WIP chart of issues closed count by milestones and month

[dotfiles.git] / data / issues.ipynb

diff --git a/data/issues.ipynb b/data/issues.ipynb
index 5b1a1b36845a5cf782d0e8f6b5a4ae6d8d75c1b9..d0a20fc3bf2e985a72e7bef2a67a0ff5e3c808a9 100644 (file)
--- a/data/issues.ipynb
+++ b/data/issues.ipynb
@@ -41,7 +41,7 @@
     "\n",
     "data_sets = []\n",
     "\n",
     "\n",
     "data_sets = []\n",
     "\n",

-    "file = data_files[0]\n",
+    "file = data_files[2]\n",

     "print(file)\n",
     "\n",
     "f = open(file, encoding='utf-16')\n",
     "print(file)\n",
     "\n",
     "f = open(file, encoding='utf-16')\n",


@@ -50,33 +50,71 @@
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",

-   "execution_count": 23,
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(df.closed[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,

    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [

-    "df['closed'] = pd.to_datetime(df['closedAt'], format='ISO8601')"
+    "import numpy as np\n",
+    "df['count'] = np.zeros(len(df))\n",
+    "\n"

    ]
   },
   {
    "cell_type": "code",
    ]
   },
   {
    "cell_type": "code",

-   "execution_count": 26,
+   "execution_count": 21,

    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [

-      "<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
+      "[Strategic] Job Aids on bootstrapping CICD Pipelines\n"

      ]
     }
    ],
    "source": [
      ]
     }
    ],
    "source": [

-    "print(type(df.closed[0]))"
+    "print(df.milestone[22].get('title'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "closed_months = pd.to_datetime(df['closedAt'], format='ISO8601')\n",
+    "df['closed_month'] = closed_months.dt.month"

    ]
   },
   {
    "cell_type": "code",
    ]
   },
   {
    "cell_type": "code",

-   "execution_count": 29,
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['mile'] = [d.get('title') if d else None for d in df.milestone]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,

    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [


@@ -85,7 +123,33 @@
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",

-   "execution_count": 33,
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "grp_df = df.groupby(['mile', 'closed_month']).count().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(grp_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot of closed issues by month for one project\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,

    "metadata": {},
    "outputs": [
     {
    "metadata": {},
    "outputs": [
     {


@@ -94,13 +158,13 @@
        "<Axes: xlabel='closed'>"
       ]
      },
        "<Axes: xlabel='closed'>"
       ]
      },

-     "execution_count": 33,
+     "execution_count": 7,

      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {

-      "image/png": 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",
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",

       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]