diff --git a/nbs/01_pandas_extra.ipynb b/nbs/01_pandas_extra.ipynb
index edb13bf..168c6c3 100644
--- a/nbs/01_pandas_extra.ipynb
+++ b/nbs/01_pandas_extra.ipynb
@@ -8,162 +8,55 @@
     "> Extra pandas functions at import"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# default_exp pdenhanced"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# export\n",
-    "import pandas as pd\n",
-    "from typing import Callable"
+    "from typing import Callable\n",
+    "from forgebox.imports import *"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Value counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# export\n",
-    "def list_vc(\n",
-    "    df, colname: str, value: str\n",
-    ") -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    count the values in a column\n",
-    "        that each cell is a list\n",
-    "    \"\"\"\n",
-    "    return df[colname].list_vc(value)\n",
-    "\n",
-    "def col_list_vc(\n",
-    "    col, value: str\n",
-    ") -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    count the values in a column\n",
-    "        that each cell is a list\n",
-    "    \"\"\"\n",
-    "    return pd.DataFrame(\n",
-    "        col.apply(lambda x: value in x).value_counts()\n",
-    "    )\n",
-    "\n",
-    "pd.DataFrame.vc = lambda self,col:pd.DataFrame(self[col].value_counts())\n",
-    "pd.Series.list_vc = col_list_vc\n",
-    "pd.DataFrame.list_vc = list_vc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rename by rule"
+    "### Testing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# export\n",
-    "def default_rename_rule(x: str) -> str:\n",
-    "    return x.replace(\" \", \"_\").replace(\"-\", \"_\").lower()\n",
-    "\n",
+    "from sklearn.datasets import california_housing\n",
     "\n",
-    "def rename_by_rule(\n",
-    "    df,\n",
-    "    rule: Callable = default_rename_rule\n",
-    ") -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    rename the columns by a rule function\n",
-    "    \"\"\"\n",
-    "    df = df.rename(\n",
-    "        columns=dict((c, rule(c)) for c in df.columns))\n",
-    "    return df\n",
+    "cdata = california_housing.fetch_california_housing()\n",
     "\n",
-    "pd.DataFrame.rename_by_rule = rename_by_rule"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rearrage Columns"
+    "df = pd.DataFrame(cdata[\"data\"], columns=cdata[\"feature_names\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# export\n",
-    "def column_order(df, *col_names) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    df = df.column_order(\"col1\", \"col2\", \"col3\")\n",
-    "    will put col1, col2, and col3 as the 1st 3 column\n",
-    "    \"\"\"\n",
-    "    cols = list(df.columns)\n",
-    "    \n",
-    "    for col_name in list(col_names)[::-1]:\n",
-    "        \n",
-    "        # warn if the column exist\n",
-    "        if col_name not in cols:\n",
-    "            print(f\"Column:'{col_name}' not in dataframe\")\n",
-    "            continue\n",
-    "        cols.insert(0, cols.pop(cols.index(col_name)))\n",
-    "    return df[cols]\n",
-    "\n",
-    "pd.DataFrame.column_order = column_order"
+    "df[\"old\"] = df.HouseAge>20"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Testing"
+    "## Value Counts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import california_housing\n",
-    "\n",
-    "cdata = california_housing.fetch_california_housing()\n",
-    "\n",
-    "df = pd.DataFrame(cdata[\"data\"], columns=cdata[\"feature_names\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"old\"] = df.HouseAge>20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -192,11 +85,11 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>True</td>\n",
+       "      <th>True</th>\n",
        "      <td>14347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>False</td>\n",
+       "      <th>False</th>\n",
        "      <td>6293</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -209,7 +102,7 @@
        "False   6293"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -257,184 +150,40 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>8.3252</td>\n",
        "      <td>41.0</td>\n",
        "      <td>6.984127</td>\n",
-       "      <td>1.023810</td>\n",
+       "      <td>1.02381</td>\n",
        "      <td>322.0</td>\n",
        "      <td>2.555556</td>\n",
        "      <td>37.88</td>\n",
        "      <td>-122.23</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.238137</td>\n",
-       "      <td>0.971880</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>2.109842</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>8.288136</td>\n",
-       "      <td>1.073446</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>2.802260</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>5.817352</td>\n",
-       "      <td>1.073059</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>2.547945</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.281853</td>\n",
-       "      <td>1.081081</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>2.181467</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20635</td>\n",
-       "      <td>1.5603</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>5.045455</td>\n",
-       "      <td>1.133333</td>\n",
-       "      <td>845.0</td>\n",
-       "      <td>2.560606</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>-121.09</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20636</td>\n",
-       "      <td>2.5568</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>6.114035</td>\n",
-       "      <td>1.315789</td>\n",
-       "      <td>356.0</td>\n",
-       "      <td>3.122807</td>\n",
-       "      <td>39.49</td>\n",
-       "      <td>-121.21</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20637</td>\n",
-       "      <td>1.7000</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>5.205543</td>\n",
-       "      <td>1.120092</td>\n",
-       "      <td>1007.0</td>\n",
-       "      <td>2.325635</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.22</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20638</td>\n",
-       "      <td>1.8672</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>5.329513</td>\n",
-       "      <td>1.171920</td>\n",
-       "      <td>741.0</td>\n",
-       "      <td>2.123209</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.32</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20639</td>\n",
-       "      <td>2.3886</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>5.254717</td>\n",
-       "      <td>1.162264</td>\n",
-       "      <td>1387.0</td>\n",
-       "      <td>2.616981</td>\n",
-       "      <td>39.37</td>\n",
-       "      <td>-121.24</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>20640 rows × 9 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       medinc  houseage  averooms  avebedrms  population  aveoccup  latitude  \\\n",
-       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
-       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
-       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
-       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
-       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
-       "...       ...       ...       ...        ...         ...       ...       ...   \n",
-       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
-       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
-       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
-       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
-       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
-       "\n",
-       "       longitude    old  \n",
-       "0        -122.23   True  \n",
-       "1        -122.22   True  \n",
-       "2        -122.24   True  \n",
-       "3        -122.25   True  \n",
-       "4        -122.25   True  \n",
-       "...          ...    ...  \n",
-       "20635    -121.09   True  \n",
-       "20636    -121.21  False  \n",
-       "20637    -121.22  False  \n",
-       "20638    -121.32  False  \n",
-       "20639    -121.24  False  \n",
+       "   medinc  houseage  averooms  avebedrms  ...  aveoccup  latitude  longitude   old\n",
+       "0  8.3252      41.0  6.984127    1.02381  ...  2.555556     37.88    -122.23  True\n",
        "\n",
-       "[20640 rows x 9 columns]"
+       "[1 rows x 9 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.rename_by_rule()"
+    "df.rename_by_rule().head(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +220,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>True</td>\n",
        "      <td>2.555556</td>\n",
        "      <td>8.3252</td>\n",
@@ -483,7 +232,7 @@
        "      <td>-122.23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>True</td>\n",
        "      <td>2.109842</td>\n",
        "      <td>8.3014</td>\n",
@@ -495,7 +244,7 @@
        "      <td>-122.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>True</td>\n",
        "      <td>2.802260</td>\n",
        "      <td>7.2574</td>\n",
@@ -506,144 +255,26 @@
        "      <td>37.85</td>\n",
        "      <td>-122.24</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.547945</td>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>5.817352</td>\n",
-       "      <td>1.073059</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.181467</td>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.281853</td>\n",
-       "      <td>1.081081</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20635</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.560606</td>\n",
-       "      <td>1.5603</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>5.045455</td>\n",
-       "      <td>1.133333</td>\n",
-       "      <td>845.0</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>-121.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20636</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3.122807</td>\n",
-       "      <td>2.5568</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>6.114035</td>\n",
-       "      <td>1.315789</td>\n",
-       "      <td>356.0</td>\n",
-       "      <td>39.49</td>\n",
-       "      <td>-121.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20637</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2.325635</td>\n",
-       "      <td>1.7000</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>5.205543</td>\n",
-       "      <td>1.120092</td>\n",
-       "      <td>1007.0</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20638</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2.123209</td>\n",
-       "      <td>1.8672</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>5.329513</td>\n",
-       "      <td>1.171920</td>\n",
-       "      <td>741.0</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20639</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2.616981</td>\n",
-       "      <td>2.3886</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>5.254717</td>\n",
-       "      <td>1.162264</td>\n",
-       "      <td>1387.0</td>\n",
-       "      <td>39.37</td>\n",
-       "      <td>-121.24</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>20640 rows × 9 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         old  AveOccup  MedInc  HouseAge  AveRooms  AveBedrms  Population  \\\n",
-       "0       True  2.555556  8.3252      41.0  6.984127   1.023810       322.0   \n",
-       "1       True  2.109842  8.3014      21.0  6.238137   0.971880      2401.0   \n",
-       "2       True  2.802260  7.2574      52.0  8.288136   1.073446       496.0   \n",
-       "3       True  2.547945  5.6431      52.0  5.817352   1.073059       558.0   \n",
-       "4       True  2.181467  3.8462      52.0  6.281853   1.081081       565.0   \n",
-       "...      ...       ...     ...       ...       ...        ...         ...   \n",
-       "20635   True  2.560606  1.5603      25.0  5.045455   1.133333       845.0   \n",
-       "20636  False  3.122807  2.5568      18.0  6.114035   1.315789       356.0   \n",
-       "20637  False  2.325635  1.7000      17.0  5.205543   1.120092      1007.0   \n",
-       "20638  False  2.123209  1.8672      18.0  5.329513   1.171920       741.0   \n",
-       "20639  False  2.616981  2.3886      16.0  5.254717   1.162264      1387.0   \n",
-       "\n",
-       "       Latitude  Longitude  \n",
-       "0         37.88    -122.23  \n",
-       "1         37.86    -122.22  \n",
-       "2         37.85    -122.24  \n",
-       "3         37.85    -122.25  \n",
-       "4         37.85    -122.25  \n",
-       "...         ...        ...  \n",
-       "20635     39.48    -121.09  \n",
-       "20636     39.49    -121.21  \n",
-       "20637     39.43    -121.22  \n",
-       "20638     39.43    -121.32  \n",
-       "20639     39.37    -121.24  \n",
+       "    old  AveOccup  MedInc  HouseAge  ...  AveBedrms  Population  Latitude  Longitude\n",
+       "0  True  2.555556  8.3252      41.0  ...   1.023810       322.0     37.88    -122.23\n",
+       "1  True  2.109842  8.3014      21.0  ...   0.971880      2401.0     37.86    -122.22\n",
+       "2  True  2.802260  7.2574      52.0  ...   1.073446       496.0     37.85    -122.24\n",
        "\n",
-       "[20640 rows x 9 columns]"
+       "[3 rows x 9 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.column_order(\"old\",\"AveOccup\")"
+    "df.column_order(\"old\",\"AveOccup\").head(3)"
    ]
   },
   {
@@ -656,7 +287,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/nbs/02_imports.ipynb b/nbs/02_imports.ipynb
deleted file mode 100644
index c7dc359..0000000
--- a/nbs/02_imports.ipynb
+++ /dev/null
@@ -1,160 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 03 Imports\n",
-    "> standard imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# default_exp imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# export\n",
-    "\n",
-    "__all__ = [\"pd\",\"np\",\"partial\",\"Path\",\"json\",\"Counter\",\n",
-    "           \"plt\",\"os\",\"sys\",\"glob\",\"Image\",]\n",
-    "\n",
-    "# import enhanced version of pandas\n",
-    "from forgebox.pdenhanced import pd\n",
-    "import numpy as np\n",
-    "from pathlib import Path\n",
-    "import json\n",
-    "from functools import partial\n",
-    "from collections import Counter\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "\n",
-    "try:\n",
-    "    from matplotlib import pyplot as plt\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "try:\n",
-    "    from forgebox.widgets import search_box,paginate\n",
-    "    pd.DataFrame.search_box = search_box\n",
-    "    pd.DataFrame.paginate = paginate\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "try:\n",
-    "    import torch\n",
-    "    from torch import nn\n",
-    "    from torch.utils.data.dataset import Dataset\n",
-    "    from torch.utils.data.dataloader import DataLoader\n",
-    "    __all__+=[\"nn\",\"torch\",\"Dataset\",\"DataLoader\"]\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "try:\n",
-    "    from ipywidgets import interact, interact_manual\n",
-    "    from IPython.display import HTML\n",
-    "    __all__+=[\"interact\",\"interact_manual\",\"HTML\"]\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "try:\n",
-    "    import redis\n",
-    "    def read_redis(key, host='localhost', port=6379, db=3):\n",
-    "        rd = redis.Redis(host=host, port=port, db=db)\n",
-    "        return pd.read_json(rd.get(key))\n",
-    "    \n",
-    "    __all__+=[\"read_redis\",]\n",
-    "    \n",
-    "    def to_redis(self, key:str, con:redis.Redis = None, ex:int=600):\n",
-    "        \"\"\"\n",
-    "        Save dataframe to redis\n",
-    "        \"\"\"\n",
-    "        if con is None:\n",
-    "            con = redis.Redis(host='localhost', port=6379, db=3)\n",
-    "        return con.set(key, self.to_json())\n",
-    "        \n",
-    "    pd.DataFrame.to_redis = to_redis\n",
-    "    \n",
-    "    def df_cache(ex=600, host='localhost', port=6379, db=3):\n",
-    "        \"\"\"\n",
-    "        Caching pd.DataFrame, use as decorator\n",
-    "        \n",
-    "        @df_cache(ex=300)\n",
-    "        def get_table_abc(uuid):\n",
-    "            return pd.read_sql(f\"select * from abc where uuid='{uuid}'\", con=conn)\n",
-    "            \n",
-    "        within 5 minutes, under same uuid, the code will query data base only once\n",
-    "        \"\"\"\n",
-    "        rd = redis.Redis(host=host, port=port, db=db)\n",
-    "        def decorator(f):\n",
-    "            def wrapper(*args, **kwargs):\n",
-    "                key = \"_\".join(str(i).lower() for i in args)\n",
-    "                if len(kwargs)>0:\n",
-    "                    key += \"_\"\n",
-    "                    key += \"_\".join(f\"{k}-{v}\" for k,v in kwargs.items())\n",
-    "                if rd.exists(key):\n",
-    "                    return pd.read_json(rd.get(key))\n",
-    "                df = f(*args, **kwargs)\n",
-    "                df.to_redis(key, con=rd)\n",
-    "                return df\n",
-    "\n",
-    "            return wrapper\n",
-    "        return decorator\n",
-    "    __all__+=[\"df_cache\",]\n",
-    "        \n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "from glob import glob\n",
-    "from PIL import Image\n",
-    "\n",
-    "Path.ls = lambda x:os.listdir(x)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/setup.py b/setup.py
index 9364ca8..4775b04 100644
--- a/setup.py
+++ b/setup.py
@@ -26,7 +26,7 @@
 ]
 
 py_versions = (
-    "2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8".split()
+    "2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10".split()
 )
 
 for exp in expected: