{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "cef5239a",
      "metadata": {},
      "source": [
        "# pandas: to_numeric() for Safe Conversion of Strings to Numbers in Pandas\n",
        "`pandas.to_numeric(arg, errors='raise', downcast=None, dtype_backend=<no_default>)`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "76c53ae1",
      "metadata": {},
      "source": [
        "In pandas, `pandas.to_numeric()` allows you to convert a column or Series of strings to numeric values (integers or floats).\n",
        "- [pandas.to_numeric() - pandas 2.3.2 documentation](https://pandas.pydata.org/docs/reference/api/pandas.to_numeric.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17aa472c",
      "metadata": {},
      "source": [
        "Basic usage:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "727b9564",
      "metadata": {},
      "source": [
        "```python\n",
        ">>> import pandas as pd\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e4601c7",
      "metadata": {},
      "source": [
        "```python\n",
        ">>> s = pd.Series(['1.0', '2', -3])\n",
        ">>> pd.to_numeric(s)\n",
        "0   1.0\n",
        "1   2.0\n",
        "2   -3.0\n",
        "dtype: float64\n",
        ">>> pd.to_numeric(s, downcast='float')\n",
        "0   1.0\n",
        "1   2.0\n",
        "2   -3.0\n",
        "dtype: float32\n",
        ">>> pd.to_numeric(s, downcast='integer')\n",
        "0    1\n",
        "1    2\n",
        "2   -3\n",
        "dtype: int8\n",
        ">>> pd.to_numeric(s, downcast='signed')\n",
        "0    1\n",
        "1    2\n",
        "2   -3\n",
        "dtype: int8\n",
        ">>> pd.to_numeric(s, downcast='unsigned')\n",
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "dtype: uint8\n",
        ">>> s = pd.Series(['apple', '1.0', '2', -3])\n",
        ">>> pd.to_numeric(s, errors='coerce')\n",
        "0    NaN\n",
        "1    1.0\n",
        "2    2.0\n",
        "3   -3.0\n",
        "dtype: float64\n",
        ">>> pd.to_numeric(s, errors='coerce').fillna(0)\n",
        "0    0.0\n",
        "1    1.0\n",
        "2    2.0\n",
        "3   -3.0\n",
        "dtype: float64"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4cd955e5",
      "metadata": {},
      "source": [
        "For more information about nullable types, refer to the following article:\n",
        "- [pandas: nullable data types](https://cornel05.github.io/cornel.ai/notebooks/snippets/pandas/pandas_nullable_dtypes.html)\n",
        "\n",
        "Downcast to nullable (can be marked as NaN/<NA>) integer or float is supported:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b55fce85",
      "metadata": {},
      "source": [
        "```python\n",
        ">>> s = pd.Series([1, 2, 3, None], dtype=\"Int64\")\n",
        ">>> pd.to_numeric(s, downcast=\"integer\")\n",
        "0       1\n",
        "1       2\n",
        "2       3\n",
        "3    <NA>\n",
        "dtype: Int8\n",
        ">>> s = pd.Series([1.1, 2.2, 3.3, None], dtype=\"Float64\")\n",
        ">>> pd.to_numeric(s, downcast=\"float\")\n",
        "0       1.1\n",
        "1       2.2\n",
        "2       3.3\n",
        "3    <NA>\n",
        "dtype: Float32\n",
        "```"
      ]
    }
  ],
  "metadata": {
    "jupytext": {
      "cell_metadata_filter": "-all",
      "formats": "ipynb,py:light",
      "main_language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}