Add utils.checks.py: Check for common issues with function inputs #86

docs/_freeze/getting-started/01_installation/execute-results/html.json

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     "markdown": "---\ntitle: Install\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\n---\n\n<!-- ::: {.callout-warning collapse=\"false\"}\n## Under Development\n\nThis library is currently under development and is not intended for general usage yet. Functionality is experimental until release 0.1.0. \n::: -->\n\n# Quick Install\n\nLet's get you up and running with `pytimetk` fast with the latest stable release. \n\n```bash\npip install pytimetk\n```\n\nYou can install from GitHub with this code. \n\n```bash\npip install git+https://github.com/business-science/pytimetk.git\n```\n\n# Next steps\n\nCheck out the [Quick Start Guide Next.](/getting-started/02_quick_start.html)\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `pytimetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n",
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docs/_freeze/guides/03_pandas_frequency/execute-results/html.json

@@ -1,9 +1,9 @@
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     "markdown": "---\ntitle: Pandas Frequencies\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\n---\n\n::: {.callout-note collapse=\"false\"}\n## How this guide benefits you\n\nThis guide covers how to use the `pandas` frequency strings within `pytimetk`. Once you understand key frequencies, you can apply them to manipulate time series data like a pro. \n:::\n\n# Pandas Frequencies\n\nPandas offers a variety of frequency strings, also known as offset aliases, to define the frequency of a time series. Here are some common frequency strings used in pandas:\n\n1. **'B'**: Business Day\n2. **'D'**: Calendar day\n3. **'W'**: Weekly\n4. **'M'**: Month end\n5. **'BM'**: Business month end\n6. **'MS'**: Month start\n7. **'BMS'**: Business month start\n8. **'Q'**: Quarter end\n9. **'BQ'**: Business quarter end\n10. **'QS'**: Quarter start\n11. **'BQS'**: Business quarter start\n12. **'A' or 'Y'**: Year end\n13. **'BA' or 'BY'**: Business year end\n14. **'AS' or 'YS'**: Year start\n15. **'BAS' or 'BYS'**: Business year start\n16. **'H'**: Hourly\n17. **'T' or 'min'**: Minutely\n18. **'S'**: Secondly\n19. **'L' or 'ms'**: Milliseconds\n20. **'U'**: Microseconds\n21. **'N'**: Nanoseconds\n\n### Custom Frequencies:\n- You can also create custom frequencies by combining base frequencies, like:\n   - **'2D'**: Every 2 days\n   - **'3W'**: Every 3 weeks\n   - **'4H'**: Every 4 hours\n   - **'1H30T'**: Every 1 hour and 30 minutes\n\n### Compound Frequencies:\n- You can combine multiple frequencies by adding them together.\n   - **'1D1H'**: 1 day and 1 hour\n   - **'1H30T'**: 1 hour and 30 minutes\n\n### Example:\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pandas as pd\n\n# Creating a date range with daily frequency\ndate_range_daily = pd.date_range(start='2023-01-01', end='2023-01-10', freq='D')\n\ndate_range_daily\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\nDatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04',\n               '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08',\n               '2023-01-09', '2023-01-10'],\n              dtype='datetime64[ns]', freq='D')\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Creating a date range with 2 days frequency\ndate_range_two_days = pd.date_range(start='2023-01-01', end='2023-01-10', freq='2D')\n\ndate_range_two_days\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\nDatetimeIndex(['2023-01-01', '2023-01-03', '2023-01-05', '2023-01-07',\n               '2023-01-09'],\n              dtype='datetime64[ns]', freq='2D')\n```\n:::\n:::\n\n\nThese frequency strings help in resampling, creating date ranges, and handling time-series data efficiently in pandas.\n\n# Timetk Incorporates Pandas Frequencies\n\nNow that you've seen pandas frequencies, you'll see them pop up in many of the `pytimetk` functions. \n\n### Example: Padding Dates\n\nThis example shows how to use Pandas frequencies inside of `pytimetk` functions. \n\nWe'll use `pad_by_time` to show how to use freq to fill in missing dates. \n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# DataFrame with missing dates\nimport pandas as pd\n\ndata = {\n    # '2023-09-05' is missing\n    'datetime': ['2023-09-01', '2023-09-02', '2023-09-03', '2023-09-04', '2023-09-06'],  \n    'value': [10, 30, 40, 50, 60]\n}\n\ndf = pd.DataFrame(data)\ndf['datetime'] = pd.to_datetime(df['datetime'])\ndf\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-09-01</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-09-02</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-09-03</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-09-04</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-09-06</td>\n      <td>60</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n```\n:::\n:::\n\n\nWe can resample to fill in the missing day using `pad_by_time` with `freq = 'D'`.\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\nimport pytimetk as tk\n\ndf.pad_by_time('datetime', freq = 'D')\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-09-01</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-09-02</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-09-03</td>\n      <td>40.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-09-04</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-09-05</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023-09-06</td>\n      <td>60.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n```\n:::\n:::\n\n\nWhat about resampling every 12 hours? Just set `freq = '12H'.\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\nimport pytimetk as tk\n\ndf.pad_by_time('datetime', freq = '12H')\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-09-01 00:00:00</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-09-01 12:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-09-02 00:00:00</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-09-02 12:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-09-03 00:00:00</td>\n      <td>40.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023-09-03 12:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023-09-04 00:00:00</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023-09-04 12:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023-09-05 00:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023-09-05 12:00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023-09-06 00:00:00</td>\n      <td>60.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n```\n:::\n:::\n\n\nYou'll see these pandas frequencies come up as the parameter `freq` in many `pytimetk` functions. \n\n# Next Steps\n\nCheck out the [Data Wrangling Guide next.](/guides/04_wrangling.html)\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `pytimetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n",
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