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brentomagic merged 2 commits into from
Jul 28, 2026
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migrate: convert boolean-data.ipynb from pandas to polars #14

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1e752fb
migrate: convert boolean-data.ipynb from pandas to polars
Jul 18, 2026
3e53e28
fix: address review feedback for boolean.ipynb migration
Jul 23, 2026
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80 changes: 54 additions & 26 deletions boolean-data.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,13 @@
"\n",
"In this chapter, we'll introduce boolean data: data that can be `True` or `False` (which can also be encoded as 1s or 0s). We'll first look at the fundamental Python true and false boolean variables before seeing how true and false work in data frames.\n",
"\n",
"### Prerequisites\n",
"\n",
"This chapter focuses on working with boolean data and expressions using **polars**. Make sure you have **polars** installed (`uv add polars`). For more on Polars boolean operations, see the [Polars boolean expression documentation](https://docs.pola.rs/api/python/stable/reference/expressions/boolean.html).\n",
"\n",
"## Booleans\n",
"\n",
"Some of the most important operations you will perform are with `True` and `False` values, also known as boolean data types. These are fundamental Python variables, just as numbers such as `1` are. \n",
"Some of the most important operations you will perform are with `True` and `False` values, also known as boolean data types. These are fundamental Python variables, just as numbers such as `1` are.\n",
"\n",
"### Boolean Variables and Conditions\n",
"\n",
Expand Down Expand Up @@ -481,11 +485,11 @@
"id": "8e06930b",
"metadata": {},
"source": [
"## Booleans in **pandas** data frames\n",
"## Booleans in **polars** DataFrames\n",
"\n",
"### Operations on booleans in data frames\n",
"\n",
"Quite often, you will run into a scenario where you're working with data that have True or False values in a data frame. It is easy to create a column of booleans in a **pandas** data frame:"
"Quite often, you will run into a scenario where you're working with data that have True or False values in a data frame. It is easy to create a column of booleans in a **polars** data frame:"
]
},
{
Expand All @@ -495,9 +499,9 @@
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import polars as pl\n",
"\n",
"df = pd.DataFrame.from_dict(\n",
"df = pl.DataFrame(\n",
" {\n",
" \"bool_col_1\": [False] * 3 + [True, True],\n",
" \"bool_col_2\": [True, False, True, False, True],\n",
Expand All @@ -511,7 +515,7 @@
"id": "c7a0cd32",
"metadata": {},
"source": [
"We can perform operations on these just like regular **pandas** data frame columns. These accept `&` (and), `|` (or), `==` (equal), and `!=` (not equal) as operations:"
"We can perform operations on these just like regular **polars** data frame columns. These accept `&` (and), `|` (or), `==` (equal), and `!=` (not equal) as operations:"
]
},
{
Expand All @@ -521,15 +525,20 @@
"metadata": {},
"outputs": [],
"source": [
"df[\"bool_col_1\"] | df[\"bool_col_2\"]"
"df.select(\n",
" [\n",
" (pl.col(\"bool_col_1\") & pl.col(\"bool_col_2\")).alias(\"and_result\"),\n",
" (pl.col(\"bool_col_1\") | pl.col(\"bool_col_2\")).alias(\"or_result\"),\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "25dede69",
"metadata": {},
"source": [
"Quite often, it's useful to have a count of the number of true values. If you take the sum of boolean columns in a **pandas** data frame, it will tot up the number of `True` values:"
"Quite often, it's useful to have a count of the number of true values. If you take the sum of boolean columns in a **polars** data frame, it will tot up the number of `True` values:"
]
},
{
Expand All @@ -539,7 +548,7 @@
"metadata": {},
"outputs": [],
"source": [
"df.sum()"
"df.select(pl.col(\"*\").sum())"
]
},
{
Expand All @@ -557,8 +566,8 @@
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame.from_dict({\"bool_col\": [0, 1, 0, 1, 1]})\n",
"df[\"bool_col\"].astype(bool)"
"df = pl.DataFrame({\"bool_col\": [0, 1, 0, 1, 1]})\n",
"df.with_columns(pl.col(\"bool_col\").cast(pl.Boolean))"
]
},
{
Expand All @@ -579,7 +588,7 @@
"metadata": {},
"outputs": [],
"source": [
"diamonds = pd.read_csv(\n",
"diamonds = pl.read_csv(\n",
" \"https://github.com/mwaskom/seaborn-data/raw/master/diamonds.csv\"\n",
")\n",
"diamonds.head()"
Expand All @@ -596,20 +605,20 @@
{
"cell_type": "code",
"execution_count": null,
"id": "7a27f0a0",
"id": "6b739dc5",
"metadata": {},
"outputs": [],
"source": [
"diamonds[\"expensive\"] = diamonds[\"price\"] > 1000\n",
"diamonds.sample(10)"
"diamonds = diamonds.with_columns((pl.col(\"price\") > 1000).alias(\"expensive\"))\n",
"diamonds.select([\"cut\", \"carat\", \"color\", \"clarity\", \"price\", \"expensive\"]).head()"
]
},
{
"cell_type": "markdown",
"id": "8383cab5",
"metadata": {},
"source": [
"Of course, this could also have been achieved in a call to assign:"
"Of course, this could also have been achieved with `select()` or `with_columns()`:"
]
},
{
Expand All @@ -619,15 +628,18 @@
"metadata": {},
"outputs": [],
"source": [
"diamonds.assign(expensive=lambda x: x[\"price\"] > 1000).head()"
"diamonds.select(\n",
" [\"cut\", \"carat\", \"color\", \"clarity\", \"price\"],\n",
" (pl.col(\"price\") > 1000).alias(\"expensive\"),\n",
").head()"
]
},
{
"cell_type": "markdown",
"id": "622ff486",
"metadata": {},
"source": [
"Another use of booleans that is quite useful when it comes to data frames is the `.isin()` function. For example, if you just want some True or False values for whether a set of columns is in a data frame:"
"Another use of booleans in data frames is checking if a set of column names exists. We can use a Python list comprehension with the Python `in` operator on `diamonds.columns`:"
]
},
{
Expand All @@ -637,7 +649,26 @@
"metadata": {},
"outputs": [],
"source": [
"diamonds.columns.isin([\"x\", \"y\", \"z\"])"
"cols_to_check = [\"x\", \"y\", \"z\"]\n",
"[col in diamonds.columns for col in cols_to_check]"
]
},
{
"cell_type": "markdown",
"id": "polars_is_in_markdown",
"metadata": {},
"source": [
"Alternatively, you can check column existence natively in **polars** using `pl.Series.is_in()`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "polars_is_in_code",
"metadata": {},
"outputs": [],
"source": [
"pl.Series(diamonds.columns).is_in(cols_to_check)"
]
},
{
Expand All @@ -647,7 +678,7 @@
"source": [
"### any() and all() in data frames\n",
"\n",
"A **pandas** column of booleans behaves a lot like a list of booleans, and we can apply the same logic to it via **pandas** built-in `.any()` and `.all()` methods. We expect some entries for `\"expensive\"` to be true, so `any()` should return true:"
"A **polars** column of booleans behaves a lot like a list of booleans, and we can apply the same logic to it via **polars** built-in `.any()` and `.all()` methods. We expect some entries for `\"expensive\"` to be true, so `any()` should return true:"
]
},
{
Expand All @@ -657,7 +688,7 @@
"metadata": {},
"outputs": [],
"source": [
"diamonds[\"expensive\"].any()"
"diamonds.select(pl.col(\"expensive\").any())"
]
},
{
Expand All @@ -677,22 +708,19 @@
"metadata": {},
"outputs": [],
"source": [
"diamonds[diamonds[\"x\"] > diamonds[\"y\"]]"
"diamonds.filter(pl.col(\"x\") > pl.col(\"y\"))"
]
},
{
"cell_type": "markdown",
"id": "9b5906ba",
"metadata": {},
"source": [
"The expression `diamonds[\"x\"] > diamonds[\"y\"]` creates a column of booleans that is used to filter to just the rows where the condition is true."
"The expression `pl.col(\"x\") > pl.col(\"y\")` creates a column of booleans that is used to filter to just the rows where the condition is true."
]
}
],
"metadata": {
"interpreter": {
"hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
},
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
Expand Down
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