diff --git a/AI_Cost_Cutting_Market_Analysis/.env.example b/AI_Cost_Cutting_Market_Analysis/.env.example index 31d5ca9..3770e5c 100644 --- a/AI_Cost_Cutting_Market_Analysis/.env.example +++ b/AI_Cost_Cutting_Market_Analysis/.env.example @@ -1,9 +1,8 @@ -# Liquid Cooling Market Watch Environment Variables -# Copy this file to .env and fill in your actual credentials +# AI Cost Cutting Market Analysis — environment variables +# Copy this file to .env and fill in your actual values -# Bigdata API credentials -BIGDATA_USERNAME=your_username -BIGDATA_PASSWORD=your_password +# Bigdata.com API key (X-API-KEY) +BIGDATA_API_KEY=your_bigdata_api_key -# OpenAI API key (for advanced features) +# OpenAI API key (for labeling / LLM features) OPENAI_API_KEY=your_openai_api_key diff --git a/AI_Cost_Cutting_Market_Analysis/AI_Cost_Cutting_Market_Analysis.html b/AI_Cost_Cutting_Market_Analysis/AI_Cost_Cutting_Market_Analysis.html index 1a0dfa3..4b9967b 100644 --- a/AI_Cost_Cutting_Market_Analysis/AI_Cost_Cutting_Market_Analysis.html +++ b/AI_Cost_Cutting_Market_Analysis/AI_Cost_Cutting_Market_Analysis.html @@ -7656,6 +7656,61 @@
import os
+
+from dotenv import load_dotenv
+from pathlib import Path
+
+script_dir = Path(__file__).parent if '__file__' in globals() else Path.cwd()
+load_dotenv(script_dir / '.env')
+
+BIGDATA_API_KEY = os.getenv('BIGDATA_API_KEY')
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+
+if not all([BIGDATA_API_KEY, OPENAI_API_KEY]):
+ print("❌ Missing required environment variables")
+ raise ValueError(
+ "Missing required environment variables. Set BIGDATA_API_KEY and OPENAI_API_KEY in your .env file."
+ )
+else:
+ print("✅ Credentials loaded from .env file")
+✅ Credentials loaded from .env file ++
Below is the Python code required for setting up our environment and importing necessary libraries.
import pandas as pd
@@ -7700,7 +7755,7 @@ Optional: Plotly Display Configu
-In [36]:
+In [6]:
-In [6]:
+In [7]:
# Define output file paths for our results
@@ -7777,59 +7832,7 @@ Define Output Paths
-Load Credentials¶
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-In [7]:
-
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-from dotenv import load_dotenv
-from pathlib import Path
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-script_dir = Path(__file__).parent if '__file__' in globals() else Path.cwd()
-load_dotenv(script_dir / '.env')
-
-BIGDATA_USERNAME = os.getenv('BIGDATA_USERNAME')
-BIGDATA_PASSWORD = os.getenv('BIGDATA_PASSWORD')
-OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
-
-if not all([BIGDATA_USERNAME, BIGDATA_PASSWORD, OPENAI_API_KEY]):
- print("❌ Missing required environment variables")
- raise ValueError("Missing required environment variables. Check your .env file.")
-else:
- print("✅ Credentials loaded from .env file")
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-✅ Credentials loaded from .env file
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@@ -7841,7 +7844,7 @@ Connecting to BigdataIn [8]:
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-Companies Using AI for Cost Cutting¶
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-This complementary chart shows companies most frequently mentioned as users of AI cost cutting technology in their operations, organized by sector and ranked by total mention volume. It identifies firms investing in AI automation, implementing cost reduction systems, or optimizing their operations through AI-driven efficiency improvements.
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-In [38]:
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-plot_top_companies_by_sector(df_users, min_companies=1, title_suffix="(AI Cost Cutting Users)", top_sectors=4, interactive=interactive_plots)
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-AI Cost Cutting Ecosystem Analysis¶
This comprehensive assessment combines both provider and user signals to create a complete picture of the AI cost cutting ecosystem. The analysis provides:
-
-- Total Exposure: Overall volume of AI cost cutting related news coverage for each company
-- Role Classification: Relative proportion of provider versus user mentions
-- Market Positioning: How companies rank in terms of their involvement in the AI cost cutting market
-
-The stacked bar chart shows the percentage breakdown of provider (green) versus user (red) mentions for the top 40 most-mentioned companies. Companies with predominantly blue bars are primarily technology providers, while those with green bars are mainly end-users implementing AI cost cutting solutions.
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-In [39]:
-
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-companies_basket = identify_basket_and_plot_confidence(df_providers,df_users, basket_size=40, theme_name='AI Cost Cutting', positive_label='Providers', negative_label='Users', interactive=interactive_plots)
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+Companies Using AI for Cost Cutting¶
+
+
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+
+This complementary chart shows companies most frequently mentioned as users of AI cost cutting technology in their operations, organized by sector and ranked by total mention volume. It identifies firms investing in AI automation, implementing cost reduction systems, or optimizing their operations through AI-driven efficiency improvements.
+
+
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+In [16]:
+
+
+plot_top_companies_by_sector(df_users, min_companies=1, title_suffix="(AI Cost Cutting Users)", top_sectors=4, interactive=interactive_plots)
+
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+AI Cost Cutting Ecosystem Analysis¶
This comprehensive assessment combines both provider and user signals to create a complete picture of the AI cost cutting ecosystem. The analysis provides:
+
+- Total Exposure: Overall volume of AI cost cutting related news coverage for each company
+- Role Classification: Relative proportion of provider versus user mentions
+- Market Positioning: How companies rank in terms of their involvement in the AI cost cutting market
+
+The stacked bar chart shows the percentage breakdown of provider (green) versus user (red) mentions for the top 40 most-mentioned companies. Companies with predominantly blue bars are primarily technology providers, while those with green bars are mainly end-users implementing AI cost cutting solutions.
+
+
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+In [15]:
+
+
+companies_basket = identify_basket_and_plot_confidence(df_providers,df_users, basket_size=40, theme_name='AI Cost Cutting', positive_label='Providers', negative_label='Users', interactive=interactive_plots)
+
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+ }) };
@@ -8399,7 +12268,7 @@ AI Cost Cutting Ecosystem Analysis
-In [24]:
+In [16]:
companies_basket.head()
@@ -8413,7 +12282,7 @@ AI Cost Cutting Ecosystem Analysis
-Out[24]:
+Out[16]: