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isaaccorley/cs210_week2.ipynb

Created September 25, 2024 00:00
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cs210_week2.ipynb
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{
"cell_type": "markdown",
"source": [
"Reading Excel/CSV files and Pandas"
],
"metadata": {
"id": "RAyAg595OW4b"
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},
{
"cell_type": "code",
"source": [
"import pandas\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set_theme()\n",
"sns.set_style()\n",
"\n",
"path = \"sample_data/california_housing_train.csv\"\n",
"data = pandas.read_csv(path)\n",
"data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 226
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"id": "an5_IhupOWqg",
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{
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" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
"0 -114.31 34.19 15.0 5612.0 1283.0 \n",
"1 -114.47 34.40 19.0 7650.0 1901.0 \n",
"2 -114.56 33.69 17.0 720.0 174.0 \n",
"3 -114.57 33.64 14.0 1501.0 337.0 \n",
"4 -114.57 33.57 20.0 1454.0 326.0 \n",
"\n",
" population households median_income median_house_value \n",
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"summary": "{\n \"name\": \"data\",\n \"rows\": 17000,\n \"fields\": [\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.0051664084261778,\n \"min\": -124.35,\n \"max\": -114.31,\n \"num_unique_values\": 827,\n \"samples\": [\n -117.56,\n -123.32,\n -118.26\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.1373397946570836,\n \"min\": 32.54,\n \"max\": 41.95,\n \"num_unique_values\": 840,\n \"samples\": [\n 38.44,\n 40.79,\n 32.69\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"housing_median_age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.586936981660399,\n \"min\": 1.0,\n \"max\": 52.0,\n \"num_unique_values\": 52,\n \"samples\": [\n 23.0,\n 52.0,\n 47.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_rooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2179.947071452767,\n \"min\": 2.0,\n \"max\": 37937.0,\n \"num_unique_values\": 5533,\n \"samples\": [\n 3564.0,\n 6955.0,\n 5451.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 421.4994515798646,\n \"min\": 1.0,\n \"max\": 6445.0,\n \"num_unique_values\": 1848,\n \"samples\": [\n 729.0,\n 719.0,\n 2075.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"population\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1147.8529591595266,\n \"min\": 3.0,\n \"max\": 35682.0,\n \"num_unique_values\": 3683,\n \"samples\": [\n 249.0,\n 1735.0,\n 235.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"households\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 384.5208408559009,\n \"min\": 1.0,\n \"max\": 6082.0,\n \"num_unique_values\": 1740,\n \"samples\": [\n 390.0,\n 1089.0,\n 1351.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_income\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.9081565183791036,\n \"min\": 0.4999,\n \"max\": 15.0001,\n \"num_unique_values\": 11175,\n \"samples\": [\n 7.2655,\n 5.6293,\n 4.2262\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_house_value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 115983.76438720885,\n \"min\": 14999.0,\n \"max\": 500001.0,\n \"num_unique_values\": 3694,\n \"samples\": [\n 162300.0,\n 346800.0,\n 116700.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 64
}
]
},
{
"cell_type": "code",
"source": [
"# Display one column\n",
"data[\"population\"]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
},
"id": "xLXsYSraOWpE",
"outputId": "2e734b52-82e7-4c02-fff8-f4296c59bbfd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 1015.0\n",
"1 1129.0\n",
"2 333.0\n",
"3 515.0\n",
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" ... \n",
"16995 907.0\n",
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"16999 806.0\n",
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"metadata": {},
"execution_count": 55
}
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},
{
"cell_type": "code",
"source": [
"# Filtering data by a column condition\n",
"condition = data[\"median_house_value\"] < 500000\n",
"data[condition]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
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"id": "bcOm55WVOWnI",
"outputId": "892cee61-bc7d-4035-c3de-e371d1105106"
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"execution_count": null,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
"0 -114.31 34.19 15.0 5612.0 1283.0 \n",
"1 -114.47 34.40 19.0 7650.0 1901.0 \n",
"2 -114.56 33.69 17.0 720.0 174.0 \n",
"3 -114.57 33.64 14.0 1501.0 337.0 \n",
"4 -114.57 33.57 20.0 1454.0 326.0 \n",
"... ... ... ... ... ... \n",
"16995 -124.26 40.58 52.0 2217.0 394.0 \n",
"16996 -124.27 40.69 36.0 2349.0 528.0 \n",
"16997 -124.30 41.84 17.0 2677.0 531.0 \n",
"16998 -124.30 41.80 19.0 2672.0 552.0 \n",
"16999 -124.35 40.54 52.0 1820.0 300.0 \n",
"\n",
" population households median_income median_house_value \n",
"0 1015.0 472.0 1.4936 66900.0 \n",
"1 1129.0 463.0 1.8200 80100.0 \n",
"2 333.0 117.0 1.6509 85700.0 \n",
"3 515.0 226.0 3.1917 73400.0 \n",
"4 624.0 262.0 1.9250 65500.0 \n",
"... ... ... ... ... \n",
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},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"# Filtering data by 2 column conditions\n",
"condition = (data[\"median_house_value\"] < 500000) & (data[\"housing_median_age\"] > 10)\n",
"data[condition]"
],
"metadata": {
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"height": 444
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"id": "IRuT3z7bOWk1",
"outputId": "416ef8b9-09cc-41be-8b9f-59a11eedea81"
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"execution_count": null,
"outputs": [
{
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" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
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"2 -114.56 33.69 17.0 720.0 174.0 \n",
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"16998 -124.30 41.80 19.0 2672.0 552.0 \n",
"16999 -124.35 40.54 52.0 1820.0 300.0 \n",
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"summary": "{\n \"name\": \"data[condition]\",\n \"rows\": 14891,\n \"fields\": [\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.0065143619130774,\n \"min\": -124.35,\n \"max\": -114.31,\n \"num_unique_values\": 816,\n \"samples\": [\n -117.64,\n -118.67,\n -123.17\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.153443748998928,\n \"min\": 32.55,\n \"max\": 41.95,\n \"num_unique_values\": 832,\n \"samples\": [\n 38.62,\n 40.94,\n 34.81\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"housing_median_age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.240978976289389,\n \"min\": 11.0,\n \"max\": 52.0,\n \"num_unique_values\": 42,\n \"samples\": [\n 12.0,\n 31.0,\n 34.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_rooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1759.329747756432,\n \"min\": 11.0,\n \"max\": 32627.0,\n \"num_unique_values\": 4957,\n \"samples\": [\n 506.0,\n 2436.0,\n 840.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 368.37227521885467,\n \"min\": 3.0,\n \"max\": 6445.0,\n \"num_unique_values\": 1661,\n \"samples\": [\n 664.0,\n 1615.0,\n 376.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"population\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1020.9055131704611,\n \"min\": 3.0,\n \"max\": 35682.0,\n \"num_unique_values\": 3392,\n \"samples\": [\n 830.0,\n 1170.0,\n 4528.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"households\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 339.1833793983472,\n \"min\": 2.0,\n \"max\": 6082.0,\n \"num_unique_values\": 1560,\n \"samples\": [\n 1789.0,\n 561.0,\n 270.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_income\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.5443641133689376,\n \"min\": 0.4999,\n \"max\": 15.0001,\n \"num_unique_values\": 9748,\n \"samples\": [\n 4.9833,\n 5.2897,\n 3.9875\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_house_value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97972.55806775189,\n \"min\": 14999.0,\n \"max\": 499100.0,\n \"num_unique_values\": 3628,\n \"samples\": [\n 219200.0,\n 334100.0,\n 446800.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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},
"metadata": {},
"execution_count": 57
}
]
},
{
"cell_type": "code",
"source": [
"# Now find rows with less than 250 households, median house value of greater than $300k, and median housing age between 10 and 40"
],
"metadata": {
"id": "heJxdzqZVnZM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Count the unique housing median ages\n",
"data[\"housing_median_age\"].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "mTCkN3v5Z_mo",
"outputId": "05cda7a4-4aad-479a-bef8-c10a03e3dd0f"
},
"execution_count": null,
"outputs": [
{
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" <td>178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47.0</th>\n",
" <td>175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48.0</th>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50.0</th>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49.0</th>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51.0</th>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> int64</label>"
]
},
"metadata": {},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"source": [
"# Plot the median house value\n",
"data[\"median_house_value\"].hist(bins=25)\n",
"plt.xlabel(\"Median House Value\")\n",
"plt.ylabel(\"Count\")\n",
"plt.title(\"Histogram of Median House Value in California\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 498
},
"id": "EQ6mLIK9ZAeJ",
"outputId": "b2057b5a-6c48-466e-c314-e5d09b5c29c9"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Histogram of Median House Value in California')"
]
},
"metadata": {},
"execution_count": 65
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Now plot the histogram of households"
],
"metadata": {
"id": "bvlccAicZuTt"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Reading SQL Databases and more Pandas"
],
"metadata": {
"id": "Gbp5VFdgOSkF"
}
},
{
"cell_type": "code",
"source": [
"# This downloads the bible database file from https://github.com/godlytalias/Bible-Database/\n",
"!wget https://github.com/godlytalias/Bible-Database/raw/refs/heads/master/English/holybible.db"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PkK1-aQzN5zd",
"outputId": "154e5d16-3baa-4969-ef1f-34839cd55de1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-09-24 23:29:35-- https://github.com/godlytalias/Bible-Database/raw/refs/heads/master/English/holybible.db\n",
"Resolving github.com (github.com)... 140.82.116.3\n",
"Connecting to github.com (github.com)|140.82.116.3|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://raw.githubusercontent.com/godlytalias/Bible-Database/refs/heads/master/English/holybible.db [following]\n",
"--2024-09-24 23:29:35-- https://raw.githubusercontent.com/godlytalias/Bible-Database/refs/heads/master/English/holybible.db\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 5089280 (4.9M) [application/octet-stream]\n",
"Saving to: ‘holybible.db’\n",
"\n",
"holybible.db 100%[===================>] 4.85M --.-KB/s in 0.06s \n",
"\n",
"2024-09-24 23:29:35 (83.5 MB/s) - ‘holybible.db’ saved [5089280/5089280]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YMhJ6huBNPGW"
},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd\n",
"\n",
"path = \"holybible.db\"\n",
"db = sqlite3.connect(path)"
]
},
{
"cell_type": "code",
"source": [
"# query the first 100 verses\n",
"query = \"\"\"\n",
" SELECT * FROM bible limit 100\n",
"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
"data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "83Yov0oMOL_Y",
"outputId": "562df8b8-5028-456a-e83f-1ff3a0976d81"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Book Chapter Versecount \\\n",
"0 0 1 1 \n",
"1 0 1 2 \n",
"2 0 1 3 \n",
"3 0 1 4 \n",
"4 0 1 5 \n",
".. ... ... ... \n",
"95 0 4 16 \n",
"96 0 4 17 \n",
"97 0 4 18 \n",
"98 0 4 19 \n",
"99 0 4 20 \n",
"\n",
" verse \n",
"0 In the beginning God created the heaven and th... \n",
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"# query the first 100 verses, only the verses\n",
"query = \"\"\"\n",
" SELECT verse FROM bible limit 100\n",
"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
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"type": "dataframe",
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"summary": "{\n \"name\": \"data\",\n \"rows\": 100,\n \"fields\": [\n {\n \"column\": \"verse\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 100,\n \"samples\": [\n \"And Abel, he also brought of the firstlings of his flock and of the fat thereof. And the LORD had respect unto Abel and to his offering:\",\n \"And Adam said, This is now bone of my bones, and flesh of my flesh: she shall be called Woman, because she was taken out of Man.\",\n \"And I will put enmity between you and the woman, and between your seed and her seed; it shall bruise your head, and you shall bruise his heel.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"source": [
"# query only verses from genesis\n",
"query = \"\"\"\n",
" SELECT * FROM bible\n",
" WHERE book = 0\n",
"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
"data"
],
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"height": 424
},
"id": "6xiJqrK0XxrM",
"outputId": "66890154-9bcf-4f37-b477-787277d6ae71"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" Book Chapter Versecount \\\n",
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"1530 And Joseph said unto his brethren, I die: and ... \n",
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"data"
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"query = \"\"\"\n",
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"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
"data"
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"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
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" }\n",
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},
{
"cell_type": "code",
"source": [
"# Count how many unique books there are in the bible\n",
"query = \"\"\"\n",
" SELECT COUNT(DISTINCT(Book)) FROM bible\n",
"\"\"\"\n",
"data = pd.read_sql_query(query, db)\n",
"data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
},
"id": "BjItvjYlYvh1",
"outputId": "14d2dfb6-9fdc-4041-c23d-4e0314d02791"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" COUNT(DISTINCT(Book))\n",
"0 66"
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"summary": "{\n \"name\": \"data\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"COUNT(DISTINCT(Book))\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 66,\n \"max\": 66,\n \"num_unique_values\": 1,\n \"samples\": [\n 66\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 53
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]
},
{
"cell_type": "markdown",
"source": [
"## Parallel Processing with `multiprocessing`"
],
"metadata": {
"id": "Gj9gwixdOMzv"
}
},
{
"cell_type": "code",
"source": [
"!wget https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/data/BSD100_HR.tar.gz\n",
"!tar -xvf BSD100_HR.tar.gz\n",
"!rm BSD100_HR.tar.gz"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "aQK06V0-NW6k",
"outputId": "8711fe80-0164-4100-caf4-c06ccbf4b705"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-09-24 23:00:11-- https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/data/BSD100_HR.tar.gz\n",
"Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.114, 3.163.189.74, ...\n",
"Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://cdn-lfs.huggingface.co/datasets/eugenesiow/BSD100/333495ccded04a0db52b33361140821ea7e0d8af8dfb54b58df79a00010858d6?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27BSD100_HR.tar.gz%3B+filename%3D%22BSD100_HR.tar.gz%22%3B&response-content-type=application%2Fgzip&Expires=1727478011&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyNzQ3ODAxMX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9kYXRhc2V0cy9ldWdlbmVzaW93L0JTRDEwMC8zMzM0OTVjY2RlZDA0YTBkYjUyYjMzMzYxMTQwODIxZWE3ZTBkOGFmOGRmYjU0YjU4ZGY3OWEwMDAxMDg1OGQ2P3Jlc3BvbnNlLWNvbnRlbnQtZGlzcG9zaXRpb249KiZyZXNwb25zZS1jb250ZW50LXR5cGU9KiJ9XX0_&Signature=gTSSE2IejPFo2QpzuydUCX2eYYcFfRjXPRdCk3XW49kR0KJzKYcyoZrzI09cFz79E9vCgrGM5Ho8nNStdoOSBFMrWBowg6D3BM5%7EZLCXOGWX0annuyQu7SlTpYMraCASXSYki4gknVvnE60J-zyLEmxEq3N-rtApRmnHuZTwXt317Cgy%7ERG2Wsp3BreuPTHUDqfrhD9CXoiB5V0B65nUoGG7Bg2VW1a7fNf1R53h9ttoQ3uK-KO1FMz4vS22O82Vk9HyVICB6p1UNgSZFm-drYLoaiZxUgzWIqOH5TdEclnAbgKyQBjRS1LPWYbg8OmkrGVVczc30fqWK32QN3j39Q__&Key-Pair-Id=K3ESJI6DHPFC7 [following]\n",
"--2024-09-24 23:00:11-- https://cdn-lfs.huggingface.co/datasets/eugenesiow/BSD100/333495ccded04a0db52b33361140821ea7e0d8af8dfb54b58df79a00010858d6?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27BSD100_HR.tar.gz%3B+filename%3D%22BSD100_HR.tar.gz%22%3B&response-content-type=application%2Fgzip&Expires=1727478011&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyNzQ3ODAxMX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9kYXRhc2V0cy9ldWdlbmVzaW93L0JTRDEwMC8zMzM0OTVjY2RlZDA0YTBkYjUyYjMzMzYxMTQwODIxZWE3ZTBkOGFmOGRmYjU0YjU4ZGY3OWEwMDAxMDg1OGQ2P3Jlc3BvbnNlLWNvbnRlbnQtZGlzcG9zaXRpb249KiZyZXNwb25zZS1jb250ZW50LXR5cGU9KiJ9XX0_&Signature=gTSSE2IejPFo2QpzuydUCX2eYYcFfRjXPRdCk3XW49kR0KJzKYcyoZrzI09cFz79E9vCgrGM5Ho8nNStdoOSBFMrWBowg6D3BM5%7EZLCXOGWX0annuyQu7SlTpYMraCASXSYki4gknVvnE60J-zyLEmxEq3N-rtApRmnHuZTwXt317Cgy%7ERG2Wsp3BreuPTHUDqfrhD9CXoiB5V0B65nUoGG7Bg2VW1a7fNf1R53h9ttoQ3uK-KO1FMz4vS22O82Vk9HyVICB6p1UNgSZFm-drYLoaiZxUgzWIqOH5TdEclnAbgKyQBjRS1LPWYbg8OmkrGVVczc30fqWK32QN3j39Q__&Key-Pair-Id=K3ESJI6DHPFC7\n",
"Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 108.138.94.23, 108.138.94.122, 108.138.94.14, ...\n",
"Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|108.138.94.23|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 27186832 (26M) [application/gzip]\n",
"Saving to: ‘BSD100_HR.tar.gz.1’\n",
"\n",
"BSD100_HR.tar.gz.1 100%[===================>] 25.93M 97.3MB/s in 0.3s \n",
"\n",
"2024-09-24 23:00:12 (97.3 MB/s) - ‘BSD100_HR.tar.gz.1’ saved [27186832/27186832]\n",
"\n",
"BSD100_HR/\n",
"BSD100_HR/101085.png\n",
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"BSD100_HR/253027.png\n",
"BSD100_HR/253055.png\n",
"BSD100_HR/260058.png\n",
"BSD100_HR/271035.png\n",
"BSD100_HR/285079.png\n",
"BSD100_HR/291000.png\n",
"BSD100_HR/295087.png\n",
"BSD100_HR/296007.png\n",
"BSD100_HR/296059.png\n",
"BSD100_HR/299086.png\n",
"BSD100_HR/300091.png\n",
"BSD100_HR/302008.png\n",
"BSD100_HR/304034.png\n",
"BSD100_HR/304074.png\n",
"BSD100_HR/306005.png\n",
"BSD100_HR/3096.png\n",
"BSD100_HR/33039.png\n",
"BSD100_HR/351093.png\n",
"BSD100_HR/361010.png\n",
"BSD100_HR/37073.png\n",
"BSD100_HR/376043.png\n",
"BSD100_HR/38082.png\n",
"BSD100_HR/38092.png\n",
"BSD100_HR/385039.png\n",
"BSD100_HR/41033.png\n",
"BSD100_HR/41069.png\n",
"BSD100_HR/42012.png\n",
"BSD100_HR/42049.png\n",
"BSD100_HR/43074.png\n",
"BSD100_HR/45096.png\n",
"BSD100_HR/54082.png\n",
"BSD100_HR/55073.png\n",
"BSD100_HR/58060.png\n",
"BSD100_HR/62096.png\n",
"BSD100_HR/65033.png\n",
"BSD100_HR/66053.png\n",
"BSD100_HR/69015.png\n",
"BSD100_HR/69020.png\n",
"BSD100_HR/69040.png\n",
"BSD100_HR/76053.png\n",
"BSD100_HR/78004.png\n",
"BSD100_HR/8023.png\n",
"BSD100_HR/85048.png\n",
"BSD100_HR/86000.png\n",
"BSD100_HR/86016.png\n",
"BSD100_HR/86068.png\n",
"BSD100_HR/87046.png\n",
"BSD100_HR/89072.png\n",
"BSD100_HR/97033.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import glob\n",
"import multiprocessing\n",
"from multiprocessing.pool import Pool\n",
"\n",
"from PIL import Image\n",
"from tqdm import tqdm\n",
"\n",
"print(\"Number of CPUs:\", multiprocessing.cpu_count())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Oh6OCmKyNW5C",
"outputId": "ee517ffd-ebfe-4b8c-d87f-a4e75539983d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of CPUs: 2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"images = glob.glob(\"BSD100_HR/*.png\")\n",
"images"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9p4U6Ml1Q9za",
"outputId": "a4ebc28e-aac1-467b-ff13-dd7edb4fb55f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['BSD100_HR/361010.png',\n",
" 'BSD100_HR/105025.png',\n",
" 'BSD100_HR/175032.png',\n",
" 'BSD100_HR/148026.png',\n",
" 'BSD100_HR/130026.png',\n",
" 'BSD100_HR/197017.png',\n",
" 'BSD100_HR/66053.png',\n",
" 'BSD100_HR/385039.png',\n",
" 'BSD100_HR/43074.png',\n",
" 'BSD100_HR/45096.png',\n",
" 'BSD100_HR/167062.png',\n",
" 'BSD100_HR/24077.png',\n",
" 'BSD100_HR/106024.png',\n",
" 'BSD100_HR/285079.png',\n",
" 'BSD100_HR/38082.png',\n",
" 'BSD100_HR/85048.png',\n",
" 'BSD100_HR/87046.png',\n",
" 'BSD100_HR/220075.png',\n",
" 'BSD100_HR/54082.png',\n",
" 'BSD100_HR/182053.png',\n",
" 'BSD100_HR/108070.png',\n",
" 'BSD100_HR/296059.png',\n",
" 'BSD100_HR/69040.png',\n",
" 'BSD100_HR/189080.png',\n",
" 'BSD100_HR/219090.png',\n",
" 'BSD100_HR/147091.png',\n",
" 'BSD100_HR/8023.png',\n",
" 'BSD100_HR/260058.png',\n",
" 'BSD100_HR/69015.png',\n",
" 'BSD100_HR/302008.png',\n",
" 'BSD100_HR/78004.png',\n",
" 'BSD100_HR/208001.png',\n",
" 'BSD100_HR/65033.png',\n",
" 'BSD100_HR/163085.png',\n",
" 'BSD100_HR/160068.png',\n",
" 'BSD100_HR/227092.png',\n",
" 'BSD100_HR/101085.png',\n",
" 'BSD100_HR/156065.png',\n",
" 'BSD100_HR/19021.png',\n",
" 'BSD100_HR/86016.png',\n",
" 'BSD100_HR/148089.png',\n",
" 'BSD100_HR/62096.png',\n",
" 'BSD100_HR/291000.png',\n",
" 'BSD100_HR/58060.png',\n",
" 'BSD100_HR/304034.png',\n",
" 'BSD100_HR/196073.png',\n",
" 'BSD100_HR/175043.png',\n",
" 'BSD100_HR/42012.png',\n",
" 'BSD100_HR/351093.png',\n",
" 'BSD100_HR/170057.png',\n",
" 'BSD100_HR/69020.png',\n",
" 'BSD100_HR/97033.png',\n",
" 'BSD100_HR/229036.png',\n",
" 'BSD100_HR/41033.png',\n",
" 'BSD100_HR/241004.png',\n",
" 'BSD100_HR/119082.png',\n",
" 'BSD100_HR/33039.png',\n",
" 'BSD100_HR/236037.png',\n",
" 'BSD100_HR/123074.png',\n",
" 'BSD100_HR/376043.png',\n",
" 'BSD100_HR/103070.png',\n",
" 'BSD100_HR/101087.png',\n",
" 'BSD100_HR/300091.png',\n",
" 'BSD100_HR/159008.png',\n",
" 'BSD100_HR/271035.png',\n",
" 'BSD100_HR/253055.png',\n",
" 'BSD100_HR/295087.png',\n",
" 'BSD100_HR/145086.png',\n",
" 'BSD100_HR/102061.png',\n",
" 'BSD100_HR/89072.png',\n",
" 'BSD100_HR/296007.png',\n",
" 'BSD100_HR/108082.png',\n",
" 'BSD100_HR/143090.png',\n",
" 'BSD100_HR/134035.png',\n",
" 'BSD100_HR/76053.png',\n",
" 'BSD100_HR/41069.png',\n",
" 'BSD100_HR/14037.png',\n",
" 'BSD100_HR/241048.png',\n",
" 'BSD100_HR/157055.png',\n",
" 'BSD100_HR/108005.png',\n",
" 'BSD100_HR/21077.png',\n",
" 'BSD100_HR/253027.png',\n",
" 'BSD100_HR/86068.png',\n",
" 'BSD100_HR/109053.png',\n",
" 'BSD100_HR/304074.png',\n",
" 'BSD100_HR/3096.png',\n",
" 'BSD100_HR/38092.png',\n",
" 'BSD100_HR/216081.png',\n",
" 'BSD100_HR/223061.png',\n",
" 'BSD100_HR/126007.png',\n",
" 'BSD100_HR/306005.png',\n",
" 'BSD100_HR/16077.png',\n",
" 'BSD100_HR/37073.png',\n",
" 'BSD100_HR/299086.png',\n",
" 'BSD100_HR/86000.png',\n",
" 'BSD100_HR/167083.png',\n",
" 'BSD100_HR/42049.png',\n",
" 'BSD100_HR/210088.png',\n",
" 'BSD100_HR/12084.png',\n",
" 'BSD100_HR/55073.png']"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"def resize(path):\n",
" img = Image.open(path).convert(\"RGB\")\n",
" img = img.resize((64, 64))\n",
" img.save(path.replace(\".png\", \"_resized.png\"))\n",
"\n",
"\n",
"pool = Pool(processes=2)\n",
"prog_bar = tqdm(pool.imap_unordered(resize, images), total=len(images))\n",
"for result in prog_bar:\n",
" pass\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b5ApbZuLRDSS",
"outputId": "8f976686-4004-413c-ba59-af62e67e98fb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 100/100 [00:00<00:00, 117.80it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"### Now try resizing all images to 28,28\n",
"\n"
],
"metadata": {
"id": "n9yKd0rwSGPz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"### Now try converting all images to grayscale (replace .convert(\"RGB\") with .convert(\"L\"))\n",
"\n"
],
"metadata": {
"id": "3p1Ra0W3SMCZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## GPU Parallelization"
],
"metadata": {
"id": "iOmGlo8mdaFL"
}
},
{
"cell_type": "code",
"source": [
"import time\n",
"import torch\n",
"\n",
"dim = 5000\n",
"\n",
"x = torch.randn(dim, dim)\n",
"y = torch.randn(dim, dim)\n",
"start_time = time.time()\n",
"z = torch.matmul(x,y)\n",
"elapsed_time = time.time() - start_time\n",
"print('CPU_time = ', elapsed_time)\n",
"\n",
"\n",
"x = torch.randn(dim, dim, device=torch.device(\"cuda:0\"))\n",
"y = torch.randn(dim, dim, device=torch.device(\"cuda:0\"))\n",
"start_time = time.time()\n",
"z = torch.matmul(x,y)\n",
"elapsed_time = time.time() - start_time\n",
"print('GPU_time = ', elapsed_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bcNC9KZqdZ21",
"outputId": "13c55133-f904-45cb-cacb-2029c5a64d4b"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU_time = 7.832552909851074\n",
"GPU_time = 0.19541239738464355\n"
]
}
]
}
]
}
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