forked from pms/uczenie-maszynowe
[L04] Zliczanie wystąpień NaN
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@ -28,7 +28,7 @@
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"Name: Piętro, dtype: int64"
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]
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"execution_count": 8,
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@ -285,7 +285,7 @@
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"execution_count": 19,
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@ -335,7 +335,7 @@
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"execution_count": 20,
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"execution_count": 7,
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_11063/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
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"/tmp/ipykernel_1089/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
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" print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n"
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]
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}
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@ -387,6 +387,30 @@
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"* [What’s the best way to handle NaN values?](https://towardsdatascience.com/whats-the-best-way-to-handle-nan-values-62d50f738fc)"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Tak można policzyć, ile jest wartości NaN w danej kolumnie:"
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]
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2983\n"
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]
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}
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],
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"source": [
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"print(np.isnan(alldata[\"Rok budowy\"]).sum())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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