{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Example usage\n",
                "\n",
                "Here, we will demonstrate how to use `pycounts_bzrudski` to count the words in a text file and plot the top 5 results."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Imports"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "from pycounts_bzrudski.pycounts_bzrudski import count_words\n",
                "from pycounts_bzrudski.plotting import plot_words"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Create a text file\n",
                "\n",
                "We'll first create a text file to work with using a famous quote from Einstein:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "quote = \"\"\"Insanity is doing the same thing\n",
                "over\n",
                "and over\n",
                "and expecting different results.\"\"\"\n",
                "\n",
                "with open(\"einstein.txt\", \"w\") as file:\n",
                "    file.write(quote)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Count Words\n",
                "\n",
                "We can count the words in our text file using the `count_words()` function. Note that this function removes punctuation and makes all words lowercase before counting."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Counter({'over': 2, 'and': 2, 'insanity': 1, 'is': 1, 'doing': 1, 'the': 1, 'same': 1, 'thing': 1, 'expecting': 1, 'different': 1, 'results': 1})\n"
                    ]
                }
            ],
            "source": [
                "counts = count_words(\"einstein.txt\")\n",
                "print(counts)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Plot words\n",
                "\n",
                "We can now plot the result using the `plot_words()` function:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig = plot_words(counts, n=5)"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "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": "3.9.18"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}