docs 2

docs/source/get_series_exp.rst

@@ -0,0 +1,125 @@
+get_series_exp Function
+===================
+
+The ``get_series_exp`` function fetches economic data series from a specified API and returns it formatted as a pandas DataFrame, making extensive use of various parameters to fine-tune the data retrieval process.
+
+.. autofunction:: evdspy.get_series_exp
+
+Parameters
+----------
+index : str or tuple
+    Identifier(s) for the data series to fetch. This can be a single string for one series or a tuple of strings for multiple series.
+
+start_date : str, optional
+    The start date for the data retrieval in 'DD-MM-YYYY' format. By default, this calls ``default_start_date_fnc()``.
+
+end_date : str, optional
+    The end date for the data retrieval in 'DD-MM-YYYY' format. By default, this calls ``default_end_date_fnc()``.
+
+frequency : str, optional
+    The frequency at which data should be retrieved. Options include:
+
+    - **daily (1)**: Data retrieved every day.
+    - **business (2)**: Data retrieved only on business days.
+    - **weekly (3)**: Data retrieved weekly on Fridays.
+    - **semimonthly (4)**: Data retrieved twice a month.
+    - **monthly (5)**: Data retrieved once a month.
+    - **quarterly (6)**: Data retrieved every quarter.
+    - **semiannually (7)**: Data retrieved twice a year.
+    - **annual/annually (8)**: Data retrieved once a year.
+
+formulas : str or tuple, optional
+    Computation methods to apply to the data series. Options include:
+
+    - **level (0)**: Raw data values.
+    - **percentage change (1)**: Percent change between consecutive data points.
+    - **difference (2)**: Difference between consecutive data points.
+    - **year to year percent change (3)**: Percent change from the same date in the previous year.
+    - **year to year differences (4)**: Difference from the same date in the previous year.
+    - **percentage change compared to end-of-previous year (5)**: Percent change relative to the end of the previous year.
+    - **difference compared to end-of-previous year (6)**: Difference relative to the end of the previous year.
+    - **moving average (7)**: Moving average of data points.
+    - **moving sum (8)**: Moving sum of data points.
+
+aggregation : str or tuple, optional
+    Aggregation methods to apply to the data, options include:
+
+    - **avg**: Average value over the specified period.
+    - **min**: Minimum value over the specified period.
+    - **max**: Maximum value over the specified period.
+    - **first**: First value within the specified period.
+    - **last**: Last value within the specified period.
+    - **sum**: Sum of all values within the specified period.
+
+cache : bool, optional [when it is False refreshes data on each call ]
+    If True, uses cached data when available to speed up the data retrieval process. Default is False.
+meta_cache : bool, optional [when it is False refreshes metadata on each call ]
+    If True, uses cached data when available to speed up the data retrieval process. Default is False.
+
+proxy : str, optional
+    The URL of the proxy server to use for the requests. Default is None.
+
+proxies : dict, optional
+    A dictionary of proxies to use for the request. Default is None.
+
+debug : bool, optional
+    If True, runs the function in debug mode, providing additional debug information without making a real API request. Default is False.
+
+api_key : str, optional
+    The API key required for accessing the data. Initially, it can be saved using the ``save("APIKEY")`` function or via command line with ``$ evdspy save``.
+
+Returns
+-------
+Result Class
+data     : pd.DataFrame
+    contains data [ same as what get_series function returns ]
+metadata : pd.DataFrame
+    contains metadata if available
+write    : Callable
+    creates and Excel file with data and metadadata of two sheets
+to_excel : Callable
+    same with write to meet pandas to_excel function
+
+Raises
+------
+ValueError
+    Raised if an invalid API key is provided or required parameters are missing.
+
+Examples
+--------
+Basic usage:
+
+.. code-block:: python
+
+    index = "TP.ODEMGZS.BDTTOPLAM"
+    result = get_series_exp(index, start_date="01-01-2020", end_date="01-01-2021", frequency="monthly")
+    print(result.data.head())
+    print(result.metadata)
+    result.write('output.xlsx')
+    result.to_excel('output.xlsx')
+
+
+Using multiple indexes and cache:
+
+.. code-block:: python
+
+    indexes = ("TP.ODEMGZS.BDTTOPLAM", "TP.ODEMGZS.ABD")
+    result = get_series_exp(indexes, start_date="01-01-2020", end_date="01-01-2021", frequency="monthly")
+    print(result.data.head())
+    print(result.metadata)
+    result.write('output.xlsx')
+    result.to_excel('output.xlsx')
+
+Applying formulas and aggregation:
+
+.. code-block:: python
+
+    template = "TP.ODEMGZS.BDTTOPLAM"
+    df = get_series(template,  formulas="level", aggregation="sum")
+    result = get_series_exp(indexes,  formulas="level", aggregation="sum"))
+    print(result.data.head())
+    print(result.metadata)
+    result.write('output.xlsx')
+    result.to_excel('output.xlsx')
+
+

docs/source/index.rst

@@ -11,6 +11,7 @@ Welcome to evdspy's documentation!
 
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+   get_series_exp
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