1.1.21

docs/requirements.txt

@@ -1,3 +1,3 @@
-evdspy
+evdspy==1.1.21
 sphinx_rtd_theme
 sphinx_book_theme

docs/source/get_series.rst

@@ -1,31 +1,68 @@
 get_series Function
 ===================
-The ``get_series`` function is designed to fetch economic data series from a specified API and return it formatted as a pandas DataFrame, making extensive use of various parameters to fine-tune the data retrieval process.
+
+The ``get_series`` 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
 
 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 'monthly', 'weekly', 'annually', 'semimonthly', 'semiannually', and 'business'.
+    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, such as 'level', 'percentage_change', 'difference', 'year_to_year_percent_change', or 'year_to_year_differences'.
+    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', 'min', 'max', 'first', 'last', and 'sum'.
+    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
     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``.
 
@@ -57,19 +94,12 @@ Using multiple indexes and cache:
     df = get_series(indexes, start_date="01-01-2020", frequency="monthly", cache=True)
     print(df.head())
 
-.. code-block:: python
-
-    template = """
-    TP.ODEMGZS.BDTTOPLAM
-    TP.ODEMGZS.ABD
-    """
-    df = get_series(template, start_date="01-01-2020", frequency="monthly", cache=True)
-    print(df.head())
-
 Applying formulas and aggregation:
 
 .. code-block:: python
 
+    template = "TP.ODEMGZS.BDTTOPLAM"
     df = get_series(template, start_date="01-01-2020", formulas="level", aggregation="sum")
     print(df.head())
 
+

evdspy/EVDSlocal/index_requests/get_series_indexes.py

@@ -63,21 +63,7 @@ def get_series(
     ------
     ValueError
         If an invalid API key is provided or required parameters are missing.
-    Examples
-    --------
-    Basic usage:
-    # >>> template = "TP.ODEMGZS.BDTTOPLAM"
-    # >>> df = get_series(template, start_date="01-01-2020", end_date="01-01-2021", frequency="monthly")
-    # >>> print(df.head())
-    #
-    # Using multiple indexes and cache:
-    # >>> indexes = ("TP.ODEMGZS.BDTTOPLAM", "TP.ODEMGZS.ABD")
-    # >>> df = get_series(indexes, start_date="01-01-2020", frequency="monthly", cache=True)
-    # >>> print(df.head())
-    #
-    # Applying formulas and aggregation:
-    # >>> df = get_series(template, start_date="01-01-2020", formulas="level", aggregation="sum")
-    # >>> print(df.head())
+
     """
     # ............initial_api_process_when_given...............
     initial_api_process_when_given(api_key)

evdspy/__version__.py

@@ -1,2 +1,2 @@
 
-#1.1.20
+#1.1.21

pyproject.toml

@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "evdspy"
-version = "1.1.20"
+version = "1.1.21"
 description = "A versatile interface for the 'EDDS' (EVDS) API of the Central Bank of the Republic of Türkiye (https://evds2.tcmb.gov.tr/index.php?/evds/userDocs). This package allows users to easily access and manage economic data through a user-friendly menu function. It features a robust caching mechanism to enhance the efficiency of data requests by storing frequently accessed data for selected periods. Required API keys can be obtained by registering on the EVDS website."
 authors = ["Sermet Pekin <[email protected]>"]
 license = "MIT"