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mw-lifecycle-analysis/text_analysis/case3/041525_phab_comments.ipynb
Matthew Gaughan b9b93fddc1 updating case studies for comparative analysis
2025-04-30 17:39:32 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import spacy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
"metadata": {},
"outputs": [],
"source": [
"phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/0422_http_phab_comments.csv\"\n",
"phab_df = pd.read_csv(phab_path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
"metadata": {},
"outputs": [],
"source": [
"def http_relevant(text):\n",
" if pd.isnull(text):\n",
" return False\n",
" # expanded dictionary for relevancy\n",
" # http, login, SSL, TLS, certificate \n",
" for word in text.split():\n",
" if \"://\" not in word.lower():\n",
" #http\n",
" if \"http\" in word.lower():\n",
" return True\n",
" #login\n",
" if \"login\" in word.lower():\n",
" return True\n",
" #ssl\n",
" if \"ssl\" in word.lower():\n",
" return True\n",
" #tls\n",
" if \"tls\" in word.lower():\n",
" return True\n",
" #cert\n",
" if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
" return True\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
"metadata": {},
"outputs": [],
"source": [
"def is_migrated(comment_text):\n",
" if pd.isnull(comment_text):\n",
" return False\n",
" text = comment_text.strip()\n",
" if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
" return True \n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d449164e-1d28-4580-9eb1-f0f69978f114",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/1321857848.py:41: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
"/tmp/ipykernel_62952/1321857848.py:44: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n"
]
}
],
"source": [
"#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
"phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
"\n",
"#cleaning df\n",
"phab_df['id'] = phab_df.index + 1\n",
"#may have to build out the reply_to column \n",
"phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
"phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
"\n",
"phab_df = phab_df.rename(columns={\n",
" 'AuthorPHID': 'speaker',\n",
" 'TaskPHID': 'conversation_id',\n",
" 'WMFaffil':'meta.affil',\n",
" 'isGerrit': 'meta.gerrit'\n",
"})\n",
"\n",
"# after 08-01-2013 before 10-01-2015\n",
"phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
"filtered_phab_df = phab_df[(phab_df['date_created'] < 1443743999) & (phab_df['date_created'] > 1375315200)]\n",
"#filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379975444)]\n",
"\n",
"#removing headless conversations\n",
"task_phab_df = filtered_phab_df[filtered_phab_df['comment_type']==\"task_description\"]\n",
"headed_task_phids = task_phab_df['conversation_id'].unique()\n",
"filtered_phab_df = filtered_phab_df[filtered_phab_df['conversation_id'].isin(headed_task_phids)]\n",
"\n",
"#removing gerrit comments \n",
"mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
"\n",
"# filter out the sourceforge migration \n",
"# Originally from: http://sourceforge.net in the task task_summary\n",
"migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\n",
"\n",
"#cut down to only the data that is relevant (mentions http)\n",
"relevant_conversation_ids = task_phab_df[\n",
" task_phab_df['comment_text'].apply(http_relevant) |\n",
" task_phab_df['task_title'].apply(http_relevant)\n",
"]['conversation_id'].unique()\n",
"\n",
"task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
"mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
"\n",
"task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
"mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
"\n",
"comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
"task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
"#comment_phab_df = mid_comment_phab_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unique conversation_ids: 2218\n",
"Unique ids: 14099\n",
"Unique speakers: 626\n"
]
}
],
"source": [
"unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
"unique_ids = len(comment_phab_df['id'].unique())\n",
"unique_speakers = len(comment_phab_df['speaker'].unique())\n",
"\n",
"print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
"print(f\"Unique ids: {unique_ids}\")\n",
"print(f\"Unique speakers: {unique_speakers}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d226d781-b002-4842-a3ae-92d4851a5878",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"\n",
"def preprocess_text(text):\n",
" text = str(text)\n",
" text = text.replace('*', ' ')\n",
" text = text.replace('-', ' ')\n",
" text = re.sub(r'http\\S+', '', text)\n",
" return text"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/2783900859.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)\n"
]
}
],
"source": [
"comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
"metadata": {},
"outputs": [],
"source": [
"#comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
"metadata": {},
"outputs": [],
"source": [
"nlp = spacy.load(\"en_core_web_sm\")\n",
"\n",
"def extract_dependency_tree(text):\n",
" doc = nlp(text)\n",
" dependency_trees = []\n",
" \n",
" for sentence in doc.sents:\n",
" for token in sentence:\n",
" token_info = (\n",
" token.text, \n",
" token.lemma_, \n",
" token.dep_, \n",
" token.head.text, \n",
" list(token.ancestors), \n",
" list(token.subtree), \n",
" list(token.children)\n",
" )\n",
" dependency_trees.append(token_info)\n",
" \n",
" return dependency_trees"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/2805711855.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)\n"
]
}
],
"source": [
"comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
"metadata": {},
"outputs": [],
"source": [
"#comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
"metadata": {},
"outputs": [],
"source": [
"#get VAD scores\n",
"import numpy as np\n",
"#https://saifmohammad.com/WebPages/nrc-vad.html\n",
"column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
"vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
"vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
"\n",
"def vad_scoring(dependency_tree):\n",
" valence = []\n",
" arousal = []\n",
" dominance = []\n",
" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
" if lemma in vad_dict:\n",
" valence.append(vad_dict[lemma]['Valence'])\n",
" arousal.append(vad_dict[lemma]['Arousal'])\n",
" dominance.append(vad_dict[lemma]['Domination'])\n",
"\n",
" # Compute average scores across the comment\n",
" avg_valence = np.mean(valence) if valence else 0\n",
" avg_arousal = np.mean(arousal) if arousal else 0\n",
" avg_dominance = np.mean(dominance) if dominance else 0\n",
"\n",
" return [avg_valence, avg_arousal, avg_dominance]\n",
"\n",
"def dominance_prevail(dependency_tree):\n",
" dominant_words = 0 \n",
" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
" if lemma in vad_dict:\n",
" if vad_dict[lemma]['Domination'] >= 0.75:\n",
" dominant_words += 1\n",
" if vad_dict[lemma]['Domination'] <= 0.25:\n",
" dominant_words += 1\n",
" return dominant_words\n",
"\n",
"def arousal_prevail(dependency_tree):\n",
" arousal_words = 0 \n",
" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
" if lemma in vad_dict:\n",
" if vad_dict[lemma]['Arousal'] >= 0.75:\n",
" arousal_words += 1\n",
" if vad_dict[lemma]['Arousal'] <= 0.25:\n",
" arousal_words += 1\n",
" return arousal_words\n",
"\n",
"def valence_prevail(dependency_tree):\n",
" valence_words = 0 \n",
" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
" if lemma in vad_dict:\n",
" if vad_dict[lemma]['Valence'] >= 0.75:\n",
" valence_words += 1\n",
" if vad_dict[lemma]['Valence'] <= 0.25:\n",
" valence_words += 1\n",
" return valence_words\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "828fb57a-e152-42ef-9c60-660648898532",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/2858732056.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
"/tmp/ipykernel_62952/2858732056.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
"/tmp/ipykernel_62952/2858732056.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
"/tmp/ipykernel_62952/2858732056.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)\n"
]
}
],
"source": [
"#establishing per-comment VAD scores \n",
"comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
"comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
"comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
"comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "27e47f6f-0257-4b70-b222-e91ef888c900",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/335308388.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
"/tmp/ipykernel_62952/335308388.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
"/tmp/ipykernel_62952/335308388.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n"
]
}
],
"source": [
"comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
"comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
"metadata": {},
"outputs": [],
"source": [
"# expand the dependency parser \n",
"\n",
"#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
"#pattern = r'\\b(WMF|Foundation)\\b'\n",
"#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
"pattern = r'\\b(http|https)\\b'\n",
"\n",
"dependency_relations = []\n",
"resolved_dependency_relations = []\n",
"\n",
"for index, row in comment_phab_df.iterrows():\n",
" text = row['comment_text']\n",
" timestamp = row['timestamp']\n",
" comment_id = row['id']\n",
" conversation_id = row['conversation_id']\n",
" WMFaffil = row['meta.affil']\n",
" \n",
" for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
" if re.search(pattern, token, re.IGNORECASE):\n",
" dependency_relations.append({\n",
" 'comment_id': comment_id,\n",
" 'timestamp': timestamp,\n",
" 'wmfAffil':WMFaffil,\n",
" 'token': token,\n",
" 'dependency': dep,\n",
" 'head': head,\n",
" 'depth': len(list(ancestors)), \n",
" 'children': len(list(children)) \n",
" })\n",
" ''' \n",
" for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
" if re.search(pattern, token, re.IGNORECASE):\n",
" resolved_dependency_relations.append({\n",
" 'comment_id': comment_id,\n",
" 'timestamp': timestamp,\n",
" 'wmfAffil':WMFaffil,\n",
" 'token': token,\n",
" 'dependency': dep,\n",
" 'head': head,\n",
" 'depth': len(list(ancestors)), \n",
" 'children': len(list(children)) \n",
" })\n",
" '''\n",
"#resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations) \n",
"dependency_relations_df = pd.DataFrame(dependency_relations)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "82498686-14f4-40c8-9e33-27b31f115b47",
"metadata": {},
"outputs": [],
"source": [
"#now analysis/plotting \n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from matplotlib.gridspec import GridSpec\n",
"import matplotlib.ticker as ticker\n",
"import matplotlib.dates as mdates"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9833922d-d69a-4f8d-96ed-b25eea626114",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 47,
"id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/2958311105.py:7: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
" task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"#task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
"task_phab_df = task_phab_df[task_phab_df['is_relevant'] == True]\n",
"task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
"#task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
"\n",
"task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
"unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
"\n",
"wmf_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] == True)]\n",
"wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
"\n",
"other_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] != True)]\n",
"other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
"\n",
"'''\n",
"task_phab_df['speakers_task'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
"\n",
"# Filter dates 06-12-2015 to 10-01-2015\n",
"bounded_task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1443743999) & (task_phab_df['date_created'] > 1434067200)]\n",
"# Bin the speakers based on the number of tasks they created\n",
"bins = [0, 6, 26, 51, float('inf')]\n",
"labels = ['0-5', '6-25', '26-50', '51+']\n",
"min_speakers_task = bounded_task_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
"min_speakers_task = bounded_task_phab_df.rename(columns={'speakers_task': 'min_speakers_task'})\n",
"bounded_task_phab_df = bounded_task_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
"bounded_task_phab_df['task_bins'] = pd.cut(bounded_task_phab_df ['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
"print(bounded_task_phab_df)\n",
"bounded_task_phab_df['week'] = bounded_task_phab_df['timestamp_y'].dt.to_period('W').dt.start_time\n",
"weekly_breakdown = bounded_task_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
"speaker_breakdown = bounded_task_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
"\n",
"# Reshape the DataFrame for use with Seaborn\n",
"weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
"speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
"\n",
"rookie_bounded_task_phab_df = weekly_breakdown[weekly_breakdown['task_bins'] == '0-5']\n",
"\n",
"#unaff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] != True)]\n",
"#unaff_new_tasks = unaff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
"\n",
"#aff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] == True)]\n",
"#aff_new_tasks = aff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
"\n",
"#sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total', marker='o')\n",
"#sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
"#sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
"#sns.lineplot(x=aff_new_tasks.index, y=aff_new_tasks.values, color='#c7756a',linestyle=\"dotted\", label=\"WMF-affiliated new authors\", marker='x')\n",
"#sns.lineplot(x=unaff_new_tasks.index, y=unaff_new_tasks.values, color='#5da2d8', linestyle=\"dotted\", label=\"Nonaffiliated new authors\", marker='x')\n",
"'''\n",
"#sns.barplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total')\n",
"sns.barplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors')\n",
"sns.barplot(x=wmf_tasks.index, y=-wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors')\n",
"#sns.lineplot(data=rookie_bounded_task_phab_df, x='week', y='count', color='green', label='Authors with ≤ 5 tasks', marker='o')\n",
"\n",
"plt.title('New Relevant Phabricator Tasks Indexed with HTTP')\n",
"plt.xlabel('Timestamp')\n",
"plt.ylabel('Unique taskPHIDs')\n",
"plt.xticks(rotation=90)\n",
"# Customize the x-axis for weekly labels\n",
"plt.grid(True)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"#plt.savefig('031825_new_tasks_fig.png')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/3303796756.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
"/tmp/ipykernel_62952/3303796756.py:17: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
" unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
"/tmp/ipykernel_62952/3303796756.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
"/tmp/ipykernel_62952/3303796756.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n"
]
},
{
"data": {
"image/png": 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++OEHa968ub399ttWr149e/zxx30cYcnp1auX9e/f3/72t79Zt27dbMeOHZaenm4LFy60s88+27p37+73ECMqLy/PFixYYEuWLAkvGzt2rEVFRdmtt95qvXv3tmbNmlmPHj38G2QJ2b17tzVs2NCeeOKJQsuzs7PtzTfftHPPPdfGjx/v0+gib+PGjTZ58uTDlvfo0cPat29v+/fv92FUJY91orCLLrqo0H3Nz883M7NNmzbZOeecY3379vVraBG3e/dumzt37mHLH374YYuPj7etW7f6MCp/fffdd9a0aVPLzMy0lStX2uDBg61q1aoWCoVs27ZtZmZWUFDg8ygjZ8uWLfbOO++Ev77tttusTZs29uSTT9q+ffvMLNj338xs27Zt9uGHHx62fNCgQVavXj3bs2ePD6Pyz6pVq6x+/fq2efNm27Vrlz3xxBMWGxtrUVFRlpaWZma/bDeDiHXCifR8AxNQJ5msrCybMGGCnXHGGXbJJZdYhQoVbOrUqeHbN2zYYFWrVi20LEgOXul37txpHTp0sGbNmtmGDRvCy3Nzc2306NHWokUL2759ux/DLDHek4HZL08IGzdutMaNG9vOnTvDt3333XclPraSkpaWZhdffLFdddVVNm7cOPvwww8tPj7ehg4damZmffv2tRtvvNHnUZaMyZMnW5cuXaxr1642cuTIQrdNmzbNmjZtaqmpqT6NLrK8bUNeXl542dq1a6127dqFdi5fe+01q1evnq1atarEx1iSsrKy7KabbrKuXbtacnLyYbf16NHDevXq5dPoSp73+Jg8ebLVr1/fFixYYGbB3pH2HLyDWFrXCe/v/MYbb1jz5s3t5ZdfPuy2l156yVq0aFHoeTUojvQ495atXbvWzj77bBs3bpwVFBSUihdX3n3fvXu3tW/f3nJycszM7PLLL7eKFStavXr1bPr06X4OsUQc+rjIzc0t9II7MzPTzMxef/11H0ZXcg5+zHtNvvrqK2vQoIH9+9//LrS8NOjWrZtt3rzZzMx69uxpcXFxVqlSJZswYYLPI4u8Q7d/pXWdOHDgQETnG8oU5+FaKH5bt27V6tWrVaZMGTVo0EA1atTQzTffrD59+mjLli3q3bu3EhMTJblD4HJzc1WjRo3AXfkqOztbsbGxkn65Qskpp5yid999V99//71q1KghyR0+XqZMGdWsWVPZ2dmKiYnxc9jFLj09PXz+kpo1a+q0006TVPgKFTk5OdqzZ48OHDggSRo5cqQ+/PBDffnll+HvP5kdqcHzzz+v0aNHa9KkSTIz3XXXXRozZowk6fTTT9ePP/7o55AjYseOHdq0aZMqVKigatWqKT4+Xh06dNDrr7+uOXPmHHalrxo1agTySia5ubmF1vPo6Ojw/zdo0EDJyclKSEgI3/eEhATFxsYqPj7eh9FGTkZGhnbu3Bm+bxUqVNC9996rrl276rHHHtPjjz+us846S5JUrlw5derUSf/85z+VmZmpihUr+jz64nFwg6pVqxa6eo13roJ+/fppzJgxGj9+vM4///zArQ/SkfcbPKVpnfD2G+ygKxh169ZNn376qaZOnary5cvrxhtvDD8GGjZsqN27d4efO4PAa3Ckc3V497t+/fpq1qyZZsyYoT/96U+SgncluIP3G2rXrq2qVavKzFSlShWVK1dOCxcu1KuvvqrFixdr4sSJ+vrrr8MXbOjZs6efQy9WW7Zs0eLFi7Vv3z4lJiaqbt264e1Afn6+ypQpoxdeeEGDBw/WtGnTVFBQoHXr1ukf//iHkpKSVLduXb/vQrE4eBvZsGFDVa9ePXybt1506NBB1apV08SJE9W7d+/APVccafvoKV++vL755ht9/vnnmj9/vqZPn67vv/9et912m6KionTLLbf4NOrid6RtQ2lcJw7uUKtWLZ1yyikaMGCAevfurZ9//lm9evUq3vmG/3GCDBG0dOlSq1u3rjVs2NBq1qxp1atXt2nTpoXfzdy9e7e1bNnS/vnPf4Z/5pFHHrGmTZsG6uNXK1eutAsvvNBmz55tZlakd+juuOMO69mzp2VlZZXACEvGsmXLrGXLlta4cWOrU6eOXXnllfbTTz8d9n3Lly+3ChUq2LZt2+zRRx+1smXL2sKFC30YcfE7tMEVV1wRPqJnz549lpGRUahJQUGB9ezZ0+69916/hhwRS5cutUaNGlmDBg2sdu3a1qpVq/BHK9auXWutW7e2qlWr2mOPPWZm7oiXUaNGWWJioqWnp/s59GL1ww8/2HXXXWf/+c9/DrvN20Ycuq249957rXv37paRkVEiYywJKSkp1q5dO2vcuLHVr1/f7rnnnvC7l4sWLbLKlSvb1VdfbV988UX4ZwYNGmSXX365ZWdn+zXsYnWkBt5HaDze0XF///vfrVGjRvb999/7MdSIOtJ+w/Tp0wv9nQ99Jz+I68SR9hu8v//69eutR48edtFFF9no0aPNzO1PPfDAA9aqVSvbtWuXP4MuZkdqcCjvsbB69Wo75ZRT7G9/+1tJDrFEHG3fqW/fvhYXF2f16tULf1R1yZIlNmTIEFu7dq1Poy5+S5cutbPOOstatGhh9evXtyZNmhT69IDZL9tI76iP2NhYi4uLs8WLF/sx5IgoyjYyNzfXzMy++OILq1Gjhr3//vt+DTcifm3b4G0Phg8fbuXLl7cGDRqE//abN2+2cePG2Q8//ODLmCOhKK+rSsM6caQO69evD9++a9cua9WqVbHONzABdYJKS0uzRo0a2QMPPGBbtmyxRYsW2dChQy06Otoef/xx27Nnj2VmZlrfvn2tbdu21q5dO+vbt6+deuqphc71cLJbv369NWzY0BISEqxly5Y2Z84cM/v1z99u2rTJHnjgAUtISLCUlJSSHGpErV692k477TQbPny4paSk2KRJk6xz58729NNPm1nhHuvXr7fzzjvPBg4caLGxsbZo0SK/hl2sfq3BU089ZWaHv6j68ccfbcSIEVa1atVAfbRk69atdsYZZ9j9999vq1evtnfffdeuvfZai4mJsTfeeMPMzNasWWN9+vSxM844w04//XS76KKLLCEhIVBPmKmpqVanTh2Lj4+3q6+++qj3befOnTZixAhLSEiwZcuWldAoI2/VqlV22mmn2bBhw+ybb76xMWPGWJs2bQp9fCQ5OdlatmxpLVu2tLPPPtuuvPJKi4uLO+yjeSerX2vgnRfu0OeL1atXW2xsrD3zzDN+DDdijrbfcOgEU1DXid/ab/BeTPz00082fPhwO/PMMy0+Pt5at25tp512WmC2kce677R3715r27at9e/fPzCT0mZH33cyM/v6668tMTHxsDfqgnQuVW8bOWLECNu6davNnTvXfve739mXX34Z/p5DP8Z+xx13WNWqVW358uW+jDkSjnUbuWXLFqtTp47dd999gfkI3m9tG7zHQEpKinXp0uWw1w8Hn+LgZHcsr6u8v30Q14mjdcjPz7eMjAy79tpri3W+gQmoE9S6deuscePGh638f/3rXy0UCtnzzz9vZm5D8uyzz1q/fv1sxIgRgZqZPnDggN111112zTXX2NSpU61Pnz7WvHnzX92R+uabb+yPf/yj1a1bN1CTcPv27bN+/frZwIEDCy2/8cYb7cILLzzs+3/88UcLhUJWpUqVwOxMH2uDtLQ0e+yxx+yMM84I1GPBzL0ze/bZZxd6d2L//v123333WUxMjL333ntm5hosW7bMnnzySZs6dWqgzv20f/9+u/HGG61Xr142fvx4u/jii+2KK6741cf7559/boMGDbL69esH6vGwZ88eu+qqq+zWW28ttPySSy6xq6++2swKn2T5/ffft3vuuceeeuqpwDxXFKXBkTz99NOB2ok0O/p+w4svvmhm7jER1HWiKPsN3ouozMxM27x5s02YMMHef/99W7dunZ9DLzbHuu/kmTlzZqDerCnKfoPXwjuvy8HLgiIjI8O6dOlid9xxR6HlXbt2tb/+9a/28ssvH3YU8cSJEy0UCgVmH9JT1G3kwRMtb775ZmCeK4qybfDOhxakCdhDHetrCrNgrhPH0mHjxo3FOt/ABNQJKjk5udBHp7wNgpnZuHHjrEyZMoedRDUos/MH++STT+zVV181M7Nvv/3WevfuXWhjebD09HT74IMPjvixtJPZjh07bOjQoTZlyhQz++WJ8YMPPrDExETLzc0t9LdPS0uz3r17B2pH8lgb5Obm2saNG23Lli2+jDeS5syZY6FQKPxiybvfBQUFduedd1rlypUD9bf/NW+//XZ42zBjxozfnIT6+eefbcqUKYd93OBkt2HDBhs4cGD4hNLe88T48ePtiiuuMDP3+AjaC6qDFaXBkd7JDKKi7Dd4L7y2bdsWyHXC7Oj7DQUFBYF+HJgd275TULcPR9tvyMnJKTTRENQOZmbTp0+3r7/+Ovz12LFjLTo62tq3b28XXXSRhUKhwz5mdvCbXEFxLNvIoD4eirJ9DOp99xzrawpP0NaJonTIy8s7bJ6hOJ4/mYA6gV155ZV2wQUXhM9jkZubG94wXH755da/f/9CT6BB32CYmc2dO/ewGfsDBw4E5t2JX3PwoeHe3/mTTz6xc88917Kzs8PLduzYYWYWqEPoPUVtcPDV/4IoLy/POnToYH379g3fV+/J4L///a916NDBHnnkkVLxIutg06ZNC09CeUd0HDhwIHwupKC2OPi8Tt468Prrr1tSUlKhZUG+lHRRGwTpHEe/pij7Dd4726Vhn8Hs1/cbVq5c6fPISk5pbVDU/YYgnRvxSA5e1z/99FM766yz7IMPPrC9e/eamTsnYJMmTWzv3r2FJmWC6GjbyD/84Q+Wk5PD9pFtg5kFf9twPK+timPdCNYp/QPm1ltvVUxMjIYPH64dO3aoTJky4asVVK9eXTt37lRMTEz4qk9BumLJoQoKCiRJ7du31+DBg9WkSRMNHjxYs2bN0vDhw3XxxRdr7969Po8yclq3bi2p8JVpMjMztW/fPkVHRysUCmnkyJG67LLLlJOTozJlgneBy6I26N69u3JycmRmfg43YqKjo9W3b19t2LBBL7zwgjIyMsJXZ6lVq5YqVaqk1atXKxQKBe6qLUeSn58vSerVq5duvfVW7d+/X6NGjdLChQs1dOhQtWnTRtnZ2YHbPnqP7y5duoS/9u7jvn37lJ6eHl42ZswY3XLLLcrLy/NtvJFwrA0GDhwYuAaHKsp+g3dF2aCtE4c62n5DUlJSoPcbJBoUdb/h0ksvDfR+w8Hretu2bfXhhx/qiiuuUKVKlSS5fYeEhARVqlQpcFePPtTRtpE7duxQTEwM20e2DYW2DUF1PK+timPdCN6r1ADp3r27UlNTNXnyZN1+++166aWXVK1aNUnuMqHx8fHKyckJ9IbSe6BHRUWFL7fevn17SdKLL76oSy65RJUrV9Znn32mypUr+zzayPEuCRoKhZSfn6/o6GjFxcWpfPnyio6O1siRI/Xss8/q66+/VtmyZf0ebkTQ4Jf14fbbb1dqaqref/99ZWVl6aGHHlJcXJwkKSEhQVWrVlV+fn64VxB5LaKjo8Pbht69eysUCunVV19V9+7dlZ+fr88++yz8gjtIvL+r1yEUCikvL09lypRRlSpVVLlyZYVCIT388MN64okntGDBgsBNTNPgcOw3sN8g0cDDfoNz8IvGKlWqqEqVKoVu37Ztm5o0aRL4bYPENpJtg8O2wfGtw/98DBWKnfeRuqysLDMzmzx5snXo0MESEhKsf//+duWVV1qlSpUCddWaI/E6/Nphf5dffrnFx8cH/uN3R+pg5s4FdNFFF9nQoUOtbNmygbna3ZGUxgZHOgGk18H7ONljjz1mF1xwgTVu3NiGDx9u1157rVWqVClQ68Rvddi9e3d42cHbhs6dO1t8fHygroRZ1A5mZv/617/syiuvtAcffDBQV8Kkwa9jv8Fhv4EGntK433Akv9bBzJ2E+OGHH7aEhITAf+SKbSTbBg/bBsfPDkxA+Sg1NdXmzp1baJn3YNiwYYOdfvrp4ctIp6am2pgxY6x///42ePBgW7FiRYmPN1KO1qFGjRr25ptvFrrtL3/5i1WoUCFQV+851g4zZsywUChklSpVOuwKJicrGjg//PCD3X///ZabmxtednCHZs2a2ezZs83MPVHcfffddumll9qAAQMCNelytA7NmzcPn3zazJ3LYfjw4RYTE2PJycklPt5IOdYOr776qoVCIatYsWJg1gsaONnZ2YWu2GVm4Salab/haB1Kw34DDZxj7RDU/YZj7fD111/bgAEDrGbNmoG6steReOe1Kk3byEMd3KC0bBuO5Lc6BHXbcCR+d2ACyidLly61GjVq2C233GJpaWmFbvvpp5+sZs2adttttxXa2Q6ionY49IRnM2fODNS7NcfTYenSpda9e/fAPGHSwFm6dKmVK1fOQqGQzZw5s9BtGzZssFq1atmtt9562LYhaCcdL2qHQ7cNb7/9dqAmn46nw2effWatW7cOzDaSBs7KlSvtuuuus/PPP99uu+22Qu/Ub9y4sdTsNxS1Q5D3G2jgHE+HIO43HE+HBQsW2NNPP22rV6/2Y8gRsWbNGnvyySft/vvvt8mTJ9v27dvDt61fv75UbCOL2iDo24bj6RDEbcOJ2oEJKB+sW7fOqlevbsOHDz/iC8bRo0fb3XffXejBEMSrMRxPhyD6Xzrs2rWrBEYYeTRwkpOTrXz58nbHHXdYnz59rH///rZ///5wk5tuuskGDRoU+G0DHZzj6eA5dBL3ZEUDZ/ny5XbKKafYTTfdZI8++qhVr17dhg4dGr79wQcftMGDBwd+nTieDkFDA+d/6RCk/Yb/pUOQJmJSUlIsISHBunfvbtdcc42VLVvWOnfubO+//76ZmT366KOB30YeT4Mg+l86BGnbcCJ3YALKB5MmTbKePXuamdv4P/nkk3bLLbfY6NGjC33OMugbCDo4x9MhaE1oYPaf//zHKleubA899JCZmT3zzDMWHx9v69atC39P0C+PbEYHz/F2CNJRcDRwMjIy7OKLL7bhw4eHl73yyis2YMCA8GXUPUHbLh6MDjTwHG+HoDXh8eDs2rXL2rVrZw8++GB4WUpKikVHR1urVq3s3Xff9W9wJYQGzvF2CNr6caJ3CP41uk9AixcvVnZ2tiR3+ej33ntPe/fu1dSpU3X33Xfr1VdflRT8SyTTwTmeDkFrUtob7N69WxdeeKEGDRqksWPHSpLuvPNO1a9fX2PGjJG5NwsCf3lkOjj/S4eoqGA8rdOgsIyMDDVp0iT8dUpKipYsWaIWLVqoZ8+emjBhgqRgbRePhA408BxPhyA24fEg5ebmKisrS926dVNBQYH279+vJk2aqF27dsrPz9eLL76olStX+j3MiKKBc7wdgrZ+nOgdgreXdhJo3ry5YmNj9fbbbysmJkbvvPOO3n77bc2ePVsNGzbU9OnTlZ6e7vcwI44ODh1oEB8fr/nz5+vpp5+W5C6TW6ZMGXXr1k2LFi3Szp07FQqFZGY+jzSy6ODQgQYH279/v3bt2qX58+frk08+0ejRo/X666/rlltu0aOPPqry5cvrrbfeUnJyst9DjSg60MBDB4cOzt69e7Vq1SqlpaUpKipKFSpU0ObNm5WTk6Phw4dr8eLFmjFjht/DjCgaOHRwTvgOJXKcFQqZP3++xcbGWqtWraxXr16Fblu5cqWFQiH78ssvfRpdyaGDQwcaeLxDX73/btmyxSpWrGh/+ctf/BxWiaODQwcaeD755BNr2LCh9ejRw6pVq2ZvvfVW+LbU1FSrUKGC/eMf//BxhCWDDjTw0MGhgzN06FCLjY210aNH2wsvvGBVqlSxW2+91czMnnrqKWvfvr1lZmYG7qNWB6OBQwfnRO5Qxr+pr9Jh06ZNWrVqlbZv366uXbuqcuXKSkxM1DPPPKPBgwfLzLR+/XrVq1dPknT66aerbdu2io+P93fgxYwODh1o4Dm0Q3x8vMqWLav8/HxFR0crPz9fNWrU0KBBg/Txxx/rhhtuUJ06dfwedrGjg0MHGngO7tClSxfFxcWpe/fumjdvnmJjY9WlSxfVrVtXklRQUKBTTz1V5513nqpUqeLzyIsXHWjgoYNDB+fQ54pTTjlFjz32mOLi4jR58mRVq1ZNw4YN06hRoyQpfAR9hQoV/Bx2saKBQwfnpOtQ4lNepcjSpUutWrVq1rJlSytbtqz97ne/s/vuu8/S09PNzGzMmDEWCoVs4MCBNnfuXEtLS7ORI0davXr1bMuWLT6PvvjQwaEDDTxH6jB8+PDwVSfy8vLC3ztz5kyrXLmyffjhhz6NNnLo4NCBBp4jdbj33ntt586dZuaOAKtfv769/vrrZuZOwj569GirU6eO/fTTTz6OvHjRgQYeOjh0cA7t0LRpU3vggQfCzxXbt28/7ApegwYNsltuucVycnICcdQLDRw6OCdjByagImT37t3WsmXL8JNDVlaWjRgxwtq1a2c9evQIv+CeOHGi1ahRw6pXr27NmjWzM844wxYvXuzz6IsPHRw60MDzWx2uuuqq8M7kwS+4u3XrZh07drT8/PzAPGHSwaEDDTxH67B9+3YzM/vzn/9soVDILrjgAuvUqZPVqlWrVG0jS0MHGjh0cOjg/FqHxMREu/LKK23Hjh1m9stHttesWWP333+/xcXF2fLly/0cerGhgUMH52TtwARUhKxfv97q169vc+bMCS/Lzs62iRMn2gUXXGDXX3+97dmzx8zM1q1bZ/PmzbPZs2fb5s2b/RpyRNDBoQMNPL/VITEx0a6//nrLyMgws18uJz9jxgxbu3atL+ONFDo4dKCB52gdrrvuunCH6dOn22233WZPPPGErVmzxq8hRwQdaOChg0MH57c6tG3btlCHnTt32siRI61169aBmoSjgUMH52TtwARUhGzfvt3OPvtse/HFF83sl5nH/Px8Gz9+vLVs2dImTZrk5xBLBB0cOtDAU5QOkydPNrPCR3wEDR0cOtDAc7QO5513XrhDkNGBBh46OHRwjuW5wsxs8+bNtm3bNl/GGik0cOjgnKwdQmal4BrGPsjNzVW/fv20detWvfXWW+GTAnouueQSxcTE6KOPPvJphCWDDg4daOChg0MHhw408BSlQ5kyZfTxxx/7NMKSQQcaeOjg0MHhuYIGHjo4J2uHKL8HEERmppiYGL388stKTU3V4MGDlZaWpoPn+q644grt2LFDBw4c8HGkkUUHhw408NDBoYNDBxp4itph586ddAh4Bxo4dHDo4PBcQQMPHZyTuQMTUBEQCoWUk5Oj008/XZ9++qkWLFigG264QYsWLVJ+fr4kKTk5WQkJCYqKCu6fgA4OHWjgoYNDB4cONPDQwaEDDTx0cOjg0IEGHjo4J3MHPoIXAfn5+YqOjtbOnTuVk5OjrKwsde/eXZUqVVJeXp7q16+vWbNmae7cuWrevLnfw40YOjh0oIGHDg4dHDrQwEMHhw408NDBoYNDBxp46OCczB1OrOmwk8yaNWuUnJxcaJn3YNiwYYOaN2+uWbNmqX79+lq4cKGGDBmirl27qk2bNlq4cOEJ92A4XnRw6EADDx0cOjh0oIGHDg4daOChg0MHhw408NDBCWSHSJ7hPMiSk5MtFArZyy+/fNhtGzdutPj4ePvjH/9oBQUF4ctGBxEdHDrQwEMHhw4OHWjgoYNDBxp46ODQwaEDDTx0cILagQmo45CcnGwVKlSwBx544Ii3T58+3YYNGxa+FGJQ0cGhAw08dHDo4NCBBh46OHSggYcODh0cOtDAQwcnyB04B9Qx+uGHH3TOOefovvvu07hx42Rmevfdd7V69WrVq1dPbdq0UYMGDcKHxgUVHRw60MBDB4cODh1o4KGDQwcaeOjg0MGhAw08dHCC3qGM3wM42Xz11VfKz8/XhRdeqIKCAnXu3Fn79+/Xtm3bFBcXp6ysLL3xxhtKTEyUmSkUCvk95Iigg0MHGnjo4NDBoQMNPHRw6EADDx0cOjh0oIGHDk7gO0T2AKtgeuSRRyw6OtoaNGhgPXv2tNWrV1teXp59//331rt3b2vdurVt27bN72FGHB0cOtDAQweHDg4daOChg0MHGnjo4NDBoQMNPHRwgtyBCagiysvLK/T12LFj7ZxzzrElS5YUWj5t2jRLSEiwZcuWleDoSg4dHDrQwEMHhw4OHWjgoYNDBxp46ODQwaEDDTx0cEpLByagjmLXrl3h/z/0QbFkyRLLysoyMwufeX7evHnWpEkTW7t2bYmNsSTQwaEDDTx0cOjg0IEGHjo4dKCBhw4OHRw60MBDB6e0dYjy+yOAJ7JVq1apZcuWGjVqlCQpOjpa+fn54dtbtGihcuXKSZKiolzKd955R6eccooSEhJKfsARQgeHDjTw0MGhg0MHGnjo4NCBBh46OHRw6EADDx2cUtnB7xmwE9XGjRutRYsWdtZZZ9nZZ59tjz76aPi2Q2cmzcxWrVplQ4YMsapVq9rSpUtLcqgRRQeHDjTw0MGhg0MHGnjo4NCBBh46OHRw6EADDx2c0tqBq+AdgZlp6tSpqlmzpoYMGaJ58+Zp6tSpkqRRo0aFZya9yx6mpKTo9ddf16xZszR79mw1b97cz+EXGzo4dKCBhw4OHRw60MBDB4cONPDQwaGDQwcaeOjglOoOJT7ldZLYunWr/fOf/zQzs23bttno0aOtSZMm9sgjj4S/x/scppn7fObWrVtLfJyRRgeHDjTw0MGhg0MHGnjo4NCBBh46OHRw6EADDx2c0tqBCagi2rJlyxEfFDNmzPBxVCWPDg4daOChg0MHhw408NDBoQMNPHRw6ODQgQYeOjilpQMfwfv/tm7dqk2bNmnXrl3q0qVL+HC3goIChUIh1ahRQ4MGDZIkvf322zIz7dmzR88//7z++9//qmbNmn4Ov9jQwaEDDTx0cOjg0IEGHjo4dKCBhw4OHRw60MBDB4cO/1+JT3mdgJYuXWp169a1Ro0aWZUqVaxJkyb21ltv2c6dO83MHfpWUFBgZm5mctSoURYKhaxq1aq2aNEiP4derOjg0IEGHjo4dHDoQAMPHRw60MBDB4cODh1o4KGDQ4dflPoJqLS0NGvSpIk9+OCDlpqaaps3b7a+ffta06ZNbfTo0ZaWlmZmFn5AmJn179/f4uLibMWKFX4Nu9jRwaEDDTx0cOjg0IEGHjo4dKCBhw4OHRw60MBDB4cOhZX6CagVK1bYmWeeedjM4gMPPGDnnHOOPfnkk5aZmRle/tprr1l8fLwtXry4pIcaUXRw6EADDx0cOjh0oIGHDg4daOChg0MHhw408NDBoUNhpX4CKjk52WrXrm1ff/21mZnt378/fNvgwYOtXr16tnTp0vCyn3/+2datW1fi44w0Ojh0oIGHDg4dHDrQwEMHhw408NDBoYNDBxp46ODQobCQmZnf56Hy2/nnn69KlSrp//7v/yRJ2dnZio2NlSS1adNGDRs21NSpU5Wfnx8+WVgQ0cGhAw08dHDo4NCBBh46OHSggYcODh0cOtDAQweHDr+I8nsAJS0zM1N79+5VRkZGeNmECRO0YsUKXXfddZKk2NhY5eXlSZI6dOigzMxMSQrUg4EODh1o4KGDQweHDjTw0MGhAw08dHDo4NCBBh46OHT4baVqAmrlypW65ppr1LFjRzVt2lRTpkyRJDVt2lTPP/+8vvjiC/Xu3Vu5ubmKinJp0tLSVLFiReXl5SkoB4vRwaEDDTx0cOjg0IEGHjo4dKCBhw4OHRw60MBDB4cORVDCH/nzzYoVKywhIcGGDh1qU6ZMsWHDhllMTEz45F6ZmZn2wQcfWO3ata1JkybWo0cP69Onj1WsWNFSUlJ8Hn3xoYNDBxp46ODQwaEDDTx0cOhAAw8dHDo4dKCBhw4OHYqmVJwDKj09Xf369VOTJk30/PPPh5cnJSXpnHPO0QsvvBBetnfvXo0dO1bp6ekqV66cbr/9djVr1syPYRc7Ojh0oIGHDg4dHDrQwEMHhw408NDBoYNDBxp46ODQoejK+D2AkpCbm6vdu3erV69ekqSCggJFRUWpXr16Sk9PlySZuyKgKleurCeeeKLQ9wUFHRw60MBDB4cODh1o4KGDQwcaeOjg0MGhAw08dHDoUHSl4t5Wq1ZNb775pi666CJJUn5+viSpVq1a4T94KBRSVFRUoZOFhUKhkh9sBNHBoQMNPHRw6ODQgQYeOjh0oIGHDg4dHDrQwEMHhw5FVyomoCTprLPOkuRmGWNiYiS5Wci0tLTw94wbN06vvfZa+Iz0QXxA0MGhAw08dHDo4NCBBh46OHSggYcODh0cOtDAQweHDkVTKj6Cd7CoqCiZWfiP7c1Ijho1SmPHjtWSJUtUpkzws9DBoQMNPHRw6ODQgQYeOjh0oIGHDg4dHDrQwEMHhw6/rdQcAXUw77zrZcqUUZ06dfT000/rySef1KJFi3Tuuef6PLqSQweHDjTw0MGhg0MHGnjo4NCBBh46OHRw6EADDx0cOvy6Ujn15s1CxsTE6O9//7vi4uI0d+5ctWzZ0ueRlSw6OHSggYcODh0cOtDAQweHDjTw0MGhg0MHGnjo4NDh15XKI6A8l1xyiSRp/vz5at26tc+j8Q8dHDrQwEMHhw4OHWjgoYNDBxp46ODQwaEDDTx0cOhwuJB5x4eVUpmZmapYsaLfw/AdHRw60MBDB4cODh1o4KGDQwcaeOjg0MGhAw08dHDoUFipn4ACAAAAAABAZJXqj+ABAAAAAAAg8piAAgAAAAAAQEQxAQUAAAAAAICIYgIKAAAAAAAAEcUEFAAAAAAAACKKCSgAAAAAAABEFBNQAAAAJ7FOnTppyJAhfg8DAADgNzEBBQAAUAxeeeUVVa5cWXl5eeFl+/btU0xMjDp16lToe+fMmaNQKKTU1NQSHiUAAIA/mIACAAAoBklJSdq3b58WLVoUXvbNN9+oevXqWrBggQ4cOBBePnv2bJ1xxhlq0KCBH0MFAAAocUxAAQAAFIPGjRurRo0amjNnTnjZnDlzdNVVV6levXr67rvvCi1PSkpSQUGBxo0bp3r16ql8+fI699xzNX369EL/7vLly9W9e3dVqlRJ1apVU//+/bVjx45fHcfHH3+sKlWqaMqUKcV+HwEAAI4XE1AAAADFJCkpSbNnzw5/PXv2bHXq1EkdO3YML8/KytKCBQuUlJSkcePGafLkyXrllVe0YsUKDR06VDfccIO++uorSdLu3bvVuXNnnXfeeVq0aJE+/fRTbdu2TX369Dni73/rrbfUr18/TZkyRddff33k7zAAAEARlfF7AAAAAEGRlJSkIUOGKC8vT1lZWVqyZIk6duyo3NxcvfLKK5Kkb7/9VtnZ2erUqZOaNWumL7/8UomJiZKk+vXra+7cuZowYYI6duyol156Seedd57+8pe/hH/HxIkTVadOHf34449q1KhRePn48eP10EMP6cMPP1THjh1L9o4DAAAcBRNQAAAAxaRTp07KzMzUwoULtWvXLjVq1EinnXaaOnbsqJtuukkHDhzQnDlzVL9+fe3bt0/79+9X165dC/0bOTk5Ou+88yRJS5cu1ezZs1WpUqXDfldqamp4Amr69OlKS0vTvHnz1KZNm8jfUQAAgGPEBBQAAEAxadiwoWrXrq3Zs2dr165d4SORatasqTp16mj+/PmaPXu2OnfurH379kly52yqVatWoX8nNjZWkruK3hVXXKEnnnjisN9Vo0aN8P+fd955Wrx4sSZOnKjWrVsrFApF6i4CAAAcFyagAAAAilFSUpLmzJmjXbt2afjw4eHlHTp00MyZM/X999/r9ttvV7NmzRQbG6uNGzf+6kfmWrZsqRkzZujMM89UmTK/vtvWoEEDPfPMM+rUqZOio6P10ksvFfv9AgAA+F9wEnIAAIBilJSUpLlz5yo5ObnQxFLHjh01YcIE5eTkKCkpSZUrV9Z9992noUOHatKkSUpNTdXixYv14osvatKkSZKkO++8U+np6erXr58WLlyo1NRUffbZZ7rpppuUn59f6Pc2atRIs2fP1owZMzRkyJCSvMsAAABHxRFQAAAAxSgpKUlZWVlq0qSJqlWrFl7esWNH7d27V40bNw5/fG7MmDE67bTTNG7cOK1bt07x8fFq2bKlHnzwQUnuo3vz5s3TAw88oG7duik7O1t169bVpZdeqqiow99HbNy4sf7v//4vfCTUM888UzJ3GgAA4ChCZmZ+DwIAAAAAAADBxUfwAAAAAAAAEFFMQAEAAAAAACCimIACAAAAAABARDEBBQAAAAAAgIhiAgoAAAAAAAARxQQUAAAAAAAAIooJKAAAAAAAAEQUE1AAAAAAAACIKCagAAAAAAAAEFFMQAEAAAAAACCimIACAAAAAABARDEBBQAAAAAAgIj6fwQjqgi7nKpPAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#task_phab_df = phab_df[phab_df['comment_type'] == \"task_description\"]\n",
"unaff_tasks_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
"# Rank speaker's task values within each group\n",
"unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
"\n",
"# Filter dates 06-12-2015 to 10-01-2015\n",
"unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1443743999) & (unaff_tasks_phab_df['date_created'] > 1434067200)]\n",
"# Bin the speakers based on the number of tasks they created\n",
"bins = [0, 6, 26, 51, float('inf')]\n",
"labels = ['0-5', '6-25', '26-50', '51+']\n",
"min_speakers_task = unaff_tasks_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
"min_speakers_task = min_speakers_task.rename(columns={'speakers_task': 'min_speakers_task'})\n",
"unaff_tasks_phab_df = unaff_tasks_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
"unaff_tasks_phab_df['task_bins'] = pd.cut(unaff_tasks_phab_df['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
"\n",
"# Calculate the weekly breakdown of binned speakers_task values\n",
"unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
"weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
"\n",
"speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
"\n",
"# Reshape the DataFrame for use with Seaborn\n",
"weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
"speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
"\n",
"# Plot the stacked bar plot using Seaborn\n",
"plt.figure(figsize=(12, 8))\n",
"sns.barplot(data=weekly_breakdown, x='week', y='count', hue='task_bins', palette='colorblind')\n",
"#sns.barplot(data=speaker_breakdown, x='week', y='speakers', hue='task_bins', palette='colorblind')\n",
"plt.title(\"06-12-2015 to 10-01-2015 Weekly Unaffiliated Task Creation by Contributor Tenure\")\n",
"plt.xlabel('Week')\n",
"plt.ylabel('Tasks')\n",
"plt.legend(title=\"Contributor had created # tasks between 8-01-2013 and 06-12-2015:\")\n",
"plt.xticks(rotation=45)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"#plt.savefig('031625_weekly_tasks_by_history.png')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/62586942.py:27: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
"/tmp/ipykernel_62952/62586942.py:28: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
"/tmp/ipykernel_62952/62586942.py:35: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of comments for each date group:\n",
"date_group\n",
"Before announcement 10232\n",
"After announcement, before deployment 798\n",
"After deployment 3069\n",
"dtype: int64\n",
"\n",
"Number of speakers for each date group:\n",
"date_group\n",
"Before announcement 512\n",
"After announcement, before deployment 142\n",
"After deployment 310\n",
"Name: speaker, dtype: int64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_62952/62586942.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Number of comments for each date group and engaged commenter subgroup:\n",
"date_group est_commenter meta.affil\n",
"Before announcement False False 9935\n",
" True 297\n",
"After announcement, before deployment False False 783\n",
" True 15\n",
"After deployment False False 2987\n",
" True 82\n",
"dtype: int64\n",
"\n",
"Number of speakers for each date group and engaged commenter subgroup:\n",
"date_group est_commenter meta.affil\n",
"Before announcement False False 509\n",
" True 56\n",
"After announcement, before deployment False False 138\n",
" True 7\n",
"After deployment False False 305\n",
" True 24\n",
"Name: speaker, dtype: int64\n",
"\n",
"Number of comments for each engaged commenter subgroup, and WMF affiliation:\n",
"est_commenter meta.affil\n",
"False False 13705\n",
" True 394\n",
"dtype: int64\n",
"\n",
"Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
"est_commenter meta.affil\n",
"False False 619\n",
" True 75\n",
"Name: speaker, dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"'\\nplot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'new_commenter\\', scatter=False, legend=False, palette=palette)\\nplot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\\nplot1.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot1.fig.subplots_adjust(top=0.9) # Adjust subplots to make room for the title\\nplot1.add_legend(title=\"Comment publication timestamp:\")\\nfig1 = plot1.fig\\n# Plot for arousal_wc\\nplot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\\nplot2.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot2.add_legend(title=\"Comment publication timestamp:\")\\n#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\\n\\nplot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\\nplot3.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot3.add_legend(title=\"Comment publication timestamp:\")\\n'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1333.5x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bins = [\n",
" pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
" pd.Timestamp('2015-06-12 00:00:01+00:00'),\n",
" pd.Timestamp('2015-07-02 00:00:01+00:00'),\n",
" pd.Timestamp('2100-08-28 00:00:01+00:00')\n",
"]\n",
"labels = ['Before announcement', 'After announcement, before deployment', 'After deployment']\n",
"\n",
"#creating variables of interest\n",
"affective_comment_phab_df = comment_phab_df\n",
"affective_comment_phab_df['date_group'] = pd.cut(affective_comment_phab_df['timestamp'], bins=bins, labels=labels, right=False)\n",
"affective_comment_phab_df['speakers_comment'] = affective_comment_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
"#all comments prior to june 1 2013\n",
"subset_comment_phab_df = affective_comment_phab_df[affective_comment_phab_df['date_created'] <= 1370044800]\n",
"#getting counts \n",
"comment_counts = subset_comment_phab_df.groupby('speaker')['speakers_comment'].max().reset_index()\n",
"comment_counts = comment_counts.rename(columns={'speakers_comment': 'pre_june_2013_comments'})\n",
"#merge back \n",
"affective_comment_phab_df = affective_comment_phab_df.merge(comment_counts, on='speaker', how='left')\n",
"affective_comment_phab_df['pre_june_2013_comments'] = affective_comment_phab_df['pre_june_2013_comments'].fillna(0)\n",
"\n",
"affective_comment_phab_df['new_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] <= 10\n",
"affective_comment_phab_df['est_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] > 50\n",
"\n",
"palette = ['#31449c', '#4a7c85', '#c5db68']\n",
"\n",
"comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
"speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
"\n",
"print(\"Number of comments for each date group:\")\n",
"print(comment_counts)\n",
"print(\"\\nNumber of speakers for each date group:\")\n",
"print(speaker_counts)\n",
"\n",
"comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
"speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n",
"\n",
"print(\"\\nNumber of comments for each date group and engaged commenter subgroup:\")\n",
"print(comment_counts_engaged)\n",
"print(\"\\nNumber of speakers for each date group and engaged commenter subgroup:\")\n",
"print(speaker_counts_engaged)\n",
"\n",
"comment_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil']).size()\n",
"speaker_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil'])['speaker'].nunique()\n",
"\n",
"print(\"\\nNumber of comments for each engaged commenter subgroup, and WMF affiliation:\")\n",
"print(comment_counts_wmf)\n",
"print(\"\\nNumber of speakers for each engaged commenter subgroup, and WMF affiliation:\")\n",
"print(speaker_counts_wmf)\n",
"\n",
"#comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
"#fig, axes = plt.subplots(2, 1, figsize=(10, 12), sharex=True)\n",
"affective_comment_phab_df['polarized_wc'] = affective_comment_phab_df['dominant_wc'] + affective_comment_phab_df['valence_wc'] + affective_comment_phab_df['arousal_wc'] \n",
"plot1 = sns.lmplot(data=affective_comment_phab_df, x=\"date_created\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", row='est_commenter', scatter=False, legend=False, palette=palette)\n",
"plot1.set_axis_labels(\"Timestamp\", \"Count of Polarized Words\")\n",
"plot1.set_titles(row_template=\"Established Author: {row_name}\", col_template=\"WMF Affiliation: {col_name}\")\n",
"plot1.fig.subplots_adjust(top=0.9) # Adjust subplots to make room for the title\n",
"plot1.add_legend(title=\"Comment publication timestamp:\")\n",
"fig1 = plot1.fig\n",
"'''\n",
"plot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row='new_commenter', scatter=False, legend=False, palette=palette)\n",
"plot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\n",
"plot1.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
"plot1.fig.subplots_adjust(top=0.9) # Adjust subplots to make room for the title\n",
"plot1.add_legend(title=\"Comment publication timestamp:\")\n",
"fig1 = plot1.fig\n",
"# Plot for arousal_wc\n",
"plot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
"plot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\n",
"plot2.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
"plot2.add_legend(title=\"Comment publication timestamp:\")\n",
"#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\n",
"\n",
"plot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
"plot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\n",
"plot3.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
"plot3.add_legend(title=\"Comment publication timestamp:\")\n",
"'''\n",
"# Show plots\n",
"#fig1.savefig('031725_engaged_commenter_D_scoring_fig.png')\n",
"#plot2.fig.savefig('031725_engaged_commenter_A_scoring_fig.png')\n",
"#plot3.fig.savefig('031725_engaged_commenter_V_scoring_fig.png')\n",
"#plt.savefig('031625_engaged_commenter_VAD_scoring_fig.png')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x15001e41ca60>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1333.5x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
"plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
"plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
"plot2.fig.subplots_adjust(top=0.9) # Adjust subplots to make room for the title\n",
"plot2.add_legend(title=\"Comment publication timestamp:\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
"metadata": {},
"outputs": [],
"source": [
"#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
"#pattern = r'\\b(WMF|Foundation)\\b'\n",
"#pattern = r'\\b(bots)\\b'\n",
"filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
"resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
"\n",
"plt.figure(figsize=(12, 8))\n",
"gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
"\n",
"# Main plot: Token depth by timestamp\n",
"'''\n",
"ax0 = plt.subplot(gs[0])\n",
"sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
"ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
"ax0.set_xlabel('')\n",
"ax0.set_ylabel('Dependency Type')\n",
"ax0.legend().set_visible(False)\n",
"'''\n",
"# Calculate the median depth over time\n",
"filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
"median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
"wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
"other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"# Plot the median depth over time\n",
"ax0 = plt.subplot(gs[0])\n",
"sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
"sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
"sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
"ax0.set_title('Median Depth of \"VE\" in Phabricator Sentence Dependency Trees')\n",
"ax0.set_ylabel('Median Depth')\n",
"ax0.set_xlabel('')\n",
"\n",
"# Calculate the median depth over time\n",
"resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
"resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
"resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
"resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
"\n",
"# Plot the median depth over time\n",
"ax1 = plt.subplot(gs[1])\n",
"sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
"sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
"sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
"ax1.set_title('Median Depth of \"VE\" in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
"ax1.set_ylabel('Median Depth')\n",
"ax1.set_xlabel('')\n",
"\n",
"plt.tight_layout()\n",
"#plt.show()\n",
"\n",
"#plt.savefig('031625_VE_depth_fig.png')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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