From 60c34c107f299c64ea9afc308daa3053a63516f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= Date: Thu, 30 Mar 2023 11:14:26 +0200 Subject: [PATCH] =?UTF-8?q?[L04]=20Zliczanie=20wyst=C4=85pie=C5=84=20NaN?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab/04_Reprezentacja_danych.ipynb | 42 ++++++++++++++++++++++++------- 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/lab/04_Reprezentacja_danych.ipynb b/lab/04_Reprezentacja_danych.ipynb index 97cd2ca..b20dee8 100644 --- a/lab/04_Reprezentacja_danych.ipynb +++ b/lab/04_Reprezentacja_danych.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -229,7 +229,7 @@ "Name: Piętro, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -352,7 +352,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_11063/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n", + "/tmp/ipykernel_1089/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n", " print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n" ] } @@ -387,6 +387,30 @@ "* [What’s the best way to handle NaN values?](https://towardsdatascience.com/whats-the-best-way-to-handle-nan-values-62d50f738fc)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tak można policzyć, ile jest wartości NaN w danej kolumnie:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2983\n" + ] + } + ], + "source": [ + "print(np.isnan(alldata[\"Rok budowy\"]).sum())" + ] + }, { "cell_type": "markdown", "metadata": {},