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theptrk/effective_pandas.ipynb

Created July 7, 2022 04:16
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effective_pandas.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "effective_pandas.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyOtPtZJLSeEfHPh71X5ocba",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/theptrk/b1c2a757379971f7b27707b410f191db/effective_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "53Fa_Agcf-3C"
},
"outputs": [],
"source": [
"# https://www.notion.so/mettalovingkindness/Book-Effective-Pandas-d6529c9908444d0cb246112b5713254a"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd"
],
"metadata": {
"id": "VcFjsaMvgEiK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pd.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "3SCl5sgigHCW",
"outputId": "8e44bbae-0cfe-4ddf-b933-2276c291154b"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'1.3.5'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"pd.show_versions()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "799wwuK3gIL8",
"outputId": "c46c16ad-3c0c-4677-8e58-6845813eaa25"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
" \"\"\")\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"INSTALLED VERSIONS\n",
"------------------\n",
"commit : 66e3805b8cabe977f40c05259cc3fcf7ead5687d\n",
"python : 3.7.13.final.0\n",
"python-bits : 64\n",
"OS : Linux\n",
"OS-release : 5.4.188+\n",
"Version : #1 SMP Sun Apr 24 10:03:06 PDT 2022\n",
"machine : x86_64\n",
"processor : x86_64\n",
"byteorder : little\n",
"LC_ALL : None\n",
"LANG : en_US.UTF-8\n",
"LOCALE : en_US.UTF-8\n",
"\n",
"pandas : 1.3.5\n",
"numpy : 1.21.6\n",
"pytz : 2022.1\n",
"dateutil : 2.8.2\n",
"pip : 21.1.3\n",
"setuptools : 57.4.0\n",
"Cython : 0.29.30\n",
"pytest : 3.6.4\n",
"hypothesis : None\n",
"sphinx : 1.8.6\n",
"blosc : None\n",
"feather : 0.4.1\n",
"xlsxwriter : None\n",
"lxml.etree : 4.2.6\n",
"html5lib : 1.0.1\n",
"pymysql : None\n",
"psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)\n",
"jinja2 : 2.11.3\n",
"IPython : 5.5.0\n",
"pandas_datareader: 0.9.0\n",
"bs4 : 4.6.3\n",
"bottleneck : None\n",
"fsspec : None\n",
"fastparquet : None\n",
"gcsfs : None\n",
"matplotlib : 3.2.2\n",
"numexpr : 2.8.1\n",
"odfpy : None\n",
"openpyxl : 3.0.10\n",
"pandas_gbq : 0.13.3\n",
"pyarrow : 6.0.1\n",
"pyxlsb : None\n",
"s3fs : None\n",
"scipy : 1.4.1\n",
"sqlalchemy : 1.4.37\n",
"tables : 3.7.0\n",
"tabulate : 0.8.9\n",
"xarray : 0.20.2\n",
"xlrd : 1.1.0\n",
"xlwt : 1.3.0\n",
"numba : 0.51.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"song = pd.Series([145, 142, 38], \n",
" name='counts',\n",
" index=['paul', 'john', 'george'])"
],
"metadata": {
"id": "gfDiOHqEgJgM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"song['paul'] # returns a value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l5x2ki3AgfbJ",
"outputId": "b022a9d1-2c79-490e-f9c0-74458ef2478e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"145"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"shirts = pd.Series(['m', 'l', 'l', 's'], dtype='category')\n",
"shirts"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3SMs023egiwZ",
"outputId": "bf10e30f-ad34-4f50-b1fe-b92622111478"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 m\n",
"1 l\n",
"2 l\n",
"3 s\n",
"dtype: category\n",
"Categories (3, object): ['l', 'm', 's']"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"# the category dtype is NOT ordered by default\n",
"shirts.cat.ordered"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sDMHaXWHg80G",
"outputId": "73fa19ca-2a26-41b3-bcd3-fe3dc96f6bb0"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"# how to order category dtype\n",
"shirts = shirts.cat.reorder_categories(['s', 'm', 'l'], ordered=True)"
],
"metadata": {
"id": "9Cy0MMH7hCXa"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shirts"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Tw98S7Lqhr1X",
"outputId": "268f287e-1115-48ee-90de-4086c73ed880"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 m\n",
"1 l\n",
"2 l\n",
"3 s\n",
"dtype: category\n",
"Categories (3, object): ['s' < 'm' < 'l']"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# now you have ordinal relations\n",
"# m, l is >= than m\n",
"# s is NOT >= than m\n",
"shirts >= 'm'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "E2ajJmhqhshX",
"outputId": "8fb6ecea-5998-4243-86a6-5170aea9d1d5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 True\n",
"1 True\n",
"2 True\n",
"3 False\n",
"dtype: bool"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"url = 'https://github.com/mattharrison/datasets/raw/master/data/vehicles.csv.zip'"
],
"metadata": {
"id": "WFhWFCKriBTL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.read_csv(url)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xHBsw5E2kUNq",
"outputId": "d3a27157-1139-4585-8db2-057b4e0a3b7b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (68,70,71,72,73,74,76,79) have mixed types.Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df.head()"
],
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"outputId": "a615589c-8b35-4bc4-aee5-84000aec9a4f"
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"execution_count": null,
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{
"output_type": "execute_result",
"data": {
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" barrels08 barrelsA08 charge120 charge240 city08 city08U cityA08 \\\n",
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"\n",
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" createdOn modifiedOn startStop \\\n",
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]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"city_mpg = df.city08"
],
"metadata": {
"id": "SVO_3bYUkXrr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
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{
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"median 17.000000\n",
"max 150.000000\n",
"std 7.905886\n",
"Name: city08, dtype: float64"
]
},
"metadata": {},
"execution_count": 23
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]
},
"metadata": {},
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]
},
{
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"source": [
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{
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"0 (18.8, 20.2]\n",
"1 (8.986, 10.4]\n",
"2 (21.6, 23.0]\n",
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"34 (14.6, 16.0]\n",
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},
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},
{
"cell_type": "code",
"source": [
"song"
],
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"john 142\n",
"george 38\n",
"Name: counts, dtype: int64"
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},
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{
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{
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"john 142\n",
"george 38\n",
"Name: counts, dtype: int64"
]
},
"metadata": {},
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{
"cell_type": "code",
"source": [
""
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{
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" 'where',\n",
" 'xs',\n",
" 'year',\n",
" 'youSaveSpend']"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"source": [
"make = df.make"
],
"metadata": {
"id": "HZN_Yc54C4U9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"make"
],
"metadata": {
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"base_uri": "https://localhost:8080/"
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"id": "4dCS9Jv0C8m3",
"outputId": "40748dc3-1a35-4fdf-ce58-44de5492289d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 Alfa Romeo\n",
"1 Ferrari\n",
"2 Dodge\n",
"3 Dodge\n",
"4 Subaru\n",
" ... \n",
"41139 Subaru\n",
"41140 Subaru\n",
"41141 Subaru\n",
"41142 Subaru\n",
"41143 Subaru\n",
"Name: make, Length: 41144, dtype: object"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"make.to_dict()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zdL4M50NC9aA",
"outputId": "f9931048-70ec-4452-c71a-17d5eb8fa86a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{0: 'Alfa Romeo',\n",
" 1: 'Ferrari',\n",
" 2: 'Dodge',\n",
" 3: 'Dodge',\n",
" 4: 'Subaru',\n",
" 5: 'Subaru',\n",
" 6: 'Subaru',\n",
" 7: 'Toyota',\n",
" 8: 'Toyota',\n",
" 9: 'Toyota',\n",
" 10: 'Toyota',\n",
" 11: 'Volkswagen',\n",
" 12: 'Volkswagen',\n",
" 13: 'Volkswagen',\n",
" 14: 'Dodge',\n",
" 15: 'Volkswagen',\n",
" 16: 'Volvo',\n",
" 17: 'Volvo',\n",
" 18: 'Audi',\n",
" 19: 'Audi',\n",
" 20: 'BMW',\n",
" 21: 'BMW',\n",
" 22: 'BMW',\n",
" 23: 'Buick',\n",
" 24: 'Buick',\n",
" 25: 'Dodge',\n",
" 26: 'Buick',\n",
" 27: 'Buick',\n",
" 28: 'Buick',\n",
" 29: 'Buick',\n",
" 30: 'Buick',\n",
" 31: 'Cadillac',\n",
" 32: 'Cadillac',\n",
" 33: 'Cadillac',\n",
" 34: 'Cadillac',\n",
" 35: 'Chevrolet',\n",
" 36: 'Dodge',\n",
" 37: 'Chevrolet',\n",
" 38: 'Chevrolet',\n",
" 39: 'Chevrolet',\n",
" 40: 'Chevrolet',\n",
" 41: 'Chrysler',\n",
" 42: 'CX Automotive',\n",
" 43: 'CX Automotive',\n",
" 44: 'Nissan',\n",
" 45: 'Nissan',\n",
" 46: 'Nissan',\n",
" 47: 'Dodge',\n",
" 48: 'Dodge',\n",
" 49: 'Dodge',\n",
" 50: 'Dodge',\n",
" 51: 'Dodge',\n",
" 52: 'Dodge',\n",
" 53: 'Dodge',\n",
" 54: 'Dodge',\n",
" 55: 'Dodge',\n",
" 56: 'Ford',\n",
" 57: 'Ford',\n",
" 58: 'Dodge',\n",
" 59: 'Ford',\n",
" 60: 'Ford',\n",
" 61: 'Ford',\n",
" 62: 'Ford',\n",
" 63: 'Ford',\n",
" 64: 'Hyundai',\n",
" 65: 'Hyundai',\n",
" 66: 'Hyundai',\n",
" 67: 'Infiniti',\n",
" 68: 'Infiniti',\n",
" 69: 'Dodge',\n",
" 70: 'Lexus',\n",
" 71: 'Mercury',\n",
" 72: 'Mercury',\n",
" 73: 'Mercury',\n",
" 74: 'Mercury',\n",
" 75: 'Mazda',\n",
" 76: 'Mazda',\n",
" 77: 'Mazda',\n",
" 78: 'Mazda',\n",
" 79: 'Mazda',\n",
" 80: 'Dodge',\n",
" 81: 'Oldsmobile',\n",
" 82: 'Oldsmobile',\n",
" 83: 'Oldsmobile',\n",
" 84: 'Oldsmobile',\n",
" 85: 'Oldsmobile',\n",
" 86: 'Oldsmobile',\n",
" 87: 'Oldsmobile',\n",
" 88: 'Oldsmobile',\n",
" 89: 'Plymouth',\n",
" 90: 'Plymouth',\n",
" 91: 'Ford',\n",
" 92: 'Plymouth',\n",
" 93: 'Plymouth',\n",
" 94: 'Plymouth',\n",
" 95: 'Pontiac',\n",
" 96: 'Pontiac',\n",
" 97: 'Pontiac',\n",
" 98: 'Pontiac',\n",
" 99: 'Rolls-Royce',\n",
" 100: 'Rolls-Royce',\n",
" 101: 'Rolls-Royce',\n",
" 102: 'Ford',\n",
" 103: 'Toyota',\n",
" 104: 'Toyota',\n",
" 105: 'Toyota',\n",
" 106: 'Toyota',\n",
" 107: 'Volkswagen',\n",
" 108: 'Volkswagen',\n",
" 109: 'Volkswagen',\n",
" 110: 'Volkswagen',\n",
" 111: 'Volvo',\n",
" 112: 'Volvo',\n",
" 113: 'Dodge',\n",
" 114: 'Ford',\n",
" 115: 'Volvo',\n",
" 116: 'Volvo',\n",
" 117: 'Volvo',\n",
" 118: 'Buick',\n",
" 119: 'Buick',\n",
" 120: 'Buick',\n",
" 121: 'Buick',\n",
" 122: 'Cadillac',\n",
" 123: 'Cadillac',\n",
" 124: 'Chevrolet',\n",
" 125: 'Ford',\n",
" 126: 'Chevrolet',\n",
" 127: 'Chevrolet',\n",
" 128: 'Chrysler',\n",
" 129: 'Chrysler',\n",
" 130: 'Chrysler',\n",
" 131: 'Chrysler',\n",
" 132: 'Dodge',\n",
" 133: 'Dodge',\n",
" 134: 'Eagle',\n",
" 135: 'Eagle',\n",
" 136: 'Ford',\n",
" 137: 'Ford',\n",
" 138: 'Lincoln',\n",
" 139: 'Mercury',\n",
" 140: 'Lincoln',\n",
" 141: 'Mercedes-Benz',\n",
" 142: 'Mercedes-Benz',\n",
" 143: 'Mercedes-Benz',\n",
" 144: 'Mercedes-Benz',\n",
" 145: 'Oldsmobile',\n",
" 146: 'Oldsmobile',\n",
" 147: 'GMC',\n",
" 148: 'Oldsmobile',\n",
" 149: 'Pontiac',\n",
" 150: 'Pontiac',\n",
" 151: 'Rolls-Royce',\n",
" 152: 'Saab',\n",
" 153: 'Saab',\n",
" 154: 'Saab',\n",
" 155: 'Saab',\n",
" 156: 'Audi',\n",
" 157: 'BMW',\n",
" 158: 'GMC',\n",
" 159: 'Chevrolet',\n",
" 160: 'Chevrolet',\n",
" 161: 'CX Automotive',\n",
" 162: 'Ford',\n",
" 163: 'Ford',\n",
" 164: 'Honda',\n",
" 165: 'Honda',\n",
" 166: 'Mercury',\n",
" 167: 'Mercury',\n",
" 168: 'Saturn',\n",
" 169: 'GMC',\n",
" 170: 'Saturn',\n",
" 171: 'Saturn',\n",
" 172: 'Saturn',\n",
" 173: 'Subaru',\n",
" 174: 'Subaru',\n",
" 175: 'Subaru',\n",
" 176: 'Subaru',\n",
" 177: 'Subaru',\n",
" 178: 'Subaru',\n",
" 179: 'Subaru',\n",
" 180: 'GMC',\n",
" 181: 'Toyota',\n",
" 182: 'Toyota',\n",
" 183: 'Buick',\n",
" 184: 'Buick',\n",
" 185: 'Buick',\n",
" 186: 'Eagle',\n",
" 187: 'Eagle',\n",
" 188: 'Eagle',\n",
" 189: 'Eagle',\n",
" 190: 'Eagle',\n",
" 191: 'GMC',\n",
" 192: 'Eagle',\n",
" 193: 'Eagle',\n",
" 194: 'Eagle',\n",
" 195: 'Ford',\n",
" 196: 'Ford',\n",
" 197: 'Mercury',\n",
" 198: 'Mercury',\n",
" 199: 'Mitsubishi',\n",
" 200: 'Mitsubishi',\n",
" 201: 'Mitsubishi',\n",
" 202: 'GMC',\n",
" 203: 'Mitsubishi',\n",
" 204: 'Mitsubishi',\n",
" 205: 'Mitsubishi',\n",
" 206: 'Mitsubishi',\n",
" 207: 'Mitsubishi',\n",
" 208: 'Mitsubishi',\n",
" 209: 'Mitsubishi',\n",
" 210: 'Mitsubishi',\n",
" 211: 'Mitsubishi',\n",
" 212: 'Oldsmobile',\n",
" 213: 'GMC',\n",
" 214: 'Oldsmobile',\n",
" 215: 'Oldsmobile',\n",
" 216: 'Mitsubishi',\n",
" 217: 'Plymouth',\n",
" 218: 'Plymouth',\n",
" 219: 'Plymouth',\n",
" 220: 'Plymouth',\n",
" 221: 'Plymouth',\n",
" 222: 'Plymouth',\n",
" 223: 'Plymouth',\n",
" 224: 'Dodge',\n",
" 225: 'GMC',\n",
" 226: 'Plymouth',\n",
" 227: 'Toyota',\n",
" 228: 'Toyota',\n",
" 229: 'Volkswagen',\n",
" 230: 'Volkswagen',\n",
" 231: 'Volkswagen',\n",
" 232: 'Volkswagen',\n",
" 233: 'Volvo',\n",
" 234: 'Volvo',\n",
" 235: 'Volvo',\n",
" 236: 'GMC',\n",
" 237: 'Volvo',\n",
" 238: 'Volvo',\n",
" 239: 'Buick',\n",
" 240: 'Chevrolet',\n",
" 241: 'Chevrolet',\n",
" 242: 'Chevrolet',\n",
" 243: 'Chevrolet',\n",
" 244: 'Chevrolet',\n",
" 245: 'Chevrolet',\n",
" 246: 'Chevrolet',\n",
" 247: 'GMC',\n",
" 248: 'Nissan',\n",
" 249: 'Nissan',\n",
" 250: 'Nissan',\n",
" 251: 'Dodge',\n",
" 252: 'Dodge',\n",
" 253: 'GMC',\n",
" 254: 'GMC',\n",
" 255: 'GMC',\n",
" 256: 'GMC',\n",
" 257: 'GMC',\n",
" 258: 'GMC',\n",
" 259: 'GMC',\n",
" 260: 'Isuzu',\n",
" 261: 'Isuzu',\n",
" 262: 'Isuzu',\n",
" 263: 'Mitsubishi',\n",
" 264: 'Mitsubishi',\n",
" 265: 'Chevrolet',\n",
" 266: 'Chevrolet',\n",
" 267: 'Chevrolet',\n",
" 268: 'Chevrolet',\n",
" 269: 'GMC',\n",
" 270: 'Chevrolet',\n",
" 271: 'Chevrolet',\n",
" 272: 'Chevrolet',\n",
" 273: 'Chevrolet',\n",
" 274: 'Chevrolet',\n",
" 275: 'Chevrolet',\n",
" 276: 'Chevrolet',\n",
" 277: 'Chevrolet',\n",
" 278: 'Chevrolet',\n",
" 279: 'Chevrolet',\n",
" 280: 'GMC',\n",
" 281: 'Chevrolet',\n",
" 282: 'Chevrolet',\n",
" 283: 'Chevrolet',\n",
" 284: 'Chevrolet',\n",
" 285: 'Dodge',\n",
" 286: 'Dodge',\n",
" 287: 'Dodge',\n",
" 288: 'Dodge',\n",
" 289: 'Dodge',\n",
" 290: 'Dodge',\n",
" 291: 'GMC',\n",
" 292: 'Dodge',\n",
" 293: 'Dodge',\n",
" 294: 'Dodge',\n",
" 295: 'Dodge',\n",
" 296: 'Dodge',\n",
" 297: 'Dodge',\n",
" 298: 'Dodge',\n",
" 299: 'Dodge',\n",
" 300: 'Dodge',\n",
" 301: 'Dodge',\n",
" 302: 'GMC',\n",
" 303: 'Dodge',\n",
" 304: 'Jeep',\n",
" 305: 'Jeep',\n",
" 306: 'Jeep',\n",
" 307: 'Jeep',\n",
" 308: 'Ford',\n",
" 309: 'Ford',\n",
" 310: 'Ford',\n",
" 311: 'Ford',\n",
" 312: 'Ford',\n",
" 313: 'Toyota',\n",
" 314: 'Ford',\n",
" 315: 'Ford',\n",
" 316: 'Ford',\n",
" 317: 'Ford',\n",
" 318: 'Ford',\n",
" 319: 'Ford',\n",
" 320: 'Ford',\n",
" 321: 'Ford',\n",
" 322: 'Ford',\n",
" 323: 'Ford',\n",
" 324: 'Toyota',\n",
" 325: 'Ford',\n",
" 326: 'Ford',\n",
" 327: 'Ford',\n",
" 328: 'Ford',\n",
" 329: 'Ford',\n",
" 330: 'Ford',\n",
" 331: 'GMC',\n",
" 332: 'GMC',\n",
" 333: 'GMC',\n",
" 334: 'GMC',\n",
" 335: 'Dodge',\n",
" 336: 'Volkswagen',\n",
" 337: 'GMC',\n",
" 338: 'GMC',\n",
" 339: 'GMC',\n",
" 340: 'GMC',\n",
" 341: 'GMC',\n",
" 342: 'GMC',\n",
" 343: 'GMC',\n",
" 344: 'GMC',\n",
" 345: 'GMC',\n",
" 346: 'GMC',\n",
" 347: 'Volkswagen',\n",
" 348: 'GMC',\n",
" 349: 'GMC',\n",
" 350: 'GMC',\n",
" 351: 'Mazda',\n",
" 352: 'Mazda',\n",
" 353: 'Mazda',\n",
" 354: 'Mazda',\n",
" 355: 'Mazda',\n",
" 356: 'Mazda',\n",
" 357: 'Toyota',\n",
" 358: 'AM General',\n",
" 359: 'Toyota',\n",
" 360: 'Toyota',\n",
" 361: 'Toyota',\n",
" 362: 'Toyota',\n",
" 363: 'Chevrolet',\n",
" 364: 'Chevrolet',\n",
" 365: 'Chevrolet',\n",
" 366: 'Chevrolet',\n",
" 367: 'Chevrolet',\n",
" 368: 'Chevrolet',\n",
" 369: 'AM General',\n",
" 370: 'Chevrolet',\n",
" 371: 'Chevrolet',\n",
" 372: 'Chevrolet',\n",
" 373: 'Chevrolet',\n",
" 374: 'Chevrolet',\n",
" 375: 'Chevrolet',\n",
" 376: 'Chevrolet',\n",
" 377: 'Chevrolet',\n",
" 378: 'Chevrolet',\n",
" 379: 'Chevrolet',\n",
" 380: 'Chevrolet',\n",
" 381: 'Chevrolet',\n",
" 382: 'Chevrolet',\n",
" 383: 'Chevrolet',\n",
" 384: 'Chevrolet',\n",
" 385: 'Nissan',\n",
" 386: 'Nissan',\n",
" 387: 'Nissan',\n",
" 388: 'Dodge',\n",
" 389: 'Dodge',\n",
" 390: 'Dodge',\n",
" 391: 'Chevrolet',\n",
" 392: 'Dodge',\n",
" 393: 'Dodge',\n",
" 394: 'Dodge',\n",
" 395: 'Dodge',\n",
" 396: 'Dodge',\n",
" 397: 'Dodge',\n",
" 398: 'Dodge',\n",
" 399: 'Dodge',\n",
" 400: 'Dodge',\n",
" 401: 'Dodge',\n",
" 402: 'Chevrolet',\n",
" 403: 'Dodge',\n",
" 404: 'Dodge',\n",
" 405: 'Jeep',\n",
" 406: 'Jeep',\n",
" 407: 'Jeep',\n",
" 408: 'Jeep',\n",
" 409: 'Ford',\n",
" 410: 'Ford',\n",
" 411: 'Ford',\n",
" 412: 'Ford',\n",
" 413: 'Chevrolet',\n",
" 414: 'Ford',\n",
" 415: 'Ford',\n",
" 416: 'Ford',\n",
" 417: 'Ford',\n",
" 418: 'Ford',\n",
" 419: 'Ford',\n",
" 420: 'Ford',\n",
" 421: 'Ford',\n",
" 422: 'Ford',\n",
" 423: 'GMC',\n",
" 424: 'Chevrolet',\n",
" 425: 'GMC',\n",
" 426: 'GMC',\n",
" 427: 'GMC',\n",
" 428: 'GMC',\n",
" 429: 'GMC',\n",
" 430: 'GMC',\n",
" 431: 'GMC',\n",
" 432: 'GMC',\n",
" 433: 'GMC',\n",
" 434: 'GMC',\n",
" 435: 'Chevrolet',\n",
" 436: 'GMC',\n",
" 437: 'GMC',\n",
" 438: 'GMC',\n",
" 439: 'GMC',\n",
" 440: 'GMC',\n",
" 441: 'GMC',\n",
" 442: 'GMC',\n",
" 443: 'GMC',\n",
" 444: 'GMC',\n",
" 445: 'GMC',\n",
" 446: 'Dodge',\n",
" 447: 'Chevrolet',\n",
" 448: 'Isuzu',\n",
" 449: 'Isuzu',\n",
" 450: 'Mitsubishi',\n",
" 451: 'Mitsubishi',\n",
" 452: 'Mitsubishi',\n",
" 453: 'Mazda',\n",
" 454: 'Mazda',\n",
" 455: 'Toyota',\n",
" 456: 'Toyota',\n",
" 457: 'Toyota',\n",
" 458: 'Chevrolet',\n",
" 459: 'Toyota',\n",
" 460: 'Toyota',\n",
" 461: 'Chevrolet',\n",
" 462: 'Chevrolet',\n",
" 463: 'Chevrolet',\n",
" 464: 'Chevrolet',\n",
" 465: 'Chevrolet',\n",
" 466: 'Chevrolet',\n",
" 467: 'Chevrolet',\n",
" 468: 'Chevrolet',\n",
" 469: 'Chevrolet',\n",
" 470: 'Dodge',\n",
" 471: 'Dodge',\n",
" 472: 'Dodge',\n",
" 473: 'Dodge',\n",
" 474: 'Dodge',\n",
" 475: 'Dodge',\n",
" 476: 'Dodge',\n",
" 477: 'Dodge',\n",
" 478: 'Dodge',\n",
" 479: 'Ford',\n",
" 480: 'Dodge',\n",
" 481: 'Ford',\n",
" 482: 'Ford',\n",
" 483: 'Ford',\n",
" 484: 'Ford',\n",
" 485: 'Ford',\n",
" 486: 'Ford',\n",
" 487: 'Ford',\n",
" 488: 'Ford',\n",
" 489: 'Ford',\n",
" 490: 'Ford',\n",
" 491: 'Dodge',\n",
" 492: 'Ford',\n",
" 493: 'GMC',\n",
" 494: 'GMC',\n",
" 495: 'GMC',\n",
" 496: 'GMC',\n",
" 497: 'GMC',\n",
" 498: 'GMC',\n",
" 499: 'GMC',\n",
" 500: 'GMC',\n",
" 501: 'Chevrolet',\n",
" 502: 'Dodge',\n",
" 503: 'Chevrolet',\n",
" 504: 'Chevrolet',\n",
" 505: 'Chevrolet',\n",
" 506: 'Chevrolet',\n",
" 507: 'Chevrolet',\n",
" 508: 'Chevrolet',\n",
" 509: 'Dodge',\n",
" 510: 'Dodge',\n",
" 511: 'Dodge',\n",
" 512: 'Dodge',\n",
" 513: 'Dodge',\n",
" 514: 'Dodge',\n",
" 515: 'Dodge',\n",
" 516: 'Dodge',\n",
" 517: 'Dodge',\n",
" 518: 'Ford',\n",
" 519: 'Ford',\n",
" 520: 'Ford',\n",
" 521: 'Ford',\n",
" 522: 'Ford',\n",
" 523: 'Ford',\n",
" 524: 'Dodge',\n",
" 525: 'Ford',\n",
" 526: 'Ford',\n",
" 527: 'GMC',\n",
" 528: 'GMC',\n",
" 529: 'GMC',\n",
" 530: 'GMC',\n",
" 531: 'GMC',\n",
" 532: 'GMC',\n",
" 533: 'GMC',\n",
" 534: 'Toyota',\n",
" 535: 'Dodge',\n",
" 536: 'Toyota',\n",
" 537: 'Toyota',\n",
" 538: 'Volkswagen',\n",
" 539: 'Volkswagen',\n",
" 540: 'Chevrolet',\n",
" 541: 'Chevrolet',\n",
" 542: 'Chevrolet',\n",
" 543: 'Chevrolet',\n",
" 544: 'Chevrolet',\n",
" 545: 'Nissan',\n",
" 546: 'Dodge',\n",
" 547: 'Nissan',\n",
" 548: 'Nissan',\n",
" 549: 'Chrysler',\n",
" 550: 'Dodge',\n",
" 551: 'Dodge',\n",
" 552: 'Dodge',\n",
" 553: 'Dodge',\n",
" 554: 'Dodge',\n",
" 555: 'Dodge',\n",
" 556: 'Dodge',\n",
" 557: 'Dodge',\n",
" 558: 'Dodge',\n",
" 559: 'Dodge',\n",
" 560: 'Dodge',\n",
" 561: 'Dodge',\n",
" 562: 'Jeep',\n",
" 563: 'Jeep',\n",
" 564: 'Jeep',\n",
" 565: 'Ford',\n",
" 566: 'Ford',\n",
" 567: 'Mercury',\n",
" 568: 'Mercury',\n",
" 569: 'Dodge',\n",
" 570: 'Geo',\n",
" 571: 'Geo',\n",
" 572: 'GMC',\n",
" 573: 'GMC',\n",
" 574: 'GMC',\n",
" 575: 'Isuzu',\n",
" 576: 'Isuzu',\n",
" 577: 'Isuzu',\n",
" 578: 'Isuzu',\n",
" 579: 'Mazda',\n",
" 580: 'Dodge',\n",
" 581: 'Mazda',\n",
" 582: 'Pontiac',\n",
" 583: 'Pontiac',\n",
" 584: 'Plymouth',\n",
" 585: 'Plymouth',\n",
" 586: 'Plymouth',\n",
" 587: 'Plymouth',\n",
" 588: 'Suzuki',\n",
" 589: 'Suzuki',\n",
" 590: 'Suzuki',\n",
" 591: 'Dodge',\n",
" 592: 'Suzuki',\n",
" 593: 'Suzuki',\n",
" 594: 'Oldsmobile',\n",
" 595: 'Oldsmobile',\n",
" 596: 'Toyota',\n",
" 597: 'Chevrolet',\n",
" 598: 'Chevrolet',\n",
" 599: 'Chevrolet',\n",
" 600: 'Chevrolet',\n",
" 601: 'Chevrolet',\n",
" 602: 'E. P. Dutton, Inc.',\n",
" 603: 'Nissan',\n",
" 604: 'Nissan',\n",
" 605: 'Chrysler',\n",
" 606: 'Dodge',\n",
" 607: 'Dodge',\n",
" 608: 'Dodge',\n",
" 609: 'Jeep',\n",
" 610: 'Jeep',\n",
" 611: 'Jeep',\n",
" 612: 'Jeep',\n",
" 613: 'GMC',\n",
" 614: 'Jeep',\n",
" 615: 'Jeep',\n",
" 616: 'Jeep',\n",
" 617: 'Jeep',\n",
" 618: 'Jeep',\n",
" 619: 'Jeep',\n",
" 620: 'Jeep',\n",
" 621: 'Ford',\n",
" 622: 'Ford',\n",
" 623: 'Ford',\n",
" 624: 'GMC',\n",
" 625: 'Ford',\n",
" 626: 'Ford',\n",
" 627: 'Geo',\n",
" 628: 'Geo',\n",
" 629: 'Geo',\n",
" 630: 'Geo',\n",
" 631: 'GMC',\n",
" 632: 'GMC',\n",
" 633: 'GMC',\n",
" 634: 'GMC',\n",
" 635: 'GMC',\n",
" 636: 'GMC',\n",
" 637: 'Isuzu',\n",
" 638: 'Isuzu',\n",
" 639: 'Isuzu',\n",
" 640: 'Isuzu',\n",
" 641: 'Isuzu',\n",
" 642: 'Land Rover',\n",
" 643: 'Land Rover',\n",
" 644: 'Land Rover',\n",
" 645: 'Mitsubishi',\n",
" 646: 'GMC',\n",
" 647: 'Mitsubishi',\n",
" 648: 'Mazda',\n",
" 649: 'Mazda',\n",
" 650: 'Mazda',\n",
" 651: 'Mazda',\n",
" 652: 'Mazda',\n",
" 653: 'PAS, Inc',\n",
" 654: 'Plymouth',\n",
" 655: 'Subaru',\n",
" 656: 'Subaru',\n",
" 657: 'GMC',\n",
" 658: 'Subaru',\n",
" 659: 'Subaru',\n",
" 660: 'Suzuki',\n",
" 661: 'Suzuki',\n",
" 662: 'Suzuki',\n",
" 663: 'Suzuki',\n",
" 664: 'Suzuki',\n",
" 665: 'Suzuki',\n",
" 666: 'Suzuki',\n",
" 667: 'Suzuki',\n",
" 668: 'Dodge',\n",
" 669: 'GMC',\n",
" 670: 'Suzuki',\n",
" 671: 'Oldsmobile',\n",
" 672: 'Toyota',\n",
" 673: 'Toyota',\n",
" 674: 'Toyota',\n",
" 675: 'Toyota',\n",
" 676: 'Toyota',\n",
" 677: 'Cadillac',\n",
" 678: 'Chevrolet',\n",
" 679: 'Chevrolet',\n",
" 680: 'GMC',\n",
" 681: 'Dodge',\n",
" 682: 'Dodge',\n",
" 683: 'Dodge',\n",
" 684: 'Dodge',\n",
" 685: 'Buick',\n",
" 686: 'Toyota',\n",
" 687: 'Toyota',\n",
" 688: 'Acura',\n",
" 689: 'Acura',\n",
" 690: 'Alfa Romeo',\n",
" 691: 'GMC',\n",
" 692: 'Chevrolet',\n",
" 693: 'Chevrolet',\n",
" 694: 'Chevrolet',\n",
" 695: 'Nissan',\n",
" 696: 'Nissan',\n",
" 697: 'Nissan',\n",
" 698: 'Nissan',\n",
" 699: 'Dodge',\n",
" 700: 'Ferrari',\n",
" 701: 'Ferrari',\n",
" 702: 'GMC',\n",
" 703: 'Ferrari',\n",
" 704: 'Honda',\n",
" 705: 'Honda',\n",
" 706: 'Honda',\n",
" 707: 'Honda',\n",
" 708: 'Honda',\n",
" 709: 'Jaguar',\n",
" 710: 'Lotus',\n",
" 711: 'Mazda',\n",
" 712: 'Mazda',\n",
" 713: 'Grumman Olson',\n",
" 714: 'Mazda',\n",
" 715: 'Mazda',\n",
" 716: 'Mercedes-Benz',\n",
" 717: 'Mercedes-Benz',\n",
" 718: 'Mercedes-Benz',\n",
" 719: 'Porsche',\n",
" 720: 'Porsche',\n",
" 721: 'Porsche',\n",
" 722: 'Porsche',\n",
" 723: 'Porsche',\n",
" 724: 'Grumman Olson',\n",
" 725: 'Toyota',\n",
" 726: 'Toyota',\n",
" 727: 'Toyota',\n",
" 728: 'Audi',\n",
" 729: 'BMW',\n",
" 730: 'BMW',\n",
" 731: 'BMW',\n",
" 732: 'BMW',\n",
" 733: 'Mercury',\n",
" 734: 'Mercury',\n",
" 735: 'Jeep',\n",
" 736: 'Mercury',\n",
" 737: 'Mercedes-Benz',\n",
" 738: 'Porsche',\n",
" 739: 'Porsche',\n",
" 740: 'Porsche',\n",
" 741: 'Porsche',\n",
" 742: 'Porsche',\n",
" 743: 'Porsche',\n",
" 744: 'Porsche',\n",
" 745: 'Porsche',\n",
" 746: 'Jeep',\n",
" 747: 'Toyota',\n",
" 748: 'Toyota',\n",
" 749: 'Toyota',\n",
" 750: 'Toyota',\n",
" 751: 'Toyota',\n",
" 752: 'Toyota',\n",
" 753: 'Acura',\n",
" 754: 'Acura',\n",
" 755: 'Acura',\n",
" 756: 'Audi',\n",
" 757: 'Jeep',\n",
" 758: 'BMW',\n",
" 759: 'BMW',\n",
" 760: 'BMW',\n",
" 761: 'BMW',\n",
" 762: 'BMW',\n",
" 763: 'BMW',\n",
" 764: 'BMW',\n",
" 765: 'BMW',\n",
" 766: 'Chevrolet',\n",
" 767: 'Chevrolet',\n",
" 768: 'Jeep',\n",
" 769: 'Chevrolet',\n",
" 770: 'Chevrolet',\n",
" 771: 'Chevrolet',\n",
" 772: 'Chevrolet',\n",
" 773: 'Chevrolet',\n",
" 774: 'Chevrolet',\n",
" 775: 'Chevrolet',\n",
" 776: 'Chevrolet',\n",
" 777: 'Chrysler',\n",
" 778: 'Nissan',\n",
" 779: 'Dodge',\n",
" 780: 'Jeep',\n",
" 781: 'Nissan',\n",
" 782: 'Nissan',\n",
" 783: 'Nissan',\n",
" 784: 'Nissan',\n",
" 785: 'Nissan',\n",
" 786: 'Dodge',\n",
" 787: 'Dodge',\n",
" 788: 'Dodge',\n",
" 789: 'Dodge',\n",
" 790: 'Dodge',\n",
" 791: 'Jeep',\n",
" 792: 'Dodge',\n",
" 793: 'Dodge',\n",
" 794: 'Dodge',\n",
" 795: 'Dodge',\n",
" 796: 'Eagle',\n",
" 797: 'Eagle',\n",
" 798: 'Eagle',\n",
" 799: 'Eagle',\n",
" 800: 'Eagle',\n",
" 801: 'Eagle',\n",
" 802: 'Plymouth',\n",
" 803: 'Eagle',\n",
" 804: 'Eagle',\n",
" 805: 'Eagle',\n",
" 806: 'Eagle',\n",
" 807: 'Eagle',\n",
" 808: 'Eagle',\n",
" 809: 'Ferrari',\n",
" 810: 'Ferrari',\n",
" 811: 'Ford',\n",
" 812: 'Ford',\n",
" 813: 'Plymouth',\n",
" 814: 'Ford',\n",
" 815: 'Ford',\n",
" 816: 'Ford',\n",
" 817: 'Ford',\n",
" 818: 'Ford',\n",
" 819: 'Ford',\n",
" 820: 'Ford',\n",
" 821: 'Ford',\n",
" 822: 'Geo',\n",
" 823: 'Geo',\n",
" 824: 'Plymouth',\n",
" 825: 'Geo',\n",
" 826: 'Honda',\n",
" 827: 'Honda',\n",
" 828: 'Honda',\n",
" 829: 'Honda',\n",
" 830: 'Honda',\n",
" 831: 'Honda',\n",
" 832: 'Honda',\n",
" 833: 'Honda',\n",
" 834: 'Honda',\n",
" 835: 'Plymouth',\n",
" 836: 'Honda',\n",
" 837: 'Honda',\n",
" 838: 'Honda',\n",
" 839: 'Honda',\n",
" 840: 'Hyundai',\n",
" 841: 'Hyundai',\n",
" 842: 'Hyundai',\n",
" 843: 'Hyundai',\n",
" 844: 'Hyundai',\n",
" 845: 'Infiniti',\n",
" 846: 'Plymouth',\n",
" 847: 'Jaguar',\n",
" 848: 'Jaguar',\n",
" 849: 'Jaguar',\n",
" 850: 'Lexus',\n",
" 851: 'Lexus',\n",
" 852: 'Lexus',\n",
" 853: 'Mazda',\n",
" 854: 'Mazda',\n",
" 855: 'Mazda',\n",
" 856: 'Mazda',\n",
" 857: 'American Motors Corporation',\n",
" 858: 'Mazda',\n",
" 859: 'Mazda',\n",
" 860: 'Mazda',\n",
" 861: 'Mazda',\n",
" 862: 'Mercedes-Benz',\n",
" 863: 'Mitsubishi',\n",
" 864: 'Mitsubishi',\n",
" 865: 'Mitsubishi',\n",
" 866: 'Mitsubishi',\n",
" 867: 'Mitsubishi',\n",
" 868: 'American Motors Corporation',\n",
" 869: 'Mitsubishi',\n",
" 870: 'Mitsubishi',\n",
" 871: 'Mitsubishi',\n",
" 872: 'Mitsubishi',\n",
" 873: 'Mitsubishi',\n",
" 874: 'Mitsubishi',\n",
" 875: 'Mitsubishi',\n",
" 876: 'Mitsubishi',\n",
" 877: 'Mitsubishi',\n",
" 878: 'Mitsubishi',\n",
" 879: 'Chevrolet',\n",
" 880: 'Plymouth',\n",
" 881: 'Plymouth',\n",
" 882: 'Plymouth',\n",
" 883: 'Plymouth',\n",
" 884: 'Plymouth',\n",
" 885: 'Plymouth',\n",
" 886: 'Plymouth',\n",
" 887: 'Plymouth',\n",
" 888: 'Plymouth',\n",
" 889: 'Plymouth',\n",
" 890: 'Dodge',\n",
" 891: 'Chevrolet',\n",
" 892: 'Plymouth',\n",
" 893: 'Plymouth',\n",
" 894: 'Pontiac',\n",
" 895: 'Pontiac',\n",
" 896: 'Pontiac',\n",
" 897: 'Pontiac',\n",
" 898: 'Pontiac',\n",
" 899: 'Pontiac',\n",
" 900: 'Pontiac',\n",
" 901: 'Pontiac',\n",
" 902: 'Chevrolet',\n",
" 903: 'Pontiac',\n",
" 904: 'Pontiac',\n",
" 905: 'Rolls-Royce',\n",
" 906: 'Rolls-Royce',\n",
" 907: 'Saab',\n",
" 908: 'Saab',\n",
" 909: 'Saab',\n",
" 910: 'Saab',\n",
" 911: 'Saab',\n",
" 912: 'Saturn',\n",
" 913: 'Chevrolet',\n",
" 914: 'Saturn',\n",
" 915: 'Saturn',\n",
" 916: 'Saturn',\n",
" 917: 'Subaru',\n",
" 918: 'Subaru',\n",
" 919: 'Subaru',\n",
" 920: 'Subaru',\n",
" 921: 'Subaru',\n",
" 922: 'Subaru',\n",
" 923: 'Subaru',\n",
" 924: 'Chevrolet',\n",
" 925: 'Subaru',\n",
" 926: 'Suzuki',\n",
" 927: 'Suzuki',\n",
" 928: 'Suzuki',\n",
" 929: 'Toyota',\n",
" 930: 'Toyota',\n",
" 931: 'Toyota',\n",
" 932: 'Toyota',\n",
" 933: 'Toyota',\n",
" 934: 'Toyota',\n",
" 935: 'Chevrolet',\n",
" 936: 'Toyota',\n",
" 937: 'Volkswagen',\n",
" 938: 'Volkswagen',\n",
" 939: 'Acura',\n",
" 940: 'Acura',\n",
" 941: 'Acura',\n",
" 942: 'Acura',\n",
" 943: 'Acura',\n",
" 944: 'Acura',\n",
" 945: 'Alfa Romeo',\n",
" 946: 'Chevrolet',\n",
" 947: 'Alfa Romeo',\n",
" 948: 'Audi',\n",
" 949: 'Audi',\n",
" 950: 'Audi',\n",
" 951: 'Audi',\n",
" 952: 'Audi',\n",
" 953: 'Audi',\n",
" 954: 'Audi',\n",
" 955: 'Audi',\n",
" 956: 'BMW',\n",
" 957: 'Chevrolet',\n",
" 958: 'BMW',\n",
" 959: 'BMW',\n",
" 960: 'BMW',\n",
" 961: 'BMW',\n",
" 962: 'BMW',\n",
" 963: 'BMW',\n",
" 964: 'Buick',\n",
" 965: 'Buick',\n",
" 966: 'Buick',\n",
" 967: 'Chevrolet',\n",
" 968: 'Chevrolet',\n",
" 969: 'Chevrolet',\n",
" 970: 'Chevrolet',\n",
" 971: 'Chevrolet',\n",
" 972: 'Chevrolet',\n",
" 973: 'Chevrolet',\n",
" 974: 'Chevrolet',\n",
" 975: 'Chevrolet',\n",
" 976: 'Chevrolet',\n",
" 977: 'Chrysler',\n",
" 978: 'Chrysler',\n",
" 979: 'Chevrolet',\n",
" 980: 'Chrysler',\n",
" 981: 'Nissan',\n",
" 982: 'Nissan',\n",
" 983: 'Dodge',\n",
" 984: 'Dodge',\n",
" 985: 'Dodge',\n",
" 986: 'Dodge',\n",
" 987: 'Dodge',\n",
" 988: 'Dodge',\n",
" 989: 'Ford',\n",
" 990: 'Chevrolet',\n",
" 991: 'Ford',\n",
" 992: 'Ford',\n",
" 993: 'Ford',\n",
" 994: 'Ford',\n",
" 995: 'Ford',\n",
" 996: 'Ford',\n",
" 997: 'Ford',\n",
" 998: 'Geo',\n",
" 999: 'Geo',\n",
" ...}"
]
},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [
"city_mpg.rename(make.to_dict()).index"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3YMrsqNIDAGC",
"outputId": "c40ae053-bbac-41a7-ee29-991df46f3624"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Alfa Romeo', 'Ferrari', 'Dodge', 'Dodge', 'Subaru', 'Subaru', 'Subaru',\n",
" 'Toyota', 'Toyota', 'Toyota',\n",
" ...\n",
" 'Saab', 'Saturn', 'Saturn', 'Saturn', 'Saturn', 'Subaru', 'Subaru',\n",
" 'Subaru', 'Subaru', 'Subaru'],\n",
" dtype='object', length=41144)"
]
},
"metadata": {},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"source": [
"city_mpg.index"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sDlrMOeWDFUS",
"outputId": "c87d1a85-1c1e-4f5a-f8ae-6d72e9813103"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RangeIndex(start=0, stop=41144, step=1)"
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [
"city_mpg.rename(make.to_dict()) == city_mpg.rename(make)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bglP8D-8Dc0j",
"outputId": "414116e5-e52e-4d22-dc7a-bc502f4a1350"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Alfa Romeo True\n",
"Ferrari True\n",
"Dodge True\n",
"Dodge True\n",
"Subaru True\n",
" ... \n",
"Subaru True\n",
"Subaru True\n",
"Subaru True\n",
"Subaru True\n",
"Subaru True\n",
"Name: city08, Length: 41144, dtype: bool"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"city_mpg"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wzvmFW_GDodq",
"outputId": "a7aba1a5-12f5-40e2-bf68-00908d522f3b"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 19\n",
"1 9\n",
"2 23\n",
"3 10\n",
"4 17\n",
" ..\n",
"41139 19\n",
"41140 20\n",
"41141 18\n",
"41142 18\n",
"41143 16\n",
"Name: city08, Length: 41144, dtype: int64"
]
},
"metadata": {},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"source": [
"make = df.make\n",
"make[:10]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tv4g16l2DuCS",
"outputId": "536e0797-955c-49fb-c32f-6838e17627a2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 Alfa Romeo\n",
"1 Ferrari\n",
"2 Dodge\n",
"3 Dodge\n",
"4 Subaru\n",
"5 Subaru\n",
"6 Subaru\n",
"7 Toyota\n",
"8 Toyota\n",
"9 Toyota\n",
"Name: make, dtype: object"
]
},
"metadata": {},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes = city_mpg[:10].rename(make.to_dict())\n",
"first_ten_makes"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0DDY2RpgXbyj",
"outputId": "5cf06636-76f6-4d2d-d647-413d39963411"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Alfa Romeo 19\n",
"Ferrari 9\n",
"Dodge 23\n",
"Dodge 10\n",
"Subaru 17\n",
"Subaru 21\n",
"Subaru 22\n",
"Toyota 23\n",
"Toyota 23\n",
"Toyota 23\n",
"Name: city08, dtype: int64"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes.loc['Ferrari']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GBNNcm9KXnt2",
"outputId": "56f40167-4706-4608-cdca-6cf00e08480a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"9"
]
},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes.loc['Toyota']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OZfBAZWKX4ZF",
"outputId": "31d4ac8f-32a4-438f-cbec-4f29060111ae"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Toyota 23\n",
"Toyota 23\n",
"Toyota 23\n",
"Name: city08, dtype: int64"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes.loc[['Ferrari']]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hGMUrz13X7ZT",
"outputId": "0449d0e5-d758-4f50-8585-290e9cb15076"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Ferrari 9\n",
"Name: city08, dtype: int64"
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes.loc[['Toyota']]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y1v5MuX2YNAl",
"outputId": "61cbff37-cc92-4e81-fdfe-72fe46e56d57"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Toyota 23\n",
"Toyota 23\n",
"Toyota 23\n",
"Name: city08, dtype: int64"
]
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"first_ten_makes.loc[['Toyota']] == first_ten_makes.loc['Toyota']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gSDh71cWYPlQ",
"outputId": "77d5cf04-ba7a-48e4-8de2-0414a05042e2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Toyota True\n",
"Toyota True\n",
"Toyota True\n",
"Name: city08, dtype: bool"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"cost = pd.Series([1, 2.25, 3.99, .99], index=['A', 'B', 'C', 'D'])"
],
"metadata": {
"id": "PPqzHwnmYlPZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cost"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rq8aJoYl3DxA",
"outputId": "5df4ced0-6300-4a4b-cd96-160c41dd1fd1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"A 1.00\n",
"B 2.25\n",
"C 3.99\n",
"D 0.99\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"source": [
"cost.loc[cost > 3]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZKuczOso3KHr",
"outputId": "51c5fd5e-71fd-4b2e-f0bd-080f43ec0b8d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"C 3.99\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"cost.mul(2).loc[cost > 3]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qNBJXOXK3Nqs",
"outputId": "df68c97e-9ab0-440b-a5de-d87fd693ba71"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"C 7.98\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 57
}
]
},
{
"cell_type": "code",
"source": [
"mask = cost > 3\n",
"cost.mul(2).loc[mask]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1ozyW3C43TNG",
"outputId": "1e2c52ee-4a6d-48a2-93ab-12933989c02b"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"C 7.98\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"source": [
"def gt3(x):\n",
" return x > 3\n",
"\n",
"cost.mul(2).loc[gt3]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "scDyZARW3Xjn",
"outputId": "7dd97532-8a33-403f-de83-71f66da73bb1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"B 4.50\n",
"C 7.98\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"source": [
"cost.iloc[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
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"metadata": {},
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{
"cell_type": "code",
"source": [
"url = 'https://github.com/mattharrison/datasets/raw/master/data/alta-noaa-1980-2019.csv'\n",
"alta_df = pd.read_csv(url)\n",
"alta_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "z9xOYFl_KsM2",
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{
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"data": {
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" STATION NAME LATITUDE LONGITUDE ELEVATION DATE DAPR \\\n",
"0 USC00420072 ALTA, UT US 40.5905 -111.6369 2660.9 1980-01-01 NaN \n",
"1 USC00420072 ALTA, UT US 40.5905 -111.6369 2660.9 1980-01-02 NaN \n",
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" DASF MDPR MDSF ... SNWD TMAX TMIN TOBS WT01 WT03 WT04 WT05 \\\n",
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" + ' to learn more about interactive tables.';\n",
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"metadata": {},
"execution_count": 83
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{
"cell_type": "code",
"source": [
"dates = pd.to_datetime(alta_df.DATE)\n",
"dates.head()"
],
"metadata": {
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"id": "9VL8UKECEOlc",
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 1980-01-01\n",
"1 1980-01-02\n",
"2 1980-01-03\n",
"3 1980-01-04\n",
"4 1980-01-05\n",
"Name: DATE, dtype: datetime64[ns]"
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"metadata": {},
"execution_count": 84
}
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{
"cell_type": "code",
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"snow = (alta_df\n",
" .SNOW\n",
" .rename(dates))\n",
"snow.head()"
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{
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"1980-01-01 2.0\n",
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"id": "TxblDOGvEmsq",
"outputId": "ef62f4f0-3621-4132-f1f9-bce82007ac83"
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{
"output_type": "execute_result",
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"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc495b3f350>"
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"metadata": {},
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{
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"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"above_zero = snow[snow>0]"
],
"metadata": {
"id": "NWjsx2usE70m"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"above_zero.plot.hist(bins=20, title='Snowfall Histogram (in)')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "HxZCtrMbFAi9",
"outputId": "adf82702-ecd2-4910-da35-2c5a0da47cf7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc49596e310>"
]
},
"metadata": {},
"execution_count": 88
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow.plot.box()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "mJ3fwJdsFGRF",
"outputId": "8261c407-aacd-46e8-a85d-1bbe3f2c57d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc4954ce2d0>"
]
},
"metadata": {},
"execution_count": 89
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow[lambda s:(s.index.month ==1) & (s>0)].plot.box()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "8CsIHqJjFgd3",
"outputId": "71258e88-5491-4779-9959-069df77583a9"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc4954ba250>"
]
},
"metadata": {},
"execution_count": 90
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"above_zero.plot.hist(bins=20, title='Snowfall Histogram (in)')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "X4KJyw4iFnuD",
"outputId": "4fd44a6e-1a59-48b3-8b7f-c439e7c2f9fd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc49542c290>"
]
},
"metadata": {},
"execution_count": 91
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow.plot.line()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 272
},
"id": "q_lhOC4VI4uS",
"outputId": "4f49fba4-7b6d-418e-b8f2-1068bf2b0ade"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc4953bbe10>"
]
},
"metadata": {},
"execution_count": 92
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow.iloc[-300:].plot.line()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "XTlzQq8zKeDL",
"outputId": "f8c34b96-efc0-4131-fe42-cb122bb92afa"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc495106710>"
]
},
"metadata": {},
"execution_count": 93
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow.resample('M').mean().plot.line()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "F7u5sk2nKlwO",
"outputId": "a73629e8-8ff7-491c-fa63-598982299d6d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc4951202d0>"
]
},
"metadata": {},
"execution_count": 94
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"snow.resample('Q').quantile([.5, .9, .99]).unstack().iloc[-100:].plot.line()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "qgDB4qGlKtNV",
"outputId": "adc1d711-fe8a-40d8-e604-176f8ad6e26f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc494f57790>"
]
},
"metadata": {},
"execution_count": 95
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"season2017 = snow.loc['2016-10':'2017-05']\n",
"season2017.resample('M').sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XXnuh_HsLJNx",
"outputId": "a45ac2d1-e369-449b-96e2-cd0947b7daaa"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2016-10-31 10.8\n",
"2016-11-30 49.0\n",
"2016-12-31 78.8\n",
"2017-01-31 127.7\n",
"2017-02-28 105.5\n",
"2017-03-31 46.5\n",
"2017-04-30 73.9\n",
"2017-05-31 9.2\n",
"Freq: M, Name: SNOW, dtype: float64"
]
},
"metadata": {},
"execution_count": 96
}
]
},
{
"cell_type": "code",
"source": [
"(season2017\n",
" .resample('M')\n",
" .sum()\n",
" .div(season2017.sum())\n",
" .mul(100)\n",
" .rename(lambda idx: idx.month_name())\n",
" .plot.bar(title='2017 Monthaly Percent of Snowfall')\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 340
},
"id": "ddm5j0DkMwDI",
"outputId": "bd7f9cda-dc37-4d86-dcd8-190f56cb5cb4"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc494eb8cd0>"
]
},
"metadata": {},
"execution_count": 97
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"(season2017\n",
" .resample('M')\n",
" .sum()\n",
" .div(season2017.sum())\n",
" .mul(100)\n",
" .rename(lambda idx: idx.month_name())\n",
" .plot.barh(title='2017 Monthaly Percent of Snowfall')\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "OAzph9JAMyhU",
"outputId": "2b6a3876-6586-477c-f46f-a0cb218b019f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc495357410>"
]
},
"metadata": {},
"execution_count": 98
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"make.value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ySdqkb4GNvJW",
"outputId": "f19f10f5-e950-4453-a4bf-de7a39d16bd5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Chevrolet 4003\n",
"Ford 3371\n",
"Dodge 2583\n",
"GMC 2494\n",
"Toyota 2071\n",
" ... \n",
"Volga Associated Automobile 1\n",
"Panos 1\n",
"Mahindra 1\n",
"Excalibur Autos 1\n",
"London Coach Co Inc 1\n",
"Name: make, Length: 136, dtype: int64"
]
},
"metadata": {},
"execution_count": 99
}
]
},
{
"cell_type": "code",
"source": [
"top10 = make.value_counts().index[:10]\n",
"(make\n",
" .where(make.isin(top10), 'Other')\n",
" .value_counts()\n",
" .plot.barh()\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "t7LMW3eFN_xQ",
"outputId": "5741d505-f2ae-457a-ab05-00a03e15e135"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc494d946d0>"
]
},
"metadata": {},
"execution_count": 100
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"make.shape, make.nunique()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UjOKlFw8OJeE",
"outputId": "8dbe80dc-171a-421d-cfad-3416a9545e52"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((41144,), 136)"
]
},
"metadata": {},
"execution_count": 101
}
]
},
{
"cell_type": "code",
"source": [
"cat_make = make.astype('category')"
],
"metadata": {
"id": "gKa-v9k4SZna"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"make.memory_usage(deep=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XGKaNBWaSzqa",
"outputId": "204b1b2a-1cb5-42b5-9f5c-dc8ca2e79833"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2738823"
]
},
"metadata": {},
"execution_count": 103
}
]
},
{
"cell_type": "code",
"source": [
"cat_make.memory_usage(deep=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G6d7cV7pS1fv",
"outputId": "554a3d13-280f-426b-f40f-ea5cd991c467"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"96204"
]
},
"metadata": {},
"execution_count": 104
}
]
},
{
"cell_type": "code",
"source": [
"make.memory_usage(deep=True) / cat_make.memory_usage(deep=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UAA7V2PR9YOF",
"outputId": "b4d4ff42-d07f-4695-b406-18d0c7492d6e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"28.46890981663964"
]
},
"metadata": {},
"execution_count": 105
}
]
},
{
"cell_type": "code",
"source": [
"%%timeit \n",
"cat_make.str.upper()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sNDidP4OS3bV",
"outputId": "67292893-1034-463a-e94a-d3ff10385da9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1000 loops, best of 5: 1.16 ms per loop\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%timeit\n",
"make.str.upper()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3-tHPoCjS6ab",
"outputId": "ad84ba71-83d0-455b-b18e-802664b04419"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"10 loops, best of 5: 28.9 ms per loop\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame({\n",
" 'growth': [.5, .7, 1.2],\n",
" 'Name': ['Paul', 'George', 'Ringo']\n",
"})"
],
"metadata": {
"id": "cNFBVh8ATAMo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 144
},
"id": "yXkSfcOs-eRm",
"outputId": "c20cf30c-8ae7-4d83-d7de-6f51e4346963"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" growth Name\n",
"0 0.5 Paul\n",
"1 0.7 George\n",
"2 1.2 Ringo"
],
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{
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],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g_uflyOd-eos",
"outputId": "1567fe45-52bf-4332-e35c-5cbc03810ac3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"growth 1.2\n",
"Name Ringo\n",
"Name: 2, dtype: object"
]
},
"metadata": {},
"execution_count": 110
}
]
},
{
"cell_type": "code",
"source": [
"type(df.iloc[2])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "muiBGGP7riqF",
"outputId": "dda782ba-03c1-4a65-e253-600216aa8c5b"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"metadata": {},
"execution_count": 111
}
]
},
{
"cell_type": "code",
"source": [
"type(df['Name'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "uiXJc_7Q_Syy",
"outputId": "41562bc3-114a-4cfa-c7f9-ebc60dbadea4"
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-978ff8b8a9ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "CuU8bGLmrfVj"
},
"execution_count": null,
"outputs": []
}
]
}
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