diff --git a/docs/examples/15_minutes_to_QCoDeS.ipynb b/docs/examples/15_minutes_to_QCoDeS.ipynb
index 58ff0dfc836..58225cd1fbe 100644
--- a/docs/examples/15_minutes_to_QCoDeS.ipynb
+++ b/docs/examples/15_minutes_to_QCoDeS.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An experimental setup comprises of many instruments. We call an experimental setup as \"station\". A station is connected to many instruments or devices. QCoDeS provides a way to interact with all these instruments to help users ",
+    "An experimental setup comprises of many instruments. We call an experimental setup as \"station\". A station is connected to many instruments or devices. QCoDeS provides a way to interact with all these instruments to help users \n",
     "the measurements and store the data in a database. To interact (read, write, trigger, etc) with the instruments, we have created a [library of drivers](http://qcodes.github.io/Qcodes/api/generated/qcodes.instrument_drivers.html) for commonly used ones. These drivers implement the most needed functionalities of the instruments. \n",
     "\n",
     "An \"Instrument\" can perform many functions. For example, on an oscilloscope instrument, we first set a correct trigger level and other parameters and then obtain a trace. In QCoDeS lingo, we call \"trigger_level\" and \"trace\" as `parameter` of this `instrument`. An instrument at any moment will have many such parameters which together define the state of the instrument, hence a parameter can be thought of as a state variable of the instrument. QCoDeS provides a method to set values of these parameters (set trigger level) and get the values from them (obtain a trace). By this way, we can interact with all the needed parameters of an instrument and are ready to set up a measurement. \n",
@@ -1243,6 +1243,30 @@
     "df.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Export data to xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's also possible to export data stored within a QCoDeS database to an `xarray.DataArray`. This can be achieved via:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xarray = dataset.to_xarray_dataarray_dict()['dmm_v1']\r\n",
+    "xarray.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1358,7 +1382,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.9-final"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/examples/DataSet/Accessing-data-in-DataSet.ipynb b/docs/examples/DataSet/Accessing-data-in-DataSet.ipynb
index 27da63ff5ec..f1e338dbb14 100644
--- a/docs/examples/DataSet/Accessing-data-in-DataSet.ipynb
+++ b/docs/examples/DataSet/Accessing-data-in-DataSet.ipynb
@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Accessing data in a DataSet\n",
-    "\n",
+    "# Accessing data in a DataSet\r\n",
+    "\r\n",
     "After a measurement is completed all the acquired data and metadata around it is accessible via a `DataSet` object. This notebook presents the useful methods and properties of the `DataSet` object which enable convenient access to the data, parameters information, and more. For general overview of the `DataSet` class, refer to [DataSet class walkthrough](DataSet-class-walkthrough.ipynb)."
    ]
   },
@@ -22,7 +22,23 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logging hadn't been started.\n",
+      "Activating auto-logging. Current session state plus future input saved.\n",
+      "Filename       : C:\\Users\\trmorgan\\.qcodes\\logs\\command_history.log\n",
+      "Mode           : append\n",
+      "Output logging : True\n",
+      "Raw input log  : False\n",
+      "Timestamping   : True\n",
+      "State          : active\n",
+      "Qcodes Logfile : C:\\Users\\trmorgan\\.qcodes\\logs\\210125-24608-qcodes.log\n"
+     ]
+    }
+   ],
    "source": [
     "import tempfile\n",
     "import os\n",
@@ -68,14 +84,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 5\n"
+      "Starting experimental run with id: 6. \n"
      ]
     }
    ],
@@ -117,12 +133,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x23f138486c8>,\n",
-       "  <matplotlib.axes._subplots.AxesSubplot at 0x23f13835348>],\n",
-       " [<matplotlib.colorbar.Colorbar at 0x23f13abe948>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x23f13b0b2c8>])"
-      ]
+      "text/plain": "([<AxesSubplot:title={'center':'Run #2, Experiment greco (draco)'}, xlabel='Voltage (V)', ylabel='Time (ks)'>,\n  <AxesSubplot:title={'center':'Run #2, Experiment greco (draco)'}, xlabel='Voltage (V)', ylabel='Time (ks)'>],\n [<matplotlib.colorbar.Colorbar at 0x25efd813608>,\n  <matplotlib.colorbar.Colorbar at 0x25efd8ad548>])"
      },
      "execution_count": 5,
      "metadata": {},
@@ -130,25 +141,25 @@
     },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
+      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
+      "image/png": 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+      "text/plain": "<Figure size 432x288 with 2 Axes>"
      },
      "metadata": {
-      "needs_background": "light"
+      "needs_background": "light",
+      "transient": {}
      },
      "output_type": "display_data"
     }
@@ -168,16 +179,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "5"
-      ]
+      "text/plain": "1"
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,16 +197,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "'greco'"
-      ]
+      "text/plain": "'greco'"
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,16 +215,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "'draco'"
-      ]
+      "text/plain": "'draco'"
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,16 +233,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "'fresco'"
-      ]
+      "text/plain": "'fresco'"
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -270,16 +273,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RunDescriber(InterDependencies_(dependencies={ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's')), ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's'))}, inferences={}, standalones=frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')})))"
-      ]
+      "text/plain": "RunDescriber(InterDependencies_(dependencies={ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's')), ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's'))}, inferences={}, standalones=frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')})), Shapes: None)"
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -308,16 +309,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "InterDependencies_(dependencies={ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's')), ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's'))}, inferences={}, standalones=frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')}))"
-      ]
+      "text/plain": "InterDependencies_(dependencies={ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's')), ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'), ParamSpecBase('t', 'numeric', 'Time', 's'))}, inferences={}, standalones=frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')}))"
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,16 +344,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{}"
-      ]
+      "text/plain": "{}"
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,16 +362,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')})"
-      ]
+      "text/plain": "frozenset({ParamSpecBase('q', 'numeric', 'Qredibility', '$')})"
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -385,19 +380,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n",
-       "  ParamSpecBase('t', 'numeric', 'Time', 's')),\n",
-       " ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n",
-       "  ParamSpecBase('t', 'numeric', 'Time', 's'))}"
-      ]
+      "text/plain": "{ParamSpecBase('y', 'numeric', 'Voltage', 'V'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n  ParamSpecBase('t', 'numeric', 'Time', 's')),\n ParamSpecBase('y2', 'numeric', 'Current', 'A'): (ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n  ParamSpecBase('t', 'numeric', 'Time', 's'))}"
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -458,20 +448,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n",
-       " ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n",
-       " ParamSpecBase('t', 'numeric', 'Time', 's'),\n",
-       " ParamSpecBase('y2', 'numeric', 'Current', 'A'),\n",
-       " ParamSpecBase('q', 'numeric', 'Qredibility', '$'))"
-      ]
+      "text/plain": "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n ParamSpecBase('x', 'numeric', 'Voltage', 'V'),\n ParamSpecBase('t', 'numeric', 'Time', 's'),\n ParamSpecBase('y2', 'numeric', 'Current', 'A'),\n ParamSpecBase('q', 'numeric', 'Qredibility', '$'))"
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -489,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -530,18 +514,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(ParamSpecBase('q', 'numeric', 'Qredibility', '$'),\n",
-       " ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n",
-       " ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
-      ]
+      "text/plain": "(ParamSpecBase('q', 'numeric', 'Qredibility', '$'),\n ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -559,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -594,17 +574,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n",
-       " ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
-      ]
+      "text/plain": "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -622,17 +599,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n",
-       " ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
-      ]
+      "text/plain": "(ParamSpecBase('y', 'numeric', 'Voltage', 'V'),\n ParamSpecBase('y2', 'numeric', 'Current', 'A'))"
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -670,20 +644,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'x': ParamSpec('x', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=[]),\n",
-       " 't': ParamSpec('t', 'numeric', 'Time', 's', inferred_from=[], depends_on=[]),\n",
-       " 'y': ParamSpec('y', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=['x', 't']),\n",
-       " 'y2': ParamSpec('y2', 'numeric', 'Current', 'A', inferred_from=[], depends_on=['x', 't']),\n",
-       " 'q': ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[])}"
-      ]
+      "text/plain": "{'x': ParamSpec('x', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=[]),\n 't': ParamSpec('t', 'numeric', 'Time', 's', inferred_from=[], depends_on=[]),\n 'y': ParamSpec('y', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=['x', 't']),\n 'y2': ParamSpec('y2', 'numeric', 'Current', 'A', inferred_from=[], depends_on=['x', 't']),\n 'q': ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[])}"
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -694,20 +662,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[ParamSpec('x', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=[]),\n",
-       " ParamSpec('t', 'numeric', 'Time', 's', inferred_from=[], depends_on=[]),\n",
-       " ParamSpec('y', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=['x', 't']),\n",
-       " ParamSpec('y2', 'numeric', 'Current', 'A', inferred_from=[], depends_on=['x', 't']),\n",
-       " ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[])]"
-      ]
+      "text/plain": "[ParamSpec('x', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=[]),\n ParamSpec('t', 'numeric', 'Time', 's', inferred_from=[], depends_on=[]),\n ParamSpec('y', 'numeric', 'Voltage', 'V', inferred_from=[], depends_on=['x', 't']),\n ParamSpec('y2', 'numeric', 'Current', 'A', inferred_from=[], depends_on=['x', 't']),\n ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[])]"
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -718,16 +680,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "'x,t,y,y2,q'"
-      ]
+      "text/plain": "'x,t,y,y2,q'"
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,16 +705,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[]),)"
-      ]
+      "text/plain": "(ParamSpec('q', 'numeric', 'Qredibility', '$', inferred_from=[], depends_on=[]),)"
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -788,35 +746,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'q': {'q': array([3.        , 2.08558738, 2.259722  , 3.31510822, 3.99537911,\n",
-       "         3.49071755, 2.40188947, 2.02507209, 2.80884137, 3.82017225,\n",
-       "         3.85514276, 2.87212284, 2.04133215, 2.3517716 , 3.43388374,\n",
-       "         3.99948622, 3.375267  , 2.30431745, 2.06153158, 2.93592978,\n",
-       "         3.88659931, 3.78183148, 2.74634542, 2.01281822, 2.4544651 ,\n",
-       "         3.5455349 , 3.98718178, 3.25365458, 2.21816852, 2.11340069,\n",
-       "         3.06407022, 3.93846842, 3.69568255, 2.624733  , 2.00051378,\n",
-       "         2.56611626, 3.6482284 , 3.95866785, 3.12787716, 2.14485724,\n",
-       "         2.17982775, 3.19115863, 3.97492791, 3.59811053, 2.50928245,\n",
-       "         2.00462089, 2.68489178, 3.740278  , 3.91441262, 3.        ])},\n",
-       " 'y': {'y': array([-0.5       , -0.41666667, -0.33333333, ...,  1.33333333,\n",
-       "          1.41666667,  1.5       ]),\n",
-       "  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n",
-       "  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n",
-       "         1416.66666667, 1500.        ])},\n",
-       " 'y2': {'y2': array([ 0.5       ,  0.41666667,  0.33333333, ..., -1.33333333,\n",
-       "         -1.41666667, -1.5       ]),\n",
-       "  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n",
-       "  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n",
-       "         1416.66666667, 1500.        ])}}"
-      ]
+      "text/plain": "{'q': {'q': array([3.        , 2.08558738, 2.259722  , 3.31510822, 3.99537911,\n         3.49071755, 2.40188947, 2.02507209, 2.80884137, 3.82017225,\n         3.85514276, 2.87212284, 2.04133215, 2.3517716 , 3.43388374,\n         3.99948622, 3.375267  , 2.30431745, 2.06153158, 2.93592978,\n         3.88659931, 3.78183148, 2.74634542, 2.01281822, 2.4544651 ,\n         3.5455349 , 3.98718178, 3.25365458, 2.21816852, 2.11340069,\n         3.06407022, 3.93846842, 3.69568255, 2.624733  , 2.00051378,\n         2.56611626, 3.6482284 , 3.95866785, 3.12787716, 2.14485724,\n         2.17982775, 3.19115863, 3.97492791, 3.59811053, 2.50928245,\n         2.00462089, 2.68489178, 3.740278  , 3.91441262, 3.        ])},\n 'y': {'y': array([-0.5       , -0.41666667, -0.33333333, ...,  1.33333333,\n          1.41666667,  1.5       ]),\n  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n         1416.66666667, 1500.        ])},\n 'y2': {'y2': array([ 0.5       ,  0.41666667,  0.33333333, ..., -1.33333333,\n         -1.41666667, -1.5       ]),\n  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n         1416.66666667, 1500.        ])}}"
      },
-     "execution_count": 26,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -845,16 +782,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "ParamSpecBase('q', 'numeric', 'Qredibility', '$')"
-      ]
+      "text/plain": "ParamSpecBase('q', 'numeric', 'Qredibility', '$')"
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,16 +801,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "ParamSpecBase('y2', 'numeric', 'Current', 'A')"
-      ]
+      "text/plain": "ParamSpecBase('y2', 'numeric', 'Current', 'A')"
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,30 +820,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'q': {'q': array([3.        , 2.08558738, 2.259722  , 3.31510822, 3.99537911,\n",
-       "         3.49071755, 2.40188947, 2.02507209, 2.80884137, 3.82017225,\n",
-       "         3.85514276, 2.87212284, 2.04133215, 2.3517716 , 3.43388374,\n",
-       "         3.99948622, 3.375267  , 2.30431745, 2.06153158, 2.93592978,\n",
-       "         3.88659931, 3.78183148, 2.74634542, 2.01281822, 2.4544651 ,\n",
-       "         3.5455349 , 3.98718178, 3.25365458, 2.21816852, 2.11340069,\n",
-       "         3.06407022, 3.93846842, 3.69568255, 2.624733  , 2.00051378,\n",
-       "         2.56611626, 3.6482284 , 3.95866785, 3.12787716, 2.14485724,\n",
-       "         2.17982775, 3.19115863, 3.97492791, 3.59811053, 2.50928245,\n",
-       "         2.00462089, 2.68489178, 3.740278  , 3.91441262, 3.        ])},\n",
-       " 'y2': {'y2': array([ 0.5       ,  0.41666667,  0.33333333, ..., -1.33333333,\n",
-       "         -1.41666667, -1.5       ]),\n",
-       "  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n",
-       "  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n",
-       "         1416.66666667, 1500.        ])}}"
-      ]
+      "text/plain": "{'q': {'q': array([3.        , 2.08558738, 2.259722  , 3.31510822, 3.99537911,\n         3.49071755, 2.40188947, 2.02507209, 2.80884137, 3.82017225,\n         3.85514276, 2.87212284, 2.04133215, 2.3517716 , 3.43388374,\n         3.99948622, 3.375267  , 2.30431745, 2.06153158, 2.93592978,\n         3.88659931, 3.78183148, 2.74634542, 2.01281822, 2.4544651 ,\n         3.5455349 , 3.98718178, 3.25365458, 2.21816852, 2.11340069,\n         3.06407022, 3.93846842, 3.69568255, 2.624733  , 2.00051378,\n         2.56611626, 3.6482284 , 3.95866785, 3.12787716, 2.14485724,\n         2.17982775, 3.19115863, 3.97492791, 3.59811053, 2.50928245,\n         2.00462089, 2.68489178, 3.740278  , 3.91441262, 3.        ])},\n 'y2': {'y2': array([ 0.5       ,  0.41666667,  0.33333333, ..., -1.33333333,\n         -1.41666667, -1.5       ]),\n  'x': array([-4., -4., -4., ...,  5.,  5.,  5.]),\n  't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n         1416.66666667, 1500.        ])}}"
      },
-     "execution_count": 29,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -923,14 +840,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `get_data_as_pandas_dataframe` - for `pandas` fans\n",
-    "\n",
-    "`DataSet` provides one main method of accessing data - `get_data_as_pandas_dataframe`. It returns data for groups of dependent-parameter-and-its-independent-parameters in a form of a dictionary of `pandas.DataFrame` s:"
+    "### `to_pandas_dataframe_dict` and `to_pandas_dataframe` - for `pandas` fans\r\n",
+    "\r\n",
+    "`DataSet` provides two methods for accessing data with `pandas` - `to_pandas_dataframe` and  `to_pandas_dataframe_dict`. The method `to_pandas_dataframe_dict` returns data for groups of dependent-parameter-and-its-independent-parameters in a form of a dictionary of `pandas.DataFrame` s, while `to_pandas_dataframe` returns a concatendated `pandas.DataFrame` for groups of dependent-parameter-and-its-independent-parameters:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -970,15 +887,15 @@
     }
    ],
    "source": [
-    "dfs = dataset.get_data_as_pandas_dataframe()\n",
-    "\n",
-    "# For the sake of making this article more readable,\n",
-    "# we will print the contents of the `dfs` dictionary\n",
-    "# manually by calling `.head()` on each of the DataFrames\n",
-    "for parameter_name, df in dfs.items():\n",
-    "    print(f\"DataFrame for parameter {parameter_name}\")\n",
-    "    print(\"-----------------------------\")\n",
-    "    print(f\"{df.head()!r}\")\n",
+    "df_dict = dataset.to_pandas_dataframe_dict()\r\n",
+    "\r\n",
+    "# For the sake of making this article more readable,\r\n",
+    "# we will print the contents of the `dfs` dictionary\r\n",
+    "# manually by calling `.head()` on each of the DataFrames\r\n",
+    "for parameter_name, df in df_dict.items():\r\n",
+    "    print(f\"DataFrame for parameter {parameter_name}\")\r\n",
+    "    print(\"-----------------------------\")\r\n",
+    "    print(f\"{df.head()!r}\")\r\n",
     "    print(\"\")"
    ]
   },
@@ -986,25 +903,71 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similar to `get_parameter_data`, `get_data_as_pandas_dataframe` also supports retrieving data for a given parameter(s), as well as `start`/`stop` arguments.\n",
-    "\n",
-    "`get_data_as_pandas_dataframe` is implemented based on `get_parameter_data`, hence the performance considerations mentioned above for `get_parameter_data` apply to `get_data_as_pandas_dataframe` as well.\n",
-    "\n",
-    "For more details on `get_data_as_pandas_dataframe` refer to [Working with pandas and xarray article](Working-With-Pandas-and-XArray.ipynb)."
+    "Alternativly to concatinate the DataSet data into a single pandas Dataframe run the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'q': array([3.        , 2.08558738, 2.259722  , 3.31510822, 3.99537911,\n",
+      "       3.49071755, 2.40188947, 2.02507209, 2.80884137, 3.82017225,\n",
+      "       3.85514276, 2.87212284, 2.04133215, 2.3517716 , 3.43388374,\n",
+      "       3.99948622, 3.375267  , 2.30431745, 2.06153158, 2.93592978,\n",
+      "       3.88659931, 3.78183148, 2.74634542, 2.01281822, 2.4544651 ,\n",
+      "       3.5455349 , 3.98718178, 3.25365458, 2.21816852, 2.11340069,\n",
+      "       3.06407022, 3.93846842, 3.69568255, 2.624733  , 2.00051378,\n",
+      "       2.56611626, 3.6482284 , 3.95866785, 3.12787716, 2.14485724,\n",
+      "       2.17982775, 3.19115863, 3.97492791, 3.59811053, 2.50928245,\n",
+      "       2.00462089, 2.68489178, 3.740278  , 3.91441262, 3.        ])}\n",
+      "{'y': array([-0.5       , -0.41666667, -0.33333333, ...,  1.33333333,\n",
+      "        1.41666667,  1.5       ]), 'x': array([-4., -4., -4., ...,  5.,  5.,  5.]), 't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n",
+      "       1416.66666667, 1500.        ])}\n",
+      "{'y2': array([ 0.5       ,  0.41666667,  0.33333333, ..., -1.33333333,\n",
+      "       -1.41666667, -1.5       ]), 'x': array([-4., -4., -4., ...,  5.,  5.,  5.]), 't': array([-500.        , -416.66666667, -333.33333333, ..., 1333.33333333,\n",
+      "       1416.66666667, 1500.        ])}\n",
+      "          q   y  y2\n",
+      "0  3.000000 NaN NaN\n",
+      "1  2.085587 NaN NaN\n",
+      "2  2.259722 NaN NaN\n",
+      "3  3.315108 NaN NaN\n",
+      "4  3.995379 NaN NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = dataset.to_pandas_dataframe()\r\n",
+    "print(f\"{df.head()!r}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Data extraction into \"other\" formats\n",
-    "\n",
-    "If the user desires to export a QCoDeS `DataSet` into a format that is not readily supported by `DataSet` methods, we recommend to use `get_data_as_pandas_dataframe` first, and then convert the resulting `DataFrame` s into a the desired format. This is becuase `pandas` package already implements converting `DataFrame` to various popular formats including comma-separated text file (`.csv`), HDF (`.hdf5`), xarray, Excel (`.xls`, `.xlsx`), and more; refer to [Working with pandas and xarray article](Working-With-Pandas-and-XArray.ipynb), and [`pandas` documentation](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#serialization-io-conversion) for more information.\n",
-    "\n",
-    "Nevertheless, `DataSet` also provides the following convenient methods:\n",
-    "\n",
-    "* `DataSet.write_data_to_text_file`\n",
-    "\n",
+    "Similar to `get_parameter_data`, `to_pandas_dataframe_dict` and `to_pandas_dataframe_dict` also supports retrieving data for a given parameter(s), as well as `start`/`stop` arguments.\r\n",
+    "\r\n",
+    "Both `to_pandas_dataframe` and `to_pandas_dataframe_dict` is implemented based on `get_parameter_data`, hence the performance considerations mentioned above for `get_parameter_data` apply to these methods as well.\r\n",
+    "\r\n",
+    "For more details on `to_pandas_dataframe` refer to [Working with pandas and xarray article](Working-With-Pandas-and-XArray.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data extraction into \"other\" formats\r\n",
+    "\r\n",
+    "If the user desires to export a QCoDeS `DataSet` into a format that is not readily supported by `DataSet` methods, we recommend to use `to_pandas_dataframe_dict` or `to_pandas_dataframe_dict` first, and then convert the resulting `DataFrame` s into a the desired format. This is becuase `pandas` package already implements converting `DataFrame` to various popular formats including comma-separated text file (`.csv`), HDF (`.hdf5`), xarray, Excel (`.xls`, `.xlsx`), and more; refer to [Working with pandas and xarray article](Working-With-Pandas-and-XArray.ipynb), and [`pandas` documentation](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#serialization-io-conversion) for more information.\r\n",
+    "\r\n",
+    "Nevertheless, `DataSet` also provides the following convenient methods:\r\n",
+    "\r\n",
+    "* `DataSet.write_data_to_text_file`\r\n",
+    "\r\n",
     "Refer to the docstrings of those methods for more information on how to use them."
    ]
   }
@@ -1025,7 +988,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
index b782599d669..874ba9fabb4 100644
--- a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
+++ b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
@@ -39,21 +39,21 @@
      "text": [
       "Logging hadn't been started.\n",
       "Activating auto-logging. Current session state plus future input saved.\n",
-      "Filename       : C:\\Users\\Jens-Work\\.qcodes\\logs\\command_history.log\n",
+      "Filename       : C:\\Users\\trmorgan\\.qcodes\\logs\\command_history.log\n",
       "Mode           : append\n",
       "Output logging : True\n",
       "Raw input log  : False\n",
       "Timestamping   : True\n",
       "State          : active\n",
-      "Qcodes Logfile : C:\\Users\\Jens-Work\\.qcodes\\logs\\200923-17516-qcodes.log\n",
+      "Qcodes Logfile : C:\\Users\\trmorgan\\.qcodes\\logs\\210125-35400-qcodes.log\n",
       "Activating auto-logging. Current session state plus future input saved.\n",
-      "Filename       : C:\\Users\\Jens-Work\\.qcodes\\logs\\command_history.log\n",
+      "Filename       : C:\\Users\\trmorgan\\.qcodes\\logs\\command_history.log\n",
       "Mode           : append\n",
       "Output logging : True\n",
       "Raw input log  : False\n",
       "Timestamping   : True\n",
       "State          : active\n",
-      "Qcodes Logfile : C:\\Users\\Jens-Work\\.qcodes\\logs\\200923-17516-qcodes.log\n"
+      "Qcodes Logfile : C:\\Users\\trmorgan\\.qcodes\\logs\\210125-35400-qcodes.log\n"
      ]
     }
    ],
@@ -95,16 +95,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qcodes.dataset.measurements.Measurement at 0x254bad0eb88>"
+       "<qcodes.dataset.measurements.Measurement at 0x1c47fe60108>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,14 +125,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 74. \n"
+      "Starting experimental run with id: 17. \n"
      ]
     }
    ],
@@ -150,19 +150,19 @@
     "                                 (dac.ch2, v2),\n",
     "                                 (dmm.v2, val))\n",
     "            \n",
-    "df1 = datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v2']"
+    "dataset1 = datasaver.dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 75. \n"
+      "Starting experimental run with id: 18. \n"
      ]
     }
    ],
@@ -179,15 +179,25 @@
     "            datasaver.add_result((dac.ch1, v1),\n",
     "                                 (dac.ch2, v2),\n",
     "                                 (dmm.v2, val))\n",
-    "            \n",
-    "df2 = datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v2']"
+    "\n",
+    "dataset2 = datasaver.dataset"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`get_data_as_pandas_dataframe` returns the data as a dict from measured (dependent) parameters to DataFrames. Here we are only interested in the dataframe of a single parameter, so we select that from the dict."
+    "`to_pandas_dataframe_dict` returns the data as a dict from measured (dependent) parameters to DataFrames. Here we are only interested in the dataframe of a single parameter, so we select that from the dict from both datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = dataset1.to_pandas_dataframe_dict()['dmm_v2']\n",
+    "df2 = dataset2.to_pandas_dataframe_dict()['dmm_v2']"
    ]
   },
   {
@@ -206,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -252,43 +262,43 @@
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000352</td>\n",
+       "      <td>0.000128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000332</td>\n",
+       "      <td>-0.000022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>-0.000201</td>\n",
+       "      <td>-0.000227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000244</td>\n",
+       "      <td>-0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.96</th>\n",
-       "      <td>-0.000056</td>\n",
+       "      <td>-0.001381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.95</th>\n",
-       "      <td>0.000188</td>\n",
+       "      <td>-0.000071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.94</th>\n",
-       "      <td>0.000082</td>\n",
+       "      <td>-0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.93</th>\n",
-       "      <td>0.000367</td>\n",
+       "      <td>0.000324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.92</th>\n",
-       "      <td>0.000349</td>\n",
+       "      <td>-0.000213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.91</th>\n",
-       "      <td>0.000050</td>\n",
+       "      <td>-0.000734</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -297,19 +307,19 @@
       "text/plain": [
        "                   dmm_v2\n",
        "dac_ch1 dac_ch2          \n",
-       "-1.0    -1.00   -0.000352\n",
-       "        -0.99    0.000332\n",
-       "        -0.98   -0.000201\n",
-       "        -0.97    0.000244\n",
-       "        -0.96   -0.000056\n",
-       "        -0.95    0.000188\n",
-       "        -0.94    0.000082\n",
-       "        -0.93    0.000367\n",
-       "        -0.92    0.000349\n",
-       "        -0.91    0.000050"
+       "-1.0    -1.00    0.000128\n",
+       "        -0.99   -0.000022\n",
+       "        -0.98   -0.000227\n",
+       "        -0.97   -0.000149\n",
+       "        -0.96   -0.001381\n",
+       "        -0.95   -0.000071\n",
+       "        -0.94   -0.000149\n",
+       "        -0.93    0.000324\n",
+       "        -0.92   -0.000213\n",
+       "        -0.91   -0.000734"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -327,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -361,61 +371,61 @@
        "      <th>0</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-1.00</td>\n",
-       "      <td>-0.000352</td>\n",
+       "      <td>0.000128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.99</td>\n",
-       "      <td>0.000332</td>\n",
+       "      <td>-0.000022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.98</td>\n",
-       "      <td>-0.000201</td>\n",
+       "      <td>-0.000227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.97</td>\n",
-       "      <td>0.000244</td>\n",
+       "      <td>-0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.96</td>\n",
-       "      <td>-0.000056</td>\n",
+       "      <td>-0.001381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.95</td>\n",
-       "      <td>0.000188</td>\n",
+       "      <td>-0.000071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.94</td>\n",
-       "      <td>0.000082</td>\n",
+       "      <td>-0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.93</td>\n",
-       "      <td>0.000367</td>\n",
+       "      <td>0.000324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.92</td>\n",
-       "      <td>0.000349</td>\n",
+       "      <td>-0.000213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.91</td>\n",
-       "      <td>0.000050</td>\n",
+       "      <td>-0.000734</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -423,19 +433,19 @@
       ],
       "text/plain": [
        "   dac_ch1  dac_ch2    dmm_v2\n",
-       "0     -1.0    -1.00 -0.000352\n",
-       "1     -1.0    -0.99  0.000332\n",
-       "2     -1.0    -0.98 -0.000201\n",
-       "3     -1.0    -0.97  0.000244\n",
-       "4     -1.0    -0.96 -0.000056\n",
-       "5     -1.0    -0.95  0.000188\n",
-       "6     -1.0    -0.94  0.000082\n",
-       "7     -1.0    -0.93  0.000367\n",
-       "8     -1.0    -0.92  0.000349\n",
-       "9     -1.0    -0.91  0.000050"
+       "0     -1.0    -1.00  0.000128\n",
+       "1     -1.0    -0.99 -0.000022\n",
+       "2     -1.0    -0.98 -0.000227\n",
+       "3     -1.0    -0.97 -0.000149\n",
+       "4     -1.0    -0.96 -0.001381\n",
+       "5     -1.0    -0.95 -0.000071\n",
+       "6     -1.0    -0.94 -0.000149\n",
+       "7     -1.0    -0.93  0.000324\n",
+       "8     -1.0    -0.92 -0.000213\n",
+       "9     -1.0    -0.91 -0.000734"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -453,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -462,13 +472,13 @@
        "<AxesSubplot:xlabel='dac_ch1', ylabel='dac_ch2'>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -492,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -501,13 +511,13 @@
        "<AxesSubplot:xlabel='dac_ch1', ylabel='dac_ch2'>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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fGYdub3W9SEO27W1IVldXdxGDbavHqycLWCemtzWJWFh5ikCEKLyoxerbvnOO17Nl92n7NaSR08vp1Mpz6bBce6s7JIkMaW+Zof93y4ggEAdbW1vY3Nwcc256ai+wO4VlHbQQiJ2dlSIb0dFRgdb1iuVetGKJRevqWV/Sx8rKiksg1rboeZGVHUMpurDn1drR7b1ZYvY8CLyaSwqplJaNHFL77fF6295YSkTVEhH1wdBOP0ikjGU+P0EgDo4dOzaWwiKiXXWR1dXVbVLRjhjYWcmur/I12aysrGzPzNDpLm2DmbfrGdKfTZnl0lLa2WuZ9Kn7s85bE5l2YDYCsW31GMSWtpv6nHJC3pWbJaUcoab+uDb68Ox5Y7X9pVBDLN44U6SV0u+zv1anBC/6DPiICGSKIKJzAbwO3RMJ38zMl5n9zwDwLgCfGYnewcy/UNO2L44dO4Zjx46NOUhNIFJnEAKxUYWexaV/PDoNtra2ti3z0lKWbGTbIwWduvEiECEKbxaWdysTTXDa+ck4RG6jF32cmgylfS515UUpQqIewaWu9lucmUckdnwlQhEdjwg9IqlNYbU65RryqRlz3/6GipCWDfZ3v2yYKYEQ0SqANwA4B8CdAG4koiPM/Amj+gFm/pc92zbj2LFjOHr06NhVuBCI/CBWV1e3U13amQK7pwFrufzRJEUmpKKjFWB8xtbq6uq289Y1Bp1W0rp2FpYQgiUQkWkHJ/bsHXalrU3TiW3tkGz/+jxosrXEa77fbVvWTo5QdL82KskhFzWUogR7Na7tiMxrX+tYWognNYaU3cD0EQQyPZwF4HbuHk8LIroKwAUAakhgkrZZPPjgg2MRiE5VATtX7pZAdPQgBGGjGO00dRqMmbG2tjYWxaSIQsZg02Na116lCwkB40V0IRF7bNbxWrkmLO9q9Pjx42MLqOS8eCkzPV4dkXipLhlrSt86VZ1205DxeH14utbxW1KoddYtxGJJsSXVVTuuFr1APyzzuZ01gZwK4Atq+04AT3X0vpuIPgrgLgD/mZlvbWgLIjoE4BAAHDx4sDgoTSACXVgXR7q5ublrHQiwcy8tYHwVqi2MawIBuqhEk5Y4OB09aEdfSyAiW1tbcyMQnZqSbUsMomvTUjriEJletKjlXgTi6cm7F5XoyE7bku9IyzXhaNvyXVjknLi1awk6la4qOecSsVjdSVNduXHMk51lwjKfj1kTiHdm7eXVhwF8MzP/AxGdD+CdAM6obNsJmQ8DOAwAGxsbxcu3o0eP4ujRo2POScgCGJ/VZIvo4szt1FwA20QB+NN4dV1D280RiDpGN4Ul/QvhebrWsetoRSARhb2at5GNJgrvqt0SiHU4eiySJrTjsykjfYz2fKSmHtvj8IhB5NLW2vAiEO9znxRWiyPOpaxm5dCDSDrI73hAe6Wa8TcC+B0AB9H5919l5v812AAMZk0gdwI4TW0/Fl2UsQ1m/nv1+RoieiMRnVjTti8efPDBXTUQTSA2tSVOTs9y0gV3HZl4jtDO2BLb2iFrJyi6loQ02eirdR1p2KhC111kvFou+mtra9ja2tqVltJEYcdr01W6ViLtdapL27NttX0v7aRtarmNQCxJpEgllSaqiUC8zzUprNqIpY9jzpHMUH3YvgIdhiJSqqv7vgzAJ5j52UR0EoBPEdHbmPnoIIMwmDWB3AjgDCJ6HIAvArgQwAu0AhE9GsA9zMxEdBaAFQBfAvC3pbZ9IRGITq/YwrgmEBuBCOHYK2OrK5GGdpo2hWVnUAmxrK2t7ZJLW3H+en2KnVllIxBBikDs9GPR1XUZTSCarLST1P1pMrDkkHLIHoFogtbEnIpAPPte5JGLRKys9DkF7zj1Po9UasnG9tGiFxHEcBjwPNbUfRnAw6jr9OsBfBnA5lADsJgpgTDzJhFdAuBadCHZFcx8KxG9dLT/cgD/CsBPENEmgP8L4ELufulu2yHGdezYMTz44INjzslGIKPxJSMQXS8R6BSWOG+vOC+2vQhEHLZEA/qqXevaqEI7ddG1JKTbr6+vb49D+pWUErDjuHO3PNHj0OPU+kJCMq1Z9C3J2JfuTxO1TZF5EYiVpSKQFnIpkVMLPFseiaTazYvzn5dxzBoDnoOauu9vADiCLhvzMAA/zMy7C34DYdYRCJj5GgDXGNnl6vNvoDspVW2HgKSwtHOx60B0BCIEIs5Up7DsFbJ2cOLwtF1LIDqqsOkuO+1XRx86ehKZjUA0CQnErq49iH077ddGIJpUdGpKjkkTmECO0bv3mEdCHinpc2xl3iwsK0uRRGoyQC4i0MeQ298nVVUjSxGO3V8buUzq/PY7idgLngJOJKKb1PZh7uq32+acNvZK4vsBfATA9wJ4PID3EdEHWJUChsTMCWQecfToUTz44IPJGgiwU+wVuU0n6TUjYsPWNTQ5ATtRCbBDNppAAOzStekSXUcRuSYFq6sL9ESEzc1NrK2tuVNwdZ9yDJZAZGxCIvYc2AjJnhetq6Mlfc71d6DfbQpLy7We9Gvhpbu0rBSR2KjB7rd9e+mu1hRYYP7R8B3ez8wbmf01dd+LAVw2ytLcTkSfAfBtAD5UO4gWBIE4sLOwLIGI89Sr0SUC8VaMC+yUX3HA2q6+xYnuKxeBaEdriQXA2Pi0M5PCuB6jJgstW1tb2zVdOVfrkLFqwpFxWydpiUCOWfevP3tX4TYV50UfdnGkFwloEvIiED0OL82VuupPkUJtuipHKjX9zhLzMo5ZwUbAE6BYMwbweQBnA/gAEZ0M4FsB3DHUACyCQBx4BKJTWMBO+kZIRDs9LwIReYlA7AwqnRbS6R99nyxgd2FcO0o9C0vLdVQhMptW0860FIHoq/fNzU2sr6+PkYONqkTu3TbFRiA6qpBtfY4F2pFLnzYiS5GCQJNaKkqwfZXSVLnUUY4USimpnI0+8r56gTSGOn9cVzN+FYC3ENHH0aW8Xs7M9w8yAAdBIA6EQIAdR6DrGsCOQ7Qr0YFxAhFdLdczq7wpv16RV0cgwPjCvlQEkiMQfYXt1RWszJKfJUBLoLb+oaMVXeuxjlrs2BSR6GpHbAla25NxeLUOHVno49X96VXq3uyuHInovrzozB5XK4Zy6jlS8/qbpN8hbCwivIh5EnC5ZnwXgO8brMMCgkAcyL2w9Bdv78YrKSwhEOvkUzUQW+zWEUhuHUiJQARebcTWQOQY7DRe7bxtcd2msDwCEf319fVtUrMFdEsu8lnPwvIiACvXhOWRhB6P/R68CCQXkeiISNvU/ZUiEA+t+72IRo+lL2qceu78tPSzH7HMhBkE4qCUwtJO3hbRgd137pUfkF4Hop11qogu8OTeWg195WxnUNlFgADGpvHqvsTJ29SPRyCeY2XmXYSj01c6EtBpOX2+tdyeR92P/XNqsrBEofsVXYE3W0vvs+fCIheB5IhlyCvzXLpK+m5t21cvsINlPl9BIA7kdu76T+4RiMh0VCG69r5Z4sR1tCKOQ24xom8LoglEHL2Wr6ysbN98UV/t5yIQbVMcqZ2FpScB6NSRrNXwCMQW1zUB6HMgY/GIRcak+9JjtWkmHaFo6GPR7bXtVA3EXtWnrpjtvhRRaLs5wsi1TbVPIUcW8+L852Uce4VlPtYgEAeykFA7KSEFYJxAvGm8NgIRudXVxKJTVcA4UWxubu4qoq+trW3ffFEgxOTdx8pbm6KduWBra2vX7KxU/cIuTLTpOi8FJzY04cox2Rla+hYnKaeq60UpuSZfTbgWIrOpMr1Pn2vbzpNPetVfk/bZa4e83whgEuhU6zIiCMTB0aNHcezYMQA7V5fylELtzLa2tnDs2LGxqEIvLrRX6iK3i/0kWrH3sbJRhSUQWxTXupr8dDpJjklslGogNlWl9e0MMk0gmnx0qkqiC9HTpKGPQx+rnhTgpbt0dKW/HxshaeQIREcDtXn7XOqqJoVlxzFJFFKrM0SbvthPJLTMxxkE4kAWEgI7Tx/c3NwsEoguKtsIRE/51QSide2dbbXD9qbxejdCtKQgDlTbljY6LSVy7ag1KdiIAsD27eGlb+vUpZ1Ns0lflqw0EWvHbdNyegw2epE+5Li187bFdi9ysmNMpbU8IvDksq9EEpM61Nr2+8lxzwuW+XwHgTiw03g1gQA7jqiWQAQ5ArEpLO0AJdqoIRB7Xy7RXV9fd2eGiQOXY00RiIzJ2taRhtUVm9rZ6xlY0o8mgVTKSMjO1nE0SYlMRx2yz661sfb1eG3E4l3510QWXttJHP1eRCG1CCKqxzKfpyAQB5pAgJ2bI9onCuoaiHaeHoFICkvXAOw0WBs5iK6QhXbsIrPOWROIdrC6YC7jSkVLNk2kU0L6FiX6+PQxARgjCnHe2mlb8rCpL+ugdDpNk4Y3uUCPT49Nnz/bbyrSsOO2yJGIdxwauTRXDRbdiS/6+GuxzMcYBOJA340X2JmWq+8lJY5d300X2IlY7BWvpIA856un5kobm8KyOXw9jVenjfSiQbGln+WhHbwmFn3lbQnEFs21Q9b79Lh1DUGOVxf3UwSi9b1Zb7Kt33XKSp9X3Yed+aXPm+fE9fdpx2hhCcOTeX2V0Opgh7DbSmKBPPT/ZxkRBOLg2LFj2wsEgZ0IxE45FaLQV9j6il/0tEO0OXaxL/ttGilFIDIm7cxXV1e3axp6JbzMrNKOU8vsNGRb19COWMjJu6K3qSHtLK3TTxGIPk8a9pyJzK7p0GRhyUPbTBXWraOvjS5SJFMbfdSiJcKp2R+YPpb5/AeBOJCV6AJ900Rg50+pCUQ7A+/hU8D4vbBkn+ewbZ5bF9EF2umLrq2xWDLSBKKnC+t6gU1z6f6lH0sW+qFYeowy/VgITWS6+C6w6Sp97vS+1G3ydaTn1Vfs2HJpq1RkkooqbBsPLY4+9bkGi0QYizLOSbHMxzlzAqHyM35fCODlo81/APATzPzR0b7PAngAwBaATc7fCrkaOgLRzlem22qi0HWRFIEIrBO3EYieaaSOfywFJjId7Wg7QiS6DiL9avISmV3fIZFMLq1jnW+N3JKAPVYbMenj1591RGGPx0YOKQKR8wb4zyixUYV3Huz3VCKa1PY0UNNHRC57h2U+jzMlEKp7xu9nADydmb9CROcBOIzxp3A9kwe+26S+x5UmEC8C0QSiIxGbr5d2qQjEkoE6R9sk4l0Ra0csBGDTXtKv7BPoKMIW0IHxWVEaNvWj+/fkck718ej9cuxejcNbZyLnXsvtlF1LIJqkvSv8VNpKdOxx5SIoL7LxzmMpyklhXtJYk9rdDyRlI+hlw6wjkOIzfpn5g0r/BnQPUZkqNDHo+oSdTaTXdtjZRl4Eogvpep9NYYnMpqFsm9RVuE1ZiW39bp2gV2dJjckiJfOm3KYIxNOxxW6dqvKOUUcVllBsZOKRRa1D9+Q1EUhLZDC0c90PznpescznfdYEUvOMX40XA/hjtc0A3ktEDOA3efzxj72hCUReQhQ2YrBEA4yv0AZ2Oy/P8et37UAsgVh7OmqwBCLOVtuWqELqE9snUrWRPnQ7YGediYaNErRjt3KbvrOLDPV+PR3XIw2xK3Z0jchGdDYyyb3r85E67/Y7sm1KWJQ0VmAY2FsLLRNmTSDer9eddE9Ez0RHIN+jxE9j5ruI6FHonv37SWa+3ml7CMAhADh48GBxUOL8tLPSMh2B6NqElnsOSd61DS+FZVNLzvGM2RCZ7ceuvNbyFFF4qbdU1OA5dL1PE4Re0GcX+NmitxC2JV7b1rbR59OOUR+njeZE3uJwLcG0OuIW552LePrY6zuOeWw/74gU1nRR84xfENETALwZwHnM/CWRc/fwFDDzvUR0NbqU2C4CGUUmhwFgY2OjeHMjj0D01FJLKtoZyrt27qkIw3NmHhHYfd7iOdnWq9G1HVuI17UbdZ633+2qdRs1pEjDkoUmRO88SV+pY7dTcHPEYyMGe4613PaVSlWl9nnIOXU9vqEc/bw433kZx7ximc/NrFe4bD/jl4gOoHvG7xGtQEQHAbwDwIuY+a+U/KFE9DD5jO4pXLcMMShNHvbdvmzUkWsj23r6r3a6er+9urY2tNym1zzbVpbSzR3nUC9gd0QmsOezRj9lQ8Nr4+237yn9EkrjSfWbkgUWF96FmPdaRMw0AuG6Z/z+LIBvAvDG0UmW6bonA7h6JFsD8LvM/J6BxrXLsXsOLOUcU87P68fC5u7tVXWqrb36t1fvHnHYvnSkYGsrtp6QOwell7Xv2bF1i5Jj9wjKi0jkHKe2h/gjD2UnsBxY5t/CrFNY4PIzfl8C4CVOuzsAPHGK40rKWwih5SoyRQoeiaSmA8u2tpe7yrcpl1S71FV733ORs5uyoc9FzRW+d+6G+jOnSGKeyWOex7bMWOZzPnMCWTTsZVpB58xToW5u28uZ68+ptt4PvjbMzunkxjYE+thc5j/3pAjCmRwS2S8rlvfIZoCU452WzZRDLvWpySClq/fn+knZsftqxpQbZ258KTulY6wdRwvC4bajT41pkTCvNRAiOo2IriKiDxDRK4hoXe17Z42NKgIhom8gosc78idUj3aBULrir3F2k1xtt17ppwjFOwZPntJNXTmljrVEbDVoiYJy/dSQ7CR/2j6EGNifmFcCAXAFgD8B8JMATgHwp0T0TaN931xjoEggRPQ8AJ8E8IdEdCsRPUXtfkvLaBcF1tHomxymHLDW9e4cm2vj2bS3I8kRguinyE5e1panZ+1oWe5WJTUy71i9NSkl0tbjSJF8blx9yGmoP3gQy/6C9gml1wxwEjNfzswfYeafBPBGANePgoWqkLCmBvIKAN/JzHcT0VkAfpuIXsHM7wDchYALj5Qzlnei3TOR5F307HO6dYhu78dk+7EEZAkqFz1Yx1zan9PTN2S0/ZXse4SZIoqUnkdm+nPOTuq78/aVyCUHr20NodXaHBLzTl7LWnOZ42NaJ6KHMPP/AwBm/h0i+ht0s2IfWmOghkBWmfnuUQcfom5F+LuJ6LGoZKlFg9y+XK+GBsaL0Hpaa2qqr316n0DPltL75A9kIwWR69u0y379eFztePVLO3O5vbomEDlekXk60l8LcVii0sdkCUb2eecl1a8ca62d0j6to8+7lZfa16CGZGbtePaqf/0fW0bMcRH9zehuHfWnImDm9xPRcwH8So2BGgJ5gIgez8x/PergbiJ6BoB3Avj2xgEvBOydbG2EYVd6C4kI9E0VU39CO+VW/4lsSKuJJeVA9d16PVIAdp5iqAlC9LQ8RSwewcjYS5GMdbyWXKwste3ZKUUBXlu7z8qtrZwzzRFRi04LSu320vkPZWdZSWTWFwIpMPNrAYCITmLm+5T8L9HdIb2IGgL5CZhUFTM/QN1zPJ5XP9zFgX5crBCIvZGgXksBjD+L2y7eA3Z+RLadvaGh2NJOzxKLdmzi5K0j1I+2tQQiTy30iEJHNJZsxIaNbqwt7fxtBOK1TX32iCZFNqU6lSBVnyp91jLP0Q0VmXiosTevTqqEGpJdZEzj9zAFfJCIPgPg7QDewcxfqW1YJBAePbzJkR8D8LbqIS4Q1tbWth2lvQOvjTg0WdgfiiYRG2nYO8qKvnaIWh/AmHPXUYlXq9BRhdgR8tDylZWVbZkQi25vCSqXGrNk4+l6xUNLhPo4vb48WYo4WmSCVESTap+LbLxFjH2imWlgHhzbstY9NOb9+Jj5DOrq2xcCeCURfQLAVcz8O6W21QsJieiHAPwygEehi0io65u/od+w5xerq6tYX1/fTlUJCQiZ2BSW3i/QaS+9alxkXu7eS2EJWVmHqgnEPsIWwBhRaFLxCGR9fX2MQJh5W2ZTWKkIxCMFrWtJwbOjycMShZdeS0Uect5ayEW2tdx+lu2WVIvX3n6eNwczb+NZdAx5PqnwBNeRzjMA/DqAdQD3M/PTS3aZ+UMAPkRE/x3ArwG4EsBwBIKuqPJsZr6toc1CYn19fduZ6rvwChlYJyJyTSAi9wgEQPJ+V7qGAYwTiK1pSKFeX7nrgruQhdjRMu1419bWto/XkoWOSmzNxIs01tbWxsjAkoUlFi1LRTwp0rLEmYpK5PhzhJHSEZTa2TsGWHj2hkI4/PnGUN8PVTzBlYgejm467rnM/HnqHnVRsvsNAJ6DLgJ5PAC5s3kRLQRyz34gD6AjkPX19e1oQ9++XRy8nl0lJOHdlly3EWgi8ohFO0rRtwQi5KQJQaIHoItA1tfXx/SFKCwRabl2xloXwBh5eHJNQiLXdm0E4aWqvEjDI49cZOGlvFL69vyXIoQciXh2Ss6jT3SSsxlkMl+Q/+1AKD7BFcAL0NUxPg8AzHxvhd2PopsU9QvM/OctAyoSCHWpKwC4iYjePuroQdk/Wg+yVDhw4AAOHDjgPvzIyoDdaSrtyCWC0Q5byEb/uDQpWLkXgYiupJpkv+jqqEL0JTVnCUTLbA3Eq6N4BCJ9abmXwioRiNW1kYn0l4o+akkm9zm1v5VISiSTwtAk0EpCgWEx4LmueYLrP0G3vuNPADwMwOuY+a0Fu9/CmbwsEf0P7hYa7kJNBPJs9flr6J67IWB0z+pYKqyvr+PAgQPY2tradso6nSWPvNUEwszY3NwEMP6D0SkwXTeR9tqRE+1MEdZXLZoUdE1ja2trV/pJE8v6+vrYeIRArH2PVLyUEoDtvmxqy9ZLZLxeCspLpdn6jCUbS256nyWSmgjEyvR50lGJJQQhY72vhSj6RBw5tEQ4Q2MI2/uByBqO8UQiukltH+bxx3R7hqzjXwPwnQDOBvCPAPw5Ed3A6llKuwyUi3pPS+2omYV1cUln2XDgwAGccMIJY89G1xGIpLU0gejCuiYKL9rQBCLOTM+0Eget+9VOVhOInpqrnZuksPQYtcxGJpaEbFRhdXOpLRs9eMRi5Tbi0TZyqa1a8vBIBBiPNCyZe/qyz7bVcvu5tN3iRGvJadoI8qhHw3Hez92zjlKoeYLrnSM7XwXwVSK6Ht1jL5IEMglaZmFdCeDfM/PfjrYfAeA1zPxj0xjYLKEJRKIKYJwQvCK66LYQiHZsOt21ttZ9NToqEblHIOJgdQSiC9qWKKTgDmCMgDwC0cSiIx6d2tKRjdb1CNEjFk0quRSWdvaaHESWIgtPL0UOKZLxohXvc0tk0kIIpe1azAsJ7QfY39eE2H6CK4Avoit6v8DovAvAbxDRGoAD6FJcrx1qABYtRfQnCHkAADN/hYi+Y9IBUGFaGnVn/3UAzkeXQvtRZv5wTdu+OOGEE7C5uYnNzc1txweUCUTn6AUSxWinbQlE9KUIb52zLrh7BKIdvRT8bYrIIwWBdtqWQLzUlpCF2LCkoHV1hCSoiUB0ukqTm5wf3U7GUEsWqejCIwwrF9REJ61oiUpq7M8zGczz2IaG/q9NAq54gisz30ZE7wHwMQDH0fnFSR/1nfyyWghkhYgewaNVikT0yMb2u0dVMS0NwHkAzhi9ngrgTQCeWtm2F6QGoh0ysLN6fHNzc9daAIlArEMRArERguhqJyp2vQikhkCEKOzVv5CQJRCd7qohEBuB5FJY2q6eTqzl8tLTkfWxSGQi50kTk440LLGkUls2RaX1RFZLDLlIpGZfSuZh0cliv2PI74YLT3Adbb8awKsH67S7SHfRQgCvQbfk/Q/QFW6eB+AXJxxYzbS0CwC8dVTouYGIHk5EpwA4vaJtL2gCscVyANtOT8slAtGRgCYF7eg8ApF+JVrRBKJrINqxSZ8eUeiUT4pAZCz2Kv/48eO7pgdrudRR9HnQKSyZeOARiLSzqTRLKsDum0LqiEcfnyY9SyBiJxdllAjEi2ysLf2e+lwbYUyTUCbpt1Uv0GHezxcRbQB4JbpngKwB24vEn4Duw1tSbasJhJnfSt0Mge8ddfBDZgHLdnTSgJppaZ7OqZVtZWyHABwCgIMHDxYHtb6+js3NTayuro4V0fVsKksg4oi1YwXG73Wl5Zubm7uuivU0YZvCkvY20rAFZm/Kr+jLFb2NQGydwRJTKgKR47ERiNi2M6vkeDQ5idyri8i5tgRi6x820tCOXu/LkYUn8+TqN1VFDC1k0JcY5t1BCfTvcL/A/m7mFG8D8NMAPo4u7VWNphTUiDBSV/jXAXhyiz3UTUtL6dS07YTdVLjDALCxsVG8D4VM4ZVowCMKTSpEtJ1O0lfE4iyFFLQdqa3YKEGnu7QcGE/d6AWKVm6v0HUE4hXcvcWIOqrQ0YqXwrJRha1fSOSjz4lX69DjE+hj04SVSlXpSE3GZklGbHhkYeViw5OJvqAmItEXItOINLT9Fnt7FX0sgDMdHEPVQKaI+5j5SJ+GE9UwDPr8MmqnpXk6Byra9oKkkvQqdGCHQOyKc2BncaCWC7HoFJb8ub11IGJbO9YUgWhdSxTa4Wrnr1NYuk9be5B+bUopN+XXk3sztuT82ohH1zq0riUL/V6qgWii0OdT3i2peBGIltvvvK/MuyptJZMWMtiPTnuesADn/+eI6M3ogoCmReJDEkifm/nXTEs7AuAS6mocTwXwd9w9k+S+ira9IKvQNzc3dz1VUBy3dSw6WskRCJBeSCj2rcOzKTJNIKUUliUQLwKx6Spvuq3Y8CKNmgjERhWaMPQx6Pai6xGFJRMdmVgS8GQ1kUdOniOBEqHUYC+dTm2kk4psAmno3+0c42IA34bu5otyxVy1SHxIAmlGzbQ0dDMOzgdwO7ppvBfn2g4xLiEQuTq2NRCJTPQPQ6e8gB0Hoxcd5ghEnH+JQPTVtU5LadvA+NRcYKfgrvW9Goi+ci8RiHa+Wi7jtnUNK9fOX/ev6yX6/NgUlhd92KgilaqytvV3po9L4EURNe+5z322W2Q5eR8swJX0XGIBCOSJzPzP+zScdQqrOC1tNPvqZbVth4DcSFEiEP3H0etAPALxIhBdAxGi0M8TsQRinWOqiK5nbGkboqsdtyUbLfem8dqoQqDTYF4KS5ONvZWJrV94NQwt1+fHRjxetGLJxN7x2KboPFJJRRt6vyf33i1yROLJpumwxfZeRBX7nXgW4PhvIKIzuccSiJaV6N8F4FZmfmC0/TAAZzLzX4xUzm7tfF5xwgknAMB2BALsTmFpUpBoQC8EFOi6iHbypQhEHLrYlvGUIhCbwpKrdltE1xGVJgWRE43PlgL8FJaeHiz7ZBzeSnavpmHTczaK82xYXS9ys6kueXn1DvsdeaTgtdc6fT7nZLX7J4k+Juk3UIcFOIffA+Ai6p5K+CAwPo03h5YI5E0Yn2X1VS1j5i832JprHDhwYIwoaiIQiSq0nGh8caA4M7HrzcKy03jFNuATiDh6G4F4N1jUck2OXqoqRyBevUTPthJdvbZE69pUlYwB2L0K35KIPZc2UtH6ligsQbZEIKntvoShUYo8UoTjTYlNyVugLy4Ck8H+duYU5/Zt2EIgxOpXxczHqbvfytLhwIEDALArhaWjBzs1V8/YSqVVNIF4t3MXAtGkAmCMbLST9+oBEvF4xXLt6LU8RyA6ipHjsNGKjoK0DZuu0udEj9sehyYWTR5aV86PHpslCt0+RRa2zpGLPrTM7tcoyXOymjYl3T7RSl+9ko0govmPQJj5c9Td2/A0jHPC50ptWwjgDiL6d+iiDgD4twDuaGi/MDhw4MC2c7EEkktheQQi6x9sqiqVwvIeKJUiEB2t2Agkl8LSEYhNS4ltALtu/W7JwkYVOQLxHHqKQLwUlpzXUmE8F4Hk5KmoROzq95SspOvplWR9dLw2tVHJ0M5u3p3ntGEviOYRRPQqAD8K4K+xM5uW0S0az6KFQF4K4PUAfmZk/DqMVncvG0444YTtL95LYWlHL/t0tKIdkCUbIaXc3Xilb/njCwHYK39v1XmOQERXRyb2tiX6qtG77YkmrFxNAthd7NZO2iMba0POYYlARFfrpz7r9xwB6WPw3lOylE4OKZ0W51vS3e+OfJZYgHP/PACPZ+ajrQ1bbmVyL7q1FksPiUB0WspGD5YsZMW5XNELNIHIy9ZAxKFpXXH0QipaV0jIRiDA+Gp5ednivCUnO2NLjtemn3KRAoAxGzlisamqVF3Di1p0jUkTk9bT35eNPjRyJJQiCns130Iepe1WvZr9tc5rKCenf4eBDgtAILcAeDiAmsffjqFlFtZDALwYwLcDeIjIeQmfB3LCCSdgZWXFjSq08wd2nIroWgfoFdHtjC1NTvZmiLo4XyIQsaGn0NoaiE0/2fUhAHZd5XvE4jleWy/Rul5UYfvT50Pb1XK7piYVQWhHb/vWx+KRiNVNOfQUieTalNCiV0pL7TV5TNvmosJeuMwhfgnAXxLRLRhfif4DpYYtKazfBvBJAN8P4BcAvBDAbW3jXAzoe0BpArHRA7DjhNbW1sbSUgJ7LyxtwzqwvgSiowJdRLcEYqMHXRgXctFkY+sX1oZ1oLUk5NVAtA07NunDq33Y1Jj+TryXoCaqSOlrlCKQlJPvG40MsS+wd7C/uznFlQB+GVO+meI/ZubnEtEFzHwlEf0uulXgS4cDBw5s343XS2HJy4tMgPErDq2rr6JriugpArG6OoUl02htncKrHdjCsRT8LbF4aalUZGKvtlIRiBCW2M0Rg62BeHr6O9LH50Uqdhx2fynisHJvX0peQxwlhzNPUYec5yFtLhsWIAK5n5lf36dhC4EcG73/LRH9MwB/g+6ZHEsHWb+gawcAdhGFyLRDB8Ydnb0XVo5AxLZ9FK2XErKFcdkPYFcEoaMKa0Mfg9ixEZNn17v690jFIwR9LNqu7NN9esdtnb118HYMVs/bl5PVyC2GIIpW/VmQx5D2lhULcH5uJqJfQnffQZ3C+nCpYQuBHKZurvDPjDr6egD/tXGgCwGpH+hUFTB+iwxg3Cl5UYnUIlK3MrHOT9/yXJymHoO1YYkCwC5S8Agk5bz1vaYsuQksCWm5J8s5dW8MHiHo8VpCKEUgVr+UqkrppvQscvp95aV9NfsDs4G9qJpTfMfoXZ6nRBhqGi8R/Ue1efHo/Q2j94dWDnChILdzryEQgY4URFeiBIF2TJaEvGhDp7CsE9V9WULQdu00Xk0KKV0baaQikJyjr1lXYf9YLaSQiixS+1Ljtf17232ijhaS6EMoLTqT6Acmx7yec+Xb343dz1iqmkZXE4E8bPT+rQCegi76AIBnA7i+ppNFg6SQxJlqp2VJBYAr11f+1pmKrr7aB7BL14tApD/RF7nuT3S0M7a1COukU+mulPNOXdGnSMFz8jmy8SINe8z6XOjPuQjE07Xj9eR7JavZV7O/r25geMzx+be+/V3oSKTatxcJhJn/GwAQ0XsBPJl3bqb48wD+d/OQFwB2nYTAOn/tbLwIxM7W0rp2hbsXgegoxnPcHoEAu6fFpkhBdDWBiEyPy3PI3gwq66S1zCOLXA1Et8+9WyL0xlEijtpIxKJECn0JYp7JQ77zQD3mlUCG8O0tNZCDAPRKxaNY4iK6JgqBnj6rUSIQ69C954Z4pOCRitW1kYYek9ixdrXcRg/Wrm6jbVvnb21YR6ojoZSNFLmliEm/e7LUu9X3tlscfEt0MQlx1Or00W3BvDrEeQTR/N/KBBP49tZ1IB8ioqvR5ceeg27+8NJB1nQAu9NKnpNMEYhNYYncy/FLX/KeIhBr29osFdG9Pr1IQ/fnOVZbLyk5fy9aSZGQ5/i9SCP1OeXQ7Rg83VbHX0sekxJHi16rbo2tiDr6YwEIt7dvb7mVyS8S0R8D+Bcj0cXM/JetIxUQ0SMBvB0d030WwPOY+StG5zQAbwXwaHQLXA4z8+tG+34ewI8DuG+k/gruHjA1MfQVg1esTv2ZrEPVhCDtPSdtnX9JbgnH0/UIROBFFFpeQza5dJe17R1ziii8d93GEkBKz8LKa6IRj7BS+i2y2v2tjmdoR1UbkQXymPdzN4lvb7odO3fzgotzgytxKYDrmPkyIrp0tP1yo7MJ4D8x84epe4DVzUT0Pt55ctZrmflXBxrPNiyBWFLIEYhAz9ZKOXWta8nGi0BE7vWnbyHipX5yBKLHZtvLPqtbIpBSZJPrT/fjfdbbKcJKOf8ap5gisJxurby0r0VnEv29trefsQjnsq9vn+XzPC4A8IzR5ysB/AkMgTDz3QDuHn1+gIhuA3AqgOZHL7bAOse+BOJFCilS8KKV2gjEG3PKRsnRe+O1ul5/qQhE61qH7KUC5b021VQbleR0U21aI5CcvHZ/rc4k+tO2E9iBvthaRsySQE4eEQSY+W4ielROmYhOR7fg5S+U+BIi+hEAN6GLVL6SaHsIo1vPHzx4sDiw1Cwsb3Gh/tN5NQHP+XukoG1oXS3L6XqptUkjkJw89afIpcFaSMHbn9PxnH+KRKy9FKnkSMnDJFFHi/MOR79YWObva6oEQkTvR1e/sHhlo52vB/CHAP4DM//9SPwmAK9CV/R5FYDXAHDvDMzMhwEcBoCNjY1iNdA+9W80hiQp2OgB2F3o1vopXS33yMZe4efIRjt6O4YasvEIxNO3zjdFIPb47Wevr5xurX5NVFHTTwqTkEarXqtuYD4QEUhPMPOzUvuI6B4iOmUUfZyCxL3oiWgdHXm8jZnfoWzfo3R+C91qykHgOfRRP64spVsTaaT6q4keUoSVcuhef3psKYK0ejXvpX2p7RbH3qpXM4bW/vrsb9Xrq7/X9gK7IdmBZcUsU1hHAFwE4LLR+7usAnW/8P8J4DZm/jWz7xRJgaGbdnbLUAPTTte7Yq0lECtL6espsVbX2vAIJBWt2GPRut54SxGD3k5FPDU2crZtVKPfS9FDqY8SOZbsDUEcsyaNadsN7MYyn+tZEshlAH6fiF4M4PMAngsARPQYAG9m5vMBPA3AiwB8nIg+Mmon03V/hYiehC6F9VkA/2aogaWu8lNRRV8C0f3V2GhJYen2+nNKVx+3deCezVK6yB5zCjVOvBTJeO2HqIHU9FWr06LXqtuKZXZoyw4iOhfA6wCsovOTlyX0ngLgBgA/zMx/MK3xzIxAmPlLAM525HcBOH/0+c8AuL92Zn7RtMYmYafnhLxIIZWC0ttaZuV9CEQX65nZTVWVnGLJUes+cwVwLyrx9peK6CLzHHqOaGrGlmrr6cyKNCZpMw+2Az6GOudEtIruRrbnALgTwI1EdIR3ljVovV/GHjyvaZYRyNxCO02BddhaV/Z5qZcaAik56doUlu2jJVoR26k0Ws3nFl353Mf51+hNixQWlTj2wn5gN+RidCCcBeB2Zr5jZPsqdMsh7LKGn0RXN37KUB2nEATiIOWQtOP16g8l519zhe6No6Wo3aKbOr6aNq2koQm4Rt8bR21k440zhSFJo1V3kjbTtBMYFg3fy4lEdJPaPszdDFLBqQC+oLbvxM4zPKSvU9HVhL8XQSCzQcsVrY1AWmzU6mrbXp2i1kZJNxVh9Rmz3m4lptaptq3RxqxJIxz9/kLD930/M2/kTDkyuyzh1wG8nJm39uJ3FgTioNX5W6fbx0ZJlnPGQ/SXCrNzNlIzsLz2qeOpmVlVY6M23TWJziT6k7abld3A5Bjwu7kTwGlq+7EA7jI6GwCuGvV5IoDziWiTmd851CA0gkAc9HXGqaihxYberklLlcbcOo7UxAHbLnfVn4sqSuP2xpAbR4uNUt9D6Q7Zdpa2A8NgwO/oRgBnENHjAHwRwIUAXqAVmPlxqt+3AHj3tMgDCAJxMXT0UJLrGV+2VtCaqvLkNQ62dJXvbZeiihq7k0QxKb2S7l4QR5BGYGgw8yYRXYJudtUqgCuY+VYieulo/+V7PaYgEAc5B5u6Qq9x8jVX4vo9dyVeO7ZcX56sZgptKTKaJHrIjbt2f1/dPvpDtw8sF4iGXYk+WgN3jZG5xMHMPzpYxwkEgThodWot6a2U7dpV1n0ikJYV3JOmn2r7yxFWqZ8S9jpqmBZp5M59YHGwzBcVQSAJtFy9e+tAtDzXtqafmjRPrdyLHqy8dsyt0UrN8e+lE1+EusYyO5/9gmX+DoNAHHhXfiXHaz972616qT5ax9bi6GvGW5K1EEVNBFLT97TbpVJwgUAJy/ybCQJxMMkVvoZdH5KrHVhiGNKhe7KWiCBFTDV/jNb+avfV9jsJ9jISWGYns59B5C/QXRYEgSSQI4scIXhta67MW1ey67G0pLZq6yWlcZf6adnX0se02g9tZ177C+w9lvk7DgJxUEoT2ahhiIilb31AX+HUprZqx5Yq4rb8IXLnclLMm53afiIdtr+wzN9zEEgCLfWLmhsm6n25qMLKU21q71hr5SVSqJ0KXIPWCCZnYyjs5Z+5NkINLDeW+fsOAnFQW6gupblaHHpNiidVzC2NrYUUhqhr9MU0/miz+PPWEntgf2CZv/+ZEQgRPRLA2wGcju6BUM9j5q84ep8F8ACALQCbcrOx2vYTjK9anpKlpvfmdPuOq3XMNZikAN5if2jM8g+7zM4i0I5lL6LP8mG9lwK4jpnPAHDdaDuFZzLzk8ydKlvaN2GIorHePykxlHRanLuNRHKvlrHlxlyyPwmmZbe1/0AghdL/bJF/Q7MkkAsAXDn6fCWAH9zj9lnUfuHel59z6Dldq1Pa540jJ8uNuQ/26k8xj386j2QX1QkEpouVlZWq1yJiljWQk5n5bgBg5ruJ6FEJPQbwXiJiAL+pHrBS235PsBckkrM9DcyDQ5w1SQQCk2KZf0dTJRAiej+ARzu7Xtlg5mnMfNeIIN5HRJ9k5usbx3EIwCEAOHjwYI3+Ut6DSNc09PasoM/zrMcisOcoEAikMVUCYeZnpfYR0T1EdMooejgFwL0JG3eN3u8loqvRPRf4egBV7UdtDwM4DAAbGxtVnqHVoXmF5tR0Xc9xevJcEX6SGVB76axLfc0DcXgXDPMwrsDiY9lTm7NMvB0BcNHo80UA3mUViOihRPQw+Qzg+wDcUtt+L5FKNXm5cr0vJ5+HNFYNSrWYeUBpfPM23sDyoKZWuKi/vVkSyGUAziGiTwM4Z7QNInoMEcn97k8G8GdE9FEAHwLwR8z8nlz7wLDwnGzOCc8LcnWllF4gMA0sM4HMrIjOzF8CcLYjvwvA+aPPdwB4Ykv7IbCsNRCgn8Ocxx93TdppHscd2H9Y1BlWNYiV6AkM6XxSNz+UfbmaiGdrPznGXFF7P52HwGJikaOLGgSB7AFyaZ5UeiX1o1vWH2MpoljW4w4EFhlBIIG5QZBEYBmxzL/rIJDA1JFaf7Lf0nGB/Yll/o0HgQSakJtgYGs7VhZF7kBguRAEEtiGV8wHdpNBjeMPcggEOsQsrH2GVEG39Yl/3kOgJumzdPVf0k2taveOqSQLBAJlxCysfYqcIy0RSWlmVer+TzXOu8X5e4SxzD/mQCCwtwgC6YFJp9jupRMPwggEZotl/g8GgQQCgcAUEQQSCAQCgV4IAgkEAoFALywzgSzv/LJAIBCYMWrvxNtQPz2XiD5FRLcT0aXO/hcS0cdGrw8SkXsz2qEQBBIIBAILACJaBfAGAOcBOBPA84noTKP2GQBPZ+YnAHgVRg/SmxYihRUIBAJTxIAprLMA3D56zAWI6CoAFwD4hCgw8weV/g0AHjtU5x6CQAKBQGCKaCCQE4noJrV9ePQ4bsGpAL6gtu8E8NSMvRcD+OPazvtgZgRCRI8E8HYApwP4LIDnMfNXjM63jnQE3wLgZ5n514no5wH8OID7RvtewczXIBAIBOYIDQRyPzNv5Ew5MvfWFET0THQE8j21nffBLGsglwK4jpnPAHDdaHsMzPwpZn4SMz8JwHcC+BqAq5XKa2V/kEcgEJhHDFhEvxPAaWr7sQDucvp7AoA3A7hg9OTWqWGWBHIBgCtHn68E8IMF/bMB/DUzf26agwoEAoGhMPAsrBsBnEFEjyOiAwAuBHDE9HcQwDsAvIiZ/2rwAzKYJYGczMx3A8Do/VEF/QsB/J6RXTKarnYFET1iGoMMBAKBeQAzbwK4BMC1AG4D8PvMfCsRvZSIXjpS+1kA3wTgjUT0EVNTGRxTrYEQ0fsBPNrZ9cpGOwcA/ACA/6LEb0I3TY1H768B8GOJ9ocAHAKAgwcPtnQdCAQCE2HIhYSjVP01Rna5+vwSAC8ZrMMCpkogzPys1D4iuoeITmHmu4noFAD3ZkydB+DDzHyPsr39mYh+C8C7M+M4jNF86I2NjfQ91QOBQGBgLPPzQGZ5ZEcAXDT6fBGAd2V0nw+TvhqRjuA5AG4ZdHSBQCAQyGKWBHIZgHOI6NMAzhltg4geQ0TbIRoRfd1o/ztM+18hoo8T0ccAPBPAT+3NsAOBQKAeQ97KZN4ws3Ugo+llZzvyuwCcr7a/hq4oZPVeNNUBBgKBwIRYZHKowfIm5wKBQCAwVcStTAKBQGCKWOYIJAgkEAgEpoggkEAgEAj0wjITSNRAAoFAINALEYEEAoHAlBCzsAKBQCAQcBARSCAQCEwRyxyBBIEEAoHAFLHMBBIprEAgEAj0QkQggUAgMEVEBBIIBAKBgEFEIIFAIDBFLHMEEgQSCAQCU0KsAwkEAoFAwEEQSCAQCAR6YWYEQkTPJaJbieg4EW1k9M4lok8R0e1EdKmSP5KI3kdEnx69P2JvRh4IBAL1WOYnEs4yArkFwA8BuD6lQESrAN4A4DwAZwJ4PhGdOdp9KYDrmPkMANeNtgOBQCCwR5gZgTDzbcz8qYLaWQBuZ+Y7mPkogKsAXDDadwGAK0efrwTwg1MZaCAQCEyAiEBmh1MBfEFt3zmSAcDJzHw3AIzeH5UyQkSHiOgmIrrpvvvum9pgA4FAwGKZCWSq03iJ6P0AHu3seiUzv6vGhCPj1nEw82EAhwFgY2OjuX0gEAgEdmOqBMLMz5rQxJ0ATlPbjwVw1+jzPUR0CjPfTUSnALh3wr4CgUBgcCxqdFGDeU9h3QjgDCJ6HBEdAHAhgCOjfUcAXDT6fBGAmogmEAgEAgNhltN4n0NEdwL4bgB/RETXjuSPIaJrAICZNwFcAuBaALcB+H1mvnVk4jIA5xDRpwGcM9oOBAKBuUFt/WNRo5SZ3cqEma8GcLUjvwvA+Wr7GgDXOHpfAnD2NMcYCAQCgTTmPYUVCAQCC40hI5DUwmq1n4jo9aP9HyOiJw9+QApBIIFAIDBFDEUghYXVgvMAnDF6HQLwpmGPZhxBIIFAILAYyC2sFlwA4K3c4QYADx/NUp0KgkACgUBgihgwhZVbWN2iMxj23fNAbr755vuJ6HMNTU4EcP+0xrNHWPRjWPTxA3EM84LWY/jmSTq7+eabryWiEyvVH0JEN6ntw6NF0IKahdWDLL6uxb4jEGY+qUWfiG5i5uTdghcBi34Miz5+II5hXrDXx8DM5w5oLrewukVnMEQKKxAIBBYDuYXVgiMAfmQ0G+u7APyd3DNwGth3EUggEAgsIph5k4hkYfUqgCuY+VYieulo/+Xo1sydD+B2AF8DcPE0xxQEUsbhssrcY9GPYdHHD8QxzAsW+hi8hdUj4pDPDOBlezUe6voLBAKBQKANUQMJBAKBQC8EgQQCgUCgF4JAAoFAINALQSCBQCAQ6IUgkEAgEAj0QhBIIBAIBHohCCQQCAQCvfD/ARSmwG4jgsgnAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -531,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -549,13 +559,13 @@
        "<AxesSubplot:xlabel='dac_ch1', ylabel='dac_ch2'>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -579,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -616,189 +626,189 @@
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-1.000</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000352</td>\n",
+       "      <td>0.000128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000332</td>\n",
+       "      <td>-0.000022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>-0.000201</td>\n",
+       "      <td>-0.000227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000244</td>\n",
+       "      <td>-0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.995</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000134</td>\n",
+       "      <td>-0.000299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000551</td>\n",
+       "      <td>-0.000296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000758</td>\n",
+       "      <td>-0.000259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000021</td>\n",
+       "      <td>0.000644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.990</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>0.000008</td>\n",
+       "      <td>0.000128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000546</td>\n",
+       "      <td>0.000922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000254</td>\n",
+       "      <td>0.000313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-0.000994</td>\n",
+       "      <td>-0.000516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.985</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>0.000023</td>\n",
+       "      <td>-0.000374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000298</td>\n",
+       "      <td>0.000094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000643</td>\n",
+       "      <td>-0.000704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-0.000076</td>\n",
+       "      <td>0.000222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.980</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000492</td>\n",
+       "      <td>0.000002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000685</td>\n",
+       "      <td>0.000058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>-0.000592</td>\n",
+       "      <td>0.000648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-0.000235</td>\n",
+       "      <td>-0.000866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.975</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000288</td>\n",
+       "      <td>0.001204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000445</td>\n",
+       "      <td>-0.000239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>-0.000052</td>\n",
+       "      <td>0.000074</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-0.000413</td>\n",
+       "      <td>0.000068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.970</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>0.000164</td>\n",
+       "      <td>-0.000439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000776</td>\n",
+       "      <td>0.000324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000899</td>\n",
+       "      <td>-0.000233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000275</td>\n",
+       "      <td>-0.000589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.965</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>0.001072</td>\n",
+       "      <td>-0.000212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000054</td>\n",
+       "      <td>0.000330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000264</td>\n",
+       "      <td>-0.000547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-0.000028</td>\n",
+       "      <td>0.000998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.960</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000784</td>\n",
+       "      <td>-0.000606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000363</td>\n",
+       "      <td>0.000359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>-0.000419</td>\n",
+       "      <td>0.000594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000659</td>\n",
+       "      <td>-0.000164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.955</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>0.000822</td>\n",
+       "      <td>-0.000344</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>-0.000334</td>\n",
+       "      <td>-0.000704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000155</td>\n",
+       "      <td>0.000893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000166</td>\n",
+       "      <td>0.000439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.950</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000072</td>\n",
+       "      <td>-0.000237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000611</td>\n",
+       "      <td>0.000416</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000182</td>\n",
+       "      <td>-0.000366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000512</td>\n",
+       "      <td>0.000570</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -807,53 +817,53 @@
       "text/plain": [
        "                   dmm_v2\n",
        "dac_ch1 dac_ch2          \n",
-       "-1.000  -1.00   -0.000352\n",
-       "        -0.99    0.000332\n",
-       "        -0.98   -0.000201\n",
-       "        -0.97    0.000244\n",
-       "-0.995  -1.00   -0.000134\n",
-       "        -0.99   -0.000551\n",
-       "        -0.98    0.000758\n",
-       "        -0.97    0.000021\n",
-       "-0.990  -1.00    0.000008\n",
-       "        -0.99   -0.000546\n",
-       "        -0.98    0.000254\n",
-       "        -0.97   -0.000994\n",
-       "-0.985  -1.00    0.000023\n",
-       "        -0.99   -0.000298\n",
-       "        -0.98    0.000643\n",
-       "        -0.97   -0.000076\n",
-       "-0.980  -1.00   -0.000492\n",
-       "        -0.99   -0.000685\n",
-       "        -0.98   -0.000592\n",
-       "        -0.97   -0.000235\n",
-       "-0.975  -1.00   -0.000288\n",
-       "        -0.99    0.000445\n",
-       "        -0.98   -0.000052\n",
-       "        -0.97   -0.000413\n",
-       "-0.970  -1.00    0.000164\n",
-       "        -0.99    0.000776\n",
-       "        -0.98    0.000899\n",
-       "        -0.97    0.000275\n",
-       "-0.965  -1.00    0.001072\n",
-       "        -0.99   -0.000054\n",
-       "        -0.98    0.000264\n",
-       "        -0.97   -0.000028\n",
-       "-0.960  -1.00   -0.000784\n",
-       "        -0.99    0.000363\n",
-       "        -0.98   -0.000419\n",
-       "        -0.97    0.000659\n",
-       "-0.955  -1.00    0.000822\n",
-       "        -0.99   -0.000334\n",
-       "        -0.98    0.000155\n",
-       "        -0.97    0.000166\n",
-       "-0.950  -1.00   -0.000072\n",
-       "        -0.99    0.000611\n",
-       "        -0.98    0.000182\n",
-       "        -0.97    0.000512"
+       "-1.000  -1.00    0.000128\n",
+       "        -0.99   -0.000022\n",
+       "        -0.98   -0.000227\n",
+       "        -0.97   -0.000149\n",
+       "-0.995  -1.00   -0.000299\n",
+       "        -0.99   -0.000296\n",
+       "        -0.98   -0.000259\n",
+       "        -0.97    0.000644\n",
+       "-0.990  -1.00    0.000128\n",
+       "        -0.99    0.000922\n",
+       "        -0.98    0.000313\n",
+       "        -0.97   -0.000516\n",
+       "-0.985  -1.00   -0.000374\n",
+       "        -0.99    0.000094\n",
+       "        -0.98   -0.000704\n",
+       "        -0.97    0.000222\n",
+       "-0.980  -1.00    0.000002\n",
+       "        -0.99    0.000058\n",
+       "        -0.98    0.000648\n",
+       "        -0.97   -0.000866\n",
+       "-0.975  -1.00    0.001204\n",
+       "        -0.99   -0.000239\n",
+       "        -0.98    0.000074\n",
+       "        -0.97    0.000068\n",
+       "-0.970  -1.00   -0.000439\n",
+       "        -0.99    0.000324\n",
+       "        -0.98   -0.000233\n",
+       "        -0.97   -0.000589\n",
+       "-0.965  -1.00   -0.000212\n",
+       "        -0.99    0.000330\n",
+       "        -0.98   -0.000547\n",
+       "        -0.97    0.000998\n",
+       "-0.960  -1.00   -0.000606\n",
+       "        -0.99    0.000359\n",
+       "        -0.98    0.000594\n",
+       "        -0.97   -0.000164\n",
+       "-0.955  -1.00   -0.000344\n",
+       "        -0.99   -0.000704\n",
+       "        -0.98    0.000893\n",
+       "        -0.97    0.000439\n",
+       "-0.950  -1.00   -0.000237\n",
+       "        -0.99    0.000416\n",
+       "        -0.98   -0.000366\n",
+       "        -0.97    0.000570"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -873,28 +883,84 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In many cases when working with data on a rectangular grids it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray"
+    "In many cases when working with data on rectangular grids it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray. This is especially true when working in multi-dimentional parameter space. \n",
+    "\n",
+    "Let's setup and rerun the above measurment with the added dependent parameter `dmm.v1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qcodes.dataset.measurements.Measurement at 0x1c47fe60108>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))  # register the 2nd dependent parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 19. \n"
+     ]
+    }
+   ],
+   "source": [
+    "# run a 2D sweep\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    for v1 in np.linspace(-1, 1, 200):\n",
+    "        for v2 in np.linspace(-1, 1, 201):\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val1 = dmm.v1.get()\n",
+    "            val2 = dmm.v2.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val1),\n",
+    "                                 (dmm.v2, val2))\n",
+    "            \n",
+    "dataset3 = datasaver.dataset"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Pandas DataSet can be directly converted to a XArray [Dataset](http://xarray.pydata.org/en/stable/data-structures.html?#dataset):"
+    "The QCoDeS DataSet can be directly converted to a XArray [Dataset](http://xarray.pydata.org/en/stable/data-structures.html?#dataset) from the `to_xarray_dataset` method. This method returns the data from measured (dependent) parameters to an XArray Dataset. It's also possible to return a dictionary of XArray DataArray's if you were only interested in a single parameter using the `to_xarray_dataarray` method. For convenience we will access the DataArray's from XArray's Dataset directly.\n",
+    "\n",
+    "Please note that the `to_xarray_dataset` is only intended to be used when all dependent parameters have the same setpoint. If this is not the case for the DataSet then `to_xarray_dataarray` should be used."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "xaDataSet = df.to_xarray()"
+    "xaDataSet = dataset3.to_xarray_dataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1215,7 +1281,8 @@
        "  grid-template-columns: 125px auto;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt, dd {\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
        "  padding: 0;\n",
        "  margin: 0;\n",
        "  float: left;\n",
@@ -1251,36 +1318,89 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:  (dac_ch1: 401, dac_ch2: 201)\n",
+       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
        "Coordinates:\n",
-       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
        "Data variables:\n",
-       "    dmm_v2   (dac_ch1, dac_ch2) float64 -0.0003524 0.0003317 ... -6.384e-05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1b994ef2-0284-4b26-be83-bcdf6644c094' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b994ef2-0284-4b26-be83-bcdf6644c094' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 401</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e6e7b2cc-50ee-45b2-98d7-68efe4226540' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6e7b2cc-50ee-45b2-98d7-68efe4226540' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.995 -0.99 ... 0.995 1.0</div><input id='attrs-d1b00a83-d28d-4b9e-9363-e5ba035be578' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1b00a83-d28d-4b9e-9363-e5ba035be578' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b1607a7-aa46-40f4-87e7-af9ff4595e7c' class='xr-var-data-in' type='checkbox'><label for='data-0b1607a7-aa46-40f4-87e7-af9ff4595e7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.   , -0.995, -0.99 , ...,  0.99 ,  0.995,  1.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-f8f5182b-e1aa-40b9-8352-7c3b7e519624' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8f5182b-e1aa-40b9-8352-7c3b7e519624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a919d2be-8f19-472c-af91-8b787a5bcac3' class='xr-var-data-in' type='checkbox'><label for='data-a919d2be-8f19-472c-af91-8b787a5bcac3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adf696c0-f9bb-44bb-bb9a-fef86845ac07' class='xr-section-summary-in' type='checkbox'  checked><label for='section-adf696c0-f9bb-44bb-bb9a-fef86845ac07' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0003524 0.0003317 ... -6.384e-05</div><input id='attrs-9f43f653-87d4-4c60-b36e-713fb7c38f64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f43f653-87d4-4c60-b36e-713fb7c38f64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0eab2e0-a8cb-4619-8f0f-f240643f1caf' class='xr-var-data-in' type='checkbox'><label for='data-b0eab2e0-a8cb-4619-8f0f-f240643f1caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.52420185e-04,  3.31706348e-04, -2.01274134e-04, ...,\n",
-       "         2.01535803e-04, -5.60901586e-05, -6.03938321e-04],\n",
-       "       [-1.34146065e-04, -5.51465473e-04,  7.57707337e-04, ...,\n",
-       "        -2.96708821e-04, -5.61339568e-04,  1.24740265e-03],\n",
-       "       [ 8.04864293e-06, -5.46450037e-04,  2.54376351e-04, ...,\n",
-       "         2.78522630e-04, -1.44431467e-03,  1.16470338e-04],\n",
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 6.173 6.039 6.066 ... 4.042 4.114 4.111\n",
+       "    dmm_v2   (dac_ch1, dac_ch2) float64 -5.117e-05 -0.0005031 ... 3.217e-05\n",
+       "Attributes:\n",
+       "    sample_name:  no sample\n",
+       "    exp_name:     working_with_pandas</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c496cd0-4b53-46c1-aba2-63baea87fe93' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c496cd0-4b53-46c1-aba2-63baea87fe93' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a330b7f5-1cbb-4c37-8e23-50ba2d96fead' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a330b7f5-1cbb-4c37-8e23-50ba2d96fead' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-3af38cb9-06a5-472b-b2c8-4202e4773929' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3af38cb9-06a5-472b-b2c8-4202e4773929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de54ebb9-513a-4ee0-abd7-dc64c1be442e' class='xr-var-data-in' type='checkbox'><label for='data-de54ebb9-513a-4ee0-abd7-dc64c1be442e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
+       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
+       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
+       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
+       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
+       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
+       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
+       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
+       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
+       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
+       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
+       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
+       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
+       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
+       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
+       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
+       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
+       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
+       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
+       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
+       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
+       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
+       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
+       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
+       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
+       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
+       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
+       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
+       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
+       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
+       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
+       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
+       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
+       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-e849fad6-4a26-44ae-a479-359da8b40c67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e849fad6-4a26-44ae-a479-359da8b40c67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d5b158b-be79-405c-a072-aeb62587796b' class='xr-var-data-in' type='checkbox'><label for='data-4d5b158b-be79-405c-a072-aeb62587796b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b49900a-e388-408d-a668-2c886e74be8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b49900a-e388-408d-a668-2c886e74be8b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.173 6.039 6.066 ... 4.114 4.111</div><input id='attrs-accb3cf7-b02a-498c-8ee8-85e7c929b9d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-accb3cf7-b02a-498c-8ee8-85e7c929b9d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae707a72-fe9d-48e8-8c58-92f022cde3cb' class='xr-var-data-in' type='checkbox'><label for='data-ae707a72-fe9d-48e8-8c58-92f022cde3cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([[6.17277633, 6.03893564, 6.06605675, ..., 6.01747266, 6.18206111,\n",
+       "        6.03082177],\n",
+       "       [6.19378707, 6.07414603, 6.0593438 , ..., 6.11205901, 6.11145516,\n",
+       "        6.14240672],\n",
+       "       [5.88571805, 6.10824856, 6.10322767, ..., 6.04910118, 6.05529314,\n",
+       "        6.1953047 ],\n",
+       "       ...,\n",
+       "       [4.04498657, 4.04571979, 4.19030665, ..., 4.15660483, 4.24847173,\n",
+       "        4.17130005],\n",
+       "       [4.12754519, 4.05884208, 4.18425914, ..., 4.24158914, 3.94623261,\n",
+       "        4.01570172],\n",
+       "       [4.04947201, 4.0927743 , 4.22786084, ..., 4.04208695, 4.11413445,\n",
+       "        4.11053143]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.117e-05 -0.0005031 ... 3.217e-05</div><input id='attrs-4fe8cb5d-ef7d-4544-af04-6a1852c8358f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fe8cb5d-ef7d-4544-af04-6a1852c8358f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99130aba-da6a-4900-ab5f-e0acfd0a4604' class='xr-var-data-in' type='checkbox'><label for='data-99130aba-da6a-4900-ab5f-e0acfd0a4604' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([[-5.11730690e-05, -5.03090582e-04, -2.97221890e-04, ...,\n",
+       "        -9.64213142e-05, -2.74387262e-04,  1.25138609e-04],\n",
+       "       [ 8.53959362e-05,  2.05418651e-04, -9.67677476e-05, ...,\n",
+       "         4.15455410e-04, -7.71958171e-05,  2.68550277e-04],\n",
+       "       [-4.61218989e-05,  3.29383022e-04,  1.07638394e-03, ...,\n",
+       "         1.39969795e-04,  4.84897369e-04, -5.85757389e-04],\n",
        "       ...,\n",
-       "       [-5.09279820e-05, -5.82869955e-04,  4.19332906e-04, ...,\n",
-       "        -3.94256532e-04,  1.65286083e-04,  4.91507650e-04],\n",
-       "       [-3.44190628e-04, -6.27987512e-04, -1.46038406e-03, ...,\n",
-       "         1.88717165e-04,  2.65590176e-04,  3.64162603e-04],\n",
-       "       [-6.35723198e-04,  1.83248714e-04, -8.25561311e-05, ...,\n",
-       "         3.46845412e-04, -3.55838321e-04, -6.38441296e-05]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c843d0e3-6e22-4a51-a7eb-160d12dfeae6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c843d0e3-6e22-4a51-a7eb-160d12dfeae6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [ 1.71057040e-04,  1.17706311e-04,  1.87608518e-05, ...,\n",
+       "         5.11138029e-04,  2.87802237e-04, -8.70092870e-04],\n",
+       "       [ 3.88158222e-04,  4.62210227e-04,  6.24519187e-04, ...,\n",
+       "        -8.69050667e-05, -6.94401435e-04, -4.40095132e-04],\n",
+       "       [ 3.06499094e-05,  1.00289282e-03, -9.92222288e-04, ...,\n",
+       "        -2.04978367e-04, -1.11987414e-03,  3.21661697e-05]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de2f7794-4afc-498b-956c-293953d36261' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de2f7794-4afc-498b-956c-293953d36261' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>working_with_pandas</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:  (dac_ch1: 401, dac_ch2: 201)\n",
+       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
        "Coordinates:\n",
-       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
        "Data variables:\n",
-       "    dmm_v2   (dac_ch1, dac_ch2) float64 -0.0003524 0.0003317 ... -6.384e-05"
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 6.173 6.039 6.066 ... 4.042 4.114 4.111\n",
+       "    dmm_v2   (dac_ch1, dac_ch2) float64 -5.117e-05 -0.0005031 ... 3.217e-05\n",
+       "Attributes:\n",
+       "    sample_name:  no sample\n",
+       "    exp_name:     working_with_pandas"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1293,21 +1413,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, in many cases it is more convenient to work with a XArray [DataArray](http://xarray.pydata.org/en/stable/data-structures.html?#dataarray). The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
+    "As mentioned above it's also possible to work with a XArray [DataArray](http://xarray.pydata.org/en/stable/data-structures.html?#dataarray) directly from the DataSet. The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "xaDataArray = xaDataSet['dmm_v2']"
+    "xaDataArray = xaDataSet['dmm_v2']# or xaDataSet.dmm_v2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1628,7 +1748,8 @@
        "  grid-template-columns: 125px auto;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt, dd {\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
        "  padding: 0;\n",
        "  margin: 0;\n",
        "  float: left;\n",
@@ -1663,57 +1784,104 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dmm_v2&#x27; (dac_ch1: 401, dac_ch2: 201)&gt;\n",
-       "array([[-3.52420185e-04,  3.31706348e-04, -2.01274134e-04, ...,\n",
-       "         2.01535803e-04, -5.60901586e-05, -6.03938321e-04],\n",
-       "       [-1.34146065e-04, -5.51465473e-04,  7.57707337e-04, ...,\n",
-       "        -2.96708821e-04, -5.61339568e-04,  1.24740265e-03],\n",
-       "       [ 8.04864293e-06, -5.46450037e-04,  2.54376351e-04, ...,\n",
-       "         2.78522630e-04, -1.44431467e-03,  1.16470338e-04],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dmm_v2&#x27; (dac_ch1: 200, dac_ch2: 201)&gt;\n",
+       "array([[-5.11730690e-05, -5.03090582e-04, -2.97221890e-04, ...,\n",
+       "        -9.64213142e-05, -2.74387262e-04,  1.25138609e-04],\n",
+       "       [ 8.53959362e-05,  2.05418651e-04, -9.67677476e-05, ...,\n",
+       "         4.15455410e-04, -7.71958171e-05,  2.68550277e-04],\n",
+       "       [-4.61218989e-05,  3.29383022e-04,  1.07638394e-03, ...,\n",
+       "         1.39969795e-04,  4.84897369e-04, -5.85757389e-04],\n",
        "       ...,\n",
-       "       [-5.09279820e-05, -5.82869955e-04,  4.19332906e-04, ...,\n",
-       "        -3.94256532e-04,  1.65286083e-04,  4.91507650e-04],\n",
-       "       [-3.44190628e-04, -6.27987512e-04, -1.46038406e-03, ...,\n",
-       "         1.88717165e-04,  2.65590176e-04,  3.64162603e-04],\n",
-       "       [-6.35723198e-04,  1.83248714e-04, -8.25561311e-05, ...,\n",
-       "         3.46845412e-04, -3.55838321e-04, -6.38441296e-05]])\n",
+       "       [ 1.71057040e-04,  1.17706311e-04,  1.87608518e-05, ...,\n",
+       "         5.11138029e-04,  2.87802237e-04, -8.70092870e-04],\n",
+       "       [ 3.88158222e-04,  4.62210227e-04,  6.24519187e-04, ...,\n",
+       "        -8.69050667e-05, -6.94401435e-04, -4.40095132e-04],\n",
+       "       [ 3.06499094e-05,  1.00289282e-03, -9.92222288e-04, ...,\n",
+       "        -2.04978367e-04, -1.11987414e-03,  3.21661697e-05]])\n",
        "Coordinates:\n",
-       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
-       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dmm_v2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 401</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df2bc417-8721-4f62-9329-4fdb20896a33' class='xr-array-in' type='checkbox' checked><label for='section-df2bc417-8721-4f62-9329-4fdb20896a33' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.0003524 0.0003317 -0.0002013 ... 0.0003468 -0.0003558 -6.384e-05</span></div><div class='xr-array-data'><pre>array([[-3.52420185e-04,  3.31706348e-04, -2.01274134e-04, ...,\n",
-       "         2.01535803e-04, -5.60901586e-05, -6.03938321e-04],\n",
-       "       [-1.34146065e-04, -5.51465473e-04,  7.57707337e-04, ...,\n",
-       "        -2.96708821e-04, -5.61339568e-04,  1.24740265e-03],\n",
-       "       [ 8.04864293e-06, -5.46450037e-04,  2.54376351e-04, ...,\n",
-       "         2.78522630e-04, -1.44431467e-03,  1.16470338e-04],\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
+       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
+       "Attributes:\n",
+       "    name:           dmm_v2\n",
+       "    paramtype:      numeric\n",
+       "    label:          Gate v2\n",
+       "    unit:           V\n",
+       "    inferred_from:  []\n",
+       "    depends_on:     [&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dmm_v2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-52f51852-05d4-4bb9-bdee-c105d07fd76a' class='xr-array-in' type='checkbox' checked><label for='section-52f51852-05d4-4bb9-bdee-c105d07fd76a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-5.117e-05 -0.0005031 -0.0002972 ... -0.000205 -0.00112 3.217e-05</span></div><div class='xr-array-data'><pre>array([[-5.11730690e-05, -5.03090582e-04, -2.97221890e-04, ...,\n",
+       "        -9.64213142e-05, -2.74387262e-04,  1.25138609e-04],\n",
+       "       [ 8.53959362e-05,  2.05418651e-04, -9.67677476e-05, ...,\n",
+       "         4.15455410e-04, -7.71958171e-05,  2.68550277e-04],\n",
+       "       [-4.61218989e-05,  3.29383022e-04,  1.07638394e-03, ...,\n",
+       "         1.39969795e-04,  4.84897369e-04, -5.85757389e-04],\n",
        "       ...,\n",
-       "       [-5.09279820e-05, -5.82869955e-04,  4.19332906e-04, ...,\n",
-       "        -3.94256532e-04,  1.65286083e-04,  4.91507650e-04],\n",
-       "       [-3.44190628e-04, -6.27987512e-04, -1.46038406e-03, ...,\n",
-       "         1.88717165e-04,  2.65590176e-04,  3.64162603e-04],\n",
-       "       [-6.35723198e-04,  1.83248714e-04, -8.25561311e-05, ...,\n",
-       "         3.46845412e-04, -3.55838321e-04, -6.38441296e-05]])</pre></div></div></li><li class='xr-section-item'><input id='section-aef456b0-476a-4214-aca0-33e9b03ad4ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aef456b0-476a-4214-aca0-33e9b03ad4ec' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.995 -0.99 ... 0.995 1.0</div><input id='attrs-6d3c07b5-2cf5-4132-889b-3100c67ef7e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d3c07b5-2cf5-4132-889b-3100c67ef7e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f763db-0e2c-4582-ad3f-41c6964a6790' class='xr-var-data-in' type='checkbox'><label for='data-23f763db-0e2c-4582-ad3f-41c6964a6790' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.   , -0.995, -0.99 , ...,  0.99 ,  0.995,  1.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-17872748-373c-440d-a87e-7c43538ca875' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17872748-373c-440d-a87e-7c43538ca875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63833386-1060-4c7f-9419-8bdb6b531526' class='xr-var-data-in' type='checkbox'><label for='data-63833386-1060-4c7f-9419-8bdb6b531526' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46454110-6195-420c-8e2c-81d769449fe6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46454110-6195-420c-8e2c-81d769449fe6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [ 1.71057040e-04,  1.17706311e-04,  1.87608518e-05, ...,\n",
+       "         5.11138029e-04,  2.87802237e-04, -8.70092870e-04],\n",
+       "       [ 3.88158222e-04,  4.62210227e-04,  6.24519187e-04, ...,\n",
+       "        -8.69050667e-05, -6.94401435e-04, -4.40095132e-04],\n",
+       "       [ 3.06499094e-05,  1.00289282e-03, -9.92222288e-04, ...,\n",
+       "        -2.04978367e-04, -1.11987414e-03,  3.21661697e-05]])</pre></div></div></li><li class='xr-section-item'><input id='section-ae9cdfe5-ea69-41c8-aee8-006c92a7918d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae9cdfe5-ea69-41c8-aee8-006c92a7918d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-15814787-8929-4b8f-9110-2c2f46707b1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15814787-8929-4b8f-9110-2c2f46707b1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f590f95-ce2f-4e79-a657-8714f5e9a42a' class='xr-var-data-in' type='checkbox'><label for='data-9f590f95-ce2f-4e79-a657-8714f5e9a42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
+       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
+       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
+       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
+       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
+       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
+       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
+       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
+       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
+       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
+       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
+       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
+       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
+       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
+       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
+       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
+       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
+       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
+       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
+       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
+       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
+       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
+       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
+       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
+       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
+       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
+       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
+       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
+       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
+       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
+       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
+       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
+       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
+       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-ff20f4c6-a3d3-4ad3-99fb-8c2d5ce585d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff20f4c6-a3d3-4ad3-99fb-8c2d5ce585d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a25712d6-15e8-4947-8eef-885e9f797bd4' class='xr-var-data-in' type='checkbox'><label for='data-a25712d6-15e8-4947-8eef-885e9f797bd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-069fc3a7-7ae3-4c7e-a7f4-66461a3461c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-069fc3a7-7ae3-4c7e-a7f4-66461a3461c5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'dmm_v2' (dac_ch1: 401, dac_ch2: 201)>\n",
-       "array([[-3.52420185e-04,  3.31706348e-04, -2.01274134e-04, ...,\n",
-       "         2.01535803e-04, -5.60901586e-05, -6.03938321e-04],\n",
-       "       [-1.34146065e-04, -5.51465473e-04,  7.57707337e-04, ...,\n",
-       "        -2.96708821e-04, -5.61339568e-04,  1.24740265e-03],\n",
-       "       [ 8.04864293e-06, -5.46450037e-04,  2.54376351e-04, ...,\n",
-       "         2.78522630e-04, -1.44431467e-03,  1.16470338e-04],\n",
+       "<xarray.DataArray 'dmm_v2' (dac_ch1: 200, dac_ch2: 201)>\n",
+       "array([[-5.11730690e-05, -5.03090582e-04, -2.97221890e-04, ...,\n",
+       "        -9.64213142e-05, -2.74387262e-04,  1.25138609e-04],\n",
+       "       [ 8.53959362e-05,  2.05418651e-04, -9.67677476e-05, ...,\n",
+       "         4.15455410e-04, -7.71958171e-05,  2.68550277e-04],\n",
+       "       [-4.61218989e-05,  3.29383022e-04,  1.07638394e-03, ...,\n",
+       "         1.39969795e-04,  4.84897369e-04, -5.85757389e-04],\n",
        "       ...,\n",
-       "       [-5.09279820e-05, -5.82869955e-04,  4.19332906e-04, ...,\n",
-       "        -3.94256532e-04,  1.65286083e-04,  4.91507650e-04],\n",
-       "       [-3.44190628e-04, -6.27987512e-04, -1.46038406e-03, ...,\n",
-       "         1.88717165e-04,  2.65590176e-04,  3.64162603e-04],\n",
-       "       [-6.35723198e-04,  1.83248714e-04, -8.25561311e-05, ...,\n",
-       "         3.46845412e-04, -3.55838321e-04, -6.38441296e-05]])\n",
+       "       [ 1.71057040e-04,  1.17706311e-04,  1.87608518e-05, ...,\n",
+       "         5.11138029e-04,  2.87802237e-04, -8.70092870e-04],\n",
+       "       [ 3.88158222e-04,  4.62210227e-04,  6.24519187e-04, ...,\n",
+       "        -8.69050667e-05, -6.94401435e-04, -4.40095132e-04],\n",
+       "       [ 3.06499094e-05,  1.00289282e-03, -9.92222288e-04, ...,\n",
+       "        -2.04978367e-04, -1.11987414e-03,  3.21661697e-05]])\n",
        "Coordinates:\n",
-       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
-       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0"
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
+       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
+       "Attributes:\n",
+       "    name:           dmm_v2\n",
+       "    paramtype:      numeric\n",
+       "    label:          Gate v2\n",
+       "    unit:           V\n",
+       "    inferred_from:  []\n",
+       "    depends_on:     ['dac_ch1', 'dac_ch2']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1724,14 +1892,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 5 Axes>"
+       "<Figure size 432x288 with 6 Axes>"
       ]
      },
      "metadata": {
@@ -1742,10 +1910,10 @@
    ],
    "source": [
     "fig, ax = plt.subplots(2,2)\n",
-    "xaDataArray.plot(ax=ax[0,0])\n",
-    "xaDataArray.mean(dim='dac_ch1').plot(ax=ax[1,0])\n",
-    "xaDataArray.mean(dim='dac_ch2').plot(ax=ax[0,1])\n",
-    "xaDataArray[200,:].plot(ax=ax[1,1])\n",
+    "xaDataSet.dmm_v2.plot(ax=ax[0,0])\n",
+    "xaDataSet.dmm_v1.plot(ax=ax[1,1])\n",
+    "xaDataSet.dmm_v2.mean(dim='dac_ch1').plot(ax=ax[1,0])\n",
+    "xaDataSet.dmm_v1.mean(dim='dac_ch2').plot(ax=ax[0,1])\n",
     "fig.tight_layout()"
    ]
   },
@@ -1755,6 +1923,13 @@
    "source": [
     "Above we demonstrated a few ways to index the data from a DataArray. For instance the DataArray can be directly plotted, the extracted mean or a specific row/column can also be plotted."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/environment.yml b/environment.yml
index 383051cdb6e..e36852e3457 100644
--- a/environment.yml
+++ b/environment.yml
@@ -22,6 +22,7 @@ dependencies:
  - scipy
  - gitpython
  - pandas
+ - xarray
  - testpath>=0.4.4
  - tqdm
  - tabulate
diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 32c06ce1454..d2f11edad55 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -2,20 +2,21 @@
 import importlib
 import json
 import logging
+import warnings
 import os
 import time
 import uuid
 from dataclasses import dataclass
 from queue import Empty, Queue
 from threading import Thread
-from typing import (TYPE_CHECKING, Any, Callable, Dict, List, Mapping,
-                    Optional, Sequence, Set, Sized, Tuple, Union)
+from typing import (Hashable, Iterator, TYPE_CHECKING, Any, Callable, Dict,
+                    List, Mapping, Optional, Sequence, Set,
+                    Sized, Tuple, Union, cast)
 
 import numpy
 
 import qcodes
-from qcodes.dataset.descriptions.dependencies import (DependencyError,
-                                                      InterDependencies_)
+from qcodes.dataset.descriptions.dependencies import InterDependencies_
 from qcodes.dataset.descriptions.param_spec import ParamSpec, ParamSpecBase
 from qcodes.dataset.descriptions.rundescriber import RunDescriber
 from qcodes.dataset.descriptions.versioning.converters import (new_to_old,
@@ -45,13 +46,14 @@
                                                  length, one,
                                                  select_one_where)
 from qcodes.instrument.parameter import _BaseParameter
+from qcodes.utils.deprecate import deprecate
 
 from .data_set_cache import DataSetCache
 from .descriptions.versioning import serialization as serial
 
 if TYPE_CHECKING:
     import pandas as pd
-
+    import xarray as xr
 
 
 log = logging.getLogger(__name__)
@@ -88,9 +90,11 @@
 class CompletedError(RuntimeError):
     pass
 
+
 class DataLengthException(Exception):
     pass
 
+
 class DataPathException(Exception):
     pass
 
@@ -108,6 +112,7 @@ class _Subscriber(Thread):
     NOTE: Special care shall be taken when using the *state* object: it is the
     user's responsibility to operate with it in a thread-safe way.
     """
+
     def __init__(self,
                  dataSet: 'DataSet',
                  id_: str,
@@ -233,7 +238,8 @@ def run(self) -> None:
             elif item['keys'] == 'finalize':
                 _WRITERS[self.path].active_datasets.remove(item['values'])
             else:
-                self.write_results(item['keys'], item['values'], item['table_name'])
+                self.write_results(
+                    item['keys'], item['values'], item['table_name'])
             self.queue.task_done()
 
     def write_results(self, keys: Sequence[str],
@@ -294,22 +300,23 @@ def __init__(self, path_to_db: Optional[str] = None,
 
         Args:
             path_to_db: path to the sqlite file on disk. If not provided, the
-              path will be read from the config.
+                path will be read from the config.
             run_id: provide this when loading an existing run, leave it
-              as None when creating a new run
+                as None when creating a new run
             conn: connection to the DB; if provided and ``path_to_db`` is
-              provided as well, then a ``ValueError`` is raised (this is to
-              prevent the possibility of providing a connection to a DB
-              file that is different from ``path_to_db``)
+                provided as well, then a ``ValueError`` is raised (this is to
+                prevent the possibility of providing a connection to a DB
+                file that is different from ``path_to_db``)
             exp_id: the id of the experiment in which to create a new run.
-              Ignored if ``run_id`` is provided.
+                Ignored if ``run_id`` is provided.
             name: the name of the dataset. Ignored if ``run_id`` is provided.
             specs: paramspecs belonging to the dataset or an ``InterDependencies_``
-              object that describes the dataset. Ignored if ``run_id`` is provided.
+                object that describes the dataset. Ignored if ``run_id``
+                is provided.
             values: values to insert into the dataset. Ignored if ``run_id`` is
-              provided.
+                provided.
             metadata: metadata to insert into the dataset. Ignored if ``run_id``
-              is provided.
+                is provided.
             shapes:
                 An optional dict from names of dependent parameters to the shape
                 of the data captured as a list of integers. The list is in the
@@ -755,7 +762,8 @@ def _perform_start_actions(self, start_bg_writer: bool) -> None:
         if start_bg_writer:
             writer_status.write_in_background = True
             if writer_status.bg_writer is None:
-                writer_status.bg_writer = _BackgroundWriter(writer_status.data_write_queue, self.conn)
+                writer_status.bg_writer = _BackgroundWriter(
+                    writer_status.data_write_queue, self.conn)
             if not writer_status.bg_writer.is_alive():
                 writer_status.bg_writer.start()
         else:
@@ -828,7 +836,8 @@ def _ensure_dataset_written(self) -> None:
         writer_status = self._writer_status
 
         if writer_status.write_in_background:
-            writer_status.data_write_queue.put({'keys': 'finalize', 'values': self.run_id})
+            writer_status.data_write_queue.put(
+                {'keys': 'finalize', 'values': self.run_id})
             while self.run_id in writer_status.active_datasets:
                 time.sleep(self.background_sleep_time)
         else:
@@ -841,14 +850,13 @@ def _ensure_dataset_written(self) -> None:
                 writer_status.bg_writer = None
 
     @staticmethod
-    def _validate_parameters(
-            *params: Union[str, ParamSpecBase, _BaseParameter]
-    ) -> List[str]:
+    def _validate_parameters(*params: Union[str, ParamSpec, _BaseParameter]
+                             ) -> List[str]:
         """
         Validate that the provided parameters have a name and return those
         names as a list.
-        The Parameters may be a mix of strings, ParamSpecBase or
-        ordinary QCoDeS parameters.
+        The Parameters may be a mix of strings, ParamSpecs or ordinary
+        QCoDeS parameters.
         """
 
         valid_param_names = []
@@ -867,7 +875,7 @@ def _validate_parameters(
 
     def get_parameter_data(
             self,
-            *params: Union[str, ParamSpecBase, _BaseParameter],
+            *params: Union[str, ParamSpec, _BaseParameter],
             start: Optional[int] = None,
             end: Optional[int] = None) -> ParameterData:
         """
@@ -901,9 +909,9 @@ def get_parameter_data(
 
         Args:
             *params: string parameter names, QCoDeS Parameter objects, and
-                ParamSpecBase objects. If no parameters are
-                supplied, data for all parameters that are not a dependency
-                of another parameter will be returned.
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
             start: start value of selection range (by result count); ignored
                 if None
             end: end value of selection range (by results count); ignored if
@@ -922,9 +930,91 @@ def get_parameter_data(
         return get_parameter_data(self.conn, self.table_name,
                                   valid_param_names, start, end)
 
+    @staticmethod
+    def _parameter_data_identical(param_dict_a: Dict[str, numpy.ndarray],
+                                  param_dict_b: Dict[str, numpy.ndarray]) -> bool:
+
+        try:
+            numpy.testing.assert_equal(param_dict_a, param_dict_b)
+        except AssertionError:
+            return False
+
+        return True
+
+    def _same_setpoints(self, datadict: ParameterData) -> bool:
+
+        def _get_setpoints(dd: ParameterData) -> Iterator[Dict[str, numpy.ndarray]]:
+
+            for dep_name, param_dict in dd.items():
+                out = {
+                    name: vals for name, vals in param_dict.items() if name != dep_name
+                }
+                yield out
+
+        sp_iterator = _get_setpoints(datadict)
+
+        try:
+            first = next(sp_iterator)
+        except StopIteration:
+            return True
+
+        return all(self._parameter_data_identical(first, rest) for rest in sp_iterator)
+
+    def to_pandas_dataframe_dict(self,
+                                 *params: Union[str,
+                                                ParamSpec,
+                                                _BaseParameter],
+                                 start: Optional[int] = None,
+                                 end: Optional[int] = None) ->\
+            Dict[str, "pd.DataFrame"]:
+        """
+        Returns the values stored in the :class:`.DataSet` for the specified parameters
+        and their dependencies as a dict of :py:class:`pandas.DataFrame` s
+        Each element in the dict is indexed by the names of the requested
+        parameters.
+
+        Each DataFrame contains a column for the data and is indexed by a
+        :py:class:`pandas.MultiIndex` formed from all the setpoints
+        of the parameter.
+
+        If no parameters are supplied data will be be
+        returned for all parameters in the :class:`.DataSet` that are not them self
+        dependencies of other parameters.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the :class:`.DataSet` – then a dict of empty :py:class:`pandas.DataFrame` s is
+        returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+            Dictionary from requested parameter names to
+            :py:class:`pandas.DataFrame` s with the requested parameter as
+            a column and a indexed by a :py:class:`pandas.MultiIndex` formed
+            by the dependencies.
+        """
+        datadict = self.get_parameter_data(*params,
+                                           start=start,
+                                           end=end)
+        dfs_dict = self._load_to_dataframe_dict(datadict)
+        return dfs_dict
+
+    @deprecate(reason='This method will be removed due to inconcise naming, please '
+               'use the renamed method to_pandas_dataframe_dict',
+               alternative='to_pandas_dataframe_dict')
     def get_data_as_pandas_dataframe(self,
                                      *params: Union[str,
-                                                    ParamSpecBase,
+                                                    ParamSpec,
                                                     _BaseParameter],
                                      start: Optional[int] = None,
                                      end: Optional[int] = None) -> \
@@ -951,9 +1041,9 @@ def get_data_as_pandas_dataframe(self,
 
         Args:
             *params: string parameter names, QCoDeS Parameter objects, and
-                ParamSpecBase objects. If no parameters are
-                supplied, data for all parameters that are not a dependency
-                of another parameter will be returned.
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
             start: start value of selection range (by result count); ignored
                 if None
             end: end value of selection range (by results count); ignored if
@@ -968,8 +1058,175 @@ def get_data_as_pandas_dataframe(self,
         datadict = self.get_parameter_data(*params,
                                            start=start,
                                            end=end)
-        dfs = self._load_to_dataframes(datadict)
-        return dfs
+        dfs_dict = self._load_to_dataframe_dict(datadict)
+        return dfs_dict
+
+    def to_pandas_dataframe(self,
+                            *params: Union[str,
+                                           ParamSpec,
+                                           _BaseParameter],
+                            start: Optional[int] = None,
+                            end: Optional[int] = None) -> "pd.DataFrame":
+        """
+        Returns the values stored in the :class:`.DataSet` for the specified parameters
+        and their dependencies as a concatinated :py:class:`pandas.DataFrame` s
+
+        The DataFrame contains a column for the data and is indexed by a
+        :py:class:`pandas.MultiIndex` formed from all the setpoints
+        of the parameter.
+
+        If no parameters are supplied data will be be
+        returned for all parameters in the :class:`.DataSet` that are not them self
+        dependencies of other parameters.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the :class:`.DataSet` – then a dict of empty :py:class:`pandas.DataFrame` s is
+        returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+            :py:class:`pandas.DataFrame` s with the requested parameter as
+            a column and a indexed by a :py:class:`pandas.MultiIndex` formed
+            by the dependencies.
+
+        Example:
+            Return a pandas DataFrame with
+                df = ds.get_data_as_pandas_dataframe()
+        """
+        import pandas as pd
+        datadict = self.get_parameter_data(*params,
+                                           start=start,
+                                           end=end)
+
+        if not self._same_setpoints(datadict):
+            warnings.warn(
+                'Independent parameter setpoints are not equal. \
+                Check concatenated output carefully.')
+
+        dfs_dict = self._load_to_dataframe_dict(datadict)
+        df = pd.concat(list(dfs_dict.values()), axis=1)
+
+        return df
+
+    def to_xarray_dataarray_dict(self,
+                                 *params: Union[str,
+                                                ParamSpec,
+                                                _BaseParameter],
+                                 start: Optional[int] = None,
+                                 end: Optional[int] = None) -> \
+            Dict[str, "xr.DataArray"]:
+        """
+        Returns the values stored in the :class:`.DataSet` for the specified parameters
+        and their dependencies as a dict of :py:class:`xr.DataArray` s
+        Each element in the dict is indexed by the names of the requested
+        parameters.
+
+        If no parameters are supplied data will be be
+        returned for all parameters in the :class:`.DataSet` that are not them self
+        dependencies of other parameters.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the :class:`.DataSet` – then a dict of empty :py:class:`xr.DataArray` s is
+        returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+            Dictionary from requested parameter names to :py:class:`xr.DataArray` s
+            with the requested parameter(s) as a column(s) and coordinates
+            formed by the dependencies.
+
+        Example:
+            Return a dict of xr.DataArray with
+
+                dataarray_dict = ds.to_xarray_dataarray_dict()
+        """
+        return self._load_to_xarray_dataarray_dict(*params, start=start, end=end)
+
+    def to_xarray_dataset(self, *params: Union[str,
+                                               ParamSpec,
+                                               _BaseParameter],
+                          start: Optional[int] = None,
+                          end: Optional[int] = None) -> "xr.Dataset":
+        """
+        Returns the values stored in the :class:`.DataSet` for the specified parameters
+        and their dependencies as a :py:class:`xr.Dataset` object.
+
+        If no parameters are supplied data will be be
+        returned for all parameters in the :class:`.DataSet` that are not then self
+        dependencies of other parameters.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the :class:`.DataSet` – then a empty :py:class:`xr.Dataset` s is
+        returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+            :py:class:`xr.Dataset` with the requested parameter(s) data as
+            :py:class:`xr.DataArray` s and coordinates formed by the dependencies.
+
+        Example:
+            Return a concatenated xr.Dataset with
+
+                xds = ds.to_xarray_dataset()
+        """
+        import xarray as xr
+
+        if not self._same_setpoints(self.get_parameter_data(*params,
+                                                            start=start,
+                                                            end=end)):
+            warnings.warn(
+                'Independent parameter setpoints are not equal. \
+                Check concatenated output carefully.')
+
+        data_xrdarray_dict = self._load_to_xarray_dataarray_dict(
+            *params, start=start, end=end)
+
+        # Casting Hashable for the key type until python/mypy#1114
+        # and python/typing#445 are resolved.
+        xrdataset = xr.Dataset(
+            cast(Dict[Hashable, xr.DataArray], data_xrdarray_dict))
+
+        for dim in xrdataset.dims:
+            paramspec_dict = self.paramspecs[str(dim)]._to_dict()
+            xrdataset.coords[str(dim)].attrs.update(paramspec_dict.items())
+
+        xrdataset.attrs["sample_name"] = self.sample_name
+        xrdataset.attrs["exp_name"] = self.exp_name
+
+        return xrdataset
 
     @staticmethod
     def _data_to_dataframe(data: Dict[str, numpy.ndarray], index: Union["pd.Index", "pd.MultiIndex"]) -> "pd.DataFrame":
@@ -1010,13 +1267,37 @@ def _generate_pandas_index(data: Dict[str, numpy.ndarray]) -> Union["pd.Index",
                 names=keys[1:])
         return index
 
-    def _load_to_dataframes(self, datadict: ParameterData) -> Dict[str, "pd.DataFrame"]:
+    def _load_to_dataframe_dict(self, datadict: ParameterData) -> Dict[str, "pd.DataFrame"]:
         dfs = {}
         for name, subdict in datadict.items():
             index = self._generate_pandas_index(subdict)
             dfs[name] = self._data_to_dataframe(subdict, index)
         return dfs
 
+    def _load_to_xarray_dataarray_dict(self,
+                                       *params: Union[str,
+                                                      ParamSpec,
+                                                      _BaseParameter],
+                                       start: Optional[int] = None,
+                                       end: Optional[int] = None) -> \
+            Dict[str, "xr.DataArray"]:
+        import xarray as xr
+        datadict = self.get_parameter_data(*params,
+                                           start=start,
+                                           end=end)
+
+        data_xrdarray_dict: Dict[str, xr.DataArray] = {}
+
+        for name, subdict in datadict.items():
+            index = self._generate_pandas_index(subdict)
+            xrdarray: xr.DataArray = self._data_to_dataframe(
+                subdict, index).to_xarray()[name]
+            paramspec_dict = self.paramspecs[name]._to_dict()
+            xrdarray.attrs.update(paramspec_dict.items())
+            data_xrdarray_dict[name] = xrdarray
+
+        return data_xrdarray_dict
+
     def write_data_to_text_file(self, path: str,
                                 single_file: bool = False,
                                 single_file_name: Optional[str] = None) -> None:
@@ -1196,8 +1477,8 @@ def _enqueue_results(
                     result_dict, all_params)
             elif toplevel_param.type in ('numeric', 'text', 'complex'):
                 res_list = self._finalize_res_dict_numeric_text_or_complex(
-                               result_dict, toplevel_param,
-                               inff_params, deps_params)
+                    result_dict, toplevel_param,
+                    inff_params, deps_params)
             else:
                 res_dict = {ps.name: result_dict[ps] for ps in all_params}
                 res_list = [res_dict]
diff --git a/qcodes/dataset/data_set_cache.py b/qcodes/dataset/data_set_cache.py
index 906c4f57625..bd66d779d93 100644
--- a/qcodes/dataset/data_set_cache.py
+++ b/qcodes/dataset/data_set_cache.py
@@ -43,7 +43,8 @@ def load_data_from_db(self) -> None:
         """
         if self._loaded_from_completed_ds:
             return
-        self._dataset._completed = completed(self._dataset.conn, self._dataset.run_id)
+        self._dataset._completed = completed(
+            self._dataset.conn, self._dataset.run_id)
         if self._dataset.completed:
             self._loaded_from_completed_ds = True
 
@@ -86,5 +87,5 @@ def to_pandas(self) -> Optional[Dict[str, "pd.DataFrame"]]:
         self.load_data_from_db()
         if self._data is None:
             return None
-        dfs = self._dataset._load_to_dataframes(self._data)
+        dfs = self._dataset._load_to_dataframe_dict(self._data)
         return dfs
diff --git a/qcodes/tests/dataset/conftest.py b/qcodes/tests/dataset/conftest.py
index f3d7ddca2c3..f33d9f51a5d 100644
--- a/qcodes/tests/dataset/conftest.py
+++ b/qcodes/tests/dataset/conftest.py
@@ -20,6 +20,7 @@
                                            DummyChannelInstrument,
                                            DummyInstrument,
                                            Multi2DSetPointParam,
+                                           Multi2DSetPointParam2Sizes,
                                            setpoint_generator)
 from qcodes.utils.validators import Arrays, ComplexNumbers, Numbers
 
@@ -199,6 +200,7 @@ def array_dataset(experiment, request):
     finally:
         datasaver.dataset.conn.close()
 
+
 @pytest.fixture(scope="function",
                 params=["array", "numeric"])
 def array_dataset_with_nulls(experiment, request):
@@ -247,6 +249,22 @@ def multi_dataset(experiment, request):
         datasaver.dataset.conn.close()
 
 
+@pytest.fixture(scope="function",
+                params=["array"])
+def different_setpoint_dataset(experiment, request):
+    meas = Measurement()
+    param = Multi2DSetPointParam2Sizes()
+
+    meas.register_parameter(param, paramtype=request.param)
+
+    with meas.run() as datasaver:
+        datasaver.add_result((param, param.get(),))
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
+
+
 @pytest.fixture(scope="function")
 def array_in_scalar_dataset(experiment):
     meas = Measurement()
@@ -431,7 +449,7 @@ def some_interdeps():
     ps5 = ParamSpecBase('ps5', paramtype='numeric', label='Signal',
                         unit='Conductance')
     ps6 = ParamSpecBase('ps6', paramtype='text', label='Goodness',
-                    unit='')
+                        unit='')
 
     idps = InterDependencies_(dependencies={ps5: (ps3, ps4), ps6: (ps3, ps4)},
                               inferences={ps4: (ps2,), ps3: (ps1,)})
@@ -545,14 +563,12 @@ def get_raw(self):
                             get_cmd=lambda: np.arange(5),
                             set_cmd=False)
 
-
     dummyinst.add_parameter('some_complex_array',
                             label='Some Array',
                             unit='some_array_unit',
                             get_cmd=lambda: np.ones(5) + 1j*np.ones(5),
                             set_cmd=False)
 
-
     yield dummyinst
     dummyinst.close()
 
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 3927c4884ff..afdfa0242dd 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -27,9 +27,10 @@
 from qcodes.utils.deprecate import QCoDeSDeprecationWarning
 from qcodes.utils.types import numpy_ints, numpy_floats
 
-pytest.register_assert_rewrite('qcodes.tests.dataset.helper_functions')
 from qcodes.tests.dataset.helper_functions import verify_data_dict
 
+pytest.register_assert_rewrite('qcodes.tests.dataset.helper_functions')
+
 n_experiments = 0
 
 
@@ -104,7 +105,7 @@ def test_dataset_location(empty_temp_db_connection):
     exp = new_experiment("test", "test1", conn=empty_temp_db_connection)
     ds = DataSet(conn=empty_temp_db_connection)
     assert path_to_dbfile(empty_temp_db_connection) == \
-           empty_temp_db_connection.path_to_dbfile
+        empty_temp_db_connection.path_to_dbfile
     assert exp.path_to_db == empty_temp_db_connection.path_to_dbfile
     assert ds.path_to_db == empty_temp_db_connection.path_to_dbfile
 
@@ -384,10 +385,14 @@ def test_add_data_1d():
 
     shadow_ds = make_shadow_dataset(mydataset)
 
-    np.testing.assert_array_equal(mydataset.get_parameter_data()['y']['x'], expected_x)
-    np.testing.assert_array_equal(mydataset.get_parameter_data()['y']['y'], expected_y)
-    np.testing.assert_array_equal(shadow_ds.get_parameter_data()['y']['x'], expected_x)
-    np.testing.assert_array_equal(shadow_ds.get_parameter_data()['y']['y'], expected_y)
+    np.testing.assert_array_equal(
+        mydataset.get_parameter_data()['y']['x'], expected_x)
+    np.testing.assert_array_equal(
+        mydataset.get_parameter_data()['y']['y'], expected_y)
+    np.testing.assert_array_equal(
+        shadow_ds.get_parameter_data()['y']['x'], expected_x)
+    np.testing.assert_array_equal(
+        shadow_ds.get_parameter_data()['y']['y'], expected_y)
 
     assert mydataset.completed is False
     mydataset.mark_completed()
@@ -425,8 +430,10 @@ def test_add_data_array():
 
     shadow_ds = make_shadow_dataset(mydataset)
 
-    np.testing.assert_array_equal(mydataset.get_parameter_data()['x']['x'], np.array(expected_x))
-    np.testing.assert_array_equal(shadow_ds.get_parameter_data()['x']['x'], np.array(expected_x))
+    np.testing.assert_array_equal(mydataset.get_parameter_data()[
+                                  'x']['x'], np.array(expected_x))
+    np.testing.assert_array_equal(shadow_ds.get_parameter_data()[
+                                  'x']['x'], np.array(expected_x))
 
     y_data = mydataset.get_parameter_data()['y']['y']
     np.testing.assert_allclose(y_data, expected_y)
@@ -518,7 +525,8 @@ def test_numpy_ints(dataset):
     results = [{"x": tp(1)} for tp in numpy_ints]
     dataset.add_results(results)
     expected_result = np.ones(len(numpy_ints))
-    np.testing.assert_array_equal(dataset.get_parameter_data()["x"]["x"], expected_result)
+    np.testing.assert_array_equal(dataset.get_parameter_data()[
+                                  "x"]["x"], expected_result)
 
 
 def test_numpy_floats(dataset):
@@ -613,7 +621,6 @@ def fb(xv, yv):
 
 def test_get_description(experiment, some_interdeps):
 
-
     ds = DataSet()
 
     assert ds.run_id == 1
@@ -629,7 +636,7 @@ def test_get_description(experiment, some_interdeps):
     # so now no description should be stored in the database
     prematurely_loaded_ds = DataSet(run_id=1)
     assert prematurely_loaded_ds.description == RunDescriber(
-                                                    InterDependencies_())
+        InterDependencies_())
 
     ds.mark_started()
 
@@ -666,7 +673,7 @@ def test_metadata(experiment, request):
     sorry_metadata = {'superman': 1, badtag: None, 'spiderman': 'two'}
 
     bad_tag_msg = (f'Tag {badtag} has value None. '
-                    ' That is not a valid metadata value!')
+                   ' That is not a valid metadata value!')
 
     with pytest.raises(RuntimeError,
                        match='Rolling back due to unhandled exception') as e:
@@ -802,7 +809,8 @@ def ds_with_vals(self, dataset):
     )
     def test_get_data_with_start_and_end_args(self, ds_with_vals,
                                               start, end, expected):
-        data = ds_with_vals.get_parameter_data(self.x, start=start, end=end)['x']
+        data = ds_with_vals.get_parameter_data(
+            self.x, start=start, end=end)['x']
         if len(expected) == 0:
             assert data == {}
         else:
@@ -902,12 +910,13 @@ def test_get_array_parameter_data(array_dataset):
     expected_shapes[par_name] = [(expected_len,), (expected_len,)]
     expected_values = {}
     expected_values[par_name] = [np.ones(expected_len) + 1,
-                                        np.linspace(5, 9, expected_len)]
+                                 np.linspace(5, 9, expected_len)]
     if 'array' in types:
         expected_shapes[par_name] = [(1, expected_len),
                                      (1, expected_len)]
         for i in range(len(expected_values[par_name])):
-            expected_values[par_name][i] = expected_values[par_name][i].reshape(1, expected_len)
+            expected_values[par_name][i] = expected_values[par_name][i].reshape(
+                1, expected_len)
     parameter_test_helper(array_dataset,
                           input_names,
                           expected_names,
@@ -934,11 +943,13 @@ def test_get_multi_parameter_data(multi_dataset):
     this_data = np.zeros((shape_1, shape_2))
     that_data = np.ones((shape_1, shape_2))
     sp_1_data = np.tile(np.linspace(5, 9, shape_1).reshape(shape_1, 1),
-                                           (1, shape_2))
+                        (1, shape_2))
     sp_2_data = np.tile(np.linspace(9, 11, shape_2), (shape_1, 1))
     if 'array' in types:
-        expected_shapes['this'] = [(1, shape_1, shape_2), (1, shape_1, shape_2)]
-        expected_shapes['that'] = [(1, shape_1, shape_2), (1, shape_1, shape_2)]
+        expected_shapes['this'] = [
+            (1, shape_1, shape_2), (1, shape_1, shape_2)]
+        expected_shapes['that'] = [
+            (1, shape_1, shape_2), (1, shape_1, shape_2)]
         expected_values['this'] = [this_data.reshape(1, shape_1, shape_2),
                                    sp_1_data.reshape(1, shape_1, shape_2),
                                    sp_2_data.reshape(1, shape_1, shape_2)]
@@ -1277,7 +1288,8 @@ def test_write_data_to_text_file_save_multi_keys(tmp_path_factory):
     xparam = ParamSpecBase("x", 'numeric')
     yparam = ParamSpecBase("y", 'numeric')
     zparam = ParamSpecBase("z", 'numeric')
-    idps = InterDependencies_(dependencies={yparam: (xparam,), zparam: (xparam,)})
+    idps = InterDependencies_(
+        dependencies={yparam: (xparam,), zparam: (xparam,)})
     dataset.set_interdependencies(idps)
 
     dataset.mark_started()
@@ -1300,7 +1312,8 @@ def test_write_data_to_text_file_save_single_file(tmp_path_factory):
     xparam = ParamSpecBase("x", 'numeric')
     yparam = ParamSpecBase("y", 'numeric')
     zparam = ParamSpecBase("z", 'numeric')
-    idps = InterDependencies_(dependencies={yparam: (xparam,), zparam: (xparam,)})
+    idps = InterDependencies_(
+        dependencies={yparam: (xparam,), zparam: (xparam,)})
     dataset.set_interdependencies(idps)
 
     dataset.mark_started()
@@ -1322,7 +1335,8 @@ def test_write_data_to_text_file_length_exception(tmp_path):
     xparam = ParamSpecBase("x", 'numeric')
     yparam = ParamSpecBase("y", 'numeric')
     zparam = ParamSpecBase("z", 'numeric')
-    idps = InterDependencies_(dependencies={yparam: (xparam,), zparam: (xparam,)})
+    idps = InterDependencies_(
+        dependencies={yparam: (xparam,), zparam: (xparam,)})
     dataset.set_interdependencies(idps)
 
     dataset.mark_started()
@@ -1346,7 +1360,8 @@ def test_write_data_to_text_file_name_exception(tmp_path):
     xparam = ParamSpecBase("x", 'numeric')
     yparam = ParamSpecBase("y", 'numeric')
     zparam = ParamSpecBase("z", 'numeric')
-    idps = InterDependencies_(dependencies={yparam: (xparam,), zparam: (xparam,)})
+    idps = InterDependencies_(
+        dependencies={yparam: (xparam,), zparam: (xparam,)})
     dataset.set_interdependencies(idps)
 
     dataset.mark_started()
@@ -1358,3 +1373,15 @@ def test_write_data_to_text_file_name_exception(tmp_path):
     with pytest.raises(Exception, match='desired file name'):
         dataset.write_data_to_text_file(path=temp_dir, single_file=True,
                                         single_file_name=None)
+
+
+def test_same_setpoint_warning_for_df_and_xarray(different_setpoint_dataset):
+
+    warning_mesage = ('Independent parameter setpoints are not equal. \
+                Check concatenated output carefully.')
+
+    with pytest.warns(UserWarning, match=warning_mesage):
+        different_setpoint_dataset.to_pandas_dataframe()
+
+    with pytest.warns(UserWarning, match=warning_mesage):
+        different_setpoint_dataset.to_xarray_dataset()
diff --git a/requirements.txt b/requirements.txt
index 41c55cbb0f2..9a873107334 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -87,6 +87,7 @@ websockets~=8.1
 widgetsnbextension~=3.5.1
 wincertstore~=0.2
 wrapt~=1.12.1
+xarray~=0.16.2
 zipp~=3.4.0
 # not strict requirements but needed for the legacy pyqtgraph based plotting
 PyQt5<5.13 # 5.12 is the last version that spyder supports at the moment
diff --git a/setup.py b/setup.py
index 760198e1b18..cbeefd55272 100644
--- a/setup.py
+++ b/setup.py
@@ -2,6 +2,7 @@
 
 import versioneer
 
+
 def readme():
     with open('README.rst') as f:
         return f.read()
@@ -28,7 +29,7 @@ def readme():
 for extra_name, extra_packages in extras.items():
     extras_require[extra_name] = [
         f'{k}>={v}' for k, v in extra_packages.items()
-        ]
+    ]
 
 
 install_requires = [
@@ -39,7 +40,8 @@ def readme():
     'jsonschema>=3.0.0',
     'ruamel.yaml>=0.16.0,!=0.16.6',
     'wrapt>=1.10.4',
-    'pandas>=0.24.0',
+    'pandas>=0.25.0',
+    'xarray>=0.16.2',
     'tabulate>=0.8.0',
     'tqdm>=4.20.0',
     'opencensus>=0.7.10, <0.8.0',