From fe3fabeb00292f04c0ff4c759a2a399a396a28d6 Mon Sep 17 00:00:00 2001 From: f82c1c4a1227cdba8ff3317d228324d6 Date: Thu, 28 Mar 2024 18:07:30 +0000 Subject: [PATCH] result3 --- module3/exo3/exercice.ipynb | 1081 ++++++++++++++++++++++++++++++++--- 1 file changed, 1006 insertions(+), 75 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index fa17440..d4a4fd9 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonal\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Décomposition de la série temporelle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mstl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CO2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseasonal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonal\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Décomposition de la série temporelle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mstl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CO2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseasonal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cannot import name 'STL'" ] } @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -258,26 +258,26 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " date size bytes from url \\\n", + " 0 1 2 3 4 \\\n", "0 [1421771203.082701] 1257 bytes from stackoverflow.com \n", "1 [1421771203.408254] 454 bytes from stackoverflow.com \n", "2 [1421771203.739730] 775 bytes from stackoverflow.com \n", "3 [1421771204.056630] 1334 bytes from stackoverflow.com \n", "4 [1421771204.372224] 83 bytes from stackoverflow.com \n", "\n", - " ip icmp ttl time ms \n", - "0 (198.252.206.140): icmp_seq=1 ttl=50 NaN ms \n", - "1 (198.252.206.140): icmp_seq=1 ttl=50 NaN ms \n", - "2 (198.252.206.140): icmp_seq=1 ttl=50 NaN ms \n", - "3 (198.252.206.140): icmp_seq=1 ttl=50 NaN ms \n", - "4 (198.252.206.140): icmp_seq=1 ttl=50 NaN ms \n" + " 5 6 7 8 9 \n", + "0 (198.252.206.140): icmp_seq=1 ttl=50 time=120 ms \n", + "1 (198.252.206.140): icmp_seq=1 ttl=50 time=120 ms \n", + "2 (198.252.206.140): icmp_seq=1 ttl=50 time=126 ms \n", + "3 (198.252.206.140): icmp_seq=1 ttl=50 time=112 ms \n", + "4 (198.252.206.140): icmp_seq=1 ttl=50 time=111 ms \n" ] } ], @@ -288,25 +288,9 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "Can only use .str accessor with string values, which use np.object_ dtype in pandas", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Procesar los datos de ping para liglab2 y stackoverflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mdf_liglab2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_ping_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_liglab2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mdf_stackoverflow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_ping_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_stackoverflow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mprocess_ping_data\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Eliminar filas incompletas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"date\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"size\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"bytes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"url\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ip\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"icmp\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"ttl\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ms\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# Nombrar las columnas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Convertir el tiempo a float\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 3608\u001b[0m if (name in self._internal_names_set or name in self._metadata or\n\u001b[1;32m 3609\u001b[0m name in self._accessors):\n\u001b[0;32m-> 3610\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3611\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3612\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;31m# this ensures that Series.str. is well defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccessor_cls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__set__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/strings.py\u001b[0m in \u001b[0;36m_make_accessor\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0;31m# (instead of test for object dtype), but that isn't practical for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0;31m# performance reasons until we have a str dtype (GH 9343)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m raise AttributeError(\"Can only use .str accessor with string \"\n\u001b[0m\u001b[1;32m 1911\u001b[0m \u001b[0;34m\"values, which use np.object_ dtype in \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1912\u001b[0m \"pandas\")\n", - "\u001b[0;31mAttributeError\u001b[0m: Can only use .str accessor with string values, which use np.object_ dtype in pandas" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -314,7 +298,9 @@ "def process_ping_data(df):\n", " df.dropna(inplace=True) # Eliminar filas incompletas\n", " df.columns = [\"date\", \"size\", \"bytes\", \"from\", \"url\", \"ip\", \"icmp\",\"ttl\",\"time\", \"ms\"] # Nombrar las columnas\n", - " df[\"time\"] = df[\"time\"].str[:-3].astype(float) # Convertir el tiempo a float\n", + " df[\"time\"] = df[\"time\"].str[5:].astype(float) # Convertir el tiempo a float\n", + " df[\"date\"] = df[\"date\"].str[1:18]\n", + " df[\"date\"] = pd.to_datetime(df[\"date\"], unit='s')\n", " return df\n", "\n", "# Procesar los datos de ping para liglab2 y stackoverflow\n", @@ -324,50 +310,995 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 17, "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'timestamp'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: an integer is required", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'timestamp'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: an integer is required", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Gráfico del tiempo de transmisión a lo largo del tiempo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_liglab2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"timestamp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_liglab2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"liglab2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tiempo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tiempo de transmisión (ms)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'timestamp'" - ] - }, { "data": { + "text/html": [ + "
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datesizebytesfromurlipicmpttltimems
02015-01-20 16:26:43.0827011257bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50120.0ms
12015-01-20 16:26:43.408254454bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50120.0ms
22015-01-20 16:26:43.739730775bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50126.0ms
32015-01-20 16:26:44.0566301334bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
42015-01-20 16:26:44.37222483bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
52015-01-20 16:26:44.688367694bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
62015-01-20 16:26:45.0055141577bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
72015-01-20 16:26:45.321112632bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
82015-01-20 16:26:45.637464405bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
92015-01-20 16:26:45.9534721419bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
102015-01-20 16:26:46.269163329bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
112015-01-20 16:26:46.585098868bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
122015-01-20 16:26:46.9019721714bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
132015-01-20 16:26:47.2178631053bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
142015-01-20 16:26:47.533900349bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
152015-01-20 16:26:47.8511481598bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
162015-01-20 16:26:48.1667941412bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
172015-01-20 16:26:48.482159167bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
182015-01-20 16:26:48.79815560bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
192015-01-20 16:26:49.1144801038bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
202015-01-20 16:26:49.430586949bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
212015-01-20 16:26:49.746729279bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
222015-01-20 16:26:50.062322757bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
232015-01-20 16:26:50.3781131355bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
242015-01-20 16:26:50.6940151151bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
252015-01-20 16:26:51.009670237bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
262015-01-20 16:26:51.3248561221bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
272015-01-20 16:26:51.6405441063bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
282015-01-20 16:26:51.956109445bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
292015-01-20 16:26:52.2725041619bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
.................................
68572015-01-20 17:04:11.530711234bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68582015-01-20 17:04:11.847515231bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68592015-01-20 17:04:12.1638371495bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68602015-01-20 17:04:12.4798341313bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68612015-01-20 17:04:12.795239182bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68622015-01-20 17:04:13.1115702000bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68632015-01-20 17:04:13.4271101396bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68642015-01-20 17:04:13.742351515bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
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68672015-01-20 17:04:14.689196806bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68682015-01-20 17:04:15.007766422bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50113.0ms
68692015-01-20 17:04:15.3245711939bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68702015-01-20 17:04:15.639814365bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
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68722015-01-20 17:04:16.2729511738bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50113.0ms
68732015-01-20 17:04:16.5919151148bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50114.0ms
68742015-01-20 17:04:16.915868294bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50119.0ms
68752015-01-20 17:04:17.2316171534bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68762015-01-20 17:04:17.5464041103bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68772015-01-20 17:04:17.8614991121bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68782015-01-20 17:04:18.1770301219bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68792015-01-20 17:04:18.4934441880bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68802015-01-20 17:04:18.808864986bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68812015-01-20 17:04:19.124524357bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68822015-01-20 17:04:19.4405171696bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68832015-01-20 17:04:19.756250561bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68842015-01-20 17:04:20.071820773bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68852015-01-20 17:04:20.3873851009bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68862015-01-20 17:04:20.7043821948bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
\n", + "

6824 rows × 10 columns

\n", + "
" + ], "text/plain": [ - "
" + " date size bytes from url \\\n", + "0 2015-01-20 16:26:43.082701 1257 bytes from stackoverflow.com \n", + "1 2015-01-20 16:26:43.408254 454 bytes from stackoverflow.com \n", + "2 2015-01-20 16:26:43.739730 775 bytes from stackoverflow.com \n", + "3 2015-01-20 16:26:44.056630 1334 bytes from stackoverflow.com \n", + "4 2015-01-20 16:26:44.372224 83 bytes from stackoverflow.com \n", + "5 2015-01-20 16:26:44.688367 694 bytes from stackoverflow.com \n", + "6 2015-01-20 16:26:45.005514 1577 bytes from stackoverflow.com \n", + "7 2015-01-20 16:26:45.321112 632 bytes from stackoverflow.com \n", + "8 2015-01-20 16:26:45.637464 405 bytes from stackoverflow.com \n", + "9 2015-01-20 16:26:45.953472 1419 bytes from stackoverflow.com \n", + "10 2015-01-20 16:26:46.269163 329 bytes from stackoverflow.com \n", + "11 2015-01-20 16:26:46.585098 868 bytes from stackoverflow.com \n", + "12 2015-01-20 16:26:46.901972 1714 bytes from stackoverflow.com \n", + "13 2015-01-20 16:26:47.217863 1053 bytes from stackoverflow.com \n", + "14 2015-01-20 16:26:47.533900 349 bytes from stackoverflow.com \n", + "15 2015-01-20 16:26:47.851148 1598 bytes from stackoverflow.com \n", + "16 2015-01-20 16:26:48.166794 1412 bytes from stackoverflow.com \n", + "17 2015-01-20 16:26:48.482159 167 bytes from stackoverflow.com \n", + "18 2015-01-20 16:26:48.798155 60 bytes from stackoverflow.com \n", + "19 2015-01-20 16:26:49.114480 1038 bytes from stackoverflow.com \n", + "20 2015-01-20 16:26:49.430586 949 bytes from stackoverflow.com \n", + "21 2015-01-20 16:26:49.746729 279 bytes from stackoverflow.com \n", + "22 2015-01-20 16:26:50.062322 757 bytes from stackoverflow.com \n", + "23 2015-01-20 16:26:50.378113 1355 bytes from stackoverflow.com \n", + "24 2015-01-20 16:26:50.694015 1151 bytes from stackoverflow.com \n", + "25 2015-01-20 16:26:51.009670 237 bytes from stackoverflow.com \n", + "26 2015-01-20 16:26:51.324856 1221 bytes from stackoverflow.com \n", + "27 2015-01-20 16:26:51.640544 1063 bytes from stackoverflow.com \n", + "28 2015-01-20 16:26:51.956109 445 bytes from stackoverflow.com \n", + "29 2015-01-20 16:26:52.272504 1619 bytes from stackoverflow.com \n", + "... ... ... ... ... ... \n", + "6857 2015-01-20 17:04:11.530711 234 bytes from stackoverflow.com \n", + "6858 2015-01-20 17:04:11.847515 231 bytes from stackoverflow.com \n", + "6859 2015-01-20 17:04:12.163837 1495 bytes from stackoverflow.com \n", + "6860 2015-01-20 17:04:12.479834 1313 bytes from stackoverflow.com \n", + "6861 2015-01-20 17:04:12.795239 182 bytes from stackoverflow.com \n", + "6862 2015-01-20 17:04:13.111570 2000 bytes from stackoverflow.com \n", + "6863 2015-01-20 17:04:13.427110 1396 bytes from stackoverflow.com \n", + "6864 2015-01-20 17:04:13.742351 515 bytes from stackoverflow.com \n", + "6865 2015-01-20 17:04:14.058100 590 bytes from stackoverflow.com \n", + "6866 2015-01-20 17:04:14.373566 229 bytes from stackoverflow.com \n", + "6867 2015-01-20 17:04:14.689196 806 bytes from stackoverflow.com \n", + "6868 2015-01-20 17:04:15.007766 422 bytes from stackoverflow.com \n", + "6869 2015-01-20 17:04:15.324571 1939 bytes from stackoverflow.com \n", + "6870 2015-01-20 17:04:15.639814 365 bytes from stackoverflow.com \n", + "6871 2015-01-20 17:04:15.954957 502 bytes from stackoverflow.com \n", + "6872 2015-01-20 17:04:16.272951 1738 bytes from stackoverflow.com \n", + "6873 2015-01-20 17:04:16.591915 1148 bytes from stackoverflow.com \n", + "6874 2015-01-20 17:04:16.915868 294 bytes from stackoverflow.com \n", + "6875 2015-01-20 17:04:17.231617 1534 bytes from stackoverflow.com \n", + "6876 2015-01-20 17:04:17.546404 1103 bytes from stackoverflow.com \n", + "6877 2015-01-20 17:04:17.861499 1121 bytes from stackoverflow.com \n", + "6878 2015-01-20 17:04:18.177030 1219 bytes from stackoverflow.com \n", + "6879 2015-01-20 17:04:18.493444 1880 bytes from stackoverflow.com \n", + "6880 2015-01-20 17:04:18.808864 986 bytes from stackoverflow.com \n", + "6881 2015-01-20 17:04:19.124524 357 bytes from stackoverflow.com \n", + "6882 2015-01-20 17:04:19.440517 1696 bytes from stackoverflow.com \n", + "6883 2015-01-20 17:04:19.756250 561 bytes from stackoverflow.com \n", + "6884 2015-01-20 17:04:20.071820 773 bytes from stackoverflow.com \n", + "6885 2015-01-20 17:04:20.387385 1009 bytes from stackoverflow.com \n", + "6886 2015-01-20 17:04:20.704382 1948 bytes from stackoverflow.com \n", + "\n", + " ip icmp ttl time ms \n", + "0 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", + "1 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", + "2 (198.252.206.140): icmp_seq=1 ttl=50 126.0 ms \n", + "3 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "4 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "5 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "7 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "8 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "9 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "10 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "11 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "12 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "13 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "14 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "15 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "16 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "17 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "18 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "19 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "20 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "21 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "22 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "23 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "24 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "25 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "26 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "27 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "28 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "29 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "... ... ... ... ... .. \n", + "6857 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6858 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", + "6859 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "6860 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6861 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", + "6862 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "6863 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6864 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", + "6865 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6866 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", + "6867 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6868 (198.252.206.140): icmp_seq=1 ttl=50 113.0 ms \n", + "6869 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "6870 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6871 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", + "6872 (198.252.206.140): icmp_seq=1 ttl=50 113.0 ms \n", + "6873 (198.252.206.140): icmp_seq=1 ttl=50 114.0 ms \n", + "6874 (198.252.206.140): icmp_seq=1 ttl=50 119.0 ms \n", + "6875 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6876 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6877 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6878 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6879 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "6880 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6881 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6882 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6883 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6884 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6885 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", + "6886 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "\n", + "[6824 rows x 10 columns]" ] }, + "execution_count": 17, "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_stackoverflow" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -376,7 +1307,7 @@ "\n", "# Gráfico del tiempo de transmisión a lo largo del tiempo\n", "plt.figure(figsize=(10, 6))\n", - "plt.plot(df_liglab2[\"timestamp\"], df_liglab2[\"time\"], label=\"liglab2\")\n", + "plt.plot(df_liglab2[\"date\"], df_liglab2[\"time\"], label=\"liglab2\")\n", "plt.xlabel(\"Tiempo\")\n", "plt.ylabel(\"Tiempo de transmisión (ms)\")\n", "plt.title(\"Evolución del tiempo de transmisión (liglab2)\")\n", -- 2.18.1