MeanModeMedian_Skew_Kurtosis.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
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    "lst = [1,2,3]"
   ]
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   "source": [
    "dir(list)"
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    {
     "data": {
      "text/plain": [
       "33.0"
      ]
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     "execution_count": 4,
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   "source": [
    "tup=(22,33,44)\n",
    "np.mean(tup)"
   ]
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       "2.0"
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  keys  vals\n",
       "0    a     1\n",
       "1    b     2\n",
       "2    c     2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([('a',1),('b',2),('c',2)], columns=['keys','vals'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee90a63e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['vals2'] = [2,4,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49d274f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "  keys  vals  vals2\n",
       "0    a     1      2\n",
       "1    b     2      4\n",
       "2    c     2      4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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   "cell_type": "code",
   "execution_count": 10,
   "id": "699c63c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_21128/3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "vals     1.666667\n",
       "vals2    3.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61ab8748",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>keys</th>\n",
       "      <th>vals</th>\n",
       "      <th>vals2</th>\n",
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       "  <tbody>\n",
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       "      <td>2.0</td>\n",
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      "text/plain": [
       "  keys  vals  vals2\n",
       "0    a   2.0    4.0\n",
       "1    b   NaN    NaN\n",
       "2    c   NaN    NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.mode()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3e9bad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_21128/3390915376.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "vals     0.577350\n",
       "vals2    1.154701\n",
       "dtype: float64"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.std()"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edae59d5",
   "metadata": {},
   "outputs": [
    {
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       " '_accessors',\n",
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       " '_align_frame',\n",
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       " '_box_col_values',\n",
       " '_can_fast_transpose',\n",
       " '_check_inplace_and_allows_duplicate_labels',\n",
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       " 'rmul',\n",
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       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
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       " 'set_flags',\n",
       " 'set_index',\n",
       " 'shape',\n",
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       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'sparse',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'style',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_feather',\n",
       " 'to_gbq',\n",
       " 'to_hdf',\n",
       " 'to_html',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_parquet',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_records',\n",
       " 'to_sql',\n",
       " 'to_stata',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'to_xml',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tshift',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(pd.DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49473803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7320508075688785"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vals'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70dce170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14254260383881612"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "#stats.norm.cdf( x, loc = meqan, scale = std)\n",
    "stats.norm.cdf(680,711,29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abbf0617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vals2'].kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07868efb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "438dda5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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