diff --git a/coverage.xml b/coverage.xml
new file mode 100644
index 00000000..1ab0d9d6
--- /dev/null
+++ b/coverage.xml
@@ -0,0 +1,5233 @@
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diff --git a/docs/table-layout.qmd b/docs/table-layout.qmd
index 223fba60..eb4d9c61 100644
--- a/docs/table-layout.qmd
+++ b/docs/table-layout.qmd
@@ -121,7 +121,7 @@ pf.etable(
)
```
-To obtain latex output use `format = "tex"`. If you want to save the table as a tex file, you can use the `filename=` argument to specify the respective path where it should be saved. If you want the latex code to be displayed in the notebook, you can use the `print_tex=True` argument.
+To obtain latex output use `format = "tex"`. If you want to save the table as a tex file, you can use the `file_name=` argument to specify the respective path where it should be saved. If you want the latex code to be displayed in the notebook, you can use the `print_tex=True` argument.
Etable will use latex packages `booktabs`, `threeparttable` and `makecell` for the table layout, so don't forget to include these packages in your latex document.
```{python}
@@ -194,6 +194,33 @@ pf.etable(
)
```
+## Rename categorical variables
+
+Categorical variables are automatically be relabeled from the formulaic "C(variable)[T.value]" notation where the default is *{variable}={value}*. This works when either the variable is categorial in the DataFrame, or the C() or i() operators are used in the regresson formula. If you instead want to keep the standard formulaic notation, specify *cat_template=""*.
+´
+```{python}
+
+# Add a categorical variable
+data['job'] = np.random.choice(["Managerial", "Admin", "Blue collar"], size=len(data), p=[1/3, 1/3, 1/3])
+# Add a label for this variable to the dictionary
+labels['job']="Job Family"
+
+fit7 = pf.feols("Y ~ X1 + X2 + job", data = data)
+
+pf.etable([fit7], labels=labels)
+```
+
+But you can also remove the variable name and only keep the levels (categories) by specifying *cat_template="{value}"*. Note that the labeling of categories also works in interaction terms:
+
+```{python}
+
+fit7 = pf.feols("Y ~ X1 + X2 + job", data = data)
+fit8 = pf.feols("Y ~ X1 + X2 + job*X2", data = data)
+
+pf.etable([fit7, fit8], labels=labels, cat_template="{value}")
+```
+
+
## Custom model headlines
You can also add custom headers for each model by passing a list of strings to the `model_headers` argument.
diff --git a/docs/table-layout.quarto_ipynb b/docs/table-layout.quarto_ipynb
new file mode 100644
index 00000000..1bfc60d4
--- /dev/null
+++ b/docs/table-layout.quarto_ipynb
@@ -0,0 +1,1013 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "format:\n",
+ " html:\n",
+ " html-table-processing: none\n",
+ "toc: true\n",
+ "toc-title: \"On this page\"\n",
+ "toc-location: left\n",
+ "---\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Table Layout with PyFixest\n",
+ "\n",
+ "Pyfixest comes with functions to generate publication-ready tables. Regression tables are generated with `pf.etable()`, which can output different formats, for instance using the [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html) package or generating formatted LaTex Tables using [booktabs](https://ctan.org/pkg/booktabs?lang=en). There are also further functions `pf.dtable()` to display descriptive statistics and `pf.make_table()` generating formatted tables from pandas dataframes in the same layout.\n",
+ "\n",
+ "To begin, we load some libraries and fit a set of regression models.\n"
+ ],
+ "id": "ada0e7f3"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "#import pylatex as pl # for the latex table; note: not a dependency of pyfixest - needs manual installation\n",
+ "from great_tables import loc, style\n",
+ "from IPython.display import FileLink, display\n",
+ "\n",
+ "import pyfixest as pf\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "data = pf.get_data()\n",
+ "\n",
+ "fit1 = pf.feols(\"Y ~ X1 + X2 | f1\", data=data)\n",
+ "fit2 = pf.feols(\"Y ~ X1 + X2 | f1 + f2\", data=data)\n",
+ "fit3 = pf.feols(\"Y ~ X1 *X2 | f1 + f2\", data=data)\n",
+ "fit4 = pf.feols(\"Y2 ~ X1 + X2 | f1\", data=data)\n",
+ "fit5 = pf.feols(\"Y2 ~ X1 + X2 | f1 + f2\", data=data)\n",
+ "fit6 = pf.feols(\"Y2 ~ X1 *X2 | f1 + f2\", data=data)"
+ ],
+ "id": "4b211a90",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Regression Tables via `pf.etable()`\n",
+ "\n",
+ "## Basic Usage\n",
+ "\n",
+ "We can compare all regression models via the pyfixest-internal `pf.etable()` function:\n"
+ ],
+ "id": "fa833139"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6])"
+ ],
+ "id": "25d5b9c4",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also estimate and display multiple regressions with one line of code using the (py)fixest stepwise notation:\n"
+ ],
+ "id": "2e58c059"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(pf.feols(\"Y+Y2~csw(X1,X2,X1:X2)\", data=data))"
+ ],
+ "id": "3e41c4f3",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Keep and drop variables\n",
+ "`etable` allows us to do a few things out of the box. For example, we can only keep the variables that we'd like, which keeps all variables that fit the provided regex match.\n"
+ ],
+ "id": "411b27b9"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], keep=\"X1\")"
+ ],
+ "id": "0fcff403",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the `exact_match` argument to select a specific set of variables:\n"
+ ],
+ "id": "d9b28b10"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], keep=[\"X1\", \"X2\"], exact_match=True)"
+ ],
+ "id": "e2c14077",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also easily **drop** variables via the `drop` argument:\n"
+ ],
+ "id": "aec06ac1"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], drop=[\"X1\"])"
+ ],
+ "id": "9351f09a",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hide fixed effects or SE-type rows\n",
+ "We can hide the rows showing the relevant fixed effects and those showing the S.E. type by setting `show_fe=False` and `show_setype=False` (for instance when the set of fixed effects or the estimation method for the std. errors is the same for all models and you want to describe this in the text or table notes rather than displaying it in the table).\n"
+ ],
+ "id": "6a865114"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], show_fe=False, show_se_type=False)"
+ ],
+ "id": "c326c470",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Display p-values or confidence intervals\n",
+ "By default, `pf.etable()` reports **standard errors**. But we can also ask to output p-values or confidence intervals via the `coef_fmt` function argument.\n"
+ ],
+ "id": "7a22812f"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], coef_fmt=\"b \\n (se) \\n [p]\")"
+ ],
+ "id": "17f9bc53",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Significance levels and rounding\n",
+ "Additionally, we can also overwrite the defaults for the reported significance levels and control the rounding of results via the `signif_code` and `digits` function arguments:\n"
+ ],
+ "id": "eca3c532"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], signif_code=[0.01, 0.05, 0.1], digits=5)"
+ ],
+ "id": "cc150b78",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other output formats\n",
+ "By default, `pf.etable()` returns a GT object (see [the Great Tables package](https://posit-dev.github.io/great-tables/articles/intro.html)), but you can also opt to dataframe, markdown, or latex output via the `type` argument.\n"
+ ],
+ "id": "6a851b7e"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Pandas styler output:\n",
+ "pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " signif_code=[0.01, 0.05, 0.1],\n",
+ " digits=5,\n",
+ " coef_fmt=\"b (se)\",\n",
+ " type=\"df\",\n",
+ ")"
+ ],
+ "id": "1842db00",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Markdown output:\n",
+ "pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " signif_code=[0.01, 0.05, 0.1],\n",
+ " digits=5,\n",
+ " type=\"md\",\n",
+ ")"
+ ],
+ "id": "5b2db128",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To obtain latex output use `format = \"tex\"`. If you want to save the table as a tex file, you can use the `file_name=` argument to specify the respective path where it should be saved. If you want the latex code to be displayed in the notebook, you can use the `print_tex=True` argument.\n",
+ "Etable will use latex packages `booktabs`, `threeparttable` and `makecell` for the table layout, so don't forget to include these packages in your latex document.\n"
+ ],
+ "id": "70dfe676"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# LaTex output (include latex packages booktabs, threeparttable, and makecell in your document):\n",
+ "tab = pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " signif_code=[0.01, 0.05, 0.1],\n",
+ " digits=2,\n",
+ " type=\"tex\",\n",
+ " print_tex=True,\n",
+ ")"
+ ],
+ "id": "a9115810",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following code generates a pdf including the regression table which you can display clicking on the link below the cell:\n"
+ ],
+ "id": "3a36e3d3"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "## Use pylatex to create a tex file with the table\n",
+ "\n",
+ "\n",
+ "def make_pdf(tab, file):\n",
+ " \"Create a PDF document with tex table.\"\n",
+ " doc = pl.Document()\n",
+ " doc.packages.append(pl.Package(\"booktabs\"))\n",
+ " doc.packages.append(pl.Package(\"threeparttable\"))\n",
+ " doc.packages.append(pl.Package(\"makecell\"))\n",
+ "\n",
+ " with (\n",
+ " doc.create(pl.Section(\"A PyFixest LateX Table\")),\n",
+ " doc.create(pl.Table(position=\"htbp\")) as table,\n",
+ " ):\n",
+ " table.append(pl.NoEscape(tab))\n",
+ "\n",
+ " doc.generate_pdf(file, clean_tex=False)\n",
+ "\n",
+ "\n",
+ "# Compile latex to pdf & display a button with the hyperlink to the pdf\n",
+ "# requires tex installation\n",
+ "run = False\n",
+ "if run:\n",
+ " make_pdf(tab, \"latexdocs/SampleTableDoc\")\n",
+ "display(FileLink(\"latexdocs/SampleTableDoc.pdf\"))"
+ ],
+ "id": "4df40b63",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rename variables\n",
+ "You can also rename variables if you want to have a more readable output. Just pass a dictionary to the `labels` argument. Note that interaction terms will also be relabeled using the specified labels for the interacted variables (if you want to manually relabel an interaction term differently, add it to the dictionary).\n"
+ ],
+ "id": "e8c07c62"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "labels = {\n",
+ " \"Y\": \"Wage\",\n",
+ " \"Y2\": \"Wealth\",\n",
+ " \"X1\": \"Age\",\n",
+ " \"X2\": \"Years of Schooling\",\n",
+ " \"f1\": \"Industry\",\n",
+ " \"f2\": \"Year\",\n",
+ "}\n",
+ "\n",
+ "pf.etable([fit1, fit2, fit3, fit4, fit5, fit6], labels=labels)"
+ ],
+ "id": "a7115aac",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want to label the rows indicating the inclusion of fixed effects not with the variable label but with a custom label, you can pass on a separate dictionary to the `felabels` argument.\n"
+ ],
+ "id": "e0c22a61"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " labels=labels,\n",
+ " felabels={\"f1\": \"Industry Fixed Effects\", \"f2\": \"Year Fixed Effects\"},\n",
+ ")"
+ ],
+ "id": "5c70b97f",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rename categorical variables\n",
+ "\n",
+ "Categorical variables will be relabeled where the default is {variable}={level}:\n",
+ "´\n"
+ ],
+ "id": "684a87fa"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Add a categorical variable\n",
+ "data['job'] = np.random.choice([\"Managerial\", \"Admin\", \"Blue collar\"], size=len(data), p=[1/3, 1/3, 1/3])\n",
+ "# Add a label for this variable to the dictionary\n",
+ "labels['job']=\"Job Family\"\n",
+ "\n",
+ "fit7 = pf.feols(\"Y ~ X1 + X2 + job\", data = data)\n",
+ "\n",
+ "pf.etable([fit7], labels=labels)"
+ ],
+ "id": "33a697bc",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But you can also remove the variable name and only keep the levels (categories) by specifying *cat_template=\"{level}\"*. Note that the labeling of categories also works in interaction terms:\n"
+ ],
+ "id": "01f0a0ad"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "fit7 = pf.feols(\"Y ~ X1 + X2 + job\", data = data)\n",
+ "fit8 = pf.feols(\"Y ~ X1 + X2 + job*X2\", data = data)\n",
+ "\n",
+ "pf.etable([fit7, fit8], labels=labels, cat_template=\"{level}\")"
+ ],
+ "id": "7ed76908",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you instead want to keep the standard formulaic notation, specify *cat_template=\"\"*.\n"
+ ],
+ "id": "2346f0e9"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "fit7 = pf.feols(\"Y ~ X1 + X2 + job\", data = data)\n",
+ "fit8 = pf.feols(\"Y ~ X1 + X2 + job*X2\", data = data)\n",
+ "\n",
+ "pf.etable([fit7, fit8], cat_template=\"\")"
+ ],
+ "id": "cd5a4c05",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom model headlines\n",
+ "You can also add custom headers for each model by passing a list of strings to the `model_headers` argument.\n"
+ ],
+ "id": "237d831d"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " labels=labels,\n",
+ " model_heads=[\"US\", \"China\", \"EU\", \"US\", \"China\", \"EU\"],\n",
+ ")"
+ ],
+ "id": "20684070",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or change the ordering of headlines having headlines first and then dependent variables using the `head_order` argument. \"hd\" stands for headlines then dependent variables, \"dh\" for dependent variables then headlines. Assigning \"d\" or \"h\" can be used to only show dependent variables or only headlines. When head_order=\"\" only model numbers are shown.\n"
+ ],
+ "id": "90710fac"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(\n",
+ " [fit1, fit4, fit2, fit5, fit3, fit6],\n",
+ " labels=labels,\n",
+ " model_heads=[\"US\", \"US\", \"China\", \"China\", \"EU\", \"EU\"],\n",
+ " head_order=\"hd\",\n",
+ ")"
+ ],
+ "id": "aba7beb7",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Remove the dependent variables from the headers:\n"
+ ],
+ "id": "dd6b5b48"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(\n",
+ " [fit1, fit4, fit2, fit5, fit3, fit6],\n",
+ " labels=labels,\n",
+ " model_heads=[\"US\", \"US\", \"China\", \"China\", \"EU\", \"EU\"],\n",
+ " head_order=\"\",\n",
+ ")"
+ ],
+ "id": "590f3275",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Further custom model information\n",
+ "You can add further custom model statistics/information to the bottom of the table by using the `custom_stats` argument to which you pass a dictionary with the name of the row and lists of values. The length of the lists must be equal to the number of models.\n"
+ ],
+ "id": "0d63456b"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " labels=labels,\n",
+ " custom_model_stats={\n",
+ " \"Number of Clusters\": [42, 42, 42, 37, 37, 37],\n",
+ " \"Additional Info\": [\"A\", \"A\", \"B\", \"B\", \"C\", \"C\"],\n",
+ " },\n",
+ ")"
+ ],
+ "id": "55cb22f6",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom table notes\n",
+ "You can replace the default table notes with your own notes using the `notes` argument.\n"
+ ],
+ "id": "1696c038"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "mynotes = \"Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.\"\n",
+ "pf.etable(\n",
+ " [fit1, fit4, fit2, fit5, fit3, fit6],\n",
+ " labels=labels,\n",
+ " model_heads=[\"US\", \"US\", \"China\", \"China\", \"EU\", \"EU\"],\n",
+ " head_order=\"hd\",\n",
+ " notes=mynotes,\n",
+ ")"
+ ],
+ "id": "8c0c7e1b",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Publication-ready LaTex tables\n",
+ "With few lines of code you thus obtain a publication-ready latex table:\n"
+ ],
+ "id": "353e3567"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "tab = pf.etable(\n",
+ " [fit1, fit4, fit2, fit5, fit3, fit6],\n",
+ " labels=labels,\n",
+ " model_heads=[\"US\", \"US\", \"China\", \"China\", \"EU\", \"EU\"],\n",
+ " head_order=\"hd\",\n",
+ " type=\"tex\",\n",
+ " notes=mynotes,\n",
+ " show_fe=True,\n",
+ " show_se_type=False,\n",
+ " custom_model_stats={\n",
+ " \"Number of Clusters\": [42, 42, 42, 37, 37, 37],\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# Compile latex to pdf & display a button with the hyperlink to the pdf\n",
+ "run = False\n",
+ "if run:\n",
+ " make_pdf(tab, \"latexdocs/SampleTableDoc2\")\n",
+ "display(FileLink(\"latexdocs/SampleTableDoc2.pdf\"))"
+ ],
+ "id": "f02296da",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Rendering Tables in Quarto\n",
+ "When you use quarto you can include latex tables generated by pyfixest when rendering the qmd file as pdf. Just specify `output: asis` in the code block options of the respective chunk and print the LaTex string returned by etable. Don't forget to include the `\\usepackage` commands for necessary latex packages in the YAML block. Here you find a sample [qmd file](https://github.com/py-econometrics/pyfixest/blob/master/docs/quarto_example/QuartoExample.qmd).\n",
+ "\n",
+ "When you render either a jupyter notebook or qmd file to html it is advisable to turn html-table-processing off in quarto as otherwise quarto adds further formatting which alters how your tables look like. You can do this in a raw cell at the top of your document.\n",
+ "\n",
+ "---\n",
+ "format:\n",
+ " html:\n",
+ " html-table-processing: none\n",
+ "---
\n",
+ "\n",
+ "# Descriptive Statistics via `pf.dtable()`\n",
+ "\n",
+ "The function `pf.dtable()` allows to display descriptive statistics for a set of variables in the same layout.\n",
+ "\n",
+ "## Basic Usage of dtable\n",
+ "Specify the variables you want to display the descriptive statistics for. You can also use a dictionary to rename the variables and add a caption.\n"
+ ],
+ "id": "50fc0dd1"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " labels=labels,\n",
+ " caption=\"Descriptive statistics\",\n",
+ " digits=2,\n",
+ ")"
+ ],
+ "id": "c36f8f9d",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Choose the set of statistics to be displayed with `stats`. You can use any pandas aggregation functions.\n"
+ ],
+ "id": "66f90bdc"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " stats=[\"count\", \"mean\", \"std\", \"min\", \"max\"],\n",
+ " labels=labels,\n",
+ " caption=\"Descriptive statistics\",\n",
+ ")"
+ ],
+ "id": "3dfb2ca8",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summarize by characteristics in columns and rows\n",
+ "You can summarize by characteristics using the `bycol` argument when groups are to be displayed in columns. When the number of observations is the same for all variables in a group, you can also opt to display the number of observations only once for each group byin a separate line at the bottom of the table with `counts_row_below==True`.\n"
+ ],
+ "id": "a61f5ba0"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Generate some categorial data\n",
+ "data[\"country\"] = np.random.choice([\"US\", \"EU\"], data.shape[0])\n",
+ "data[\"occupation\"] = np.random.choice([\"Blue collar\", \"White collar\"], data.shape[0])\n",
+ "\n",
+ "# Drop nan values to have balanced data\n",
+ "data.dropna(inplace=True)\n",
+ "\n",
+ "pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " labels=labels,\n",
+ " bycol=[\"country\", \"occupation\"],\n",
+ " stats=[\"count\", \"mean\", \"std\"],\n",
+ " caption=\"Descriptive statistics\",\n",
+ " stats_labels={\"count\": \"Number of observations\"},\n",
+ " counts_row_below=True,\n",
+ ")"
+ ],
+ "id": "921d4a02",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also use custom aggregation functions to compute further statistics or affect how statistics are presented. Pyfixest provides two such functions `mean_std` and `mean_newline_std` which compute the mean and standard deviation and display both the same cell (either with line break between them or not). This allows to have more compact tables when you want to show statistics for many characteristcs in the columns.\n",
+ "\n",
+ "You can also hide the display of the statistics labels in the header with `hide_stats_labels=True`. In that case a table note will be added naming the statistics displayed using its label (if you have not provided a custom note).\n"
+ ],
+ "id": "d3e65a6c"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " labels=labels,\n",
+ " bycol=[\"country\", \"occupation\"],\n",
+ " stats=[\"mean_newline_std\", \"count\"],\n",
+ " caption=\"Descriptive statistics\",\n",
+ " stats_labels={\"count\": \"Number of observations\"},\n",
+ " counts_row_below=True,\n",
+ " hide_stats=True,\n",
+ ")"
+ ],
+ "id": "eeb3b5fe",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also split by characteristics in both columns and rows. Note that you can only use one grouping variable in rows, but several in columns (as shown above).\n"
+ ],
+ "id": "7273b21d"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " labels=labels,\n",
+ " bycol=[\"country\"],\n",
+ " byrow=\"occupation\",\n",
+ " stats=[\"count\", \"mean\", \"std\"],\n",
+ " caption=\"Descriptive statistics\",\n",
+ ")"
+ ],
+ "id": "8f46b537",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And you can again export descriptive statistics tables also to LaTex:\n"
+ ],
+ "id": "58855a0c"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "dtab = pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " labels=labels,\n",
+ " bycol=[\"country\"],\n",
+ " byrow=\"occupation\",\n",
+ " stats=[\"count\", \"mean\", \"std\"],\n",
+ " type=\"tex\",\n",
+ ")\n",
+ "\n",
+ "run = False\n",
+ "if run:\n",
+ " make_pdf(dtab, \"latexdocs/SampleTableDoc3\")\n",
+ "display(FileLink(\"latexdocs/SampleTableDoc3.pdf\"))"
+ ],
+ "id": "b997b509",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Table Layout for DataFrames with `pf.make_table()`\n",
+ "\n",
+ "`pf.make_table()` is called by `pf.etable()` and `pf.dtable()` to generate the tables in \"gt\" and \"tex\" format. But you can also use it directly to generate tables in the same layout from other pandas dataframes.\n",
+ "\n",
+ "## Basic Usage of make_table\n"
+ ],
+ "id": "792aa5dd"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "df = pd.DataFrame(np.random.randn(4, 4).round(2), columns=[\"A\", \"B\", \"C\", \"D\"])\n",
+ "\n",
+ "# Make Booktabs style table\n",
+ "pf.make_table(df=df, caption=\"This is a caption\", notes=\"These are notes\")"
+ ],
+ "id": "697b38b4",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Mutiindex DataFrames\n",
+ "When the respective dataframe has a mutiindex for the columns, columns spanners are generated from the index. The row index can also be a multiindex (of at most two levels). In this case the first index level is used to generate group rows (for instance using the index name as headline and separating the groups by a horizontal line) and the second index level is used to generate the row labels.\n"
+ ],
+ "id": "fc3fcfc7"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Create a multiindex dataframe with random data\n",
+ "row_index = pd.MultiIndex.from_tuples(\n",
+ " [\n",
+ " (\"Group 1\", \"Variable 1\"),\n",
+ " (\"Group 1\", \"Variable 2\"),\n",
+ " (\"Group 1\", \"Variable 3\"),\n",
+ " (\"Group 2\", \"Variable 4\"),\n",
+ " (\"Group 2\", \"Variable 5\"),\n",
+ " (\"Group 3\", \"Variable 6\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "col_index = pd.MultiIndex.from_product([[\"A\", \"B\"], [\"X\", \"Y\"], [\"High\", \"Low\"]])\n",
+ "df = pd.DataFrame(np.random.randn(6, 8).round(3), index=row_index, columns=col_index)\n",
+ "\n",
+ "pf.make_table(df=df, caption=\"This is a caption\", notes=\"These are notes\")"
+ ],
+ "id": "1dd3d888",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also hide column group names: This just creates a table where variables on the second level of the row index are displayed in groups based on the first level separated by horizontal lines.\n"
+ ],
+ "id": "922f3b00"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "pf.make_table(\n",
+ " df=df, caption=\"This is a caption\", notes=\"These are notes\", rgroup_display=False\n",
+ ").tab_style(style=style.text(style=\"italic\"), locations=loc.body(rows=[1, 5]))"
+ ],
+ "id": "575afd69",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Custom Styling with Great Tables\n",
+ "You can use the rich set of methods offered by [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html) to further customize the table display when the type is \"gt\".\n",
+ "\n",
+ "## Example Styling\n"
+ ],
+ "id": "79b5136d"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "(\n",
+ " pf.etable([fit1, fit2, fit3, fit4, fit5, fit6])\n",
+ " .tab_options(\n",
+ " column_labels_background_color=\"cornsilk\",\n",
+ " stub_background_color=\"whitesmoke\",\n",
+ " )\n",
+ " .tab_style(\n",
+ " style=style.fill(color=\"mistyrose\"),\n",
+ " locations=loc.body(columns=\"(3)\", rows=[\"X2\"]),\n",
+ " )\n",
+ ")"
+ ],
+ "id": "adf65c78",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Defining Table Styles: Some Examples\n",
+ "\n",
+ "You can easily define table styles that you can apply to all tables in your project. Just define a dictionary with the respective values for the tab options (see the [Great Tables documentation](https://posit-dev.github.io/great-tables/reference/GT.tab_options.html#great_tables.GT.tab_options)) and use the style with `.tab_options(**style_dict)`.\n"
+ ],
+ "id": "0787c805"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "style_print = {\n",
+ " \"table_font_size\": \"12px\",\n",
+ " \"heading_title_font_size\": \"12px\",\n",
+ " \"source_notes_font_size\": \"8px\",\n",
+ " \"data_row_padding\": \"3px\",\n",
+ " \"column_labels_padding\": \"3px\",\n",
+ " \"row_group_border_top_style\": \"hidden\",\n",
+ " \"table_body_border_top_style\": \"None\",\n",
+ " \"table_body_border_bottom_width\": \"1px\",\n",
+ " \"column_labels_border_top_width\": \"1px\",\n",
+ " \"table_width\": \"14cm\",\n",
+ "}\n",
+ "\n",
+ "\n",
+ "style_presentation = {\n",
+ " \"table_font_size\": \"16px\",\n",
+ " \"table_font_color_light\": \"white\",\n",
+ " \"table_body_border_top_style\": \"hidden\",\n",
+ " \"table_body_border_bottom_style\": \"hidden\",\n",
+ " \"heading_title_font_size\": \"18px\",\n",
+ " \"source_notes_font_size\": \"12px\",\n",
+ " \"data_row_padding\": \"3px\",\n",
+ " \"column_labels_padding\": \"6px\",\n",
+ " \"column_labels_background_color\": \"midnightblue\",\n",
+ " \"stub_background_color\": \"whitesmoke\",\n",
+ " \"row_group_background_color\": \"whitesmoke\",\n",
+ " \"table_background_color\": \"whitesmoke\",\n",
+ " \"heading_background_color\": \"white\",\n",
+ " \"source_notes_background_color\": \"white\",\n",
+ " \"column_labels_border_bottom_color\": \"white\",\n",
+ " \"column_labels_font_weight\": \"bold\",\n",
+ " \"row_group_font_weight\": \"bold\",\n",
+ " \"table_width\": \"18cm\",\n",
+ "}"
+ ],
+ "id": "1db18acc",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "t1 = pf.dtable(\n",
+ " data,\n",
+ " vars=[\"Y\", \"Y2\", \"X1\", \"X2\"],\n",
+ " stats=[\"count\", \"mean\", \"std\", \"min\", \"max\"],\n",
+ " labels=labels,\n",
+ " caption=\"Descriptive statistics\",\n",
+ ")\n",
+ "\n",
+ "t2 = pf.etable(\n",
+ " [fit1, fit2, fit3, fit4, fit5, fit6],\n",
+ " labels=labels,\n",
+ " show_se=False,\n",
+ " felabels={\"f1\": \"Industry Fixed Effects\", \"f2\": \"Year Fixed Effects\"},\n",
+ " caption=\"Regression results\",\n",
+ ")"
+ ],
+ "id": "91d2110d",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "display(t1.tab_options(**style_print))\n",
+ "display(t2.tab_options(**style_print))"
+ ],
+ "id": "f69526ba",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "style_printDouble = {\n",
+ " \"table_font_size\": \"12px\",\n",
+ " \"heading_title_font_size\": \"12px\",\n",
+ " \"source_notes_font_size\": \"8px\",\n",
+ " \"data_row_padding\": \"3px\",\n",
+ " \"column_labels_padding\": \"3px\",\n",
+ " \"table_body_border_bottom_style\": \"double\",\n",
+ " \"column_labels_border_top_style\": \"double\",\n",
+ " \"column_labels_border_bottom_width\": \"0.5px\",\n",
+ " \"row_group_border_top_style\": \"hidden\",\n",
+ " \"table_body_border_top_style\": \"None\",\n",
+ " \"table_width\": \"14cm\",\n",
+ "}\n",
+ "display(t1.tab_options(**style_printDouble))\n",
+ "display(t2.tab_options(**style_printDouble))"
+ ],
+ "id": "15e8020e",
+ "execution_count": null,
+ "outputs": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "myenv",
+ "language": "python",
+ "display_name": "Python (myenv)",
+ "path": "C:\\Users\\dirks\\AppData\\Roaming\\jupyter\\kernels\\myenv"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
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+ md5: dbcace4706afdfb7eb891f7b37d07c04
license: LicenseRef-Public-Domain
purls: []
- size: 122354
- timestamp: 1728047496079
+ size: 122921
+ timestamp: 1737119101255
- conda: https://conda.anaconda.org/conda-forge/linux-64/tzlocal-5.2-py312h7900ff3_1.conda
sha256: 555504b27493789b597e8ac1c40e833f66b71b11868737dbf053c307fecbd9b8
md5: 38824686e2715e2da796fddf09343de9
depends:
- python >=3.12,<3.13.0a0
- python_abi 3.12.* *_cp312
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls:
@@ -12925,6 +13781,8 @@ packages:
depends:
- python >=3.12,<3.13.0a0
- python_abi 3.12.* *_cp312
+ arch: x86_64
+ platform: osx
license: MIT
license_family: MIT
purls:
@@ -12938,6 +13796,8 @@ packages:
- python >=3.12,<3.13.0a0
- python >=3.12,<3.13.0a0 *_cpython
- python_abi 3.12.* *_cp312
+ arch: arm64
+ platform: osx
license: MIT
license_family: MIT
purls:
@@ -12951,6 +13811,8 @@ packages:
- python >=3.12,<3.13.0a0
- python-tzdata
- python_abi 3.12.* *_cp312
+ arch: x86_64
+ platform: win
license: MIT
license_family: MIT
purls:
@@ -12962,6 +13824,8 @@ packages:
md5: 6797b005cd0f439c4c5c9ac565783700
constrains:
- vs2015_runtime >=14.29.30037
+ arch: x86_64
+ platform: win
license: LicenseRef-MicrosoftWindowsSDK10
purls: []
size: 559710
@@ -13003,34 +13867,38 @@ packages:
- pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
- zstandard>=0.18.0 ; extra == 'zstd'
requires_python: '>=3.9'
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-ha32ba9b_23.conda
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- md5: 7c10ec3158d1eb4ddff7007c9101adb0
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h5fd82a7_24.conda
+ sha256: 7ce178cf139ccea5079f9c353b3d8415d1d49b0a2f774662c355d3f89163d7b4
+ md5: 00cf3a61562bd53bd5ea99e6888793d0
depends:
- - vc14_runtime >=14.38.33135
+ - vc14_runtime >=14.40.33810
+ arch: x86_64
+ platform: win
track_features:
- vc14
license: BSD-3-Clause
license_family: BSD
purls: []
- size: 17479
- timestamp: 1731710827215
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34433-he29a5d6_23.conda
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+ size: 17693
+ timestamp: 1737627189024
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34433-h6356254_24.conda
+ sha256: abda97b8728cf6e3c37df8f1178adde7219bed38b96e392cb3be66336386d32e
+ md5: 2441e010ee255e6a38bf16705a756e94
depends:
- ucrt >=10.0.20348.0
constrains:
- - vs2015_runtime 14.42.34433.* *_23
+ - vs2015_runtime 14.42.34433.* *_24
+ arch: x86_64
+ platform: win
license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
license_family: Proprietary
purls: []
- size: 754247
- timestamp: 1731710681163
-- pypi: https://files.pythonhosted.org/packages/10/f9/0919cf6f1432a8c4baa62511f8f8da8225432d22e83e3476f5be1a1edc6e/virtualenv-20.28.0-py3-none-any.whl
+ size: 753531
+ timestamp: 1737627061911
+- pypi: https://files.pythonhosted.org/packages/89/9b/599bcfc7064fbe5740919e78c5df18e5dceb0887e676256a1061bb5ae232/virtualenv-20.29.1-py3-none-any.whl
name: virtualenv
- version: 20.28.0
- sha256: 23eae1b4516ecd610481eda647f3a7c09aea295055337331bb4e6892ecce47b0
+ version: 20.29.1
+ sha256: 4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779
requires_dist:
- distlib>=0.3.7,<1
- filelock>=3.12.2,<4
@@ -13056,16 +13924,18 @@ packages:
- setuptools>=68 ; extra == 'test'
- time-machine>=2.10 ; platform_python_implementation == 'CPython' and extra == 'test'
requires_python: '>=3.8'
-- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34433-hdffcdeb_23.conda
- sha256: 568ce8151eaae256f1cef752fc78651ad7a86ff05153cc7a4740b52ae6536118
- md5: 5c176975ca2b8366abad3c97b3cd1e83
+- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34433-hfef2bbc_24.conda
+ sha256: 09102e0bd283af65772c052d85028410b0c31989b3cd96c260485d28e270836e
+ md5: 117fcc5b86c48f3b322b0722258c7259
depends:
- vc14_runtime >=14.42.34433
+ arch: x86_64
+ platform: win
license: BSD-3-Clause
license_family: BSD
purls: []
- size: 17572
- timestamp: 1731710685291
+ size: 17669
+ timestamp: 1737627066773
- pypi: https://files.pythonhosted.org/packages/12/87/48361531f70b1f87928b045df868a9fd4e253d9ae087fa4cf3f7113be363/watchdog-6.0.0-cp312-cp312-macosx_10_13_x86_64.whl
name: watchdog
version: 6.0.0
@@ -13142,25 +14012,25 @@ packages:
- statsmodels>=0.13
- tabulate>=0.9.0
requires_python: '>=3.8,<4.0'
-- pypi: https://files.pythonhosted.org/packages/4b/d9/a8ba5e9507a9af1917285d118388c5eb7a81834873f45df213a6fe923774/wrapt-1.17.0-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/15/06/1dbf478ea45c03e78a6a8c4be4fdc3c3bddea5c8de8a93bc971415e47f0f/wrapt-1.17.2-cp312-cp312-win_amd64.whl
name: wrapt
- version: 1.17.0
- sha256: d2c63b93548eda58abf5188e505ffed0229bf675f7c3090f8e36ad55b8cbc371
+ version: 1.17.2
+ sha256: ec89ed91f2fa8e3f52ae53cd3cf640d6feff92ba90d62236a81e4e563ac0e991
requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/85/82/518605474beafff11f1a34759f6410ab429abff9f7881858a447e0d20712/wrapt-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+- pypi: https://files.pythonhosted.org/packages/2a/5a/04cde32b07a7431d4ed0553a76fdb7a61270e78c5fd5a603e190ac389f14/wrapt-1.17.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
name: wrapt
- version: 1.17.0
- sha256: 89fc28495896097622c3fc238915c79365dd0ede02f9a82ce436b13bd0ab7569
+ version: 1.17.2
+ sha256: bc570b5f14a79734437cb7b0500376b6b791153314986074486e0b0fa8d71d98
requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/b3/4f/243f88ac49df005b9129194c6511b3642818b3e6271ddea47a15e2ee4934/wrapt-1.17.0-cp312-cp312-win_amd64.whl
+- pypi: https://files.pythonhosted.org/packages/48/2a/97928387d6ed1c1ebbfd4efc4133a0633546bec8481a2dd5ec961313a1c7/wrapt-1.17.2-cp312-cp312-macosx_11_0_arm64.whl
name: wrapt
- version: 1.17.0
- sha256: 3c34f6896a01b84bab196f7119770fd8466c8ae3dfa73c59c0bb281e7b588ce7
+ version: 1.17.2
+ sha256: 8fdbdb757d5390f7c675e558fd3186d590973244fab0c5fe63d373ade3e99d40
requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/d2/50/dbef1a651578a3520d4534c1e434989e3620380c1ad97e309576b47f0ada/wrapt-1.17.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/53/18/75ddc64c3f63988f5a1d7e10fb204ffe5762bc663f8023f18ecaf31a332e/wrapt-1.17.2-cp312-cp312-macosx_10_13_x86_64.whl
name: wrapt
- version: 1.17.0
- sha256: 18b956061b8db634120b58f668592a772e87e2e78bc1f6a906cfcaa0cc7991c1
+ version: 1.17.2
+ sha256: 3fc7cb4c1c744f8c05cd5f9438a3caa6ab94ce8344e952d7c45a8ed59dd88392
requires_python: '>=3.8'
- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda
sha256: c12396aabb21244c212e488bbdc4abcdef0b7404b15761d9329f5a4a39113c4b
@@ -13168,6 +14038,8 @@ packages:
depends:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13181,6 +14053,8 @@ packages:
- libgcc >=13
- libuuid >=2.38.1,<3.0a0
- xorg-libice >=1.1.2,<2.0a0
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13193,6 +14067,8 @@ packages:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
- libxcb >=1.17.0,<2.0a0
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13204,6 +14080,8 @@ packages:
depends:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13215,6 +14093,8 @@ packages:
depends:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13227,6 +14107,8 @@ packages:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
- xorg-libx11 >=1.8.10,<2.0a0
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13239,6 +14121,8 @@ packages:
- __glibc >=2.17,<3.0.a0
- libgcc >=13
- xorg-libx11 >=1.8.10,<2.0a0
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13253,6 +14137,8 @@ packages:
- xorg-libice >=1.1.1,<2.0a0
- xorg-libsm >=1.2.4,<2.0a0
- xorg-libx11 >=1.8.10,<2.0a0
+ arch: x86_64
+ platform: linux
license: MIT
license_family: MIT
purls: []
@@ -13275,6 +14161,8 @@ packages:
depends:
- __osx >=10.13
- libzlib 1.3.1 hd23fc13_2
+ arch: x86_64
+ platform: osx
license: Zlib
license_family: Other
purls: []
@@ -13286,6 +14174,8 @@ packages:
depends:
- __osx >=11.0
- libzlib 1.3.1 h8359307_2
+ arch: arm64
+ platform: osx
license: Zlib
license_family: Other
purls: []
@@ -13298,6 +14188,8 @@ packages:
- libgcc-ng >=12
- libstdcxx-ng >=12
- libzlib >=1.2.13,<2.0.0a0
+ arch: x86_64
+ platform: linux
license: BSD-3-Clause
license_family: BSD
purls: []
@@ -13309,6 +14201,8 @@ packages:
depends:
- __osx >=10.9
- libzlib >=1.2.13,<2.0.0a0
+ arch: x86_64
+ platform: osx
license: BSD-3-Clause
license_family: BSD
purls: []
@@ -13320,6 +14214,8 @@ packages:
depends:
- __osx >=11.0
- libzlib >=1.2.13,<2.0.0a0
+ arch: arm64
+ platform: osx
license: BSD-3-Clause
license_family: BSD
purls: []
diff --git a/pyfixest/report/summarize.py b/pyfixest/report/summarize.py
index 04b5e399..b630622c 100644
--- a/pyfixest/report/summarize.py
+++ b/pyfixest/report/summarize.py
@@ -31,6 +31,7 @@ def etable(
drop: Optional[Union[list, str]] = None,
exact_match: Optional[bool] = False,
labels: Optional[dict] = None,
+ cat_template: Optional[str] = "{variable}={value}",
show_fe: Optional[bool] = True,
show_se_type: Optional[bool] = True,
felabels: Optional[dict] = None,
@@ -83,6 +84,9 @@ def etable(
names and the values the new names. Note that interaction terms will also be
relabeled using the labels of the individual variables.
The command is applied after the `keep` and `drop` commands.
+ cat_template: str, optional
+ Template to relabel categorical variables. When empty, the function will not
+ relabel categorical variables. Default is "{variable}={value}".
show_fe: bool, optional
Whether to show the rows with fixed effects markers. Default is True.
show_se_type: bool, optional
@@ -156,6 +160,8 @@ def etable(
models = _post_processing_input_checks(models)
+ if labels is None:
+ labels = {}
if custom_stats is None:
custom_stats = dict()
if keep is None:
@@ -215,16 +221,19 @@ def etable(
r2_list = []
r2_within_list: list[float] = [] # noqa: F841
- # Define code for R2 & interaction symbol depending on output type
+ # Define code for R2, interaction & line break depending on output type
if type in ["gt", "html"]:
interactionSymbol = " × "
R2code = "R2"
+ lbcode = "
"
elif type == "tex":
interactionSymbol = " $\\times$ "
R2code = "$R^2$"
+ lbcode = r"\\"
else:
interactionSymbol = " x "
R2code = "R2"
+ lbcode = "\n"
for model in models:
dep_var_list.append(model._depvar)
@@ -345,12 +354,7 @@ def etable(
custom_stats[element][i]
).apply(_number_formatter, **kwargs)
elif element == "\n": # Replace output specific code for newline
- if type in ["html", "gt"]:
- model_tidy_df[coef_fmt_title] += "
"
- elif type == "tex":
- model_tidy_df[coef_fmt_title] += r"\\"
- elif type in ["md", "df"]:
- model_tidy_df[coef_fmt_title] += "\n"
+ model_tidy_df[coef_fmt_title] += lbcode
else:
model_tidy_df[coef_fmt_title] += element
model_tidy_df[coef_fmt_title] = pd.Categorical(model_tidy_df[coef_fmt_title])
@@ -392,14 +396,14 @@ def etable(
res = pd.concat([res, pd.DataFrame([intercept_row])])
# Relabel variables
- if labels is not None:
+ if (labels != {}) or (cat_template != ""):
# Relabel dependent variables
dep_var_list = [labels.get(k, k) for k in dep_var_list]
# Relabel explanatory variables
res_index = res.index.to_series()
res_index = res_index.apply(
- lambda x: _relabel_expvar(x, labels or {}, interactionSymbol)
+ lambda x: _relabel_expvar(x, labels or {}, interactionSymbol, cat_template)
)
res.set_index(res_index, inplace=True)
@@ -991,8 +995,6 @@ def make_table(
with open(file_name, "w") as f:
f.write(latex_res) # Write the latex code to a file
- # Only when type is 'tex' return the latex code as
- # otherwise a GT object will be returned
if type == "tex":
return latex_res
diff --git a/pyfixest/report/utils.py b/pyfixest/report/utils.py
index e381b64f..c58b07a7 100644
--- a/pyfixest/report/utils.py
+++ b/pyfixest/report/utils.py
@@ -1,10 +1,16 @@
import re
+from typing import Optional
+import pandas as pd
-def _relabel_expvar(varname: str, labels: dict, interaction_symbol: str):
+
+def _relabel_expvar(
+ varname: str, labels: dict, interaction_symbol: str, cat_template=""
+):
"""
Relabel a variable name using the labels dictionary
- Also automatically relabel interaction terms using the labels of the individual variables.
+ Also automatically relabel interaction terms using the labels of the individual variables
+ and categorical variables using the cat_template.
Parameters
----------
@@ -14,39 +20,58 @@ def _relabel_expvar(varname: str, labels: dict, interaction_symbol: str):
A dictionary to relabel the variables. The keys are the original variable names and the values the new names.
interaction_symbol: str
The symbol to use for displaying the interaction term.
+ cat_template: str
+ Template to relabel categorical variables. When empty, the function will not relabel categorical variables.
Returns
-------
str
The relabeled variable
"""
- # When varname in labels dictionary, then relabel (note: this allows also to manually rename interaction terms in the dictionary)
- # Otherwise: When interaction term, then split by vars, relabel, and join using interaction symbol
- if varname in labels:
- return labels[varname]
- elif ":" in varname:
- vars = varname.split(":")
- relabeled_vars = [labels.get(v, v) for v in vars]
- return interaction_symbol.join(relabeled_vars)
- return varname
-
-
-def _rename_categorical(col_name):
- pattern = r"C\(([^,]+)(?:,[^]]+)?\)\[T\.([^]]+)\]"
-
- # Apply the regex to extract the variable and level
+ # First split the variable name by the interaction symbol
+ # Note: will just be equal to varname when no interaction term
+ vars = varname.split(":")
+ # Loop over the variables and relabel them
+ for i in range(len(vars)):
+ # Check whether template for categorical variables is provided &
+ # whether the variable is a categorical variable
+ v = vars[i]
+ if cat_template != "" and ("C(" in v or "[T." in v):
+ vars[i] = _rename_categorical(v, template=cat_template, labels=labels)
+ else:
+ vars[i] = labels.get(v, v)
+ # Finally join the variables using the interaction symbol
+ return interaction_symbol.join(vars)
+
+
+def _rename_categorical(
+ col_name, template="{variable}::{value}", labels: Optional[dict] = None
+):
+ # Here two patterns are used to extract the variable and level
+ # Note the second pattern matches the notation when the variable is categorical at the outset
+ if col_name.startswith("C("):
+ pattern = r"C\(([^,]+)(?:,[^]]+)?\)\[T\.([^]]+)\]"
+ else:
+ pattern = r"([^[]+)\[T\.([^]]+)\]"
+ # Replace labels with empty dictionary if not provided
+ if labels is None:
+ labels = {}
+ # Apply the regex to extract the variable and value
match = re.search(pattern, col_name)
-
if match:
variable = match.group(1)
- level = match.group(2)
- result = f"{variable}::{level}"
+ # Relabel the variable if it is in the labels dictionary
+ variable = labels.get(variable, variable)
+ value = match.group(2)
+ result = template.format(variable=variable, value=value)
return result
else:
return col_name
-def rename_categoricals(coef_names_list: list) -> dict:
+def rename_categoricals(
+ coef_names_list: list, template="{variable}::{value}", labels: Optional[dict] = None
+) -> dict:
"""
Rename the categorical variables in the coef_names_list.
@@ -54,6 +79,11 @@ def rename_categoricals(coef_names_list: list) -> dict:
----------
coef_names_list: list
The list of coefficient names.
+ template: str
+ The template to use for renaming the categorical variables.
+ It can contain the placeholders {variable} and {level}.
+ labels: dict
+ A dictionary with the variable names as keys and the new names as values.
Returns
-------
@@ -65,7 +95,51 @@ def rename_categoricals(coef_names_list: list) -> dict:
rename_categorical(["C(var)[T.1]", "C(var)[T.2]", "C(var2)[T.1]", "C(var2)[T.2]"])
{'C(var)[T.1]': 'var::1', 'C(var)[T.2]': 'var::2', 'C(var2)[T.1]': 'var2::1', 'C(var2)[T.2]': 'var2::2'}
"""
- return {col: _rename_categorical(col) for col in coef_names_list}
+ return {
+ col: _rename_categorical(col, template=template, labels=labels)
+ for col in coef_names_list
+ }
+
+
+def set_first_cat(df, var_value_dict):
+ """
+ Set the first category of the categorical variables in a DataFrame.
+ This function is useful to change the reference categories of categorical variables.
+ Regressions of the form "y ~ c" will use the first category of c as reference.
+
+ Parameters
+ ----------
+ df: pd.DataFrame
+ The DataFrame containing the categorical variables.
+ var_value_dict: dict
+ A dictionary with the variable names as keys and the value to set as first category as values.
+
+ Returns
+ -------
+ pd.DataFrame
+ The DataFrame with the first category set for the categorical variables.
+ """
+ assert all(column in df.columns for column in var_value_dict), (
+ "All keys in var_value_dict must be columns in df."
+ )
+ assert all(df[column].dtype.name == "category" for column in var_value_dict), (
+ "All variables in var_value_dict must be categorical."
+ )
+ assert all(
+ value in df[column].cat.categories for column, value in var_value_dict.items()
+ ), (
+ "All values in var_value_dict must be in the categories of the corresponding column."
+ )
+ for column, value in var_value_dict.items():
+ df[column] = pd.Categorical(
+ df[column],
+ categories=[
+ value,
+ *list(x for x in df[column].cat.categories if x != value),
+ ],
+ ordered=True,
+ )
+ return df
def _rename_event_study_coefs(col_name: str):
diff --git a/tests/test_others.py b/tests/test_others.py
index aa617074..96209198 100644
--- a/tests/test_others.py
+++ b/tests/test_others.py
@@ -3,7 +3,11 @@
import polars as pl
from pyfixest.estimation.estimation import feols, fepois
-from pyfixest.report.utils import rename_categoricals, rename_event_study_coefs
+from pyfixest.report.utils import (
+ rename_categoricals,
+ rename_event_study_coefs,
+ set_first_cat,
+)
from pyfixest.utils.utils import capture_context, get_data, ssc
@@ -135,6 +139,33 @@ def test_rename_categoricals():
"C(f4, contr.treatment(base='A'))[T.C]": "f4::C",
}
+ # with categoricals:
+ coefnames = ["Intercept", "variable1[T.value1]", "variable1[T.value2]"]
+ renamed = rename_categoricals(coefnames)
+ assert renamed == {
+ "Intercept": "Intercept",
+ "variable1[T.value1]": "variable1::value1",
+ "variable1[T.value2]": "variable1::value2",
+ }
+
+ # Test with labels
+ coefnames = ["C(variable1)[T.value1]", "variable2[T.value2]"]
+ labels = {"variable1": "var1", "variable2": "var2"}
+ renamed = rename_categoricals(coefnames, labels=labels)
+ assert renamed == {
+ "C(variable1)[T.value1]": "var1::value1",
+ "variable2[T.value2]": "var2::value2",
+ }
+
+ # Test with custom template
+ coefnames = ["C(variable1)[T.value1]", "variable2[T.value2]"]
+ template = "{variable}--{value}"
+ renamed = rename_categoricals(coefnames, template=template)
+ assert renamed == {
+ "C(variable1)[T.value1]": "variable1--value1",
+ "variable2[T.value2]": "variable2--value2",
+ }
+
def test_rename_event_study_coefs():
coefnames = [
@@ -151,6 +182,28 @@ def test_rename_event_study_coefs():
}
+def test_set_first_cat():
+ # Create a sample DataFrame
+ df = pd.DataFrame(
+ {
+ "job": pd.Categorical(
+ ["Admin", "Blue collar", "White collar", "Admin", "Blue collar"]
+ ),
+ "gender": pd.Categorical(["Male", "Female", "Female", "Male", "Female"]),
+ }
+ )
+
+ # Define the variable-value dictionary
+ var_value_dict = {"job": "Admin", "gender": "Female"}
+
+ # Call the function
+ result_df = set_first_cat(df, var_value_dict)
+
+ # Check the results
+ assert result_df["job"].cat.categories[0] == "Admin"
+ assert result_df["gender"].cat.categories[0] == "Female"
+
+
def _foo():
"Simulate a callable for testing context capture behavior."
...