mode_column_test.ipynb

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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <td>2</td>\n",
       "      <td>6</td>\n",
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       "   0  1  2  3  4  5\n",
       "0  0  1  3  3  4  5\n",
       "1  0  1  2  4  4  5\n",
       "2  0  1  2  5  4  5\n",
       "3  0  1  2  6  4  5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df =  pd.DataFrame([ \n",
    "    [0\t,1\t,3\t,3,\t4,\t5], \n",
    "    [0\t,1\t,2\t,4,\t4,\t5], \n",
    "    [0\t,1\t,2\t,5,\t4,\t5], \n",
    "    [0\t,1\t,2\t,6,\t4,\t5], \n",
    "    ])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "     0\n",
       "0  3.0\n",
       "1  4.0\n",
       "2  5.0\n",
       "3  0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode(axis=1, numeric_only=True).dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_mode(arr: np.array) -> bool: \n",
    "    \"\"\"Checks if mode is available with the given array\n",
    "\n",
    "    Args:\n",
    "        arr (np.array): _description_\n",
    "\n",
    "    Returns:\n",
    "        bool: _description_\n",
    "    \"\"\"\n",
    "    unique = len(set(arr.tolist()))\n",
    "    length = len(arr.tolist())\n",
    "    has_similar = unique != length\n",
    "    \n",
    "    if(has_similar):\n",
    "        try\n",
    "    \n",
    "    \n",
    "    return \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "nums = np.array([1\t,2\t,3,\t4,\t5, 6])\n",
    "\n",
    "stats.mode(nums)\n",
    "max(set(nums.tolist()), key=nums.tolist().count)\n",
    "\n",
    "has_mode(nums)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statistics import mode, StatisticsError\n",
    "\n",
    "nums_no_mode = np.array([1\t,1\t,3, 3\t,6,\t4,\t5])\n",
    "\n",
    "stats.mode(nums_no_mode)\n",
    "# max(set(nums_no_mode.tolist()), key=nums_no_mode.tolist().count)\n",
    "\n",
    "has_mode(nums_no_mode)\n",
    "# mode(nums_no_mode.tolist())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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