From e4a36e63138f9aaabea5663a4f4d1c0befede860 Mon Sep 17 00:00:00 2001 From: nalcancia Date: Sun, 27 Oct 2024 02:52:33 -0400 Subject: [PATCH] Submit answers to assignment-1 --- 02_activities/assignments/assignment_1.ipynb | 1569 +++++++++++++++++- 1 file changed, 1551 insertions(+), 18 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 0825486c4..745641a62 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -26,17 +26,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 201, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dotenv extension is already loaded. To reload it, use:\n", + " %reload_ext dotenv\n" + ] + } + ], "source": [ "# Write your code below.\n", - "\n" + "%load_ext dotenv\n", + "%dotenv ../../01_materials/labs/.env" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ @@ -55,15 +65,1036 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 203, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['../../05_src/data/prices\\\\A\\\\A_2000.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\A\\\\A_2001.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\A\\\\A_2002.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\A\\\\A_2003.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\A\\\\A_2004.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\A\\\\A_2005.parquet\\\\part.0.parquet',\n", + " 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'../../05_src/data/prices\\\\AVB\\\\AVB_2015.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2016.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2017.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2018.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2019.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2020.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2021.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2022.parquet\\\\part.0.parquet',\n", + " '../../05_src/data/prices\\\\AVB\\\\AVB_2023.parquet\\\\part.0.parquet',\n", + " ...]" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from glob import glob\n", "\n", "# Write your code below.\n", - "\n" + "\n", + "import os\n", + "from glob import glob\n", + "\n", + "price_data_dir = os.getenv(\"PRICE_DATA\")\n", + "\n", + "price_data_all_parquet_glob = glob(price_data_dir + '**/*.parquet', recursive=True)\n", + "\n", + "price_data_files_only_glob = [file for file in price_data_all_parquet_glob if os.path.isfile(file)]\n", + "\n", + "price_data_files_only_glob\n" ] }, { @@ -88,11 +1119,201 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 204, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nalca\\.conda\\envs\\dsi_participant\\lib\\site-packages\\dask\\dataframe\\core.py:5517: UserWarning: New index has same name as existing, this is a no-op.\n", + " warnings.warn(\n", + "C:\\Users\\nalca\\AppData\\Local\\Temp\\ipykernel_54248\\3534232673.py:6: UserWarning: `meta` is not specified, inferred from partial data. 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", + " dd_feat = dd_price_data.groupby('ticker', group_keys=False).apply(\n" + ] + }, + { + "data": { + "text/html": [ + "
Dask DataFrame Structure:
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DateOpenHighLowCloseAdj CloseVolumesectorsubsectoryearClose_lagAdj_Close_lagreturnshi_lo_range
npartitions=5749
datetime64[ns]float64float64float64float64float64int64objectobjectint32float64float64float64float64
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Dask Name: assign, 18 graph layers
" + ], + "text/plain": [ + "Dask DataFrame Structure:\n", + " Date Open High Low Close Adj Close Volume sector subsector year Close_lag Adj_Close_lag returns hi_lo_range\n", + "npartitions=5749 \n", + " datetime64[ns] float64 float64 float64 float64 float64 int64 object object int32 float64 float64 float64 float64\n", + " ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "Dask Name: assign, 18 graph layers" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Write your code below.\n", + "import dask.dataframe as dd\n", + "\n", + "dd_price_data = dd.read_parquet(price_data_files_only_glob).set_index(\"ticker\")\n", + "\n", + "dd_feat = dd_price_data.groupby('ticker', group_keys=False).apply(\n", + " lambda x: x.assign(Close_lag = x['Close'].shift(1), \n", + " Adj_Close_lag = x['Adj Close'].shift(1))\n", + ").assign(\n", + " returns = lambda x: (x['Adj Close']/x['Adj_Close_lag']) - 1\n", + ").assign(\n", + " hi_lo_range = lambda x: (x['High'] - x['Low'])\n", + ")\n", + "\n", + "dd_feat\n", "\n" ] }, @@ -109,12 +1330,322 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 205, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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tickerDateOpenHighLowCloseAdj CloseVolumesectorsubsectoryearClose_lagAdj_Close_lagreturnshi_lo_rangerolling_average
0MMM2001-01-0249.38336250.79431549.12207449.82754925.6593405562835IndustrialsIndustrial Conglomerates2001NaNNaNNaN1.672241NaN
1MMM2001-01-0349.33110450.11496748.65175649.25271625.3633385686262IndustrialsIndustrial Conglomerates200149.82754925.659340-0.0115361.463211NaN
2MMM2001-01-0449.27884749.95819548.31208049.74916525.6189984856238IndustrialsIndustrial Conglomerates200149.25271625.3633380.0100801.646114NaN
3MMM2001-01-0548.91304448.91304446.22178347.89402424.66365610006932IndustrialsIndustrial Conglomerates200149.74916525.618998-0.0372902.691261NaN
4MMM2001-01-0847.24080348.23369645.80372248.18143824.8116656561974IndustrialsIndustrial Conglomerates200147.89402424.6636560.0060012.429974NaN
...................................................
1425342ED2011-12-2361.79999962.18999961.68999961.98000038.1618351110600UtilitiesMulti-Utilities201161.61999937.9401780.0058420.5000000.004575
1425343ED2011-12-2761.98000062.54000161.84000062.50000038.481998852100UtilitiesMulti-Utilities201161.98000038.1618350.0083900.7000010.006123
1425344ED2011-12-2862.50000062.54999962.13999962.18999938.291145879400UtilitiesMulti-Utilities201162.50000038.481998-0.0049600.4100000.005083
1425345ED2011-12-2962.18999962.66999862.18999962.59000038.537422935900UtilitiesMulti-Utilities201162.18999938.2911450.0064320.4800000.006301
1425346ED2011-12-3062.66999862.74000262.00999862.02999938.1926191189400UtilitiesMulti-Utilities201162.59000038.537422-0.0089470.7300030.003756
\n", + "

1425347 rows × 16 columns

\n", + "
" + ], + "text/plain": [ + " ticker Date Open High Low Close \\\n", + "0 MMM 2001-01-02 49.383362 50.794315 49.122074 49.827549 \n", + "1 MMM 2001-01-03 49.331104 50.114967 48.651756 49.252716 \n", + "2 MMM 2001-01-04 49.278847 49.958195 48.312080 49.749165 \n", + "3 MMM 2001-01-05 48.913044 48.913044 46.221783 47.894024 \n", + "4 MMM 2001-01-08 47.240803 48.233696 45.803722 48.181438 \n", + "... ... ... ... ... ... ... \n", + "1425342 ED 2011-12-23 61.799999 62.189999 61.689999 61.980000 \n", + "1425343 ED 2011-12-27 61.980000 62.540001 61.840000 62.500000 \n", + "1425344 ED 2011-12-28 62.500000 62.549999 62.139999 62.189999 \n", + "1425345 ED 2011-12-29 62.189999 62.669998 62.189999 62.590000 \n", + "1425346 ED 2011-12-30 62.669998 62.740002 62.009998 62.029999 \n", + "\n", + " Adj Close Volume sector subsector year \\\n", + "0 25.659340 5562835 Industrials Industrial Conglomerates 2001 \n", + "1 25.363338 5686262 Industrials Industrial Conglomerates 2001 \n", + "2 25.618998 4856238 Industrials Industrial Conglomerates 2001 \n", + "3 24.663656 10006932 Industrials Industrial Conglomerates 2001 \n", + "4 24.811665 6561974 Industrials Industrial Conglomerates 2001 \n", + "... ... ... ... ... ... \n", + "1425342 38.161835 1110600 Utilities Multi-Utilities 2011 \n", + "1425343 38.481998 852100 Utilities Multi-Utilities 2011 \n", + "1425344 38.291145 879400 Utilities Multi-Utilities 2011 \n", + "1425345 38.537422 935900 Utilities Multi-Utilities 2011 \n", + "1425346 38.192619 1189400 Utilities Multi-Utilities 2011 \n", + "\n", + " Close_lag Adj_Close_lag returns hi_lo_range rolling_average \n", + "0 NaN NaN NaN 1.672241 NaN \n", + "1 49.827549 25.659340 -0.011536 1.463211 NaN \n", + "2 49.252716 25.363338 0.010080 1.646114 NaN \n", + "3 49.749165 25.618998 -0.037290 2.691261 NaN \n", + "4 47.894024 24.663656 0.006001 2.429974 NaN \n", + "... ... ... ... ... ... \n", + "1425342 61.619999 37.940178 0.005842 0.500000 0.004575 \n", + "1425343 61.980000 38.161835 0.008390 0.700001 0.006123 \n", + "1425344 62.500000 38.481998 -0.004960 0.410000 0.005083 \n", + "1425345 62.189999 38.291145 0.006432 0.480000 0.006301 \n", + "1425346 62.590000 38.537422 -0.008947 0.730003 0.003756 \n", + "\n", + "[1425347 rows x 16 columns]" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Write your code below.\n", - "\n" + "import pandas as pd\n", + "\n", + "pdf_feat = dd_feat.compute()\n", + "\n", + "pdf_feat.reset_index(inplace=True)\n", + "\n", + "pdf_feat['rolling_average'] = pdf_feat.groupby('ticker', group_keys=False)['returns'].rolling(10).mean().reset_index(level=0, drop=True)\n", + "\n", + "pdf_feat\n" ] }, { @@ -124,7 +1655,9 @@ "Please comment:\n", "\n", "+ Was it necessary to convert to pandas to calculate the moving average return?\n", + " - No, it is also possible to perform the calculation in Dask\n", "+ Would it have been better to do it in Dask? Why?\n", + " - Depending on the need, Dask would be better to ensure computational efficiency, but if we want to just add the rolling average on the data without re-running compute, pandas is a good option. This is also dependent on the amount of data, if we know that the data would be too large for in-memory computations, then Dask would almost always be better.\n", "\n", "(1 pt)" ] @@ -155,10 +1688,10 @@ " * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", "\n", "Checklist:\n", - "- [ ] Created a branch with the correct naming convention.\n", - "- [ ] Ensured that the repository is public.\n", - "- [ ] Reviewed the PR description guidelines and adhered to them.\n", - "- [ ] Verify that the link is accessible in a private browser window.\n", + "- [x] Created a branch with the correct naming convention.\n", + "- [x] Ensured that the repository is public.\n", + "- [x] Reviewed the PR description guidelines and adhered to them.\n", + "- [x] Verify that the link is accessible in a private browser window.\n", "\n", "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-3-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." ] @@ -166,7 +1699,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env", + "display_name": "dsi_participant", "language": "python", "name": "python3" }, @@ -180,7 +1713,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.9.15" } }, "nbformat": 4,