diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index 2c0be1f44..a9e490b43 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -1366,10 +1366,84 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "841c60ea", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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First NameLast NamePlace of birthDate of Birth T.A.
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AragornAragorn IIElessarEriador2931
SamSamwiseGamgeeShire2980
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" + ], + "text/plain": [ + " First Name Last Name Place of birth Date of Birth T.A.\n", + "Frodo Frodo Baggins Shire 2968\n", + "Bilbo Bilbo Baggins Shire 2890\n", + "Aragorn Aragorn II Elessar Eriador 2931\n", + "Sam Samwise Gamgee Shire 2980" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n", "display(data_pandas)" @@ -1414,10 +1560,24 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "a1e96976", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "First Name Aragorn II\n", + "Last Name Elessar\n", + "Place of birth Eriador\n", + "Date of Birth T.A. 2931\n", + "Name: Aragorn, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(data_pandas.loc['Aragorn'])" ] @@ -1432,10 +1592,90 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "cd7e4a5f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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First NameLast NamePlace of birthDate of Birth T.A.
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BilboBilboBagginsShire2890
AragornAragorn IIElessarEriador2931
SamSamwiseGamgeeShire2980
PippinPeregrinTookShire2990
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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "0 2.478941 0.067160 1.433694 0.021727 0.421696\n", + "1 0.475437 0.018948 1.052080 0.044243 0.005919\n", + "2 0.079934 0.123648 0.290803 1.480317 0.116132\n", + "3 0.005025 0.377988 1.153619 0.001467 2.103247\n", + "4 3.219448 2.125991 0.043075 0.195895 0.021733\n", + "5 1.237395 0.419221 1.976527 0.005417 0.076321\n", + "6 0.158165 2.330948 0.506970 1.479361 0.175147\n", + "7 0.078763 1.223446 2.710247 0.915014 2.447265\n", + "8 0.136263 0.565051 0.002668 0.045413 0.936654\n", + "9 2.426726 1.138142 0.161477 1.473172 1.296808" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -1471,7 +1990,9 @@ "cols = 5\n", "a = np.random.randn(rows,cols)\n", "df = pd.DataFrame(a)\n", + "v = df.mean()\n", "display(df)\n", + "df = df - v\n", "print(df.mean())\n", "print(df.std())\n", "display(df**2)" @@ -1626,13 +2147,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 25, "id": "e8326ead", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1820,7 +2341,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "id": "0e0fd7b4", "metadata": {}, "outputs": [ @@ -1840,7 +2361,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1858,7 +2379,7 @@ "from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error\n", "\n", "x = np.random.rand(100,1)\n", - "y = 2.0+ 5*x#+0.01*np.random.randn(100,1)\n", + "y = 2.0+ 5*x#+np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "ypredict = linreg.predict(x)\n",