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--git a/_sources/auto-research-outputs.ipynb b/_sources/auto-research-outputs.ipynb index 57b38a9..ffbaa4c 100644 --- a/_sources/auto-research-outputs.ipynb +++ b/_sources/auto-research-outputs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "615bc61b", + "id": "bb5b88be", "metadata": {}, "source": [ "(auto-research-outputs)=\n", @@ -76,67 +76,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "033d9c5a", + "execution_count": null, + "id": "5c1fc048", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
timeLunchDinner
smoker
Yes2.833.07
No2.673.13
\n", - "
" - ], - "text/plain": [ - "time Lunch Dinner\n", - "smoker \n", - "Yes 2.83 3.07\n", - "No 2.67 3.13" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "import pandas as pd\n", @@ -149,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "01b32733", + "id": "9cc82578", "metadata": {}, "source": [ "This can be turned into a $\\LaTeX$ table using the following command" @@ -157,28 +100,17 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "78fccfc0", + "execution_count": null, + "id": "67c5537e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\\\begin{table}\\n\\\\caption{A Table}\\n\\\\label{tab:descriptive}\\n\\\\begin{tabular}{lrr}\\ntime & Lunch & Dinner \\\\\\\\\\nsmoker & & \\\\\\\\\\nYes & 2.830000 & 3.070000 \\\\\\\\\\nNo & 2.670000 & 3.130000 \\\\\\\\\\n\\\\end{tabular}\\n\\\\end{table}\\n'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "table.style.to_latex(caption='A Table', label='tab:descriptive')" ] }, { "cell_type": "markdown", - "id": "82792a7e", + "id": "3dfbc077", "metadata": {}, "source": [ "Or perhaps you have a regression table, for example" @@ -186,45 +118,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "bcc9b1b9", + "execution_count": null, + "id": "a78e4cdb", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
Dependent variable: target
(1)(2)
ABP416.673***397.581***
(69.495)(70.870)
Age37.24124.703
(64.117)(65.411)
BMI787.182***789.744***
(65.424)(66.887)
Intercept152.133***152.133***
(2.853)(2.853)
S1197.848
(143.812)
S2-169.243
(142.744)
Sex-106.576*-82.862
(62.125)(64.851)
Observations442442
R20.4000.403
Adjusted R20.3950.395
Residual Std. Error59.976 (df=437)59.982 (df=435)
F Statistic72.913*** (df=4; 437)48.915*** (df=6; 435)
Note:*p<0.1; **p<0.05; ***p<0.01
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "from sklearn import datasets\n", @@ -245,7 +142,7 @@ }, { "cell_type": "markdown", - "id": "5cbd52db", + "id": "a888db4f", "metadata": {}, "source": [ "which can similarly be cast into $\\LaTeX$ using `reg_results.render_latex()`.\n", @@ -421,18 +318,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, "source_map": [ 14, 83, diff --git a/_sources/code-best-practice.ipynb b/_sources/code-best-practice.ipynb index 26a4eff..28d87ac 100644 --- a/_sources/code-best-practice.ipynb +++ b/_sources/code-best-practice.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d71139c6", + "id": "0dde4ef4", "metadata": {}, "source": [ "(code-best-practice)=\n", @@ -296,45 +296,17 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "fc8c0cbb", + "execution_count": null, + "id": "30829869", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Zen of Python, by Tim Peters\n", - "\n", - "Beautiful is better than ugly.\n", - "Explicit is better than implicit.\n", - "Simple is better than complex.\n", - "Complex is better than complicated.\n", - "Flat is better than nested.\n", - "Sparse is better than dense.\n", - "Readability counts.\n", - "Special cases aren't special enough to break the rules.\n", - "Although practicality beats purity.\n", - "Errors should never pass silently.\n", - "Unless explicitly silenced.\n", - "In the face of ambiguity, refuse the temptation to guess.\n", - "There should be one-- and preferably only one --obvious way to do it.\n", - "Although that way may not be obvious at first unless you're Dutch.\n", - "Now is better than never.\n", - "Although never is often better than *right* now.\n", - "If the implementation is hard to explain, it's a bad idea.\n", - "If the implementation is easy to explain, it may be a good idea.\n", - "Namespaces are one honking great idea -- let's do more of those!\n" - ] - } - ], + "outputs": [], "source": [ "import this" ] }, { "cell_type": "markdown", - "id": "1e8cfc79", + "id": "28364c1c", "metadata": {}, "source": [ "## Advanced Coding Tips\n", @@ -417,18 +389,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, "source_map": [ 14, 303, diff --git a/_sources/code-preliminaries.ipynb b/_sources/code-preliminaries.ipynb index e0cd753..616cf61 100644 --- a/_sources/code-preliminaries.ipynb +++ b/_sources/code-preliminaries.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "72769ddf", + "id": "22b82598", "metadata": {}, "source": [ "(code-preliminaries)=\n", @@ -52,22 +52,14 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "49255628", + "execution_count": null, + "id": "8d278d09", "metadata": { "tags": [ "remove-input" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compiled with Python version: 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:35:25) [Clang 16.0.6 ]\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "\n", @@ -76,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "8636742c", + "id": "bebb8571", "metadata": {}, "source": [ "### An Integrated Development Environment, or IDE\n", @@ -357,18 +349,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, "source_map": [ 14, 59, diff --git a/_sources/coming-from-stata.ipynb b/_sources/coming-from-stata.ipynb index 90bac73..a064dc7 100644 --- a/_sources/coming-from-stata.ipynb +++ b/_sources/coming-from-stata.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f3c0c4ab", + "id": "88640f7e", "metadata": {}, "source": [ "# Coming from Stata\n", diff --git a/_sources/craft-research-blogs.ipynb b/_sources/craft-research-blogs.ipynb index 4a0df23..e143fbe 100644 --- a/_sources/craft-research-blogs.ipynb +++ b/_sources/craft-research-blogs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a8bc1dd3", + "id": "c3ad870d", "metadata": {}, "source": [ "(craft-research-blogs)=\n", @@ -30,28 +30,16 @@ { "cell_type": "code", "execution_count": 1, - "id": "6ef65f4e", + "id": "83a85b1d", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ - { - "ename": "TypeError", - "evalue": "RegularPolygon.__init__() takes 3 positional arguments but 4 positional arguments (and 1 keyword-only argument) were given", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcraft_diss_pyramid\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcraft\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mcraft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_pyramid\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/git_projects/coding-for-economists/craft_diss_pyramid.py:37\u001b[0m, in \u001b[0;36mplot_pyramid\u001b[0;34m()\u001b[0m\n\u001b[1;32m 35\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m3\u001b[39m), dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m)\n\u001b[1;32m 36\u001b[0m \u001b[38;5;66;03m# add a Polygon\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m diss \u001b[38;5;241m=\u001b[39m \u001b[43mmpatches\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mRegularPolygon\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mstart_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_y\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpi\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 40\u001b[0m base_blog_triangle \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m \u001b[38;5;241m*\u001b[39m start_x \u001b[38;5;241m+\u001b[39m delta_blog\n\u001b[1;32m 41\u001b[0m blog \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRegularPolygon(\n\u001b[1;32m 42\u001b[0m [base_blog_triangle, start_y], \u001b[38;5;241m3\u001b[39m, height, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.4\u001b[39m, orientation\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi\n\u001b[1;32m 43\u001b[0m )\n", - "\u001b[0;31mTypeError\u001b[0m: RegularPolygon.__init__() takes 3 positional arguments but 4 positional arguments (and 1 keyword-only argument) were given" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -68,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "4eb4d31c", + "id": "87e40ec3", "metadata": {}, "source": [ "Each stage of the inverted pyramid is valuable, but it's important to recognise that:\n", diff --git a/_sources/data-read-and-write.ipynb b/_sources/data-read-and-write.ipynb index 8a138d7..9e4ef4b 100644 --- a/_sources/data-read-and-write.ipynb +++ b/_sources/data-read-and-write.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "8f208c9b", + "id": "89b12ac7", "metadata": {}, "source": [ "(data-read-and-write)=\n", @@ -21,8 +21,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "2a0436ee", + "execution_count": null, + "id": "a25b3f13", "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "markdown", - "id": "5b37f7e6", + "id": "88387cc7", "metadata": {}, "source": [ "## Reading in data from a file\n", @@ -107,100 +107,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "b005990b", + "execution_count": null, + "id": "a0428b16", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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windpressure
month
149.857143992.400000
438.863636998.121212
535.1741291003.875622
.........
1051.581085991.398440
1149.427412992.440938
1245.542453997.877358
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10 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " wind pressure\n", - "month \n", - "1 49.857143 992.400000\n", - "4 38.863636 998.121212\n", - "5 35.174129 1003.875622\n", - "... ... ...\n", - "10 51.581085 991.398440\n", - "11 49.427412 992.440938\n", - "12 45.542453 997.877358\n", - "\n", - "[10 rows x 2 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/dplyr/storms.csv')\n", "table = (df.groupby(['month'])\n", @@ -211,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "bbd3ac1c", + "id": "e8fa4ff9", "metadata": {}, "source": [ "Our options for export of the `table` variable (which has datatype `pandas.core.frame.DataFrame`) are varied. For instance, we could use\n", @@ -262,18 +172,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, "source_map": [ 14, 28, diff --git a/_sources/data-sharing.ipynb b/_sources/data-sharing.ipynb index 59f98e3..38a34c5 100644 --- a/_sources/data-sharing.ipynb +++ b/_sources/data-sharing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "bc42f045", + "id": "fbf7e16c", "metadata": {}, "source": [ "(data-sharing)=\n", diff --git a/_sources/econmt-regression.ipynb b/_sources/econmt-regression.ipynb index e78e766..56dbe52 100644 --- a/_sources/econmt-regression.ipynb +++ b/_sources/econmt-regression.ipynb @@ -901,26 +901,10 @@ "metadata": {}, "outputs": [], "source": [ - "results_cigs_ols = feols(\"np.log(packs) ~ np.log(rincome) + np.log(rprice) | year + state\", data=dfiv, vcov={\"CRV1\": \"year\"})\n", + "results_cigs_ols = feols(\"np.log(packs) ~ np.log(rprice) + np.log(rincome) | year + state\", data=dfiv, vcov={\"CRV1\": \"year\"})\n", "results_cigs_ols.summary()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now select these two models to compare:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "etable([results_iv, results_cigs_ols], type=\"md\")" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/_sources/econmt-statistics.ipynb b/_sources/econmt-statistics.ipynb index 5a0d86b..18f52a4 100644 --- a/_sources/econmt-statistics.ipynb +++ b/_sources/econmt-statistics.ipynb @@ -287,7 +287,6 @@ "source": [ "import scipy.stats as st\n", "\n", - "\n", "def plot_t_stat(x, mu):\n", " T = np.linspace(-7, 7, 500)\n", " pdf_vals = st.t.pdf(T, len(x) - 1)\n", @@ -304,14 +303,14 @@ " fig, ax = plt.subplots()\n", " ax.plot(T, pdf_vals, label=f\"Student t: dof={len(x)-1}\", zorder=2)\n", " ax.fill_between(\n", - " interval_T, 0, interval_y, alpha=0.2, label=r\"95% interval\", zorder=1\n", + " interval_T, 0, interval_y, alpha=0.2, label=\"95% interval\", zorder=1\n", " )\n", " ax.plot(\n", " actual_T_stat,\n", " st.t.pdf(actual_T_stat, len(x) - 1),\n", " \"bo\",\n", " ms=15,\n", - " label=r\"$\\sqrt{n}(\\bar{x} - \\mu)/\\hat{\\sigma}}$\",\n", + " label=r\"$\\sqrt{n} (\\bar{x} - \\mu) / \\hat{\\sigma}$\",\n", " color=\"orchid\",\n", " zorder=4,\n", " )\n", @@ -859,7 +858,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/_sources/vis-dashboards.ipynb b/_sources/vis-dashboards.ipynb index 577b001..ac73c3b 100644 --- a/_sources/vis-dashboards.ipynb +++ b/_sources/vis-dashboards.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5533e8e9", + "id": "2a7e8616", "metadata": {}, "source": [ "(dashboards)=\n", diff --git a/_sources/wrkflow-command-line.ipynb b/_sources/wrkflow-command-line.ipynb index 63d23ee..d29d7d1 100644 --- a/_sources/wrkflow-command-line.ipynb +++ b/_sources/wrkflow-command-line.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f7ce96e1", + "id": "0b72f8f7", "metadata": {}, "source": [ "(wrkflow-command-line)=\n", diff --git a/_sources/wrkflow-markdown.ipynb b/_sources/wrkflow-markdown.ipynb index 1b9d985..5e5f366 100644 --- a/_sources/wrkflow-markdown.ipynb +++ b/_sources/wrkflow-markdown.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3bd840ce", + "id": "16e36858", "metadata": {}, "source": [ "(wrkflow-markdown)=\n", diff --git a/_sources/wrkflow-version-control.ipynb b/_sources/wrkflow-version-control.ipynb index 484b74b..aee3f4b 100644 --- a/_sources/wrkflow-version-control.ipynb +++ b/_sources/wrkflow-version-control.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "35b15049", + "id": "c07c6a3b", "metadata": {}, "source": [ "(wrkflow-version-control)=\n", @@ -135,18 +135,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "d24dc9ae", + "execution_count": null, + "id": "6e8c4c7b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "aeturrell\n" - ] - } - ], + "outputs": [], "source": [ "%%bash\n", "git config --get user.name" @@ -154,7 +146,7 @@ }, { "cell_type": "markdown", - "id": "2dcb526a", + "id": "ed90c49f", "metadata": {}, "source": [ "You can check other settings with `git config --list`.\n", @@ -807,18 +799,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, "source_map": [ 14, 142, diff --git a/_sources/zreferences.ipynb b/_sources/zreferences.ipynb index c5e7c2c..61f8a81 100644 --- a/_sources/zreferences.ipynb +++ b/_sources/zreferences.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "711dfaa8", + "id": "ff52d012", "metadata": {}, "source": [ "# Bibliography\n", diff --git a/craft-research-blogs.html b/craft-research-blogs.html index 63a2429..94bcdf4 100644 --- a/craft-research-blogs.html +++ b/craft-research-blogs.html @@ -581,27 +581,7 @@

Dissemination
-
---------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-Cell In[1], line 3
-      1 import craft_diss_pyramid as craft
-----> 3 craft.plot_pyramid()
-
-File ~/Documents/git_projects/coding-for-economists/craft_diss_pyramid.py:37, in plot_pyramid()
-     35 fig, ax = plt.subplots(figsize=(8, 3), dpi=200)
-     36 # add a Polygon
----> 37 diss = mpatches.RegularPolygon(
-     38     [start_x, start_y], 3, height, alpha=0.4, orientation=np.pi
-     39 )
-     40 base_blog_triangle = 4 * start_x + delta_blog
-     41 blog = mpatches.RegularPolygon(
-     42     [base_blog_triangle, start_y], 3, height, alpha=0.4, orientation=np.pi
-     43 )
-
-TypeError: RegularPolygon.__init__() takes 3 positional arguments but 4 positional arguments (and 1 keyword-only argument) were given
-
-
-_images/38122ab9c267826d5b61f900f72cde001b7fd8eaac26bb6f5c9e6f3af4af7866.png +_images/61e02fa870dc2bf8a587335e5cc566bc71c0d8771b755e7615bf6ad21a4900d2.png

Each stage of the inverted pyramid is valuable, but it’s important to recognise that:

diff --git a/econmt-regression.html b/econmt-regression.html index 5fb08f0..a8d085f 100644 --- a/econmt-regression.html +++ b/econmt-regression.html @@ -662,7 +662,7 @@

Imports
-
+
-
+
@@ -838,12 +838,6 @@

Regression basics -
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
-
-
-

Well, where are the results!? They’re stored in the object we created. To peek at them we need to call the summary function (and, for easy reading, I’ll print it out too using print)

@@ -865,7 +859,7 @@

Regression basics -_images/e4f12782cee242e0d7a873d66ce6828917d4000307730d0b33ebf465ae38ddf0.svg

+_images/b41a550d502d35af42df4859bb4072dc43291f34d14c6ee1cca017b1a048d258.svg

Oh dear, Jabba’s been on the paddy frogs again, and he’s a bit of different case. When we’re estimating statistical relationships, we have all kinds of choices and should be wary about arbitrary decisions of what to include or exclude in case we fool ourselves about the generality of the relationship we are capturing. Let’s say we knew that we weren’t interested in Hutts though, but only in other species: in that case, it’s fair enough to filter out Jabba and run the regression without this obvious outlier. We’ll exclude any entry that contains the string ‘Jabba’ in the name column:

@@ -985,14 +979,10 @@

Regression basics
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
-
-

This looks a lot more healthy. Not only is the model explaining a lot more of the data, but the coefficients are now significant.

@@ -1018,11 +1008,7 @@

Standard errors
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
+RMSE: 166.502   R2: 0.018
@@ -1047,7 +1033,7 @@

Standard errors -
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
-
-
###
 
 Estimation:  OLS
@@ -1088,7 +1070,7 @@ 
Clustered standard errors
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
+RMSE: 172.239   R2: 0.014
@@ -1277,11 +1255,7 @@

Fixed effects and categorical variables
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
+RMSE: 2.943   R2: 0.754
@@ -1308,10 +1282,6 @@

Fixed effects and categorical variables -
/Users/aet/mambaforge/envs/codeforecon/lib/python3.10/site-packages/pyfixest/model_matrix_fixest.py:162: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  if Y.dtypes[0] == "int64":
-
-
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
est1est2est3
0depvarmpgmpgmpg
1Intercept72.64*** (6.004)40.409*** (2.741)39.674*** (2.914)
2np.log(hp)-10.764*** (1.224)
...............
7R20.720.7560.82
8S.E. typeiidiidiid
9Observations323232
+

10 rows × 4 columns

+

There are a few different type= options, including dataframe, markdown, and latex:

@@ -2133,6 +2065,27 @@

Comparing a set of models you’ve already created

+
+
                              est1               est2               est3
+--------------  ------------------  -----------------  -----------------
+depvar                         mpg                mpg                mpg
+------------------------------------------------------------------------
+Intercept         72.64*** (6.004)  40.409*** (2.741)  39.674*** (2.914)
+np.log(hp)      -10.764*** (1.224)
+hp                                  -0.213*** (0.035)  -0.098*** (0.025)
+np.power(hp,2)                           0.0*** (0.0)
+disp                                                   -0.073*** (0.014)
+hp:disp                                                      0.0** (0.0)
+------------------------------------------------------------------------
+------------------------------------------------------------------------
+R2                            0.72              0.756               0.82
+S.E. type                      iid                iid                iid
+Observations                    32                 32                 32
+------------------------------------------------------------------------
+Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001
+
+
+

And of course the latex one (type="tex") can be written to a file using open('regression.tex', 'w').write(...) where the ellipsis is the relevant etable command.

@@ -2151,6 +2104,82 @@

Stepwise multiple models +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
yx1x2x3speciesid
05.13.51.40.2setosa1
14.93.01.40.2setosa1
24.73.21.30.2setosa1
34.63.11.50.2setosa1
45.03.61.40.2setosa1
+

Let’s try a regular (not cumulative) stepwise regression:

@@ -2159,6 +2188,49 @@

Stepwise multiple models +
###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      6.481 |        0.481 |    13.466 |      0.000 |   5.530 |    7.432 |
+| x1            |     -0.209 |        0.156 |    -1.339 |      0.183 |  -0.517 |    0.099 |
+---
+RMSE: 0.82   R2: 0.012
+###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      4.306 |        0.078 |    54.895 |      0.000 |   4.151 |    4.461 |
+| x2            |      0.409 |        0.019 |    21.646 |      0.000 |   0.372 |    0.446 |
+---
+RMSE: 0.404   R2: 0.76
+###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      4.779 |        0.073 |    65.616 |      0.000 |   4.636 |    4.923 |
+| x3            |      0.888 |        0.051 |    17.297 |      0.000 |   0.786 |    0.989 |
+---
+RMSE: 0.475   R2: 0.669
+
+
+

And a cumulative stepwise regression:

@@ -2167,6 +2239,52 @@

Stepwise multiple models +
###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      6.481 |        0.481 |    13.466 |      0.000 |   5.530 |    7.432 |
+| x1            |     -0.209 |        0.156 |    -1.339 |      0.183 |  -0.517 |    0.099 |
+---
+RMSE: 0.82   R2: 0.012
+###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      2.251 |        0.247 |     9.104 |      0.000 |   1.763 |    2.740 |
+| x1            |      0.597 |        0.069 |     8.602 |      0.000 |   0.460 |    0.734 |
+| x2            |      0.471 |        0.017 |    27.616 |      0.000 |   0.437 |    0.504 |
+---
+RMSE: 0.33   R2: 0.84
+###
+
+Estimation:  OLS
+Dep. var.: y
+Inference:  iid
+Observations:  150
+
+| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
+| Intercept     |      1.845 |        0.250 |     7.368 |      0.000 |   1.350 |    2.340 |
+| x1            |      0.655 |        0.067 |     9.823 |      0.000 |   0.523 |    0.787 |
+| x2            |      0.711 |        0.057 |    12.560 |      0.000 |   0.599 |    0.823 |
+| x3            |     -0.563 |        0.127 |    -4.426 |      0.000 |  -0.814 |   -0.311 |
+---
+RMSE: 0.31   R2: 0.859
+
+
+

This is a very verbose way to get regression coefficients out! We can get a summary of the info by using etable again, but this time as a method rather than a stand-alone function:

@@ -2176,6 +2294,118 @@

Stepwise multiple models +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fmly~x1y~x1+x2y~x1+x2+x3
CoefficientInterceptx1Interceptx1x2Interceptx1x2x3
Estimate6.481-0.2092.2510.5970.4711.8450.6550.711-0.563
Std. Error0.4810.1560.2470.0690.0170.2500.0670.0570.127
t value13.466-1.3399.1048.60227.6167.3689.82312.560-4.426
Pr(>|t|)0.0000.1830.0000.0000.0000.0000.0000.0000.000
2.5 %5.530-0.5171.7630.4600.4371.3500.5230.599-0.814
97.5 %7.4320.0992.7400.7340.5042.3400.7870.823-0.311
+

Even handier, pyfixest lets you visualise these results (via Lets-Plot):

@@ -2184,6 +2414,91 @@

Stepwise multiple models +
+

@@ -2213,6 +2528,118 @@

Instrumental variables +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
rownamesstateyearcpipopulationpacksincometaxpricetaxsrpricerincome
01AL19851.0763973000116.4862824601496832.500004102.18167133.34833594.96438110.763866
12AR19851.0762327000128.5345922621073637.000000101.47499837.00000094.30762210.468165
23AZ19851.0763184000104.5226144395693631.000000108.57875136.170418100.90962212.830456
34CA19851.07626444000100.36303744710281626.000000107.83734132.104000100.22058015.713321
45CO19851.0763209000112.9635394946667231.00000094.26666331.00000087.60842514.326190
+

Now we’ll specify the model. It’s going to be in the form dep ~ exog + [endog ~ instruments], where endog will be regressed on instruments and dep will be regressed on both exog and the predicted values of endog.

In this case, the model will be

@@ -2233,20 +2660,45 @@

Instrumental variables +
+
###
+
+Estimation:  IV
+Dep. var.: np.log(packs), Fixed effects: year+state
+Inference:  CRV1
+Observations:  96
+
+| Coefficient     |   Estimate |   Std. Error |               t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:----------------|-----------:|-------------:|----------------------:|-----------:|--------:|---------:|
+| np.log(rprice)  |     -1.279 |        0.000 | -6058735433955890.000 |      0.000 |  -1.279 |   -1.279 |
+| np.log(rincome) |      0.443 |        0.000 |  3295534227163963.000 |      0.000 |   0.443 |    0.443 |
+---
+
+
+

We can compare the IV results against (naive) OLS. First, run the OLS equivalent:

-
results_cigs_ols = feols("np.log(packs) ~ np.log(rincome) + np.log(rprice) | year + state", data=dfiv, vcov={"CRV1": "year"})
+
results_cigs_ols = feols("np.log(packs) ~ np.log(rprice) + np.log(rincome) | year + state", data=dfiv, vcov={"CRV1": "year"})
 results_cigs_ols.summary()
-
-

Now select these two models to compare:

-
-
-
etable([results_iv, results_cigs_ols], type="md")
+

+
###
+
+Estimation:  OLS
+Dep. var.: np.log(packs), Fixed effects: year+state
+Inference:  CRV1
+Observations:  96
+
+| Coefficient     |   Estimate |   Std. Error |               t value |   Pr(>|t|) |   2.5 % |   97.5 % |
+|:----------------|-----------:|-------------:|----------------------:|-----------:|--------:|---------:|
+| np.log(rprice)  |     -1.056 |        0.000 | -6960957983563793.000 |      0.000 |  -1.056 |   -1.056 |
+| np.log(rincome) |      0.497 |        0.000 |  5834939776353078.000 |      0.000 |   0.497 |    0.497 |
+---
+RMSE: 0.044   R2: 0.967   R2 Within: 0.556
 

 

 

diff --git a/econmt-statistics.html b/econmt-statistics.html
index 5046562..1c67e71 100644
--- a/econmt-statistics.html
+++ b/econmt-statistics.html
@@ -791,7 +791,6 @@ 
One-sample t-test
 
import scipy.stats as st
 
-
 def plot_t_stat(x, mu):
     T = np.linspace(-7, 7, 500)
     pdf_vals = st.t.pdf(T, len(x) - 1)
@@ -808,14 +807,14 @@ 
One-sample t-testfig, ax = plt.subplots()
     ax.plot(T, pdf_vals, label=f"Student t: dof={len(x)-1}", zorder=2)
     ax.fill_between(
-        interval_T, 0, interval_y, alpha=0.2, label=r"95% interval", zorder=1
+        interval_T, 0, interval_y, alpha=0.2, label="95% interval", zorder=1
     )
     ax.plot(
         actual_T_stat,
         st.t.pdf(actual_T_stat, len(x) - 1),
         "bo",
         ms=15,
-        label=r"$\sqrt{n}(\bar{x} - \mu)/\hat{\sigma}}$",
+        label=r"$\sqrt{n} (\bar{x} - \mu) / \hat{\sigma}$",
         color="orchid",
         zorder=4,
     )
@@ -836,268 +835,7 @@ 
One-sample t-test
-
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/IPython/core/formatters.py:340, in BaseFormatter.__call__(self, obj)
-    338     pass
-    339 else:
---> 340     return printer(obj)
-    341 # Finally look for special method names
-    342 method = get_real_method(obj, self.print_method)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/IPython/core/pylabtools.py:152, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
-    149     from matplotlib.backend_bases import FigureCanvasBase
-    150     FigureCanvasBase(fig)
---> 152 fig.canvas.print_figure(bytes_io, **kw)
-    153 data = bytes_io.getvalue()
-    154 if fmt == 'svg':
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/backend_bases.py:2164, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
-   2161     # we do this instead of `self.figure.draw_without_rendering`
-   2162     # so that we can inject the orientation
-   2163     with getattr(renderer, "_draw_disabled", nullcontext)():
--> 2164         self.figure.draw(renderer)
-   2165 if bbox_inches:
-   2166     if bbox_inches == "tight":
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:95, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
-     93 @wraps(draw)
-     94 def draw_wrapper(artist, renderer, *args, **kwargs):
----> 95     result = draw(artist, renderer, *args, **kwargs)
-     96     if renderer._rasterizing:
-     97         renderer.stop_rasterizing()
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/figure.py:3154, in Figure.draw(self, renderer)
-   3151         # ValueError can occur when resizing a window.
-   3153 self.patch.draw(renderer)
--> 3154 mimage._draw_list_compositing_images(
-   3155     renderer, self, artists, self.suppressComposite)
-   3157 for sfig in self.subfigs:
-   3158     sfig.draw(renderer)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/axes/_base.py:3070, in _AxesBase.draw(self, renderer)
-   3067 if artists_rasterized:
-   3068     _draw_rasterized(self.figure, artists_rasterized, renderer)
--> 3070 mimage._draw_list_compositing_images(
-   3071     renderer, self, artists, self.figure.suppressComposite)
-   3073 renderer.close_group('axes')
-   3074 self.stale = False
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/legend.py:769, in Legend.draw(self, renderer)
-    765     self._legend_box.set_width(self.get_bbox_to_anchor().width - pad)
-    767 # update the location and size of the legend. This needs to
-    768 # be done in any case to clip the figure right.
---> 769 bbox = self._legend_box.get_window_extent(renderer)
-    770 self.legendPatch.set_bounds(bbox.bounds)
-    771 self.legendPatch.set_mutation_scale(fontsize)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:399, in OffsetBox.get_window_extent(self, renderer)
-    397 if renderer is None:
-    398     renderer = self.figure._get_renderer()
---> 399 bbox = self.get_bbox(renderer)
-    400 try:  # Some subclasses redefine get_offset to take no args.
-    401     px, py = self.get_offset(bbox, renderer)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in VPacker._get_bbox_and_child_offsets(self, renderer)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in <listcomp>(.0)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in HPacker._get_bbox_and_child_offsets(self, renderer)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in <listcomp>(.0)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in VPacker._get_bbox_and_child_offsets(self, renderer)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in <listcomp>(.0)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in HPacker._get_bbox_and_child_offsets(self, renderer)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in <listcomp>(.0)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:799, in TextArea.get_bbox(self, renderer)
-    794 def get_bbox(self, renderer):
-    795     _, h_, d_ = renderer.get_text_width_height_descent(
-    796         "lp", self._text._fontproperties,
-    797         ismath="TeX" if self._text.get_usetex() else False)
---> 799     bbox, info, yd = self._text._get_layout(renderer)
-    800     w, h = bbox.size
-    802     self._baseline_transform.clear()
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:381, in Text._get_layout(self, renderer)
-    379 clean_line, ismath = self._preprocess_math(line)
-    380 if clean_line:
---> 381     w, h, d = _get_text_metrics_with_cache(
-    382         renderer, clean_line, self._fontproperties,
-    383         ismath=ismath, dpi=self.figure.dpi)
-    384 else:
-    385     w = h = d = 0
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:69, in _get_text_metrics_with_cache(renderer, text, fontprop, ismath, dpi)
-     66 """Call ``renderer.get_text_width_height_descent``, caching the results."""
-     67 # Cached based on a copy of fontprop so that later in-place mutations of
-     68 # the passed-in argument do not mess up the cache.
----> 69 return _get_text_metrics_with_cache_impl(
-     70     weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:77, in _get_text_metrics_with_cache_impl(renderer_ref, text, fontprop, ismath, dpi)
-     73 @functools.lru_cache(4096)
-     74 def _get_text_metrics_with_cache_impl(
-     75         renderer_ref, text, fontprop, ismath, dpi):
-     76     # dpi is unused, but participates in cache invalidation (via the renderer).
----> 77     return renderer_ref().get_text_width_height_descent(text, fontprop, ismath)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/backends/backend_svg.py:1287, in RendererSVG.get_text_width_height_descent(self, s, prop, ismath)
-   1285 def get_text_width_height_descent(self, s, prop, ismath):
-   1286     # docstring inherited
--> 1287     return self._text2path.get_text_width_height_descent(s, prop, ismath)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/textpath.py:60, in TextToPath.get_text_width_height_descent(self, s, prop, ismath)
-     57     prop = prop.copy()
-     58     prop.set_size(self.FONT_SCALE)
-     59     width, height, descent, *_ = \
----> 60         self.mathtext_parser.parse(s, 72, prop)
-     61     return width * scale, height * scale, descent * scale
-     63 font = self._get_font(prop)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/mathtext.py:79, in MathTextParser.parse(self, s, dpi, prop, antialiased)
-     77 prop = prop.copy() if prop is not None else None
-     78 antialiased = mpl._val_or_rc(antialiased, 'text.antialiased')
----> 79 return self._parse_cached(s, dpi, prop, antialiased)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/mathtext.py:100, in MathTextParser._parse_cached(self, s, dpi, prop, antialiased)
-     97 if self._parser is None:  # Cache the parser globally.
-     98     self.__class__._parser = _mathtext.Parser()
---> 100 box = self._parser.parse(s, fontset, fontsize, dpi)
-    101 output = _mathtext.ship(box)
-    102 if self._output_type == "vector":
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/_mathtext.py:2165, in Parser.parse(self, s, fonts_object, fontsize, dpi)
-   2162     result = self._expression.parseString(s)
-   2163 except ParseBaseException as err:
-   2164     # explain becomes a plain method on pyparsing 3 (err.explain(0)).
--> 2165     raise ValueError("\n" + ParseException.explain(err, 0)) from None
-   2166 self._state_stack = []
-   2167 self._in_subscript_or_superscript = False
-
-ValueError: 
-$\sqrt{n}(\bar{x} - \mu)/\hat{\sigma}}$
-^
-ParseException: Expected end of text, found '$'  (at char 0), (line:1, col:1)
-

-

-
<Figure size 1200x600 with 1 Axes>
-

-

-

+_images/aa5dff916161a1b75fe37bcb35446182974851c1ba7ace977e095f36e44036e2.svg

In this case, we would reject the alternative hypothesis. You can see why from the plot; the test statistic we have constructed lies within the interval where we cannot reject the null hypothesis. \(\bar{x}-\mu\) is close enough to zero to give us cause for concern. (You can also see from the plot why this is a two-tailed test: we don’t care if \(\bar{x}\) is greater or less than \(\mu\), just that it’s different–and so the test statistic could appear in either tail of the distribution for us to accept \(H_1\).)

We accept the null here, but about if there were many more data points? Let’s try adding some generated data (pretend it is from making extra observations).

@@ -1166,268 +904,7 @@

One-sample t-test -
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/IPython/core/formatters.py:340, in BaseFormatter.__call__(self, obj)
-    338     pass
-    339 else:
---> 340     return printer(obj)
-    341 # Finally look for special method names
-    342 method = get_real_method(obj, self.print_method)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/IPython/core/pylabtools.py:152, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
-    149     from matplotlib.backend_bases import FigureCanvasBase
-    150     FigureCanvasBase(fig)
---> 152 fig.canvas.print_figure(bytes_io, **kw)
-    153 data = bytes_io.getvalue()
-    154 if fmt == 'svg':
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/backend_bases.py:2164, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
-   2161     # we do this instead of `self.figure.draw_without_rendering`
-   2162     # so that we can inject the orientation
-   2163     with getattr(renderer, "_draw_disabled", nullcontext)():
--> 2164         self.figure.draw(renderer)
-   2165 if bbox_inches:
-   2166     if bbox_inches == "tight":
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:95, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
-     93 @wraps(draw)
-     94 def draw_wrapper(artist, renderer, *args, **kwargs):
----> 95     result = draw(artist, renderer, *args, **kwargs)
-     96     if renderer._rasterizing:
-     97         renderer.stop_rasterizing()
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/figure.py:3154, in Figure.draw(self, renderer)
-   3151         # ValueError can occur when resizing a window.
-   3153 self.patch.draw(renderer)
--> 3154 mimage._draw_list_compositing_images(
-   3155     renderer, self, artists, self.suppressComposite)
-   3157 for sfig in self.subfigs:
-   3158     sfig.draw(renderer)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/axes/_base.py:3070, in _AxesBase.draw(self, renderer)
-   3067 if artists_rasterized:
-   3068     _draw_rasterized(self.figure, artists_rasterized, renderer)
--> 3070 mimage._draw_list_compositing_images(
-   3071     renderer, self, artists, self.figure.suppressComposite)
-   3073 renderer.close_group('axes')
-   3074 self.stale = False
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-    130 if not_composite or not has_images:
-    131     for a in artists:
---> 132         a.draw(renderer)
-    133 else:
-    134     # Composite any adjacent images together
-    135     image_group = []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/artist.py:72, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
-     69     if artist.get_agg_filter() is not None:
-     70         renderer.start_filter()
----> 72     return draw(artist, renderer)
-     73 finally:
-     74     if artist.get_agg_filter() is not None:
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/legend.py:769, in Legend.draw(self, renderer)
-    765     self._legend_box.set_width(self.get_bbox_to_anchor().width - pad)
-    767 # update the location and size of the legend. This needs to
-    768 # be done in any case to clip the figure right.
---> 769 bbox = self._legend_box.get_window_extent(renderer)
-    770 self.legendPatch.set_bounds(bbox.bounds)
-    771 self.legendPatch.set_mutation_scale(fontsize)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:399, in OffsetBox.get_window_extent(self, renderer)
-    397 if renderer is None:
-    398     renderer = self.figure._get_renderer()
---> 399 bbox = self.get_bbox(renderer)
-    400 try:  # Some subclasses redefine get_offset to take no args.
-    401     px, py = self.get_offset(bbox, renderer)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in VPacker._get_bbox_and_child_offsets(self, renderer)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in <listcomp>(.0)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in HPacker._get_bbox_and_child_offsets(self, renderer)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in <listcomp>(.0)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in VPacker._get_bbox_and_child_offsets(self, renderer)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:484, in <listcomp>(.0)
-    481         if isinstance(c, PackerBase) and c.mode == "expand":
-    482             c.set_width(self.width)
---> 484 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    485 (x0, x1), xoffsets = _get_aligned_offsets(
-    486     [bbox.intervalx for bbox in bboxes], self.width, self.align)
-    487 height, yoffsets = _get_packed_offsets(
-    488     [bbox.height for bbox in bboxes], self.height, sep, self.mode)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:366, in OffsetBox.get_bbox(self, renderer)
-    364 def get_bbox(self, renderer):
-    365     """Return the bbox of the offsetbox, ignoring parent offsets."""
---> 366     bbox, offsets = self._get_bbox_and_child_offsets(renderer)
-    367     return bbox
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in HPacker._get_bbox_and_child_offsets(self, renderer)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:511, in <listcomp>(.0)
-    508 pad = self.pad * dpicor
-    509 sep = self.sep * dpicor
---> 511 bboxes = [c.get_bbox(renderer) for c in self.get_visible_children()]
-    512 if not bboxes:
-    513     return Bbox.from_bounds(0, 0, 0, 0).padded(pad), []
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/offsetbox.py:799, in TextArea.get_bbox(self, renderer)
-    794 def get_bbox(self, renderer):
-    795     _, h_, d_ = renderer.get_text_width_height_descent(
-    796         "lp", self._text._fontproperties,
-    797         ismath="TeX" if self._text.get_usetex() else False)
---> 799     bbox, info, yd = self._text._get_layout(renderer)
-    800     w, h = bbox.size
-    802     self._baseline_transform.clear()
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:381, in Text._get_layout(self, renderer)
-    379 clean_line, ismath = self._preprocess_math(line)
-    380 if clean_line:
---> 381     w, h, d = _get_text_metrics_with_cache(
-    382         renderer, clean_line, self._fontproperties,
-    383         ismath=ismath, dpi=self.figure.dpi)
-    384 else:
-    385     w = h = d = 0
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:69, in _get_text_metrics_with_cache(renderer, text, fontprop, ismath, dpi)
-     66 """Call ``renderer.get_text_width_height_descent``, caching the results."""
-     67 # Cached based on a copy of fontprop so that later in-place mutations of
-     68 # the passed-in argument do not mess up the cache.
----> 69 return _get_text_metrics_with_cache_impl(
-     70     weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/text.py:77, in _get_text_metrics_with_cache_impl(renderer_ref, text, fontprop, ismath, dpi)
-     73 @functools.lru_cache(4096)
-     74 def _get_text_metrics_with_cache_impl(
-     75         renderer_ref, text, fontprop, ismath, dpi):
-     76     # dpi is unused, but participates in cache invalidation (via the renderer).
----> 77     return renderer_ref().get_text_width_height_descent(text, fontprop, ismath)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/backends/backend_svg.py:1287, in RendererSVG.get_text_width_height_descent(self, s, prop, ismath)
-   1285 def get_text_width_height_descent(self, s, prop, ismath):
-   1286     # docstring inherited
--> 1287     return self._text2path.get_text_width_height_descent(s, prop, ismath)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/textpath.py:60, in TextToPath.get_text_width_height_descent(self, s, prop, ismath)
-     57     prop = prop.copy()
-     58     prop.set_size(self.FONT_SCALE)
-     59     width, height, descent, *_ = \
----> 60         self.mathtext_parser.parse(s, 72, prop)
-     61     return width * scale, height * scale, descent * scale
-     63 font = self._get_font(prop)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/mathtext.py:79, in MathTextParser.parse(self, s, dpi, prop, antialiased)
-     77 prop = prop.copy() if prop is not None else None
-     78 antialiased = mpl._val_or_rc(antialiased, 'text.antialiased')
----> 79 return self._parse_cached(s, dpi, prop, antialiased)
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/mathtext.py:100, in MathTextParser._parse_cached(self, s, dpi, prop, antialiased)
-     97 if self._parser is None:  # Cache the parser globally.
-     98     self.__class__._parser = _mathtext.Parser()
---> 100 box = self._parser.parse(s, fontset, fontsize, dpi)
-    101 output = _mathtext.ship(box)
-    102 if self._output_type == "vector":
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/matplotlib/_mathtext.py:2165, in Parser.parse(self, s, fonts_object, fontsize, dpi)
-   2162     result = self._expression.parseString(s)
-   2163 except ParseBaseException as err:
-   2164     # explain becomes a plain method on pyparsing 3 (err.explain(0)).
--> 2165     raise ValueError("\n" + ParseException.explain(err, 0)) from None
-   2166 self._state_stack = []
-   2167 self._in_subscript_or_superscript = False
-
-ValueError: 
-$\sqrt{n}(\bar{x} - \mu)/\hat{\sigma}}$
-^
-ParseException: Expected end of text, found '$'  (at char 0), (line:1, col:1)
-
-
-
<Figure size 1200x600 with 1 Axes>
-
-
-

+_images/063f289be6ecd79290397b59d5012a860da34c2e91a2447e8bb02377e750527e.svg

Now our test statistic is safely outside the interval.

@@ -2100,7 +1577,7 @@

Power calculations -_images/4a2bc04d84c2c0a774092642e61f2faff452ca44af9905c7e46c55e08fa08c6e.svg

+_images/71f34048984d5245c482304d7b21ba8bc4f04e72bce543a120004394929e2f83.svg

From this, we can see we need a sample size of at least 200 in order to have a power of 0.8.

The pg.power_ttest function takes any three of the four of d, n, power, and alpha (ie leave one of these out), and then returns what the missing parameter should be. We passed in d, n, and alpha, and so the power was returned.

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25, 27, 31, 32, 34, 38, 39, 41, 42, 43, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67, 69, 73, 74], "30": [1, 3, 4, 5, 21, 22, 23, 25, 26, 27, 30, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 48, 50, 54, 56, 57, 59, 63, 64, 67, 68, 71, 73], "dai": [1, 2, 3, 5, 8, 17, 19, 22, 25, 26, 27, 30, 34, 42, 44, 56, 57, 59, 61, 64, 65, 67, 71, 72], "hitchhik": [1, 5], "guid": [1, 4, 5, 15, 18, 21, 27, 32, 34, 36, 40, 41, 43, 52, 53, 63, 69, 71, 72, 73, 74, 75], "Being": [1, 55], "abl": [1, 2, 3, 6, 10, 16, 17, 18, 19, 20, 24, 25, 26, 28, 32, 38, 39, 40, 46, 47, 48, 50, 54, 55, 57, 60, 63, 64, 67, 68, 72, 73, 74], "sometim": [1, 2, 3, 6, 9, 11, 13, 14, 15, 16, 18, 19, 20, 23, 24, 25, 26, 27, 29, 30, 32, 34, 40, 41, 42, 43, 44, 53, 57, 59, 60, 61, 64, 66, 67, 68, 69, 70, 73, 74], "dictionari": [1, 2, 21, 23, 26, 27, 29, 30, 33, 34, 35, 40, 52, 53, 57, 58, 59, 66, 67], "ar": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 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43, 44, 45, 47, 48, 50, 53, 55, 56, 57, 63, 67, 69, 70, 72, 73, 74], "forev": 1, "10": [1, 2, 3, 4, 5, 6, 8, 9, 10, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 68, 69, 70, 73, 74, 75], "9": [1, 2, 3, 5, 9, 10, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 47, 48, 50, 52, 53, 54, 55, 56, 57, 59, 60, 64, 68, 69, 70, 71, 73, 74, 75], "8": [1, 2, 3, 4, 5, 9, 10, 16, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 66, 67, 68, 70, 71, 73], "7": [1, 2, 3, 5, 10, 16, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 59, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 75], "nb": [1, 2, 3, 27, 50, 64, 70, 73], "case": [1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 70, 72, 73, 74], "doe": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 15, 16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59, 61, 63, 66, 68, 69, 73, 74], "compound": [1, 10, 30], "assign": [1, 3, 4, 5, 6, 9, 10, 15, 22, 24, 25, 26, 30, 35, 36, 41, 57, 59, 60, 62, 66, 69], "left": [1, 2, 3, 5, 6, 7, 18, 19, 21, 24, 27, 28, 29, 30, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 47, 52, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 69, 71, 73, 74], "hand": [1, 2, 3, 5, 6, 7, 11, 15, 17, 19, 21, 27, 28, 30, 36, 41, 46, 50, 54, 63, 68, 70, 72, 73, 74, 75], "side": [1, 3, 4, 5, 6, 7, 16, 17, 19, 21, 22, 27, 36, 38, 39, 40, 41, 42, 49, 52, 54, 57, 61, 63, 68, 70, 71, 72, 74], "equal": [1, 3, 4, 19, 20, 22, 27, 30, 35, 36, 38, 39, 40, 42, 43, 44, 47, 51, 53, 57, 59, 67], "minu": [1, 30, 34, 42, 57, 74], "keyword": [1, 2, 4, 5, 16, 18, 19, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 40, 41, 44, 47, 48, 50, 52, 53, 56, 57, 59, 62, 63, 64, 65, 67, 71], "break": [1, 4, 18, 19, 27, 43, 44, 50, 52, 53, 55, 58, 59, 62, 69, 71, 73, 74], "reach": [1, 2, 3, 5, 15, 16, 19, 24, 25, 38, 39, 49, 61, 72], "certain": [1, 2, 3, 5, 19, 20, 21, 23, 26, 29, 32, 36, 38, 41, 42, 49, 52, 55, 59, 63, 73, 74], "iter": [1, 2, 3, 22, 26, 27, 33, 35, 36, 39, 43, 44, 50, 52, 53, 55, 57, 58, 59], "without": [1, 2, 3, 4, 5, 7, 15, 16, 18, 20, 21, 22, 24, 33, 34, 35, 36, 41, 43, 50, 53, 56, 57, 63, 66, 68, 72, 73, 74], "converg": [1, 35, 36, 39, 40, 48, 50, 57], "make": [1, 2, 3, 5, 6, 7, 10, 12, 15, 16, 17, 18, 19, 20, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 74, 75], "ascii_lowercas": [1, 52, 55], "charact": [1, 3, 4, 10, 13, 21, 26, 27, 31, 41, 42, 48, 53, 56, 57, 59, 60, 67, 68, 69, 73, 74], "alphabet": [1, 3, 21, 27, 41, 42, 52, 69], "backward": [1, 4, 29, 43, 52, 54, 61], "through": [1, 2, 3, 4, 5, 6, 15, 16, 17, 18, 19, 20, 24, 26, 32, 34, 35, 36, 41, 43, 44, 47, 48, 50, 52, 53, 55, 56, 57, 58, 59, 61, 63, 64, 67, 68, 69, 71, 73], "start": [1, 2, 3, 5, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 57, 59, 61, 63, 64, 65, 66, 67, 69, 72, 73, 74], "z": [1, 18, 19, 20, 22, 39, 41, 47, 51, 54, 55, 56, 59, 62, 69], "befor": [1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 15, 17, 18, 20, 22, 23, 24, 25, 26, 27, 30, 35, 36, 39, 41, 45, 46, 48, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 63, 64, 67, 68, 70, 72, 73, 74], "collect": [1, 2, 3, 5, 6, 15, 17, 19, 24, 33, 38, 40, 52, 53, 59, 61, 62, 63, 74], "unord": [1, 61, 71], "unindex": 1, "distinct": [1, 3, 8, 10, 16, 35, 36, 40, 41, 44, 51, 53, 55, 56, 57, 61, 63, 66, 69, 74], "analog": [1, 5, 7, 43, 52, 55], "mathemat": [1, 3, 19, 27, 36, 48, 55, 58, 61, 73], "definit": [1, 2, 4, 5, 6, 7, 13, 14, 15, 16, 19, 30, 35, 38, 39, 46, 47, 48, 53, 54, 55, 56, 57, 61, 68], "These": [1, 2, 3, 4, 6, 7, 10, 15, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 31, 32, 33, 34, 35, 36, 38, 39, 43, 46, 48, 50, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 73, 74], "veri": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 67, 69, 70, 71, 72, 73, 74], "interest": [1, 2, 3, 5, 15, 17, 19, 20, 23, 24, 25, 26, 27, 28, 30, 31, 32, 34, 36, 38, 41, 42, 43, 44, 46, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 64, 69, 72, 74], "though": [1, 2, 3, 4, 5, 6, 7, 10, 13, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 29, 31, 34, 35, 36, 38, 40, 41, 43, 44, 45, 46, 47, 50, 52, 53, 55, 56, 57, 58, 59, 60, 61, 63, 67, 69, 70, 73, 74], "people_set": 1, "robinson": [1, 5, 46, 72, 75], "fawcett": [1, 5], "ostrom": [1, 5, 52], "length": [1, 3, 10, 13, 19, 21, 24, 27, 30, 35, 36, 42, 43, 47, 52, 57, 59, 62, 63, 64, 65, 67, 73], "len": [1, 3, 5, 10, 11, 17, 40, 42, 43, 44, 51, 52, 53, 57, 58, 59, 60, 61, 63, 72, 73], "ask": [1, 2, 4, 10, 15, 17, 19, 20, 22, 24, 25, 26, 27, 30, 37, 38, 40, 41, 42, 43, 44, 45, 46, 52, 53, 54, 57, 61, 63, 64, 68, 73, 74], "whether": [1, 2, 3, 4, 5, 6, 7, 10, 17, 18, 19, 20, 22, 23, 26, 30, 31, 32, 35, 36, 38, 39, 40, 42, 43, 46, 47, 48, 50, 52, 53, 54, 56, 57, 59, 60, 63, 69, 72, 74], "particular": [1, 2, 4, 6, 7, 13, 15, 17, 18, 19, 20, 23, 25, 26, 27, 34, 36, 37, 38, 39, 40, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 57, 59, 61, 63, 64, 73, 74], "within": [1, 2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 19, 20, 21, 26, 27, 28, 31, 32, 34, 36, 37, 38, 39, 41, 42, 43, 44, 48, 51, 52, 53, 54, 55, 57, 58, 60, 61, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74], "multipl": [1, 2, 3, 4, 6, 9, 10, 18, 20, 22, 24, 25, 26, 27, 30, 33, 36, 39, 43, 44, 46, 47, 48, 50, 52, 53, 55, 56, 57, 58, 59, 60, 62, 64, 65, 68, 69, 70, 71, 73, 74], "union": [1, 19, 41, 43, 46, 53, 55, 75], "martineau": 1, "entry_nam": 1, "last": [1, 2, 3, 4, 5, 11, 16, 19, 20, 21, 24, 25, 26, 27, 29, 33, 34, 38, 41, 42, 43, 48, 52, 53, 54, 55, 56, 57, 58, 61, 63, 64, 67, 69, 73, 74], "pop": [1, 2, 3, 6, 19, 22, 26, 28, 39, 41, 44, 50, 59, 63, 73], "easili": [1, 4, 6, 7, 15, 16, 18, 21, 24, 25, 26, 27, 30, 31, 32, 38, 39, 43, 46, 48, 49, 50, 53, 56, 59, 72, 74], "real": [1, 2, 3, 4, 5, 7, 15, 19, 20, 22, 24, 26, 29, 30, 33, 34, 35, 36, 38, 40, 41, 42, 44, 46, 47, 48, 50, 51, 53, 57, 59, 60, 63, 72, 73], "support": [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 15, 16, 17, 19, 20, 24, 25, 27, 32, 33, 34, 35, 36, 38, 39, 40, 44, 46, 48, 49, 53, 54, 57, 59, 60, 61, 63, 66, 67, 69, 71, 72, 73], "intersect": [1, 43, 46], "st1": 1, "item1": 1, "item2": 1, "item3": 1, "item4": 1, "st2": 1, "symmetr": 1, "symmetric_differ": 1, "return": [1, 2, 3, 4, 5, 6, 8, 10, 15, 17, 20, 21, 22, 24, 25, 26, 27, 29, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 46, 47, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 66, 67, 68, 69, 73], "boolean": [1, 4, 5, 10, 20, 21, 25, 27, 29, 33, 35, 38, 47, 52], "built": [1, 2, 3, 5, 6, 9, 10, 17, 20, 21, 22, 24, 27, 29, 30, 31, 32, 34, 36, 38, 40, 44, 47, 48, 50, 52, 53, 57, 58, 59, 60, 61, 63, 66, 68, 69, 70, 72, 73, 74], "usual": [1, 2, 3, 4, 6, 7, 10, 12, 15, 18, 19, 22, 24, 25, 26, 27, 31, 32, 36, 38, 41, 43, 47, 48, 49, 50, 52, 53, 57, 58, 59, 60, 61, 63, 67, 70, 73, 74], "said": [1, 18, 22, 27, 31, 34, 39, 42, 49, 53, 54], "contrast": [1, 17, 22, 36, 39, 42, 55, 57], "own": [1, 2, 3, 4, 5, 7, 15, 16, 17, 19, 22, 24, 26, 27, 30, 32, 33, 34, 35, 36, 40, 41, 43, 44, 50, 52, 56, 57, 59, 60, 63, 68, 69, 73], "class": [1, 2, 3, 4, 6, 9, 10, 11, 21, 24, 25, 26, 27, 30, 33, 35, 38, 39, 41, 43, 46, 48, 50, 52, 53, 56, 57, 61, 63, 66, 74], "behaviour": [1, 5, 22, 38, 58, 75], "__bool__": 1, "dunder": [1, 50], "method": [1, 2, 4, 5, 7, 9, 10, 13, 15, 18, 20, 21, 22, 23, 26, 27, 29, 30, 35, 36, 37, 38, 39, 41, 42, 43, 44, 47, 48, 50, 51, 53, 56, 57, 59, 60, 61, 62, 63, 65, 66, 67, 68, 73, 75], "def": [1, 2, 3, 4, 5, 10, 20, 25, 27, 34, 38, 40, 41, 42, 43, 46, 47, 50, 52, 53, 54, 55, 57, 58, 59, 61, 64, 68, 73], "bool_check_var": 1, "input_vari": 1, "els": [1, 2, 3, 4, 5, 19, 20, 22, 24, 32, 34, 39, 41, 42, 46, 55, 59, 60, 61, 63, 68, 72, 73, 74], "other_listi": [1, 22], "doesn": [1, 2, 3, 5, 6, 7, 10, 15, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 31, 34, 36, 39, 40, 41, 43, 44, 45, 46, 47, 50, 52, 53, 55, 57, 59, 61, 64, 66, 67, 72, 73, 74], "variou": [1, 2, 3, 8, 16, 17, 18, 19, 20, 22, 27, 29, 30, 44, 52, 56, 58, 59, 63, 64, 65, 66, 73], "zero": [1, 2, 3, 5, 9, 10, 19, 22, 23, 25, 27, 30, 34, 35, 36, 38, 41, 42, 43, 47, 48, 50, 52, 53, 55, 56, 57, 61, 63, 73], "wa": [1, 2, 4, 5, 6, 7, 10, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 31, 32, 33, 35, 36, 38, 40, 41, 42, 43, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 61, 63, 64, 68, 69, 71, 73, 74], "three": [1, 2, 3, 4, 6, 7, 8, 10, 15, 16, 18, 19, 22, 23, 24, 25, 27, 28, 30, 32, 33, 34, 36, 38, 40, 41, 42, 47, 48, 50, 52, 53, 54, 59, 60, 61, 62, 63, 64, 66, 68, 71, 72, 73, 74], "none": [1, 2, 3, 5, 10, 18, 20, 21, 22, 24, 25, 27, 29, 33, 39, 40, 41, 42, 43, 44, 52, 54, 55, 56, 57, 58, 59, 61, 63, 64, 66, 75], "specif": [1, 2, 3, 5, 6, 7, 13, 15, 16, 17, 18, 19, 20, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 39, 40, 41, 43, 44, 49, 51, 52, 53, 55, 56, 57, 61, 63, 68, 69, 70, 73, 74, 75], "list_val": [1, 22], "simpli": [1, 5, 13, 19, 21, 22, 24, 27, 29, 43, 46, 51, 52, 62, 63, 69, 71, 74], "old": [1, 5, 31, 42, 52, 63, 73, 74], "idea": [1, 2, 3, 4, 5, 7, 8, 13, 15, 16, 18, 19, 27, 29, 30, 35, 36, 45, 49, 53, 56, 57, 58, 60, 62, 63, 70, 71, 73, 74], "part": [1, 2, 3, 6, 7, 8, 13, 15, 16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 50, 52, 54, 55, 56, 57, 59, 61, 63, 64, 67, 68, 69, 70, 72, 73, 74], "paradigm": [1, 10, 36], "tend": [1, 4, 6, 10, 18, 27, 31, 48, 52, 53, 58, 60, 61, 69], "orient": [1, 5, 10, 16, 24, 25, 42, 46, 59, 61, 63, 64], "approach": [1, 5, 15, 18, 19, 21, 24, 35, 36, 38, 39, 44, 47, 48, 50, 53, 54, 55, 61, 63, 64, 65, 67, 68, 72], "origin": [1, 2, 7, 15, 17, 19, 20, 21, 27, 28, 30, 34, 35, 36, 38, 41, 43, 47, 48, 51, 53, 57, 59, 60, 63, 64, 67], "alan": [1, 33, 73, 74], "ture": [1, 15, 27, 73, 74], "machin": [1, 2, 5, 6, 17, 19, 20, 31, 34, 36, 46, 52, 53, 57, 61, 67, 69, 74, 75], "alonso": 1, "church": [1, 27, 43, 44], "calculu": 1, "high": [1, 2, 3, 6, 10, 11, 15, 18, 19, 22, 24, 25, 35, 36, 38, 39, 40, 42, 43, 46, 49, 50, 51, 54, 57, 58, 60, 61, 64, 69, 73, 74], "level": [1, 2, 3, 4, 5, 10, 11, 15, 16, 18, 19, 24, 34, 35, 38, 39, 41, 42, 43, 44, 45, 46, 49, 57, 59, 60, 61, 62, 63, 64, 66, 67, 69, 71, 73, 74], "mix": [1, 3, 6, 7, 18, 24, 25, 27, 33, 36, 38, 52, 53, 62, 63, 66, 72, 73], "haskel": [1, 7], "strongli": [1, 4, 6, 18, 23, 61, 63], "r": [1, 2, 4, 5, 6, 7, 9, 11, 19, 20, 24, 26, 31, 32, 36, 38, 39, 40, 41, 42, 43, 46, 47, 50, 52, 53, 54, 55, 57, 59, 60, 63, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75], "lean": [1, 11, 50, 51, 53, 61], "toward": [1, 2, 7, 20, 36, 39, 40, 73], "powerhous": 1, "fortran": [1, 6, 46], "despit": [1, 10, 13, 19, 53, 73], "less": [1, 3, 6, 9, 10, 11, 15, 16, 18, 19, 20, 22, 23, 27, 31, 32, 34, 36, 42, 43, 45, 46, 48, 50, 52, 53, 55, 57, 58, 60, 61, 64, 66, 67, 68, 69, 71, 73], "plus_on": 1, "x": [1, 2, 3, 4, 5, 6, 9, 11, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 74], "liner": 1, "actual": [1, 2, 3, 4, 5, 6, 7, 10, 15, 17, 19, 23, 24, 25, 26, 27, 29, 32, 33, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 66, 67, 69, 72, 73, 74], "shouldn": [1, 4, 10, 15, 18, 19, 32, 55, 73, 74], "form": [1, 3, 4, 5, 6, 15, 16, 19, 20, 24, 26, 31, 34, 35, 36, 41, 43, 47, 50, 52, 53, 55, 56, 57, 59, 60, 61, 64, 66, 69, 71, 72, 73, 74], "wild": [1, 13, 24], "had": [1, 2, 3, 6, 15, 21, 26, 27, 30, 31, 39, 42, 46, 50, 52, 53, 56, 60, 61, 63, 68, 69, 73, 74], "replac": [1, 3, 6, 8, 10, 11, 18, 19, 20, 21, 27, 29, 33, 34, 39, 44, 48, 53, 57, 60, 63, 64, 67, 69, 73], "phrase": [1, 13, 19, 27, 52, 53], "hello": [1, 2, 3, 5, 6, 7, 10, 18, 31, 54, 68, 69, 73, 74], "my": [1, 2, 3, 16, 17, 19, 26, 53, 60, 61, 69, 72, 73, 74], "blah": [1, 54], "ada": [1, 2, 3, 5, 22, 46, 52, 53], "IS": [1, 55], "adam": [1, 3, 5, 22, 46, 52, 53, 63, 75], "dtype": [1, 2, 11, 21, 22, 23, 25, 27, 29, 30, 33, 34, 35, 36, 38, 40, 41, 42, 43, 46, 47, 48, 52, 53, 56, 57, 59, 63], "Is": [1, 3, 15, 16, 22, 25, 26, 32, 71], "complex": [1, 2, 3, 4, 5, 10, 15, 19, 20, 22, 26, 27, 32, 33, 36, 43, 44, 46, 47, 50, 54, 59, 60, 63, 66, 69, 73, 74, 75], "construct": [1, 3, 15, 19, 20, 22, 25, 32, 35, 36, 42, 43, 44, 48, 57, 66, 75], "y": [1, 2, 3, 5, 9, 11, 18, 19, 20, 22, 25, 26, 27, 30, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 69, 73, 75], "troubl": [1, 3, 5, 16, 20, 31, 41], "declar": [1, 2, 3, 10, 20, 34, 35, 40, 41, 54, 59, 61, 62, 63, 65, 66], "compos": [1, 3, 5, 24, 43, 52, 53, 55, 62, 63, 65, 66, 75], "seri": [1, 7, 17, 20, 21, 23, 24, 26, 29, 30, 32, 41, 43, 44, 45, 48, 52, 53, 56, 58, 60, 61, 63, 64, 67, 69, 70, 73], "specifi": [1, 2, 5, 9, 11, 15, 18, 20, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 38, 41, 42, 43, 46, 47, 52, 54, 57, 59, 61, 62, 64, 70, 71, 72, 73, 74], "after": [1, 2, 3, 4, 5, 6, 15, 17, 18, 20, 25, 30, 36, 38, 41, 44, 50, 52, 54, 57, 58, 59, 60, 63, 64, 66, 69, 70, 73, 74], "alon": [1, 16, 19, 27, 35, 41, 53, 68, 69, 73], "new": [1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 19, 22, 24, 25, 29, 34, 35, 36, 40, 41, 42, 43, 44, 48, 50, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 75], "With": [1, 3, 7, 17, 19, 27, 33, 34, 36, 39, 41, 42, 43, 44, 48, 50, 52, 57, 68, 70, 73], "again": [1, 2, 3, 4, 5, 6, 7, 10, 19, 20, 25, 27, 33, 34, 35, 36, 41, 42, 43, 44, 48, 55, 57, 58, 59, 63, 64, 67, 69, 73, 74], "demonstr": [1, 5, 7, 19, 22, 25, 27, 34, 35, 38, 39, 41, 43, 44, 46, 47, 48, 53, 54, 58, 59, 61, 62, 63, 64, 67, 68, 69, 72], "principl": [1, 2, 13, 15, 32, 43, 55, 57, 58, 63, 68, 73], "func": [1, 2, 5, 9, 48], "16": [1, 2, 3, 4, 5, 6, 20, 25, 27, 29, 30, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 47, 48, 56, 57, 59, 60, 64, 73, 74, 75], "peopl": [1, 2, 3, 5, 6, 7, 10, 15, 16, 17, 18, 19, 24, 26, 27, 29, 32, 36, 37, 38, 39, 40, 42, 44, 45, 46, 48, 50, 52, 53, 56, 58, 59, 60, 61, 64, 69, 73, 74], "convent": [1, 3, 4, 9, 25, 31, 35, 44, 48, 68], "stop": [1, 2, 3, 6, 18, 20, 21, 26, 27, 47, 50, 52, 54, 73], "hors": 1, "apart": [1, 2, 25, 36, 53, 54, 63], "take": [1, 2, 3, 4, 5, 6, 7, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74], "squar": [1, 3, 5, 6, 19, 21, 27, 36, 38, 39, 41, 47, 50, 51, 52, 55, 57, 68, 71], "root": [1, 3, 4, 6, 10, 11, 19, 26, 46, 47, 53, 55, 63, 69, 73, 74], "short": [1, 2, 3, 4, 7, 11, 15, 16, 18, 19, 20, 24, 26, 30, 31, 34, 35, 40, 41, 46, 50, 52, 56, 60, 61, 62, 63, 67, 68, 70, 73, 74], "video": [1, 3, 6, 26, 31, 34, 38, 58, 60], "tutori": [1, 2, 3, 6, 7, 20, 21, 26, 27, 34, 36, 60, 63, 69, 73], "read": [1, 3, 4, 5, 6, 7, 10, 13, 16, 18, 19, 22, 25, 26, 27, 29, 34, 35, 38, 41, 42, 43, 48, 50, 57, 59, 61, 62, 63, 67, 69, 70, 73], "known": [1, 3, 4, 5, 6, 15, 19, 20, 22, 23, 25, 36, 37, 39, 42, 43, 44, 47, 50, 51, 52, 53, 57, 59, 60, 61, 64, 73, 74], "unpack": [1, 30, 63], "fed": [1, 16, 40], "argument": [1, 2, 4, 5, 15, 16, 18, 19, 21, 23, 25, 26, 27, 28, 29, 30, 33, 34, 40, 41, 42, 43, 44, 47, 48, 50, 52, 53, 56, 57, 61, 62, 63, 65, 66, 67, 68, 69, 74], "effici": [1, 2, 4, 5, 18, 23, 24, 26, 31, 32, 36, 42, 47, 48, 50, 52, 53, 56, 61, 63, 66, 69, 73], "send": [1, 2, 4, 5, 6, 7, 15, 19, 70, 72, 73, 74], "labouri": [1, 63], "accompani": [1, 18, 32], "pair": [1, 3, 6, 7, 27, 28, 33, 36, 40, 42, 48, 53, 57], "func_arg": 1, "11": [1, 2, 3, 9, 20, 22, 24, 25, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 48, 50, 55, 56, 57, 59, 63, 64, 74, 75], "17": [1, 2, 21, 22, 24, 25, 27, 29, 30, 33, 34, 35, 36, 38, 39, 40, 41, 42, 46, 47, 51, 56, 57, 59, 62, 67, 70, 74], "surprisingli": [1, 3, 46, 63, 73], "sum_el": 1, "sum": [1, 2, 3, 5, 8, 11, 15, 22, 24, 27, 30, 34, 40, 43, 47, 48, 51, 53, 55, 57, 59, 67, 73, 75], "num": [1, 3, 5, 40, 55, 59], "more_num": 1, "multipli": [1, 2, 3, 5, 9, 27, 30, 36, 41, 47], "togeth": [1, 2, 3, 6, 7, 19, 26, 27, 28, 34, 35, 36, 41, 42, 46, 48, 53, 55, 57, 58, 59, 60, 61, 62, 63, 64, 67, 69, 70, 74], "12": [1, 2, 3, 5, 20, 21, 22, 24, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 52, 54, 55, 56, 57, 59, 61, 63, 64, 65, 70, 73], "aka": [1, 4, 5, 7, 10, 19, 21, 26, 28, 29, 35, 39, 43, 47, 52, 53, 55, 59, 61, 62, 63, 65, 68, 70], "kwarg": [1, 2, 3, 5, 38, 41, 42, 52, 57, 61, 63], "function_with_kwarg": 1, "quick": [1, 2, 4, 6, 13, 15, 18, 20, 25, 26, 27, 31, 32, 36, 39, 40, 43, 50, 52, 55, 56, 59, 64, 68, 73, 75], "dive": [1, 24, 30, 49], "deal": [1, 2, 5, 15, 19, 20, 24, 26, 27, 30, 35, 36, 42, 48, 51, 53, 56, 57, 74], "scratch": [1, 2, 27, 45, 46, 48, 53, 57, 58], "surfac": [1, 2, 24, 26, 43, 46, 47, 48, 53, 57, 59], "should": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 66, 69, 70, 71, 72, 73, 74], "sens": [1, 2, 16, 19, 32, 33, 36, 38, 41, 45, 48, 50, 52, 53, 56, 57, 59, 63, 64, 73], "datetim": [1, 2, 5, 8, 20, 23, 25, 27, 33, 34, 44, 59, 62, 63, 64, 65], "precis": [1, 2, 3, 9, 15, 19, 24, 25, 33, 34, 39, 40, 43, 50, 56, 57, 67], "account": [1, 2, 6, 17, 19, 25, 26, 32, 35, 36, 38, 39, 41, 53, 57, 60, 61], "2024": [1, 35, 36, 38, 39, 56, 57], "46": [1, 22, 25, 27, 30, 34, 35, 36, 38, 41, 42, 44, 57, 59, 63], "912755": 1, "bit": [1, 2, 3, 4, 5, 6, 7, 10, 11, 16, 17, 18, 19, 20, 22, 24, 25, 26, 27, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 46, 50, 52, 53, 54, 57, 58, 59, 61, 63, 64, 67, 69, 72, 73, 74], "month": [1, 2, 6, 17, 23, 26, 27, 30, 31, 32, 34, 38, 44, 56, 57, 59, 64, 73], "year": [1, 2, 4, 7, 15, 19, 21, 25, 26, 27, 29, 30, 32, 33, 34, 35, 38, 41, 44, 46, 53, 54, 56, 57, 58, 59, 62, 63, 64, 65, 66, 73], "hour": [1, 4, 6, 26, 27, 30, 42, 56, 57], "minut": [1, 13, 15, 24, 27, 30, 56, 57], "subtract": [1, 3, 21, 30, 34, 56], "timedelta": [1, 56, 57], "new_tim": 1, "365": [1, 27, 35, 42], "2025": 1, "04": [1, 25, 27, 30, 33, 34, 35, 41, 44, 56, 57, 59, 63], "year_select": 1, "2030": 1, "new_year": 1, "time_till_ni": 1, "2188": 1, "includ": [1, 2, 3, 4, 6, 7, 11, 13, 15, 16, 17, 18, 20, 21, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 73], "inform": [1, 2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 38, 41, 43, 44, 48, 51, 52, 53, 56, 57, 59, 60, 61, 62, 63, 67, 68, 69, 72, 73, 74], "wherea": [1, 4, 19, 39, 66, 69], "show": [1, 2, 3, 5, 6, 10, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 47, 48, 50, 51, 52, 53, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74], "delight": [1, 21, 45, 61], "unicod": [1, 2, 3, 4, 52, 54, 71], "\u03b1": [1, 36, 46], "\u03b2": [1, 36, 46], "swap": [1, 52], "itertool": [1, 53, 57, 59, 63], "offer": [1, 2, 6, 15, 17, 25, 26, 35, 38, 39, 42, 43, 46, 48, 51, 53, 56, 57, 60, 61, 74], "count": [1, 2, 3, 4, 5, 8, 11, 19, 21, 22, 23, 24, 25, 26, 27, 29, 32, 33, 34, 38, 40, 42, 44, 50, 57, 59, 61, 62, 63, 66, 67, 69], "repeat": [1, 2, 3, 16, 19, 27, 33, 36, 46, 49, 52, 55, 57, 58, 59, 68, 69, 73], "cycl": [1, 5, 38, 57, 59, 63, 73], "chain": [1, 5, 10, 20, 24, 27, 35, 36, 40, 48, 53, 63, 69], "next": [1, 2, 3, 5, 6, 7, 16, 18, 19, 21, 22, 24, 25, 26, 34, 36, 41, 43, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 69, 71, 72, 73, 74], "iteract": 1, "lorri": 1, "red": [1, 5, 21, 35, 38, 39, 41, 43, 55, 57, 59, 64], "yellow": [1, 5, 21, 41, 63, 72], "lorry_it": 1, "product": [1, 2, 5, 7, 10, 19, 20, 24, 26, 28, 30, 31, 34, 38, 42, 45, 54, 57, 59, 61, 64, 67, 68, 73, 74], "permut": [1, 40], "abc": [1, 29, 48, 54], "pass": [1, 2, 3, 4, 5, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 34, 35, 38, 40, 41, 42, 43, 44, 47, 48, 50, 52, 53, 56, 57, 58, 59, 61, 62, 63, 65, 66, 72, 74], "timezon": [1, 56, 57, 69], "isdigit": [1, 52], "islow": [1, 27, 52], "isupp": [1, 52], "raw_str": 1, "asdfaa3fa": 1, "str_func": 1, "__name__": [1, 5, 50, 53, 68, 69], "recurs": [1, 6, 46], "instanc": [1, 2, 3, 5, 9, 16, 21, 25, 26, 31, 32, 34, 41, 42, 50, 54, 56, 59, 63, 66, 67, 69, 73], "fibonacci": [1, 2], "sequenc": [1, 2, 3, 7, 43, 44, 48, 54, 57, 58, 63, 66, 69], "a_n": 1, "a_": 1, "pleas": [1, 15, 19, 25, 26, 44, 45, 48, 50, 51, 61, 64, 66], "enter": [1, 2, 3, 4, 6, 7, 20, 21, 23, 25, 27, 31, 41, 44, 60, 68, 69, 73, 74], "elif": [1, 2, 3, 5, 22, 41, 55, 60], "21": [1, 3, 22, 25, 27, 30, 33, 34, 35, 36, 38, 39, 41, 42, 44, 48, 56, 57, 59, 64, 69, 73, 74, 75], "34": [1, 21, 22, 25, 26, 27, 29, 34, 35, 36, 39, 41, 42, 43, 44, 50, 53, 54, 57, 59], "knowledg": [2, 4, 13, 15, 17, 18, 19, 26, 34, 61, 69, 73, 74, 75], "faster": [2, 3, 5, 20, 21, 24, 31, 39, 44, 47, 63], "understand": [2, 3, 4, 5, 6, 15, 17, 18, 19, 24, 27, 30, 38, 45, 51, 53, 59, 61, 68, 69, 74], "goe": [2, 3, 4, 6, 15, 18, 20, 23, 24, 35, 36, 43, 44, 46, 64, 69, 70, 73], "wrong": [2, 4, 5, 18, 20, 27, 30, 34, 43, 44, 49, 52, 55, 61, 73], "topic": [2, 4, 7, 15, 16, 17, 19, 26, 27, 34, 37, 45, 48, 50, 53, 54, 61, 64, 74], "advanc": [2, 6, 7, 13, 15, 19, 20, 26, 32, 33, 34, 36, 43, 44, 50, 58, 61, 70, 73, 74, 75], "warn": [2, 18, 24, 25, 26, 29, 30, 35, 36, 39, 41, 44, 47, 53, 56, 57, 59, 64, 72, 73], "tell": [2, 3, 4, 5, 6, 7, 13, 15, 16, 19, 22, 24, 25, 26, 28, 36, 40, 41, 42, 43, 44, 46, 52, 53, 56, 57, 58, 60, 61, 62, 63, 64, 65, 68, 69, 72, 73, 74], "ever": [2, 3, 4, 6, 16, 18, 20, 22, 23, 27, 30, 42, 50, 51, 53, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67, 73, 74], "conda": [2, 6, 7, 21, 25, 27, 34, 43, 44, 57, 68, 69, 73], "packagenam": [2, 7, 10, 21, 27, 31, 34, 68, 69, 70, 73], "pip": [2, 3, 6, 7, 18, 21, 24, 25, 27, 30, 31, 32, 33, 34, 38, 43, 44, 53, 57, 58, 60, 62, 68, 69, 70, 73, 74], "termin": [2, 3, 7, 10, 20, 21, 30, 31, 33, 39, 47, 60, 68, 72, 73, 74], "previou": [2, 7, 13, 15, 18, 20, 22, 25, 27, 30, 32, 34, 35, 36, 39, 48, 50, 53, 57, 69, 72, 73], "met": [2, 4, 7, 30, 52, 58, 73], "matter": [2, 4, 5, 6, 7, 15, 19, 23, 24, 27, 32, 39, 40, 48, 56, 57, 61, 64, 66, 73, 74, 75], "readabl": [2, 3, 4, 5, 19, 26, 31, 34, 35, 36, 43, 44, 56, 57, 58, 67, 69, 70, 71, 73], "bug": [2, 4, 12, 72, 74], "catch": [2, 16, 39, 72], "flag": [2, 4, 20, 25, 63, 69, 73], "issu": [2, 3, 4, 5, 6, 7, 12, 15, 16, 19, 20, 25, 26, 34, 40, 41, 43, 44, 45, 48, 51, 53, 57, 61, 63, 64, 67, 68, 69, 73, 74], "compli": [2, 38], "function": [2, 4, 6, 8, 9, 10, 11, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 61, 62, 63, 65, 66, 67, 68, 69, 72, 73, 74], "linter": 2, "analys": [2, 6, 15, 19, 20, 21, 24, 33, 35, 38, 43, 53, 57, 60, 69], "programmat": [2, 24, 32, 48, 50, 60], "stylist": 2, "potenti": [2, 23, 25, 30, 32, 35, 39, 41, 44, 49, 50, 53, 55], "deviat": [2, 21, 25, 30, 34, 35, 36, 38, 39, 42, 47, 48, 51, 55, 57, 61, 67], "been": [2, 3, 4, 5, 6, 15, 17, 19, 21, 22, 24, 25, 30, 31, 32, 34, 35, 36, 37, 39, 41, 44, 45, 46, 47, 48, 50, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 66, 73, 74], "rais": [2, 3, 5, 12, 15, 25, 27, 29, 41, 42, 44, 53, 55, 59, 63, 68, 74], "supposedli": [2, 55], "trap": [2, 53, 57], "cloth": 2, "dryer": 2, "small": [2, 4, 5, 6, 8, 13, 16, 17, 19, 24, 25, 26, 27, 30, 33, 36, 42, 44, 48, 50, 51, 53, 57, 58, 59, 62, 69, 74], "blazingli": 2, "fast": [2, 3, 4, 10, 20, 24, 26, 27, 30, 32, 36, 41, 46, 47, 48, 52, 54, 73], "ruff": [2, 70], "across": [2, 4, 5, 6, 7, 17, 19, 25, 30, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 53, 54, 57, 58, 60, 66, 67, 69], "flake8": [2, 74], "pycodestyl": 2, "pylint": 2, "vs": [2, 3, 5, 6, 7, 18, 30, 33, 35, 38, 41, 50, 63, 66, 69, 73], "integr": [2, 4, 7, 10, 20, 21, 32, 36, 40, 46, 58, 60, 73, 74], "mac": [2, 4, 6, 7, 31, 69, 73], "select": [2, 3, 4, 5, 6, 7, 8, 10, 15, 17, 19, 20, 24, 26, 27, 29, 32, 34, 35, 36, 38, 40, 41, 42, 43, 48, 53, 55, 57, 60, 63, 68, 69, 73, 74], "pylanc": [2, 6], "extens": [2, 3, 5, 6, 7, 8, 10, 15, 17, 18, 19, 24, 26, 31, 32, 39, 62, 63, 68, 71, 72, 73, 74], "script": [2, 4, 5, 6, 8, 54, 60, 70, 74], "list_defn": 2, "this_is_a_func": 2, "numpi": [2, 3, 5, 6, 8, 9, 10, 21, 23, 25, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 68, 70, 72, 73], "np": [2, 3, 5, 6, 8, 9, 16, 18, 21, 23, 25, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 72, 73], "press": [2, 3, 6, 7, 17, 28, 64, 69, 74, 75], "ctrl": [2, 6, 7, 69, 73], "otherwis": [2, 3, 5, 15, 16, 19, 22, 35, 36, 41, 46, 48, 52, 55, 57, 59, 60, 63, 67, 69, 72, 73], "navig": [2, 3, 5, 6, 18, 20, 26, 27, 70, 73], "problem": [2, 3, 5, 6, 13, 15, 17, 19, 24, 25, 27, 29, 30, 35, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 52, 53, 55, 57, 59, 60, 61, 63, 64, 68, 73, 74], "load": [2, 5, 6, 7, 20, 25, 26, 27, 30, 33, 39, 40, 42, 43, 44, 48, 50, 51, 53, 57, 58, 62, 63, 69, 73, 74], "messag": [2, 3, 5, 6, 7, 16, 19, 20, 22, 41, 47, 58, 60, 61, 64, 73, 74], "few": [2, 3, 5, 6, 11, 13, 15, 16, 17, 18, 19, 20, 24, 25, 29, 30, 33, 36, 38, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 61, 63, 64, 66, 67, 69, 71, 73, 74], "miss": [2, 19, 20, 23, 25, 30, 34, 41, 42, 53, 57, 59, 61, 65, 67], "whitespac": [2, 3, 5, 8, 10, 41, 52, 53, 54, 74], "e231": 2, "under": [2, 3, 4, 5, 6, 7, 10, 15, 17, 18, 19, 30, 35, 36, 39, 40, 42, 44, 46, 48, 50, 58, 61, 68, 69, 71, 73], "indent": [2, 4, 5, 6, 63], "visual": [2, 3, 4, 7, 8, 10, 11, 19, 21, 24, 32, 36, 40, 43, 52, 54, 57, 59, 60, 61, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 75], "e128": 2, "expect": [2, 3, 5, 13, 15, 18, 22, 24, 25, 26, 27, 30, 34, 35, 36, 40, 41, 42, 43, 50, 53, 56, 58, 59, 61, 62, 63, 67, 70, 73], "blank": [2, 4, 6, 57], "found": [2, 6, 15, 16, 19, 26, 29, 32, 35, 36, 38, 39, 42, 46, 47, 48, 51, 53, 57, 61, 63, 69, 73, 74], "e302": 2, "e111": 2, "undefin": 2, "f821": 2, "e402": 2, "unus": [2, 42], "f3401": 2, "reportundefinedvari": 2, "newlin": [2, 18, 46, 54, 59], "w292": 2, "78": [2, 22, 25, 27, 30, 34, 35, 36, 42, 43], "338": [2, 42, 61], "mostli": [2, 3, 7, 15, 16, 20, 26, 30, 36, 50, 55, 69, 73], "rule": [2, 4, 6, 13, 15, 16, 19, 26, 36, 39, 47, 50, 57, 61, 68, 73, 74], "broken": [2, 20, 30], "row": [2, 4, 5, 8, 10, 11, 22, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 43, 44, 48, 50, 52, 53, 55, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67], "posit": [2, 3, 6, 10, 15, 16, 21, 27, 28, 30, 36, 38, 41, 42, 43, 47, 48, 50, 52, 57, 59, 60, 61, 62, 63, 64, 67, 73], "helpfulli": 2, "handi": [2, 3, 18, 23, 29, 30, 36, 46, 50, 54, 71], "caus": [2, 3, 6, 11, 15, 19, 20, 24, 28, 29, 30, 33, 41, 42, 47, 53, 55, 56, 57, 64, 68, 73, 74], "customis": [2, 19, 25, 26, 44, 59, 60, 61], "conveni": [2, 5, 6, 18, 26, 27, 29, 31, 39, 41, 43, 47, 48, 50, 53, 56, 57, 59, 60, 61, 63, 67, 68, 72, 73, 74], "fail": [2, 10, 19, 20, 42, 50, 68, 73], "wouldn": [2, 20, 24, 26, 67, 74], "fix": [2, 4, 5, 11, 12, 15, 23, 25, 27, 29, 33, 34, 42, 44, 48, 51, 55, 60, 68, 73, 74], "formatt": [2, 4, 42, 61, 67], "valid": [2, 3, 4, 10, 15, 19, 29, 38, 41, 48, 52, 53, 73], "forcibl": 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39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74], "entir": [2, 3, 6, 15, 19, 24, 26, 29, 34, 35, 36, 38, 39, 40, 43, 46, 48, 52, 59, 61, 66, 68, 69, 73, 74], "bring": [2, 3, 6, 11, 14, 16, 19, 20, 22, 24, 26, 37, 41, 43, 44, 55, 64, 66, 69, 74], "k": [2, 35, 36, 38, 40, 41, 42, 43, 44, 46, 47, 48, 50, 55, 57, 58, 59, 63, 69, 72, 75], "poorli": [2, 39, 50], "content": [2, 5, 6, 7, 12, 16, 17, 18, 19, 21, 25, 26, 27, 31, 51, 53, 59, 67, 68, 69, 71, 73, 74, 75], "very_important_funct": 2, "y_val": 2, "keyword_arg": 2, "another_arg": 2, "linspac": [2, 9, 39, 40, 42, 47, 50, 57, 58, 59, 60, 63, 64, 72], "did": [2, 3, 4, 16, 19, 20, 25, 26, 28, 35, 36, 37, 38, 39, 43, 45, 47, 53, 54, 55, 57, 61, 63, 68, 72, 73], "move": [2, 6, 7, 15, 18, 19, 22, 27, 29, 30, 39, 44, 52, 53, 55, 56, 57, 58, 59, 60, 62, 66, 68, 69, 73, 74], "around": [2, 4, 5, 6, 7, 9, 10, 17, 18, 19, 20, 26, 30, 32, 35, 36, 38, 39, 40, 41, 43, 44, 46, 48, 52, 57, 58, 59, 63, 67, 69, 70, 73, 74], "ad": [2, 3, 4, 5, 7, 8, 15, 19, 21, 24, 25, 27, 28, 33, 34, 35, 36, 41, 42, 43, 44, 48, 52, 53, 54, 56, 57, 59, 61, 64, 68, 71], "strength": [2, 6, 7, 19, 24, 36, 50, 51, 59], "weak": [2, 6, 14, 19, 35, 51], "split": [2, 4, 10, 15, 19, 20, 26, 35, 41, 42, 44, 45, 47, 50, 53, 54, 59, 61, 67, 69, 73], "troublesom": 2, "isort": 2, "pressur": [2, 26, 31, 42, 60], "off": [2, 3, 5, 6, 8, 19, 20, 24, 27, 29, 35, 36, 41, 43, 44, 50, 53, 57, 59, 60, 61, 67, 73], "think": [2, 3, 4, 5, 6, 11, 12, 13, 15, 16, 17, 19, 20, 24, 25, 26, 27, 29, 30, 32, 34, 35, 36, 39, 40, 42, 43, 44, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 63, 64, 72, 73], "collabor": [2, 5, 33, 61, 72, 74], "sourc": [2, 4, 6, 7, 9, 13, 15, 16, 17, 18, 19, 21, 25, 29, 32, 38, 41, 42, 43, 44, 49, 59, 60, 61, 64, 67, 69, 72, 73, 74, 75], "maintain": [2, 4, 5, 6, 12, 26, 60, 74], "incorpor": [2, 15, 19, 36, 44], "branch": [2, 3, 17, 22, 46, 49, 51, 59, 73], "consist": [2, 3, 4, 10, 13, 15, 20, 21, 23, 26, 27, 31, 33, 36, 39, 41, 42, 46, 53, 57, 73], "who": [2, 3, 6, 7, 15, 16, 17, 18, 19, 20, 22, 24, 27, 32, 42, 45, 52, 60, 61, 64, 67, 69, 72, 73, 74], "autom": [2, 4, 7, 8, 18, 20, 25, 32, 60, 61, 69, 71, 73, 74], "someon": [2, 4, 5, 16, 19, 24, 64, 67, 68, 73, 74], "commit": [2, 4, 5, 7, 16, 24, 70, 73], "share": [2, 5, 6, 7, 14, 15, 17, 19, 24, 26, 27, 31, 58, 61, 66, 73, 74], "repositori": [2, 5, 7, 12, 18, 24, 52, 57, 59, 60, 71, 73], "hook": [2, 7, 73, 74], "jupyt": [2, 3, 4, 5, 6, 10, 18, 48, 50, 51, 66, 67, 69, 70, 71, 73, 74], "notebook": [2, 3, 4, 5, 6, 10, 18, 20, 21, 27, 34, 35, 44, 48, 50, 51, 60, 66, 67, 68, 69, 70, 71, 73, 74], "instead": [2, 3, 4, 5, 7, 9, 10, 16, 18, 19, 20, 21, 24, 27, 29, 30, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 48, 50, 51, 52, 53, 55, 56, 61, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 74], "render": [2, 3, 5, 6, 7, 15, 18, 25, 38, 42, 43, 47, 48, 50, 51, 53, 59, 61, 62, 63, 65, 66, 67, 71, 73], "interact": [2, 3, 7, 10, 11, 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72, 73, 74], "revers": [2, 3, 15, 19, 24, 34, 43, 46, 52, 57, 59, 64, 74], "IN": [2, 53], "sort": [2, 6, 9, 16, 17, 23, 24, 25, 26, 27, 30, 34, 40, 51, 58, 59, 61, 63, 69], "ascend": [2, 10, 21, 27, 30, 42, 53, 55, 59], "order": [2, 3, 6, 7, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 26, 29, 30, 32, 34, 39, 40, 42, 44, 45, 46, 47, 48, 53, 55, 57, 59, 60, 61, 63, 64, 65, 66, 69, 73, 74], "option": [2, 3, 4, 5, 6, 7, 10, 11, 15, 16, 19, 20, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 41, 42, 44, 46, 47, 48, 50, 51, 52, 53, 54, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 70, 73, 74], "ran": [2, 36, 54, 62], "plenti": [2, 5, 16, 31, 35, 53, 56, 60, 63, 71, 74], "map": [2, 3, 10, 15, 21, 25, 27, 33, 34, 35, 40, 43, 48, 49, 52, 55, 58, 59, 62, 63, 65, 67], "letter": [2, 3, 19, 27, 28, 40, 41, 42, 43, 52, 54, 61, 74, 75], "dict": [2, 3, 5, 10, 34, 40, 41, 43, 46, 53, 58, 59, 63, 64, 66], "programm": [2, 3, 5, 6, 8, 18, 39, 52, 57, 69, 73, 74], "halt": [2, 72], "won": [2, 3, 4, 5, 6, 10, 15, 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34, 41, 48, 57, 63], "arithmeticerror": 2, "anywai": [2, 13, 16, 59], "tantrum": 2, "person": [2, 5, 17, 53, 57, 73, 74], "wrote": [2, 45, 68, 74], "hood": [2, 10, 18, 35, 46, 48, 50, 61], "philosophi": [2, 10, 20, 61, 63, 73], "loudli": [2, 10, 20], "downstream": [2, 20, 25, 48, 73], "valueerror": [2, 3, 42, 55, 58, 59, 61], "neg": [2, 3, 5, 15, 30, 36, 41, 42, 48, 52, 61, 64], "ones": [2, 5, 6, 9, 10, 15, 18, 19, 25, 30, 34, 35, 36, 39, 40, 41, 43, 46, 47, 48, 52, 55, 59, 61, 63, 69, 70, 71, 73, 74], "specialis": [2, 6, 13, 24], "handl": [2, 24, 26, 29, 39, 73], "low": [2, 5, 6, 15, 19, 25, 36, 39, 40, 41, 42, 50, 51, 61], "thrown": [2, 20], "deep": [2, 16, 18, 19, 36, 48, 49, 61, 64, 66, 75], "down": [2, 4, 14, 16, 18, 19, 25, 26, 27, 30, 31, 32, 33, 35, 36, 38, 44, 49, 50, 51, 53, 56, 57, 58, 61, 63, 64, 69, 70, 71, 72, 73, 74], "middl": [2, 30, 33, 61, 73], "awai": [2, 5, 6, 13, 16, 24, 27, 33, 36, 41, 43, 48, 50, 53, 59, 61, 63, 66], "sum_reciproc": 2, "caught": [2, 13, 74], "elsewher": [2, 26, 60, 62, 66], "v": [2, 9, 18, 20, 36, 42, 53, 55, 59, 69, 75], "one_ov": 2, "b_year": 2, "1852": 2, "name_input": 2, "process_input": 2, "year_born": 2, "typeerror": [2, 16, 41, 63], "172": [2, 21, 38, 41, 42, 59], "comput": [2, 3, 4, 5, 7, 10, 17, 19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 38, 41, 42, 45, 46, 47, 48, 50, 52, 53, 55, 56, 57, 58, 60, 63, 68, 69, 74, 75], "liter": [2, 3, 18, 36, 43, 46, 52, 54, 56, 64, 69], "unless": [2, 4, 5, 9, 10, 15, 16, 19, 32, 36, 40, 52, 63, 66, 74], "difficult": [2, 6, 15, 32, 58, 61, 73], "fundament": [2, 10, 15, 18, 19, 22, 24, 30, 43, 47, 56, 57, 61, 63, 75], "fact": [2, 10, 15, 18, 24, 25, 27, 39, 40, 41, 42, 43, 44, 50, 52, 53, 57, 63, 69], "term": [2, 3, 4, 10, 11, 15, 17, 19, 24, 26, 35, 38, 42, 46, 49, 54, 57, 61, 63, 64, 69, 73, 74], "moth": 2, "earli": [2, 7, 17, 18, 19, 30, 57, 58, 67, 73], "gave": [2, 38], "produc": [2, 3, 6, 7, 10, 14, 15, 18, 19, 20, 22, 23, 25, 26, 30, 34, 35, 36, 38, 39, 40, 44, 46, 47, 50, 53, 55, 57, 58, 59, 61, 62, 63, 66, 69, 71, 72, 73, 74], "outcom": [2, 14, 18, 19, 27, 35, 36, 38, 39, 40, 41, 50, 52, 63], "base": [2, 3, 4, 5, 6, 7, 8, 10, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 47, 48, 50, 51, 52, 53, 56, 58, 59, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74], "tricki": [2, 13, 15, 17, 19, 26, 40, 54, 55, 57, 58, 67, 73], "track": [2, 6, 18, 20, 23, 24, 27, 28, 34, 48, 53, 63], "fear": [2, 10, 57, 73], "simplest": [2, 5, 6, 21, 24, 26, 27, 42, 43, 57, 60], "m": [2, 3, 5, 9, 11, 19, 20, 23, 24, 30, 34, 36, 42, 44, 46, 47, 53, 54, 55, 56, 57, 59, 68, 74, 75], "afraid": [2, 30], "plonk": 2, "arrai": [2, 3, 6, 9, 10, 17, 22, 27, 30, 32, 35, 36, 40, 41, 42, 46, 48, 50, 51, 53, 55, 56, 58, 59, 63, 68], "array_oper": 2, "in_arr_on": 2, "in_arr_two": 2, "out_arr": 2, "in_vals_on": 2, "22": [2, 3, 5, 20, 21, 22, 25, 27, 30, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 48, 56, 57, 59, 63, 64, 66, 69], "in_vals_two": 2, 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"back": [2, 4, 6, 7, 10, 15, 16, 18, 19, 20, 24, 26, 27, 30, 32, 33, 34, 36, 39, 41, 43, 48, 50, 53, 55, 56, 57, 58, 59, 60, 61, 63, 67, 68, 69, 73, 74], "decid": [2, 6, 16, 27, 32, 42, 43, 53, 59, 60, 61, 72, 73, 74], "ebd3efde9b3": 2, "operand": 2, "broadcast": [2, 30, 47, 68], "shape": [2, 9, 10, 11, 17, 20, 21, 25, 26, 27, 30, 32, 34, 36, 38, 40, 43, 44, 47, 48, 50, 51, 53, 59, 61, 63, 64, 68], "still": [2, 4, 6, 10, 11, 15, 16, 18, 19, 23, 24, 25, 30, 38, 46, 47, 48, 50, 52, 53, 55, 58, 61, 63, 66, 68, 69, 71, 72, 73], "relat": [2, 8, 13, 15, 16, 17, 18, 19, 20, 26, 27, 29, 30, 39, 42, 43, 52, 53, 57, 58, 60, 63, 66, 71, 73, 74], "intermedi": [2, 4, 21, 31, 34, 57, 58, 70, 73], "4961f476c7eb": 2, "vector": [2, 3, 10, 16, 18, 27, 30, 36, 41, 42, 43, 44, 46, 47, 48, 52, 56, 57, 59, 61, 62], "dimens": [2, 9, 30, 35, 36, 41, 42, 44, 47, 49, 50, 58, 59, 61, 63, 66, 75], "space": [2, 3, 4, 5, 13, 15, 16, 19, 31, 39, 40, 44, 47, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 69, 71, 73], 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Grammar of Graphics with Plotnine", "Data Visualisation with Seaborn", "Tables", "Workflow Basics", "The Command Line", "Virtual Code Environments", "Markdown", "Combining Code and Text in Quarto Markdown", "Reproducible Analysis", "Version Control", "Bibliography"], "terms": {"In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 72, 73, 74, 75], "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74], "chapter": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 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57, 58, 59, 61, 63, 64, 67, 68, 69, 71, 73], "start": [1, 2, 3, 5, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 57, 59, 61, 63, 64, 65, 66, 67, 69, 72, 73, 74], "z": [1, 18, 19, 20, 22, 39, 41, 47, 51, 54, 55, 56, 59, 62, 69], "befor": [1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 15, 17, 18, 20, 22, 23, 24, 25, 26, 27, 30, 35, 36, 39, 41, 45, 46, 48, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 63, 64, 67, 68, 70, 72, 73, 74], "collect": [1, 2, 3, 5, 6, 15, 17, 19, 24, 33, 38, 40, 52, 53, 59, 61, 62, 63, 74], "unord": [1, 61, 71], "unindex": 1, "distinct": [1, 3, 8, 10, 16, 35, 36, 40, 41, 44, 51, 53, 55, 56, 57, 61, 63, 66, 69, 74], "analog": [1, 5, 7, 43, 52, 55], "mathemat": [1, 3, 19, 27, 36, 48, 55, 58, 61, 73], "definit": [1, 2, 4, 5, 6, 7, 13, 14, 15, 16, 19, 30, 35, 38, 39, 46, 47, 48, 53, 54, 55, 56, 57, 61, 68], "These": [1, 2, 3, 4, 6, 7, 10, 15, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 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43, 48, 52, 54, 55, 56, 57, 58, 61, 63, 64, 67, 69, 73, 74], "pop": [1, 2, 3, 6, 19, 22, 26, 28, 39, 41, 44, 50, 59, 63, 73], "easili": [1, 4, 6, 7, 15, 16, 18, 21, 24, 25, 26, 27, 30, 31, 32, 38, 39, 43, 46, 48, 49, 50, 53, 56, 59, 72, 74], "real": [1, 2, 3, 4, 5, 7, 15, 19, 20, 22, 24, 26, 29, 30, 33, 34, 35, 36, 38, 40, 41, 42, 44, 46, 47, 48, 50, 51, 53, 57, 59, 60, 63, 72, 73], "support": [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 15, 16, 17, 19, 20, 24, 25, 27, 32, 33, 34, 35, 36, 38, 39, 40, 44, 46, 48, 49, 53, 54, 57, 59, 60, 61, 63, 66, 67, 69, 71, 72, 73], "intersect": [1, 43, 46], "st1": 1, "item1": 1, "item2": 1, "item3": 1, "item4": 1, "st2": 1, "symmetr": 1, "symmetric_differ": 1, "return": [1, 2, 3, 4, 5, 6, 8, 10, 15, 17, 20, 21, 22, 24, 25, 26, 27, 29, 34, 35, 36, 37, 38, 40, 42, 43, 44, 46, 47, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 66, 67, 68, 69, 73], "boolean": [1, 4, 5, 10, 20, 21, 25, 27, 29, 33, 35, 38, 47, 52], "built": [1, 2, 3, 5, 6, 9, 10, 17, 20, 21, 22, 24, 27, 29, 30, 31, 32, 34, 36, 38, 40, 44, 47, 48, 50, 52, 53, 57, 58, 59, 60, 61, 63, 66, 68, 69, 70, 72, 73, 74], "usual": [1, 2, 3, 4, 6, 7, 10, 12, 15, 18, 19, 22, 24, 25, 26, 27, 31, 32, 36, 38, 41, 43, 47, 48, 49, 50, 52, 53, 57, 58, 59, 60, 61, 63, 67, 70, 73, 74], "said": [1, 18, 22, 27, 31, 34, 39, 42, 49, 53, 54], "contrast": [1, 17, 22, 36, 39, 42, 55, 57], "own": [1, 2, 3, 4, 5, 7, 15, 16, 17, 19, 22, 24, 26, 27, 30, 32, 33, 34, 35, 36, 40, 41, 43, 44, 50, 52, 56, 57, 59, 60, 63, 68, 69, 73], "class": [1, 2, 3, 4, 6, 9, 10, 11, 21, 24, 25, 26, 27, 30, 33, 35, 38, 39, 41, 43, 46, 48, 50, 52, 53, 56, 57, 61, 63, 66, 74], "behaviour": [1, 5, 22, 38, 58, 75], "__bool__": 1, "dunder": [1, 50], "method": [1, 2, 4, 5, 7, 9, 10, 13, 15, 18, 20, 21, 22, 23, 26, 27, 29, 30, 35, 36, 37, 38, 39, 41, 42, 43, 44, 47, 48, 50, 51, 53, 56, 57, 59, 60, 61, 62, 63, 65, 66, 67, 68, 73, 75], "def": [1, 2, 3, 4, 5, 10, 20, 25, 27, 34, 38, 40, 42, 43, 46, 47, 50, 52, 54, 55, 57, 58, 59, 61, 64, 68, 73], "bool_check_var": 1, "input_vari": 1, "els": [1, 2, 3, 4, 5, 19, 20, 22, 24, 32, 34, 39, 46, 55, 59, 60, 61, 63, 68, 72, 73, 74], "other_listi": [1, 22], "doesn": [1, 2, 3, 5, 6, 7, 10, 15, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 31, 34, 36, 39, 40, 41, 43, 44, 45, 46, 47, 50, 52, 53, 55, 57, 59, 61, 64, 66, 67, 72, 73, 74], "variou": [1, 2, 3, 8, 16, 17, 18, 19, 20, 22, 27, 29, 30, 44, 52, 56, 58, 59, 63, 64, 65, 66, 73], "zero": [1, 2, 3, 5, 9, 10, 19, 22, 23, 25, 27, 30, 34, 35, 36, 38, 41, 42, 43, 47, 48, 50, 52, 53, 55, 56, 57, 61, 63, 73], "wa": [1, 2, 4, 5, 6, 7, 10, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 31, 32, 33, 35, 36, 38, 40, 41, 42, 43, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 61, 63, 64, 68, 69, 71, 73, 74], "three": [1, 2, 3, 4, 6, 7, 8, 10, 15, 16, 18, 19, 22, 23, 24, 25, 27, 28, 30, 32, 33, 34, 36, 38, 40, 41, 42, 47, 48, 50, 52, 53, 54, 59, 60, 61, 62, 63, 64, 66, 68, 71, 72, 73, 74], "none": [1, 2, 3, 5, 10, 18, 20, 21, 22, 24, 25, 27, 29, 33, 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13, 19, 53, 73], "less": [1, 3, 6, 9, 10, 11, 15, 16, 18, 19, 20, 22, 23, 27, 31, 32, 34, 36, 42, 43, 45, 46, 48, 50, 52, 53, 55, 57, 58, 60, 61, 64, 66, 67, 68, 69, 71, 73], "plus_on": 1, "x": [1, 2, 3, 4, 5, 6, 9, 11, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 74], "liner": 1, "actual": [1, 2, 3, 4, 5, 6, 7, 10, 15, 17, 19, 23, 24, 25, 26, 27, 29, 32, 33, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 66, 67, 69, 72, 73, 74], "shouldn": [1, 4, 10, 15, 18, 19, 32, 55, 73, 74], "form": [1, 3, 4, 5, 6, 15, 16, 19, 20, 24, 26, 31, 34, 35, 36, 41, 43, 47, 50, 52, 53, 55, 56, 57, 59, 60, 61, 64, 66, 69, 71, 72, 73, 74], "wild": [1, 13, 24], "had": [1, 2, 3, 6, 15, 21, 26, 27, 30, 31, 39, 42, 46, 50, 52, 53, 56, 60, 61, 63, 68, 69, 73, 74], "replac": [1, 3, 6, 8, 10, 11, 18, 19, 20, 21, 27, 29, 33, 34, 39, 44, 48, 53, 57, 60, 63, 64, 67, 69, 73], "phrase": [1, 13, 19, 27, 52, 53], "hello": [1, 2, 3, 5, 6, 7, 10, 18, 31, 54, 68, 69, 73, 74], "my": [1, 2, 3, 16, 17, 19, 26, 53, 60, 61, 69, 72, 73, 74], "blah": [1, 54], "ada": [1, 2, 3, 5, 22, 46, 52, 53], "IS": [1, 55], "adam": [1, 3, 5, 22, 46, 52, 53, 63, 75], "dtype": [1, 2, 11, 21, 22, 23, 25, 27, 29, 30, 33, 34, 35, 36, 38, 40, 41, 42, 43, 46, 47, 48, 52, 53, 56, 57, 59, 63], "Is": [1, 3, 15, 16, 22, 25, 26, 32, 71], "complex": [1, 2, 3, 4, 5, 10, 15, 19, 20, 22, 26, 27, 32, 33, 36, 43, 44, 46, 47, 50, 54, 59, 60, 63, 66, 69, 73, 74, 75], "construct": [1, 3, 15, 19, 20, 22, 25, 32, 35, 36, 42, 43, 44, 48, 57, 66, 75], "y": [1, 2, 3, 5, 9, 11, 18, 19, 20, 22, 25, 26, 27, 30, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 69, 73, 75], "troubl": [1, 3, 5, 16, 20, 31, 41], "declar": [1, 2, 3, 10, 20, 34, 35, 40, 41, 54, 59, 61, 62, 63, 65, 66], "compos": [1, 3, 5, 24, 43, 52, 53, 55, 62, 63, 65, 66, 75], "seri": [1, 7, 17, 20, 21, 23, 24, 26, 29, 30, 32, 43, 44, 45, 48, 52, 53, 56, 58, 60, 61, 63, 64, 67, 69, 70, 73], "specifi": [1, 2, 5, 9, 11, 15, 18, 20, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 38, 41, 42, 43, 46, 47, 52, 54, 57, 59, 61, 62, 64, 70, 71, 72, 73, 74], "after": [1, 2, 3, 4, 5, 6, 15, 17, 18, 20, 25, 30, 36, 38, 41, 44, 50, 52, 54, 57, 58, 59, 60, 63, 64, 66, 69, 70, 73, 74], "alon": [1, 16, 19, 27, 35, 41, 53, 68, 69, 73], "new": [1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 19, 22, 24, 25, 29, 34, 35, 36, 40, 41, 42, 43, 44, 48, 50, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 75], "With": [1, 3, 7, 17, 19, 27, 33, 34, 36, 39, 41, 42, 43, 44, 48, 50, 52, 57, 68, 70, 73], "again": [1, 2, 3, 4, 5, 6, 7, 10, 19, 20, 25, 27, 33, 34, 35, 36, 41, 42, 43, 44, 48, 55, 57, 58, 59, 63, 64, 67, 69, 73, 74], "demonstr": [1, 5, 7, 19, 22, 25, 27, 34, 35, 38, 39, 41, 43, 44, 46, 47, 48, 53, 54, 58, 59, 61, 62, 63, 64, 67, 68, 69, 72], "principl": [1, 2, 13, 15, 32, 43, 55, 57, 58, 63, 68, 73], "func": [1, 2, 5, 9, 48], "16": [1, 2, 3, 4, 5, 6, 20, 25, 27, 29, 30, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 47, 48, 53, 56, 57, 59, 60, 64, 73, 74, 75], "peopl": [1, 2, 3, 5, 6, 7, 10, 15, 16, 17, 18, 19, 24, 26, 27, 29, 32, 36, 37, 38, 39, 40, 42, 44, 45, 46, 48, 50, 52, 53, 56, 58, 59, 60, 61, 64, 69, 73, 74], "convent": [1, 3, 4, 9, 25, 31, 35, 44, 48, 68], "stop": [1, 2, 3, 6, 18, 20, 21, 26, 27, 47, 50, 52, 54, 73], "hors": 1, "apart": [1, 2, 25, 36, 53, 54, 63], "take": [1, 2, 3, 4, 5, 6, 7, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 72, 73, 74], "squar": [1, 3, 5, 6, 19, 21, 27, 36, 38, 39, 41, 47, 50, 51, 52, 55, 57, 68, 71], "root": [1, 3, 4, 6, 10, 11, 19, 26, 46, 47, 53, 55, 63, 69, 73, 74], "short": [1, 2, 3, 4, 7, 11, 15, 16, 18, 19, 20, 24, 26, 30, 31, 34, 35, 40, 41, 46, 50, 52, 56, 60, 61, 62, 63, 67, 68, 70, 73, 74], "video": [1, 3, 6, 26, 31, 34, 38, 58, 60], "tutori": [1, 2, 3, 6, 7, 20, 21, 26, 27, 34, 36, 60, 63, 69, 73], "read": [1, 3, 4, 5, 6, 7, 10, 13, 16, 18, 19, 22, 25, 26, 27, 29, 34, 35, 38, 41, 42, 43, 48, 50, 57, 59, 61, 62, 63, 67, 69, 70, 73], "known": [1, 3, 4, 5, 6, 15, 19, 20, 22, 23, 25, 36, 37, 39, 42, 43, 44, 47, 50, 51, 52, 53, 57, 59, 60, 61, 64, 73, 74], "unpack": [1, 30, 63], "fed": [1, 16, 40], "argument": [1, 2, 4, 5, 15, 16, 18, 19, 21, 23, 25, 26, 27, 28, 29, 30, 33, 34, 40, 41, 43, 44, 47, 48, 50, 52, 53, 56, 57, 61, 62, 63, 65, 66, 67, 68, 69, 74], "effici": [1, 2, 4, 5, 18, 23, 24, 26, 31, 32, 36, 42, 47, 48, 50, 52, 53, 56, 61, 63, 66, 69, 73], "send": [1, 2, 4, 5, 6, 7, 15, 19, 70, 72, 73, 74], "labouri": [1, 63], "accompani": [1, 18, 32], "pair": [1, 3, 6, 7, 27, 28, 33, 36, 40, 42, 48, 53, 57], "func_arg": 1, "11": [1, 2, 3, 9, 20, 22, 24, 25, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 48, 50, 55, 56, 57, 59, 63, 64, 74, 75], "17": [1, 2, 21, 22, 24, 25, 27, 29, 30, 33, 34, 35, 36, 38, 39, 40, 41, 42, 46, 47, 51, 53, 56, 57, 59, 62, 67, 70, 74], "surprisingli": [1, 3, 46, 63, 73], "sum_el": 1, "sum": [1, 2, 3, 5, 8, 11, 15, 22, 24, 27, 30, 34, 40, 43, 47, 48, 51, 53, 55, 57, 59, 67, 73, 75], "num": [1, 3, 5, 40, 55, 59], "more_num": 1, "multipli": [1, 2, 3, 5, 9, 27, 30, 36, 41, 47], "togeth": [1, 2, 3, 6, 7, 19, 26, 27, 28, 34, 35, 36, 41, 46, 48, 53, 55, 57, 58, 59, 60, 61, 62, 63, 64, 67, 69, 70, 74], "12": [1, 2, 3, 5, 20, 21, 22, 24, 25, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 52, 53, 54, 55, 56, 57, 59, 61, 63, 64, 65, 70, 73], "aka": [1, 4, 5, 7, 10, 19, 21, 26, 28, 29, 35, 39, 43, 47, 52, 53, 55, 59, 61, 62, 63, 65, 68, 70], "kwarg": [1, 2, 3, 5, 38, 52, 57, 61, 63], "function_with_kwarg": 1, "quick": [1, 2, 4, 6, 13, 15, 18, 20, 25, 26, 27, 31, 32, 36, 39, 40, 43, 50, 52, 55, 56, 59, 64, 68, 73, 75], "dive": [1, 24, 30, 49], "deal": [1, 2, 5, 15, 19, 20, 24, 26, 27, 30, 35, 36, 42, 48, 51, 53, 56, 57, 74], "scratch": [1, 2, 27, 45, 46, 48, 53, 57, 58], "surfac": [1, 2, 24, 26, 43, 46, 47, 48, 53, 57, 59], "should": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 66, 69, 70, 71, 72, 73, 74], "sens": [1, 2, 16, 19, 32, 33, 36, 38, 41, 45, 48, 50, 52, 53, 56, 57, 59, 63, 64, 73], "datetim": [1, 2, 5, 8, 20, 23, 25, 27, 33, 34, 44, 59, 62, 63, 64, 65], "precis": [1, 2, 3, 9, 15, 19, 24, 25, 33, 34, 39, 40, 43, 50, 56, 57, 67], "account": [1, 2, 6, 17, 19, 25, 26, 32, 35, 36, 38, 39, 41, 53, 57, 60, 61], "2024": [1, 35, 36, 38, 39, 56, 57], "46": [1, 22, 25, 27, 30, 34, 35, 36, 38, 41, 42, 44, 57, 59, 63], "912755": 1, "bit": [1, 2, 3, 4, 5, 6, 7, 10, 11, 16, 17, 18, 19, 20, 22, 24, 25, 26, 27, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 46, 50, 52, 53, 54, 57, 58, 59, 61, 63, 64, 67, 69, 72, 73, 74], "month": [1, 2, 6, 17, 23, 26, 27, 30, 31, 32, 34, 38, 44, 56, 57, 59, 64, 73], "year": [1, 2, 4, 7, 15, 19, 21, 25, 26, 27, 29, 30, 32, 33, 34, 35, 38, 41, 44, 46, 53, 54, 56, 57, 58, 59, 62, 63, 64, 65, 66, 73], "hour": [1, 4, 6, 26, 27, 30, 42, 56, 57], "minut": [1, 13, 15, 24, 27, 30, 56, 57], "subtract": [1, 3, 21, 30, 34, 56], "timedelta": [1, 56, 57], "new_tim": 1, "365": [1, 27, 35], "2025": 1, "04": [1, 25, 27, 30, 33, 34, 35, 41, 44, 56, 57, 59, 63], "year_select": 1, "2030": 1, "new_year": 1, "time_till_ni": 1, "2188": 1, "includ": [1, 2, 3, 4, 6, 7, 11, 13, 15, 16, 17, 18, 20, 21, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 73], "inform": [1, 2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 38, 41, 43, 44, 48, 51, 52, 53, 56, 57, 59, 60, 61, 62, 63, 67, 68, 69, 72, 73, 74], "wherea": [1, 4, 19, 39, 66, 69], "show": [1, 2, 3, 5, 6, 10, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 47, 48, 50, 51, 52, 53, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74], "delight": [1, 21, 45, 61], "unicod": [1, 2, 3, 4, 52, 54, 71], "\u03b1": [1, 36, 46], "\u03b2": [1, 36, 46], "swap": [1, 52], "itertool": [1, 53, 57, 59, 63], "offer": [1, 2, 6, 15, 17, 25, 26, 35, 38, 39, 42, 43, 46, 48, 51, 53, 56, 57, 60, 61, 74], "count": [1, 2, 3, 4, 5, 8, 11, 19, 21, 22, 23, 24, 25, 26, 27, 29, 32, 33, 34, 38, 40, 42, 44, 50, 57, 59, 61, 62, 63, 66, 67, 69], "repeat": [1, 2, 3, 16, 19, 27, 33, 36, 46, 49, 52, 55, 57, 58, 59, 68, 69, 73], "cycl": [1, 5, 38, 57, 59, 63, 73], "chain": [1, 5, 10, 20, 24, 27, 35, 36, 40, 48, 53, 63, 69], "next": [1, 2, 3, 5, 6, 7, 16, 18, 19, 21, 22, 24, 25, 26, 34, 36, 41, 43, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 69, 71, 72, 73, 74], "iteract": 1, "lorri": 1, "red": [1, 5, 21, 35, 38, 39, 41, 43, 55, 57, 59, 64], "yellow": [1, 5, 21, 41, 63, 72], "lorry_it": 1, "product": [1, 2, 5, 7, 10, 19, 20, 24, 26, 28, 30, 31, 34, 38, 42, 45, 54, 57, 59, 61, 64, 67, 68, 73, 74], "permut": [1, 40], "abc": [1, 29, 48, 54], "pass": [1, 2, 3, 4, 5, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 34, 35, 38, 40, 41, 42, 43, 44, 47, 48, 50, 52, 53, 56, 57, 58, 59, 61, 62, 63, 65, 66, 72, 74], "timezon": [1, 56, 57, 69], "isdigit": [1, 52], "islow": [1, 27, 52], "isupp": [1, 52], "raw_str": 1, "asdfaa3fa": 1, "str_func": 1, "__name__": [1, 5, 50, 53, 68, 69], "recurs": [1, 6, 46], "instanc": [1, 2, 3, 5, 9, 16, 21, 25, 26, 31, 32, 34, 41, 42, 50, 54, 56, 59, 63, 66, 67, 69, 73], "fibonacci": [1, 2], "sequenc": [1, 2, 3, 7, 43, 44, 48, 54, 57, 58, 63, 66, 69], "a_n": 1, "a_": 1, "pleas": [1, 15, 19, 25, 26, 44, 45, 48, 50, 51, 61, 64, 66], "enter": [1, 2, 3, 4, 6, 7, 20, 21, 23, 25, 27, 31, 41, 44, 60, 68, 69, 73, 74], "elif": [1, 2, 3, 5, 22, 55, 60], "21": [1, 3, 22, 25, 27, 30, 33, 34, 35, 36, 38, 39, 41, 42, 44, 48, 56, 57, 59, 64, 69, 73, 74, 75], "34": [1, 21, 22, 25, 26, 27, 29, 34, 35, 36, 39, 41, 42, 43, 44, 50, 54, 57, 59], "knowledg": [2, 4, 13, 15, 17, 18, 19, 26, 34, 61, 69, 73, 74, 75], "faster": [2, 3, 5, 20, 21, 24, 31, 39, 44, 47, 63], "understand": [2, 3, 4, 5, 6, 15, 17, 18, 19, 24, 27, 30, 38, 45, 51, 53, 59, 61, 68, 69, 74], "goe": [2, 3, 4, 6, 15, 18, 20, 23, 24, 35, 36, 43, 44, 46, 64, 69, 70, 73], "wrong": [2, 4, 5, 18, 20, 27, 30, 34, 43, 44, 49, 52, 55, 61, 73], "topic": [2, 4, 7, 15, 16, 17, 19, 26, 27, 34, 37, 45, 48, 50, 53, 54, 61, 64, 74], "advanc": [2, 6, 7, 13, 15, 19, 20, 26, 32, 33, 34, 36, 43, 44, 50, 58, 61, 70, 73, 74, 75], "warn": [2, 18, 24, 25, 26, 29, 30, 35, 36, 39, 41, 44, 47, 53, 56, 57, 59, 64, 72, 73], "tell": [2, 3, 4, 5, 6, 7, 13, 15, 16, 19, 22, 24, 25, 26, 28, 36, 40, 41, 42, 43, 44, 46, 52, 53, 56, 57, 58, 60, 61, 62, 63, 64, 65, 68, 69, 72, 73, 74], "ever": [2, 3, 4, 6, 16, 18, 20, 22, 23, 27, 30, 42, 50, 51, 53, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67, 73, 74], "conda": [2, 6, 7, 21, 25, 27, 34, 43, 44, 57, 68, 69, 73], "packagenam": [2, 7, 10, 21, 27, 31, 34, 68, 69, 70, 73], "pip": [2, 3, 6, 7, 18, 21, 24, 25, 27, 30, 31, 32, 33, 34, 38, 43, 44, 53, 57, 58, 60, 62, 68, 69, 70, 73, 74], "termin": [2, 3, 7, 10, 20, 21, 30, 31, 33, 39, 47, 60, 68, 72, 73, 74], "previou": [2, 7, 13, 15, 18, 20, 22, 25, 27, 30, 32, 34, 35, 36, 39, 48, 50, 53, 57, 69, 72, 73], "met": [2, 4, 7, 30, 52, 58, 73], "matter": [2, 4, 5, 6, 7, 15, 19, 23, 24, 27, 32, 39, 40, 48, 56, 57, 61, 64, 66, 73, 74, 75], "readabl": [2, 3, 4, 5, 19, 26, 31, 34, 35, 36, 43, 44, 56, 57, 58, 67, 69, 70, 71, 73], "bug": [2, 4, 12, 72, 74], "catch": [2, 16, 39, 72], "flag": [2, 4, 20, 25, 63, 69, 73], "issu": [2, 3, 4, 5, 6, 7, 12, 15, 16, 19, 20, 25, 26, 34, 40, 43, 44, 45, 48, 51, 53, 57, 61, 63, 64, 67, 68, 69, 73, 74], "compli": [2, 38], "function": [2, 4, 6, 8, 9, 10, 11, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 61, 62, 63, 65, 66, 67, 68, 69, 72, 73, 74], "linter": 2, "analys": [2, 6, 15, 19, 20, 21, 24, 33, 35, 38, 43, 53, 57, 60, 69], "programmat": [2, 24, 32, 48, 50, 60], "stylist": 2, "potenti": [2, 23, 25, 30, 32, 35, 39, 41, 44, 49, 50, 53, 55], "deviat": [2, 21, 25, 30, 34, 35, 36, 38, 39, 42, 47, 48, 51, 55, 57, 61, 67], "been": [2, 3, 4, 5, 6, 15, 17, 19, 21, 22, 24, 25, 30, 31, 32, 34, 35, 36, 37, 39, 41, 44, 45, 46, 47, 48, 50, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 66, 73, 74], "rais": [2, 3, 5, 12, 15, 25, 27, 29, 44, 53, 55, 59, 63, 68, 74], "supposedli": [2, 55], "trap": [2, 53, 57], "cloth": 2, "dryer": 2, "small": [2, 4, 5, 6, 8, 13, 16, 17, 19, 24, 25, 26, 27, 30, 33, 36, 42, 44, 48, 50, 51, 53, 57, 58, 59, 62, 69, 74], "blazingli": 2, "fast": [2, 3, 4, 10, 20, 24, 26, 27, 30, 32, 36, 41, 46, 47, 48, 52, 54, 73], "ruff": [2, 70], "across": [2, 4, 5, 6, 7, 17, 19, 25, 30, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 53, 54, 57, 58, 60, 66, 67, 69], "flake8": [2, 74], "pycodestyl": 2, "pylint": 2, "vs": [2, 3, 5, 6, 7, 18, 30, 33, 35, 38, 41, 50, 63, 66, 69, 73], "integr": [2, 4, 7, 10, 20, 21, 32, 36, 40, 46, 58, 60, 73, 74], "mac": [2, 4, 6, 7, 31, 69, 73], "select": [2, 3, 4, 5, 6, 7, 8, 10, 15, 17, 19, 20, 24, 26, 27, 29, 32, 34, 35, 36, 38, 40, 42, 43, 48, 53, 55, 57, 60, 63, 68, 69, 73, 74], "pylanc": [2, 6], "extens": [2, 3, 5, 6, 7, 8, 10, 15, 17, 18, 19, 24, 26, 31, 32, 39, 62, 63, 68, 71, 72, 73, 74], "script": [2, 4, 5, 6, 8, 54, 60, 70, 74], "list_defn": 2, "this_is_a_func": 2, "numpi": [2, 3, 5, 6, 8, 9, 10, 21, 23, 25, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 68, 70, 72, 73], "np": [2, 3, 5, 6, 8, 9, 18, 21, 23, 25, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 53, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 72, 73], "press": [2, 3, 6, 7, 17, 28, 64, 69, 74, 75], "ctrl": [2, 6, 7, 69, 73], "otherwis": [2, 3, 5, 15, 16, 19, 22, 35, 36, 41, 46, 48, 52, 55, 57, 59, 60, 63, 67, 69, 72, 73], "navig": [2, 3, 5, 6, 18, 20, 26, 27, 70, 73], "problem": [2, 3, 5, 6, 13, 15, 17, 19, 24, 25, 27, 29, 30, 35, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 52, 53, 55, 57, 59, 60, 61, 63, 64, 68, 73, 74], "load": [2, 5, 6, 7, 20, 25, 26, 27, 30, 33, 39, 40, 42, 43, 44, 48, 50, 51, 53, 57, 58, 62, 63, 69, 73, 74], "messag": [2, 3, 5, 6, 7, 16, 19, 20, 22, 41, 47, 58, 60, 61, 64, 73, 74], "few": [2, 3, 5, 6, 11, 13, 15, 16, 17, 18, 19, 20, 24, 25, 29, 30, 33, 36, 38, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 61, 63, 64, 66, 67, 69, 71, 73, 74], "miss": [2, 19, 20, 23, 25, 30, 34, 41, 42, 53, 57, 59, 61, 65, 67], "whitespac": [2, 3, 5, 8, 10, 41, 52, 53, 54, 74], "e231": 2, "under": [2, 3, 4, 5, 6, 7, 10, 15, 17, 18, 19, 30, 35, 36, 39, 40, 42, 44, 46, 48, 50, 58, 61, 68, 69, 71, 73], "indent": [2, 4, 5, 6, 63], "visual": [2, 3, 4, 7, 8, 10, 11, 19, 21, 24, 32, 36, 40, 43, 52, 54, 57, 59, 60, 61, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 75], "e128": 2, "expect": [2, 3, 5, 13, 15, 18, 22, 24, 25, 26, 27, 30, 34, 35, 36, 40, 41, 42, 43, 50, 53, 56, 58, 59, 61, 62, 63, 67, 70, 73], "blank": [2, 4, 6, 57], "found": [2, 6, 15, 16, 19, 26, 29, 32, 35, 36, 38, 39, 42, 46, 47, 48, 51, 53, 57, 61, 63, 69, 73, 74], "e302": 2, "e111": 2, "undefin": 2, "f821": 2, "e402": 2, "unus": 2, "f3401": 2, "reportundefinedvari": 2, "newlin": [2, 18, 46, 54, 59], "w292": 2, "78": [2, 22, 25, 27, 30, 34, 35, 36, 42, 43], "338": [2, 61], "mostli": [2, 3, 7, 15, 16, 20, 26, 30, 36, 50, 55, 69, 73], "rule": [2, 4, 6, 13, 15, 16, 19, 26, 36, 39, 47, 50, 57, 61, 68, 73, 74], "broken": [2, 20, 30], "row": [2, 4, 5, 8, 10, 11, 22, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 43, 44, 48, 50, 52, 53, 55, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67], "posit": [2, 3, 6, 10, 15, 21, 27, 28, 30, 36, 38, 42, 43, 47, 48, 50, 52, 57, 59, 60, 61, 62, 63, 64, 67, 73], "helpfulli": 2, "handi": [2, 3, 18, 23, 29, 30, 36, 46, 50, 54, 71], "caus": [2, 3, 6, 11, 15, 19, 20, 24, 28, 29, 30, 33, 41, 42, 47, 53, 55, 56, 57, 64, 68, 73, 74], "customis": [2, 19, 25, 26, 44, 59, 60, 61], "conveni": [2, 5, 6, 18, 26, 27, 29, 31, 39, 41, 43, 47, 48, 50, 53, 56, 57, 59, 60, 61, 63, 67, 68, 72, 73, 74], "fail": [2, 10, 19, 20, 42, 50, 68, 73], "wouldn": [2, 20, 24, 26, 67, 74], "fix": [2, 4, 5, 11, 12, 15, 23, 25, 27, 29, 33, 34, 42, 44, 48, 51, 55, 60, 68, 73, 74], "formatt": [2, 4, 61, 67], "valid": [2, 3, 4, 10, 15, 19, 29, 38, 48, 52, 73], "forcibl": [2, 38], "kind": [2, 3, 4, 5, 6, 7, 17, 19, 24, 26, 27, 29, 34, 35, 41, 43, 44, 46, 47, 48, 50, 52, 53, 59, 61, 63, 64, 66, 67, 68, 72, 73, 74], "probabl": [2, 3, 4, 5, 6, 10, 15, 16, 18, 19, 20, 21, 24, 25, 26, 29, 35, 36, 39, 42, 48, 49, 50, 52, 54, 57, 58, 60, 61, 67, 70, 73, 74], "yapf": 2, "yet": [2, 4, 5, 6, 13, 18, 25, 26, 30, 35, 57, 59, 60, 61, 65, 68, 73, 74], "googl": [2, 3, 4, 6, 7, 16, 19, 20, 21, 24, 26, 27, 32, 33, 34, 36, 43, 45, 46, 52, 53, 60, 61, 64, 71, 72, 74, 75], "autopep8": 2, "pep8": [2, 4], "black": [2, 21, 39, 43, 44, 52, 57, 59, 61, 63, 70, 73, 74, 75], "uncompromis": 2, "opinion": [2, 15, 16, 18], "colour": [2, 3, 5, 6, 15, 25, 38, 43, 44, 58, 59, 60, 63, 64, 65, 69], "long": [2, 3, 4, 5, 6, 7, 11, 13, 15, 16, 17, 18, 19, 20, 24, 26, 30, 34, 35, 36, 39, 40, 41, 43, 44, 45, 50, 52, 56, 57, 61, 63, 66, 67, 69, 73], "There": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74], "entir": [2, 3, 6, 15, 19, 24, 26, 29, 34, 35, 36, 38, 39, 40, 43, 46, 48, 52, 59, 61, 66, 68, 69, 73, 74], "bring": [2, 3, 6, 11, 14, 16, 19, 20, 22, 24, 26, 37, 41, 43, 44, 55, 64, 66, 69, 74], "k": [2, 35, 36, 38, 40, 41, 42, 43, 44, 46, 47, 48, 50, 55, 57, 58, 59, 63, 69, 72, 75], "poorli": [2, 39, 50], "content": [2, 5, 6, 7, 12, 16, 17, 18, 19, 21, 25, 26, 27, 31, 51, 53, 59, 67, 68, 69, 71, 73, 74, 75], "very_important_funct": 2, "y_val": 2, "keyword_arg": 2, "another_arg": 2, "linspac": [2, 9, 39, 40, 42, 47, 50, 57, 58, 59, 60, 63, 64, 72], "did": [2, 3, 4, 16, 19, 20, 25, 26, 28, 35, 36, 37, 38, 39, 43, 45, 47, 53, 54, 55, 57, 61, 63, 68, 72, 73], "move": [2, 6, 7, 15, 18, 19, 22, 27, 29, 30, 39, 44, 52, 53, 55, 56, 57, 58, 59, 60, 62, 66, 68, 69, 73, 74], "around": [2, 4, 5, 6, 7, 9, 10, 17, 18, 19, 20, 26, 30, 32, 35, 36, 38, 39, 40, 41, 43, 44, 46, 48, 52, 57, 58, 59, 63, 67, 69, 70, 73, 74], "ad": [2, 3, 4, 5, 7, 8, 15, 19, 21, 24, 25, 27, 28, 33, 34, 35, 36, 41, 42, 43, 44, 48, 52, 53, 54, 56, 57, 59, 61, 64, 68, 71], "strength": [2, 6, 7, 19, 24, 36, 50, 51, 59], "weak": [2, 6, 14, 19, 35, 51], "split": [2, 4, 10, 15, 19, 20, 26, 35, 41, 42, 44, 45, 47, 50, 53, 54, 59, 61, 67, 69, 73], "troublesom": 2, "isort": 2, "pressur": [2, 26, 31, 42, 60], "off": [2, 3, 5, 6, 8, 19, 20, 24, 27, 29, 35, 36, 41, 43, 44, 50, 53, 57, 59, 60, 61, 67, 73], "think": [2, 3, 4, 5, 6, 11, 12, 13, 15, 16, 17, 19, 20, 24, 25, 26, 27, 29, 30, 32, 34, 35, 36, 39, 40, 42, 43, 44, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 63, 64, 72, 73], "collabor": [2, 5, 33, 61, 72, 74], "sourc": [2, 4, 6, 7, 9, 13, 15, 16, 17, 18, 19, 21, 25, 29, 32, 38, 41, 42, 43, 44, 49, 59, 60, 61, 64, 67, 69, 72, 73, 74, 75], "maintain": [2, 4, 5, 6, 12, 26, 60, 74], "incorpor": [2, 15, 19, 36, 44], "branch": [2, 3, 17, 22, 46, 49, 51, 59, 73], "consist": [2, 3, 4, 10, 13, 15, 20, 21, 23, 26, 27, 31, 33, 36, 39, 41, 42, 46, 53, 57, 73], "who": [2, 3, 6, 7, 15, 16, 17, 18, 19, 20, 22, 24, 27, 32, 42, 45, 52, 60, 61, 64, 67, 69, 72, 73, 74], "autom": [2, 4, 7, 8, 18, 20, 25, 32, 60, 61, 69, 71, 73, 74], "someon": [2, 4, 5, 16, 19, 24, 64, 67, 68, 73, 74], "commit": [2, 4, 5, 7, 16, 24, 70, 73], "share": [2, 5, 6, 7, 14, 15, 17, 19, 24, 26, 27, 31, 58, 61, 66, 73, 74], "repositori": [2, 5, 7, 12, 18, 24, 52, 57, 59, 60, 71, 73], "hook": [2, 7, 73, 74], "jupyt": [2, 3, 4, 5, 6, 10, 18, 48, 50, 51, 66, 67, 69, 70, 71, 73, 74], "notebook": [2, 3, 4, 5, 6, 10, 18, 20, 21, 27, 34, 35, 44, 48, 50, 51, 60, 66, 67, 68, 69, 70, 71, 73, 74], "instead": [2, 3, 4, 5, 7, 9, 10, 16, 18, 19, 20, 21, 24, 27, 29, 30, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 48, 50, 51, 52, 53, 55, 56, 61, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 74], "render": [2, 3, 5, 6, 7, 15, 18, 25, 38, 43, 47, 48, 50, 51, 53, 59, 61, 62, 63, 65, 66, 67, 71, 73], "interact": [2, 3, 7, 10, 11, 24, 25, 32, 52, 53, 59, 61, 62, 63, 69, 70, 73, 74, 75], "window": [2, 3, 5, 7, 10, 16, 26, 31, 36, 39, 48, 52, 60, 61, 62, 69, 70, 72, 73], "fantast": [2, 4, 5, 10, 21, 24, 27, 34, 61, 73], "interfac": [2, 6, 7, 14, 20, 24, 26, 32, 35, 43, 63, 66, 69, 74], "valu": [2, 4, 5, 6, 8, 10, 13, 15, 17, 20, 23, 24, 25, 26, 28, 30, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 73, 74], "my_list": 2, "foo": [2, 10, 27, 28, 34, 69], "bar": [2, 3, 5, 6, 10, 15, 17, 21, 25, 28, 30, 34, 40, 41, 42, 43, 44, 57, 61, 63, 66, 69, 70, 74], "gt": [2, 10, 25, 29, 30, 35, 36, 41, 52, 56, 57, 67, 68, 70], "mutabl": [2, 3], "clear": [2, 3, 4, 5, 7, 13, 14, 15, 16, 18, 19, 21, 34, 36, 41, 42, 50, 55, 57, 59, 61, 63, 64, 67, 73], "copi": [2, 3, 18, 32, 34, 40, 41, 43, 52, 55, 57, 67, 68, 69, 73, 74], "shallow": 2, "occurr": [2, 8, 40, 44, 52, 53, 54], "9223372036854775807": 2, "insert": [2, 7, 19, 24, 63, 67, 69, 71, 72, 73, 74], "revers": [2, 3, 15, 19, 24, 34, 43, 46, 52, 57, 59, 64, 74], "IN": [2, 53], "sort": [2, 6, 9, 16, 17, 23, 24, 25, 26, 27, 30, 34, 40, 51, 58, 59, 61, 63, 69], "ascend": [2, 10, 21, 27, 30, 42, 53, 55, 59], "order": [2, 3, 6, 7, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 26, 29, 30, 32, 34, 39, 40, 42, 44, 45, 46, 47, 48, 53, 55, 57, 59, 60, 61, 63, 64, 65, 66, 69, 73, 74], "option": [2, 3, 4, 5, 6, 7, 10, 11, 15, 16, 19, 20, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 41, 42, 44, 46, 47, 48, 50, 51, 52, 53, 54, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 70, 73, 74], "ran": [2, 36, 54, 62], "plenti": [2, 5, 16, 31, 35, 53, 56, 60, 63, 71, 74], "map": [2, 3, 10, 15, 21, 25, 27, 33, 34, 35, 40, 43, 48, 49, 52, 55, 58, 59, 62, 63, 65, 67], "letter": [2, 3, 19, 27, 28, 40, 41, 42, 43, 52, 54, 61, 74, 75], "dict": [2, 3, 5, 10, 34, 40, 41, 43, 46, 53, 58, 59, 63, 64, 66], "programm": [2, 3, 5, 6, 8, 18, 39, 52, 57, 69, 73, 74], "halt": [2, 72], "won": [2, 3, 4, 5, 6, 10, 15, 16, 18, 19, 20, 24, 25, 26, 27, 28, 32, 36, 42, 45, 49, 53, 57, 59, 61, 63, 67, 72], "hit": [2, 3, 6, 7, 18, 26, 29, 54, 56, 60, 64, 68, 69, 72, 73, 74], "special": [2, 3, 4, 6, 7, 19, 23, 26, 27, 29, 32, 37, 39, 43, 46, 48, 55, 57, 59, 60, 61, 69, 72, 74], "trigger": [2, 20, 64, 66], "life": [2, 4, 15, 19, 26, 27, 36, 38, 58], "action": [2, 3, 4, 7, 8, 11, 15, 16, 19, 20, 24, 25, 26, 32, 34, 38, 39, 40, 41, 43, 44, 51, 52, 56, 57, 68], "denom": 2, "zerodivisionerror": 2, "traceback": [2, 5, 20, 61], "recent": [2, 5, 7, 13, 16, 19, 20, 21, 24, 32, 36, 38, 46, 51, 52, 57, 59, 61, 62, 72, 74], "ipython": [2, 5, 7, 61, 66, 72], "39": [2, 3, 6, 22, 25, 26, 27, 33, 34, 35, 36, 41, 42, 48, 51, 56, 57, 59, 62, 67], "e45c0e0a3e37": 2, "divis": [2, 3, 5, 22, 27, 30, 31, 53, 59, 73], "oh": [2, 41, 43, 55, 63, 69, 74], "got": [2, 3, 6, 7, 16, 20, 27, 29, 32, 36, 39, 41, 42, 57, 61, 63, 69, 72, 74], "crash": 2, "went": [2, 20, 35, 52], "aris": [2, 29, 40], "pattern": [2, 3, 22, 25, 27, 30, 33, 34, 35, 36, 49, 52, 53, 54, 55, 56, 57, 62, 63, 66, 68], "occur": [2, 5, 22, 26, 30, 40, 41, 42, 44, 52, 53, 54, 56, 57, 61], "block": [2, 3, 6, 7, 9, 18, 43, 53, 55, 60, 63, 68], "isn": [2, 3, 4, 6, 10, 15, 16, 17, 19, 20, 21, 23, 24, 25, 27, 29, 30, 31, 33, 35, 38, 39, 40, 43, 44, 48, 52, 53, 57, 59, 63, 64, 67, 68, 69, 70, 72, 73], "cannot": [2, 7, 15, 16, 19, 27, 31, 42, 43, 45, 50, 52, 60, 61, 72], "divid": [2, 3, 4, 5, 15, 30, 34, 36, 38, 40, 44, 47, 48, 59, 68, 73], "fine": [2, 6, 19, 31, 58, 59, 61, 63, 67, 73], "told": [2, 3, 19, 20, 23, 52, 57, 63], "user": [2, 3, 4, 6, 17, 20, 24, 25, 26, 32, 35, 36, 38, 43, 46, 49, 50, 57, 59, 60, 61, 62, 63, 65, 69, 72, 73, 74], "reciproc": 2, "sadli": [2, 17], "encount": [2, 24, 46], "sever": [2, 3, 6, 11, 19, 20, 25, 29, 30, 32, 39, 40, 41, 43, 47, 48, 50, 53, 54, 55, 58, 59, 60, 61, 63, 64, 66, 68, 69, 73, 74], "claus": [2, 3, 22, 24], "indexerror": [2, 61], "nest": [2, 3, 4, 5, 22, 26, 34, 41, 48, 57, 63], "arithmeticerror": 2, "anywai": [2, 13, 16, 59], "tantrum": 2, "person": [2, 5, 17, 53, 57, 73, 74], "wrote": [2, 45, 68, 74], "hood": [2, 10, 18, 35, 46, 48, 50, 61], "philosophi": [2, 10, 20, 61, 63, 73], "loudli": [2, 10, 20], "downstream": [2, 20, 25, 48, 73], "valueerror": [2, 3, 55, 58, 59, 61], "neg": [2, 3, 5, 15, 30, 36, 41, 42, 48, 52, 61, 64], "ones": [2, 5, 6, 9, 10, 15, 18, 19, 25, 30, 34, 35, 36, 39, 40, 41, 43, 46, 47, 48, 52, 55, 59, 61, 63, 69, 70, 71, 73, 74], "specialis": [2, 6, 13, 24], "handl": [2, 24, 26, 29, 39, 73], "low": [2, 5, 6, 15, 19, 25, 36, 39, 40, 41, 42, 50, 51, 61], "thrown": [2, 20], "deep": [2, 16, 18, 19, 36, 48, 49, 61, 64, 66, 75], "down": [2, 4, 14, 16, 18, 19, 25, 26, 27, 30, 31, 32, 33, 35, 36, 38, 44, 49, 50, 51, 53, 56, 57, 58, 61, 63, 64, 69, 70, 71, 72, 73, 74], "middl": [2, 30, 33, 61, 73], "awai": [2, 5, 6, 13, 16, 24, 27, 33, 36, 41, 43, 48, 50, 53, 59, 61, 63, 66], "sum_reciproc": 2, "caught": [2, 13, 74], "elsewher": [2, 26, 60, 62, 66], "v": [2, 9, 18, 20, 36, 42, 53, 55, 59, 69, 75], "one_ov": 2, "b_year": 2, "1852": 2, "name_input": 2, "process_input": 2, "year_born": 2, "typeerror": [2, 63], "172": [2, 21, 38, 41, 42, 59], "comput": [2, 3, 4, 5, 7, 10, 17, 19, 20, 21, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 38, 41, 42, 45, 46, 47, 48, 50, 52, 53, 55, 56, 57, 58, 60, 63, 68, 69, 74, 75], "liter": [2, 3, 18, 36, 43, 46, 52, 54, 56, 64, 69], "unless": [2, 4, 5, 9, 10, 15, 16, 19, 32, 36, 40, 52, 63, 66, 74], "difficult": [2, 6, 15, 32, 58, 61, 73], "fundament": [2, 10, 15, 18, 19, 22, 24, 30, 43, 47, 56, 57, 61, 63, 75], "fact": [2, 10, 15, 18, 24, 25, 27, 39, 40, 41, 42, 43, 44, 50, 52, 53, 57, 63, 69], "term": [2, 3, 4, 10, 11, 15, 17, 19, 24, 26, 35, 38, 42, 46, 49, 54, 57, 61, 63, 64, 69, 73, 74], "moth": 2, "earli": [2, 7, 17, 18, 19, 30, 57, 58, 67, 73], "gave": [2, 38], "produc": [2, 3, 6, 7, 10, 14, 15, 18, 19, 20, 22, 23, 25, 26, 30, 34, 35, 36, 38, 39, 40, 44, 46, 47, 50, 53, 55, 57, 58, 59, 61, 62, 63, 66, 69, 71, 72, 73, 74], "outcom": [2, 14, 18, 19, 27, 35, 36, 38, 39, 40, 41, 50, 52, 63], "base": [2, 3, 4, 5, 6, 7, 8, 10, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 47, 48, 50, 51, 52, 53, 56, 58, 59, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74], "tricki": [2, 13, 15, 17, 19, 26, 40, 54, 55, 57, 58, 67, 73], 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"sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 56}}) \ No newline at end of file diff --git a/text-nlp.html b/text-nlp.html index eb06f0c..8a08dee 100644 --- a/text-nlp.html +++ b/text-nlp.html @@ -1358,7 +1358,7 @@

TF-IDF
-_images/d9c5820a34e6925e1c0257a6c07eac777b2c9f3c8b3fe1e862ee3052a0a67c40.svg
+_images/aebe91dc9dd57f0f802d3585460444d12704b4b6249e44766d9822f66016be5d.svg

Let’s see what happens when we ask only for bi-grams.

@@ -1389,7 +1389,7 @@

TF-IDF

-_images/9d7e0862dec4dced49b32549f02f3b089ac37055737c9d473976216429a070c3.svg
+_images/386dab22a3df1ef2681be45bd6b20866dc89f73e33c2acf56dd75d62a412d874.svg

As you might expect, the highest frequency with which 2-grams occur is less than the highest frequency with which 1-grams occur.

Now let’s move on to the inverse document frequency. The most common definition is

@@ -1430,7 +1430,7 @@

TF-IDF
-_images/6303ef8a6ad8223512a4670885343b71b6ea35a7db2e6276879df24e83083fe8.svg
+_images/cf69385f852253eddf38fdd2665fd6b0e5e13a5a1c5d2de70e1447409d114f31.svg

There are small differences between this ranking of terms versus the original tf 1-gram version above. In the previous one, words such as ‘one’ were slightly higher in the ranking but their common appearance in multiple documents (lines) downweights them here. In this case, we also used the sublinear option, which uses \(1+\log(\mathrm{tf})\) in place of \(\mathrm{tf}\).

@@ -1577,7 +1577,7 @@

Using a Special Vocabulary -_images/cc80647a354f271b67615af3684500fb54da48081d7184a1baf749009de78a51.svg +_images/a11d75e77779c8972d2f7d41f7166bc3595b9aebc635df59c9d3b8cdecadb255.svg

Note that we did not pass stopwords in this case; there’s no need, because passing a vocab effectively says to categorise any word that is not in the special vocabulary as a stopword. We also still passed an n-gram range to ensure our longest n-gram, with \(n=2\), was counted.

@@ -1645,7 +1645,7 @@

Context of Terms -_images/dbb6dabc362c482fc5f898fc2b0f0606b06e9d513ca49f85c47e08d32d87128d.svg +_images/edae624e802700923b2b9f97c3a67e0c3c2f348c195e58561941236c5eb839f6.svg
@@ -1811,48 +1811,84 @@

Part of Speech Tagging
-
---------------------------------------------------------------------------
-OSError                                   Traceback (most recent call last)
-Cell In[38], line 3
-      1 import spacy
-----> 3 nlp = spacy.load("en_core_web_sm")
-      5 doc = nlp(example_sent)
-      7 pos_df = pd.DataFrame(
-      8     [(token.text, token.lemma_, token.pos_, token.tag_) for token in doc],
-      9     columns=["text", "lemma", "pos", "tag"],
-     10 )
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/spacy/__init__.py:51, in load(name, vocab, disable, enable, exclude, config)
-     27 def load(
-     28     name: Union[str, Path],
-     29     *,
-   (...)
-     34     config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
-     35 ) -> Language:
-     36     """Load a spaCy model from an installed package or a local path.
-     37 
-     38     name (str): Package name or model path.
-   (...)
-     49     RETURNS (Language): The loaded nlp object.
-     50     """
----> 51     return util.load_model(
-     52         name,
-     53         vocab=vocab,
-     54         disable=disable,
-     55         enable=enable,
-     56         exclude=exclude,
-     57         config=config,
-     58     )
-
-File ~/mambaforge/envs/codeforecon/lib/python3.10/site-packages/spacy/util.py:472, in load_model(name, vocab, disable, enable, exclude, config)
-    470 if name in OLD_MODEL_SHORTCUTS:
-    471     raise IOError(Errors.E941.format(name=name, full=OLD_MODEL_SHORTCUTS[name]))  # type: ignore[index]
---> 472 raise IOError(Errors.E050.format(name=name))
-
-OSError: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.
-
-
-
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textlemmapostag
0IfifSCONJIN
1wewePRONPRP
2arebeAUXVBP
...............
14folkfolkNOUNNN
15bandbandNOUNNN
16..PUNCT.
+

17 rows × 4 columns

+

For those brave enough for the pun, spacy also has some nifty visualisation tools.

@@ -1865,6 +1901,130 @@

Part of Speech Tagging

+
+
+ + When + SCONJ + + + + you + PRON + + + + light + VERB + + + + a + DET + + + + candle, + NOUN + + + + you + PRON + + + + also + ADV + + + + cast + VERB + + + + a + DET + + + + shadow. + NOUN + + + + + + advmod + + + + + + + + nsubj + + + + + + + + advcl + + + + + + + + det + + + + + + + + dobj + + + + + + + + nsubj + + + + + + + + advmod + + + + + + + + det + + + + + + + + dobj + + + +

@@ -1881,6 +2041,38 @@

Named Entity Recognition +
+ + TAE Technologies + ORG + +, a + + California + GPE + +-based firm building technology to generate power from nuclear fusion, said on + + Thursday + DATE + + it had raised + + $280 million + MONEY + + from new and existing investors, including + + Google + ORG + + and + + New Enterprise Associates + ORG + +.

Pretty impressive stuff, but a health warning that there are plenty of texts that are not quite as clean as this one! As with the PoS tagger, you can extract the named entities in a tabular format for onward use:

@@ -1892,6 +2084,77 @@

Named Entity Recognition +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textstart_posend_poslabel
0TAE Technologies016ORG
1California2030GPE
2Thursday109117DATE
3$280 million132144MONEY
4Google188194ORG
5New Enterprise Associates199224ORG
+

The table below gives the different label meanings in Named Entity Recognition:

@@ -1968,6 +2231,52 @@

Readability Statistics +
+
+ +

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
score
flesch_reading_ease52.23
flesch_kincaid_grade12.80
automated_readability_index15.50
dale_chall_readability_score7.30
difficult_words9.00
+