diff --git a/README.md b/README.md
index 777be4a..adc1a16 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,4 @@
+******  I had technical difficulties with ipython, so I had to turn in python code***
 # Chart data
 
 ## Description
diff --git a/charting.ipynb b/charting.ipynb
new file mode 100644
index 0000000..34b327b
--- /dev/null
+++ b/charting.ipynb
@@ -0,0 +1,464 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:926b9bf5d6b559dd6e84c76e36d2f85d88423594a6a6116c519cee66c5d008a3"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "import seaborn\n",
+      "import math\n",
+      "import statistics as st\n",
+      "import matplotlib.pyplot as plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def flipcoin():\n",
+      "    coin = [\"H\", \"T\"]\n",
+      "    return random.choice(coin)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def flip_simulation(n=100000):\n",
+      "    obs_to_store = [pow(2, num) for num in range(n) if pow(2, num) <= n]\n",
+      "    flip_counts = []\n",
+      "    counter = {'Heads': 0, 'Tails': 0}\n",
+      "    for flip in range(1, n+1):\n",
+      "        result = flipcoin()\n",
+      "        if result == \"H\":\n",
+      "            counter['Heads'] += 1\n",
+      "        elif result == \"T\":\n",
+      "            counter['Tails'] += 1\n",
+      "        if flip in obs_to_store:\n",
+      "            flip_counts.append(dict(counter))\n",
+      "    flip_counts.append(dict(counter))\n",
+      "    return flip_counts"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def head_minus_tail(object_list):\n",
+      "    return [object_list[ind]['Heads'] - object_list[ind]['Tails']\n",
+      "            for ind in range(0, len(object_list))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def ratios(object_list):\n",
+      "    return ([object_list[ind]['Heads'] /\n",
+      "            (object_list[ind]['Tails'] + object_list[ind]['Heads'])\n",
+      "            for ind in range(0, len(object_list))])\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def line_plot(object_list, xlabel, ylabel, title):\n",
+      "    plt.xlabel(xlabel)\n",
+      "    plt.ylabel(ylabel)\n",
+      "    plt.title(title)\n",
+      "    plt.plot(object_list)\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def log_line_plot(object_list, title, x=True, y=False):\n",
+      "    plt.plot(object_list, 'bo')\n",
+      "    if x:\n",
+      "        plt.xscale('log')\n",
+      "    if y:\n",
+      "        plt.yscale('log')\n",
+      "    plt.title(title)\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def run_trial(trials, flips):\n",
+      "    return [flip_simulation(flips)\n",
+      "            for flip in range(trials)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def trial_ratio(trial_object_list):\n",
+      "    return [ratios(trial_object_list[trial])\n",
+      "            for trial in range(len(trial_object_list))]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def means(all_list):\n",
+      "    return [st.mean(all_list[ratio])\n",
+      "            for ratio in range(len(all_list))]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def std_dev(all_list):\n",
+      "    return [st.stdev(all_list[ratio])\n",
+      "            for ratio in range(len(all_list))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def histogram(list_to_plot, title):\n",
+      "    plt.hist(list_to_plot)\n",
+      "    plt.xlabel(\"Number\")\n",
+      "    plt.ylabel(\"frequency\")\n",
+      "    plt.title(title)\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def box_plot(list_to_plot, title, xticks):\n",
+      "    plt.boxplot(list_to_plot)\n",
+      "    plt.title(title)\n",
+      "    plt.xticks(range(1, len(list_to_plot)), xticks)\n",
+      "    plt.show()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "heads_tails = flip_simulation(100000)\n",
+      "diff_head_tail = head_minus_tail(heads_tails)\n",
+      "ratio_head_tail = ratios(heads_tails)\n",
+      "    "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "line_plot(diff_head_tail, \"Obs#\", \"Difference\", \"100,000 Coin Flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Lr5fyzLIN1G/fzb+cdThDiqKs2pR8XZW/s4HMEuipDr5ERKQDA3kSYKelz92f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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c965470>"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "line_plot(ratio_head_tail, \"Obs#\", \"Heads Ratio\", \"100,000 Coin Flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10caf6978>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "log_line_plot(diff_head_tail, \"Difference - Log Scale - 100,000 Coin Flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Y0rrWWpmMct00m3CjWTclvQi4mPwu0L8caZot6xFIOhv4MjAzFb0FmBERRwDn\nAJel8i8BXyQfIrp2ottp5TOGddNsQo1h3bwMmAf8naTlI023lT2Ch4G38tzG/Sjg2wAR8V1Jr0l/\nf5/nrl42mwijWjcHRMQ7MZsYo91urhzLRFvWI4iIG3nuPkWQ35BuR83rvanbYzahvG5auxqvdbOd\nVuYd7H+n0o6I2NeqxpjV8Lpp7aqQdbOdguBu8kdaIulwYNjzXs0mkNdNa1eFrJvtcEHZwF3vbgLe\nKOnu9NrHBazVvG5auyp03fTdR83MSq6dhobMzKwFHARmZiXnIDAzKzkHgZlZyTkIzMxKzkFgZlZy\nDgIzs5JzEJiZlZyDwMys5BwEZmYl9/8BMJWOhdOrekYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c965e48>"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "log_line_plot(ratio_head_tail, \" Heads Ratio - Log Scale - 100,000 Coin Flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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u77et0kA1m6MenxpKs+l89Ar5i4BjI2JjvXxKRJwE7JeZ50XE+cA3I+K3wPXAR/puiSRp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\nM+QlqWD/HxnVG/4m/JYgAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ccc4d68>"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trials = run_trial(20, 100000)\n",
+      "trial_ratios = trial_ratio(trials)\n",
+      "ratio_means = means(trial_ratios)\n",
+      "ratio_std = std_dev(trial_ratios)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "log_line_plot(ratio_means, \"Means\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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sWa/HessG3VhSqCYfDlM/VSb8rVkPnyUFqT4qEf67d5+1xV8BPjhWbd7lpX6qRM2/LMvy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\nyPCXpBoy/CWphv4/lrq8VcAxnYkAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ca8ea20>"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "log_line_plot(ratio_std, \"Standard Deviations\", y=True)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10ca95240>"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trials100 = run_trial(100000, 100)\n",
+      "trial_ratios100 = trial_ratio(trials100)\n",
+      "means100 = means(trial_ratios100)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "histogram(means100, \"Histogram for 100,000 Trials of 100 flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ad8rvDNwDfFTS/VnxoxFxtKQHgeOB+4CHgcsiYiSwPbA3qcN+PjAZWJDVnUsZ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\nAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ccf9dd8>"
+       ]
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trials1000 = run_trial(100000, 1000)\n",
+      "trial_ratios1000 = trial_ratio(trials1000)\n",
+      "means1000 = means(trial_ratios1000)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "histogram(means1000, \"Histogram for 100,000 Trials of 1000 flips\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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DOwN7kwbsFwATgEVZ3XmUobW1Pc9m9Fnex13NbGBoa1vXL/ZXlSa1ep823AFc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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10cd048d0>"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "box_plot([means100, means1000], \"Box Plot\",  [\"100 flips\", \"1000 flips\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEKCAYAAADkYmWmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKnAvcDN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+       "text": [
+        "<matplotlib.figure.Figure at 0x10ccee470>"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/dice_simulation.py b/dice_simulation.py
new file mode 100644
index 0000000..e8bc248
--- /dev/null
+++ b/dice_simulation.py
@@ -0,0 +1,123 @@
+import random
+import seaborn
+import math
+import statistics as st
+import matplotlib.pyplot as plt
+
+
+def flipcoin():
+    coin = ["H", "T"]
+    return random.choice(coin)
+
+
+def flip_simulation(n=100000):
+    obs_to_store = [pow(2, num) for num in range(n) if pow(2, num) <= n]
+    flip_counts = []
+    counter = {'Heads': 0, 'Tails': 0}
+    for flip in range(1, n+1):
+        result = flipcoin()
+        if result == "H":
+            counter['Heads'] += 1
+        elif result == "T":
+            counter['Tails'] += 1
+        if flip in obs_to_store:
+            flip_counts.append(dict(counter))
+    flip_counts.append(dict(counter))
+    return flip_counts
+
+
+def head_minus_tail(object_list):
+    return [object_list[ind]['Heads'] - object_list[ind]['Tails']
+            for ind in range(0, len(object_list))]
+
+
+def ratios(object_list):
+    return ([object_list[ind]['Heads'] /
+            (object_list[ind]['Tails'] + object_list[ind]['Heads'])
+            for ind in range(0, len(object_list))])
+
+
+def line_plot(object_list, xlabel, ylabel, title):
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title(title)
+    plt.plot(object_list)
+    plt.show()
+
+
+def log_line_plot(object_list, title, x=True, y=False):
+    plt.plot(object_list, 'bo')
+    if x:
+        plt.xscale('log')
+    if y:
+        plt.yscale('log')
+    plt.title(title)
+    plt.show()
+
+
+def run_trial(trials, flips):
+    return [flip_simulation(flips)
+            for flip in range(trials)]
+
+
+def trial_ratio(trial_object_list):
+    return [ratios(trial_object_list[trial])
+            for trial in range(len(trial_object_list))]
+
+
+def means(all_list):
+    return [st.mean(all_list[ratio])
+            for ratio in range(len(all_list))]
+
+
+def std_dev(all_list):
+    return [st.stdev(all_list[ratio])
+            for ratio in range(len(all_list))]
+
+
+def histogram(list_to_plot, title):
+    plt.hist(list_to_plot)
+    plt.xlabel("Number")
+    plt.ylabel("frequency")
+    plt.title(title)
+    plt.show()
+
+
+def box_plot(list_to_plot, title, xticks):
+    plt.boxplot(list_to_plot)
+    plt.title(title)
+    plt.xticks(range(1, len(list_to_plot)), xticks)
+    plt.show()
+
+
+def box_plot_seaborn(list_to_plot):
+    sns.set_style("whitegrid")
+    data = list_to_plot
+    sns.boxplot(data)
+
+if __name__ == '__main__':
+    heads_tails = flip_simulation(100000)
+    diff_head_tail = head_minus_tail(heads_tails)
+    ratio_head_tail = ratios(heads_tails)
+    # Charts
+    line_plot(diff_head_tail, "Obs#", "Difference", "100,000 Coin Flips")
+    line_plot(ratio_head_tail, "Obs#", "Heads Ratio", "100,000 Coin Flips")
+    log_line_plot(diff_head_tail, "Log Scale - 100,000 Coin Flips")
+    log_line_plot(ratio_head_tail, "Log Scale - 100,000 Coin Flips")
+
+    trials = run_trial(20, 100000)
+    trial_ratios = trial_ratio(trials)
+    ratio_means = means(trial_ratios)
+    ratio_std = std_dev(trial_ratios)
+    log_line_plot(ratio_means, "Means")
+    log_line_plot(ratio_std, "Standard Deviations", y=True)
+
+    trials100 = run_trial(100000, 100)
+    trial_ratios100 = trial_ratio(trials100)
+    means100 = means(trial_ratios100)
+    histogram(means100, "Histogram for 100,000 Trials of 100 flips")
+    trials1000 = run_trial(100000, 1000)
+    trial_ratios1000 = trial_ratio(trials1000)
+    means1000 = means(trial_ratios1000)
+    histogram(means1000, "Histogram for 100,000 Trials of 1000 flips")
+    box_plot([means100, means1000], "Box Plot",  ["100 flips", "1000 flips"])
diff --git a/test_dice.py b/test_dice.py
new file mode 100644
index 0000000..4bc9440
--- /dev/null
+++ b/test_dice.py
@@ -0,0 +1,47 @@
+from dice_simulation import *
+
+sim = flip_simulation(10)
+le_sim = int(len(sim))
+
+
+def test_coin_flip():
+    assert flipcoin() in ["H", "T"]
+
+
+def test_flip_simulation():
+    assert le_sim == 5
+
+
+def test_minus():
+    minuslist = head_minus_tail(sim)
+    assert minuslist[0] == sim[0]['Heads'] - sim[0]['Tails']
+    assert len(minuslist) == len(sim)
+
+
+def test_ratios():
+    ratiolist = ratios(sim)
+    assert ratiolist[0] == (sim[0]['Heads']
+                            / (sim[0]['Heads'] + sim[0]['Tails']))
+    assert len(ratiolist) == le_sim
+
+
+def test_run_trial():
+    trials = run_trial(2, 10)
+    assert len(trials) == 2
+    assert len(trials[0]) == le_sim
+
+
+def test_trial_ratios():
+    trials = run_trial(2, 10)
+    trial_ratios = trial_ratio(trials)
+    assert len(trial_ratios[0]) == le_sim
+
+
+def test_means():
+    a = [[1, 2, 3, 4], [5, 6, 7, 6]]
+    assert means(a) == [2.5, 6]
+
+
+def test_st_dev():
+    a = [[1, 2, 3, 4], [5, 6, 7, 6]]
+    assert std_dev(a) == [1.2909944487358056, 0.816496580927726]
diff --git a/tests_coins.py.ipynb b/tests_coins.py.ipynb
new file mode 100644
index 0000000..a9e8871
--- /dev/null
+++ b/tests_coins.py.ipynb
@@ -0,0 +1,91 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:86c8fb0e4fe6c4c520c241d41fb5efd84b3120e17822ea82db072e2ea88c054e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "\n",
+      "from charting import *\n",
+      "\n",
+      "sim = flip_simulation(10)\n",
+      "le_sim = int(len(sim))\n",
+      "\n",
+      "\n",
+      "def test_coin_flip():\n",
+      "    assert flipcoin() in [\"H\", \"T\"]\n",
+      "\n",
+      "\n",
+      "def test_flip_simulation():\n",
+      "    assert le_sim == 5\n",
+      "\n",
+      "\n",
+      "def test_minus():\n",
+      "    minuslist = head_minus_tail(sim)\n",
+      "    assert minuslist[0] == sim[0]['Heads'] - sim[0]['Tails']\n",
+      "    assert len(minuslist) == len(sim)\n",
+      "\n",
+      "\n",
+      "def test_ratios():\n",
+      "    ratiolist = ratios(sim)\n",
+      "    assert ratiolist[0] == (sim[0]['Heads']\n",
+      "                            / (sim[0]['Heads'] + sim[0]['Tails']))\n",
+      "    assert len(ratiolist) == le_sim\n",
+      "\n",
+      "\n",
+      "def test_run_trial():\n",
+      "    trials = run_trial(2, 10)\n",
+      "    assert len(trials) == 2\n",
+      "    assert len(trials[0]) == le_sim\n",
+      "\n",
+      "\n",
+      "def test_trial_ratios():\n",
+      "    trials = run_trial(2, 10)\n",
+      "    trial_ratios = trial_ratio(trials)\n",
+      "    assert len(trial_ratios[0]) == le_sim\n",
+      "\n",
+      "\n",
+      "def test_means():\n",
+      "    a = [[1, 2, 3, 4], [5, 6, 7, 6]]\n",
+      "    assert means(a) == [2.5, 6]\n",
+      "\n",
+      "\n",
+      "def test_st_dev():\n",
+      "    a = [[1, 2, 3, 4], [5, 6, 7, 6]]\n",
+      "    assert std_dev(a) == [1.2909944487358056, 0.816496580927726]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file