diff --git a/blogposts/cut_material.ipynb b/blogposts/cut_material.ipynb index 963ec3eb..78a5128a 100644 --- a/blogposts/cut_material.ipynb +++ b/blogposts/cut_material.ipynb @@ -578,6 +578,335 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "20501524-6f72-480e-b68a-b765fe221373", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 184, + "width": 783 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# FIGURES ONLY\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "with plt.rc_context({\"figure.figsize\":(8,2)}):\n", + " fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n", + "\n", + " ax1.set_title(\"(a) strip plot\")\n", + " sns.stripplot(x=prices, ax=ax1, jitter=0)\n", + " ax1.set_xticks(range(6,13))\n", + "\n", + " ax2.set_title(\"(b) hist plot\")\n", + " sns.histplot(x=prices, ax=ax2)\n", + " ax2.set_xticks(range(6,13))\n", + " ax2.set_yticks(range(0,5))\n", + "\n", + " ax3.set_title(\"(c) box plot\")\n", + " sns.boxplot(x=prices, ax=ax3)\n", + " ax3.set_xticks(range(6,13))\n", + "\n", + "savefig(fig, \"figures/epricesW_strip_hist_box_plots.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "404fd7b5-b026-42c4-99f6-72e22c352746", + "metadata": {}, + "source": [ + "#### Manual calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "aec8c48a-1329-49e9-9b36-40302158a03a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3e0bf784-5445-41b6-a95d-d681ae5b9534", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9.155555555555555" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "57191815-819d-4a18-b77d-7e9fa82790b3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.5621388471508473" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def std(values):\n", + " avg = mean(values)\n", + " sqdevs = sum([(v-avg)**2 for v in values])\n", + " var = sqdevs / (len(values) - 1)\n", + " return var**(1/2)\n", + "\n", + "std(prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7f9dc1e6-3a55-49fe-908d-91cf8de570b8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.4142135623730951" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2**(1/2)" + ] + }, + { + "cell_type": "markdown", + "id": "216b2fdc-41a9-481f-bd4a-787afa764108", + "metadata": {}, + "source": [ + "### Data cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9ce20b65-783c-47e0-8379-c2521fd67391", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " East West\n", + "0 7.7 11.8\n", + "1 5.9 10.0\n", + "2 7.0 11.0\n", + "3 4.8 8.6\n", + "4 6.3 8.3\n", + "5 6.3 9.4\n", + "6 5.5 8.0\n", + "7 5.4 6.8\n", + "8 6.5 8.5\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "epriceswide = pd.read_csv(\"https://nobsstats.com/datasets/epriceswide.csv\")\n", + "print(epriceswide)" + ] + }, + { + "cell_type": "markdown", + "id": "fa5baf34-a1b1-4d64-ab51-6f2b336fd891", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Data transformation\n", + "\n", + "Once you have your hands on the data in some form, Pandas offers many functions for data transformations like selection of subsets, combining datasets, and other pre-processing steps on the raw data, in preparation for statistical analysis.\n", + "\n", + "One of the most common data transformation steps is to convert data from \"wide\" format into \"long\" format, which is also known as \"tidy data.\"\n", + "\n", + "\n", + "Click [here](https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0A%0Aepriceswide_csv%20%3D%20'''%0AEast,West%0A7.7,11.8%0A5.9,10.0%0A7.0,11.0%0A4.8,8.6%0A6.3,8.3%0A6.3,9.4%0A5.5,8.0%0A5.4,6.8%0A6.5,8.5%0A'''%0A%0Aepriceswide%20%3D%20pd.read_csv%28io.StringIO%28epriceswide_csv%29%29%0A%0Aepriceswide.melt%28var_name%3D%22end%22,%20value_name%3D%22price%22%29&d=2023-07-02&lang=py&v=v1) to see a visualization of the `melt` operation on the `epriceswide` data frame into a tidy data frame `eprices`.\n", + "\n", + "A discussion about tidy data is beyond the scope of this blog post, so I'll refer you to this blog post and video tutorials that will explain why tidy data is useful for doing statistics.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d283bdbc-76cd-466b-a3ae-376e14fd0666", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Click [here](https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0A%0Aepriceswide_csv%20%3D%20'''%0AEast,West%0A7.7,11.8%0A5.9,10.0%0A7.0,11.0%0A4.8,8.6%0A6.3,8.3%0A6.3,9.4%0A5.5,8.0%0A5.4,6.8%0A6.5,8.5%0A'''%0A%0Aepriceswide%20%3D%20pd.read_csv%28io.StringIO%28epriceswide_csv%29%29%0A%0Aepriceswide.melt%28var_name%3D%22end%22,%20value_name%3D%22price%22%29&d=2023-07-02&lang=py&v=v1) to see a visualization of the above melt operation." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "91a31e75-af25-4fa7-8a6d-607ba12030d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " end price\n", + "0 East 7.7\n", + "1 East 5.9\n", + "2 East 7.0\n", + "3 East 4.8\n", + "4 East 6.3\n", + "5 East 6.3\n", + "6 East 5.5\n", + "7 East 5.4\n", + "8 East 6.5\n", + "9 West 11.8\n", + "10 West 10.0\n", + "11 West 11.0\n", + "12 West 8.6\n", + "13 West 8.3\n", + "14 West 9.4\n", + "15 West 8.0\n", + "16 West 6.8\n", + "17 West 8.5\n" + ] + } + ], + "source": [ + "eprices = pd.melt(epriceswide, var_name=\"end\", value_name='price')\n", + "print(eprices)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b17e9a3f-6535-4db3-9e05-bef685a64534", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([11.8, 10. , 11. , 8.6, 8.3, 9.4, 8. , 6.8, 8.5])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pricesW = eprices[eprices[\"end\"]==\"West\"][\"price\"]\n", + "pricesE = eprices[eprices[\"end\"]==\"East\"][\"price\"]\n", + "\n", + "pricesW.values" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "24002cfe-4356-47f1-9647-5db13760f801", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7.7, 5.9, 7. , 4.8, 6.3, 6.3, 5.5, 5.4, 6.5])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pricesE.values" + ] } ], "metadata": { diff --git a/blogposts/python_for_stats.ipynb b/blogposts/python_for_stats.ipynb index 5a562e08..2df135fe 100644 --- a/blogposts/python_for_stats.ipynb +++ b/blogposts/python_for_stats.ipynb @@ -20,55 +20,18 @@ "For example,\n", "you can copy-paste some of the commands in previous cells,\n", "modify them and run to see what happens.\n", - "Try to break things.\n", + "Try to break things, that's the best way to learn!\n", "\n", "**To run a code cell, press** the play button in the menu bar, or use the keyboard shortcut **SHIFT+ENTER**." ] }, - { - "cell_type": "markdown", - "id": "ff950e24-931e-454c-8c45-45eb6823aa7d", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Notebook setup" - ] - }, { "cell_type": "code", - "execution_count": 1, - "id": "db0ba656-cf8d-47fb-a889-52f03722f26d", + "execution_count": null, + "id": "ed5832a3-6014-47df-96f9-f0d969c94077", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Figures setup\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "plt.clf() # needed otherwise `sns.set_theme` doesn't work\n", - "sns.set_theme(\n", - " style=\"whitegrid\",\n", - " rc={'figure.figsize': (6.25, 2.0)},\n", - ")\n", - "# High-resolution figures please\n", - "%config InlineBackend.figure_format = 'retina'\n", - "\n", - "def savefig(fig, filename):\n", - " fig.tight_layout()\n", - " fig.savefig(filename, dpi=300, bbox_inches=\"tight\", pad_inches=0)" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -88,9 +51,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "6ff440f2-f2c4-4821-8af0-1bec72c2b608", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -98,7 +67,7 @@ "5.5" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -109,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "a5d156a0-ac2f-4271-95e6-1f22f499b535", "metadata": {}, "outputs": [], @@ -119,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "1d87588a-e92e-4225-9ace-dbe533c4f4f6", "metadata": {}, "outputs": [], @@ -129,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "a7eaa89b-cf12-477c-94b6-30641373ea55", "metadata": {}, "outputs": [ @@ -139,7 +108,7 @@ "5.5" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -151,7 +120,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f5770975-0a8e-4192-bc38-c41041b0e280", + "id": "27790060-6a22-4f9f-93e4-b85ff7273287", "metadata": {}, "outputs": [], "source": [] @@ -166,9 +135,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "305c829d-680e-4652-8399-2f55b40bee57", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -176,7 +151,7 @@ "2.75" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -188,16 +163,28 @@ { "cell_type": "code", "execution_count": null, - "id": "cafb1d20-3c46-486c-856b-235a33f33b9b", - "metadata": {}, + "id": "08222c31-5369-4801-b65b-4733726f534b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "08222c31-5369-4801-b65b-4733726f534b", - "metadata": {}, + "id": "d84a4c83-e628-4d28-a559-883c3af78a13", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [] }, @@ -211,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "57b9fab6-d2bd-4ebf-a191-eb83f81d6a0b", "metadata": {}, "outputs": [ @@ -221,7 +208,7 @@ "75.0" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "e097b267-e125-4f0f-bb06-5e6ef837fb48", "metadata": {}, "outputs": [ @@ -260,7 +247,7 @@ "75.0" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -277,14 +264,26 @@ "cell_type": "code", "execution_count": null, "id": "31dca516-7c74-42a8-9b93-44098a0249df", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "960b0808-0fff-47eb-9d7e-9069d3074c14", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Functions" ] @@ -337,30 +336,50 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "ab6e7d58-79f9-4afd-9f85-b814b45eb1bb", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ - "def mean(sample):\n", - " total = sum(sample)\n", - " avg = total / len(sample)\n", + "def mean(values):\n", + " total = 0\n", + " for value in values:\n", + " total = total + value\n", + " avg = total / len(values)\n", " return avg" ] }, { "cell_type": "markdown", "id": "346a10de-ad20-425a-b65b-c471874a7d3e", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "To **call** the function `mean` with input `grades`, we use the Python code `mean(grades)`." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "ab56d567-0758-4b70-9b97-fb2cd1283deb", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -368,7 +387,7 @@ "75.0" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -381,7 +400,7 @@ { "cell_type": "code", "execution_count": null, - "id": "27fb12e5-59f2-4374-9331-34c5919ee15d", + "id": "bf781976-47b4-4a89-9f99-fdda0c48dd8a", "metadata": {}, "outputs": [], "source": [] @@ -389,7 +408,13 @@ { "cell_type": "markdown", "id": "795538a1-fa68-43c8-b8ce-461d67d57842", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "#### Exmample 2: math function (bonus topic)\n", "\n", @@ -406,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "00fd3024-4329-4899-af02-2023a5e722f1", "metadata": {}, "outputs": [], @@ -427,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "88d9656c-913f-4617-999b-d98460ec070d", "metadata": {}, "outputs": [ @@ -437,7 +462,7 @@ "11" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -446,24 +471,6 @@ "f(3)" ] }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2d8c7281-2d21-4f92-8951-4ef76cd99144", - "metadata": {}, - "outputs": [], - "source": [ - "## Exercise: plot the graph of the function f(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e594bc0-f90c-43fd-ac9f-a82082d42ce1", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -490,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "a70a4bc4-50a7-4b50-8706-b2295bf57eb4", "metadata": {}, "outputs": [], @@ -500,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "5891ee22-6a6c-416d-a1bb-b283bb6a6519", "metadata": {}, "outputs": [ @@ -510,23 +517,18 @@ "" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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" ] }, - "metadata": { - "image/png": { - "height": 201, - "width": 508 - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -537,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "02caade4-678c-4681-893b-04e22a865903", "metadata": {}, "outputs": [ @@ -547,23 +549,18 @@ "" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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tkiQtLS0plUp57733MmrUqIwaNWqZ36NUKuXPf/5zW0UEAAAAqKhqcTJv3ryPfK9cLlczAgAAAMAyq2pxctBBB1XzdAAAAAAfS1WLk8suu6yapwMAAAD4WKr6VB0AAACAjkRxAgAAAFBAcQIAAABQoKprnBx33HEf+z1KpVJuvPHGlZAGAAAAYMmqWpz8/ve/T6lUWqFjFz62eEWPBwAAAFheVS1Okr8XIMujVCplk002yeqrr94GiQAAAAAWr6rFycMPP7zUfcrlcubNm5e33347f/rTn/Kzn/0sb775Znr27JkbbrghAwYMqEJSAAAAgCoXJxtssMEy77vZZptlyJAhOfroo/O1r30tTz75ZE477bTccsstbtcBAAAAqqLdP1WnR48eufrqq1NfX5+nn346d911V60jAQAAAJ1Euy9OkmTAgAHZc889Uy6Xc+edd9Y6DgAAANBJdIjiJEk+8YlPJEmmTJlS4yQAAABAZ1H1p+qsqFmzZiVJ3n///TY7x3vvvZeRI0dm7NixefnllzN79uysscYa2XLLLbPPPvvk4IMPTn19fZudHwAAAGhfOkRx0tLSkjFjxiRJmz1VZ/z48Rk+fHhmzJixyPdnzJiR8ePHZ/z48bnlllty/fXXZ6ONNmqTDAAAAED70u6Lk9mzZ+eiiy7Kq6++mlKplJ133nmln2PSpEk5+eSTM3fu3CTJLrvskj322CN9+/bNX/7yl9x555154YUX8sILL+TEE0/M//3f/6V3794rPQcAAADQvlS1OPm3f/u3Zd63sbExM2bMyB//+MdKoZEkRxxxxErPdfHFF1fOMWLEiBx55JGLvH788cfnnHPOyT333JNXX3011113Xc4555yVngMAAABoX6panIwePTqlUmm5jyuXy0kWFBjbbbfdSs304osv5oknnkiS7Lnnnh8pTZKka9euueSSS/L4449n2rRpueOOO/KNb3wjdXV1KzULAAAA0L5U/ak65XJ5uf/ZfPPN853vfCdnn332Ss8zfvz4yvYBBxxQuF/37t2z++67J1mwiOwrr7yy0rMAAAAA7UtVZ5zcdNNNy7xvly5dsvrqq2e99dZL//792yxTly5dsvnmm+evf/1rBg4cuMR9+/TpU9meOXNmm2UCAAAA2oeqFief+cxnqnm6ZXLUUUflqKOOWqZ9p0yZUtnu27dvGyUCAAAA2ouq36rTUb311lv5zW9+kyTp169fNtlkkxonAgAAANpau3gc8WuvvZbXX3897733XpKkd+/e2WijjdpVOXH55ZenqakpSbLffvulSxedEwAAAKzqalacNDQ05H/+53/y85//PO+8885i9+nXr1+GDh2ak08+OQMGDKhywr8bOXJkfvGLXyRJevToka9+9as1yzJlyhSlzXJYWHY1NTVlwoQJNU5DRzB48ODU19enXC5n1uxZNc1SbilX/l3rLHQMC59C19jYmEmTJtU0i+svHZnxy/IaNGhQunZtF3+TzuDBgyvbjY2NNUxCRzN//vxFlqeolSVdg1taWmoRqTbFyRNPPJGvf/3rmT59epK/f9D7sBkzZuRnP/tZ7rvvvnzve9/L5z73uWrGTJI89NBDueiiiypfjxgxIuuss07VcyzU3Nyc5ubmmp2/I1v4AwjLopy/FxftQXvKQvvVepS0p2tee8oCy8v4ZVl07do19fX1tY4BH1t7u+a1lzxVL04mTJiQr3zlK2lsbKwUJn379s3gwYPTt2/ftLS05N13383kyZMzc+bMlMvlvPPOOznppJMyatSofPKTn6xa1gceeCDDhw+vFBXHHXfcEh9ZXA11dXVmnCyH1j9o3bp1q2ESOppSklKXUk0ztC5Lap2FjqH1KKn1Nc/1l47M+GVFtbSUM3P2vJpmaF2i+/TAsujds3u6/O2zZnu45i3pGtzS0lKTiQRVLU6ampoyfPjwzJu34GKy1VZb5Rvf+EY++9nPLnb/xx57LFdeeWWeffbZNDY25owzzsi9995blf8xb7/99owYMSLz589Pkhx00EE599xz2/y8SzNo0KD06tWr1jE6jAkTJqSpqSndunXLtttuW+s4dCClUim9etb2Z23W7Fkpt5RT6lL7LHQMpdKCDz319fU1v+a5/tKRGb+sqPfnNOZ7Nz9V0ww+P7C8zj5ux/Tp1b1dfH5IlnwNnjVrViZPnlz1TFWdunDnnXfmjTfeSKlUym677ZZRo0YVliZJ8vnPfz6jRo3KF7/4xSQLFpG999572zzntddem29/+9uV0uSQQw7JpZdeWvlACgAAAHQOVS1OHn744STJGmuskcsvv3yZZo507do1l19+eXr37p0kuf/++9ssX0tLS84///xcc801le8dd9xxueSSS9weAwAAAJ1QVduA5557LqVSKXvssUf69u27zMf16dMne+yxR8rlciZOnNgm2VpaWvLNb34zo0aNqnzv9NNPz7e+9S0zTQAAAKCTquoaJ++++26SZJNNNlnuYxce09DQsDIjVYwYMSL33HNPkqRLly45//zzc+SRR7bJuQAAAICOoarFSffu3dPU1JQ5c+Ys97ELj+nRo8fKjpU77rijMtOkS5cuueyyy3LggQeu9PMAAAAAHUtVb9VZf/31Uy6X8/jjjy/3sQuPWXfddVdqpnfeeSeXXHJJ5euzzz5baQIAAAAkqfKMk5133jmTJ0/OhAkTMmbMmPzjP/7jMh338MMP549//GNKpVJ23nnnlZrpxhtvzKxZs5IsKHY23HDDPPTQQ0s9bquttsr666+/UrMAAAAA7UtVi5PDDz88N998c5LkG9/4Rn7wgx9UHjVcZOzYsfnmN7+ZJCmVSjn00ENXaqbRo0dXtv/yl7/kX//1X5fpuMsuuywHH3zwSs0CAAAAtC9VLU4222yzHHnkkbn11lszZ86cnHzyydlpp52y9957Z/DgwZUn7bz77ruZPHly7rvvvjz55JMpl8splUr58pe/nC222GKl5ZkxY0befvvtlfZ+AAAAwKqlqsVJkpxzzjmZOnVqxo0bl1KplCeeeCJPPPFE4f7lcjlJ8rnPfS7nn3/+Ss3Sv3//TJ48eaW+JwAAALDqqOrisElSX1+f6667Lv/6r/+aHj16pFwuL/GfHj165JRTTskNN9yQrl2r3vMAAAAAnVhNmoi6urqceuqpOfbYYzN27Nj87ne/yxtvvJGGhoaUy+X07t07AwcOzA477JC99947vXv3rkVMAAAAoJOr6RSOvn375qCDDspBBx1UyxgAAAAAi1XVW3Vee+21Zdrvxz/+ce6///40Nja2cSIAAACAYlWZcXLnnXfm2muvTbdu3fKrX/1qifs2NTXlxz/+cebNm5d11lknp556ag455JBqxAQAAABYRJsWJ3Pnzs2ZZ56ZsWPHVh4pPGPGjPTv37/wmD/84Q/54IMPUiqV8uabb+bb3/52fv3rX+f73/9+6uvr2zIuAAAAwCLa7Fad5ubmnHLKKRk7duwi33/++eeXeFz37t2z2267pa6uLsmCxxE/8MAD+frXv155NDEAAABANbRZcXL99ddn/Pjxla8PPvjgPPDAAxkyZMgSj9t2223zH//xH3n44Ycri8aWy+WMGTMmN954Y1vFBQAAAPiINilOZsyYkf/8z/9MsuDRwz/84Q9z6aWXZqONNlrm91hnnXVy2WWX5aKLLkqpVEq5XM4111yT2bNnt0VkAAAAgI9ok+Lk7rvvrqxTctZZZ2WvvfZa4fc67LDDctRRRyVJ5syZk7vvvntlxQQAAABYojYpTn73u98lSQYMGJBjjz32Y7/fqaeemtVWWy1J8thjj33s9wMAAABYFm1SnEyePDmlUimf+9znKou8fhx9+/bNkCFDUi6X8+c//3klJAQAAABYujYpThoaGpIkm2yyyUp7zy233DJJ8u6776609wQAAABYkjYpTubPn58k6dat20p7z549eyZJmpqaVtp7AgAAACxJmxQnvXv3TrJyZ4fMnDkzyd8LFAAAAIC21ibFyaabbrrS1yOZOHFikmTdddddae8JAAAAsCRtUpxsv/32SZKnnnoq06dP/9jvN23atDz55JMplUoZPHjwx34/AAAAgGXRJsXJXnvtlSRpbm7Otdde+7Hf79prr62sm7Lbbrt97PcDAAAAWBZtUpxss802+Yd/+IeUy+WMHDky99xzzwq/11133ZX//d//TalUyoABA7LHHnusxKQAAAAAxdqkOEmSb37zmymVSkmSs88+O//+7/+exsbGZT7+gw8+yFVXXZVzzz238r3TTz893bt3X+lZAQAAABana1u98ac//emcfPLJue6665Ik119/fUaNGpWhQ4dmyJAh+fSnP51+/fpV9i+Xy5k+fXqeeuqpPPbYY3nwwQfT0NCQcrmcJNl///1z6KGHtlVcAAAAgI9os+IkSU477bTMnTs3//3f/51SqZTp06fnlltuyS233JIk6datW/r27ZvGxsbMnDmzUpIkWWT70EMPzYUXXtiWUQEAAAA+ok2Lk2TBbTrbbbddLr300kybNi3lcjmlUinlcjmNjY2ZNm1a4bEbb7xxzjrrrMpiswAAAADV1ObFSZLss88++cd//Mfceeedueeee/LMM89UnpLzYX369MnOO++cf/qnf8ruu++eurq6akQEAAAA+IiqFCdJUl9fn8MOOyyHHXZY5s2blxdffDF//etfM2fOnNTV1WWNNdbIJptsko022qiyqCwAAABALVWtOGmte/fu2WqrrbLVVlvV4vQAAAAAy6TNHkcMAAAA0NEpTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE4AAAAACihOAAAAAAooTgAAAAAKKE6W4MILL8yWW26Za665ptZRAAAAgBpQnBQYP358Ro4cWesYAAAAQA0pThZj4sSJGTZsWFpaWmodBQAAAKghxcmHjBs3LieccEJmzZpV6ygAAABAjXWtdYD2orGxMT/+8Y9z/fXXm2kCAAAAJDHjJEny29/+Nvvuu29+9KMfpaWlJT169MgJJ5xQ61gAAABAjSlOktx999154403kiSf+tSncvvtt2f33XevcSoAAACg1tyq8zf9+/fPsGHDcsQRR6Suri7Tp0+vdSQAAACgxhQnSY4++uiMGDEiq622Wq2jAAAAAO2I4iTJNttsU+sIAAAAQDukOOlgpkyZki5d2vfSNIMGDUrXru1jaA0ePLiy3djYWMMkdBTdunVLqVRKksyaXdvHkpdbypV/1zoLHUPP1bslScrlcpqammqaxfWX5dW1a9eUSqWUy+XMnz+/plmMX5aXzw90ZOXygjHT2NiYSZMm1ThNKp9hmpqaMmHChEVeq9UTcNvHb7css+bm5jQ3N9c6xhJ17do19fX1tY4BH9vCDx7tQXvKQvvV5W8f2kulkuswHZbxS0fXnv4/uz1lof1qPUpq/YeXD2sveRQnHUxdXV27n3GyUEtLOTNnz6tphtYXgVLNUtCR9OnVvfIXo1KX2o6a1h92ap2FjsX1l45o4fXX+KUj8vmBjqz1KOnWrVvNcizUuiz5cJ6WlpaaTCRQnHQwgwYNSq9evWodY5m8P6cx37v5qZpmmDV7Vsot5ZS6lNKrZ8f470ZtXXzS5/K3zz01HzPGLyvK9ZeOaOH11/ilI/L5gY5sYelXX1+fbbfdtsZpkgkTJqSpqSndunX7SJ5Zs2Zl8uTJVc/UMaYuAAAAANSA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoEDXWgdor3beeedMnjy51jEAAACAGjLjBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACggOIEAAAAoIDiBAAAAKCA4gQAAACgQNdaB2gvyuVy7r333vz85z/Pc889lzlz5mSttdbKTjvtlKOPPjrbbrttrSMCAAAAVaY4SfLBBx/k9NNPzyOPPLLI96dOnZqpU6fmnnvuyRlnnJGvfvWrtQkIAAAA1ITiJMm3vvWtSmmy2Wab5bDDDsuaa66ZZ599NiNHjsycOXPygx/8IOuss04OOOCA2oYFAAAAqqbTFyePPfZY7r333iTJkCFD8pOf/CTdu3dPkuy///758pe/nKOOOioNDQ259NJLs8cee6RXr161jAwAAABUSadfHPanP/1pkqRr1665+OKLK6XJQptttlnOO++8JElDQ0Nuv/32qmcEAAAAaqNTFycNDQ357W9/myTZdddds9FGGy12v6FDh2bAgAFJkvvuu69q+QAAAIDa6tTFyZNPPpmWlpYkC27TKdKlS5fstNNOSZI//vGPee+996qSDwAAAKitTl2cvPDCC5XtLbbYYon7Dho0KMmCxxY///zzbZoLAAAAaB86dXEyderUyvYGG2ywxH3XXXfdxR4HAAAArLo69VN1ZsyYUdnu16/fEvft27dvZbuhoaGNEn1Uc3PzIl/PmTOnauf+uObOacyAXqWaZujRtWvKLeWUupSy+mq1zULHMGvWrHTpUjJ+6ZCMXzoy45eOzPilI5s7Z3bq0lTrGBULl9NoaWnJrFmzFnntw78Pf/j35bbSqYuTDz74oLL94afpfFh9ff1ij2tr8+bNW+Tr119/vWrnXhm+uPWS/7u2vVqfn47mhRf+fiue8UtHY/zSkRm/dGTGLx3Z1NdfrnWExWpubs7kyZOXuM+Hf19uK536Vp358+dXtlsXI4vT+vXWxwEAAACrrk5dnKy22mqV7aamJU9NamxsrGwvrWQBAAAAVg2d+ladHj16VLbnzZu3xEKkdXGytNt6VqbWa6ssPHddXV3Vzg8AAADtQXNz8yK353z49+W20qmLk969e1e2GxoassYaaxTu23pB2P79+7dlrEXU19dn7bXXrtr5AAAAgL/r1LfqDBw4sLL917/+dYn7vvnmm5Xt9ddfv60iAQAAAO1Ipy5ONttss8r2Cy+8sMR9F75eKpWy+eabt2kuAAAAoH3o1MXJ9ttvn27duiVJHn/88cL9mpub88QTTyRJBg8evMgtPgAAAMCqq1MXJ717986QIUOSJGPGjMlf/vKXxe73i1/8IjNmzEiS7LvvvlXLBwAAANRWpy5OkuT4449PsuBxxMOHD8+sWbMWeX3KlCm55JJLkiQ9e/bMoYceWu2IAAAAQI2UyuVyudYhau20007L/fffnyTZcMMNc+SRR2a99dbLn//859x2222ZPXt2kuTCCy/MEUccUcuoAAAAQBUpTpLMnTs3w4YNy6OPPrrY10ulUoYNG5Zhw4ZVORkAAABQS4qTvymXy7nnnnty55135rnnnsv777+fvn37Zocddshxxx2XHXbYodYRAQAAgCpTnAAAAAAU6PSLwwIAAAAUUZwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAU6FrrALAyXHPNNbn22muX+7iDDjoo3/3ud9sgEayYxsbG/N///V9+9atfZfLkyZkzZ0769OmTbbbZJgceeGD23nvvlEqlWseExZo1a1ZuueWWPPjgg3nllVfS2NiY9ddfP7vuumuOO+64bLzxxrWOCIu48MILc+utt2bYsGE59dRTl7r/uHHjctttt2XChAmZOXNmBgwYkG233TZHHnlkPve5z1UhMSxqecfwQl/72tfyyCOP5LLLLsvBBx/chglh8ZZn7M6dOzc///nP8+CDD+b555/P+++/n549e+YTn/hE9thjjxx55JHp2bNnm+ZVnNCp+QWU9uStt97K1772tTz33HOLfP+dd97J2LFjM3bs2Oy22265+uqr06NHjxqlhMWbMGFCTjnllLz99tuLfP+VV17JK6+8kpEjR+ass87K8ccfX5uA8CHjx4/PyJEjl2nflpaWnH/++bn99tsX+f6bb76ZN998Mw888ECOOeaYfPvb3/bZgqpZnjHc2h133JFHHnlk5QeCZbQ8Y3fSpEkZNmxYXn/99UW+39DQkKeffjpPP/10br755lx33XXZeuut2yJuEsUJq4ihQ4fmk5/85FL3e/fddzNixIjMnz8/PXv2zIknnliFdLB0TU1Ni5Qmm2yySQ455JCst956efnllzNy5MjMmDEj48aNy5lnnpnrr7++xonh71566aUcf/zxmT17dpJk4MCBOfTQQ7P++uvn5Zdfzm233Za33347l112WZqbm117qbmJEydm2LBhaWlpWab9f/jDH1ZKk/XWWy9HHXVUNthgg7z00ku59dZbM2PGjNxyyy0ZMGBATjnllLaMDkmWfwwvNG7cuJx//vltlAqWbnnG7ltvvZUTTzwx77zzTpLkH/7hHzJ06NCsvfbaefvtt/PLX/4yzzzzTN58882ceOKJ+fnPf54NNtigTXIrTlglbLbZZtlss82WuE+5XM5XvvKVzJ8/P0ly8cUXZ9CgQdWIB0s1evToSmmy++675+qrr85qq61Wef3YY4/NCSeckEmTJmXMmDF59NFHs8suu9QqLizi3HPPrZQme++9d6644orU19dXXj/mmGNywgkn5Nlnn82VV16ZXXfdNVtssUWt4tLJjRs3LmeddVZmzZq1TPu/+OKLueGGG5Ikm2++eX72s5+lT58+ldcPP/zwHHPMMXn11Vdz3XXX5YADDmizD+6QLP8YXuiOO+7IBRdckKampjZKBku2vGP3yiuvrJQm//Iv/5KzzjprkdePO+64XHHFFbnhhhvy7rvv5rvf/W6uueaalZ47sTgsnchtt92WRx99NEnyT//0Txk6dGiNE8HfPfDAA0mSLl265KKLLlqkNEmS/v3751vf+tZH9odae/rpp/PMM88kST7xiU/k+9///iKlSZL06dMnP/zhD9OtW7fMnz8/P/7xj2sRlU6usbEx//7v/56TTjopM2fOXObjbrzxxjQ3NydJRowYsUhpkiRrr712Lr/88iQLZg/+93//98oLDa2s6Bh+//33c/755+ff/u3f0tjY2IYJYfFWZOy+//77+cUvfpEk2XrrrXPmmWcudr8zzzyzcovOQw89lHfffXflhP4QxQmdwrRp03LFFVckSfr27Ztzzz23xolgUW+88UaSBQXJ2muvvdh9tttuu8r21KlTq5ILluY3v/lNZfv/+//+v3Tv3n2x+2200UbZfffdkyQPP/xw5syZU5V8kCS//e1vs+++++ZHP/pRWlpa0qNHj5xwwglLPa6lpSX3339/kmSLLbbIjjvuuNj9tt9++8oH9/vvvz/lcnnlhYes+Bi+9957s/fee2fUqFFJFnzOOOKII9o6LlSs6Nh98sknK7Oj/t//+3+F60eVSqXsvffeSRZcs//0pz+tvPCtKE7oFK644orKNPKzzjor/fr1q3EiWNQaa6yRJJk+fXplrH5Y67Kkf//+VckFS/Piiy9Wtpf2VJFtt902SfLBBx9k4sSJbZoLWrv77rsrBfWnPvWp3H777ZUib0mef/75NDQ0JEmGDBmyxH0Xvj5t2rRMnjz54wWGD1nRMTxq1KhMnz49SbLLLrtk9OjRi/whBtraio7d+fPnZ8stt0yfPn0ycODAJe7beibg8szGWh7WOGGVN2nSpNx9991JFvy16JBDDqlxIviobbfdNhMmTEi5XM5Pf/rTxT6W7T//8z8r29Y3ob14//33K9tFs6UW6tu3b2X7hRdeyGc+85m2igUf0b9//wwbNixHHHFE6urqKr9MLsnzzz9f2V7aujyt102bNGlSBg8evOJhYTFWZAwnyQYbbJAzzzwz++23XxsnhMVbkbH7pS99KV/60peW6f2nTJlS2W79WWNlUpywyrvmmmsqU2ZPP/30dOliohXtzz//8z/njjvuyJw5c3Lddddl5syZOeKII7L++uvntddey3/913/lrrvuSpJ85jOfyf7771/jxLBA60djz5s3L926dSvct/VicNOmTWvTXNDa0UcfnREjRnxk/ailaT3Tb2kLvq633nqLPQ5WhhUdw8OHD88222yTrl392kdtrOjYXVYffPBBZS2Uurq6fOpTn2qT8/gJYpX22muvZcyYMUkWLFq4xx571DgRLN7GG2+cG264IcOHD89bb72Vm266KTfddNMi+3Tr1i1HHHFEzjzzzNTV1dUoKSxqww03rGxPnDhxibczPPvss5XttppKC4uzzTbbrNBxM2bMqGwv7Tbf1lPFF97eAyvLio7h7bfffiUngeWzomN3Wf3oRz+qXKu/8IUvtNmME396Z5V2yy23VJ4RfsIJJxQuKgTtwY477pgrr7yy8ILfu3fvbLrppkoT2pXWRcktt9xSuN/06dPz8MMPV772ZAc6gg8++KCy/eGnRX1Y64WRWx8HQNsYN25c5Vb2urq6nHbaaW12LjNOWGXNnj07d9xxR5JkzTXXzIEHHljbQLAETU1NOfvssytTDYcMGZK99tor/fr1yxtvvJG77rorU6ZMyUUXXZT77rsvN9xwQ5tNeYTl8YUvfCEbb7xxXnvttTz44IO54YYb8i//8i+L7DN37twMHz58kSfpKLLpCObPn1/ZXlpx0vr11scBsPL94Q9/yBlnnFH5I/mwYcOy1VZbtdn5FCessn75y19WFi089NBDl/qBB2rpzDPPrDzy8rzzzssxxxyzyOsnnnhiLrzwwowaNSq///3vc+655+bKK6+sRVRYRF1dXS6++OKccMIJaW5uzhVXXJFx48Zl3333Tf/+/fPqq6/mf//3fzN16tTstttuGTduXJIUPrYY2pPWBfXCx2IWaT2Laklr/QDw8Tz11FP56le/WvmDzJe+9KWcdNJJbXpOt+qwynrooYcq2/vss08Nk8CS/f73v6+UJgcddNBHSpNkwS+nF1xwQeVxrr/4xS/ywgsvVDUnFNl5551z5ZVXVn7JfOKJJ3LRRRfljDPOyFVXXZWpU6dmv/32y9lnn105ZuEjuKE9a7348dJuL5s3b15l24xAgLYxbty4fOUrX6ksOP/5z38+V155ZZs/AERxwipp7ty5GT9+fJJk4MCBHglIu/bAAw9Uto866qjC/erq6hYpVR555JG2jAXLZZ999skvf/nLHHXUUdloo41SX1+fNddcM7vttluuv/76XHnllZk9e3Zl/7XWWquGaWHZ9O7du7K9tAVf33vvvcp2//792yoSQKd1++2355RTTqnMNNl1111z/fXXV+XOArfqsEp67LHHKn/52XvvvWucBpbs1VdfrWxvueWWS9y39b2bb7zxRptlghWxwQYb5IILLih8ffLkyZXtTTfdtBqR4GMZOHBgZfuvf/3rEvdt/fr666/fVpEAOqXrrrsuP/zhDytf77333rniiiuqthyDGSeskn7zm99Utvfcc88aJoGlK5fLle3WU70Xp/U0RE/XoaP5/e9/n2TBwrCf/OQna5wGlm7QoEGV7aXdHtn69S222KLNMgF0Nj/4wQ8WKU0OP/zwXH311VVdw1JxwirpmWeeSbJgcTa36dDerbvuupXtiRMnLnHf559/vrLtL5q0B5MnT85pp52WI444Io8++mjhfh988EF+/etfJ0k+9alPpV+/ftWKCCts0003zZprrpkkefzxx5e478LX+/btqzgBWEl+8pOf5Cc/+Unl65NOOikXXXRRm69p8mGKE1Y58+bNy5QpU5Is+IuPp+nQ3n32s5+tbN90002F+5XL5dx6662Vr3fZZZc2zQXLomfPnrn//vvzzDPPVB6nvTi33nprZY2IL3/5y1VKBx9Ply5dKjNXJ06cmD/84Q+L3e+pp57Ks88+m2TB9PFqf6AHWBX97ne/W+QpkmeccUa+/vWv1ySLqzqrnJdeeinNzc1JYio4HcKXvvSlbLDBBkmSsWPH5vrrr//IPuVyOd///vcrtzrssssuZlPRLmy44YbZZpttkiT33HPPYn+xHDduXK666qokyUYbbZSDDz64mhHhYzn22GPTteuCZQHPPvvsvP3224u8/tZbb+Wcc85JsuAWyn/+53+uekaAVc28efNy7rnnVm5pP/bYY3PyySfXLI/FYVnlvPbaa5XtAQMG1DAJLJv6+vpcfvnlOeGEE9LU1JSrr746Dz30UPbff/+ss846mTZtWu6+++7KXzP79++f73znOzVODX935plnVsbvMccck0MPPTTbbbddmpqa8uijj+aBBx5IS0tLunXrlu9973tmAtKhDBo0KMcdd1x++tOf5pVXXskBBxyQo446Kptuumlefvnl3HrrrZk+fXqS5MQTT8xmm21W48QAHd/o0aMzderUJAtmt37605/OQw89tNTjNt100za5DitOWOW89dZble1evXrVMAksu5122in/8R//keHDh6ehoSETJ05c7HonAwcOzHXXXWd9E9qVz372s7ngggty8cUXp6mpKbfeeusit5UlSb9+/XLVVVfl05/+dI1Swoo766yz8u6772b06NGZPn16rrnmmo/sc+ihh9ZsCjnAqmb06NGV7dmzZy/z9XXYsGE59dRTV3oexQmrnNmzZ1e2e/fuXcMksHw+//nP58EHH8xtt92WsWPH5qWXXsrs2bPTu3fvDB48OHvttVcOOeQQf62nXTryyCOz/fbb58Ybb8zjjz+eadOmpVu3btl0002zxx575Oijj07fvn1rHRNWSF1dXb773e9m3333zciRI/OnP/0pDQ0N6dWrV7bbbrsceeSR+eIXv1jrmACrjNYPRGgPSuXWz8EEAAAAoMLisAAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAFFCcAAAAABRQnAAAAAAUUJwAAAAAF/n8dYRsg/HuzEAAAAABJRU5ErkJggg==\n", 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\n", 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" + "
" ] }, - "metadata": { - "image/png": { - "height": 201, - "width": 551 - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -573,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "8455cb4d-6c7b-4341-80a2-bd67126f5383", "metadata": {}, "outputs": [ @@ -583,23 +580,18 @@ "" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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"text/plain": [ - "
" + "
" ] }, - "metadata": { - "image/png": { - "height": 201, - "width": 508 - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -607,51 +599,6 @@ "sns.boxplot(x=prices)" ] }, - { - "cell_type": "code", - "execution_count": 18, - "id": "7d6743af-1055-416f-9e1d-517c2ef1e988", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 184, - "width": 783 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# FIGURES ONLY\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "with plt.rc_context({\"figure.figsize\":(8,2)}):\n", - " fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n", - "\n", - " ax1.set_title(\"(a) strip plot\")\n", - " sns.stripplot(x=prices, ax=ax1, jitter=0)\n", - " ax1.set_xticks(range(6,13))\n", - "\n", - " ax2.set_title(\"(b) hist plot\")\n", - " sns.histplot(x=prices, ax=ax2)\n", - " ax2.set_xticks(range(6,13))\n", - " ax2.set_yticks(range(0,5))\n", - "\n", - " ax3.set_title(\"(c) box plot\")\n", - " sns.boxplot(x=prices, ax=ax3)\n", - " ax3.set_xticks(range(6,13))\n", - "\n", - "savefig(fig, \"figures/epricesW_strip_hist_box_plots.png\")" - ] - }, { "cell_type": "code", "execution_count": null, @@ -676,110 +623,126 @@ "### Descriptive statistics" ] }, + { + "cell_type": "markdown", + "id": "5727115b-f58d-44ca-bf14-98eca6ac0ea4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Data manipulations using Pandas" + ] + }, { "cell_type": "code", - "execution_count": 19, - "id": "a746d4e7-a90b-498f-ac4b-67521aadd149", + "execution_count": 16, + "id": "e4b22ff8-db8a-413b-bc55-61f2b64a0de9", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " East West\n", + "0 7.7 11.8\n", + "1 5.9 10.0\n", + "2 7.0 11.0\n", + "3 4.8 8.6\n", + "4 6.3 8.3\n", + "5 6.3 9.4\n", + "6 5.5 8.0\n", + "7 5.4 6.8\n", + "8 6.5 8.5\n" + ] } ], "source": [ - "len(prices)" + "import pandas as pd\n", + "epriceswide = pd.read_csv(\"https://nobsstats.com/datasets/epriceswide.csv\")\n", + "print(epriceswide)" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "71a0a111-acc0-4140-ad01-49a3959e5ee9", + "execution_count": 17, + "id": "386037ed-cadc-455c-81b7-7877bbbbd706", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "9.155555555555555" + "pandas.core.frame.DataFrame" ] }, - "execution_count": 20, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mean(prices)" + "type(epriceswide)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "a955b21c-5666-4483-ad88-c70a769ea8b4", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "5727115b-f58d-44ca-bf14-98eca6ac0ea4", + "id": "3b7b850f-2022-4a84-ab3b-bf7bf145cd7b", "metadata": {}, "source": [ - "#### Data manipulations using Pandas" + "We want to extract only the second column which is called \"West\":" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "e4b22ff8-db8a-413b-bc55-61f2b64a0de9", - "metadata": {}, + "execution_count": 18, + "id": "38c52689-1b5a-4053-8f76-c10a7b76cd72", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " East West\n", - "0 7.7 11.8\n", - "1 5.9 10.0\n", - "2 7.0 11.0\n", - "3 4.8 8.6\n", - "4 6.3 8.3\n", - "5 6.3 9.4\n", - "6 5.5 8.0\n", - "7 5.4 6.8\n", - "8 6.5 8.5\n" - ] + "data": { + "text/plain": [ + "0 11.8\n", + "1 10.0\n", + "2 11.0\n", + "3 8.6\n", + "4 8.3\n", + "5 9.4\n", + "6 8.0\n", + "7 6.8\n", + "8 8.5\n", + "Name: West, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "DATA_URL = \"https://nobsstats.com/datasets/epriceswide.csv\"\n", - "\n", - "import pandas as pd\n", - "epriceswide = pd.read_csv(DATA_URL)\n", - "print(epriceswide)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "38c52689-1b5a-4053-8f76-c10a7b76cd72", - "metadata": {}, - "outputs": [], - "source": [ - "pricesW = epriceswide[\"West\"]" + "pricesW = epriceswide[\"West\"]\n", + "pricesW" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "id": "927b21a6-7b75-4ac5-b93c-2b5632e0b608", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -787,7 +750,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 23, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -798,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "id": "cd3b487a-545b-4680-b315-2e7d0471d63d", "metadata": {}, "outputs": [], @@ -825,9 +788,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "id": "70075246-d2b0-48f0-9c26-b00b20c05bc7", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -835,7 +804,7 @@ "9" ] }, - "execution_count": 25, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -846,9 +815,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "id": "4bf01229-90ea-4352-a26e-7d4fa6c766c2", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -856,7 +831,7 @@ "9.155555555555557" ] }, - "execution_count": 26, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -867,9 +842,36 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "aaff543c-6428-48b7-922a-3b3273324402", + "execution_count": 23, + "id": "083041a3-4007-4ccd-aec5-2dbaba3fd41b", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.6" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pricesW.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "aaff543c-6428-48b7-922a-3b3273324402", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -877,7 +879,7 @@ "1.5621388471508475" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -888,9 +890,15 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "id": "81a08643-8207-4130-93f8-8e9227f54c2e", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -906,7 +914,7 @@ "Name: West, dtype: float64" ] }, - "execution_count": 28, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -919,123 +927,34 @@ "cell_type": "code", "execution_count": null, "id": "0060b041-0300-4c5d-b92b-49e0b7f26511", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "345999ef-eab8-4c4d-b590-38cffc41460f", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Data cleaning" ] }, - { - "cell_type": "code", - "execution_count": 29, - "id": "70161723-bfab-4810-9cef-49cff61aa74d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " East West\n", - "0 7.7 11.8\n", - "1 5.9 10.0\n", - "2 7.0 11.0\n", - "3 4.8 8.6\n", - "4 6.3 8.3\n", - "5 6.3 9.4\n", - "6 5.5 8.0\n", - "7 5.4 6.8\n", - "8 6.5 8.5\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "DATA_URL = \"https://nobsstats.com/datasets/epriceswide.csv\"\n", - "epriceswide = pd.read_csv(DATA_URL)\n", - "print(epriceswide)" - ] - }, - { - "cell_type": "markdown", - "id": "7a20430b-47bc-4eed-b536-d2c220f6bc3b", - "metadata": {}, - "source": [ - "Click [here](https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0A%0Aepriceswide_csv%20%3D%20'''%0AEast,West%0A7.7,11.8%0A5.9,10.0%0A7.0,11.0%0A4.8,8.6%0A6.3,8.3%0A6.3,9.4%0A5.5,8.0%0A5.4,6.8%0A6.5,8.5%0A'''%0A%0Aepriceswide%20%3D%20pd.read_csv%28io.StringIO%28epriceswide_csv%29%29%0A%0Aepriceswide.melt%28var_name%3D%22end%22,%20value_name%3D%22price%22%29&d=2023-07-02&lang=py&v=v1) to see a visualization of the above melt operation." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "f2172968-641c-4951-a722-029ac2bc853b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " end price\n", - "0 East 7.7\n", - "1 East 5.9\n", - "2 East 7.0\n", - "3 East 4.8\n", - "4 East 6.3\n", - "5 East 6.3\n", - "6 East 5.5\n", - "7 East 5.4\n", - "8 East 6.5\n", - "9 West 11.8\n", - "10 West 10.0\n", - "11 West 11.0\n", - "12 West 8.6\n", - "13 West 8.3\n", - "14 West 9.4\n", - "15 West 8.0\n", - "16 West 6.8\n", - "17 West 8.5\n" - ] - } - ], - "source": [ - "eprices = pd.melt(epriceswide, var_name=\"end\", value_name='price')\n", - "print(eprices)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "37ed70f2-0a06-42bb-be5f-9bef646f44f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([11.8, 10. , 11. , 8.6, 8.3, 9.4, 8. , 6.8, 8.5]),\n", - " array([7.7, 5.9, 7. , 4.8, 6.3, 6.3, 5.5, 5.4, 6.5]))" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pricesW = eprices[eprices[\"end\"]==\"West\"][\"price\"]\n", - "pricesE = eprices[eprices[\"end\"]==\"East\"][\"price\"]\n", - "\n", - "pricesW.values, pricesE.values" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "157b54eb-72ff-404a-a5c9-7b330fdba7dc", + "id": "c63e5acf-1ba4-4ee7-a392-cea2a5be8b13", "metadata": {}, "outputs": [], "source": [] @@ -1056,38 +975,16 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "a90f6557-3795-4d72-915d-68f279a142a4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56f42acc-f9a3-41f9-ad8d-6a0f7b4354ab", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "28fae377-556f-4e40-b477-5add145298ea", "metadata": {}, "source": [ - "## Conclusion" + "## Conclusion\n", + "\n", + "Python = good for your life!" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd49e584-9391-41a1-b94b-345720cb3e5c", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "e959a84d-532e-4138-9652-239cf888d279", @@ -1096,56 +993,67 @@ "## Links\n", "\n", "- Book website [noBSstats.com](https://nobsstats.com/intro.html): contains all the notebooks, demos, and visualizations from the book.\n", - "- [Detailed book outline](https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit): continuously updated list of the topics that are covered in each section.\n", - "- [Python tutorial](https://nobsstats.com/tutorials/python_tutorial.html)\n", - "- [Pandas tutorial](https://nobsstats.com/tutorials/pandas_tutorial.html)\n", - "- [Seaborn tutorial](https://nobsstats.com/tutorials/seaborn_tutorial.html)\n", - "- Previous blog posts:\n", + "- [Detailed book outline](https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit): continuously updated list of the topics that are covered in each section\n", + "- [Python tutorial](https://nobsstats.com/tutorials/python_tutorial.html): introduction to Python syntax, data types, functions and other constructs.\n", + " See also the [Pandas tutorial](https://nobsstats.com/tutorials/pandas_tutorial.html) (WIP) and the [Seaborn tutorial](https://nobsstats.com/tutorials/seaborn_tutorial.html) (WIP).\n", + "- Previous blog posts about statistics book:\n", " - [Outline of the stats curriculum research](https://minireference.com/blog/fixing-the-introductory-statistics-curriculum/)\n", " - [Book proposal](https://minireference.com/blog/no-bullshit-guide-to-statistics-progress-update/)\n", " - [Stats survey results](https://minireference.com/blog/what-stats-do-people-want-to-learn/)\n", - " - Part 2\n", - " - Part 3" + " - [Python coding skills for statistics - PART 2](https://docs.google.com/document/d/1XusbfJoZ7CQxbeWPPXMM84BA-VeUn8lkPUuUy7RcW8M/edit): probability and statistics procedures\n", + " - PART 3: coming soon" ] }, { "cell_type": "code", "execution_count": null, - "id": "6b8b889a-ed0b-430d-aa3c-41cdf55eb398", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da44e889-59e3-4e3b-976b-ae212d605da1", + "id": "87d99089-d5ef-4202-81ab-0b6634e21ede", "metadata": {}, "outputs": [], "source": [] }, { - "cell_type": "code", - "execution_count": null, - "id": "25a8565a-46ed-45e4-9515-719c8ab0dbfa", - "metadata": {}, - "outputs": [], - "source": [] + "cell_type": "markdown", + "id": "77d4957e-93ec-4dac-b79f-4ef446e01a44", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Python error messages" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "fc178ca0-6e03-43cf-8ad5-c70bd3acb284", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "b82d61a6-7c4b-4118-b9ca-c0850aba6621", - "metadata": {}, + "execution_count": 26, + "id": "9753d6e6-ed60-4991-b1ff-877bc0a3f8c7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "raises-exception" + ] + }, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m3\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], "source": [ - "_____" + "3/0" ] } ],