"outputs": [],
"source": [
"import platform\n",
+ "import matplotlib.pyplot as plt\n",
"print(platform.python_version())"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Todo List\n",
+ "\n",
+ "- [ ] trim each chart to just a single, selected year\n",
+ "- [ ] make one chart per year\n",
+ "- [x] make sure all the data files are getting loaded"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"source": [
"import pandas as pd\n",
"\n",
- "data_sets = []\n",
+ "data_frames = []\n",
+ "for file in data_files:\n",
"\n",
- "# file = data_files[6] # CLI Tools\n",
- "file = data_files[2] # Job Aids\n",
- "print(file)\n",
+ "\tf = open(file, encoding='utf-8')\n",
+ "\tdf_temp = pd.read_json(f)\n",
+ "\tdata_frames.append(df_temp)\n",
"\n",
- "f = open(file, encoding='utf-8')\n",
- "df = pd.read_json(f)"
+ "df = pd.concat(data_frames, ignore_index=True)"
]
},
{
"source": [
"dfp = df\n",
"dfp['mile'] = [d.get('title') if d else None for d in dfp.milestone]\n",
- "dfp.drop(columns=['closedAt','milestone', 'url'], inplace=True)"
+ "closed_dates = pd.to_datetime(df['closedAt'], format='ISO8601')\n",
+ "dfp['year'] = closed_dates.dt.year\n",
+ "\n",
+ "# TODO: Split by YEAR before dropping 'closedAt'\n",
+ "dfp.drop(columns=['closedAt','milestone', 'url'], inplace=True)\n",
+ "print(dfp)"
]
},
{
"source": [
"mile_counts = df.groupby('mile')['count'].sum()\n",
"mile_counts = mile_counts.loc[lambda x: x > 5]\n",
- "plt.pie(mile_counts, labels=mile_counts.index, autopct='%1.1f%%')\n",
+ "\n",
+ "\n",
+ "print(mile_counts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "plt.pie(mile_counts, labels=mile_counts.index, autopct='%0.1f%%')\n",
"plt.title(f\"Tasks by Milestone\")\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "type(mile_counts)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},