Repository: corriporai/runpandas (language: python)

Issue:
Activity summary (describe function)
A describe function that presents some basic summaries about the
sessions, including the sports present in the sessions, the number of sessions, training coverage,
total training duration and the units for each of the variables.


Discussion / hints:

Fix patch:
diff --git a/README.rst b/README.rst
index d3ca9d9..fa3c704 100644
--- a/README.rst
+++ b/README.rst
@@ -399,6 +399,36 @@ active.
 
 
 
+Runpandas also provides a method ``summary`` for summarising the
+activity through common statistics. Such a session summary includes
+estimates of several metrics computed above with a single call.
+
+.. code:: ipython3
+
+    activity_only_moving.summary()
+
+
+
+
+.. parsed-literal::
+
+    Session                           Running: 26-12-2012 21:29:53
+    Total distance (meters)                                4686.31
+    Total ellapsed time                            0 days 00:33:11
+    Total moving time                              0 days 00:33:05
+    Average speed (km/h)                                   8.47656
+    Average moving speed (km/h)                            8.49853
+    Average pace (per 1 km)                        0 days 00:07:04
+    Average pace moving (per 1 km)                 0 days 00:07:03
+    Average cadence                                            NaN
+    Average moving cadence                                     NaN
+    Average heart rate                                     156.653
+    Average moving heart rate                                157.4
+    Average temperature                                        NaN
+    dtype: object
+
+
+
 Now, let’s play with the data. Let’s show distance vs as an example of
 what and how we can create visualizations. In this example, we will use
 the built in, matplotlib based plot function.
diff --git a/examples/overview.ipynb b/examples/overview.ipynb
index 215ada1..045c8bd 100644
--- a/examples/overview.ipynb
+++ b/examples/overview.ipynb
@@ -28,16 +28,8 @@
    ],
    "cell_type": "code",
    "metadata": {},
-   "execution_count": 74,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
-     ]
-    }
-   ]
+   "execution_count": 1,
+   "outputs": []
   },
   {
    "source": [
@@ -50,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,12 +58,12 @@
    ],
    "cell_type": "code",
    "metadata": {},
-   "execution_count": 76,
+   "execution_count": 3,
    "outputs": []
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +81,7 @@
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>alt</th>\n      <th>dist</th>\n      <th>hr</th>\n      <th>lon</th>\n      <th>lat</th>\n    </tr>\n    <tr>\n      <th>time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00:00:00</th>\n      <td>178.942627</td>\n      <td>0.000000</td>\n      <td>62</td>\n      <td>-79.093187</td>\n      <td>35.951880</td>\n    </tr>\n    <tr>\n      <th>00:00:01</th>\n      <td>178.942627</td>\n      <td>0.000000</td>\n      <td>62</td>\n      <td>-79.093184</td>\n      <td>35.951880</td>\n    </tr>\n    <tr>\n      <th>00:00:06</th>\n      <td>178.942627</td>\n      <td>1.106947</td>\n      <td>62</td>\n      <td>-79.093172</td>\n      <td>35.951868</td>\n    </tr>\n    <tr>\n      <th>00:00:12</th>\n      <td>177.500610</td>\n      <td>13.003035</td>\n      <td>62</td>\n      <td>-79.093228</td>\n      <td>35.951774</td>\n    </tr>\n    <tr>\n      <th>00:00:16</th>\n      <td>177.500610</td>\n      <td>22.405027</td>\n      <td>60</td>\n      <td>-79.093141</td>\n      <td>35.951732</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 77
+     "execution_count": 4
     }
    ],
    "source": [
@@ -115,7 +107,7 @@
    ],
    "cell_type": "code",
    "metadata": {},
-   "execution_count": 78,
+   "execution_count": 5,
    "outputs": [
     {
      "output_type": "stream",
@@ -135,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -153,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -224,7 +216,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 82
+     "execution_count": 9
     }
    ],
    "source": [
@@ -235,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +244,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 83
+     "execution_count": 10
     }
    ],
    "source": [
@@ -270,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +279,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 84
+     "execution_count": 11
     }
    ],
    "source": [
@@ -306,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -331,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -342,13 +334,53 @@
       ]
      },
      "metadata": {},
-     "execution_count": 86
+     "execution_count": 13
     }
    ],
    "source": [
     "activity_only_moving.moving_time"
    ]
   },
+  {
+   "source": [
+    "Runpandas also provides a method  ``summary`` for summarising the activity through common statistics.  Such a session summary includes estimates of several metrics computed above with a single call."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Session                           Running: 26-12-2012 21:29:53\n",
+       "Total distance (meters)                                4686.31\n",
+       "Total ellapsed time                            0 days 00:33:11\n",
+       "Total moving time                              0 days 00:33:05\n",
+       "Average speed (km/h)                                   8.47656\n",
+       "Average moving speed (km/h)                            8.49853\n",
+       "Average pace (per 1 km)                        0 days 00:07:04\n",
+       "Average pace moving (per 1 km)                 0 days 00:07:03\n",
+       "Average cadence                                            NaN\n",
+       "Average moving cadence                                     NaN\n",
+       "Average heart rate                                     156.653\n",
+       "Average moving heart rate                                157.4\n",
+       "Average temperature                                        NaN\n",
+       "dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "source": [
+    "activity_only_moving.summary()"
+   ]
+  },
   {
    "source": [
     "Now, let's play with the data. Let's show distance vs as an example of what and how we can create visualizations.  In this example, we will use the built in, matplotlib based plot function."
@@ -358,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -369,13 +401,13 @@
       ]
      },
      "metadata": {},
-     "execution_count": 87
+     "execution_count": 15
     },
     {
      "output_type": "display_data",
      "data": {
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      },
      "metadata": {
@@ -396,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -407,13 +439,13 @@
       ]
      },
      "metadata": {},
-     "execution_count": 88
+     "execution_count": 16
     },
     {
      "output_type": "display_data",
      "data": {
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      },
      "metadata": {
@@ -434,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -445,13 +477,13 @@
       ]
      },
      "metadata": {},
-     "execution_count": 89
+     "execution_count": 17
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
@@ -472,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -483,13 +515,13 @@
       ]
      },
      "metadata": {},
-     "execution_count": 90
+     "execution_count": 18
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
@@ -510,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -547,7 +579,7 @@
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-79.093617),\n                new google.maps.LatLng(35.951331, -79.093818),\n                new google.maps.LatLng(35.951333, -79.093848),\n                new google.maps.LatLng(35.951335, -79.093876),\n                new google.maps.LatLng(35.951339, -79.093905),\n                new google.maps.LatLng(35.951345, -79.094048),\n                new google.maps.LatLng(35.951331, -79.094189),\n                new google.maps.LatLng(35.951277, -79.094358),\n                new google.maps.LatLng(35.951256, -79.094492),\n                new google.maps.LatLng(35.951260, -79.094588),\n                new google.maps.LatLng(35.951193, -79.094823),\n                new google.maps.LatLng(35.951086, -79.094987),\n                new google.maps.LatLng(35.951019, -79.095058),\n                new google.maps.LatLng(35.950899, -79.095169),\n                new google.maps.LatLng(35.950789, -79.095241),\n                new google.maps.LatLng(35.950657, -79.095301),\n                new google.maps.LatLng(35.950612, -79.095313),\n                new google.maps.LatLng(35.950590, -79.095322),\n                new google.maps.LatLng(35.950477, -79.095344),\n                new google.maps.LatLng(35.950358, -79.095351),\n                new google.maps.LatLng(35.950228, -79.095336),\n                new google.maps.LatLng(35.950180, -79.095338),\n                new google.maps.LatLng(35.950061, -79.095341),\n                new google.maps.LatLng(35.949987, -79.095339),\n                new google.maps.LatLng(35.949909, -79.095339),\n                new google.maps.LatLng(35.949889, -79.095340),\n                new google.maps.LatLng(35.949776, -79.095345),\n                new google.maps.LatLng(35.949673, -79.095347),\n                new google.maps.LatLng(35.949536, -79.095355),\n                new google.maps.LatLng(35.949392, -79.095373),\n                new google.maps.LatLng(35.949347, -79.095481),\n                new google.maps.LatLng(35.949347, -79.095508),\n                new google.maps.LatLng(35.949339, -79.095676),\n                new google.maps.LatLng(35.949337, -79.095824),\n                new google.maps.LatLng(35.949344, -79.095956),\n                new google.maps.LatLng(35.949346, -79.096094),\n                new google.maps.LatLng(35.949345, -79.096249),\n                new google.maps.LatLng(35.949350, -79.096333),\n                new google.maps.LatLng(35.949351, -79.096362),\n                new google.maps.LatLng(35.949351, -79.096416),\n                new google.maps.LatLng(35.949351, -79.096442),\n                new google.maps.LatLng(35.949348, -79.096465),\n                new google.maps.LatLng(35.949345, -79.096548),\n                new google.maps.LatLng(35.949345, -79.096577),\n                new google.maps.LatLng(35.949343, -79.096810),\n                new google.maps.LatLng(35.949345, -79.096995),\n                new google.maps.LatLng(35.949299, -79.097163),\n                new 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      },
      "metadata": {},
-     "execution_count": 92
+     "execution_count": 20
     }
    ],
    "source": [
@@ -567,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -620,7 +652,7 @@
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>enhanced_speed</th>\n      <th>enhanced_altitude</th>\n      <th>unknown_87</th>\n      <th>fractional_cadence</th>\n      <th>lap</th>\n      <th>session</th>\n      <th>unknown_108</th>\n      <th>dist</th>\n      <th>cad</th>\n      <th>hr</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>temp</th>\n    </tr>\n    <tr>\n      <th>time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00:00:00</th>\n      <td>0.000</td>\n      <td>254.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>101</td>\n      <td>13.843376</td>\n      <td>51.066280</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>00:00:01</th>\n      <td>0.000</td>\n      <td>254.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>101</td>\n      <td>13.843374</td>\n      <td>51.066274</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>00:00:10</th>\n      <td>1.698</td>\n      <td>254.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2362.0</td>\n      <td>0.00</td>\n      <td>83</td>\n      <td>97</td>\n      <td>13.843176</td>\n      <td>51.066249</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>00:00:12</th>\n      <td>2.267</td>\n      <td>254.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2362.0</td>\n      <td>3.95</td>\n      <td>84</td>\n      <td>99</td>\n      <td>13.843118</td>\n      <td>51.066250</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>00:00:21</th>\n      <td>2.127</td>\n      <td>254.6</td>\n      <td>0</td>\n      <td>0.5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2552.0</td>\n      <td>16.67</td>\n      <td>87</td>\n      <td>100</td>\n      <td>13.842940</td>\n      <td>51.066231</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 94
+     "execution_count": 22
     }
    ],
    "source": [
@@ -636,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
diff --git a/runpandas/_utils.py b/runpandas/_utils.py
index 90b8854..301a298 100644
--- a/runpandas/_utils.py
+++ b/runpandas/_utils.py
@@ -5,7 +5,17 @@
 from xml.etree.cElementTree import iterparse
 from functools import wraps
 from runpandas import exceptions
-from pandas import Series
+from pandas import Series, Timedelta
+
+
+def convert_pace_secmeters2minkms(seconds):
+    """
+    Convert pace from sec/meters to min/km.
+    """
+    pace_min = int((seconds * 1000) / 60)
+    pace_sec = int(seconds * 1000 - (pace_min * 60))
+    total_seconds = (pace_min * 60) + pace_sec
+    return Timedelta(seconds=total_seconds)
 
 
 def file_exists(fname):
diff --git a/runpandas/types/__init__.py b/runpandas/types/__init__.py
index c9dcd90..6fdd856 100644
--- a/runpandas/types/__init__.py
+++ b/runpandas/types/__init__.py
@@ -2,3 +2,4 @@
 from runpandas.types.frame import Activity  # noqa
 from runpandas.types.acessors import _InactivityAssessor  # noqa
 from runpandas.types.acessors import MetricsAcessor  # noqa
+from runpandas.types import summary  # noqa
diff --git a/runpandas/types/frame.py b/runpandas/types/frame.py
index e901721..c81027e 100644
--- a/runpandas/types/frame.py
+++ b/runpandas/types/frame.py
@@ -277,3 +277,16 @@ def mean_cadence(self, only_moving=False):
             activity = self
 
         return activity["cad"].mean()
+
+    def summary(self):
+        """
+        Display the common basic statistics for the activity.
+
+        Returns:
+        --------
+            pandas.Dataframe:  A pandas DataFrame containing the summary statistics, which
+            inclues estimates of the total distance covered, the total duration,
+            the time spent moving, and many others.
+
+        """
+        return runpandas.types.summary.activity_summary(self)
diff --git a/runpandas/types/summary.py b/runpandas/types/summary.py
new file mode 100644
index 0000000..51493ae
--- /dev/null
+++ b/runpandas/types/summary.py
@@ -0,0 +1,142 @@
+"""
+    Helper module for evaluation and display of the summary of training sessions.
+
+ """
+
+import numpy as np
+import pandas as pd
+from runpandas._utils import convert_pace_secmeters2minkms
+
+
+def _build_summary_statistics(obj):
+    """
+    Generate basic statistics from a given pandas Series.
+
+    Parameters
+    ----------
+    obj:  The DataFrame to generate basic commute statistics from.
+
+    Returns:
+    --------
+
+    A Series containing the following statistics:
+    - Session times
+    - Total distance
+    - Total ellapsed time
+    - Total moving time
+    - Total and average elevation gain
+    - Average speed
+    - Average moving speed
+    - Average pace (per 1 km)
+    - Average pace moving (per 1 km)
+    - Average cadence running
+    - Average cadence running moving
+    - Average heart rate
+    - Average heart rate moving
+    - Average temperature
+    - Total elevation gain
+    """
+    # special conditions for methods that raise Exception
+    try:
+        moving_time = obj.moving_time
+    except AttributeError:
+        moving_time = np.nan
+
+    try:
+        mean_speed = obj.mean_speed()
+        mean_pace = convert_pace_secmeters2minkms(obj.mean_pace().total_seconds())
+    except AttributeError:
+        mean_speed = np.nan
+        mean_pace = np.nan
+
+    try:
+        mean_moving_speed = obj.mean_speed(only_moving=True)
+        mean_moving_pace = convert_pace_secmeters2minkms(
+            obj.mean_pace(only_moving=True).total_seconds()
+        )
+    except (AttributeError, KeyError):
+        mean_moving_speed = np.nan
+        mean_moving_pace = np.nan
+
+    try:
+        mean_cadence = obj.mean_cadence()
+    except AttributeError:
+        mean_cadence = np.nan
+
+    try:
+        mean_moving_cadence = obj.mean_cadence(only_moving=True)
+    except (AttributeError, KeyError):
+        mean_moving_cadence = np.nan
+
+    try:
+        mean_heart_rate = obj.mean_heart_rate()
+    except AttributeError:
+        mean_heart_rate = np.nan
+
+    try:
+        mean_moving_heart_rate = obj.mean_heart_rate(only_moving=True)
+    except (AttributeError, KeyError):
+        mean_moving_heart_rate = np.nan
+
+    try:
+        mean_temperature = obj["temp"].mean()
+    except KeyError:
+        mean_temperature = np.nan
+
+    rows = {
+        "Session": "Running: %s" % obj.start.strftime("%d-%m-%Y %H:%M:%S"),
+        "Total distance (meters)": obj.distance,
+        "Total ellapsed time": obj.ellapsed_time,
+        "Total moving time": moving_time,
+        "Average speed (km/h)": mean_speed * 3.6,
+        "Average moving speed (km/h)": mean_moving_speed * 3.6,
+        "Average pace (per 1 km)": mean_pace,
+        "Average pace moving (per 1 km)": mean_moving_pace,
+        "Average cadence": mean_cadence,
+        "Average moving cadence": mean_moving_cadence,
+        "Average heart rate": mean_heart_rate,
+        "Average moving heart rate": mean_moving_heart_rate,
+        "Average temperature": mean_temperature,
+    }
+
+    series = pd.Series(
+        rows,
+        index=[
+            "Session",
+            "Total distance (meters)",
+            "Total ellapsed time",
+            "Total moving time",
+            "Average speed (km/h)",
+            "Average moving speed (km/h)",
+            "Average pace (per 1 km)",
+            "Average pace moving (per 1 km)",
+            "Average cadence",
+            "Average moving cadence",
+            "Average heart rate",
+            "Average moving heart rate",
+            "Average temperature",
+        ],
+    )
+
+    return series
+
+
+def activity_summary(activity):
+    """
+    Returns the a pandas Dataframe with the common basic statistics for the
+    given activity.
+
+    Parameters
+    ----------
+    activity:  runpandas.types.Activity
+    Runpandas Activity to be computed the statistics
+
+    Returns:
+    --------
+        pandas.Dataframe:  A pandas DataFrame containing the summary statistics, which
+        inclues estimates of the total distance covered, the total duration,
+        the time spent moving, and many others.
+
+    """
+    summary_statistics = _build_summary_statistics(activity)
+    return summary_statistics.T


Test patch:
diff --git a/runpandas/tests/test_summary.py b/runpandas/tests/test_summary.py
new file mode 100644
index 0000000..e514a3b
--- /dev/null
+++ b/runpandas/tests/test_summary.py
@@ -0,0 +1,143 @@
+"""
+Test module for summary statistics module
+"""
+import os
+import pytest
+import numpy as np
+from pandas import Timedelta, Series
+from runpandas import reader
+from pandas.testing import assert_series_equal
+
+
+pytestmark = pytest.mark.stable
+
+
+@pytest.fixture
+def dirpath(datapath):
+    return datapath("io", "data")
+
+
+def test_activity_summary_with_missing_values(dirpath):
+    tcx_file = os.path.join(dirpath, "tcx", "basic.tcx")
+    frame_tcx = reader._read_file(tcx_file, to_df=False)
+    result = frame_tcx.summary()
+
+    expected = Series(
+        [
+            "Running: 26-12-2012 21:29:53",
+            4686.31,
+            Timedelta("0 days 00:33:11"),
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            156.653,
+            np.nan,
+            np.nan,
+        ],
+        index=[
+            "Session",
+            "Total distance (meters)",
+            "Total ellapsed time",
+            "Total moving time",
+            "Average speed (km/h)",
+            "Average moving speed (km/h)",
+            "Average pace (per 1 km)",
+            "Average pace moving (per 1 km)",
+            "Average cadence",
+            "Average moving cadence",
+            "Average heart rate",
+            "Average moving heart rate",
+            "Average temperature",
+        ],
+    )
+    assert_series_equal(result, expected)
+
+
+def test_activity_summary_missing_moving(dirpath):
+    tcx_file = os.path.join(dirpath, "tcx", "basic.tcx")
+    frame_tcx = reader._read_file(tcx_file, to_df=False)
+    frame_tcx["distpos"] = frame_tcx.compute.distance(correct_distance=False)
+    frame_tcx["speed"] = frame_tcx.compute.speed(from_distances=True)
+
+    # removing hr to simulate the missing values
+    frame_tcx.drop("hr", axis=1, inplace=True)
+
+    result = frame_tcx.summary()
+
+    expected = Series(
+        [
+            "Running: 26-12-2012 21:29:53",
+            4686.31,
+            Timedelta("0 days 00:33:11"),
+            np.nan,
+            8.476556294170631,
+            np.nan,
+            Timedelta("0 days 00:07:04"),
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+            np.nan,
+        ],
+        index=[
+            "Session",
+            "Total distance (meters)",
+            "Total ellapsed time",
+            "Total moving time",
+            "Average speed (km/h)",
+            "Average moving speed (km/h)",
+            "Average pace (per 1 km)",
+            "Average pace moving (per 1 km)",
+            "Average cadence",
+            "Average moving cadence",
+            "Average heart rate",
+            "Average moving heart rate",
+            "Average temperature",
+        ],
+    )
+    assert_series_equal(result, expected)
+
+
+def test_activity_full_summary(dirpath):
+    fit_file = os.path.join(dirpath, "fit", "run.fit")
+    frame_fit = reader._read_file(fit_file, to_df=False)
+    frame_only_moving_fit = frame_fit.only_moving()
+    result = frame_only_moving_fit.summary()
+    expected = Series(
+        [
+            "Running: 14-09-2019 15:22:05",
+            5839.77,
+            Timedelta("0 days 00:52:47"),
+            Timedelta("0 days 00:52:21"),
+            6.521134575307862,
+            6.564336962750716,
+            Timedelta("0 days 00:09:12"),
+            Timedelta("0 days 00:09:08"),
+            63.963394342762065,
+            64.0092050209205,
+            129.72129783693845,
+            129.7623430962343,
+            26.905158069883527,
+        ],
+        index=[
+            "Session",
+            "Total distance (meters)",
+            "Total ellapsed time",
+            "Total moving time",
+            "Average speed (km/h)",
+            "Average moving speed (km/h)",
+            "Average pace (per 1 km)",
+            "Average pace moving (per 1 km)",
+            "Average cadence",
+            "Average moving cadence",
+            "Average heart rate",
+            "Average moving heart rate",
+            "Average temperature",
+        ],
+    )
+    assert_series_equal(result, expected)
diff --git a/runpandas/tests/test_utils.py b/runpandas/tests/test_utils.py
index 831dfdb..296b735 100644
--- a/runpandas/tests/test_utils.py
+++ b/runpandas/tests/test_utils.py
@@ -3,6 +3,7 @@
 """
 import os
 import pytest
+from pandas import Timedelta
 from runpandas import _utils as utils
 
 pytestmark = pytest.mark.stable
@@ -53,3 +54,9 @@ def is_valid_nok(invalid_tcx_filename):
 def test_camecase_to_snakecase():
     assert utils.camelcase_to_snakecase("LatitudeDegrees") == "latitude_degrees"
     assert utils.camelcase_to_snakecase("Time") == "time"
+
+
+def test_convert_pace_secmeters2minkms():
+    assert utils.convert_pace_secmeters2minkms(
+        Timedelta("0 days 00:00:00.407078").total_seconds()
+    ) == Timedelta("0 days 00:06:47")