From 2127d985f2562476b7e09459465eb235e1db4ca9 Mon Sep 17 00:00:00 2001 From: Matthew Gaughan Date: Thu, 6 Mar 2025 10:28:22 -0800 Subject: [PATCH] updating look at phab tasks --- .../ve_dependency-checkpoint.ipynb | 82 ++++++++----------- text_analysis/case1/ve_dependency.ipynb | 44 ++++++---- 2 files changed, 61 insertions(+), 65 deletions(-) diff --git a/text_analysis/case1/.ipynb_checkpoints/ve_dependency-checkpoint.ipynb b/text_analysis/case1/.ipynb_checkpoints/ve_dependency-checkpoint.ipynb index 31dac06..c7f8606 100644 --- a/text_analysis/case1/.ipynb_checkpoints/ve_dependency-checkpoint.ipynb +++ b/text_analysis/case1/.ipynb_checkpoints/ve_dependency-checkpoint.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "fa6a7cea-1375-4153-a388-1847dfa5b257", "metadata": {}, "outputs": [], @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "812ab4c8-2561-466b-bc57-defc93f5c893", "metadata": {}, "outputs": [], @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "60bcef32-67be-44f5-a51a-84e6e63d29ed", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "7babf07b-4f91-4e48-88a9-4fe10f8b668d", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "558d1638-abe9-4fc2-896e-6fc1bc396ca3", "metadata": {}, "outputs": [ @@ -462,7 +462,7 @@ "[32488 rows x 15 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "acb87a1a-c3e0-4d3f-8450-e2af96150e94", "metadata": {}, "outputs": [], @@ -488,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "b67c136e-16c4-4002-a2d6-f92c88252baf", "metadata": {}, "outputs": [], @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "f749706a-f2bb-42e3-aae5-3876b00c48ad", "metadata": {}, "outputs": [ @@ -506,7 +506,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/2706376531.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_19414/2706376531.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", @@ -521,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "82c48463-5a90-4105-9ee9-5763d0b1e35b", "metadata": {}, "outputs": [], @@ -777,7 +777,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/3477839074.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/tmp/ipykernel_19414/3477839074.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n" ] } @@ -873,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 21, "id": "dae8ebc0-05f3-48c1-946f-fb70e074e5ea", "metadata": {}, "outputs": [ @@ -881,9 +881,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/1281015390.py:3: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n", + "/tmp/ipykernel_19414/3977280642.py:3: 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", - "/tmp/ipykernel_56151/1281015390.py:3: SettingWithCopyWarning: \n", + "/tmp/ipykernel_19414/3977280642.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", @@ -891,43 +891,25 @@ " task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n" ] }, - { - "ename": "TypeError", - "evalue": "agg function failed [how->median,dtype->object]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1942\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1942\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_grouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1943\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/ops.py:864\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m 862\u001b[0m preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 864\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aggregate_series_pure_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m npvalues \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result, try_float\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/ops.py:885\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[0;32m--> 885\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 886\u001b[0m res \u001b[38;5;241m=\u001b[39m extract_result(res)\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:2534\u001b[0m, in \u001b[0;36mGroupBy.median..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 2461\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2462\u001b[0m \u001b[38;5;124;03mCompute median of groups, excluding missing values.\u001b[39;00m\n\u001b[1;32m 2463\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2530\u001b[0m \u001b[38;5;124;03mFreq: MS, dtype: float64\u001b[39;00m\n\u001b[1;32m 2531\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2532\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m 2533\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m-> 2534\u001b[0m alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 2535\u001b[0m numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m 2536\u001b[0m )\n\u001b[1;32m 2537\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/series.py:6559\u001b[0m, in \u001b[0;36mSeries.median\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 6551\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 6552\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmedian\u001b[39m(\n\u001b[1;32m 6553\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6557\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 6558\u001b[0m ):\n\u001b[0;32m-> 6559\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/generic.py:12431\u001b[0m, in \u001b[0;36mNDFrame.median\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12424\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmedian\u001b[39m(\n\u001b[1;32m 12425\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 12426\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12429\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 12430\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 12431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12432\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmedian\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 12433\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12378\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m 12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/series.py:6457\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 6453\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 6454\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6455\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6456\u001b[0m )\n\u001b[0;32m-> 6457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/nanops.py:787\u001b[0m, in \u001b[0;36mnanmedian\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 787\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalues\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 788\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[0;31mTypeError\u001b[0m: Cannot convert ['PHID-TASK-yvetu3dh7pwz6hn5x7ii'] to numeric", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[41], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m unique_taskPHIDs \u001b[38;5;241m=\u001b[39m task_phab_df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweek\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTaskPHID\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mnunique()\n\u001b[1;32m 6\u001b[0m wmf_task_phab_df \u001b[38;5;241m=\u001b[39m task_phab_df[task_phab_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWMFaffil\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m]\n\u001b[0;32m----> 7\u001b[0m wmf_tasks \u001b[38;5;241m=\u001b[39m \u001b[43mwmf_task_phab_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweek\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTaskPHID\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m 9\u001b[0m other_task_phab_df \u001b[38;5;241m=\u001b[39m task_phab_df[task_phab_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWMFaffil\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m]\n\u001b[1;32m 10\u001b[0m other_tasks \u001b[38;5;241m=\u001b[39m other_task_phab_df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweek\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTaskPHID\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian()\u001b[38;5;241m.\u001b[39mreset_index()\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:2532\u001b[0m, in \u001b[0;36mGroupBy.median\u001b[0;34m(self, numeric_only)\u001b[0m\n\u001b[1;32m 2459\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 2460\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmedian\u001b[39m(\u001b[38;5;28mself\u001b[39m, numeric_only: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[1;32m 2461\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2462\u001b[0m \u001b[38;5;124;03m Compute median of groups, excluding missing values.\u001b[39;00m\n\u001b[1;32m 2463\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2530\u001b[0m \u001b[38;5;124;03m Freq: MS, dtype: float64\u001b[39;00m\n\u001b[1;32m 2531\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2532\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cython_agg_general\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2533\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2534\u001b[0m \u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2535\u001b[0m \u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2536\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2537\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 1995\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m 1996\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouped_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m 2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/internals/base.py:367\u001b[0m, in \u001b[0;36mSingleDataManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mgrouped_reduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func):\n\u001b[1;32m 366\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray\n\u001b[0;32m--> 367\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 368\u001b[0m index \u001b[38;5;241m=\u001b[39m default_index(\u001b[38;5;28mlen\u001b[39m(res))\n\u001b[1;32m 370\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_array(res, index)\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1995\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general..array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m 1994\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m alt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1995\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_agg_py_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1996\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1946\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1944\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg function failed [how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mser\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;66;03m# preserve the kind of exception that raised\u001b[39;00m\n\u001b[0;32m-> 1946\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(msg) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1948\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ser\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m 1949\u001b[0m res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[0;31mTypeError\u001b[0m: agg function failed [how->median,dtype->object]" - ] - }, { "data": { + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "9816" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -943,15 +925,17 @@ "other_tasks = other_task_phab_df.groupby('week')['TaskPHID'].nunique()\n", "\n", "sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', marker='o')\n", - "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n", - "sns.lineplot(x=other_tasks.index, y=other_tasks.values, ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n", - "plt.title('Unique taskPHIDs')\n", + "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='x')\n", + "sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n", + "plt.title('Task Descriptions (Task head)')\n", "plt.xlabel('Timestamp')\n", "plt.ylabel('Unique taskPHIDs')\n", "plt.xticks(rotation=45)\n", "plt.grid(True)\n", "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n", + "\n", + "#len(filtered_phab_df[filtered_phab_df['TaskPHID'].isin(task_phab_df['TaskPHID'])])" ] }, { diff --git a/text_analysis/case1/ve_dependency.ipynb b/text_analysis/case1/ve_dependency.ipynb index 080b055..c7f8606 100644 --- a/text_analysis/case1/ve_dependency.ipynb +++ b/text_analysis/case1/ve_dependency.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "fa6a7cea-1375-4153-a388-1847dfa5b257", "metadata": {}, "outputs": [], @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "812ab4c8-2561-466b-bc57-defc93f5c893", "metadata": {}, "outputs": [], @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "60bcef32-67be-44f5-a51a-84e6e63d29ed", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "7babf07b-4f91-4e48-88a9-4fe10f8b668d", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "558d1638-abe9-4fc2-896e-6fc1bc396ca3", "metadata": {}, "outputs": [ @@ -462,7 +462,7 @@ "[32488 rows x 15 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "acb87a1a-c3e0-4d3f-8450-e2af96150e94", "metadata": {}, "outputs": [], @@ -488,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "b67c136e-16c4-4002-a2d6-f92c88252baf", "metadata": {}, "outputs": [], @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "f749706a-f2bb-42e3-aae5-3876b00c48ad", "metadata": {}, "outputs": [ @@ -506,7 +506,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/2706376531.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_19414/2706376531.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", @@ -521,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "82c48463-5a90-4105-9ee9-5763d0b1e35b", "metadata": {}, "outputs": [], @@ -777,7 +777,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/3477839074.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/tmp/ipykernel_19414/3477839074.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n" ] } @@ -873,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 21, "id": "dae8ebc0-05f3-48c1-946f-fb70e074e5ea", "metadata": {}, "outputs": [ @@ -881,9 +881,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_56151/4021189635.py:3: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n", + "/tmp/ipykernel_19414/3977280642.py:3: 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", - "/tmp/ipykernel_56151/4021189635.py:3: SettingWithCopyWarning: \n", + "/tmp/ipykernel_19414/3977280642.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", @@ -900,6 +900,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "9816" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -923,7 +933,9 @@ "plt.xticks(rotation=45)\n", "plt.grid(True)\n", "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n", + "\n", + "#len(filtered_phab_df[filtered_phab_df['TaskPHID'].isin(task_phab_df['TaskPHID'])])" ] }, {