diff --git a/10. Working with datetime.ipynb b/10. Working with datetime.ipynb
index fbe18e6..5139688 100644
--- a/10. Working with datetime.ipynb	
+++ b/10. Working with datetime.ipynb	
@@ -1,148 +1,282 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Working with datetime\n",
-    "---\n",
-    "Numpy has core array data types which natively support datetime functionality. The data type is called “datetime64”, so named because “datetime” is already taken by the datetime library included in Python.\n"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "10. Working with datetime.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "YYYY-MM-DD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# creating a date\n",
-    "today = np.datetime64('2017-12-31')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "today"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get year in numpy datetime object\n",
-    "np.datetime64(today, 'Y')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# creating array of dates in a month\n",
-    "dates = np.arange('2019-01', '2020-02', dtype='datetime64[M]')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# arithmetic operation on dates\n",
-    "dur = np.datetime64('2018-05-22') - np.datetime64('2017-05-22')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dur"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# duration in weeks\n",
-    "np.timedelta64(dur, 'W')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sorting dates\n",
-    "a = np.array(['2017-02-12', '2016-10-13', '2019-05-22'], dtype='datetime64')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a"
-   ]
-  }
- ],
- "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": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FIwinC3trguJ",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Working with datetime\n",
+        "---\n",
+        "Numpy has core array data types which natively support datetime functionality. The data type is called “datetime64”, so named because “datetime” is already taken by the datetime library included in Python.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Am5LVH7_rguP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EQsr1s8irguY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "YYYY-MM-DD"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zfAqIyWrrguf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# creating a date\n",
+        "today = np.datetime64('2017-12-31')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Sz5d19Srrguk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "today"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MayiCTEGrguq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# get year in numpy datetime object\n",
+        "np.datetime64(today, 'Y')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fA9gFfHarguu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# creating array of dates in a month\n",
+        "dates = np.arange('2019-01', '2020-02', dtype='datetime64[M]')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e9QYJKzXrguy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "dates"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "L7IG23cOrgu6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# arithmetic operation on dates\n",
+        "dur = np.datetime64('2018-05-22') - np.datetime64('2017-05-22')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HfRyDQkyrgvC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "dur"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hfD62uKtrgvi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# duration in weeks\n",
+        "np.timedelta64(dur, 'W')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IOXP38yirgvr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# sorting dates\n",
+        "a = np.array(['2017-02-12', '2016-10-13', '2019-05-22'], dtype='datetime64')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iIEy1VoZrgv0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f9c1e68d-2e10-4544-e5a9-8b3cfa02b63d"
+      },
+      "source": [
+        "a"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['2017-02-12', '2016-10-13', '2019-05-22'], dtype='datetime64[D]')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IpI_ZqGrrplI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f7538e98-e9db-46b6-aa3b-0be7d910e3ae"
+      },
+      "source": [
+        "a.sort\n",
+        "a"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['2016-10-13', '2017-02-12', '2019-05-22'], dtype='datetime64[D]')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SVO7uEBZsKpL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "c3df6d40-c9af-4207-81ab-9661e04bffaf"
+      },
+      "source": [
+        "np.flip(a,0)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['2019-05-22', '2017-02-12', '2016-10-13'], dtype='datetime64[D]')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file