From 4ac2875f3d05b053a55fdead9d1dbb40d41305aa Mon Sep 17 00:00:00 2001
From: Lyudmila Vaseva <vaseva@mi.fu-berlin.de>
Date: Tue, 16 Jul 2019 17:12:00 +0200
Subject: [PATCH] Clean up broken and useless viz's
---
src/explore.ipynb | 443 +---------------------------------------------
1 file changed, 4 insertions(+), 439 deletions(-)
diff --git a/src/explore.ipynb b/src/explore.ipynb
index 85a07a5..8d2e5dc 100644
--- a/src/explore.ipynb
+++ b/src/explore.ipynb
@@ -9916,7 +9916,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "A per month break-down is difficult to plot on DIN A4 paper (maybe?); I'm aggregating them per year to start."
+ "A per month break-down is difficult to plot on DIN A4 paper (maybe?); I'm aggregating them per year to start. -> that's less useful than the per month aggregation (see above)"
]
},
{
@@ -10218,198 +10218,6 @@
"Where does this data come from? It looks kind of fishy that there are \"disallow,tag\" but not \"warn,tag\" actions, where there are a bunch of filters with \"warn,tag\" actions set."
]
},
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>LogYear</th>\n",
- " <th>FilterActions</th>\n",
- " <th>Freq</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2019-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2018-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2017-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>961</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2016-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>27752</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2015-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>36971</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2014-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>3970</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>2010-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>2009-01-01 00:00:00</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>321</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>2013-01-01</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>2012-01-01</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>2011-01-01</td>\n",
- " <td>disallow,tag</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " LogYear FilterActions Freq\n",
- "0 2019-01-01 00:00:00 disallow,tag 18\n",
- "1 2018-01-01 00:00:00 disallow,tag 71\n",
- "2 2017-01-01 00:00:00 disallow,tag 961\n",
- "3 2016-01-01 00:00:00 disallow,tag 27752\n",
- "4 2015-01-01 00:00:00 disallow,tag 36971\n",
- "5 2014-01-01 00:00:00 disallow,tag 3970\n",
- "6 2010-01-01 00:00:00 disallow,tag 24\n",
- "7 2009-01-01 00:00:00 disallow,tag 321\n",
- "8 2013-01-01 disallow,tag 0\n",
- "9 2012-01-01 disallow,tag 0\n",
- "10 2011-01-01 disallow,tag 0"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#df_actions_year.fillna('log only')[df_actions_year [\"FilterActions\"]== \"log only\"]\n",
- "test = df_actions_year.fillna('log only').query('FilterActions==\"disallow,tag\"')\n",
- "df2 = pd.DataFrame([['2013-01-01', 'disallow,tag', 0], ['2012-01-01', 'disallow,tag', 0], ['2011-01-01', 'disallow,tag', 0]], columns=['LogYear', 'FilterActions', 'Freq'])\n",
- "test.append(df2, ignore_index=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-30-c11e9b229550>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mp3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_actions_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==\"warn\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mp4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_actions_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==\"disallow\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mp5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_actions_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==\"disallow,tag\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\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 14\u001b[0m \u001b[0mp6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_actions_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==\"blockautopromote,tag\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mp7\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_actions_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==\"blockautopromote\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1808\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1809\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1812\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m 2249\u001b[0m x, height, width, y, linewidth = np.broadcast_arrays(\n\u001b[1;32m 2250\u001b[0m \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2251\u001b[0;31m np.atleast_1d(x), height, width, y, linewidth)\n\u001b[0m\u001b[1;32m 2252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2253\u001b[0m \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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 253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\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 188\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Trying to plot a grouped bar chart\n",
- "\n",
- "N = 11\n",
- "\n",
- "fig, ax = plt.subplots()\n",
- "\n",
- "ind = np.arange(N) # the x locations for the groups\n",
- "width = 0.2 # the width of the bars\n",
- "p1 = ax.bar(ind, df_actions_year.fillna('log only').query('FilterActions==\"log only\"')['Freq'], width, bottom=0)\n",
- "p2 = ax.bar(ind + width, df_actions_year.fillna('log only').query('FilterActions==\"tag\"')['Freq'], width, bottom=0)\n",
- "p3 = ax.bar(ind + 2*width, df_actions_year.fillna('log only').query('FilterActions==\"warn\"')['Freq'], width, bottom=0)\n",
- "p4 = ax.bar(ind + 3*width, df_actions_year.fillna('log only').query('FilterActions==\"disallow\"')['Freq'], width, bottom=0)\n",
- "p5 = ax.bar(ind + 4*width, df_actions_year.fillna('log only').query('FilterActions==\"disallow,tag\"')['Freq'], width, bottom=0)\n",
- "p6 = ax.bar(ind + 5*width, df_actions_year.fillna('log only').query('FilterActions==\"blockautopromote,tag\"')['Freq'], width, bottom=0)\n",
- "p7 = ax.bar(ind + 6*width, df_actions_year.fillna('log only').query('FilterActions==\"blockautopromote\"')['Freq'], width, bottom=0)\n",
- "p8 = ax.bar(ind + 7*width, df_actions_year.fillna('log only').query('FilterActions==\"aftv5flagabuse\"')['Freq'], width, bottom=0)\n",
- "\n",
- "plt.xlabel('actions')\n",
- "plt.xticks(rotation='20')\n",
- "plt.ylabel('Num filters')\n",
- "ax.set_title('Scores by group and gender')\n",
- "ax.set_xticks(ind + 8*width / 9)\n",
- "ax.set_xticklabels(('2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019'))\n",
- "\n",
- "ax.legend((p1[0], p2[0], p3[0], p4[0], p5[0], p6[0], p7[0], p8[0]), ('log only', 'tag', 'warn', 'disallow', 'disallow,tag', 'blockautopromote,tag', 'blockautopromote', 'aftv5flagabuse'))\n",
- "ax.autoscale_view()\n",
- "plt.show()"
- ]
- },
{
"cell_type": "code",
"execution_count": 8,
@@ -10706,251 +10514,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " LogMonth FilterActions Freq\n",
- "14 2018-12-01 NaN 83958\n",
- "15 2018-12-01 disallow 33417\n",
- "16 2018-12-01 disallow,tag 6\n",
- "17 2018-12-01 tag 76257\n",
- "18 2018-12-01 warn 32649\n",
- "19 2018-11-01 NaN 83866\n",
- "20 2018-11-01 disallow 43244\n",
- "21 2018-11-01 disallow,tag 5\n",
- "22 2018-11-01 tag 86062\n",
- "23 2018-11-01 warn 40056\n",
- "24 2018-10-01 NaN 87070\n",
- "25 2018-10-01 disallow 38163\n",
- "26 2018-10-01 disallow,tag 8\n",
- "27 2018-10-01 tag 86140\n",
- "28 2018-10-01 warn 45057\n",
- "29 2018-09-01 NaN 88941\n",
- "30 2018-09-01 disallow 27452\n",
- "31 2018-09-01 disallow,tag 1\n",
- "32 2018-09-01 tag 75443\n",
- "33 2018-09-01 warn 38517\n",
- "34 2018-08-01 NaN 97404\n",
- "35 2018-08-01 disallow 22387\n",
- "36 2018-08-01 disallow,tag 2\n",
- "37 2018-08-01 tag 69333\n",
- "38 2018-08-01 warn 26919\n",
- "39 2018-07-01 NaN 93867\n",
- "40 2018-07-01 disallow 18885\n",
- "41 2018-07-01 disallow,tag 3\n",
- "42 2018-07-01 tag 68355\n",
- "43 2018-07-01 warn 24367\n",
- "44 2018-06-01 NaN 87889\n",
- "45 2018-06-01 disallow 21996\n",
- "46 2018-06-01 disallow,tag 3\n",
- "47 2018-06-01 tag 70779\n",
- "48 2018-06-01 warn 28707\n",
- "49 2018-05-01 NaN 83190\n",
- "50 2018-05-01 disallow 30660\n",
- "51 2018-05-01 disallow,tag 2\n",
- "52 2018-05-01 tag 81854\n",
- "53 2018-05-01 warn 42529\n",
- "54 2018-04-01 NaN 88356\n",
- "55 2018-04-01 disallow 30991\n",
- "56 2018-04-01 disallow,tag 2\n",
- "57 2018-04-01 tag 83492\n",
- "58 2018-04-01 warn 40016\n",
- "59 2018-03-01 NaN 89214\n",
- "60 2018-03-01 disallow 34215\n",
- "61 2018-03-01 disallow,tag 14\n",
- "62 2018-03-01 tag 87523\n",
- "63 2018-03-01 warn 44465\n",
- "64 2018-02-01 NaN 62171\n",
- "65 2018-02-01 disallow 32644\n",
- "66 2018-02-01 disallow,tag 9\n",
- "67 2018-02-01 tag 76656\n",
- "68 2018-02-01 warn 42068\n",
- "69 2018-01-01 NaN 73898\n",
- "70 2018-01-01 disallow 32164\n",
- "71 2018-01-01 disallow,tag 16\n",
- "72 2018-01-01 tag 82561\n",
- "73 2018-01-01 warn 47066\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "15 33417\n",
- "20 43244\n",
- "25 38163\n",
- "30 27452\n",
- "35 22387\n",
- "40 18885\n",
- "45 21996\n",
- "50 30660\n",
- "55 30991\n",
- "60 34215\n",
- "65 32644\n",
- "70 32164\n",
- "Name: Freq, dtype: int64"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mask2018 = (df_actions['LogMonth'] >= '2018-01-01') & (df_actions['LogMonth'] < '2019-01-01')\n",
- "actions_2018 = df_actions.loc[mask2018]\n",
- "print (actions_2018)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-51-46c8522bf62c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mactions_2018\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'FilterActions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log_only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactions_2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log_only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FilterActions==@a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_offset\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 8\u001b[0m \u001b[0my_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_offset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m 2455\u001b[0m return gca().bar(\n\u001b[1;32m 2456\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2457\u001b[0;31m **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m 2458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1808\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1809\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1812\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m 2249\u001b[0m x, height, width, y, linewidth = np.broadcast_arrays(\n\u001b[1;32m 2250\u001b[0m \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2251\u001b[0;31m np.atleast_1d(x), height, width, y, linewidth)\n\u001b[0m\u001b[1;32m 2252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2253\u001b[0m \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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 253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\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 188\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ind = np.arange(12) # the x locations for the groups\n",
- "width = 0.35 # the width of the bars: can also be len(x) sequence\n",
- "y_offset = np.zeros(len(ind))\n",
- "\n",
- "for a in actions_2018['FilterActions'].fillna('log_only').unique():\n",
- " ac = actions_2018.fillna('log_only').query('FilterActions==@a')\n",
- " plt.bar(ind, ac['Freq'], width, bottom=y_offset)\n",
- " y_offset = y_offset + ac['Freq']\n",
- "\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-36-c599c7c7dd11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisallow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisallow_tag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisallow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mp3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisallow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdisallow_tag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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 14\u001b[0m \u001b[0mp4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisallow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdisallow_tag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mp5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisallow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdisallow_tag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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- "\u001b[0;32m~/uni/ma-arbeit-filters/src/env3/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\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 188\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
- ]
- },
- {
- "data": {
- "image/png": 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p2nn4dUyH36uBlyb5s2G7Gr+avt1zUd7yudTDYU6P7ViIkpzAdDB8qaq+NnQ/Y3Au8M4kjzJ9+u+8JP80bEtjsRvYXVUHjuxuZTosFro/An5UVfuq6r+BrwF/MHBP4/KTJKcDtPe9A/dzTCz1cFiUj+1IEqbPYT9YVZ8aup9xqKqrq2plVU0y/e90R1Ut+N9Eq+pJ4PEkr2ul84EHBmxpXH4MnJPkJe3/4/ksggvtzVZgfZteD9w2YC/HzLz5hvQQFvFjO84F3gfcn+T7rfbR9i10zT9/AXyp/YLyCPD+gfs5alV1d5Jbge8xfffcfSzAbxUn+TLwNuDUJLuBa4DrgFuSXA48BrxnuA6PHb8hLUnqLPXTSpKkGRgOkqSO4SBJ6hgOkqSO4SBJ6hgOkqSO4SBJ6hgOkqTO/wBIryJP5WoCgQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "disallow = actions_2018.query('FilterActions==\"disallow\"')\n",
- "disallow_tag = actions_2018.query('FilterActions==\"disallow,tag\"')\n",
- "tag = actions_2018.query('FilterActions==\"tag\"')\n",
- "warn = actions_2018.query('FilterActions==\"warn\"')\n",
- "log = actions_2018.fillna('log_only').query('FilterActions==\"log_only\"')\n",
- "\n",
- "\n",
- "ind = np.arange(12) # the x locations for the groups\n",
- "width = 0.35 # the width of the bars: can also be len(x) sequence\n",
- "\n",
- "'''\n",
- "p1 = plt.bar(ind, disallow['Freq'], width)\n",
- "p2 = plt.bar(ind, disallow_tag['Freq'], width, bottom=disallow['Freq'])\n",
- "p3 = plt.bar(ind, tag['Freq'], width, bottom=(disallow['Freq']+disallow_tag['Freq']))\n",
- "p4 = plt.bar(ind, warn['Freq'], width, bottom=(disallow['Freq']+disallow_tag['Freq']+tag['Freq']))\n",
- "p5 = plt.bar(ind, log['Freq'], width, bottom=(disallow['Freq']+disallow_tag['Freq']+tag['Freq']+warn['Freq']))\n",
- "\n",
- "plt.ylabel('Frequency of edit filter actions')\n",
- "plt.title('Edit filter actions per month')\n",
- "plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
- "plt.legend((p1[0], p2[0], p3[0], p4[0], p5[0]), ('disallow', 'disallow, tag', 'tag', 'warn', 'log only'))\n",
- "'''\n",
- "\n",
- "\n",
- "\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[('createaccount', 121),\n",
- " ('edit', 121),\n",
- " ('move', 121),\n",
- " ('delete', 61),\n",
- " ('autocreateaccount', 58),\n",
- " ('upload', 35),\n",
- " ('feedback', 24),\n",
- " ('gatheredit', 11),\n",
- " ('moodbar', 11),\n",
- " ('stashupload', 2)]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Which editors' actions triggered a filter\n",
- "df_ed_actions = pd.read_csv(\"quarry-34050-which-actions-triggered-an-abuse-filter-en-wiki-run346498.csv\", sep=',')\n",
- "collections.Counter(list(df_ed_actions['EditorActions'])).most_common()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
+ "execution_count": 56,
"metadata": {
"scrolled": true
},
@@ -11420,13 +10984,14 @@
"[565 rows x 3 columns]"
]
},
- "execution_count": 23,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Which editors' actions triggered a filter over time\n",
+ "df_ed_actions = pd.read_csv(\"quarry-34050-which-actions-triggered-an-abuse-filter-en-wiki-run346498.csv\", sep=',')\n",
"df_ed_actions['LogMonth'] = pd.to_datetime(df_ed_actions['LogMonth'], format=\"%Y%m\")\n",
"df_ed_actions\n",
"\n",
--
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