Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

LICENSE

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+MIT License
+
+Copyright (c) 2023 tony hollaar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Streamlit Connection API
+The Streamlit Connection API is a custom-built Python package that allows you to easily interact with the U.S. Bureau of Labor Statistics (BLS) API and retrieve data as pandas dataframes.
+
+## Installation
+
+To install the Streamlit Connection API, simply run the following command:
+```python
+pip install streamlit-bls-connection
+```
+
+## Example Streamlit API:
+
+```python
+import streamlit as st
+from streamlit_bls_connection import BLSConnection
+
+# Step 1: Setup connection to US Bureau of Labor Statistics
+connection = BLSConnection("bls_connection")
+
+# Step 2: Define Input parameters for the API call
+# Tip: one or multiple Series ID's* can be retrieved
+seriesids_list = ['APU000074714', 'APU000072610']
+start_year_str = '2014'  # start of date range
+end_year_str = '2023'    # end of date range
+
+# Step 3: Fetch data using the custom connection
+dataframes_dict = connection.query(seriesids_list, start_year_str, end_year_str)
+
+# Step 4: Create dataframes
+gas_df = dataframes_dict['APU000074714']
+electricity_df = dataframes_dict['APU000072610']
+
+# Step 5: Show Dataframes in Streamlit
+st.dataframe(gas_df, electricity_df)
+```
+
+## License
+This project is licensed under the MIT License. See the LICENSE file for details.

setup.py

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+from setuptools import setup, find_packages
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setup(
+    name='streamlit_bls_connection',
+    version='0.5',
+    license='MIT',  
+    description='A package to fetch Bureau of Labor Statistics data using Streamlit',
+    author='Tony Hollaar',
+    author_email='[email protected]',
+    packages=find_packages(),
+    keywords = ['streamlit', 'api', 'bls']
+    url = 'https://github.com/tonyhollaar/',
+    install_requires=[
+        'streamlit',
+        'requests',
+        'pandas',
+    ],
+classifiers=[
+    'Development Status :: 4 - Beta',      # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package
+    'Intended Audience :: Developers',      # Define that your audience are developers
+    'Topic :: Software Development :: Build Tools',
+    'License :: OSI Approved :: MIT License',   # Again, pick a license
+    'Programming Language :: Python :: 3.7',      #Specify which pyhton versions that you want to support
+    'Programming Language :: Python :: 3.8',
+    'Programming Language :: Python :: 3.9',
+    ],
+)

streamlit_bls_connection.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: streamlit-bls-connection
+Version: 0.4
+Summary: A package to fetch Bureau of Labor Statistics data using Streamlit
+Author: Tony Hollaar
+Author-email: [email protected]
+License-File: LICENSE

streamlit_bls_connection.egg-info/SOURCES.txt

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+LICENSE
+README.md
+setup.py
+streamlit_bls_connection/__init__.py
+streamlit_bls_connection/streamlit_bls_connection.py
+streamlit_bls_connection.egg-info/PKG-INFO
+streamlit_bls_connection.egg-info/SOURCES.txt
+streamlit_bls_connection.egg-info/dependency_links.txt
+streamlit_bls_connection.egg-info/requires.txt
+streamlit_bls_connection.egg-info/top_level.txt

streamlit_bls_connection.egg-info/dependency_links.txt

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+

streamlit_bls_connection.egg-info/requires.txt

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+streamlit
+requests
+pandas

streamlit_bls_connection.egg-info/top_level.txt

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+streamlit_bls_connection

streamlit_bls_connection/__init__.py

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+from streamlit_bls_connection.bls_connection import BLSConnection

streamlit_bls_connection/bls_connection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 24 15:12:23 2023
+
+@author: tholl
+"""
+import streamlit as st
+from streamlit.connections import ExperimentalBaseConnection
+import requests
+import pandas as pd
+import json
+
+class BLSConnection(ExperimentalBaseConnection):
+    def __init__(self, connection_name, **kwargs):
+        super().__init__(connection_name=connection_name, **kwargs)
+        # Load any connection-specific configuration or credentials here if needed.
+
+    def _connect(self, **kwargs):
+        # Implement the connection setup here.
+        # We don't need to explicitly set up a connection in this case,
+        # as we'll be making direct API calls in the methods below.
+        pass
+
+    def fetch_data(self, seriesids, start_year, end_year):
+        dataframes_dict = {}
+        headers = {'Content-type': 'application/json'}
+
+        # iterate over one or more timeseries
+        for series_id in seriesids:
+            # create empty list to save data for the current seriesId
+            parsed_data = []
+            # set the variable to retrieve from the public dataset
+            data = json.dumps({"seriesid": [series_id], "startyear": start_year, "endyear": end_year})
+            p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)
+            json_data = json.loads(p.text)
+
+            # iterate over the json file
+            for series in json_data['Results']['series']:
+                # iterate over the list of lists that contains the data
+                for item in series['data']:
+                    # within each list retrieve the year, period, value and footnotes
+                    year = item['year']
+                    period = item['period']
+                    value = item['value']
+                    footnotes = ""
+                    for footnote in item['footnotes']:
+                        if footnote:
+                            footnotes = footnotes + footnote['text'] + ','
+                    parsed_data.append([series_id, year, period, value, footnotes[0:-1]])
+
+            df = pd.DataFrame(parsed_data, columns=['seriesID', 'year', 'period', 'value', 'footnotes'])
+            df['value'] = pd.to_numeric(df['value'])
+            df['month'] = pd.to_numeric(df['period'].replace({'M': ''}, regex=True))
+            df['date'] = df['month'].map(str) + '-' + df['year'].map(str)
+            df['date'] = pd.to_datetime(pd.to_datetime(df['date'], format='%m-%Y').dt.strftime('%m-%Y'))
+            df = df.sort_values(by='date', ascending=True)
+            df['perct_change_value'] = df['value'].pct_change()
+
+            # add the dataframe to the dictionary with the seriesid as the key
+            dataframes_dict[series_id] = df
+        return dataframes_dict
+
+    @staticmethod
+    @st.cache_data(ttl="1d")  # Cache the data for one day (24 hours)
+    def query(series_id, start_year, end_year):
+        # This method will be called by the Streamlit app to retrieve data using the custom connection.
+        # You can implement any caching logic or other data processing here.
+        connection = BLSConnection("bls_connection")
+        try:
+            return connection.fetch_data(series_id, start_year, end_year)
+        except KeyError:
+            with st.sidebar:
+                st.error("😒 **Error**: Failed to fetch latest data. Daily query limit is exceeded, retrieving stored data from backup source.")
+           #st.stop()  # Stop the app execution and display the error message to the user
+            return None

streamlit_bls_connection/setup.cfg

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+# Inside of setup.cfg
+[metadata]
+description-file = README.md