diff --git "a/parquetmerge.ipynb" "b/parquetmerge.ipynb" new file mode 100644--- /dev/null +++ "b/parquetmerge.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO2tU1d7j8p2fX59S3XS9Bp"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MUNy1bseizoy","executionInfo":{"status":"ok","timestamp":1763680533556,"user_tz":300,"elapsed":367,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"660462ff-6722-49ad-a9e9-6c5c30cbd0ed"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/nchs_yearly_context.parquet',\n"," '/content/newspaper_yearly_context.parquet',\n"," '/content/famous_people_yearly_context.parquet',\n"," '/content/gdp_growth_yearly_context.parquet',\n"," '/content/disaster_deaths_yearly_context.parquet',\n"," '/content/patent_yearly_contextmerged.parquet',\n"," '/content/arxiv_yearly_context.parquet',\n"," '/content/space_yearly_context.parquet',\n"," '/content/nuclear_yearly_context.parquet',\n"," '/content/energy_yearly_context.parquet',\n"," '/content/global_temp_yearly_context.parquet',\n"," '/content/market_yearly_contextmerged.parquet',\n"," '/content/gdelt_yearly_context.parquet',\n"," '/content/ucdp_yearly_contextmerged.parquet',\n"," '/content/air_travel_yearly_context.parquet']"]},"metadata":{},"execution_count":1}],"source":["import pandas as pd\n","import glob\n","import os\n","\n","# ======================================\n","# 1. FIND ALL PARQUET FILES IN /content\n","# ======================================\n","parquets = glob.glob(\"/content/*.parquet\")\n","parquets\n"]},{"cell_type":"code","source":["# ======================================\n","# 2. LOAD EACH FILE INTO A DICT\n","# ======================================\n","tables = {}\n","\n","for p in parquets:\n"," name = os.path.basename(p).replace(\".parquet\", \"\")\n"," try:\n"," df = pd.read_parquet(p)\n"," if \"year\" in df.columns:\n"," df = df.sort_values(\"year\")\n"," tables[name] = df\n"," print(f\"Loaded {name}: {df.shape}\")\n"," else:\n"," print(f\"⚠️ Skipped {name} (no year column)\")\n"," except Exception as e:\n"," print(f\"❌ Error loading {p}: {e}\")\n","\n","print(\"\\nLoaded tables:\", list(tables.keys()))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_c2weM0QjhFD","executionInfo":{"status":"ok","timestamp":1763680591923,"user_tz":300,"elapsed":239,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"3f1c8818-0d63-4afc-947b-003b77aabd7b"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded nchs_yearly_context: (118, 13)\n","Loaded newspaper_yearly_context: (8, 28)\n","Loaded famous_people_yearly_context: (2451, 18)\n","Loaded gdp_growth_yearly_context: (64, 6)\n","Loaded disaster_deaths_yearly_context: (13, 8)\n","Loaded patent_yearly_contextmerged: (50, 5)\n","Loaded arxiv_yearly_context: (19, 179)\n","Loaded space_yearly_context: (68, 10)\n","Loaded nuclear_yearly_context: (80, 8)\n","Loaded energy_yearly_context: (60, 273)\n","Loaded global_temp_yearly_context: (86, 7)\n","Loaded market_yearly_contextmerged: (237, 19)\n","Loaded gdelt_yearly_context: (36, 22)\n","Loaded ucdp_yearly_contextmerged: (34, 20)\n","Loaded air_travel_yearly_context: (52, 8)\n","\n","Loaded tables: ['nchs_yearly_context', 'newspaper_yearly_context', 'famous_people_yearly_context', 'gdp_growth_yearly_context', 'disaster_deaths_yearly_context', 'patent_yearly_contextmerged', 'arxiv_yearly_context', 'space_yearly_context', 'nuclear_yearly_context', 'energy_yearly_context', 'global_temp_yearly_context', 'market_yearly_contextmerged', 'gdelt_yearly_context', 'ucdp_yearly_contextmerged', 'air_travel_yearly_context']\n"]}]},{"cell_type":"code","source":["# ======================================\n","# 3. MERGE THEM ALL ON YEAR\n","# ======================================\n","\n","merged = None\n","\n","for name, df in tables.items():\n","\n"," # prefix all non-year columns with filename\n"," rename_map = {c: f\"{name}::{c}\" for c in df.columns if c != \"year\"}\n"," df = df.rename(columns=rename_map)\n","\n"," if merged is None:\n"," merged = df\n"," else:\n"," merged = pd.merge(merged, df, on=\"year\", how=\"outer\")\n","\n","print(\"\\nAfter merge:\", merged.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vvj0XdnHj01W","executionInfo":{"status":"ok","timestamp":1763680674357,"user_tz":300,"elapsed":201,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"df4c4dcb-3fc0-4ec7-fc70-77a8ac6f2081"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","After merge: (2455, 610)\n"]}]},{"cell_type":"code","source":["# ======================================\n","# 4. SORT YEARS + FILL MISSING\n","# ======================================\n","merged = merged.sort_values(\"year\")\n","\n","# Fill missing numeric values with NaN (no zero-fill!)\n","for c in merged.columns:\n"," if c != \"year\":\n"," merged[c] = pd.to_numeric(merged[c], errors=\"ignore\")\n","\n","print(\"Years:\", merged[\"year\"].min(), \"→\", merged[\"year\"].max())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PWeMXxXcj6i_","executionInfo":{"status":"ok","timestamp":1763680696783,"user_tz":300,"elapsed":103,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"8607db89-af14-49eb-a7ae-6e85c953e2ce"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Years: -2700 → 2025\n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-3040674674.py:9: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n"," merged[c] = pd.to_numeric(merged[c], errors=\"ignore\")\n"]}]},{"cell_type":"code","source":["# ======================================\n","# 5. SAVE FINAL MASTER FILE\n","# ======================================\n","out_path = \"/content/macro_yearly_context.parquet\"\n","merged.to_parquet(out_path)\n","\n","print(\"\\nSaved master file →\", out_path)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Gxhgyn8j6fa","executionInfo":{"status":"ok","timestamp":1763680738573,"user_tz":300,"elapsed":141,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"9aae5114-d2cc-4c42-9fe7-8f12ae2ac759"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Saved master file → /content/macro_yearly_context.parquet\n"]}]},{"cell_type":"code","source":["merged = pd.read_parquet(\"/content/macro_yearly_context.parquet\")\n","print(\"Shape:\", merged.shape)\n","merged.head(50)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"8dWw0oH8j6b2","executionInfo":{"status":"ok","timestamp":1763680893569,"user_tz":300,"elapsed":1278,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"c0519b89-0636-462e-d217-24080118a8dc"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape: (2455, 610)\n"]},{"output_type":"execute_result","data":{"text/plain":[" year \\\n","0 -2700 \n","1 -2659 \n","2 -2284 \n","3 -2200 \n","4 -1800 \n","5 -1750 \n","6 -1734 \n","7 -1700 \n","8 -1675 \n","9 -1648 \n","10 -1646 \n","11 -1550 \n","12 -1549 \n","13 -1510 \n","14 -1503 \n","15 -1481 \n","16 -1479 \n","17 -1425 \n","18 -1420 \n","19 -1400 \n","20 -1303 \n","21 -1300 \n","22 -1220 \n","23 -1217 \n","24 -1213 \n","25 -1200 \n","26 -1175 \n","27 -1155 \n","28 -1150 \n","29 -1149 \n","30 -1145 \n","31 -1136 \n","32 -1129 \n","33 -1111 \n","34 -1100 \n","35 -1098 \n","36 -1060 \n","37 -1000 \n","38 -935 \n","39 -850 \n","40 -805 \n","41 -800 \n","42 -795 \n","43 -792 \n","44 -790 \n","45 -782 \n","46 -771 \n","47 -753 \n","48 -752 \n","49 -750 \n","\n"," nchs_yearly_context::nchs-top-five-leading-causes-of-death-united-states-1990-1950-2000::Number of Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-age-adjusted-death-rates-for-selected-major-causes-of-death::Age Adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Average Life Expectancy (Years) \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Age-adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Age-adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::HHS Region \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Observed Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Population \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," ... ucdp_yearly_contextmerged::countries_covered \\\n","0 ... NaN \n","1 ... NaN \n","2 ... NaN \n","3 ... NaN \n","4 ... NaN \n","5 ... NaN \n","6 ... NaN \n","7 ... NaN \n","8 ... NaN \n","9 ... NaN \n","10 ... NaN \n","11 ... NaN \n","12 ... NaN \n","13 ... NaN \n","14 ... NaN \n","15 ... NaN \n","16 ... NaN \n","17 ... NaN \n","18 ... NaN \n","19 ... NaN \n","20 ... NaN \n","21 ... NaN \n","22 ... NaN \n","23 ... NaN \n","24 ... NaN \n","25 ... NaN \n","26 ... NaN \n","27 ... NaN \n","28 ... NaN \n","29 ... NaN \n","30 ... NaN \n","31 ... NaN \n","32 ... NaN \n","33 ... NaN \n","34 ... NaN \n","35 ... NaN \n","36 ... NaN \n","37 ... NaN \n","38 ... NaN \n","39 ... NaN \n","40 ... NaN \n","41 ... NaN \n","42 ... NaN \n","43 ... NaN \n","44 ... NaN \n","45 ... NaN \n","46 ... NaN \n","47 ... NaN \n","48 ... NaN \n","49 ... NaN \n","\n"," ucdp_yearly_contextmerged::clear_events \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," ucdp_yearly_contextmerged::unclear_events \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," air_travel_yearly_context::air_passengers_total \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," air_travel_yearly_context::air_passengers_mean \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN \n","30 NaN \n","31 NaN \n","32 NaN \n","33 NaN \n","34 NaN \n","35 NaN \n","36 NaN \n","37 NaN \n","38 NaN \n","39 NaN \n","40 NaN \n","41 NaN \n","42 NaN \n","43 NaN \n","44 NaN \n","45 NaN \n","46 NaN \n","47 NaN \n","48 NaN \n","49 NaN \n","\n"," air_travel_yearly_context::air_passengers_std \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","5 NaN \n","6 NaN \n","7 NaN \n","8 NaN \n","9 NaN \n","10 NaN \n","11 NaN \n","12 NaN \n","13 NaN \n","14 NaN \n","15 NaN \n","16 NaN \n","17 NaN \n","18 NaN \n","19 NaN \n","20 NaN \n","21 NaN \n","22 NaN \n","23 NaN \n","24 NaN \n","25 NaN \n","26 NaN \n","27 NaN \n","28 NaN \n","29 NaN 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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"merged"}},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["merged = pd.read_parquet(\"/content/macro_yearly_context.parquet\")\n","\n","# Keep only years >= 1850 (or choose 1900 / 1950 depending on you)\n","clean = merged[merged[\"year\"] >= 1850].reset_index(drop=True)\n","\n","print(clean.head())\n","print(clean.tail())\n","print(clean.shape)\n","\n","clean.to_parquet(\"/content/macro_yearly_context_clean.parquet\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-XWopT7Kj6R8","executionInfo":{"status":"ok","timestamp":1763681111517,"user_tz":300,"elapsed":1845,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"fe077d79-da3a-485e-f319-ec0a19ae2c1a"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":[" year \\\n","0 1850 \n","1 1851 \n","2 1852 \n","3 1853 \n","4 1854 \n","\n"," nchs_yearly_context::nchs-top-five-leading-causes-of-death-united-states-1990-1950-2000::Number of Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-age-adjusted-death-rates-for-selected-major-causes-of-death::Age Adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Average Life Expectancy (Years) \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Age-adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Age-adjusted Death Rate \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::HHS Region \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Observed Deaths \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Population \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," ... ucdp_yearly_contextmerged::countries_covered \\\n","0 ... NaN \n","1 ... NaN \n","2 ... NaN \n","3 ... NaN \n","4 ... NaN \n","\n"," ucdp_yearly_contextmerged::clear_events \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," ucdp_yearly_contextmerged::unclear_events \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_total \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_mean \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_std \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_min \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_max \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_count \\\n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n"," air_travel_yearly_context::air_passengers_pct_change \n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN \n","\n","[5 rows x 610 columns]\n"," year \\\n","171 2021 \n","172 2022 \n","173 2023 \n","174 2024 \n","175 2025 \n","\n"," nchs_yearly_context::nchs-top-five-leading-causes-of-death-united-states-1990-1950-2000::Number of Deaths \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-age-adjusted-death-rates-for-selected-major-causes-of-death::Age Adjusted Death Rate \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Average Life Expectancy (Years) \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-death-rates-and-life-expectancy-at-birth::Age-adjusted Death Rate \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Deaths \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-leading-causes-of-death-united-states::Age-adjusted Death Rate \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::HHS Region \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Observed Deaths \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," nchs_yearly_context::nchs-potentially-excess-deaths-from-the-five-leading-causes-of-death::Population \\\n","171 NaN \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," ... ucdp_yearly_contextmerged::countries_covered \\\n","171 ... 58.0 \n","172 ... 59.0 \n","173 ... NaN \n","174 ... NaN \n","175 ... NaN \n","\n"," ucdp_yearly_contextmerged::clear_events \\\n","171 0.0 \n","172 0.0 \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," ucdp_yearly_contextmerged::unclear_events \\\n","171 16812.0 \n","172 17545.0 \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_total \\\n","171 9.413078e+09 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_mean \\\n","171 5.603023e+07 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_std \\\n","171 2.325693e+08 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_min \\\n","171 523.761 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_max \\\n","171 2.279975e+09 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_count \\\n","171 168.0 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n"," air_travel_yearly_context::air_passengers_pct_change \n","171 0.288239 \n","172 NaN \n","173 NaN \n","174 NaN \n","175 NaN \n","\n","[5 rows x 610 columns]\n","(176, 610)\n"]}]},{"cell_type":"code","source":["import re\n","import pandas as pd\n","\n","df = pd.read_parquet(\"/content/macro_yearly_context_clean.parquet\")\n","\n","def clean_col(col):\n"," # 1. lower case\n"," col = col.lower()\n","\n"," # 2. replace separators :: with _\n"," col = col.replace(\"::\", \"_\")\n","\n"," # 3. remove dataset prefixes that repeat\n"," col = col.replace(\"nchs_yearly_context_\", \"nchs_\")\n"," col = col.replace(\"air_travel_yearly_context_\", \"air_\")\n"," col = col.replace(\"market_yearly_contextmerged_\", \"market_\")\n"," col = col.replace(\"global_temp_yearly_context_\", \"global_temp_\")\n"," col = col.replace(\"space_yearly_context_\", \"space_\")\n"," col = col.replace(\"disaster_deaths_yearly_context_\", \"disaster_\")\n"," col = col.replace(\"gdp_growth_yearly_context_\", \"gdp_\")\n"," col = col.replace(\"ucdp_yearly_contextmerged_\", \"ucdp_\")\n"," col = col.replace(\"energy_yearly_context_\", \"energy_\")\n"," col = col.replace(\"arxiv_yearly_context_\", \"arxiv_\")\n"," col = col.replace(\"famous_people_yearly_context_\", \"famous_\")\n"," col = col.replace(\"newspaper_yearly_context_\", \"news_\")\n","\n"," # 4. kill spaces + punctuation\n"," col = re.sub(r\"[^a-z0-9_]+\", \"_\", col)\n","\n"," # 5. collapse repeated ___\n"," col = re.sub(r\"_+\", \"_\", col)\n","\n"," # 6. trim leading/trailing _\n"," col = col.strip(\"_\")\n","\n"," return col\n","\n","df.columns = [clean_col(c) for c in df.columns]\n","\n","df.to_parquet(\"/content/macro_yearly_context_clean_named.parquet\")\n","\n","print(\"Done! New shape:\", df.shape)\n","print(\"Sample columns:\", df.columns[:25].tolist())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ya_xCJZmp-VS","executionInfo":{"status":"ok","timestamp":1763682286374,"user_tz":300,"elapsed":172,"user":{"displayName":"bewithyourbreath","userId":"03865340285477226404"}},"outputId":"5137efa9-de4d-460f-e4f6-6e5a6ee8f836"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Done! New shape: (176, 610)\n","Sample columns: ['year', 'nchs_nchs_top_five_leading_causes_of_death_united_states_1990_1950_2000_number_of_deaths', 'nchs_nchs_age_adjusted_death_rates_for_selected_major_causes_of_death_age_adjusted_death_rate', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_average_life_expectancy_years', 'nchs_nchs_death_rates_and_life_expectancy_at_birth_age_adjusted_death_rate', 'nchs_nchs_leading_causes_of_death_united_states_deaths', 'nchs_nchs_leading_causes_of_death_united_states_age_adjusted_death_rate', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_hhs_region', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_observed_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_population', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_expected_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_potentially_excess_deaths', 'nchs_nchs_potentially_excess_deaths_from_the_five_leading_causes_of_death_percent_potentially_excess_deaths', 'news_articles_total', 'news_unique_sources', 'news_source_entropy', 'news_total_words', 'news_mean_words', 'news_median_words', 'news_vocab_size', 'news_vocab_entropy', 'news_lexical_diversity', 'news_sentiment_mean', 'news_sentiment_std', 'news_ufo']\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"CjfpfjJssgwH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"HFMZBZaxp-R3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Ge4eAMUZp-In"},"execution_count":null,"outputs":[]}]} \ No newline at end of file