diff --git a/src/explore.ipynb b/src/explore.ipynb
index 14a0c19c4fac490220b7d8667fdbd145543992d5..85a07a51ac7bdc7f54411f4ad560a32be9a6365b 100644
--- a/src/explore.ipynb
+++ b/src/explore.ipynb
@@ -1756,7 +1756,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -1781,52 +1781,52 @@
"94.181.170.143 247\n",
"Theadityapratap 239\n",
"94.181.156.128 229\n",
- "Achat cialis 222\n",
"93.124.46.78 222\n",
+ "Achat cialis 222\n",
"93.124.28.116 213\n",
"176.97.116.140 202\n",
"5.165.186.39 192\n",
"Acquistare cialis 190\n",
"93.124.74.221 187\n",
- "37.113.34.32 186\n",
"AbhiJahazi 186\n",
+ "37.113.34.32 186\n",
"37.113.28.187 185\n",
"64.62.219.98 180\n",
" ... \n",
- "39.54.130.54 1\n",
- "VerrillSabinenEeci 1\n",
- "94.72.204.28 1\n",
- "59.101.86.233 1\n",
- "Valde21 1\n",
- "Frank L Mason 1\n",
- "71.107.27.56 1\n",
- "76.69.22.109 1\n",
- "Fbdave 1\n",
- "Lvtd 1\n",
- "95.105.29.221 1\n",
- "24.180.180.56 1\n",
- "2001:558:6017:144:7422:40DB:D32E:FDB1 1\n",
- "Ahmadvc 1\n",
- "Josephine18 1\n",
- "NZ Raven 1\n",
- "McaleerKoganGhOOO 1\n",
- "2600:1006:B123:3E61:79FA:2751:C2D2:69BF 1\n",
- "Laurenbrown44 1\n",
- "MarkeeStillionsDjntn 1\n",
- "31.54.171.137 1\n",
- "Romp48 1\n",
- "Moverhleo 1\n",
- "188.39.41.194 1\n",
- "Rainbow Joint 1\n",
- "Fateh Ali Samejo 1\n",
- "135.23.188.13 1\n",
- "2600:1012:B019:2196:2C2A:154A:5E7D:4489 1\n",
- "UmfleetVanhoffCbdjL 1\n",
- "124.189.132.154 1\n",
+ "HortonTimsonOlvsL 1\n",
+ "75.108.207.104 1\n",
+ "Ambalamb 1\n",
+ "98.183.198.159 1\n",
+ "2600:1003:B460:10FF:3580:4CD7:FCA8:76A8 1\n",
+ "Arth21 1\n",
+ "LinaPom774069 1\n",
+ "50.133.191.168 1\n",
+ "37.44.110.64 1\n",
+ "76.9.63.87 1\n",
+ "2602:306:BCCB:3360:A9C9:3DCF:17AC:FE29 1\n",
+ "82.40.75.53 1\n",
+ "2601:646:8801:FB67:542A:8A08:C2C6:ED03 1\n",
+ "95.239.155.109 1\n",
+ "2600:1002:B001:834C:AB:517:31CF:FEB6 1\n",
+ "120.145.0.23 1\n",
+ "94.3.229.99 1\n",
+ "49.204.22.233 1\n",
+ "71.85.159.151 1\n",
+ "204.174.144.1 1\n",
+ "96.29.192.142 1\n",
+ "CGrater 1\n",
+ "Lotta21 1\n",
+ "JuwelRana 1\n",
+ "207.165.194.249 1\n",
+ "Sujithbdu 1\n",
+ "Arsyad44 1\n",
+ "108.89.84.24 1\n",
+ "41.174.157.12 1\n",
+ "PorthBohnVGUK 1\n",
"Name: afl_user_text, Length: 139586, dtype: int64"
]
},
- "execution_count": 6,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -1838,12 +1838,35 @@
"df_jan2016['afl_user_text'].value_counts() #TODO intersect users with actions/pages"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12876"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "most_user_hits = [1689, 1249, 1133, 715, 697, 674, 559, 556, 533, 473, 430, 317, 307, 291, 279, 274, 247, 239, 229, 222, 222, \n",
+ " 213, 202, 192, 190, 187, 186, 186, 185]\n",
+ "sum(most_user_hits)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"37.113.52.15 is a Russian IP (whois --> netname: ERTH-PENZA-PPPOE-16-NET)\n",
- "95.152.44.52 too (whois--> netname: RU-PENZA-VT-DSL-200901)"
+ "95.152.44.52 too (whois--> netname: RU-PENZA-VT-DSL-200901). However, together they are responsible for just 2.000 out of 300.000 edits...\n",
+ "64.62.219.98 for example (180 edits) look completely legit."
]
},
{
@@ -6820,269 +6843,1860 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "count 138.000000\n",
- "mean 2702.224638\n",
- "std 7454.825274\n",
- "min 1.000000\n",
- "25% 25.250000\n",
- "50% 250.500000\n",
- "75% 2799.000000\n",
- "max 71853.000000\n",
- "Name: afl_filter, dtype: float64"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Number of hits per filter\n",
- "df_jan2016['afl_filter'].value_counts().describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "UserLogin 71920\n",
- "Skateboard 660\n",
- "Conchobar_Lads_Tomlinson 584\n",
- "Billboard_(magazine) 536\n",
- "Chris_Stark 467\n",
- "Tyler_Joseph 431\n",
- "Bulletin_board_system 339\n",
- "Hailey_Baldwin 328\n",
- "Flickr_-_Official_U.S._Navy_Imagery_-_Sailors_play_board_games_with_children_at_the_Cameron_Community_Ministries_during_Rochester_Navy_Week.jpg 322\n",
- "Ryback 287\n",
- "Aditya_Pratap 247\n",
- "95.152.44.52 232\n",
- "WikiProject_Film 232\n",
- "British_Board_of_Film_Classification 224\n",
- "10_Cloverfield_Lane 219\n",
- "Keyboard_instrument 217\n",
- "Board_game 203\n",
- "176.97.116.140 202\n",
- "93.124.46.78 198\n",
- "AbhiJahazi 186\n",
- "Board 184\n",
- "Entertainment_Software_Rating_Board 178\n",
- "Battle_of_the_Alamo 169\n",
- "Computer_keyboard 161\n",
- "Cultural_history_of_the_buttocks 155\n",
- "Conchobar_Tomlinson 155\n",
- "History_of_the_internal_combustion_engine 154\n",
- "Skateboarder_(magazine) 146\n",
- "WikiProject_Video_games 140\n",
- "Cian_Mitchell 140\n",
- " ... \n",
- "Surfix 1\n",
- "British_Colonial_Research_Committee 1\n",
- "Nassau_County_Police_Department 1\n",
- "Institute_of_Health_Sciences_Bhubaneswar 1\n",
- "Underwater_Pompeii 1\n",
- "El_Valle_de_Antón 1\n",
- "Make_My_Journeys 1\n",
- "Ten_Fold_Engineering 1\n",
- "LNER_Class_A3_4472_Flying_Scotsman 1\n",
- "North_Eastern_College 1\n",
- "Zillow 1\n",
- "Ww57614wW 1\n",
- "Junkyard_Dog 1\n",
- "John_Steptoe 1\n",
- "Financial_doctor 1\n",
- "Music_of_Portugal 1\n",
- "Northwoods_Mall_(North_Charleston,_South_Carolina) 1\n",
- "Niuman_Romero 1\n",
- "Chaudhry_Faisal_Mushtaq 1\n",
- "Big_Island_(Bay_of_Quinte) 1\n",
- "Venus_of_Willendorf 1\n",
- "Alexis_Argüello 1\n",
- "Pdfpdf/Images_of_Adelaide 1\n",
- "Chickasaw_State_Park_(Alabama) 1\n",
- "Torin's_Passage 1\n",
- "Gulshanthakurbohani 1\n",
- "WPCH-TV 1\n",
- "List_of_countries_with_IKEA_stores 1\n",
- "Phelps,_New_York 1\n",
- "Micheál_Quirke 1\n",
- "Name: afl_title, Length: 91387, dtype: int64"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Number of hits per intended edit page\n",
- "df_jan2016['afl_title'].value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To be fair, I don't see any particularly interesting or conspicious pattern, beside the 71920 attempts at account creations. Neither the pages edited are extraordinary, nor are there particular pages with extra-orbitant hits; users are not particularly interesting either and it's mostly the most active filters that got triggered anyway.\n",
- "\n",
- "The 3 IP editors with +1000 filter triggers triggered above all (or maybe exclusively) filters dealing with linkspam.\n",
- "\n",
- "But maybe it's exactly them that make the 71920 hits difference to all the \"standard\" numbers. I'm comparing this with September 2016 (238406 hits) for reference."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Filter hits in September 2016"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "edit 213279\n",
- "createaccount 24824\n",
- "move 196\n",
- "autocreateaccount 88\n",
- "delete 19\n",
- "Name: afl_action, dtype: int64"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_sep2016 = pd.read_csv(\"quarry-37496-abuselog-entries-en-wiki-in-september-2016-run389243.csv\", sep=',')\n",
- "\n",
- "# Number of hits per editor's actions\n",
- "df_sep2016['afl_action'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "527 24743\n",
- "61 21609\n",
- "384 18895\n",
- "633 16897\n",
- "650 16766\n",
- "636 15372\n",
- "135 8195\n",
- "172 6582\n",
- "3 5206\n",
- "432 5005\n",
- "380 4814\n",
- "30 4801\n",
- "712 4520\n",
- "364 4519\n",
- "686 4370\n",
- "260 3342\n",
- "279 3324\n",
- "189 3103\n",
- "231 3059\n",
- "784 2893\n",
- "98 2794\n",
- "50 2745\n",
- "550 2696\n",
- "225 2667\n",
- "631 2602\n",
- "491 2317\n",
- "752 2291\n",
- "46 2223\n",
- "132 2215\n",
- "680 2053\n",
+ "527 71853\n",
+ "61 27072\n",
+ "650 24264\n",
+ "633 21099\n",
+ "279 18460\n",
+ "636 17279\n",
+ "384 15080\n",
+ "135 10028\n",
+ "30 7829\n",
+ "172 7471\n",
+ "271 7192\n",
+ "380 6554\n",
+ "80 6530\n",
+ "364 6238\n",
+ "686 6089\n",
+ "712 5597\n",
+ "466 5555\n",
+ "432 5398\n",
+ "220 5385\n",
+ "550 5215\n",
+ "189 4730\n",
+ "3 4656\n",
+ "148 4470\n",
+ "260 4148\n",
+ "614 4120\n",
+ "231 3398\n",
+ "631 3349\n",
+ "225 3245\n",
+ "46 3174\n",
+ "680 3134\n",
" ... \n",
- "68 17\n",
- "770 17\n",
- "345 17\n",
- "751 16\n",
- "224 15\n",
- "744 14\n",
+ "242 19\n",
+ "706 19\n",
+ "16 19\n",
+ "734 18\n",
+ "264 18\n",
+ "710 17\n",
+ "666 15\n",
+ "722 14\n",
"167 13\n",
- "781 11\n",
- "723 11\n",
- "745 10\n",
- "2 9\n",
- "755 9\n",
- "624 7\n",
- "242 6\n",
- "762 6\n",
- "722 6\n",
- "667 5\n",
- "651 5\n",
- "642 5\n",
- "694 4\n",
- "792 4\n",
- "734 3\n",
- "757 3\n",
- "710 2\n",
- "795 1\n",
- "294 1\n",
- "690 1\n",
- "743 1\n",
- "1 1\n",
- "639 1\n",
- "Name: afl_filter, Length: 139, dtype: int64"
+ "294 10\n",
+ "624 9\n",
+ "727 8\n",
+ "651 8\n",
+ "637 8\n",
+ "745 6\n",
+ "52 6\n",
+ "674 6\n",
+ "748 5\n",
+ "690 5\n",
+ "2 5\n",
+ "597 4\n",
+ "579 4\n",
+ "709 4\n",
+ "68 4\n",
+ "554 3\n",
+ "749 3\n",
+ "596 1\n",
+ "718 1\n",
+ "459 1\n",
+ "694 1\n",
+ "Name: afl_filter, Length: 138, dtype: int64"
]
},
- "execution_count": 10,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df_sep2016['afl_filter'].value_counts()"
+ "# Number of hits per filter\n",
+ "df_jan2016['afl_filter'].value_counts()"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
- "text/plain": [
- "count 139.000000\n",
- "mean 1715.151079\n",
- "std 4035.023411\n",
- "min 1.000000\n",
- "25% 25.500000\n",
- "50% 231.000000\n",
- "75% 1714.500000\n",
- "max 24743.000000\n",
- "Name: afl_filter, dtype: float64"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Number of hits per filter\n",
- "df_sep2016['afl_filter'].value_counts().describe()"
+ "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>afl_id</th>\n",
+ " <th>afl_filter</th>\n",
+ " <th>afl_user</th>\n",
+ " <th>afl_user_text</th>\n",
+ " <th>afl_ip</th>\n",
+ " <th>afl_action</th>\n",
+ " <th>afl_actions</th>\n",
+ " <th>afl_var_dump</th>\n",
+ " <th>afl_timestamp</th>\n",
+ " <th>afl_namespace</th>\n",
+ " <th>afl_title</th>\n",
+ " <th>afl_wiki</th>\n",
+ " <th>afl_deleted</th>\n",
+ " <th>afl_patrolled_by</th>\n",
+ " <th>afl_rev_id</th>\n",
+ " <th>afl_log_id</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>14158231</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>108.35.116.14</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704052095</td>\n",
+ " <td>20160101000928</td>\n",
+ " <td>0</td>\n",
+ " <td>Ronnie_Burrell</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>14158246</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>151.198.253.240</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704052601</td>\n",
+ " <td>20160101001411</td>\n",
+ " <td>0</td>\n",
+ " <td>Electrical_alternans</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>697663267.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71</th>\n",
+ " <td>14158267</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>94.136.95.150</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704053460</td>\n",
+ " <td>20160101002056</td>\n",
+ " <td>0</td>\n",
+ " <td>Budapest_Ferenc_Liszt_International_Airport</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>697664110.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78</th>\n",
+ " <td>14158274</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>90.193.162.233</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704053709</td>\n",
+ " <td>20160101002223</td>\n",
+ " <td>0</td>\n",
+ " <td>Standing_but_not_operating</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80</th>\n",
+ " <td>14158276</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>162.194.172.150</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704053742</td>\n",
+ " <td>20160101002235</td>\n",
+ " <td>0</td>\n",
+ " <td>Natalya_(wrestler)</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>697664400.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83</th>\n",
+ " <td>14158279</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>178.197.226.193</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704053996</td>\n",
+ " <td>20160101002412</td>\n",
+ " <td>0</td>\n",
+ " <td>Dinner_for_One</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>697664640.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102</th>\n",
+ " <td>14158298</td>\n",
+ " <td>61</td>\n",
+ " <td>27192988</td>\n",
+ " <td>Brevor777</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704054811</td>\n",
+ " <td>20160101003049</td>\n",
+ " <td>0</td>\n",
+ " <td>Indiana_State_Road_267</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>697665446.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107</th>\n",
+ " <td>14158303</td>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>122.109.193.184</td>\n",
+ " <td>NaN</td>\n",
+ " <td>edit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>stored-text:704054980</td>\n",
+ " <td>20160101003209</td>\n",
+ " <td>0</td>\n",
+ " <td>Haplogroup_R1a</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
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+ "372877 GLwithAssignment NaN edit \n",
+ "372889 GLwithAssignment NaN edit \n",
+ "372903 74.103.198.95 NaN edit \n",
+ "372905 Stevan95 NaN edit \n",
+ "\n",
+ " afl_actions afl_var_dump afl_timestamp afl_namespace \\\n",
+ "35 NaN stored-text:704052095 20160101000928 0 \n",
+ "50 NaN stored-text:704052601 20160101001411 0 \n",
+ "71 NaN stored-text:704053460 20160101002056 0 \n",
+ "78 NaN stored-text:704053709 20160101002223 0 \n",
+ "80 NaN stored-text:704053742 20160101002235 0 \n",
+ "83 NaN stored-text:704053996 20160101002412 0 \n",
+ "102 NaN stored-text:704054811 20160101003049 0 \n",
+ "107 NaN stored-text:704054980 20160101003209 0 \n",
+ "109 NaN stored-text:704055027 20160101003229 0 \n",
+ "110 NaN stored-text:704055053 20160101003245 0 \n",
+ "111 NaN stored-text:704055061 20160101003249 0 \n",
+ "112 NaN stored-text:704055076 20160101003259 0 \n",
+ "113 NaN stored-text:704055082 20160101003301 0 \n",
+ "114 NaN stored-text:704055097 20160101003307 0 \n",
+ "115 NaN stored-text:704055148 20160101003335 0 \n",
+ "117 NaN stored-text:704055180 20160101003353 0 \n",
+ "121 NaN stored-text:704055197 20160101003405 0 \n",
+ "124 NaN stored-text:704055212 20160101003416 0 \n",
+ "148 NaN stored-text:704055805 20160101003904 0 \n",
+ "150 NaN stored-text:704055827 20160101003914 0 \n",
+ "155 NaN stored-text:704055869 20160101003936 0 \n",
+ "159 NaN stored-text:704055884 20160101003943 0 \n",
+ "162 NaN stored-text:704055895 20160101003947 0 \n",
+ "167 NaN stored-text:704055902 20160101003949 0 \n",
+ "170 NaN stored-text:704055925 20160101004001 0 \n",
+ "225 NaN stored-text:704056754 20160101004655 0 \n",
+ "226 NaN stored-text:704056780 20160101004706 0 \n",
+ "228 NaN stored-text:704056801 20160101004717 0 \n",
+ "231 NaN stored-text:704056851 20160101004742 0 \n",
+ "236 NaN stored-text:704057157 20160101005030 0 \n",
+ "... ... ... ... ... \n",
+ "372519 NaN stored-text:709299799 20160131230945 0 \n",
+ "372520 NaN stored-text:709299829 20160131230959 0 \n",
+ "372538 NaN stored-text:709300095 20160131231213 0 \n",
+ "372552 NaN stored-text:709300229 20160131231312 0 \n",
+ "372553 NaN stored-text:709300247 20160131231324 0 \n",
+ "372556 NaN stored-text:709300294 20160131231344 0 \n",
+ "372564 NaN stored-text:709300487 20160131231512 0 \n",
+ "372572 NaN stored-text:709300629 20160131231615 0 \n",
+ "372594 NaN stored-text:709300818 20160131231751 0 \n",
+ "372598 NaN stored-text:709300905 20160131231837 0 \n",
+ "372637 NaN stored-text:709301661 20160131232412 0 \n",
+ "372655 NaN stored-text:709301928 20160131232613 0 \n",
+ "372685 NaN stored-text:709302313 20160131232932 0 \n",
+ "372686 NaN stored-text:709302328 20160131232937 0 \n",
+ "372710 NaN stored-text:709302921 20160131233451 0 \n",
+ "372717 NaN stored-text:709303116 20160131233641 0 \n",
+ "372751 NaN stored-text:709303356 20160131233834 0 \n",
+ "372752 NaN stored-text:709303381 20160131233847 0 \n",
+ "372792 NaN stored-text:709304087 20160131234451 0 \n",
+ "372801 NaN stored-text:709304270 20160131234625 0 \n",
+ "372805 NaN stored-text:709304318 20160131234642 0 \n",
+ "372828 NaN stored-text:709304755 20160131235017 0 \n",
+ "372833 NaN stored-text:709304799 20160131235038 0 \n",
+ "372848 NaN stored-text:709305056 20160131235251 0 \n",
+ "372860 NaN stored-text:709305204 20160131235419 0 \n",
+ "372874 NaN stored-text:709305406 20160131235555 0 \n",
+ "372877 NaN stored-text:709305418 20160131235600 0 \n",
+ "372889 NaN stored-text:709305594 20160131235723 0 \n",
+ "372903 NaN stored-text:709305721 20160131235822 0 \n",
+ "372905 NaN stored-text:709305816 20160131235906 0 \n",
+ "\n",
+ " afl_title afl_wiki afl_deleted \\\n",
+ "35 Ronnie_Burrell NaN 0 \n",
+ "50 Electrical_alternans NaN 0 \n",
+ "71 Budapest_Ferenc_Liszt_International_Airport NaN 0 \n",
+ "78 Standing_but_not_operating NaN 0 \n",
+ "80 Natalya_(wrestler) NaN 0 \n",
+ "83 Dinner_for_One NaN 0 \n",
+ "102 Indiana_State_Road_267 NaN 0 \n",
+ "107 Haplogroup_R1a NaN 0 \n",
+ "109 Pingdom NaN 0 \n",
+ "110 Pingdom NaN 0 \n",
+ "111 Haplogroup_R1a NaN 0 \n",
+ "112 Indiana_State_Road_267 NaN 0 \n",
+ "113 Pingdom NaN 0 \n",
+ "114 Turkey_Lacrosse_Association NaN 0 \n",
+ "115 Jordan_Pruitt NaN 0 \n",
+ "117 Sidhu NaN 0 \n",
+ "121 Sidhu NaN 0 \n",
+ "124 Jordan_Pruitt NaN 0 \n",
+ "148 Islam_in_Australia NaN 0 \n",
+ "150 Islam_in_Australia NaN 0 \n",
+ "155 Hassan_Shehata NaN 0 \n",
+ "159 Hassan_Shehata NaN 0 \n",
+ "162 Hassan_Shehata NaN 0 \n",
+ "167 Hassan_Shehata NaN 0 \n",
+ "170 Hassan_Shehata NaN 0 \n",
+ "225 Celia_(telenovela) NaN 0 \n",
+ "226 Celia_(telenovela) NaN 0 \n",
+ "228 Celia_(telenovela) NaN 0 \n",
+ "231 Mankri NaN 0 \n",
+ "236 Criticism_of_ESPN NaN 0 \n",
+ "... ... ... ... \n",
+ "372519 Google_Glass NaN 0 \n",
+ "372520 The_Nueva_School NaN 0 \n",
+ "372538 Granger_House_and_The_Perch NaN 0 \n",
+ "372552 Freedom,_Outagamie_County,_Wisconsin NaN 0 \n",
+ "372553 Freedom,_Outagamie_County,_Wisconsin NaN 0 \n",
+ "372556 Freedom,_Outagamie_County,_Wisconsin NaN 0 \n",
+ "372564 Hypercarnivore NaN 0 \n",
+ "372572 Michael_Wigglesworth NaN 0 \n",
+ "372594 Busch_Gardens_Williamsburg NaN 0 \n",
+ "372598 Busch_Gardens_Williamsburg NaN 0 \n",
+ "372637 I_Know_What_You_Did_Last_Summer_(song) NaN 0 \n",
+ "372655 Shawn_Mendes_discography NaN 0 \n",
+ "372685 2016_Sundance_Film_Festival NaN 0 \n",
+ "372686 Iman_Foundation NaN 0 \n",
+ "372710 Bedford_Level_experiment NaN 0 \n",
+ "372717 Nick_Grimshaw NaN 0 \n",
+ "372751 Twoja_twarz_brzmi_znajomo_(season_5) NaN 0 \n",
+ "372752 Twoja_twarz_brzmi_znajomo_(season_5) NaN 0 \n",
+ "372792 Vermont NaN 0 \n",
+ "372801 Maqsudul_Alam NaN 0 \n",
+ "372805 Otto_Liman_von_Sanders NaN 0 \n",
+ "372828 Julia_Lescova NaN 0 \n",
+ "372833 Andersonville_National_Historic_Site NaN 0 \n",
+ "372848 George_Wassouf NaN 0 \n",
+ "372860 J._J._Watt NaN 0 \n",
+ "372874 Object-relational_mapping NaN 0 \n",
+ "372877 Object-relational_mapping NaN 0 \n",
+ "372889 Object-relational_mapping NaN 0 \n",
+ "372903 Amelia_Warner NaN 0 \n",
+ "372905 George_Wassouf NaN 0 \n",
+ "\n",
+ " afl_patrolled_by afl_rev_id afl_log_id \n",
+ "35 0 NaN NaN \n",
+ "50 0 697663267.0 NaN \n",
+ "71 0 697664110.0 NaN \n",
+ "78 0 NaN NaN \n",
+ "80 0 697664400.0 NaN \n",
+ "83 0 697664640.0 NaN \n",
+ "102 0 697665446.0 NaN \n",
+ "107 0 NaN NaN \n",
+ "109 0 NaN NaN \n",
+ "110 0 NaN NaN \n",
+ "111 0 697665683.0 NaN \n",
+ "112 0 697665701.0 NaN \n",
+ "113 0 697665702.0 NaN \n",
+ "114 0 697665717.0 NaN \n",
+ "115 0 NaN NaN \n",
+ "117 0 NaN NaN \n",
+ "121 0 697665813.0 NaN \n",
+ "124 0 697665827.0 NaN \n",
+ "148 0 NaN NaN \n",
+ "150 0 697666425.0 NaN \n",
+ "155 0 NaN NaN \n",
+ "159 0 NaN NaN \n",
+ "162 0 NaN NaN \n",
+ "167 0 NaN NaN \n",
+ "170 0 NaN NaN \n",
+ "225 0 NaN NaN \n",
+ "226 0 NaN NaN \n",
+ "228 0 697667361.0 NaN \n",
+ "231 0 697667411.0 NaN \n",
+ "236 0 697667720.0 NaN \n",
+ "... ... ... ... \n",
+ "372519 0 702660807.0 NaN \n",
+ "372520 0 NaN NaN \n",
+ "372538 0 702661086.0 NaN \n",
+ "372552 0 NaN NaN \n",
+ "372553 0 NaN NaN \n",
+ "372556 0 702661270.0 NaN \n",
+ "372564 0 702661457.0 NaN \n",
+ "372572 0 702661594.0 NaN \n",
+ "372594 0 NaN NaN \n",
+ "372598 0 702661858.0 NaN \n",
+ "372637 0 702662591.0 NaN \n",
+ "372655 0 702662840.0 NaN \n",
+ "372685 0 702663195.0 NaN \n",
+ "372686 0 702663209.0 NaN \n",
+ "372710 0 702663781.0 NaN \n",
+ "372717 0 702663971.0 NaN \n",
+ "372751 0 NaN NaN \n",
+ "372752 0 702664208.0 NaN \n",
+ "372792 0 702664905.0 NaN \n",
+ "372801 0 702665061.0 NaN \n",
+ "372805 0 702665103.0 NaN \n",
+ "372828 0 702665526.0 NaN \n",
+ "372833 0 NaN NaN \n",
+ "372848 0 702665813.0 NaN \n",
+ "372860 0 NaN NaN \n",
+ "372874 0 NaN NaN \n",
+ "372877 0 702666161.0 NaN \n",
+ "372889 0 702666328.0 NaN \n",
+ "372903 0 702666450.0 NaN \n",
+ "372905 0 702666542.0 NaN \n",
+ "\n",
+ "[27072 rows x 16 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# What were the filters with most hits filtering\n",
+ "df_jan2016[df_jan2016['afl_filter'] == 61]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "UserLogin 71920\n",
+ "Skateboard 660\n",
+ "Conchobar_Lads_Tomlinson 584\n",
+ "Billboard_(magazine) 536\n",
+ "Chris_Stark 467\n",
+ "Tyler_Joseph 431\n",
+ "Bulletin_board_system 339\n",
+ "Hailey_Baldwin 328\n",
+ "Flickr_-_Official_U.S._Navy_Imagery_-_Sailors_play_board_games_with_children_at_the_Cameron_Community_Ministries_during_Rochester_Navy_Week.jpg 322\n",
+ "Ryback 287\n",
+ "Aditya_Pratap 247\n",
+ "95.152.44.52 232\n",
+ "WikiProject_Film 232\n",
+ "British_Board_of_Film_Classification 224\n",
+ "10_Cloverfield_Lane 219\n",
+ "Keyboard_instrument 217\n",
+ "Board_game 203\n",
+ "176.97.116.140 202\n",
+ "93.124.46.78 198\n",
+ "AbhiJahazi 186\n",
+ "Board 184\n",
+ "Entertainment_Software_Rating_Board 178\n",
+ "Battle_of_the_Alamo 169\n",
+ "Computer_keyboard 161\n",
+ "Cultural_history_of_the_buttocks 155\n",
+ "Conchobar_Tomlinson 155\n",
+ "History_of_the_internal_combustion_engine 154\n",
+ "Skateboarder_(magazine) 146\n",
+ "WikiProject_Video_games 140\n",
+ "Cian_Mitchell 140\n",
+ " ... \n",
+ "Surfix 1\n",
+ "British_Colonial_Research_Committee 1\n",
+ "Nassau_County_Police_Department 1\n",
+ "Institute_of_Health_Sciences_Bhubaneswar 1\n",
+ "Underwater_Pompeii 1\n",
+ "El_Valle_de_Antón 1\n",
+ "Make_My_Journeys 1\n",
+ "Ten_Fold_Engineering 1\n",
+ "LNER_Class_A3_4472_Flying_Scotsman 1\n",
+ "North_Eastern_College 1\n",
+ "Zillow 1\n",
+ "Ww57614wW 1\n",
+ "Junkyard_Dog 1\n",
+ "John_Steptoe 1\n",
+ "Financial_doctor 1\n",
+ "Music_of_Portugal 1\n",
+ "Northwoods_Mall_(North_Charleston,_South_Carolina) 1\n",
+ "Niuman_Romero 1\n",
+ "Chaudhry_Faisal_Mushtaq 1\n",
+ "Big_Island_(Bay_of_Quinte) 1\n",
+ "Venus_of_Willendorf 1\n",
+ "Alexis_Argüello 1\n",
+ "Pdfpdf/Images_of_Adelaide 1\n",
+ "Chickasaw_State_Park_(Alabama) 1\n",
+ "Torin's_Passage 1\n",
+ "Gulshanthakurbohani 1\n",
+ "WPCH-TV 1\n",
+ "List_of_countries_with_IKEA_stores 1\n",
+ "Phelps,_New_York 1\n",
+ "Micheál_Quirke 1\n",
+ "Name: afl_title, Length: 91387, dtype: int64"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Number of hits per intended edit page\n",
+ "df_jan2016['afl_title'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To be fair, I don't see any particularly interesting or conspicious pattern, beside the 71920 attempts at account creations. Neither the pages edited are extraordinary, nor are there particular pages with extra-orbitant hits; users are not particularly interesting either and it's mostly the most active filters that got triggered anyway.\n",
+ "\n",
+ "The 3 IP editors with +1000 filter triggers triggered above all (or maybe exclusively) filters dealing with linkspam.\n",
+ "\n",
+ "But maybe it's exactly them that make the 71920 hits difference to all the \"standard\" numbers. I'm comparing this with September 2016 (238406 hits) for reference."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Filter hits in September 2016"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "edit 213279\n",
+ "createaccount 24824\n",
+ "move 196\n",
+ "autocreateaccount 88\n",
+ "delete 19\n",
+ "Name: afl_action, dtype: int64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sep2016 = pd.read_csv(\"quarry-37496-abuselog-entries-en-wiki-in-september-2016-run389243.csv\", sep=',')\n",
+ "\n",
+ "# Number of hits per editor's actions\n",
+ "df_sep2016['afl_action'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "527 24743\n",
+ "61 21609\n",
+ "384 18895\n",
+ "633 16897\n",
+ "650 16766\n",
+ "636 15372\n",
+ "135 8195\n",
+ "172 6582\n",
+ "3 5206\n",
+ "432 5005\n",
+ "380 4814\n",
+ "30 4801\n",
+ "712 4520\n",
+ "364 4519\n",
+ "686 4370\n",
+ "260 3342\n",
+ "279 3324\n",
+ "189 3103\n",
+ "231 3059\n",
+ "784 2893\n",
+ "98 2794\n",
+ "50 2745\n",
+ "550 2696\n",
+ "225 2667\n",
+ "631 2602\n",
+ "491 2317\n",
+ "752 2291\n",
+ "46 2223\n",
+ "132 2215\n",
+ "680 2053\n",
+ " ... \n",
+ "68 17\n",
+ "770 17\n",
+ "345 17\n",
+ "751 16\n",
+ "224 15\n",
+ "744 14\n",
+ "167 13\n",
+ "781 11\n",
+ "723 11\n",
+ "745 10\n",
+ "2 9\n",
+ "755 9\n",
+ "624 7\n",
+ "242 6\n",
+ "762 6\n",
+ "722 6\n",
+ "667 5\n",
+ "651 5\n",
+ "642 5\n",
+ "694 4\n",
+ "792 4\n",
+ "734 3\n",
+ "757 3\n",
+ "710 2\n",
+ "795 1\n",
+ "294 1\n",
+ "690 1\n",
+ "743 1\n",
+ "1 1\n",
+ "639 1\n",
+ "Name: afl_filter, Length: 139, dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sep2016['afl_filter'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 139.000000\n",
+ "mean 1715.151079\n",
+ "std 4035.023411\n",
+ "min 1.000000\n",
+ "25% 25.500000\n",
+ "50% 231.000000\n",
+ "75% 1714.500000\n",
+ "max 24743.000000\n",
+ "Name: afl_filter, dtype: float64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Number of hits per filter\n",
+ "df_sep2016['afl_filter'].value_counts().describe()"
]
},
{
@@ -7626,7 +9240,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 54,
"metadata": {},
"outputs": [
{
@@ -8223,6 +9837,81 @@
" print(df_actions.fillna('log only'))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Score')"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#TODO Multi line chart: a line per action;\n",
+ "# TODO why are there combis such as \"disallow,tag\", but no \"warn,tag\"?\n",
+ "\n",
+ "# style\n",
+ "plt.style.use('seaborn-darkgrid')\n",
+ " \n",
+ "# create a color palette\n",
+ "palette = plt.get_cmap('Set1')\n",
+ "\n",
+ "plt.yscale('linear') # bot linear and log scales kinda suck\n",
+ "\n",
+ "plt.plot(df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"log only\"].fillna('log only')['LogMonth'],\n",
+ " df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"log only\"].fillna('log only')['Freq'],\n",
+ " color=palette(1),\n",
+ " label='log only')\n",
+ "plt.plot(df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"disallow\"]['LogMonth'],\n",
+ " df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"disallow\"]['Freq'],\n",
+ " color=palette(2),\n",
+ " label='disallow')\n",
+ "plt.plot(df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"warn\"]['LogMonth'],\n",
+ " df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"warn\"]['Freq'],\n",
+ " color=palette(3),\n",
+ " label='warn') \n",
+ "plt.plot(df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"tag\"]['LogMonth'],\n",
+ " df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"tag\"]['Freq'],\n",
+ " color=palette(4),\n",
+ " label='tag')\n",
+ "plt.plot(df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"blockautopromote\"]['LogMonth'],\n",
+ " df_actions[df_actions.fillna('log only')[\"FilterActions\"] == \"blockautopromote\"]['Freq'],\n",
+ " color=palette(5),\n",
+ " label='blockautopromote')\n",
+ " \n",
+ "# Add legend\n",
+ "plt.legend(loc=2, ncol=2)\n",
+ " \n",
+ "# Add titles\n",
+ "plt.title(\"A (bad) Spaghetti plot\", loc='left', fontsize=12, fontweight=0, color='orange')\n",
+ "plt.xlabel(\"Time\")\n",
+ "plt.ylabel(\"Score\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "OMG, this is quite interesting: compare with num of hits diagram (it's num of hits but plotted by action); so, it's above all a lot of logging only filters that got triggered during the peak period of 2016. Disallows are quite constant the whole time."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -8232,9 +9921,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 36,
"metadata": {
- "scrolled": true
+ "scrolled": false
},
"outputs": [
{
@@ -8315,11 +10004,211 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 42,
"metadata": {},
- "outputs": [],
+ "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</td>\n",
+ " <td>log only</td>\n",
+ " <td>498557</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>2018-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>1019817</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>2017-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>727988</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>2016-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>1188795</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>884479</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>2014-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>128362</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>2013-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>85726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>2012-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>92226</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>2011-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>170676</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>2010-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>56999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>2009-01-01</td>\n",
+ " <td>log only</td>\n",
+ " <td>192680</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " LogYear FilterActions Freq\n",
+ "0 2019-01-01 log only 498557\n",
+ "5 2018-01-01 log only 1019817\n",
+ "10 2017-01-01 log only 727988\n",
+ "15 2016-01-01 log only 1188795\n",
+ "20 2015-01-01 log only 884479\n",
+ "25 2014-01-01 log only 128362\n",
+ "30 2013-01-01 log only 85726\n",
+ "34 2012-01-01 log only 92226\n",
+ "40 2011-01-01 log only 170676\n",
+ "46 2010-01-01 log only 56999\n",
+ "52 2009-01-01 log only 192680"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "#TODO Multi line chart: a line per action"
+ "df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"log only\"].fillna('log only')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "' \\n# multiple line plot\\nnum=0\\nfor column in df.drop(\\'x\\', axis=1):\\n num+=1\\n plt.plot(df[\\'x\\'], df[column], marker=\\'\\', color=palette(num), linewidth=1, alpha=0.9, label=column)\\n \\n# Add legend\\nplt.legend(loc=2, ncol=2)\\n \\n# Add titles\\nplt.title(\"A (bad) Spaghetti plot\", loc=\\'left\\', fontsize=12, fontweight=0, color=\\'orange\\')\\nplt.xlabel(\"Time\")\\nplt.ylabel(\"Score\")\\n'"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#TODO Multi line chart: a line per action;\n",
+ "# TODO why are there combis such as \"disallow,tag\", but no \"warn,tag\"?\n",
+ "\n",
+ "# style\n",
+ "plt.style.use('seaborn-darkgrid')\n",
+ " \n",
+ "# create a color palette\n",
+ "palette = plt.get_cmap('Set1')\n",
+ "\n",
+ "plt.yscale('linear') # bot linear and log scales kinda suck\n",
+ "\n",
+ "plt.plot(df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"log only\"].fillna('log only')['LogYear'],\n",
+ " df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"log only\"].fillna('log only')['Freq'],\n",
+ " color=palette(1),\n",
+ " label='log only')\n",
+ "plt.plot(df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"disallow\"]['LogYear'],\n",
+ " df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"disallow\"]['Freq'],\n",
+ " color=palette(2),\n",
+ " label='disallow')\n",
+ "plt.plot(df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"warn\"]['LogYear'],\n",
+ " df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"warn\"]['Freq'],\n",
+ " color=palette(3),\n",
+ " label='warn') \n",
+ "plt.plot(df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"tag\"]['LogYear'],\n",
+ " df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"tag\"]['Freq'],\n",
+ " color=palette(4),\n",
+ " label='tag')\n",
+ "plt.plot(df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"blockautopromote\"]['LogYear'],\n",
+ " df_actions_year[df_actions_year.fillna('log only')[\"FilterActions\"] == \"blockautopromote\"]['Freq'],\n",
+ " color=palette(5),\n",
+ " label='blockautopromote')\n",
+ " \n",
+ "# Add legend\n",
+ "plt.legend(loc=2, ncol=2)\n",
+ " \n",
+ "# Add titles\n",
+ "plt.title(\"A (bad) Spaghetti plot\", loc='left', fontsize=12, fontweight=0, color='orange')\n",
+ "plt.xlabel(\"Time\")\n",
+ "plt.ylabel(\"Score\")\n",
+ "''' \n",
+ "# multiple line plot\n",
+ "num=0\n",
+ "for column in df.drop('x', axis=1):\n",
+ " num+=1\n",
+ " plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)\n",
+ " \n",
+ "# Add legend\n",
+ "plt.legend(loc=2, ncol=2)\n",
+ " \n",
+ "# Add titles\n",
+ "plt.title(\"A (bad) Spaghetti plot\", loc='left', fontsize=12, fontweight=0, color='orange')\n",
+ "plt.xlabel(\"Time\")\n",
+ "plt.ylabel(\"Score\")\n",
+ "'''"
]
},
{