diff --git a/Numpy.ipynb b/Numpy.ipynb
index 387f7177b1fb673167bd0cf4a1321e9721f2b331..0b9cb35800aecee75c7ac9f3e4d86ee55a5eb012 100644
--- a/Numpy.ipynb
+++ b/Numpy.ipynb
@@ -311,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/Optimization.ipynb b/Optimization.ipynb
index fb73519d66b2e4602771b3e2de48953ad9089153..9dae9c6978ade382afd4e39efb3103fa425de622 100644
--- a/Optimization.ipynb
+++ b/Optimization.ipynb
@@ -46,15 +46,36 @@
    "outputs": [],
    "source": [
     "#renaming columns\n",
-    "df.columns=[\"h\",\"Q\"]\n",
-    "\n",
+    "df.columns=[\"h\",\"Q\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# h -> [m], Q -> [m³/s]\n",
     "df[\"h\"] = df[\"h\"]/1000\n",
-    "df[\"Q\"] = df[\"Q\"]/1000\n",
-    "\n",
+    "df[\"Q\"] = df[\"Q\"]/1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# dropping na's\n",
-    "df.dropna(how=\"any\",inplace=True)\n",
-    "\n",
+    "df.dropna(how=\"any\",inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# removing h<=0, Q<=0\n",
     "df = df[(df[\"h\"]>0) & (df[\"Q\"]>0)]"
    ]
@@ -223,8 +244,8 @@
     "# optimization result!\n",
     "kst = result_manning.x[0]\n",
     "\n",
-    "# converting predicted values\n",
-    "manning_data = df[[\"h\"]]\n",
+    "# converting predicted values (creating a new dataframe that contain our manning h and Q)\n",
+    "manning_data = df[[\"h\"]].copy()\n",
     "manning_data[\"Q\"] = ManningCIR(df.h.values,r,kst,I)\n",
     "\n",
     "# sorting for plot\n",
@@ -383,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/Pandas.ipynb b/Pandas.ipynb
index 43099e279b75987922f293d79b1c7ebad5d20276..f28d5984992f1529b05c1a92e4f81da1191ec786 100755
--- a/Pandas.ipynb
+++ b/Pandas.ipynb
@@ -93,6 +93,23 @@
     "df2"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1 \n",
+    "\n",
+    "Create a Dataframe from a list.\n",
+    "Call the DataFrame **df_mine**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -162,6 +179,25 @@
     "print(df[[\"a\",\"b\"]][:3])\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2\n",
+    "\n",
+    "Play around with indexing:\n",
+    "- try to get a column\n",
+    "- try to get row\n",
+    "- try to get a cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -189,6 +225,22 @@
     "plt.tight_layout()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 3\n",
+    "\n",
+    "Plot column 'a' with a title!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -361,7 +413,7 @@
    "outputs": [],
    "source": [
     "# column bind (axis=1) (row bind axis=0)\n",
-    "newdf=pd.concat([df[\"A\"],df[\"exponential_column\"]],axis=1)"
+    "newdf=pd.concat([df[\"a\"],df[\"exponential_column\"]],axis=1)"
    ]
   },
   {
@@ -595,7 +647,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/Python Tutorial - Basics.ipynb b/Python Tutorial - Basics.ipynb
index 6a826c33e1245a509177e4e3ce327ef1d85db8ec..d37735fbcfb1cbef7f4ca499d78546c5cc894297 100755
--- a/Python Tutorial - Basics.ipynb	
+++ b/Python Tutorial - Basics.ipynb	
@@ -72,13 +72,6 @@
     "a"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Mind the difference between \\/ and \\/\\/**"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -105,6 +98,27 @@
     "print(r\"C:\\adg\\ggj\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'42323dsfdsagfdsafsa      .. . .99.900'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2**99%57\n",
+    "\"42323dsfdsagfdsafsa      .. . .%.3f\" %99.9"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -632,7 +646,7 @@
     "- What's the remainder of $2^{99}$ divided by $57$?  \n",
     "- If you were to write a little database, that gets one file for every day containing data for that day.  \n",
     "And you want to store the file locations in a basic python data structure. Which one would you use and why?  \n",
-    "- If you want to check two large python lists for commen values, how would you do it?"
+    "- If you want to check two large python lists for common values, how would you do it?"
    ]
   },
   {
@@ -654,7 +668,7 @@
     "# summing up the values of a list\n",
     "summe=0\n",
     "for num in [1,2,3,4]:\n",
-    "    # everything that is endeted is within the loop\n",
+    "    # everything that is indeted is within the loop\n",
     "    summe+=num\n",
     "    print(num)\n",
     "    \n",