diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 45cfc9cd7..3a6224edc 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -26,12 +26,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Write your code below.\n", - "\n" + "%load_ext dotenv\n", + "%dotenv\n" ] }, { @@ -55,15 +55,3126 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../05_src/data/prices/ACN/ACN_2001/part.0.parquet\n", + "../../05_src/data/prices/ACN/ACN_2001/part.1.parquet\n", + "../../05_src/data/prices/ACN/ACN_2002/part.0.parquet\n", + "../../05_src/data/prices/ACN/ACN_2002/part.1.parquet\n", + "../../05_src/data/prices/ACN/ACN_2003/part.0.parquet\n", + 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"../../05_src/data/prices/ZIXI/ZIXI_2018/part.0.parquet\n", + "../../05_src/data/prices/ZIXI/ZIXI_2018/part.1.parquet\n", + "../../05_src/data/prices/ZIXI/ZIXI_2019/part.0.parquet\n", + "../../05_src/data/prices/ZIXI/ZIXI_2019/part.1.parquet\n", + "../../05_src/data/prices/ZIXI/ZIXI_2020/part.0.parquet\n", + "../../05_src/data/prices/ZIXI/ZIXI_2020/part.1.parquet\n" + ] + } + ], "source": [ "import os\n", "from glob import glob\n", "\n", - "# Write your code below.\n", - "\n" + "price_data_dir = os.getenv(\"PRICE_DATA\")\n", + "parquet_files = glob(os.path.join(price_data_dir, \"**\", \"*.parquet\"), recursive=True)\n", + "normalized_paths = [path.replace(\"\\\\\", \"/\") for path in parquet_files]\n", + "\n", + "\n", + "for file_path in normalized_paths:\n", + " print(file_path)" ] }, { @@ -88,12 +3199,35 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\annam\\AppData\\Local\\Temp\\ipykernel_13864\\4139134364.py:11: UserWarning: `meta` is not specified, inferred from partial data.\n", + "Please provide `meta` if the result is unexpected.\n", + " Before: .apply(func)\n", + " After: .apply(func, meta={'x': 'f8', 'y': 'f8'}) for dataframe result\n", + " or: .apply(func, meta=('x', 'f8')) for series result\n", + "\n", + " dd_feat = dd_px.groupby('ticker', group_keys=False).apply(add_features)#.reset_index()\n" + ] + } + ], "source": [ - "# Write your code below.\n", - "\n" + "dd_px = dd.read_parquet(parquet_files).set_index(\"ticker\")\n", + "\n", + "def add_features(x):\n", + " return x.assign(\n", + " Close_lag_1 = x['Close'].shift(1),\n", + " Adj_Close_lag_1 = x['Adj Close'].shift(1),\n", + " returns = x['Close'] / x['Close'].shift(1) - 1,\n", + " hi_lo_range = x['High'] - x['Low']\n", + " )\n", + "\n", + "dd_feat = dd_px.groupby('ticker', group_keys=False).apply(add_features)" ] }, { @@ -108,12 +3242,177 @@ }, 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tickerDateOpenHighLowCloseAdj CloseVolumesourceYearClose_lag_1Adj_Close_lag_1returnshi_lo_rangereturns_ma_10
362767ZIXI2020-03-264.064.533.884.514.511668500.0ZIXI.csv20204.004.000.1275000.65-0.000807
362768ZIXI2020-03-274.494.714.104.604.601146800.0ZIXI.csv20204.514.510.0199560.610.004262
362769ZIXI2020-03-304.834.874.444.644.641212000.0ZIXI.csv20204.604.600.0086960.430.015491
362770ZIXI2020-03-314.604.694.104.314.311057200.0ZIXI.csv20204.644.64-0.0711210.590.015926
362771ZIXI2020-04-014.114.163.803.823.82539500.0ZIXI.csv20204.314.31-0.1136890.360.027261
\n", + "
" + ], + "text/plain": [ + " ticker Date Open High Low Close Adj Close Volume \\\n", + "362767 ZIXI 2020-03-26 4.06 4.53 3.88 4.51 4.51 1668500.0 \n", + "362768 ZIXI 2020-03-27 4.49 4.71 4.10 4.60 4.60 1146800.0 \n", + "362769 ZIXI 2020-03-30 4.83 4.87 4.44 4.64 4.64 1212000.0 \n", + "362770 ZIXI 2020-03-31 4.60 4.69 4.10 4.31 4.31 1057200.0 \n", + "362771 ZIXI 2020-04-01 4.11 4.16 3.80 3.82 3.82 539500.0 \n", + "\n", + " source Year Close_lag_1 Adj_Close_lag_1 returns hi_lo_range \\\n", + "362767 ZIXI.csv 2020 4.00 4.00 0.127500 0.65 \n", + "362768 ZIXI.csv 2020 4.51 4.51 0.019956 0.61 \n", + "362769 ZIXI.csv 2020 4.60 4.60 0.008696 0.43 \n", + "362770 ZIXI.csv 2020 4.64 4.64 -0.071121 0.59 \n", + "362771 ZIXI.csv 2020 4.31 4.31 -0.113689 0.36 \n", + "\n", + " returns_ma_10 \n", + "362767 -0.000807 \n", + "362768 0.004262 \n", + "362769 0.015491 \n", + "362770 0.015926 \n", + "362771 0.027261 " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Write your code below.\n", - "\n" + "df = dd_feat.compute().reset_index()\n", + "df[\"returns_ma_10\"] = (\n", + " df.sort_values(\"Date\")\n", + " .groupby(\"ticker\")[\"returns\"]\n", + " .transform(lambda x: x.rolling(window=10).mean()))\n", + "df.tail()" ] }, { @@ -128,6 +3427,14 @@ "(1 pt)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It depends on the use case. If data is small to medium-size, converting to Pandas is fine and as it gives access to rolling(), .groupby(), and other rich features. But with large/distributed data, converting to Pandas can blow up memory usage, slow down the pipeline, result in a loss of parallelism and scalability. Advantages of staying in Dask are parallelism, memory efficiencyp, ipeline consistency. But there are some challenges. Dask’s API is more limited—especially for .rolling() and .groupby().apply() across partitions.\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -165,7 +3472,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env", + "display_name": "dsi_participant", "language": "python", "name": "python3" }, @@ -179,7 +3486,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.13.5" } }, "nbformat": 4,