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Split the data into more a more (computer) readable file

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Silver-T 2018-05-02 16:28:53 +10:00
parent e0e869183a
commit 3ce5dc5e95
2 changed files with 164 additions and 434 deletions
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212
wk7/wk7.ipynb
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@ -9,16 +9,27 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: MacOSX\n",
"Using matplotlib backend: TkAgg\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"ename": "ImportError",
"evalue": "No module named 'seaborn'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-ad52641fb291>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: No module named 'seaborn'"
]
}
],
"source": [
@ -35,196 +46,19 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >ranking</th> \n",
" <th class=\"col_heading level0 col1\" >age</th> \n",
" <th class=\"col_heading level0 col2\" >points</th> \n",
" <th class=\"col_heading level0 col3\" >tournplayed</th> \n",
" <th class=\"col_heading level0 col4\" >born</th> \n",
" <th class=\"col_heading level0 col5\" >weight</th> \n",
" <th class=\"col_heading level0 col6\" >height</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row0\" class=\"row_heading level0 row0\" >ranking</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row0_col0\" class=\"data row0 col0\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row0_col1\" class=\"data row0 col1\" >-0.165935</td> \n",
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" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row0_col4\" class=\"data row0 col4\" >0.17403</td> \n",
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" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row1\" class=\"row_heading level0 row1\" >age</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col0\" class=\"data row1 col0\" >-0.165935</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col1\" class=\"data row1 col1\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col2\" class=\"data row1 col2\" >0.121731</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col3\" class=\"data row1 col3\" >-0.140033</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col4\" class=\"data row1 col4\" >-0.994296</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col5\" class=\"data row1 col5\" >0.157223</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row1_col6\" class=\"data row1 col6\" >-0.0282972</td> \n",
" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row2\" class=\"row_heading level0 row2\" >points</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col0\" class=\"data row2 col0\" >-0.586707</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col1\" class=\"data row2 col1\" >0.121731</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col3\" class=\"data row2 col3\" >-0.004905</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col4\" class=\"data row2 col4\" >-0.129971</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col5\" class=\"data row2 col5\" >0.159385</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row2_col6\" class=\"data row2 col6\" >-0.0153843</td> \n",
" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row3\" class=\"row_heading level0 row3\" >tournplayed</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col0\" class=\"data row3 col0\" >-0.244073</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col1\" class=\"data row3 col1\" >-0.140033</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col2\" class=\"data row3 col2\" >-0.004905</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col4\" class=\"data row3 col4\" >0.13293</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col5\" class=\"data row3 col5\" >-0.139194</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row3_col6\" class=\"data row3 col6\" >-0.0712482</td> \n",
" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row4\" class=\"row_heading level0 row4\" >born</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col0\" class=\"data row4 col0\" >0.17403</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col1\" class=\"data row4 col1\" >-0.994296</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col2\" class=\"data row4 col2\" >-0.129971</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col3\" class=\"data row4 col3\" >0.13293</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col4\" class=\"data row4 col4\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col5\" class=\"data row4 col5\" >-0.163677</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row4_col6\" class=\"data row4 col6\" >0.0333731</td> \n",
" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row5\" class=\"row_heading level0 row5\" >weight</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col0\" class=\"data row5 col0\" >-0.0826093</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col1\" class=\"data row5 col1\" >0.157223</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col2\" class=\"data row5 col2\" >0.159385</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col3\" class=\"data row5 col3\" >-0.139194</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col4\" class=\"data row5 col4\" >-0.163677</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col5\" class=\"data row5 col5\" >1</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row5_col6\" class=\"data row5 col6\" >0.757689</td> \n",
" </tr> <tr> \n",
" <th id=\"T_60af3fba_4c3b_11e8_903f_32001d384000level0_row6\" class=\"row_heading level0 row6\" >height</th> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col0\" class=\"data row6 col0\" >0.0196139</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col1\" class=\"data row6 col1\" >-0.0282972</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col2\" class=\"data row6 col2\" >-0.0153843</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col3\" class=\"data row6 col3\" >-0.0712482</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col4\" class=\"data row6 col4\" >0.0333731</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col5\" class=\"data row6 col5\" >0.757689</td> \n",
" <td id=\"T_60af3fba_4c3b_11e8_903f_32001d384000row6_col6\" class=\"data row6 col6\" >1</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1103ce6a0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-b834b3dc0d59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackground_gradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Wistia'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [

386
wk8/wk8.ipynb
View File

@ -40,31 +40,29 @@
"\n",
"**\\*\\* Double Check this with Murray \\*\\***\n",
"\n",
"\\*\\* Now we need to read the excel file in a nice way given that the spreadsheet has double headings in some places"
"TO DO:\n",
"\\*\\* Need to test the statistical significance of the data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_excel('Dong_etal_2018_data.xlsx', sheetname='A1')"
"# Contains data about the number of customers during the recorded time period\n",
"cust_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='# of Customers')\n",
"# Contains data about the customers' median spending amounts during the recorded time period\n",
"spend_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='Median spending amount')\n",
"# Contains data about the number of transactions during the recorded time period\n",
"trans_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='# of transactions')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/matplotlib/colors.py:494: RuntimeWarning: invalid value encountered in less\n",
" cbook._putmask(xa, xa < 0.0, -1)\n"
]
},
{
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@ -243,22 +187,17 @@
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@ -389,79 +310,33 @@
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@ -471,25 +346,46 @@
" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x7f4b0a308898>"
"<pandas.formats.style.Styler at 0x7f4b09f5a630>"
]
},
"execution_count": 14,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.corr().style.background_gradient(cmap='Wistia')"
"cust_data.corr().style.background_gradient(cmap='Wistia')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"****\\*\\*\\*\\* We gon' need to get rid of some of them headins' \\*\\*\\*\\* ****"
"Now to plot some data..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},