From 61b4e9ec9eab86c3d96f75b00fd9eb30a462405a Mon Sep 17 00:00:00 2001 From: Lyudmila Vaseva <vaseva@mi.fu-berlin.de> Date: Mon, 11 Mar 2019 08:54:26 +0100 Subject: [PATCH] Restructure general stats, add piechart --- src/explore.ipynb | 123 ++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 109 insertions(+), 14 deletions(-) diff --git a/src/explore.ipynb b/src/explore.ipynb index 9bd0d22..9413ade 100644 --- a/src/explore.ipynb +++ b/src/explore.ipynb @@ -24,14 +24,29 @@ "We import a cleaned version of manually annotated edit filters:" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"20190106115600_filters-sorted-by-hits-manual-tags.csv\", sep='\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As well as an orignial query against the abuse_filter table:" + ] + }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv(\"20190106115600_filters-sorted-by-hits-manual-tags.csv\", sep='\\t')\n", - "df_origin = pd.read_csv(\"quarry-32518-all-filters-sorted-num-hits.csv\", sep=',')\n" + "df_origin = pd.read_csv(\"quarry-32518-all-filters-sorted-num-hits.csv\", sep=',')" ] }, { @@ -64,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -74,31 +89,65 @@ "201\n", "753\n", "600\n", - "110\n", "0\n" ] } ], "source": [ "# Active (enabled) filters\n", - "print (len(df.query('af_enabled==1')))\n", + "num_enabled = len(df.query('af_enabled==1'))\n", + "print (num_enabled)\n", "\n", "# Disabled filters\n", - "print (len(df.query('af_enabled==0')))\n", + "num_disabled = len(df.query('af_enabled==0'))\n", + "print (num_disabled)\n", "\n", "# Deleted filters\n", - "print (len(df.query('af_deleted==1')))\n", + "num_deleted = len(df.query('af_deleted==1'))\n", + "print (num_deleted)\n", + "\n", + "# Deleted and enabled -- make sure it's 0 \n", + "num_enabled_deleted = len(df.query('af_deleted==1 and af_enabled==1'))\n", + "print (num_enabled_deleted)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "361\n", + "110\n", + "35\n", + "216\n" + ] + } + ], + "source": [ + "# public filters\n", + "num_public = len(df.query('af_hidden==0'))\n", + "print (num_public)\n", "\n", "# Active public filters\n", - "print (len(df.query('af_hidden==0 and af_enabled==1')))\n", + "num_enabled_public = len(df.query('af_hidden==0 and af_enabled==1'))\n", + "print (num_enabled_public)\n", "\n", - "# Deleted and enabled\n", - "print (len(df.query('af_deleted==1 and af_enabled==1')))" + "# disabled (but not deleted) public filters\n", + "num_disabled_public = len(df.query('af_hidden==0 and af_enabled==0 and af_deleted==0'))\n", + "print (num_disabled_public)\n", + "\n", + "# deleted public filters\n", + "num_deleted_public = num_public - num_enabled_public - num_disabled_public\n", + "print (num_deleted_public)" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -106,16 +155,62 @@ "output_type": "stream", "text": [ "593\n", - "91\n" + "91\n", + "118\n", + "384\n" ] } ], "source": [ "# hidden filters\n", - "print (len(df.query('af_hidden==1')))\n", + "num_hidden = len(df.query('af_hidden==1'))\n", + "print (num_hidden)\n", "\n", "# active hidden filters\n", - "print (len(df.query('af_hidden==1 and af_enabled==1')))" + "num_enabled_hidden = len(df.query('af_hidden==1 and af_enabled==1'))\n", + "print (num_enabled_hidden)\n", + "\n", + "# disabled (but not deleted) hidden filters\n", + "num_disabled_hidden = len(df.query('af_hidden==1 and af_enabled==0 and af_deleted==0'))\n", + "print (num_disabled_hidden)\n", + "\n", + "# deleted hidden filters\n", + "num_deleted_hidden = num_hidden - num_enabled_hidden - num_disabled_hidden\n", + "print (num_deleted_hidden)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot general overview\n", + "\n", + "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n", + "# here, we mean \"disabled, but not deleted\"\n", + "labels = ['public deleted', 'hidden deleted', 'public enabled', 'hidden enabled', 'public disabled', 'hidden disabled']\n", + "sizes = [num_deleted_public, num_deleted_hidden, num_enabled_public, num_enabled_hidden, num_disabled_public, num_disabled_hidden]\n", + "\n", + "\n", + "fig1, ax1 = plt.subplots()\n", + "ax1.set_prop_cycle(color=['lightskyblue', 'steelblue', 'yellowgreen', 'olivedrab', 'lightgrey', 'grey'])\n", + "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n", + "ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.\n", + "\n", + "\n", + "plt.show()" ] }, { -- GitLab