### Initialize DataFrame for JSON examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html

Setup a sample DataFrame to demonstrate JSON serialization and deserialization.

```python
>>> from io import StringIO
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
```

--------------------------------

### DataFrame Setup for GroupBy Pipe Example

Source: https://pandas.pydata.org/docs/whatsnew/v0.21.0.html

Initializes a DataFrame with store, product, revenue, and quantity data for demonstrating the GroupBy pipe method.

```python
import numpy as np

n = 1000

df = pd.DataFrame({'Store': np.random.choice(['Store_1', 'Store_2'], n),
                   'Product': np.random.choice(['Product_1',
                                                 'Product_2',
                                                 'Product_3'
                                                 ], n),
                   'Revenue': (np.random.random(n) * 50 + 10).round(2),
                   'Quantity': np.random.randint(1, 10, size=n)})

df.head(2)
```

--------------------------------

### Initialize a Pandas Series

Source: https://pandas.pydata.org/docs/whatsnew/v0.13.0.html

Demonstrates the initialization of a Pandas Series. This is a basic setup for subsequent examples.

```python
In [96]: s = pd.Series([1, 2, 3, 4])
```

--------------------------------

### Create a Pandas DataFrame

Source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html

Initializes a sample DataFrame for demonstrating JSON conversion. This setup is common across various `to_json` examples.

```python
from json import loads, dumps
import pandas as pd
df = pd.DataFrame(
    [["a", "b"], ["c", "d"]],
    index=["row 1", "row 2"],
    columns=["col 1", "col 2"],
)
```

--------------------------------

### Serialize a pandas DataFrame to a pickle file

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.to_pickle.html

This example demonstrates how to create a pandas DataFrame and then serialize it to a file named 'dummy.pkl' using the `to_pickle` method. Ensure you have pandas installed (`pip install pandas`).

```python
import pandas as pd

original_df = pd.DataFrame(
    {"foo": range(5), "bar": range(5, 10)}
)
print(original_df)
original_df.to_pickle("./dummy.pkl")
```

--------------------------------

### Get Fiscal Year of Period with pandas Series.dt.qyear

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.qyear.html

Demonstrates how to use the Series.dt.qyear accessor to retrieve the fiscal year of a pandas Period. It shows examples where the fiscal year aligns with the calendar year and where it differs due to a custom fiscal year start.

```python
import pandas as pd

# Example 1: Natural and fiscal year are the same
per_same_year = pd.Period('2018Q1', freq='Q')
print(f'Period: {per_same_year}, qyear: {per_same_year.qyear}, year: {per_same_year.year}')

# Example 2: Fiscal year starts in April (Q-MAR)
per_diff_year = pd.Period('2018Q1', freq='Q-MAR')
print(f'Period: {per_diff_year}, start_time: {per_diff_year.start_time}, qyear: {per_diff_year.qyear}, year: {per_diff_year.year}')
```

--------------------------------

### Build pandas with setuptools

Source: https://pandas.pydata.org/docs/development/contributing_environment.html

Legacy method to compile and install pandas using setup.py. Note that this method is deprecated.

```bash
python setup.py develop
```

--------------------------------

### Get Day of Week for a Period (pandas)

Source: https://pandas.pydata.org/docs/reference/api/pandas.Period.day_of_week.html

Demonstrates how to retrieve the day of the week (Monday=0, Sunday=6) for a pandas Period object. It shows examples for hourly, multi-day spanning, and monthly frequencies, illustrating how the start or end of the period is used depending on the frequency.

```python
import pandas as pd

# Hourly frequency
per_h = pd.Period('2017-12-31 22:00', 'h')
print(f"Hourly period day of week: {per_h.day_of_week}")

# Multi-day spanning frequency (e.g., 4 hours)
per_4h = pd.Period('2017-12-31 22:00', '4h')
print(f"4-hour period day of week: {per_4h.day_of_week}")
print(f"Start time of 4-hour period day of week: {per_4h.start_time.day_of_week}")

# Monthly frequency
per_m = pd.Period('2018-01', 'M')
print(f"Monthly period day of week: {per_m.day_of_week}")
print(f"End time of monthly period day of week: {per_m.end_time.day_of_week}")
```

--------------------------------

### Setting Startup Options in IPython

Source: https://pandas.pydata.org/docs/user_guide/options.html

Provides an example of a Python script to be used in the IPython startup directory for automatically setting Pandas options when an IPython session begins.

```python
import pandas as pd

# Set common display options for an IPython session
pd.set_option("display.max_rows", 999)
pd.set_option("display.precision", 5)
```

--------------------------------

### Get Starting Month for BHalfYearBegin Offset

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.HalfYearBegin.startingMonth.html

Retrieves the starting month for the BHalfYearBegin offset. This indicates that half-years start in January.

```python
>>> pd.offsets.BHalfYearBegin().startingMonth
1
```

--------------------------------

### Initialize DataFrame for idxmin examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmin.html

Create a sample DataFrame representing food consumption and CO2 emissions to demonstrate index retrieval.

```python
>>> df = pd.DataFrame(
...     {
...         "consumption": [10.51, 103.11, 55.48],
...         "co2_emissions": [37.2, 19.66, 1712],
...     },
...     index=["Pork", "Wheat Products", "Beef"],
... )
```

```python
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00
```

--------------------------------

### Get BHalfYearEnd starting month

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearBegin.startingMonth.html

Retrieve the default starting month for BHalfYearEnd offsets. This is typically June.

```python
>>> pd.offsets.BHalfYearEnd().startingMonth
6
```

--------------------------------

### Get BHalfYearBegin starting month

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearBegin.startingMonth.html

Retrieve the default starting month for BHalfYearBegin offsets. This is typically January.

```python
>>> pd.offsets.BHalfYearBegin().startingMonth
1
```

--------------------------------

### Get HalfYearBegin with custom starting month

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearBegin.startingMonth.html

Instantiate HalfYearBegin with a custom starting month and retrieve its value. This demonstrates how to define non-default half-year start points.

```python
>>> pd.offsets.HalfYearBegin(startingMonth=3).startingMonth
3
```

--------------------------------

### Initialize DataFrame for clipping examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.clip.html

Sets up a sample DataFrame to demonstrate clipping operations.

```python
>>> data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5
```

--------------------------------

### Setup for eval() Performance Comparison

Source: https://pandas.pydata.org/docs/user_guide/enhancingperf.html

Initializes DataFrames and Series for performance testing of pandas.eval().

```python
nrows, ncols = 20000, 100
df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for _ in range(4)]
```

--------------------------------

### Get Period Start Time - Python

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.start_time.html

Retrieves the Timestamp representing the start of a pandas Period object. This is useful for time-series analysis where precise start times are needed.

```python
import pandas as pd

period = pd.Period('2012-1-1', freq='D')
start_time = period.start_time
print(start_time)
```

--------------------------------

### Setup MultiIndex DataFrame

Source: https://pandas.pydata.org/docs/whatsnew/v0.14.0.html

Demonstrates creating a MultiIndex DataFrame using from_product and sorting indices for proper slicing performance.

```python
import pandas as pd
import numpy as np
def mklbl(prefix, n):
    return ["%s%s" % (prefix, i) for i in range(n)]
index = pd.MultiIndex.from_product([mklbl('A', 4), mklbl('B', 2), mklbl('C', 4), mklbl('D', 2)])
columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), ('b', 'foo'), ('b', 'bah')], names=['lvl0', 'lvl1'])
df = pd.DataFrame(np.arange(len(index) * len(columns)).reshape((len(index), len(columns))), index=index, columns=columns).sort_index().sort_index(axis=1)
```

--------------------------------

### Get Starting Month for BHalfYearEnd Offset

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.HalfYearBegin.startingMonth.html

Retrieves the starting month for the BHalfYearEnd offset. This indicates that half-years end in June.

```python
>>> pd.offsets.BHalfYearEnd().startingMonth
6
```

--------------------------------

### Create DataFrame for examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.shift.html

Initializes a pandas DataFrame with a date range index and three columns for demonstration purposes.

```python
df = pd.DataFrame(
    [[10, 13, 17], [20, 23, 27], [15, 18, 22], [30, 33, 37], [45, 48, 52]],
    columns=["Col1", "Col2", "Col3"],
    index=pd.date_range("2020-01-01", "2020-01-05"),
)
df
```

--------------------------------

### Get Business Hour Start Time - Python

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.CustomBusinessHour.start.html

Retrieves the start time(s) of a business hour offset. This property returns a tuple of datetime.time objects representing the start times.

```python
>>> pd.offsets.BusinessHour().start
(datetime.time(9, 0),)
```

--------------------------------

### Set and Get Starting Month for QuarterBegin

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.QuarterBegin.startingMonth.html

Demonstrates how to set a custom starting month for a quarter begin offset and then retrieve its value. This allows for defining quarters that start in months other than the default.

```python
>>> pd.offsets.QuarterBegin(startingMonth=1).startingMonth
1
```

--------------------------------

### Display HDFStore information

Source: https://pandas.pydata.org/docs/reference/api/pandas.HDFStore.info.html

This example demonstrates how to initialize an HDFStore, save multiple DataFrames to it, and print the summary information of the store using the info() method.

```python
import pandas as pd
df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["C", "D"])
store = pd.HDFStore("store.h5", "w")
store.put("data1", df1)
store.put("data2", df2)
print(store.info())
store.close()
```

--------------------------------

### Initialize DataFrame for JSON Export

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html

Setup a sample DataFrame with index and columns for demonstration purposes.

```python
>>> from json import loads, dumps
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
```

--------------------------------

### Set and Get Starting Month for HalfYearBegin Offset

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.HalfYearBegin.startingMonth.html

Demonstrates how to set a custom starting month for the HalfYearBegin offset and then retrieve its value.

```python
>>> pd.offsets.HalfYearBegin(startingMonth=3).startingMonth
3
```

--------------------------------

### Create Example Series and DataFrame

Source: https://pandas.pydata.org/docs/user_guide/basics.html

Demonstrates the creation of a date range index, a Series with random data, and a DataFrame with random data, index, and columns.

```python
index = pd.date_range("1/1/2000", periods=8)

s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=["A", "B", "C"])
```

--------------------------------

### Get BHalfYearEnd rule code

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearBegin.rule_code.html

Instantiate a BHalfYearEnd offset with a specific starting month and access its rule_code property to get the string representation.

```python
offset = pd.offsets.BHalfYearEnd(startingMonth=6)
offset.rule_code
```

--------------------------------

### Create DataFrame for examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.api.typing.SeriesGroupBy.mean.html

Initializes a pandas DataFrame with sample data including NaN values for use in subsequent examples.

```python
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {"A": [1, 1, 2, 1, 2], "B": [np.nan, 2, 3, 4, 5], "C": [1, 2, 1, 1, 2]},
    columns=["A", "B", "C"],
)

```

--------------------------------

### Get Default Starting Month for QuarterEnd

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.QuarterBegin.startingMonth.html

Retrieves the default starting month for a quarter end offset. The default is typically March (3).

```python
>>> pd.offsets.QuarterEnd().startingMonth
3
```

--------------------------------

### Get Default Starting Month for BQuarterBegin

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.QuarterBegin.startingMonth.html

Retrieves the default starting month for a business quarter begin offset. The default is typically March (3).

```python
>>> pd.offsets.BQuarterBegin().startingMonth
3
```

--------------------------------

### Install Pandas with Clipboard Support

Source: https://pandas.pydata.org/docs/getting_started/install.html

Install Pandas with support for reading from and writing to the system clipboard. This requires PyQt4/PyQt5 or qtpy.

```bash
pip install "pandas[clipboard]"
```

--------------------------------

### GET /pandas/tseries/offsets/SemiMonthEnd/is_quarter_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.SemiMonthEnd.is_quarter_start.html

Checks if a provided timestamp occurs on the start of a quarter.

```APIDOC
## GET /pandas/tseries/offsets/SemiMonthEnd/is_quarter_start

### Description
Returns a boolean value indicating whether the provided timestamp occurs on the start of a quarter.

### Method
GET

### Endpoint
/pandas/tseries/offsets/SemiMonthEnd/is_quarter_start

### Parameters
#### Path Parameters
- **ts** (Timestamp) - Required - The timestamp to check against the quarter start criteria.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - True if the timestamp is the start of a quarter, False otherwise.

#### Response Example
{
  "result": true
}
```

--------------------------------

### Create DataFrame for Styling

Source: https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.background_gradient.html

Initializes a pandas DataFrame with sample data for demonstration purposes.

```python
df = pd.DataFrame(
    columns=["City", "Temp (c)", "Rain (mm)", "Wind (m/s)"],
    data=[
        ["Stockholm", 21.6, 5.0, 3.2],
        ["Oslo", 22.4, 13.3, 3.1],
        ["Copenhagen", 24.5, 0.0, 6.7],
    ],
)

```

--------------------------------

### GET /pandas/tseries/offsets/Tick/is_year_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.Tick.is_year_start.html

Checks if a specific timestamp corresponds to the start of a year.

```APIDOC
## GET /pandas/tseries/offsets/Tick/is_year_start

### Description
Return a boolean value indicating whether the provided timestamp occurs on the start of the year.

### Method
GET

### Endpoint
/pandas/tseries/offsets/Tick/is_year_start

### Parameters
#### Path Parameters
- **ts** (Timestamp) - Required - The timestamp to check.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - Returns True if the timestamp is January 1st, otherwise False.

#### Response Example
{
  "result": true
}
```

--------------------------------

### Initialize DataFrame

Source: https://pandas.pydata.org/docs/user_guide/cookbook.html

Demonstrates how to create a pandas DataFrame from a dictionary.

```python
df = pd.DataFrame(
    {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]}
)
```

--------------------------------

### Initialize DataFrame for ffill examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.ffill.html

Create a sample DataFrame with NaN values to demonstrate forward filling behavior.

```python
>>> df = pd.DataFrame(
...     [
...         [np.nan, 2, np.nan, 0],
...         [3, 4, np.nan, 1],
...         [np.nan, np.nan, np.nan, np.nan],
...         [np.nan, 3, np.nan, 4],
...     ],
...     columns=list("ABCD"),
... )
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0
```

--------------------------------

### GET /pandas/tseries/offsets/Micro/is_month_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.Micro.is_month_start.html

Determines if a provided timestamp occurs on the start of a month.

```APIDOC
## GET /pandas/tseries/offsets/Micro/is_month_start

### Description
Returns a boolean value indicating whether the provided timestamp occurs on the first day of the month.

### Method
GET

### Endpoint
/pandas/tseries/offsets/Micro/is_month_start

### Parameters
#### Path Parameters
- **ts** (Timestamp) - Required - The timestamp object to evaluate.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - Returns True if the timestamp is the first day of the month, otherwise False.

#### Response Example
{
  "result": true
}
```

--------------------------------

### Install Pandas with Compression Support

Source: https://pandas.pydata.org/docs/getting_started/install.html

Install Pandas with support for additional compression libraries, such as Zstandard.

```bash
pip install "pandas[compression]"
```

--------------------------------

### GET /pandas/tseries/offsets/DateOffset/is_month_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.DateOffset.is_month_start.html

Checks if a given timestamp occurs on the start of a month.

```APIDOC
## GET /pandas/tseries/offsets/DateOffset/is_month_start

### Description
Determines if a given timestamp falls on the first day of a month, considering the frequency of the offset.

### Method
GET

### Endpoint
/pandas/tseries/offsets/DateOffset/is_month_start

### Parameters
#### Query Parameters
- **ts** (Timestamp) - Required - The timestamp to check.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - Returns True if the timestamp is the first day of the month, False otherwise.

#### Response Example
{
  "result": true
}
```

--------------------------------

### Registering Pandas Plotting Backend via Entry Points (Python Setup)

Source: https://pandas.pydata.org/docs/development/extending.html

Illustrates how a third-party library can register its plotting backend with pandas using Python's `entry_points` mechanism in `setup.py`. This makes the backend discoverable by pandas.

```python
# in setup.py
# setup(
#     ...,
#     entry_points={
#         "pandas_plotting_backends": [
#             "matplotlib = pandas:plotting._matplotlib",
#         ],
#     },
# )

```

--------------------------------

### GET /pandas/tseries/offsets/BusinessMonthEnd/is_month_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BusinessMonthEnd.is_month_start.html

Checks if a provided timestamp occurs on the start of a month.

```APIDOC
## GET /pandas/tseries/offsets/BusinessMonthEnd/is_month_start

### Description
Determines if a given timestamp falls on the first day of a month, considering the frequency of the offset.

### Method
GET

### Endpoint
/pandas/tseries/offsets/BusinessMonthEnd/is_month_start

### Parameters
#### Query Parameters
- **ts** (Timestamp) - Required - The timestamp to check.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - Returns true if the timestamp is the start of the month, false otherwise.

#### Response Example
{
  "result": true
}
```

--------------------------------

### GET /pandas/tseries/offsets/BusinessDay/is_month_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BusinessDay.is_month_start.html

Checks if a given timestamp occurs on the start of a month.

```APIDOC
## GET /pandas/tseries/offsets/BusinessDay/is_month_start

### Description
Returns a boolean indicating whether a provided timestamp occurs on the first day of the month.

### Method
GET

### Endpoint
/pandas/tseries/offsets/BusinessDay/is_month_start

### Parameters
#### Query Parameters
- **ts** (Timestamp) - Required - The timestamp to evaluate against the month start criteria.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - True if the timestamp is the first day of the month, False otherwise.

#### Response Example
{
  "result": true
}
```

--------------------------------

### Pandas Documentation Example Patterns

Source: https://pandas.pydata.org/docs/development/contributing_docstring.html

Examples demonstrating standard pandas documentation practices, including method definitions, docstring formatting, and the use of keyword arguments for clarity.

```python
class Series:
    def mean(self):
        """
        Compute the mean of the input.

        Examples
        --------
        >>> ser = pd.Series([1, 2, 3])
        >>> ser.mean()
        2
        """
        pass

    def fillna(self, value):
        """
        Replace missing values by ``value``.

        Examples
        --------
        >>> ser = pd.Series([1, np.nan, 3])
        >>> ser.fillna(value=0)
        [1, 0, 3]
        """
        pass

    def groupby_mean(self):
        """
        Group by index and return mean.

        Examples
        --------
        >>> ser = pd.Series([380., 370., 24., 26],
        ...               name='max_speed',
        ...               index=['falcon', 'falcon', 'parrot', 'parrot'])
        >>> ser.groupby_mean()
        """
        pass
```

--------------------------------

### GET BYearEnd.is_year_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BYearEnd.is_year_start.html

Checks if a provided timestamp occurs on the start of a year.

```APIDOC
## METHOD BYearEnd.is_year_start

### Description
Returns a boolean value indicating whether the provided timestamp occurs on the start of a year.

### Method
N/A (Class Method)

### Endpoint
pandas.tseries.offsets.BYearEnd.is_year_start(ts)

### Parameters
#### Path Parameters
- **ts** (Timestamp) - Required - The timestamp to check.

### Request Example
```python
import pandas as pd
from pandas.tseries.offsets import BYearEnd

ts = pd.Timestamp(2022, 1, 1)
offset = BYearEnd()
offset.is_year_start(ts)
```

### Response
#### Success Response (200)
- **result** (boolean) - True if the timestamp is at the start of the year, False otherwise.

#### Response Example
```json
True
```
```

--------------------------------

### Create DataFrame for Examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nlargest.html

This snippet demonstrates how to create a pandas DataFrame with sample data including population, GDP, and alpha-2 codes, which will be used in subsequent examples.

```python
import pandas as pd

df = pd.DataFrame({
    "population": [
        59000000,
        65000000,
        434000,
        434000,
        434000,
        337000,
        11300,
        11300,
        11300,
    ],
    "GDP": [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],
    "alpha-2": ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"],
},
    index=[
        "Italy",
        "France",
        "Malta",
        "Maldives",
        "Brunei",
        "Iceland",
        "Nauru",
        "Tuvalu",
        "Anguilla",
    ],
)
print(df)
```

--------------------------------

### GET QuarterEnd.startingMonth

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.QuarterEnd.startingMonth.html

Retrieves the starting month configuration for a QuarterEnd offset instance.

```APIDOC
## GET QuarterEnd.startingMonth

### Description
Returns the month of the year from which quarters start for the QuarterEnd offset instance. This value determines the quarterly cycle (e.g., 1 for Jan/Apr/Jul/Oct).

### Method
GET

### Endpoint
pandas.tseries.offsets.QuarterEnd.startingMonth

### Parameters
#### Path Parameters
- None

### Request Example
N/A (Property access)

### Response
#### Success Response (200)
- **startingMonth** (int) - The month integer (1-12) representing the start of the quarter.

#### Response Example
```python
>>> pd.offsets.QuarterEnd(startingMonth=1).startingMonth
1
```
```

--------------------------------

### Create a DataFrame

Source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.clip.html

Initializes a pandas DataFrame for demonstration purposes.

```python
import pandas as pd
data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]}
df = pd.DataFrame(data)
print(df)
```

--------------------------------

### DataFrame Initialization for Eval Example

Source: https://pandas.pydata.org/docs/whatsnew/v0.18.0.html

Initializes a DataFrame with columns 'a' and 'b' for demonstrating the eval method.

```python
In [86]: df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)})

In [87]: df
Out[87]: 
      a  b
0   0.0  0
1   2.5  1
2   5.0  2
3   7.5  3
4  10.0  4
```

--------------------------------

### GET /pandas/tseries/offsets/BQuarterEnd/startingMonth

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BQuarterEnd.startingMonth.html

Retrieves the starting month of the year for the BQuarterEnd offset instance.

```APIDOC
## GET /pandas/tseries/offsets/BQuarterEnd/startingMonth

### Description
Returns the month of the year from which quarters start for the BQuarterEnd offset. This value determines the quarterly period cycle.

### Method
GET

### Endpoint
pandas.tseries.offsets.BQuarterEnd.startingMonth

### Parameters
#### Path Parameters
- None

#### Query Parameters
- None

### Request Example
N/A (Property access)

### Response
#### Success Response (200)
- **startingMonth** (int) - The month index (1-12) representing the start of the quarter.

#### Response Example
```python
>>> pd.offsets.BQuarterEnd().startingMonth
3
```
```

--------------------------------

### GET BHalfYearEnd.startingMonth

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearEnd.startingMonth.html

Retrieves the starting month configuration for the BHalfYearEnd offset object.

```APIDOC
## GET BHalfYearEnd.startingMonth

### Description
Returns the month of the year from which half-years start for the BHalfYearEnd offset instance.

### Method
GET

### Endpoint
pandas.tseries.offsets.BHalfYearEnd.startingMonth

### Parameters
#### Path Parameters
- None

#### Query Parameters
- None

#### Request Body
- None

### Request Example
N/A (Property access)

### Response
#### Success Response (200)
- **startingMonth** (int) - The month index (1-12) representing the start of the half-year period.

#### Response Example
```python
>>> pd.offsets.BHalfYearEnd().startingMonth
6
```
```

--------------------------------

### Describe a specific pandas option

Source: https://pandas.pydata.org/docs/reference/api/pandas.describe_option.html

This example demonstrates how to use describe_option to retrieve the documentation for a specific configuration key, such as display.max_columns. It prints the description to the console by default.

```python
import pandas as pd

# Print the description for a specific option
pd.describe_option("display.max_columns")
```

--------------------------------

### GET /pandas/tseries/offsets/BHalfYearBegin/is_quarter_start

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BHalfYearBegin.is_quarter_start.html

Checks whether a provided timestamp occurs on the start of a quarter.

```APIDOC
## GET /pandas/tseries/offsets/BHalfYearBegin/is_quarter_start

### Description
Returns a boolean value indicating whether the specified timestamp occurs on the start of a quarter.

### Method
GET

### Endpoint
/pandas/tseries/offsets/BHalfYearBegin/is_quarter_start

### Parameters
#### Path Parameters
- **ts** (Timestamp) - Required - The timestamp to check against the quarter start criteria.

### Request Example
{
  "ts": "2022-01-01"
}

### Response
#### Success Response (200)
- **result** (boolean) - True if the timestamp is the start of a quarter, False otherwise.

#### Response Example
{
  "result": true
}
```

--------------------------------

### See Also Section Example

Source: https://pandas.pydata.org/docs/development/contributing_docstring.html

Example of how to structure the 'See Also' section in pandas documentation, listing related functions with brief descriptions.

```APIDOC
## See Also

Series.tail : Return the last 5 elements of the Series.
Series.iloc : Return a slice of the elements in the Series, which can also be used to return the first or last n.
```

--------------------------------

### GET Period.start_time

Source: https://pandas.pydata.org/docs/reference/api/pandas.Period.start_time.html

Retrieves the start Timestamp for a given pandas Period object.

```APIDOC
## GET Period.start_time

### Description
Returns the Timestamp representing the start of the period defined by the Period object.

### Method
GET (Property Access)

### Endpoint
Period.start_time

### Parameters
None

### Request Example
```python
import pandas as pd
period = pd.Period('2012-1-1', freq='D')
print(period.start_time)
```

### Response
#### Success Response (200)
- **Timestamp** (object) - The Timestamp object corresponding to the start of the period.

#### Response Example
```python
Timestamp('2012-01-01 00:00:00')
```
```

--------------------------------

### Check if Timestamp is Quarter Start with FY5253

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.FY5253.is_quarter_start.html

Demonstrates how to use the is_quarter_start method on a pandas Timestamp. This example initializes a timestamp and checks its alignment with the fiscal quarter start.

```python
import pandas as pd
from pandas.tseries.offsets import FY5253

ts = pd.Timestamp(2022, 1, 1)
freq = FY5253(startingMonth=1)
print(freq.is_quarter_start(ts))
```

--------------------------------

### Initialize HDFStore

Source: https://pandas.pydata.org/docs/user_guide/io.html

Opens an HDF5 file for storing pandas objects. Ensure PyTables is installed.

```python
import pandas as pd
import numpy as np

store = pd.HDFStore("store.h5")
print(store)
```

--------------------------------

### Example: Hour Offset Nanoseconds (Python)

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BusinessMonthBegin.nanos.html

Demonstrates how to get the total number of nanoseconds for the Hour offset in pandas. This is an example of a fixed frequency where the .nanos attribute returns a valid integer.

```python
import pandas as pd

# Get nanoseconds for 1 hour
hour_nanos = pd.offsets.Hour(n=1).nanos
print(f"Nanoseconds in one hour: {hour_nanos}")
```

--------------------------------

### Create a Pandas DataFrame

Source: https://pandas.pydata.org/docs/user_guide/10min.html

Initializes a sample Pandas DataFrame with columns 'A', 'B', 'C', and 'D' for demonstrating grouping operations. This setup is required before performing any group by actions.

```python
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
        "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
        "C": np.random.randn(8),
        "D": np.random.randn(8),
    }
)

```

--------------------------------

### Check if Timestamp is Month Start with pandas Offset

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BQuarterEnd.is_month_start.html

This example demonstrates how to use the is_month_start method on a pandas frequency offset object. It checks if a specific timestamp corresponds to the start of a month.

```python
import pandas as pd
ts = pd.Timestamp(2022, 1, 1)
freq = pd.offsets.Hour(5)
result = freq.is_month_start(ts)
print(result)
```

--------------------------------

### Create a DataFrame for ffill examples

Source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ffill.html

Initializes a pandas DataFrame with NaN values for demonstrating the ffill method.

```python
import pandas as pd
import numpy as np

df = pd.DataFrame(
    [
        [np.nan, 2, np.nan, 0],
        [3, 4, np.nan, 1],
        [np.nan, np.nan, np.nan, np.nan],
        [np.nan, 3, np.nan, 4],
    ],
    columns=list("ABCD"),
)
df
```

--------------------------------

### Retrieve HDFStore keys

Source: https://pandas.pydata.org/docs/reference/api/pandas.HDFStore.keys.html

This example demonstrates how to initialize an HDFStore, save a DataFrame, and retrieve the list of keys present in the store. It highlights the use of the keys() method to inspect the contents of the HDF5 file.

```python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
store = pd.HDFStore("store.h5", "w")
store.put("data", df)
print(store.keys())
store.close()
```

--------------------------------

### Create a DataFrame

Source: https://pandas.pydata.org/docs/reference/api/pandas.melt.html

Initializes a sample DataFrame for demonstrating the melt function.

```python
df = pd.DataFrame(
    {
        "A": {0: "a", 1: "b", 2: "c"},
        "B": {0: 1, 1: 3, 2: 5},
        "C": {0: 2, 1: 4, 2: 6},
    }
)
df
```

--------------------------------

### Get Period Start Time with pandas

Source: https://pandas.pydata.org/docs/reference/api/pandas.PeriodIndex.start_time.html

Demonstrates how to create a pandas Period object and access its `start_time` property to get the Timestamp for the beginning of the period. It also shows the `end_time` property for comparison.

```python
>>> period = pd.Period('2012-1-1', freq='D')
>>> period
Period('2012-01-01', 'D')

>>> period.start_time
Timestamp('2012-01-01 00:00:00')

>>> period.end_time
Timestamp('2012-01-01 23:59:59.999999')
```

--------------------------------

### Check if timestamp is quarter start using BusinessMonthBegin

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BusinessMonthBegin.is_quarter_start.html

This example demonstrates how to instantiate a pandas Timestamp and check if it represents the start of a quarter using the is_quarter_start method. It returns a boolean value indicating the result.

```python
import pandas as pd
from pandas.tseries.offsets import BusinessMonthBegin

ts = pd.Timestamp(2022, 1, 1)
freq = BusinessMonthBegin()
result = freq.is_quarter_start(ts)
print(result)
```

--------------------------------

### Install ASV Benchmark Tool

Source: https://pandas.pydata.org/docs/development/contributing_codebase.html

Installs the Airspeed Velocity (ASV) tool from the official GitHub repository using pip. This is a prerequisite for running the pandas performance test suite.

```bash
pip install git+https://github.com/airspeed-velocity/asv
```

--------------------------------

### Check if Timestamp is Month Start with pandas

Source: https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.BusinessDay.is_month_start.html

This example demonstrates how to use the is_month_start method on a pandas offset object. It creates a Timestamp and an offset frequency, then evaluates whether the timestamp represents the start of the month.

```python
import pandas as pd

ts = pd.Timestamp(2022, 1, 1)
freq = pd.offsets.Hour(5)
result = freq.is_month_start(ts)
print(result) # Output: True
```

--------------------------------

### Create DataFrame and Series for Orient Options

Source: https://pandas.pydata.org/docs/user_guide/io.html

Sets up a sample DataFrame and Series used to illustrate different JSON orientation options.

```python
In [221]: dfjo = pd.DataFrame(
   .....:     dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
   .....:     columns=list("ABC"),
   .....:     index=list("xyz"),
   .....: )
   .....: 

In [222]: dfjo
Out[222]: 
   A  B  C
x  1  4  7
y  2  5  8
z  3  6  9

In [223]: sjo = pd.Series(dict(x=15, y=16, z=17), name="D")

In [224]: sjo
Out[224]: 
x    15
y    16
z    17
Name: D, dtype: int64
```

--------------------------------

### Retrieve Data from HDFStore

Source: https://pandas.pydata.org/docs/reference/api/pandas.HDFStore.get.html

This example demonstrates how to create an HDFStore, store a DataFrame, retrieve it using the get method, and close the store. It requires an existing HDF5 file or creates a new one in write mode.

```python
import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
store = pd.HDFStore("store.h5", "w")
store.put("data", df)
# Retrieve the stored DataFrame
result = store.get("data")
store.close()
```

--------------------------------

### Create a custom Styler subclass with from_custom_template

Source: https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.from_custom_template.html

This example demonstrates how to initialize a custom Styler class by providing a search path and a template filename. The resulting class can then be used to style a pandas DataFrame.

```python
from pandas.io.formats.style import Styler
import pandas as pd

# Create a custom Styler subclass
EasyStyler = Styler.from_custom_template(
    "path/to/template",
    "template.tpl",
)

# Apply the custom styler to a DataFrame
df = pd.DataFrame({"A": [1, 2]})
EasyStyler(df)
```

--------------------------------

### GET Series.str.startswith

Source: https://pandas.pydata.org/docs/reference/api/pandas.Series.str.startswith.html

Tests if the start of each string element in a Series matches a given pattern or tuple of patterns.

```APIDOC
## GET Series.str.startswith

### Description
Test if the start of each string element matches a pattern. This method is equivalent to the Python standard library str.startswith() method but applied to a pandas Series.

### Method
GET

### Endpoint
Series.str.startswith(pat, na=<no_default>)

### Parameters
#### Path Parameters
- **pat** (str or tuple[str, ...]) - Required - Character sequence or tuple of strings to match. Regular expressions are not accepted.

#### Query Parameters
- **na** (scalar) - Optional - Object shown if element tested is not a string. Default depends on dtype (False for 'str', np.nan for object, pandas.NA for StringDtype).

### Request Example
{
  "pat": "b"
}

### Response
#### Success Response (200)
- **Series/Index of bool** (bool) - A Series of booleans indicating whether the pattern matches the start of each string element.

#### Response Example
{
  "0": true,
  "1": false,
  "2": false,
  "3": false
}
```

--------------------------------

### GET /pandas/bdate_range

Source: https://pandas.pydata.org/docs/reference/api/pandas.bdate_range.html

Generates a DatetimeIndex with business day frequency based on provided start, end, or period constraints.

```APIDOC
## GET /pandas/bdate_range

### Description
Return a fixed frequency DatetimeIndex with business day as the default. Of the four parameters (start, end, periods, freq), exactly three must be specified.

### Method
GET

### Endpoint
pandas.bdate_range

### Parameters
#### Query Parameters
- **start** (str/datetime) - Optional - Left bound for generating dates.
- **end** (str/datetime) - Optional - Right bound for generating dates.
- **periods** (int) - Optional - Number of periods to generate.
- **freq** (str/Timedelta) - Optional - Frequency string, default 'B' (business daily).
- **tz** (str) - Optional - Time zone name.
- **normalize** (bool) - Optional - Normalize start/end dates to midnight.
- **weekmask** (str) - Optional - Weekmask of valid business days.
- **holidays** (list) - Optional - Dates to exclude.
- **inclusive** (str) - Optional - Boundary inclusion ('both', 'neither', 'left', 'right').

### Request Example
{
  "start": "2018-01-01",
  "end": "2018-01-08",
  "freq": "B"
}

### Response
#### Success Response (200)
- **DatetimeIndex** (object) - A fixed frequency DatetimeIndex object.

#### Response Example
{
  "result": ["2018-01-01", "2018-01-02", "2018-01-03", "2018-01-04", "2018-01-05", "2018-01-08"],
  "freq": "B"
}
```