time_series_group_example.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame({'foo': [2,2,3,4,5,5,5,6]})\n",
    "df.head()"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   foo    bar  count\n",
       "0    2  False      1\n",
       "1    2   True      1\n",
       "2    3  False      2\n",
       "3    4  False      3\n",
       "4    5  False      4\n",
       "5    5   True      4\n",
       "6    5   True      4\n",
       "7    6  False      5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit df0=df[\"foo\"].copy()\n",
    "num = 0\n",
    "df0['bar'] = (df0.foo == df0.foo.shift())\n",
    "def prange(bar):\n",
    "    global num\n",
    "    if bar:\n",
    "        return num\n",
    "    else:\n",
    "        num+=1\n",
    "        return num\n",
    "df0['count'] = df0['bar'].apply(lambda x: prange(x))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "742 µs ± 272 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit df1=df[\"foo\"].copy()\n",
    "df1[\"count\"]=df1.rolling(2).apply(lambda x: 0 if x[0]==x[1] else 1).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.4 µs ± 7.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 100 df2=df[\"foo\"].copy()\n",
    "res=np.ndarray(len(df2),dtype=int)\n",
    "src=df2.values\n",
    "res[0]=0\n",
    "for i in range(1,len(df2)):\n",
    "    if src[i]==src[i-1]:\n",
    "        res[i]=res[i-1]\n",
    "    else:\n",
    "        res[i]=res[i-1]+1\n",
    "df2[\"count\"]=res"
   ]
  }
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