{ "cells": [ { "cell_type": "markdown", "id": "11c89ea9", "metadata": {}, "source": [ "# Lumen.ai - Chat Logs 2024-12-26 12:09:33.168493" ] }, { "cell_type": "code", "execution_count": null, "id": "391fe320", "metadata": {}, "outputs": [], "source": [ "import lumen as lm\n", "import panel as pn\n", "\n", "pn.extension('tabulator')" ] }, { "cell_type": "markdown", "id": "95b9778f", "metadata": {}, "source": [ "
🧑User
\n", "\n", "What datasets do you have?" ] }, { "cell_type": "markdown", "id": "94e31efc", "metadata": {}, "source": [ "
🧑User
\n", "\n", "Can you join these two?" ] }, { "cell_type": "code", "execution_count": null, "id": "ce8a0d57", "metadata": {}, "outputs": [], "source": [ "pipeline = lm.Pipeline.from_spec({\n", " \"source\": {\n", " \"tables\": {\n", " \"join_passengers_flights\": \"WITH passengers_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_passengers\\n FROM read_csv('passengers.csv')\\n),\\nflights_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_flights\\n FROM read_csv('flights.csv')\\n)\\nSELECT\\n p.\\\"Year\\\",\\n p.\\\"Month\\\",\\n COALESCE(p.domestic_passengers, 0) AS domestic_passengers,\\n COALESCE(p.international_passengers, 0) AS international_passengers,\\n COALESCE(p.total_passengers, 0) AS total_passengers,\\n COALESCE(f.domestic_flights, 0) AS domestic_flights,\\n COALESCE(f.international_flights, 0) AS international_flights,\\n COALESCE(f.total_flights, 0) AS total_flights\\nFROM passengers_data p\\nJOIN flights_data f ON p.\\\"Year\\\" = f.\\\"Year\\\" AND p.\\\"Month\\\" = f.\\\"Month\\\"\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"sql_transforms\": [\n", " {\n", " \"limit\": 1000000,\n", " \"type\": \"sql_limit\"\n", " }\n", " ],\n", " \"table\": \"join_passengers_flights\"\n", "})\n", "pipeline" ] }, { "cell_type": "markdown", "id": "b7f19729", "metadata": {}, "source": [ "
🤖Agent
\n", "\n", "> The SQL query successfully combines data from the two datasets: `passengers.csv` and `flights.csv`, using the common keys of \"Year\" and \"Month.\" This allows us to analyze the relationship between passenger numbers and flight operations over time. Here are the key insights from the combined data:\n", "> \n", "> 1. **Passenger Trends**:\n", "> - The average number of domestic passengers is approximately 55.8 million per month, while international passengers average around 14.3 million. This indicates that domestic travel significantly outweighs international travel.\n", "> - The total number of passengers per month averages about 70.1 million, suggesting a robust travel market.\n", "> \n", "> 2. **Flight Operations**:\n", "> - There are, on average, about 706,248 domestic flights and 110,531 international flights per month. The total monthly flights average around 816,779.\n", "> - The data shows that the number of domestic flights is substantially higher than international flights, reflecting the trends in passenger numbers.\n", "> \n", "> 3. **Growth Patterns**:\n", "> - The standard deviation for both domestic and international passengers indicates variability in travel patterns, which could be influenced by seasonal factors, economic conditions, or global events (e.g., pandemics).\n", "> - Notably, the maximum number of domestic passengers recorded is 78.8 million, while the maximum for international passengers is 24.6 million. This disparity highlights the potential for growth in international travel.\n", "> \n", "> 4. **Operational Efficiency**:\n", "> - The ratio of passengers to flights can provide insights into operational efficiency. For instance, if we take the average total passengers (70.1 million) and divide it by the average total flights (816,779), we find that each flight carries an average of about 86 passengers. This metric can help assess whether airlines are operating efficiently or if there is room for improvement.\n", "> \n", "> 5. **Anomalies and Considerations**:\n", "> - If there are months with significantly lower or higher passenger or flight numbers, it would be worth investigating the causes, such as economic downturns, travel restrictions, or major events that could have impacted travel behavior.\n", "> \n", "> In summary, the combined dataset reveals strong domestic travel trends, with opportunities for growth in international travel. Monitoring these patterns over time can help inform strategic decisions regarding capacity, marketing, and operational adjustments." ] }, { "cell_type": "markdown", "id": "b6ca707b", "metadata": {}, "source": [ "
🧑User
\n", "\n", "Can you plot normalized total passengers against normalized total flights?" ] }, { "cell_type": "code", "execution_count": null, "id": "1b7345a5", "metadata": {}, "outputs": [], "source": [ "view = lm.View.from_spec({\n", " \"pipeline\": {\n", " \"source\": {\n", " \"mirrors\": {\n", " \"passengers.csv\": [\n", " {\n", " \"tables\": {\n", " \"passengers.csv\": \"read_csv('passengers.csv')\",\n", " \"flights.csv\": \"read_csv('flights.csv')\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"passengers.csv\"\n", " ],\n", " \"flights.csv\": [\n", " {\n", " \"tables\": {\n", " \"passengers.csv\": \"read_csv('passengers.csv')\",\n", " \"flights.csv\": \"read_csv('flights.csv')\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"flights.csv\"\n", " ],\n", " \"normalize_total_passengers_flights\": [\n", " {\n", " \"mirrors\": {\n", " \"passengers.csv\": [\n", " {\n", " \"tables\": {\n", " \"passengers.csv\": \"read_csv('passengers.csv')\",\n", " \"flights.csv\": \"read_csv('flights.csv')\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"passengers.csv\"\n", " ],\n", " \"flights.csv\": [\n", " {\n", " \"tables\": {\n", " \"passengers.csv\": \"read_csv('passengers.csv')\",\n", " \"flights.csv\": \"read_csv('flights.csv')\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"flights.csv\"\n", " ],\n", " \"join_passengers_flights\": [\n", " {\n", " \"tables\": {\n", " \"join_passengers_flights\": \"WITH passengers_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_passengers\\n FROM read_csv('passengers.csv')\\n),\\nflights_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_flights\\n FROM read_csv('flights.csv')\\n)\\nSELECT\\n p.\\\"Year\\\",\\n p.\\\"Month\\\",\\n COALESCE(p.domestic_passengers, 0) AS domestic_passengers,\\n COALESCE(p.international_passengers, 0) AS international_passengers,\\n COALESCE(p.total_passengers, 0) AS total_passengers,\\n COALESCE(f.domestic_flights, 0) AS domestic_flights,\\n COALESCE(f.international_flights, 0) AS international_flights,\\n COALESCE(f.total_flights, 0) AS total_flights\\nFROM passengers_data p\\nJOIN flights_data f ON p.\\\"Year\\\" = f.\\\"Year\\\" AND p.\\\"Month\\\" = f.\\\"Month\\\"\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"join_passengers_flights\"\n", " ]\n", " },\n", " \"tables\": {\n", " \"normalize_total_passengers_flights\": \"WITH combined_data AS (\\n WITH passengers_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_passengers,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_passengers\\n FROM read_csv('passengers.csv')\\n ),\\n flights_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"DOMESTIC\\\", ',', ''), ' ', '') AS INTEGER) AS domestic_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"INTERNATIONAL\\\", ',', ''), ' ', '') AS INTEGER) AS international_flights,\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_flights\\n FROM read_csv('flights.csv')\\n )\\n SELECT\\n p.\\\"Year\\\",\\n p.\\\"Month\\\",\\n COALESCE(p.domestic_passengers, 0) AS domestic_passengers,\\n COALESCE(p.international_passengers, 0) AS international_passengers,\\n COALESCE(p.total_passengers, 0) AS total_passengers,\\n COALESCE(f.domestic_flights, 0) AS domestic_flights,\\n COALESCE(f.international_flights, 0) AS international_flights,\\n COALESCE(f.total_flights, 0) AS total_flights\\n FROM passengers_data p\\n JOIN flights_data f ON p.\\\"Year\\\" = f.\\\"Year\\\" AND p.\\\"Month\\\" = f.\\\"Month\\\"\\n),\\noverall_totals AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n SUM(total_passengers) AS overall_total_passengers,\\n SUM(total_flights) AS overall_total_flights\\n FROM combined_data\\n GROUP BY \\\"Year\\\", \\\"Month\\\"\\n)\\nSELECT\\n c.\\\"Year\\\",\\n c.\\\"Month\\\",\\n c.domestic_passengers,\\n c.international_passengers,\\n c.total_passengers,\\n c.domestic_flights,\\n c.international_flights,\\n c.total_flights,\\n CASE WHEN ot.overall_total_passengers > 0 THEN c.total_passengers::FLOAT / ot.overall_total_passengers END AS normalized_total_passengers,\\n CASE WHEN ot.overall_total_flights > 0 THEN c.total_flights::FLOAT / ot.overall_total_flights END AS normalized_total_flights\\nFROM combined_data c\\nJOIN overall_totals ot ON c.\\\"Year\\\" = ot.\\\"Year\\\" AND c.\\\"Month\\\" = ot.\\\"Month\\\"\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"normalize_total_passengers_flights\"\n", " ]\n", " },\n", " \"tables\": {\n", " \"total_columns_normalized\": \"WITH combined_data AS (\\n WITH passengers_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_passengers\\n FROM read_csv('passengers.csv')\\n ),\\n flights_data AS (\\n SELECT\\n \\\"Year\\\",\\n \\\"Month\\\",\\n TRY_CAST(REPLACE(REPLACE(\\\"TOTAL\\\", ',', ''), ' ', '') AS INTEGER) AS total_flights\\n FROM read_csv('flights.csv')\\n )\\n SELECT\\n p.\\\"Year\\\",\\n p.\\\"Month\\\",\\n COALESCE(p.total_passengers, 0) AS total_passengers,\\n COALESCE(f.total_flights, 0) AS total_flights\\n FROM passengers_data p\\n JOIN flights_data f ON p.\\\"Year\\\" = f.\\\"Year\\\" AND p.\\\"Month\\\" = f.\\\"Month\\\"\\n),\\noverall_totals AS (\\n SELECT\\n \\\"Month\\\",\\n SUM(total_passengers) AS overall_total_passengers,\\n SUM(total_flights) AS overall_total_flights\\n FROM combined_data\\n GROUP BY \\\"Month\\\"\\n)\\nSELECT\\n c.\\\"Year\\\",\\n c.\\\"Month\\\",\\n c.total_passengers,\\n c.total_flights,\\n CASE WHEN ot.overall_total_passengers > 0 THEN c.total_passengers::FLOAT / ot.overall_total_passengers END AS normalized_total_passengers,\\n CASE WHEN ot.overall_total_flights > 0 THEN c.total_flights::FLOAT / ot.overall_total_flights END AS normalized_total_flights\\nFROM combined_data c\\nJOIN overall_totals ot ON c.\\\"Month\\\" = ot.\\\"Month\\\"\"\n", " },\n", " \"uri\": \":memory:\",\n", " \"type\": \"duckdb\"\n", " },\n", " \"sql_transforms\": [\n", " {\n", " \"limit\": 1000000,\n", " \"type\": \"sql_limit\"\n", " }\n", " ],\n", " \"table\": \"total_columns_normalized\"\n", " },\n", " \"spec\": {\n", " \"description\": \"A scatter plot of normalized total passengers against normalized total flights.\",\n", " \"data\": {\n", " \"url\": \"data/total_columns_normalized.csv\"\n", " },\n", " \"mark\": \"point\",\n", " \"encoding\": {\n", " \"x\": {\n", " \"field\": \"normalized_total_flights\",\n", " \"type\": \"quantitative\",\n", " \"title\": \"Normalized Total Flights\"\n", " },\n", " \"y\": {\n", " \"field\": \"normalized_total_passengers\",\n", " \"type\": \"quantitative\",\n", " \"title\": \"Normalized Total Passengers\"\n", " },\n", " \"tooltip\": [\n", " {\n", " \"field\": \"normalized_total_flights\",\n", " \"type\": \"quantitative\",\n", " \"title\": \"Normalized Total Flights\"\n", " },\n", " {\n", " \"field\": \"normalized_total_passengers\",\n", " \"type\": \"quantitative\",\n", " \"title\": \"Normalized Total Passengers\"\n", " }\n", " ]\n", " },\n", " \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",\n", " \"width\": \"container\",\n", " \"height\": \"container\"\n", " },\n", " \"type\": \"vegalite\",\n", " \"sizing_mode\": \"stretch_both\",\n", " \"min_height\": 500\n", "})\n", "view" ] }, { "cell_type": "markdown", "id": "9a607b91", "metadata": {}, "source": [ "
⚙️System
\n", "\n", "> Error cannot be resolved:\n", "> \n", "> while parsing a block mapping\n", "> in \"\", line 90, column 7:\n", "> total_columns_normalized: \"WITH ... \n", "> ^\n", "> expected , but found ''\n", "> in \"\", line 95, column 16:\n", "> \\ \"Month\\\",\\n TRY_CAST(R ... \n", "> ^" ] }, { "cell_type": "markdown", "id": "33aaa5d9", "metadata": {}, "source": [ "
⚙️System
\n", "\n", "> Error cannot be resolved:\n", "> \n", "> could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple'\n", "> in \"\", line 5, column 20:\n", "> flights.csv: !!python/tuple\n", "> ^" ] }, { "cell_type": "markdown", "id": "eb563df5", "metadata": {}, "source": [ "
🧑User
\n", "\n", "Can you color it by year?" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }