{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Example `stata_kernel` Jupyter notebook\n", "\n", "This Jupyter notebook is an example of how you can use Stata in the Jupyter ecosystem using `stata_kernel`.\n", "\n", "Full documentation, including how to install, is available at https://kylebarron.dev/stata_kernel/." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "The Jupyter Notebook is a file format that permits interactive coding with text, code, and results in a single document. You can share a notebook file (with extension `.ipynb`), and results will be viewable without running the code, but as long as the recipient also has Jupyter installed, he or she can edit and re-run the code cells.\n", "\n", "Jupyter itself is language agnostic, i.e. it permits writing code in any language. This document uses Stata code, but you can also code in Jupyter using Python, [R](https://irkernel.github.io/), [Julia](https://github.com/JuliaLang/IJulia.jl), [Matlab](https://github.com/calysto/matlab_kernel), and [SAS](https://github.com/sassoftware/sas_kernel). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running code\n", "\n", "In contrast to [IPyStata](https://github.com/TiesdeKok/ipystata), no special commands are needed. Just write code as you would normally in Stata.\n", "\n", "Let's make sure that the connection with Stata is working properly." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, world!\n" ] } ], "source": [ "display \"Hello, world!\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can run a cell by pressing Ctrl+Enter or Shift+Enter. If a number appears in the brackets to the left of the input cell, that means that the code was successfully run (sometimes a cell doesn't produce any output).\n", "\n", "If you don't see `Hello, world!` as output, check out the [troubleshooting tips](https://kylebarron.dev/stata_kernel/using_stata_kernel/troubleshooting/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's load the included `auto` dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1978 Automobile Data)\n" ] } ], "source": [ "sysuse auto.dta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the `auto` dataset is in memory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic descriptive statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nearly all commands that work in Stata work through Jupyter as well. A couple commands that depend on the Graphical User Interface, such as `browse` and `edit`, only work on Windows." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " | Headroom (in.)\n", " Car type | 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 | Total\n", "-----------+----------------------------------------------------------------------------------------+----------\n", " Domestic | 3 10 4 7 13 10 4 1 | 52 \n", " Foreign | 1 3 10 6 2 0 0 0 | 22 \n", "-----------+----------------------------------------------------------------------------------------+----------\n", " Total | 4 13 14 13 15 10 4 1 | 74 \n" ] } ], "source": [ "tabulate foreign headroom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No special syntax is needed to generate graphs. Just write commands like you're used to.\n", "The display order of graphs will always be the same as the order in the code." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "(highschool and beyond (200 cases))\n" ] }, { "data": { "application/pdf": 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"\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t30\n", "\t\n", "\t40\n", "\t\n", "\t50\n", "\t\n", "\t60\n", "\t\n", "\t70\n", "\t\n", "\t80\n", "\tmath score\n", "\t\n", "\t\n", "\t20\n", "\t\n", "\t40\n", "\t\n", "\t60\n", "\t\n", "\t80\n", "\tscience score\n", "\tMath score vs Science score\n", "\n" ], "text/html": [ " \n" ], "text/plain": [ "This front-end cannot display the desired image type." ] }, "metadata": { "image/svg+xml": { "height": 436, "width": 600 }, "text/html": { "height": 436, "width": 600 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "// Dataset with test scores\n", "use \"https://stats.idre.ucla.edu/stat/stata/notes/hsb2\", clear\n", "scatter read math, title(\"Reading score vs Math score\")\n", "scatter math science, title(\"Math score vs Science score\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't want to display a graph, just prefix the command with [`quietly`](https://www.stata.com/help.cgi?quietly)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "quietly scatter read math, title(\"Reading score vs Math score\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comments in code\n", "\n", "`stata_kernel` lets you use _any_ format of comments, including `//`, `///`, `*`, and `/*`-`*/`, even in an interactive console environment where the Stata command line normally wouldn't accept them." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "displayed\n" ] } ], "source": [ "display \"displayed\"\n", "// display \"comment\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "line continuation comment\n" ] } ], "source": [ "display \"line continuation \" /// comment\n", " \"comment\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "* display \"not displayed\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "displayed1\n", "\n", "displayed3\n" ] } ], "source": [ "display \"displayed1\"\n", "/*\n", "display \"displayed2\"\n", "*/\n", "display \"displayed3\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autocompletion\n", "\n", "`stata_kernel` provides autocompletion for locals, globals, variables, scalars, and matrices based on the contents in memory. It also suggests file paths to load or save files. Press Tab while typing to activate it.\n", "\n", "![](https://raw.githubusercontent.com/kylebarron/stata_kernel/master/docs/src/img/jupyterlab_autocompletion.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Magics\n", "\n", "[_Magics_](https://kylebarron.dev/stata_kernel/using_stata_kernel/magics/) are special commands that `stata_kernel` provides to give extra functionality, especially regarding the connection with Jupyter. \n", "\n", "These commands all start with `%`. You can run `%help magics` or [go here](https://kylebarron.dev/stata_kernel/using_stata_kernel/magics/) to see a list of available magics. You can also run `%magic_name --help` to see the help for any given magic.\n", "\n", "In order to prevent confusion, these commands **must** occur at the beginning of a cell." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `%head`, `%browse`, `%tail`\n", "\n", "**`%head`**, **`%browse`**, and **`%tail`** show a well-formatted portion of the dataset in memory." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", " +-------------------------------------------------------------------------------------------+\n", " | id female race ses schtyp prog read write math science socst |\n", " |-------------------------------------------------------------------------------------------|\n", " 1. | 70 male white low public general 57 52 41 47 57 |\n", " 2. | 121 female white middle public vocation 68 59 53 63 61 |\n", " 3. | 86 male white high public general 44 33 54 58 31 |\n", " 4. | 141 male white high public vocation 63 44 47 53 56 |\n", " 5. | 172 male white middle public academic 47 52 57 53 61 |\n", " +-------------------------------------------------------------------------------------------+\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%head 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `%help`\n", "**`%help`** shows the help menu for a given command. The links in this help file are clickable, just like the official Stata documentation. (This command requires internet access.)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "Stata 15 help for summarize\n", "\n", "\n", "\n", "\n", "\n", "

\n", "\n", "
\n", "\n", "\n", "
\n",
       "
\n",
       "[R] summarize -- Summary statistics\n",
       "
\n",
       "
\n",
       "Syntax\n",
       "
\n",
       "        summarize [varlist] [if] [in] [weight] [, options]\n",
       "
\n",
       "    options           Description\n",
       "    -------------------------------------------------------------------------\n",
       "    Main\n",
       "      detail          display additional statistics\n",
       "      meanonly        suppress the display; calculate only the mean;\n",
       "                        programmer's option\n",
       "      format          use variable's display format\n",
       "      separator(#)    draw separator line after every # variables; default is\n",
       "                        separator(5)\n",
       "      display_options control spacing, line width, and base and empty cells\n",
       "
\n",
       "    -------------------------------------------------------------------------\n",
       "    varlist may contain factor variables; see fvvarlist.\n",
       "    varlist may contain time-series operators; see tsvarlist.\n",
       "    by, rolling, and statsby are allowed; see prefix.\n",
       "  \n",
       "    aweights, fweights, and iweights are allowed.  However, iweights may not\n",
       "      be used with the detail option; see weight.\n",
       "
\n",
       "
\n",
       "Menu\n",
       "
\n",
       "    Statistics > Summaries, tables, and tests > Summary and descriptive\n",
       "        statistics > Summary statistics\n",
       "
\n",
       "
\n",
       "Description\n",
       "
\n",
       "    summarize calculates and displays a variety of univariate summary\n",
       "    statistics.  If no varlist is specified, summary statistics are\n",
       "    calculated for all the variables in the dataset.\n",
       "
\n",
       "
\n",
       "Options\n",
       "
\n",
       "        +------+\n",
       "    ----+ Main +-------------------------------------------------------------\n",
       "
\n",
       "    detail produces additional statistics, including skewness, kurtosis, the\n",
       "        four smallest and four largest values, and various percentiles.\n",
       "
\n",
       "    meanonly, which is allowed only when detail is not specified, suppresses\n",
       "        the display of results and calculation of the variance.  Ado-file\n",
       "        writers will find this useful for fast calls.\n",
       "
\n",
       "    format requests that the summary statistics be displayed using the\n",
       "        display formats associated with the variables rather than the default\n",
       "        g display format; see [D] format.\n",
       "
\n",
       "    separator(#) specifies how often to insert separation lines into the\n",
       "        output.  The default is separator(5), meaning that a line is drawn\n",
       "        after every five variables.  separator(10) would draw a line after\n",
       "        every 10 variables.  separator(0) suppresses the separation line.\n",
       "
\n",
       "    display_options:  vsquish, noemptycells, baselevels, allbaselevels,\n",
       "        nofvlabel, fvwrap(#), and fvwrapon(style); see [R] estimation\n",
       "        options.\n",
       "
\n",
       "
\n",
       "Examples\n",
       "
\n",
       "    . sysuse auto\n",
       "    . summarize\n",
       "    . summarize mpg weight\n",
       "    . summarize mpg weight if foreign\n",
       "    . summarize mpg weight if foreign, detail\n",
       "    . summarize i.rep78\n",
       "
\n",
       "
\n",
       "Video example\n",
       "
\n",
       "    Descriptive statistics in Stata\n",
       "
\n",
       "
\n",
       "Stored results\n",
       "
\n",
       "    summarize stores the following in r():\n",
       "
\n",
       "    Scalars   \n",
       "      r(N)           number of observations\n",
       "      r(mean)        mean\n",
       "      r(skewness)    skewness (detail only)\n",
       "      r(min)         minimum\n",
       "      r(max)         maximum\n",
       "      r(sum_w)       sum of the weights\n",
       "      r(p1)          1st percentile (detail only)\n",
       "      r(p5)          5th percentile (detail only)\n",
       "      r(p10)         10th percentile (detail only)\n",
       "      r(p25)         25th percentile (detail only)\n",
       "      r(p50)         50th percentile (detail only)\n",
       "      r(p75)         75th percentile (detail only)\n",
       "      r(p90)         90th percentile (detail only)\n",
       "      r(p95)         95th percentile (detail only)\n",
       "      r(p99)         99th percentile (detail only)\n",
       "      r(Var)         variance\n",
       "      r(kurtosis)    kurtosis (detail only)\n",
       "      r(sum)         sum of variable\n",
       "      r(sd)          standard deviation\n",
       "
\n",
       "
\n", "\n", "
\n", "\n", "\n", "

\n", "\n" ], "text/plain": [ "This front-end cannot display HTML help." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%help summarize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `%locals`, `%globals`\n", "\n", "**`%locals`** and **`%globals`** display the local or global macros in the current environment." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "local local1 \"foo\"\n", "local local2 \"bar\"\n", "local abcd \"foo bar\"" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd: foo bar\n", "local2: bar\n", "local1: foo\n" ] } ], "source": [ "%locals" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "local2: bar\n", "local1: foo\n" ] } ], "source": [ "%locals loc" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(note: showing first line of global values; run with --verbose)\n", "\n", "T_gm_fix_span: 0\n", "stata_kernel_graph_counter: 2\n", "S_FNDATE: 17 Jun 2002 08:48\n", "S_FN: https://stats.idre.ucla.edu/stat/stata/notes/hsb2.dta\n", "S_ADO: BASE;SITE;.;PERSONAL;PLUS;OLDPLACE;`\"/Users/kyle/github/stata/stata-kernel/stata_kernel/ado\"'\n", "S_level: 95\n", "F1: help advice;\n", "F2: describe;\n", "F7: save\n", "F8: use\n", "S_StataSE: SE\n", "S_CONSOLE: console\n", "S_FLAVOR: Intercooled\n", "S_OS: Unix\n", "S_MACH: Macintosh (Intel 64-bit)\n" ] } ], "source": [ "%globals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `%html`, `%latex`\n", "\n", "**`%html`** and **`%latex`** attempt to display either type of _output_ (not user input). This could be used, for example, with `estout` to display several regression results side-by-side.\n", "\n", "**Note:** Jupyter can display a math subset of LaTeX but doesn't support tables. _However_, it's really easy to export a Jupyter Notebook file to PDF through LaTeX (see File > Export Notebook As > Export Notebook to PDF). In this PDF export, LaTeX tables _will_ be properly displayed." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "(1978 Automobile Data)\n", "\n", "\n", "(est1 stored)\n", "\n", "(est2 stored)\n", "\n", "(est3 stored)\n" ] } ], "source": [ "cap ssc install estout\n", "sysuse auto, clear\n", "eststo clear\n", "eststo: qui regress price mpg rep78\n", "eststo: qui regress price mpg rep78 gear_ratio trunk\n", "eststo: qui regress price mpg rep78 gear_ratio trunk weight displacement" ] }, { "cell_type": "code", "execution_count": 17, "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", "\n", "\n", "\n", "\n", "
Regression Table
(1) (2) (3)
Price Price Price
Mileage (mpg) -271.6*** -206.3* -76.96
(-4.70) (-2.65) (-0.92)
 
Repair Record 1978 667.0 767.1* 899.1**
(1.95) (2.17) (2.76)
 
Gear Ratio -1289.9 1479.7
(-1.38) (1.30)
 
Trunk space (cu. ft.) 12.49 -110.3
(0.14) (-1.23)
 
Weight (lbs.) 1.140
(1.00)
 
Displacement (cu. in.) 17.82
(1.88)
 
Constant 9657.8*** 11620.1*** -5163.3
(7.17) (3.65) (-0.94)
Observations 69 69 69
\n", "t statistics in parentheses\n", "
* p < 0.05, ** p < 0.01, *** p < 0.001\n", "
\n", "\n" ], "text/plain": [ "This front-end or document format cannot display HTML" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%html\n", "esttab, label title(\"Regression Table\") html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `%show_gui`, `%hide_gui`\n", "\n", "On Windows, **`%show_gui`** and **`%hide_gui`** show and hide the traditional Stata Graphical User Interface window. These magics do not work on macOS or Linux because those platforms communicate with Stata in a different manner." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `;`-delimited commands\n", "\n", "Often with long commands, such as graphs, using [`#delimit ;`](https://www.stata.com/help.cgi?delimit) helps prevent very long lines and helps to keep code more readable. This is supported in `stata_kernel`, despite it not being allowed in the normal Stata command-line environment." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1978 Automobile Data)\n" ] } ], "source": [ "sysuse auto, clear" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, world!\n", "delimiter now ;" ] } ], "source": [ "#delimit ;\n", "display \"Hello, world!\";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's important to note that the `;`-delimiter mode persists across cells. `stata_kernel` will expect cells to include `;` for each command, and will raise an error if `;` is missing." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "stata_kernel error: code entered was incomplete.\n", "\n", "This usually means that a loop or program was not correctly terminated.\n", "This can also happen if you are in `#delimit ;` mode and did not end the\n", "command with `;`. Use `%delimit` to see the current delimiter mode and\n", "use `#delimit cr` to switch back to the default mode where `;` is\n", "unnecessary.\n" ] } ], "source": [ "display \"Hello, world!\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can check the current delimiter with the **`%delimit`** magic." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The delimiter is currently: ;\n" ] } ], "source": [ "%delimit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can switch back to normal line-break delimited commands (i.e. where `;` is unnecessary) with `#delimit cr`." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delimiter now cr" ] } ], "source": [ "#delimit cr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Mata\n", "\n", "You can start an interactive Mata session by typing `mata`. This persists across cells; cells will continue being Mata cells until you run `end` to exit the mata session.\n", "\n", "You can run the **`%status`** magic to check if you're in Mata or Stata mode." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1978 Automobile Data)\n" ] } ], "source": [ "sysuse auto, clear" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------- mata (type end to exit) -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "mata" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stata_kernel 1.9.0 for Stata 15.1\n", "\n", "\n", "\tDelimiter: cr\n", "\tEnvironment: Mata\n" ] } ], "source": [ "%status" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "y = st_data(., \"price\")\n", "X = st_data(., \"mpg trunk\")\n", "n = rows(X)\n", "X = X,J(n,1,1)\n", "XpX = quadcross(X, X)\n", "XpXi = invsym(XpX)\n", "b = XpXi*quadcross(X, y)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1 2 3\n", " +----------------------------------------------+\n", " 1 | -220.1648801 43.55851009 10254.94983 |\n", " +----------------------------------------------+\n" ] } ], "source": [ "b'" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "end" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stata_kernel 1.9.0 for Stata 15.1\n", "\n", "\n", "\tDelimiter: cr\n", "\tEnvironment: Stata\n" ] } ], "source": [ "%status" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Stata", "language": "stata", "name": "stata" }, "language_info": { "codemirror_mode": "stata", "file_extension": ".do", "mimetype": "text/x-stata", "name": "stata", "version": "15.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "464px", "width": "252px" }, "navigate_menu": true, "number_sections": false, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }