{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Few Gotchas with pandas \n", "\n", "Doing Data Analysis with pandas is pretty straightforward until it is NOT! In this blog post, I present a few gotchas with pandas that you don't need to repeat.\n", "\n", "To run the examples download this Jupyter notebook.\n", "\n", "To Step Up Your Pandas Game, read:\n", "- [5 lesser-known pandas tricks](https://towardsdatascience.com/5-lesser-known-pandas-tricks-e8ab1dd21431),\n", "- [Exploratory Data Analysis with pandas](https://towardsdatascience.com/exploratory-data-analysis-with-pandas-508a5e8a5964),\n", "- [How NOT to write pandas code](https://towardsdatascience.com/how-not-to-write-pandas-code-ef88599c6e8f)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import glob\n", "from platform import python_version\n", "\n", "import pandas as pd\n", "import matplotlib as mpl" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "mpl.pyplot.rcParams['figure.facecolor'] = 'w'" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "python version==3.7.3\n", "pandas==0.25.3\n", "matplotlib==3.1.2\n" ] } ], "source": [ "print(\"python version==%s\" % python_version())\n", "print(\"pandas==%s\" % pd.__version__)\n", "print(\"matplotlib==%s\" % mpl.__version__)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['data/20191210.csv.gz', 'data/20191209.csv.gz', 'data/20191211.csv.gz']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filepaths = glob.glob('data/*.gz')\n", "filepaths" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['data/20191209.csv.gz', 'data/20191210.csv.gz', 'data/20191211.csv.gz']" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filepaths = sorted(filepaths)\n", "filepaths" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['100', '1000', '200', '20000', '9']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(['100', '200', '1000', '20000', '9'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[9, 100, 200, 1000, 20000]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted([100, 200, 1000, 20000, 9])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1578327, 10)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_list = []\n", "for filepath in filepaths:\n", " df_ = pd.read_csv(filepath)\n", " df_list.append(df_)\n", "\n", "df = pd.concat(df_list)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timestampsymbolsidesizepricetickDirectiontrdMatchIDgrossValuehomeNotionalforeignNotional
02019-12-09D00:00:18.265072000ADAZ19Buy53190.000005ZeroPlusTick7317fb7d-1bec-af18-e030-d8d6d9c684ac27499235319.00.027499
12019-12-09D00:00:42.503391000ADAZ19Sell100000.000005MinusTick43a3c08b-2c86-20a4-9722-f59f0a3e12c8516000010000.00.051600
22019-12-09D00:01:50.513620000ADAZ19Buy200000.000005PlusTick643f6fea-a72e-f3ac-994f-e923a0c2212c1034000020000.00.103400
32019-12-09D00:02:35.189775000ADAZ19Buy185430.000005ZeroPlusTick153fe5ef-b974-606e-2321-b3ccff0caee3958673118543.00.095867
42019-12-09D00:03:49.934950000ADAZ19Buy11380.000005ZeroPlusTickdfc5efdd-76fa-9ef7-38bf-ffec4a5645305883461138.00.005883
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" ], "text/plain": [ " timestamp symbol side size price tickDirection \\\n", "0 2019-12-09D00:00:18.265072000 ADAZ19 Buy 5319 0.000005 ZeroPlusTick \n", "1 2019-12-09D00:00:42.503391000 ADAZ19 Sell 10000 0.000005 MinusTick \n", "2 2019-12-09D00:01:50.513620000 ADAZ19 Buy 20000 0.000005 PlusTick \n", "3 2019-12-09D00:02:35.189775000 ADAZ19 Buy 18543 0.000005 ZeroPlusTick \n", "4 2019-12-09D00:03:49.934950000 ADAZ19 Buy 1138 0.000005 ZeroPlusTick \n", "\n", " trdMatchID grossValue homeNotional \\\n", "0 7317fb7d-1bec-af18-e030-d8d6d9c684ac 2749923 5319.0 \n", "1 43a3c08b-2c86-20a4-9722-f59f0a3e12c8 5160000 10000.0 \n", "2 643f6fea-a72e-f3ac-994f-e923a0c2212c 10340000 20000.0 \n", "3 153fe5ef-b974-606e-2321-b3ccff0caee3 9586731 18543.0 \n", "4 dfc5efdd-76fa-9ef7-38bf-ffec4a564530 588346 1138.0 \n", "\n", " foreignNotional \n", "0 0.027499 \n", "1 0.051600 \n", "2 0.103400 \n", "3 0.095867 \n", "4 0.005883 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2019-12-09D00:00:18.265072000\n", "1 2019-12-09D00:00:42.503391000\n", "2 2019-12-09D00:01:50.513620000\n", "3 2019-12-09D00:02:35.189775000\n", "4 2019-12-09D00:03:49.934950000\n", " ... \n", "389905 2019-12-11D23:35:26.637330000\n", "389906 2019-12-11D23:35:26.637330000\n", "389907 2019-12-11D23:37:25.166632000\n", "389908 2019-12-11D23:40:09.701271000\n", "389909 2019-12-11D23:55:46.793512000\n", "Name: timestamp, Length: 1578327, dtype: object" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.timestamp" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 100\n", "2 1000\n", "1 200\n", "3 20000\n", "4 9\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(['100', '200', '1000', '20000', '9']).sort_values()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2019-12-09 00:00:18.265072\n", "1 2019-12-09 00:00:42.503391\n", "2 2019-12-09 00:01:50.513620\n", "3 2019-12-09 00:02:35.189775\n", "4 2019-12-09 00:03:49.934950\n", " ... \n", "389905 2019-12-11 23:35:26.637330\n", "389906 2019-12-11 23:35:26.637330\n", "389907 2019-12-11 23:37:25.166632\n", "389908 2019-12-11 23:40:09.701271\n", "389909 2019-12-11 23:55:46.793512\n", "Name: timestamp, Length: 1578327, dtype: datetime64[ns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.timestamp = pd.to_datetime(df.timestamp.str.replace(\"D\", \"T\"))\n", "df.timestamp" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1309671, 10)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[df.symbol == 'XBTUSD']\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "df = df.sort_values(\"timestamp\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timestampsymbolsidesizepricetickDirectiontrdMatchIDgrossValuehomeNotionalforeignNotional
02019-12-09 00:00:03.137637XBTUSDBuy5007512.5ZeroPlusTick084e9218-9596-3457-fee1-3012bb3ce1a666555000.066555500.0
12019-12-09 00:00:03.157349XBTUSDBuy147512.5ZeroPlusTickd25b3551-5850-0974-b844-80d591dff4d21863540.00186414.0
22019-12-09 00:00:03.165542XBTUSDSell57512.0MinusTickbad21a77-12eb-aed6-809b-8d3090d8efb2665600.0006665.0
32019-12-09 00:00:03.174043XBTUSDSell3837512.0ZeroMinusTick57da5cfd-a646-d014-d8b3-3c18e7565abc50984960.050985383.0
42019-12-09 00:00:03.178405XBTUSDSell207512.0ZeroMinusTickd95cdd09-7784-21e4-b008-192762fbf1992662400.00266220.0
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" ], "text/plain": [ " timestamp symbol side size price tickDirection \\\n", "0 2019-12-09 00:00:03.137637 XBTUSD Buy 500 7512.5 ZeroPlusTick \n", "1 2019-12-09 00:00:03.157349 XBTUSD Buy 14 7512.5 ZeroPlusTick \n", "2 2019-12-09 00:00:03.165542 XBTUSD Sell 5 7512.0 MinusTick \n", "3 2019-12-09 00:00:03.174043 XBTUSD Sell 383 7512.0 ZeroMinusTick \n", "4 2019-12-09 00:00:03.178405 XBTUSD Sell 20 7512.0 ZeroMinusTick \n", "\n", " trdMatchID grossValue homeNotional \\\n", "0 084e9218-9596-3457-fee1-3012bb3ce1a6 6655500 0.066555 \n", "1 d25b3551-5850-0974-b844-80d591dff4d2 186354 0.001864 \n", "2 bad21a77-12eb-aed6-809b-8d3090d8efb2 66560 0.000666 \n", "3 57da5cfd-a646-d014-d8b3-3c18e7565abc 5098496 0.050985 \n", "4 d95cdd09-7784-21e4-b008-192762fbf199 266240 0.002662 \n", "\n", " foreignNotional \n", "0 500.0 \n", "1 14.0 \n", "2 5.0 \n", "3 383.0 \n", "4 20.0 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.reset_index(inplace=True, drop=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sizepricegrossValuehomeNotionalforeignNotional
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mean3.857544e+037.336222e+035.254340e+075.254340e-013.857544e+03
std1.679764e+041.224793e+022.284365e+082.284365e+001.679764e+04
min1.000000e+007.122000e+031.300600e+041.300600e-041.000000e+00
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75%1.627000e+037.460500e+032.222291e+072.222291e-011.627000e+03
max2.486453e+067.689000e+033.328615e+103.328615e+022.486453e+06
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" ], "text/plain": [ " size price grossValue homeNotional foreignNotional\n", "count 1.309671e+06 1.309671e+06 1.309671e+06 1.309671e+06 1.309671e+06\n", "mean 3.857544e+03 7.336222e+03 5.254340e+07 5.254340e-01 3.857544e+03\n", "std 1.679764e+04 1.224793e+02 2.284365e+08 2.284365e+00 1.679764e+04\n", "min 1.000000e+00 7.122000e+03 1.300600e+04 1.300600e-04 1.000000e+00\n", "25% 3.200000e+01 7.223000e+03 4.367360e+05 4.367360e-03 3.200000e+01\n", "50% 2.000000e+02 7.326500e+03 2.732200e+06 2.732200e-02 2.000000e+02\n", "75% 1.627000e+03 7.460500e+03 2.222291e+07 2.222291e-01 1.627000e+03\n", "max 2.486453e+06 7.689000e+03 3.328615e+10 3.328615e+02 2.486453e+06" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['timestamp', 'symbol', 'side', 'size', 'price', 'tickDirection',\n", " 'trdMatchID', 'grossValue', 'homeNotional', 'foreignNotional'],\n", " dtype='object')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timestampsymbolsidesizepricetickDirectiontrdMatchIDgrossValuehomeNotionalforeignNotional
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freq14211309671679823NaNNaN6038211NaNNaNNaN
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last2019-12-11 23:59:59.861272NaNNaNNaNNaNNaNNaNNaNNaNNaN
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minNaNNaNNaN1.000000e+007.122000e+03NaNNaN1.300600e+041.300600e-041.000000e+00
25%NaNNaNNaN3.200000e+017.223000e+03NaNNaN4.367360e+054.367360e-033.200000e+01
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75%NaNNaNNaN1.627000e+037.460500e+03NaNNaN2.222291e+072.222291e-011.627000e+03
maxNaNNaNNaN2.486453e+067.689000e+03NaNNaN3.328615e+103.328615e+022.486453e+06
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" ], "text/plain": [ " timestamp symbol side size \\\n", "count 1309671 1309671 1309671 1.309671e+06 \n", "unique 407403 1 2 NaN \n", "top 2019-12-09 07:04:18.260564 XBTUSD Sell NaN \n", "freq 1421 1309671 679823 NaN \n", "first 2019-12-09 00:00:03.137637 NaN NaN NaN \n", "last 2019-12-11 23:59:59.861272 NaN NaN NaN \n", "mean NaN NaN NaN 3.857544e+03 \n", "std NaN NaN NaN 1.679764e+04 \n", "min NaN NaN NaN 1.000000e+00 \n", "25% NaN NaN NaN 3.200000e+01 \n", "50% NaN NaN NaN 2.000000e+02 \n", "75% NaN NaN NaN 1.627000e+03 \n", "max NaN NaN NaN 2.486453e+06 \n", "\n", " price tickDirection trdMatchID \\\n", "count 1.309671e+06 1309671 1309671 \n", "unique NaN 4 1309671 \n", "top NaN ZeroMinusTick 34370c9e-38b5-fdcd-a900-ec25969cffc1 \n", "freq NaN 603821 1 \n", "first NaN NaN NaN \n", "last NaN NaN NaN \n", "mean 7.336222e+03 NaN NaN \n", "std 1.224793e+02 NaN NaN \n", "min 7.122000e+03 NaN NaN \n", "25% 7.223000e+03 NaN NaN \n", "50% 7.326500e+03 NaN NaN \n", "75% 7.460500e+03 NaN NaN \n", "max 7.689000e+03 NaN NaN \n", "\n", " grossValue homeNotional foreignNotional \n", "count 1.309671e+06 1.309671e+06 1.309671e+06 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "first NaN NaN NaN \n", "last NaN NaN NaN \n", "mean 5.254340e+07 5.254340e-01 3.857544e+03 \n", "std 2.284365e+08 2.284365e+00 1.679764e+04 \n", "min 1.300600e+04 1.300600e-04 1.000000e+00 \n", "25% 4.367360e+05 4.367360e-03 3.200000e+01 \n", "50% 2.732200e+06 2.732200e-02 2.000000e+02 \n", "75% 2.222291e+07 2.222291e-01 1.627000e+03 \n", "max 3.328615e+10 3.328615e+02 2.486453e+06 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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i9OKLL2rAgAH67W9/q7feeksvvvhiv79CGnAbKxDAJVu3btXQoUP1wgsvaNiwYXrhhRc0dOhQbd261e3WgH4hQACXPPLII3r77bc1bdo0RUVFadq0aXr77bf5ECEiBgECAHCEayCAS3iUCSIdKxDAJRs3btSjjz6q1atXa8iQIVq9erUeffRRHmWCiMEKBHDJ8ePH1d7erpKSkvAKJC8vT1988YXbrQH9QoAALhk0aJC8Xq/uu+8+dXR0aPDgwQoEAmpqanK7NaBfOIUFuKSjo0Mff/yx8vLydPbsWeXl5enjjz9WR0eH260B/UKAAC7xeDxasGCBPvzwQ8XExOjDDz/UggUL5PF43G4N6BcCBHCJmeno0aNqb2+XJLW3t+vo0aMyM5c7A/qHayCAS6Kjo3XhwgWdO3dO3d3damhoUFRUlKKj+bNEZGAFArhk8ODB6ujo0JNPPqmzZ8/qySefDF9MByIBAQK4pL29XVlZWSopKdHo0aNVUlKirKys8Ckt4MeOAAFclJSUJL/fr6ioKPn9fiUlJbndEtBvBAjgkpiYGG3evFl5eXm6cOGC8vLytHnzZh7njohBgAAuGTp0qIYPH64tW7ZoxIgR2rJli4YPH66hQ4e63RrQLwQI4JLGxkZt2bJFw4YNkyQNGzZMW7ZsUWNjo8udAf3TZ4Dk5eVpwoQJSk5ODtdaW1uVkZGhhIQEZWRkqK2tTdK397WvWbNGfr9fqampOnLkSHifYDCohIQEJSQkKBgMhutVVVVKSUmR3+/XmjVrwvfAO5kDiCSJiYl6/fXXdfz4cXV3d+v48eN6/fXXlZiY6HZrQL/0GSBPPPGEKioqetQKCws1f/581dTUaP78+SosLJQkvf/++6qpqVFNTY2Kioq0cuVKSd+Gwfr16/Xpp5+qsrJS69evDwfCypUr9c4774T3+26u650DiDRRUVEKhUJatGiRmpubtWjRIoVCIUVFcWIAkaHP/1LvvffeKy7qlZWVKTc3V5KUm5ur3bt3h+s5OTnyeDyaM2eOzp49q6amJu3du1cZGRmKiYnRmDFjlJGRoYqKCjU1Nen8+fOaM2eOPB6PcnJyeox1PXMAkaa6uloLFixQbW2tJk6cqNraWi1YsEDV1dVutwb0i6P/1Tl9+rRiY2MlSZMmTdLp06clSQ0NDZo8eXJ4u7i4ODU0NPRaj4uLu6LuZI6rKSoqUiAQUCAQUHNzs5O3Ctw0ZqalS5f2qC1dupRHmSBi3PAzEzwez01/+JvTOfLz85Wfny9JCgQCP3RbwA179tlnVVZWFv4+kMWLF7vdEtBvjlYgEydODJ82ampq0oQJEyRJXq9XdXV14e3q6+vl9Xp7rdfX119RdzIHEGmGDRumCxcuaMmSJRo8eLCWLFmiCxcuhO/KAn7sHAVIVlZW+E6qYDAY/r+mrKwsbdu2TWamQ4cOadSoUYqNjVVmZqb27duntrY2tbW1ad++fcrMzFRsbKxGjhypQ4cOycy0bdu2HmNdzxxApPn66681cOBAtbW1yczU1tamgQMH6uuvv3a7NaB/rA/Lli2zSZMmWXR0tHm9Xnv33XftzJkzNm/ePPP7/TZ//nxraWkxM7Pu7m57+umnzefzWXJysh0+fDg8TnFxscXHx1t8fLyVlJSE64cPH7akpCTz+Xy2atUq6+7uNjNzNEdvZs2a1a/tgFslOjrahg8fblOmTDGPx2NTpkyx4cOHW3R0tNutAWG9HTs9ZrfHFbtAIKBQKOR2G0DYd9f1Jk6cqC+//FITJkwI3yxym/xZIgL0duzkhnPARYMGDVJra6vMTK2trRo0aJDbLQH9RoAALurs7FRhYaHa29tVWFiozs5Ot1sC+o1TWIBLPB6PoqOje4TGd7/fJn+WiACcwgJ+pDo7O8OPLomKimIFgohCgAAuGz9+fI+fQKQgQAAXDRkyRK2trZK+fejokCFDXO4I6D8CBHDRgAED5PV65fF45PV6NWDAALdbAvrthp+FBcCZAQMGqL29Xe3t7ZKkU6dOhetAJGAFArikq6vruurAjw0BAgBwhAABXDZmzJgeP4FIQYAALvvqq696/AQiBQECuCwtLU2NjY1KS0tzuxXgunAXFuCyf//737rzzjvdbgO4bqxAAACOECAAAEcIEMBl332x1Hc/gUhBgAAu++7R7TzCHZGGAAEAOEKAAAAcIUAAAI4QIAAARwgQAIAjBAgAwBECBADgCAECAHCEAAEAOEKAAAAcIUAAAI4QIAAARwgQAIAjNxQgU6ZMUUpKimbOnKlAICBJam1tVUZGhhISEpSRkaG2tjZJ3z5pdM2aNfL7/UpNTdWRI0fC4wSDQSUkJCghIUHBYDBcr6qqUkpKivx+v9asWRN+Wum15gAA3Do3vAI5cOCAjh49qlAoJEkqLCzU/PnzVVNTo/nz56uwsFCS9P7776umpkY1NTUqKirSypUrJX0bBuvXr9enn36qyspKrV+/PhwIK1eu1DvvvBPer6Kiotc5AAC3zg9+CqusrEy5ubmSpNzcXO3evTtcz8nJkcfj0Zw5c3T27Fk1NTVp7969ysjIUExMjMaMGaOMjAxVVFSoqalJ58+f15w5c+TxeJSTk9NjrKvNAQC4dW4oQDwej375y19q1qxZKioqkiSdPn1asbGxkqRJkybp9OnTkqSGhgZNnjw5vG9cXJwaGhp6rcfFxV1R722O7ysqKlIgEFAgEFBzc/ONvFUAwPdE38jOBw8elNfr1ZdffqmMjAz99Kc/7fG6x+O56V/T2dsc+fn5ys/Pl6TwNRoAwA/jhlYgXq9XkjRhwgQ98MADqqys1MSJE9XU1CRJampq0oQJE8Lb1tXVhfetr6+X1+vttV5fX39FXdI15wAA3DqOA6S9vV0XLlwI/3vfvn1KTk5WVlZW+E6qYDCoxYsXS5KysrK0bds2mZkOHTqkUaNGKTY2VpmZmdq3b5/a2trU1tamffv2KTMzU7GxsRo5cqQOHTokM9O2bdt6jHW1OQAAt47jU1inT5/WAw88IEnq7OzUo48+qoULFyotLU0PP/ywiouL9ZOf/ER///vfJUn333+/ysvL5ff7NXToUP3lL3+RJMXExOiPf/yj0tLSJEl/+tOfFBMTI0l688039cQTT+ibb77Rfffdp/vuu0+S9MILL1x1DgDAreOx7z5c8T8uEAiEbzUGfgx6uz54m/xZIgL0duzkk+gAAEcIEACAIwQIAMARAgQA4AgBAgBwhAABADhCgAAAHCFAAACOECAAAEcIEACAIwQIAMARAgQA4AgBAgBwhAABADhCgAAAHCFAAACOECAAAEcIEACAIwQIAMARAgQA4AgBAgBwhAABADhCgAAAHCFAAACOECAAAEcIEACAIwQIAMARAgQA4AgBAgBwhAABADhCgAAAHInoAKmoqND06dPl9/tVWFjodjsAcFuJ2ADp6urSqlWr9P777+v48eMqLS3V8ePH3W4LAG4bERsglZWV8vv98vl8GjRokJYtW6aysjK32wKA20a02w041dDQoMmTJ4d/j4uL06efftpjm6KiIhUVFUmSmpubb2l/+N+QEky5aWMn/zX5ls/739z/3pRxcXuK2ADpj/z8fOXn50uSAoGAy90gEt3MA67H47nma2Z20+YFfigRewrL6/Wqrq4u/Ht9fb28Xq+LHQHA7SViAyQtLU01NTU6efKkLl26pB07digrK8vttoB+u9Yqg9UHIkXEnsKKjo7W1q1blZmZqa6uLuXl5SkpKcnttoDrQlggkkVsgEjS/fffr/vvv9/tNgDgthSxp7AAAO4iQAAAjhAgAABHCBAAgCMeu01uAxk3bpymTJnidhvAVTU3N2v8+PFutwFc4dSpUzpz5sxVX7ttAgT4MQsEAgqFQm63AVwXTmEBABwhQAAAjhAgwI/Adw/9BCIJ10AAAI6wAgEAOEKAAAAcIUAAFzz55JM6fvy4220AN4RrIAAAR1iBADdZe3u7fvWrX2nGjBlKTk7W3/72N/3iF79QKBTSP/7xD82cOVMzZ87U9OnTNXXqVElSVVWVfv7zn2vWrFnKzMxUU1OTy+8CuBIBAtxkFRUVuvPOO/Wf//xH1dXVWrhwYfi1rKwsHT16VEePHtWMGTO0du1aXb58WatXr9auXbtUVVWlvLw8FRQUuPgOgKuL6C+UAiJBSkqKnnvuOf3+97/Xr3/9a/3sZz+7YpvNmzfrjjvu0KpVq1RdXa3q6mplZGRIkrq6uhQbG3ur2wb6RIAAN9m0adN05MgRlZeX6w9/+IPmz5/f4/V//vOf2rlzpz788ENJ337NbVJSkj755BM32gX6jVNYwE3W2NiooUOH6vHHH9fzzz+vI0eOhF/74osvtGrVKu3cuVN33HGHJGn69Olqbm4OB8jly5d17NgxV3oHesMKBLjJ/vvf/+r5559XVFSUBg4cqD//+c9au3atJOmvf/2rWlpa9Jvf/EaSdOedd6q8vFy7du3SmjVrdO7cOXV2dup3v/udkpKS3HwbwBW4jRcA4AinsAAAjhAgAABHCBAAgCMECADAEQIEAOAIAQIAcIQAAQA48n9/MnTixaYubQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"size\"].plot(kind=\"box\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df['size_clip'] = df['size'].clip(df['size'].quantile(0.01), df['size'].quantile(0.99))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"size\"].plot(kind=\"box\", ylim=(0, 5000))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 (7462.2, 7575.6]\n", "1 (7462.2, 7575.6]\n", "2 (7462.2, 7575.6]\n", "3 (7462.2, 7575.6]\n", "4 (7462.2, 7575.6]\n", " ... \n", "1309666 (7121.433, 7235.4]\n", "1309667 (7121.433, 7235.4]\n", "1309668 (7121.433, 7235.4]\n", "1309669 (7121.433, 7235.4]\n", "1309670 (7121.433, 7235.4]\n", "Name: price_discrete_bins, Length: 1309671, dtype: category\n", "Categories (5, interval[float64]): [(7121.433, 7235.4] < (7235.4, 7348.8] < (7348.8, 7462.2] < (7462.2, 7575.6] < (7575.6, 7689.0]]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['price_discrete_bins'] = pd.cut(df.price, 5)\n", "df['price_discrete_bins']" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7121.433, 7235.4] 419820\n", "(7235.4, 7348.8] 367043\n", "(7462.2, 7575.6] 301654\n", "(7348.8, 7462.2] 200231\n", "(7575.6, 7689.0] 20923\n", "Name: price_discrete_bins, dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['price_discrete_bins'].value_counts()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 high\n", "1 high\n", "2 high\n", "3 high\n", "4 high\n", " ... \n", "1309666 very low\n", "1309667 very low\n", "1309668 very low\n", "1309669 very low\n", "1309670 very low\n", "Name: price_discrete_labels, Length: 1309671, dtype: category\n", "Categories (5, object): [very low < low < mid < high < very high]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['price_discrete_labels'] = pd.cut(df.price, 5, labels=['very low', 'low', 'mid', 'high', 'very high'])\n", "df['price_discrete_labels']" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "very low 0.320554\n", "low 0.280256\n", "high 0.230328\n", "mid 0.152886\n", "very high 0.015976\n", "Name: price_discrete_labels, dtype: float64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.price_discrete_labels.value_counts(normalize=True)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 (7477.0, 7689.0]\n", "1 (7477.0, 7689.0]\n", "2 (7477.0, 7689.0]\n", "3 (7477.0, 7689.0]\n", "4 (7477.0, 7689.0]\n", " ... \n", "1309666 (7121.999, 7212.5]\n", "1309667 (7121.999, 7212.5]\n", "1309668 (7121.999, 7212.5]\n", "1309669 (7121.999, 7212.5]\n", "1309670 (7121.999, 7212.5]\n", "Name: price_discrete_equal_bins, Length: 1309671, dtype: category\n", "Categories (5, interval[float64]): [(7121.999, 7212.5] < (7212.5, 7292.5] < (7292.5, 7348.5] < (7348.5, 7477.0] < (7477.0, 7689.0]]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['price_discrete_equal_bins'] = pd.qcut(df.price, 5)\n", "df['price_discrete_equal_bins']" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7121.999, 7212.5] 264009\n", "(7292.5, 7348.5] 262610\n", "(7348.5, 7477.0] 262396\n", "(7477.0, 7689.0] 260412\n", "(7212.5, 7292.5] 260244\n", "Name: price_discrete_equal_bins, dtype: int64" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.price_discrete_equal_bins.value_counts()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "None == None" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.np.nan == pd.np.nan" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/r.orac/envs/r/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "nan" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.np.log(-1)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/r.orac/envs/r/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in arccos\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "nan" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.np.arccos(2)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/r.orac/envs/r/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/Users/r.orac/envs/r/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in arccos\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.np.log(-1) == pd.np.arccos(2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", 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