rolling windows. The `window` tuple (1,3) will generate window \n# sizes of 1, 2, and 3.\n# Note - It's preferred to use built-in or configurable functions instead of \n# lambda functions for performance reasons.\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = (1,3), # Specifying a range of window sizes\n window_func = [\n 'mean', # Using built-in mean function\n ('std', lambda x: x.std()) # Lambda function for standard deviation\n ],\n threads = 1, # Disabling parallel processing\n engine = 'pandas' # Using pandas engine\n )\n)\ndisplay(rolled_df) \n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_1value_rolling_std_win_1value_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_3value_rolling_std_win_3
    0D102014-07-032076.22076.20.02076.200.002076.2000000.000000
    1D102014-07-042073.42073.40.02074.801.402074.8000001.400000
    2D102014-07-052048.72048.70.02061.0512.352066.10000012.356645
    3D102014-07-062048.92048.90.02048.800.102057.00000011.596839
    4D102014-07-072006.42006.40.02027.6521.252034.66666719.987718
    ..............................
    9738D5002012-09-199418.89418.80.09425.356.559429.4666677.905413
    9739D5002012-09-209365.79365.70.09392.2526.559405.46666728.623339
    9740D5002012-09-219445.99445.90.09405.8040.109410.13333333.310092
    9741D5002012-09-229497.99497.90.09471.9026.009436.50000054.378182
    9742D5002012-09-239545.39545.30.09521.6023.709496.36666740.594362
    \n

    9743 rows × 9 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# Example 3 - Multiple groups, polars engine\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = (1,3), # Specifying a range of window sizes\n window_func = [\n 'mean', # Using built-in mean function\n 'std', # Using built-in standard deviation function\n ],\n engine = 'polars' # Using polars engine\n )\n)\ndisplay(rolled_df) \n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_1value_rolling_std_win_1value_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_3value_rolling_std_win_3
    0D102014-07-032076.22076.20.02076.200.0000002076.2000000.000000
    1D102014-07-042073.42073.40.02074.801.9798992074.8000001.979899
    2D102014-07-052048.72048.70.02061.0517.4655372066.10000015.133737
    3D102014-07-062048.92048.90.02048.800.1414212057.00000014.203169
    4D102014-07-072006.42006.40.02027.6530.0520382034.66666724.479856
    ..............................
    9738D5002012-09-199418.89418.80.09425.359.2630999429.4666679.682114
    9739D5002012-09-209365.79365.70.09392.2537.5473709405.46666735.056288
    9740D5002012-09-219445.99445.90.09405.8056.7099649410.13333340.796364
    9741D5002012-09-229497.99497.90.09471.9036.7695539436.50000066.599399
    9742D5002012-09-239545.39545.30.09521.6033.5168619496.36666749.717737
    \n

    9743 rows × 9 columns

    \n
    \n```\n:::\n:::\n\n\n", + "markdown": "---\ntitle: augment_rolling\n---\n\n\n\n`augment_rolling(data, date_column, value_column, window_func='mean', window=2, min_periods=None, engine='pandas', center=False, threads=1, show_progress=True, reduce_memory=False, **kwargs)`\n\nApply one or more Series-based rolling functions and window sizes to one or more columns of a DataFrame.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|-----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `data` | Union\\[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy\\] | Input data to be processed. Can be a Pandas DataFrame or a GroupBy object. | _required_ |\n| `date_column` | str | Name of the datetime column. Data is sorted by this column within each group. | _required_ |\n| `value_column` | Union\\[str, list\\] | Column(s) to which the rolling window functions should be applied. Can be a single column name or a list. | _required_ |\n| `window_func` | Union\\[str, list, Tuple\\[str, Callable\\]\\] | The `window_func` parameter in the `augment_rolling` function specifies the function(s) to be applied to the rolling windows of the value column(s). 1. It can be either: - A string representing the name of a standard function (e.g., 'mean', 'sum'). 2. For custom functions: - Provide a list of tuples. Each tuple should contain a custom name for the function and the function itself. - Each custom function should accept a Pandas Series as its input and operate on that series. Example: (\"range\", lambda x: x.max() - x.min()) (See more Examples below.) Note: If your function needs to operate on multiple columns (i.e., it requires access to a DataFrame rather than just a Series), consider using the `augment_rolling_apply` function in this library. | `'mean'` |\n| `window` | Union\\[int, tuple, list\\] | Specifies the size of the rolling windows. - An integer applies the same window size to all columns in `value_column`. - A tuple generates windows from the first to the second value (inclusive). - A list of integers designates multiple window sizes for each respective column. | `2` |\n| `min_periods` | int | Minimum observations in the window to have a value. Defaults to the window size. If set, a value will be produced even if fewer observations are present than the window size. | `None` |\n| `center` | bool | If `True`, the rolling window will be centered on the current value. For even-sized windows, the window will be left-biased. Otherwise, it uses a trailing window. | `False` |\n| `threads` | int | Number of threads to use for parallel processing. If `threads` is set to 1, parallel processing will be disabled. Set to -1 to use all available CPU cores. | `1` |\n| `show_progress` | bool | If `True`, a progress bar will be displayed during parallel processing. | `True` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is False. | `False` |\n| `engine` | str | Specifies the backend computation library for augmenting expanding window functions. The options are: - \"pandas\" (default): Uses the `pandas` library. - \"polars\": Uses the `polars` library, which may offer performance benefits for larger datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|--------------|--------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame | The `augment_rolling` function returns a DataFrame with new columns for each applied function, window size, and value column. |\n\n## Notes\n\n## Performance\n\nThis function uses parallel processing to speed up computation for large \ndatasets with many time series groups: \n\nParallel processing has overhead and may not be faster on small datasets.\n\nTo use parallel processing, set `threads = -1` to use all available processors.\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pytimetk as tk\nimport pandas as pd\nimport numpy as np\n\ndf = tk.load_dataset(\"m4_daily\", parse_dates = ['date'])\n```\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Example 1 - Using a single window size and a single function name, pandas engine\n# This example demonstrates the use of both string-named functions and lambda \n# functions on a rolling window. We specify a list of window sizes: [2,7]. \n# As a result, the output will have computations for both window sizes 2 and 7.\n# Note - It's preferred to use built-in or configurable functions instead of \n# lambda functions for performance reasons.\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = [2,7], # Specifying multiple window sizes\n window_func = [\n 'mean', # Built-in mean function\n ('std', lambda x: x.std()) # Lambda function to compute standard deviation\n ],\n threads = 1, # Disabling parallel processing\n engine = 'pandas' # Using pandas engine\n )\n)\ndisplay(rolled_df)\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_7value_rolling_std_win_7
    0D102014-07-032076.2NaNNaNNaNNaN
    1D102014-07-042073.42074.801.402074.8000001.400000
    2D102014-07-052048.72061.0512.352066.10000012.356645
    3D102014-07-062048.92048.800.102061.80000013.037830
    4D102014-07-072006.42027.6521.252050.72000025.041038
    ........................
    9738D5002012-09-199418.89425.356.559382.07142974.335988
    9739D5002012-09-209365.79392.2526.559396.40000058.431303
    9740D5002012-09-219445.99405.8040.109419.11428639.184451
    9741D5002012-09-229497.99471.9026.009438.92857138.945336
    9742D5002012-09-239545.39521.6023.709449.02857153.379416
    \n

    9743 rows × 7 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Example 2 - Multiple groups, pandas engine\n# Example showcasing the use of string function names and lambda functions \n# applied on rolling windows. The `window` tuple (1,3) will generate window \n# sizes of 1, 2, and 3.\n# Note - It's preferred to use built-in or configurable functions instead of \n# lambda functions for performance reasons.\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = (1,3), # Specifying a range of window sizes\n window_func = [\n 'mean', # Using built-in mean function\n ('std', lambda x: x.std()) # Lambda function for standard deviation\n ],\n threads = 1, # Disabling parallel processing\n engine = 'pandas' # Using pandas engine\n )\n)\ndisplay(rolled_df) \n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_1value_rolling_std_win_1value_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_3value_rolling_std_win_3
    0D102014-07-032076.22076.20.02076.200.002076.2000000.000000
    1D102014-07-042073.42073.40.02074.801.402074.8000001.400000
    2D102014-07-052048.72048.70.02061.0512.352066.10000012.356645
    3D102014-07-062048.92048.90.02048.800.102057.00000011.596839
    4D102014-07-072006.42006.40.02027.6521.252034.66666719.987718
    ..............................
    9738D5002012-09-199418.89418.80.09425.356.559429.4666677.905413
    9739D5002012-09-209365.79365.70.09392.2526.559405.46666728.623339
    9740D5002012-09-219445.99445.90.09405.8040.109410.13333333.310092
    9741D5002012-09-229497.99497.90.09471.9026.009436.50000054.378182
    9742D5002012-09-239545.39545.30.09521.6023.709496.36666740.594362
    \n

    9743 rows × 9 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# Example 3 - Multiple groups, polars engine\n\nrolled_df = (\n df\n .groupby('id')\n .augment_rolling(\n date_column = 'date', \n value_column = 'value', \n window = (1,3), # Specifying a range of window sizes\n window_func = [\n 'mean', # Using built-in mean function\n 'std', # Using built-in standard deviation function\n ],\n engine = 'polars' # Using polars engine\n )\n)\ndisplay(rolled_df) \n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevaluevalue_rolling_mean_win_1value_rolling_std_win_1value_rolling_mean_win_2value_rolling_std_win_2value_rolling_mean_win_3value_rolling_std_win_3
    0D102014-07-032076.22076.2NaN2076.20NaN2076.200000NaN
    1D102014-07-042073.42073.4NaN2074.801.9798992074.8000001.979899
    2D102014-07-052048.72048.7NaN2061.0517.4655372066.10000015.133737
    3D102014-07-062048.92048.9NaN2048.800.1414212057.00000014.203169
    4D102014-07-072006.42006.4NaN2027.6530.0520382034.66666724.479856
    ..............................
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    \n

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    \n```\n:::\n:::\n\n\n", "supporting": [ - "augment_rolling_files/figure-html" + "augment_rolling_files" ], "filters": [], "includes": { @@ -11,7 +11,7 @@ "\n\n\n\n" ], "include-after-body": [ - "\n" + "\n" ] } } diff --git a/docs/_freeze/reference/future_frame/execute-results/html.json b/docs/_freeze/reference/future_frame/execute-results/html.json index e5c4231e..1f363530 100644 --- a/docs/_freeze/reference/future_frame/execute-results/html.json +++ b/docs/_freeze/reference/future_frame/execute-results/html.json @@ -1,9 +1,9 @@ { - "hash": "3a41d334a24c8719eab655e163d3fae8", + "hash": "b506985a7c47c60e215804dc41582504", "result": { - "markdown": "---\ntitle: future_frame\n---\n\n\n\n`future_frame(data, date_column, length_out, freq=None, force_regular=False, bind_data=True, threads=1, show_progress=True, reduce_memory=False, engine='pandas')`\n\nExtend a DataFrame or GroupBy object with future dates.\n\nThe `future_frame` function extends a given DataFrame or GroupBy object with \nfuture dates based on a specified length, optionally binding the original data.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `data` | pd.DataFrame or pd.core.groupby.generic.DataFrameGroupBy | The `data` parameter is the input DataFrame or DataFrameGroupBy object that you want to extend with future dates. | _required_ |\n| `date_column` | str | The `date_column` parameter is a string that specifies the name of the column in the DataFrame that contains the dates. This column will be used to generate future dates. | _required_ |\n| `freq` | str | | `None` |\n| `length_out` | int | The `length_out` parameter specifies the number of future dates to be added to the DataFrame. | _required_ |\n| `force_regular` | bool | The `force_regular` parameter is a boolean flag that determines whether the frequency of the future dates should be forced to be regular. If `force_regular` is set to `True`, the frequency of the future dates will be forced to be regular. If `force_regular` is set to `False`, the frequency of the future dates will be inferred from the input data (e.g. business calendars might be used). The default value is `False`. | `False` |\n| `bind_data` | bool | The `bind_data` parameter is a boolean flag that determines whether the extended data should be concatenated with the original data or returned separately. If `bind_data` is set to `True`, the extended data will be concatenated with the original data using `pd.concat`. If `bind_data` is set to `False`, the extended data will be returned separately. The default value is `True`. | `True` |\n| `threads` | int | The `threads` parameter specifies the number of threads to use for parallel processing. If `threads` is set to `None`, it will use all available processors. If `threads` is set to `-1`, it will use all available processors as well. | `1` |\n| `show_progress` | bool | A boolean parameter that determines whether to display progress using tqdm. If set to True, progress will be displayed. If set to False, progress will not be displayed. | `True` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is True. | `False` |\n| `engine` | str | The `engine` parameter specifies the engine to use for computation. - Currently only `pandas` is supported. - `polars` will be supported in the future. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|--------------|------------------------------------------|\n| pd.DataFrame | An extended DataFrame with future dates. |\n\n## Notes\n\n\n## Performance\n\nThis function uses a number of techniques to speed up computation for large \ndatasets with many time series groups: \n\n- We vectorize where possible and use parallel processing to speed up. \n- The `threads` parameter controls the number of threads to use for parallel \n processing.\n\n - Set threads = -1 to use all available processors. \n - Set threads = 1 to disable parallel processing.\n\n\n\n\n## See Also\n\nmake_future_timeseries: Generate future dates for a time series.\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\ndf = tk.load_dataset('m4_hourly', parse_dates = ['date'])\ndf\n\n# Example 1 - Extend the data for a single time series group by 12 hours\nextended_df = (\n df\n .query('id == \"H10\"')\n .future_frame(\n date_column = 'date', \n length_out = 12\n )\n)\nextended_df\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevalue
    0H102015-07-01 12:00:00+00:00513.0
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    3H102015-07-01 15:00:00+00:00500.0
    4H102015-07-01 16:00:00+00:00490.0
    ............
    707H102015-07-30 23:00:00+00:00NaN
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    \n

    712 rows × 3 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Example 2 - Extend the data for each group by 12 hours\nextended_df = (\n df\n .groupby('id', sort = False) # Use sort = False to preserve the original order of the data\n .future_frame(\n date_column = 'date', \n length_out = 12,\n threads = 1 # Use 2 threads for parallel processing\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n0 2015-07-01 12:00:00+00:00\n1 2015-07-01 13:00:00+00:00\n2 2015-07-01 14:00:00+00:00\n3 2015-07-01 15:00:00+00:00\n4 2015-07-01 16:00:00+00:00\n ... \n695 2015-07-30 11:00:00+00:00\n696 2015-07-30 12:00:00+00:00\n697 2015-07-30 13:00:00+00:00\n698 2015-07-30 14:00:00+00:00\n699 2015-07-30 15:00:00+00:00\nName: date, Length: 700, dtype: datetime64[ns, UTC]\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevalue
    0H102015-07-01 12:00:00+00:00513.0
    1H102015-07-01 13:00:00+00:00512.0
    2H102015-07-01 14:00:00+00:00506.0
    3H102015-07-01 15:00:00+00:00500.0
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    ............
    3103H4102017-02-10 19:00:00+00:00NaN
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    3107H4102017-02-10 23:00:00+00:00NaN
    \n

    3108 rows × 3 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Example 3 - Same as above, but just return the extended data with bind_data=False\nextended_df = (\n df\n .groupby('id', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n bind_data = False # Returns just future data\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n0 2015-07-01 12:00:00+00:00\n1 2015-07-01 13:00:00+00:00\n2 2015-07-01 14:00:00+00:00\n3 2015-07-01 15:00:00+00:00\n4 2015-07-01 16:00:00+00:00\n ... \n695 2015-07-30 11:00:00+00:00\n696 2015-07-30 12:00:00+00:00\n697 2015-07-30 13:00:00+00:00\n698 2015-07-30 14:00:00+00:00\n699 2015-07-30 15:00:00+00:00\nName: date, Length: 700, dtype: datetime64[ns, UTC]\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateid
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    42015-07-30 20:00:00+00:00H10
    52015-07-30 21:00:00+00:00H10
    62015-07-30 22:00:00+00:00H10
    72015-07-30 23:00:00+00:00H10
    82015-07-31 00:00:00+00:00H10
    92015-07-31 01:00:00+00:00H10
    102015-07-31 02:00:00+00:00H10
    112015-07-31 03:00:00+00:00H10
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    132015-07-30 17:00:00+00:00H50
    142015-07-30 18:00:00+00:00H50
    152015-07-30 19:00:00+00:00H50
    162015-07-30 20:00:00+00:00H50
    172015-07-30 21:00:00+00:00H50
    182015-07-30 22:00:00+00:00H50
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    202015-07-31 00:00:00+00:00H50
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    252013-09-30 17:00:00+00:00H150
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    342013-10-01 02:00:00+00:00H150
    352013-10-01 03:00:00+00:00H150
    362017-02-10 12:00:00+00:00H410
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    382017-02-10 14:00:00+00:00H410
    392017-02-10 15:00:00+00:00H410
    402017-02-10 16:00:00+00:00H410
    412017-02-10 17:00:00+00:00H410
    422017-02-10 18:00:00+00:00H410
    432017-02-10 19:00:00+00:00H410
    442017-02-10 20:00:00+00:00H410
    452017-02-10 21:00:00+00:00H410
    462017-02-10 22:00:00+00:00H410
    472017-02-10 23:00:00+00:00H410
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# Example 4 - Working with irregular dates: Business Days (Stocks Data)\n\nimport pytimetk as tk\nimport pandas as pd\n\n# Stock data\ndf = tk.load_dataset('stocks_daily', parse_dates = ['date'])\ndf\n\n# Allow irregular future dates (i.e. business days)\nextended_df = (\n df\n .groupby('symbol', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n force_regular = False, # Allow irregular future dates (i.e. business days)),\n bind_data = True,\n threads = 1\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n0 2013-01-02\n1 2013-01-03\n2 2013-01-04\n3 2013-01-07\n4 2013-01-08\n ... \n2694 2023-09-15\n2695 2023-09-18\n2696 2023-09-19\n2697 2023-09-20\n2698 2023-09-21\nName: date, Length: 2699, dtype: datetime64[ns]\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    symboldateopenhighlowclosevolumeadjusted
    0META2013-01-0227.44000128.18000027.42000028.00000069846400.028.000000
    1META2013-01-0327.87999928.46999927.59000027.77000063140600.027.770000
    2META2013-01-0428.01000028.93000027.83000028.76000072715400.028.760000
    3META2013-01-0728.69000129.79000128.65000029.42000083781800.029.420000
    4META2013-01-0829.51000029.60000028.86000129.05999945871300.029.059999
    ...........................
    16261GOOG2023-09-29NaNNaNNaNNaNNaNNaN
    16262GOOG2023-09-30NaNNaNNaNNaNNaNNaN
    16263GOOG2023-10-01NaNNaNNaNNaNNaNNaN
    16264GOOG2023-10-02NaNNaNNaNNaNNaNNaN
    16265GOOG2023-10-03NaNNaNNaNNaNNaNNaN
    \n

    16266 rows × 8 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# Force regular: Include Weekends\nextended_df = (\n df\n .groupby('symbol', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n force_regular = True, # Force regular future dates (i.e. include weekends)),\n bind_data = True\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n0 2013-01-02\n1 2013-01-03\n2 2013-01-04\n3 2013-01-07\n4 2013-01-08\n ... \n2694 2023-09-15\n2695 2023-09-18\n2696 2023-09-19\n2697 2023-09-20\n2698 2023-09-21\nName: date, Length: 2699, dtype: datetime64[ns]\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    symboldateopenhighlowclosevolumeadjusted
    0META2013-01-0227.44000128.18000027.42000028.00000069846400.028.000000
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    4META2013-01-0829.51000029.60000028.86000129.05999945871300.029.059999
    ...........................
    16261GOOG2023-09-29NaNNaNNaNNaNNaNNaN
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    16265GOOG2023-10-03NaNNaNNaNNaNNaNNaN
    \n

    16266 rows × 8 columns

    \n
    \n```\n:::\n:::\n\n\n", + "markdown": "---\ntitle: future_frame\n---\n\n\n\n`future_frame(data, date_column, length_out, freq=None, force_regular=False, bind_data=True, threads=1, show_progress=True, reduce_memory=False, engine='pandas')`\n\nExtend a DataFrame or GroupBy object with future dates.\n\nThe `future_frame` function extends a given DataFrame or GroupBy object with \nfuture dates based on a specified length, optionally binding the original data.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|-----------------|----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `data` | pd.DataFrame or pd.core.groupby.generic.DataFrameGroupBy | The `data` parameter is the input DataFrame or DataFrameGroupBy object that you want to extend with future dates. | _required_ |\n| `date_column` | str | The `date_column` parameter is a string that specifies the name of the column in the DataFrame that contains the dates. This column will be used to generate future dates. | _required_ |\n| `freq` | str | | `None` |\n| `length_out` | int | The `length_out` parameter specifies the number of future dates to be added to the DataFrame. | _required_ |\n| `force_regular` | bool | The `force_regular` parameter is a boolean flag that determines whether the frequency of the future dates should be forced to be regular. If `force_regular` is set to `True`, the frequency of the future dates will be forced to be regular. If `force_regular` is set to `False`, the frequency of the future dates will be inferred from the input data (e.g. business calendars might be used). The default value is `False`. | `False` |\n| `bind_data` | bool | The `bind_data` parameter is a boolean flag that determines whether the extended data should be concatenated with the original data or returned separately. If `bind_data` is set to `True`, the extended data will be concatenated with the original data using `pd.concat`. If `bind_data` is set to `False`, the extended data will be returned separately. The default value is `True`. | `True` |\n| `threads` | int | The `threads` parameter specifies the number of threads to use for parallel processing. If `threads` is set to `None`, it will use all available processors. If `threads` is set to `-1`, it will use all available processors as well. | `1` |\n| `show_progress` | bool | A boolean parameter that determines whether to display progress using tqdm. If set to True, progress will be displayed. If set to False, progress will not be displayed. | `True` |\n| `reduce_memory` | bool | The `reduce_memory` parameter is used to specify whether to reduce the memory usage of the DataFrame by converting int, float to smaller bytes and str to categorical data. This reduces memory for large data but may impact resolution of float and will change str to categorical. Default is True. | `False` |\n| `engine` | str | The `engine` parameter specifies the engine to use for computation. - Currently only `pandas` is supported. - `polars` will be supported in the future. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|--------------|------------------------------------------|\n| pd.DataFrame | An extended DataFrame with future dates. |\n\n## Notes\n\n\n## Performance\n\nThis function uses a number of techniques to speed up computation for large \ndatasets with many time series groups: \n\n- We vectorize where possible and use parallel processing to speed up. \n- The `threads` parameter controls the number of threads to use for parallel \n processing.\n\n - Set threads = -1 to use all available processors. \n - Set threads = 1 to disable parallel processing.\n\n## See Also\n\nmake_future_timeseries: Generate future dates for a time series.\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\ndf = tk.load_dataset('m4_hourly', parse_dates = ['date'])\ndf\n\n# Example 1 - Extend the data for a single time series group by 12 hours\nextended_df = (\n df\n .query('id == \"H10\"')\n .future_frame(\n date_column = 'date', \n length_out = 12\n )\n)\nextended_df\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevalue
    0H102015-07-01 12:00:00+00:00513.0
    1H102015-07-01 13:00:00+00:00512.0
    2H102015-07-01 14:00:00+00:00506.0
    3H102015-07-01 15:00:00+00:00500.0
    4H102015-07-01 16:00:00+00:00490.0
    ............
    707H102015-07-30 23:00:00+00:00NaN
    708H102015-07-31 00:00:00+00:00NaN
    709H102015-07-31 01:00:00+00:00NaN
    710H102015-07-31 02:00:00+00:00NaN
    711H102015-07-31 03:00:00+00:00NaN
    \n

    712 rows × 3 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Example 2 - Extend the data for each group by 12 hours\nextended_df = (\n df\n .groupby('id', sort = False) # Use sort = False to preserve the original order of the data\n .future_frame(\n date_column = 'date', \n length_out = 12,\n threads = 1 # Use 2 threads for parallel processing\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    iddatevalue
    0H102015-07-01 12:00:00+00:00513.0
    1H102015-07-01 13:00:00+00:00512.0
    2H102015-07-01 14:00:00+00:00506.0
    3H102015-07-01 15:00:00+00:00500.0
    4H102015-07-01 16:00:00+00:00490.0
    ............
    3103H4102017-02-10 19:00:00+00:00NaN
    3104H4102017-02-10 20:00:00+00:00NaN
    3105H4102017-02-10 21:00:00+00:00NaN
    3106H4102017-02-10 22:00:00+00:00NaN
    3107H4102017-02-10 23:00:00+00:00NaN
    \n

    3108 rows × 3 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Example 3 - Same as above, but just return the extended data with bind_data=False\nextended_df = (\n df\n .groupby('id', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n bind_data = False # Returns just future data\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateid
    02015-07-30 16:00:00+00:00H10
    12015-07-30 17:00:00+00:00H10
    22015-07-30 18:00:00+00:00H10
    32015-07-30 19:00:00+00:00H10
    42015-07-30 20:00:00+00:00H10
    52015-07-30 21:00:00+00:00H10
    62015-07-30 22:00:00+00:00H10
    72015-07-30 23:00:00+00:00H10
    82015-07-31 00:00:00+00:00H10
    92015-07-31 01:00:00+00:00H10
    102015-07-31 02:00:00+00:00H10
    112015-07-31 03:00:00+00:00H10
    122015-07-30 16:00:00+00:00H50
    132015-07-30 17:00:00+00:00H50
    142015-07-30 18:00:00+00:00H50
    152015-07-30 19:00:00+00:00H50
    162015-07-30 20:00:00+00:00H50
    172015-07-30 21:00:00+00:00H50
    182015-07-30 22:00:00+00:00H50
    192015-07-30 23:00:00+00:00H50
    202015-07-31 00:00:00+00:00H50
    212015-07-31 01:00:00+00:00H50
    222015-07-31 02:00:00+00:00H50
    232015-07-31 03:00:00+00:00H50
    242013-09-30 16:00:00+00:00H150
    252013-09-30 17:00:00+00:00H150
    262013-09-30 18:00:00+00:00H150
    272013-09-30 19:00:00+00:00H150
    282013-09-30 20:00:00+00:00H150
    292013-09-30 21:00:00+00:00H150
    302013-09-30 22:00:00+00:00H150
    312013-09-30 23:00:00+00:00H150
    322013-10-01 00:00:00+00:00H150
    332013-10-01 01:00:00+00:00H150
    342013-10-01 02:00:00+00:00H150
    352013-10-01 03:00:00+00:00H150
    362017-02-10 12:00:00+00:00H410
    372017-02-10 13:00:00+00:00H410
    382017-02-10 14:00:00+00:00H410
    392017-02-10 15:00:00+00:00H410
    402017-02-10 16:00:00+00:00H410
    412017-02-10 17:00:00+00:00H410
    422017-02-10 18:00:00+00:00H410
    432017-02-10 19:00:00+00:00H410
    442017-02-10 20:00:00+00:00H410
    452017-02-10 21:00:00+00:00H410
    462017-02-10 22:00:00+00:00H410
    472017-02-10 23:00:00+00:00H410
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\n# Example 4 - Working with irregular dates: Business Days (Stocks Data)\n\nimport pytimetk as tk\nimport pandas as pd\n\n# Stock data\ndf = tk.load_dataset('stocks_daily', parse_dates = ['date'])\ndf\n\n# Allow irregular future dates (i.e. business days)\nextended_df = (\n df\n .groupby('symbol', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n force_regular = False, # Allow irregular future dates (i.e. business days)),\n bind_data = True,\n threads = 1\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    symboldateopenhighlowclosevolumeadjusted
    0META2013-01-0227.44000128.18000027.42000028.00000069846400.028.000000
    1META2013-01-0327.87999928.46999927.59000027.77000063140600.027.770000
    2META2013-01-0428.01000028.93000027.83000028.76000072715400.028.760000
    3META2013-01-0728.69000129.79000128.65000029.42000083781800.029.420000
    4META2013-01-0829.51000029.60000028.86000129.05999945871300.029.059999
    ...........................
    16261GOOG2023-09-29NaNNaNNaNNaNNaNNaN
    16262GOOG2023-09-30NaNNaNNaNNaNNaNNaN
    16263GOOG2023-10-01NaNNaNNaNNaNNaNNaN
    16264GOOG2023-10-02NaNNaNNaNNaNNaNNaN
    16265GOOG2023-10-03NaNNaNNaNNaNNaNNaN
    \n

    16266 rows × 8 columns

    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\n# Force regular: Include Weekends\nextended_df = (\n df\n .groupby('symbol', sort = False)\n .future_frame(\n date_column = 'date', \n length_out = 12,\n force_regular = True, # Force regular future dates (i.e. include weekends)),\n bind_data = True\n )\n) \nextended_df\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    symboldateopenhighlowclosevolumeadjusted
    0META2013-01-0227.44000128.18000027.42000028.00000069846400.028.000000
    1META2013-01-0327.87999928.46999927.59000027.77000063140600.027.770000
    2META2013-01-0428.01000028.93000027.83000028.76000072715400.028.760000
    3META2013-01-0728.69000129.79000128.65000029.42000083781800.029.420000
    4META2013-01-0829.51000029.60000028.86000129.05999945871300.029.059999
    ...........................
    16261GOOG2023-09-29NaNNaNNaNNaNNaNNaN
    16262GOOG2023-09-30NaNNaNNaNNaNNaNNaN
    16263GOOG2023-10-01NaNNaNNaNNaNNaNNaN
    16264GOOG2023-10-02NaNNaNNaNNaNNaNNaN
    16265GOOG2023-10-03NaNNaNNaNNaNNaNNaN
    \n

    16266 rows × 8 columns

    \n
    \n```\n:::\n:::\n\n\n", "supporting": [ - "future_frame_files/figure-html" + "future_frame_files" ], "filters": [], "includes": { @@ -11,7 +11,7 @@ "\n\n\n\n" ], "include-after-body": [ - "\n" + "\n" ] } } diff --git a/docs/_freeze/reference/get_holiday_signature/execute-results/html.json b/docs/_freeze/reference/get_holiday_signature/execute-results/html.json index 6bac77c6..0bfdfa0b 100644 --- a/docs/_freeze/reference/get_holiday_signature/execute-results/html.json +++ b/docs/_freeze/reference/get_holiday_signature/execute-results/html.json @@ -1,9 +1,9 @@ { - "hash": "2d6a2bf877c3a6abc8ad713f69e1b6b7", + "hash": "0355de02b1801810842cf6e38aa1c9b9", "result": { - "markdown": "---\ntitle: get_holiday_signature\n---\n\n\n\n`get_holiday_signature(idx, country_name='UnitedStates', engine='pandas')`\n\nEngineers 4 different holiday features from a single datetime for 137 countries \nand 2 financial markets.\n\nNote: Requires the `holidays` package to be installed. See \nhttps://pypi.org/project/holidays/ for more information.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------|--------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `idx` | Union\\[pd.DatetimeIndex, pd.Series\\] | The input series. | _required_ |\n| `country_name` | str | The name of the country for which to generate holiday features. Defaults to United States holidays, but the following countries are currently available and accessible by the full name or ISO code: See NOTES. | `'UnitedStates'` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for getting holidays. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for getting holidays. This can be faster than using \"pandas\" for large datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame: | A pandas DataFrame with three holiday-specific features: - is_holiday: (0, 1) indicator for holiday - before_holiday: (0, 1) indicator for day before holiday - after_holiday: (0, 1) indicator for day after holiday - holiday_name: name of the holiday |\n\n## Notes\n\nAny of the following are acceptable keys for `country_name`:\n\n| Available Countries | Full Country | Code |\n|:-----------------------------------------:|:--------------------------------:|:----:|\n| Albania | Albania | AL |\n| Algeria | Algeria | DZ |\n| American Samoa | AmericanSamoa | AS |\n| Andorra | Andorra | AD |\n| Angola | Angola | AO |\n| Argentina | Argentina | AR |\n| Armenia | Armenia | AM |\n| Aruba | Aruba | AW |\n| Australia | Australia | AU |\n| Austria | Austria | AT |\n| Azerbaijan | Azerbaijan | AZ |\n| Bahrain | Bahrain | BH |\n| Bangladesh | Bangladesh | BD |\n| Barbados | Barbados | BB |\n| Belarus | Belarus | BY |\n| Belgium | Belgium | BE |\n| Belize | Belize | BZ |\n| Bolivia | Bolivia | BO |\n| Bosnia and Herzegovina | BosniaandHerzegovina | BA |\n| Botswana | Botswana | BW |\n| Brazil | Brazil | BR |\n| Brunei | Brunei | BN |\n| Bulgaria | Bulgaria | BG |\n| Burkina Faso | BurkinaFaso | BF |\n| Burundi | Burundi | BI |\n| Laos | Laos | LA |\n| Latvia | Latvia | LV |\n| Lesotho | Lesotho | LS |\n| Liechtenstein | Liechtenstein | LI |\n| Lithuania | Lithuania | LT |\n| Luxembourg | Luxembourg | LU |\n| Madagascar | Madagascar | MG |\n| Malawi | Malawi | MW |\n| Malaysia | Malaysia | MY |\n| Maldives | Maldives | MV |\n| Malta | Malta | MT |\n| Marshall Islands | MarshallIslands | MH |\n| Mexico | Mexico | MX |\n| Moldova | Moldova | MD |\n| Monaco | Monaco | MC |\n| Montenegro | Montenegro | ME |\n| Morocco | Morocco | MA |\n| Mozambique | Mozambique | MZ |\n| Namibia | Namibia | NA |\n| Netherlands | Netherlands | NL |\n| New Zealand | NewZealand | NZ |\n| Nicaragua | Nicaragua | NI |\n| Nigeria | Nigeria | NG |\n| Northern Mariana Islands | NorthernMarianaIslands | MP |\n| North Macedonia | NorthMacedonia | MK |\n| Norway | Norway | NO |\n| Pakistan | Pakistan | PK |\n| Panama | Panama | PA |\n| Paraguay | Paraguay | PY |\n| Peru | Peru | PE |\n| Philippines | Philippines | PH |\n| Poland | Poland | PL |\n| Portugal | Portugal | PT |\n| Puerto Rico | PuertoRico | PR |\n| Romania | Romania | RO |\n| Russia | Russia | RU |\n| San Marino | SanMarino | SM |\n| Saudi Arabia | SaudiArabia | SA |\n| Serbia | Serbia | RS |\n| Singapore | Singapore | SG |\n| Slovakia | Slovakia | SK |\n| Slovenia | Slovenia | SI |\n| South Africa | SouthAfrica | ZA |\n| South Korea | SouthKorea | KR |\n| Spain | Spain | ES |\n| Sweden | Sweden | SE |\n| Switzerland | Switzerland | CH |\n| Taiwan | Taiwan | TW |\n| Tanzania | Tanzania | TZ |\n| Thailand | Thailand | TH |\n| Tunisia | Tunisia | TN |\n| Turkey | Turkey | TR |\n| Ukraine | Ukraine | UA |\n| United Arab Emirates | UnitedArabEmirates | AE |\n| United Kingdom | UnitedKingdom | GB |\n| United States Minor Outlying Islands | UnitedStatesMinorOutlyingIslands | UM |\n| United States of America | UnitedStatesofAmerica | US |\n| United States Virgin Islands | UnitedStatesVirginIslands | VI |\n| Uruguay | Uruguay | UY |\n| Uzbekistan | Uzbekistan | UZ |\n| Vanuatu | Vanuatu | VU |\n| Vatican City | VaticanCity | VA |\n| Venezuela | Venezuela | VE |\n| Vietnam | Vietnam | VN |\n| Virgin Islands (U.S.) | VirginIslandsUS | VI |\n| Zambia | Zambia | ZM |\n| Zimbabwe | Zimbabwe | ZW |\n\n\nThese are the Available Financial Markets:\n\n| Available Financial Markets | Full Country | Code |\n|:----------------------------:|:----------------------:|:----:|\n| European Central Bank | EuropeanCentralBank | ECB |\n| New York Stock Exchange | NewYorkStockExchange | XNYS |\n\n\n\n## Example\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\n# Make a DataFrame with a date column\nstart_date = '2023-01-01'\nend_date = '2023-01-10'\ndf = pd.DataFrame(pd.date_range(start=start_date, end=end_date), columns=['date'])\n\n# Add holiday features for US\ntk.get_holiday_signature(df['date'], 'UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01110New Year's Day
    12023-01-02101New Year's Day (Observed)
    22023-01-03001NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.get_holiday_signature(df['date'], 'France')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n", + "markdown": "---\ntitle: get_holiday_signature\n---\n\n\n\n`get_holiday_signature(idx, country_name='UnitedStates', engine='pandas')`\n\nEngineers 4 different holiday features from a single datetime for 137 countries \nand 2 financial markets.\n\nNote: Requires the `holidays` package to be installed. See \nhttps://pypi.org/project/holidays/ for more information.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------|--------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `idx` | Union\\[pd.DatetimeIndex, pd.Series\\] | The input series. | _required_ |\n| `country_name` | str | The name of the country for which to generate holiday features. Defaults to United States holidays, but the following countries are currently available and accessible by the full name or ISO code: See NOTES. | `'UnitedStates'` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for getting holidays. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for getting holidays. This can be faster than using \"pandas\" for large datasets. | `'pandas'` |\n\n## Returns\n\n| Type | Description |\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| pd.DataFrame: | A pandas DataFrame with three holiday-specific features: - is_holiday: (0, 1) indicator for holiday - before_holiday: (0, 1) indicator for day before holiday - after_holiday: (0, 1) indicator for day after holiday - holiday_name: name of the holiday |\n\n## Notes\n\nAny of the following are acceptable keys for `country_name`:\n\n| Available Countries | Full Country | Code |\n|:-----------------------------------------:|:--------------------------------:|:----:|\n| Albania | Albania | AL |\n| Algeria | Algeria | DZ |\n| American Samoa | AmericanSamoa | AS |\n| Andorra | Andorra | AD |\n| Angola | Angola | AO |\n| Argentina | Argentina | AR |\n| Armenia | Armenia | AM |\n| Aruba | Aruba | AW |\n| Australia | Australia | AU |\n| Austria | Austria | AT |\n| Azerbaijan | Azerbaijan | AZ |\n| Bahrain | Bahrain | BH |\n| Bangladesh | Bangladesh | BD |\n| Barbados | Barbados | BB |\n| Belarus | Belarus | BY |\n| Belgium | Belgium | BE |\n| Belize | Belize | BZ |\n| Bolivia | Bolivia | BO |\n| Bosnia and Herzegovina | BosniaandHerzegovina | BA |\n| Botswana | Botswana | BW |\n| Brazil | Brazil | BR |\n| Brunei | Brunei | BN |\n| Bulgaria | Bulgaria | BG |\n| Burkina Faso | BurkinaFaso | BF |\n| Burundi | Burundi | BI |\n| Laos | Laos | LA |\n| Latvia | Latvia | LV |\n| Lesotho | Lesotho | LS |\n| Liechtenstein | Liechtenstein | LI |\n| Lithuania | Lithuania | LT |\n| Luxembourg | Luxembourg | LU |\n| Madagascar | Madagascar | MG |\n| Malawi | Malawi | MW |\n| Malaysia | Malaysia | MY |\n| Maldives | Maldives | MV |\n| Malta | Malta | MT |\n| Marshall Islands | MarshallIslands | MH |\n| Mexico | Mexico | MX |\n| Moldova | Moldova | MD |\n| Monaco | Monaco | MC |\n| Montenegro | Montenegro | ME |\n| Morocco | Morocco | MA |\n| Mozambique | Mozambique | MZ |\n| Namibia | Namibia | NA |\n| Netherlands | Netherlands | NL |\n| New Zealand | NewZealand | NZ |\n| Nicaragua | Nicaragua | NI |\n| Nigeria | Nigeria | NG |\n| Northern Mariana Islands | NorthernMarianaIslands | MP |\n| North Macedonia | NorthMacedonia | MK |\n| Norway | Norway | NO |\n| Pakistan | Pakistan | PK |\n| Panama | Panama | PA |\n| Paraguay | Paraguay | PY |\n| Peru | Peru | PE |\n| Philippines | Philippines | PH |\n| Poland | Poland | PL |\n| Portugal | Portugal | PT |\n| Puerto Rico | PuertoRico | PR |\n| Romania | Romania | RO |\n| Russia | Russia | RU |\n| San Marino | SanMarino | SM |\n| Saudi Arabia | SaudiArabia | SA |\n| Serbia | Serbia | RS |\n| Singapore | Singapore | SG |\n| Slovakia | Slovakia | SK |\n| Slovenia | Slovenia | SI |\n| South Africa | SouthAfrica | ZA |\n| South Korea | SouthKorea | KR |\n| Spain | Spain | ES |\n| Sweden | Sweden | SE |\n| Switzerland | Switzerland | CH |\n| Taiwan | Taiwan | TW |\n| Tanzania | Tanzania | TZ |\n| Thailand | Thailand | TH |\n| Tunisia | Tunisia | TN |\n| Turkey | Turkey | TR |\n| Ukraine | Ukraine | UA |\n| United Arab Emirates | UnitedArabEmirates | AE |\n| United Kingdom | UnitedKingdom | GB |\n| United States Minor Outlying Islands | UnitedStatesMinorOutlyingIslands | UM |\n| United States of America | UnitedStatesofAmerica | US |\n| United States Virgin Islands | UnitedStatesVirginIslands | VI |\n| Uruguay | Uruguay | UY |\n| Uzbekistan | Uzbekistan | UZ |\n| Vanuatu | Vanuatu | VU |\n| Vatican City | VaticanCity | VA |\n| Venezuela | Venezuela | VE |\n| Vietnam | Vietnam | VN |\n| Virgin Islands (U.S.) | VirginIslandsUS | VI |\n| Zambia | Zambia | ZM |\n| Zimbabwe | Zimbabwe | ZW |\n\n\nThese are the Available Financial Markets:\n\n| Available Financial Markets | Full Country | Code |\n|:----------------------------:|:----------------------:|:----:|\n| European Central Bank | EuropeanCentralBank | ECB |\n| New York Stock Exchange | NewYorkStockExchange | XNYS |\n\n## Example\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\nimport pytimetk as tk\n\n# Make a DataFrame with a date column\nstart_date = '2023-01-01'\nend_date = '2023-01-10'\ndf = pd.DataFrame(pd.date_range(start=start_date, end=end_date), columns=['date'])\n\n# Add holiday features for US\ntk.get_holiday_signature(df['date'], 'UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01110New Year's Day
    12023-01-02101New Year's Day (Observed)
    22023-01-03001NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Add holiday features for France\ntk.get_holiday_signature(df['date'], 'France')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    dateis_holidaybefore_holidayafter_holidayholiday_name
    02023-01-01100New Year's Day
    12023-01-02001NaN
    22023-01-03000NaN
    32023-01-04000NaN
    42023-01-05000NaN
    52023-01-06000NaN
    62023-01-07000NaN
    72023-01-08000NaN
    82023-01-09000NaN
    92023-01-10000NaN
    \n
    \n```\n:::\n:::\n\n\n", "supporting": [ - "get_holiday_signature_files/figure-html" + "get_holiday_signature_files" ], "filters": [], "includes": { diff --git a/docs/_freeze/reference/is_holiday/execute-results/html.json b/docs/_freeze/reference/is_holiday/execute-results/html.json index 8f94587f..4738754b 100644 --- a/docs/_freeze/reference/is_holiday/execute-results/html.json +++ b/docs/_freeze/reference/is_holiday/execute-results/html.json @@ -1,9 +1,9 @@ { - "hash": "b1a6cdf0d14f0c157c90d2e4a63c4f93", + "hash": "7617beb9b6018fefcbeae892703fdab0", "result": { - "markdown": "---\ntitle: is_holiday\n---\n\n\n\n`is_holiday(idx, country_name='UnitedStates', country=None, engine='pandas')`\n\nCheck if a given list of dates are holidays for a specified country.\n\nNote: This function requires the `holidays` package to be installed.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------|-------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `idx` | Union\\[str, datetime, List\\[Union\\[str, datetime\\]\\], pd.Series\\] | The dates to check for holiday status. | _required_ |\n| `country_name` | str | The name of the country for which to check the holiday status. Defaults to 'UnitedStates' if not specified. | `'UnitedStates'` |\n| `country` | str | An alternative parameter to specify the country for holiday checking, overriding country_name. | `None` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for generating the boolean series. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for generating a boolean of holidays or not holidays. This can be faster than using \"pandas\" for long series. | `'pandas'` |\n\n## Returns:\n\npd.Series:\n Series containing True if the date is a holiday, False otherwise.\n\n\n\n## Raises:\n\nValueError:\n If the specified country is not found in the holidays package.\n\n\n\n## Examples:\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport polars as pl\nimport pytimetk as tk\n\ntk.is_holiday('2023-01-01', country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n0 True\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# List of dates\ntk.is_holiday(['2023-01-01', '2023-01-02', '2023-01-03'], country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\n0 True\n1 True\n2 False\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Polars Series\ntk.is_holiday(pl.Series(['2023-01-01', '2023-01-02', '2023-01-03']), country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n0 True\n1 True\n2 False\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n", + "markdown": "---\ntitle: is_holiday\n---\n\n\n\n`is_holiday(idx, country_name='UnitedStates', country=None, engine='pandas')`\n\nCheck if a given list of dates are holidays for a specified country.\n\nNote: This function requires the `holidays` package to be installed.\n\n## Parameters\n\n| Name | Type | Description | Default |\n|----------------|-------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|\n| `idx` | Union\\[str, datetime, List\\[Union\\[str, datetime\\]\\], pd.Series\\] | The dates to check for holiday status. | _required_ |\n| `country_name` | str | The name of the country for which to check the holiday status. Defaults to 'UnitedStates' if not specified. | `'UnitedStates'` |\n| `country` | str | An alternative parameter to specify the country for holiday checking, overriding country_name. | `None` |\n| `engine` | str | The `engine` parameter is used to specify the engine to use for generating the boolean series. It can be either \"pandas\" or \"polars\". - The default value is \"pandas\". - When \"polars\", the function will internally use the `polars` library for generating a boolean of holidays or not holidays. This can be faster than using \"pandas\" for long series. | `'pandas'` |\n\n## Returns:\n\npd.Series:\n Series containing True if the date is a holiday, False otherwise.\n\n## Raises:\n\nValueError:\n If the specified country is not found in the holidays package.\n\n## Examples:\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport polars as pl\nimport pytimetk as tk\n\ntk.is_holiday('2023-01-01', country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n0 True\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# List of dates\ntk.is_holiday(['2023-01-01', '2023-01-02', '2023-01-03'], country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\n0 True\n1 True\n2 False\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Polars Series\ntk.is_holiday(pl.Series(['2023-01-01', '2023-01-02', '2023-01-03']), country_name='UnitedStates')\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n0 True\n1 True\n2 False\nName: is_holiday, dtype: bool\n```\n:::\n:::\n\n\n", "supporting": [ - "is_holiday_files/figure-html" + "is_holiday_files" ], "filters": [], "includes": {} diff --git a/docs/_site/changelog-news.html b/docs/_site/changelog-news.html index a7d24cb0..8e5f000c 100644 --- a/docs/_site/changelog-news.html +++ b/docs/_site/changelog-news.html @@ -7,7 +7,7 @@ -pytimetk - Changelog for PyTimeTK +pytimetk - Changelog for Pytimetk
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