{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "534b3332",
   "metadata": {},
   "source": [
    "## `dtype`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7f69f2",
   "metadata": {},
   "source": [
    "`ndarray` is a container for homogeneous data, i.e. all elements must be of the same type. Each array has a `dtype`, an object that describes the data type of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "04e4b903",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T15:01:57.826369Z",
     "iopub.status.busy": "2026-05-17T15:01:57.826144Z",
     "iopub.status.idle": "2026-05-17T15:01:57.866663Z",
     "shell.execute_reply": "2026-05-17T15:01:57.866405Z",
     "shell.execute_reply.started": "2026-05-17T15:01:57.826345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "rng = np.random.default_rng()\n",
    "data = rng.random((7, 3))\n",
    "dt = data.dtype\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a09a4c5",
   "metadata": {},
   "source": [
    "NumPy data types:\n",
    "\n",
    "Type              | Type code     | Description\n",
    ":---------------- | :------------ | :----------\n",
    "`int8`, `uint8`   | `i1`, `u1`    | Signed and unsigned 8-bit (1-byte) integer types\n",
    "`int16`, `uint16` | `i2`, `u2`    | Signed and unsigned 16-Bit (2 Byte) integer types\n",
    "`int32`, `uint32` | `i4`, `u4`    | Signed and unsigned 32-Bit (4 Byte) integer types\n",
    "`int64`, `uint64` | `i8`, `u8`    | Signed and unsigned 64-Bit (8 Byte) integer types\n",
    "`float16`         | `f2`          | Standard floating point with half precision\n",
    "`float32`         | `f4` or `f`   | Standard floating point with single precision; compatible with C `float`\n",
    "`float64`         | `f8` or `d`   | Standard floating point with double precision; compatible with C `double` and Python `float` object\n",
    "`complex64`, `complex128`, `complex256` | `c8`, `c16`, `c32` | Complex numbers represented by two 32, 64 or 128 floating point numbers respectively\n",
    "`bool`            | `?`           | Boolean type that stores the values `True` and `False`\n",
    "`object`          | `O`           | Python object type; a value can be any Python object\n",
    "`string_`         | `S`           | ASCII string type with fixed length (1 byte per character); to create a string type with length 7, for example, use `S7`; longer inputs are truncated without warning\n",
    "`unicode_`        | `U`           | Unicode type with fixed length where the number of bytes is platform-specific; uses the same specification semantics as `string_`, e.g. `U7`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a0b6b2",
   "metadata": {},
   "source": [
    "Determine the number of elements with `itemsize`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba581655",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T15:01:57.867084Z",
     "iopub.status.busy": "2026-05-17T15:01:57.866960Z",
     "iopub.status.idle": "2026-05-17T15:01:57.869673Z",
     "shell.execute_reply": "2026-05-17T15:01:57.869423Z",
     "shell.execute_reply.started": "2026-05-17T15:01:57.867058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc3981f",
   "metadata": {},
   "source": [
    "Determine the name of the data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "465e5fbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T15:01:57.870067Z",
     "iopub.status.busy": "2026-05-17T15:01:57.870003Z",
     "iopub.status.idle": "2026-05-17T15:01:57.872216Z",
     "shell.execute_reply": "2026-05-17T15:01:57.871962Z",
     "shell.execute_reply.started": "2026-05-17T15:01:57.870060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'float64'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1bbf89d",
   "metadata": {},
   "source": [
    "Check data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8771580",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T15:01:57.872615Z",
     "iopub.status.busy": "2026-05-17T15:01:57.872529Z",
     "iopub.status.idle": "2026-05-17T15:01:57.874755Z",
     "shell.execute_reply": "2026-05-17T15:01:57.874530Z",
     "shell.execute_reply.started": "2026-05-17T15:01:57.872608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.type is np.float64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d304951",
   "metadata": {},
   "source": [
    "Change data type with `astype`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "372a4baf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T15:01:57.875104Z",
     "iopub.status.busy": "2026-05-17T15:01:57.875008Z",
     "iopub.status.idle": "2026-05-17T15:01:57.877163Z",
     "shell.execute_reply": "2026-05-17T15:01:57.876975Z",
     "shell.execute_reply.started": "2026-05-17T15:01:57.875093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09647385, 0.18739372, 0.19808419],\n",
       "       [0.90809816, 0.6350481 , 0.9251651 ],\n",
       "       [0.11977272, 0.6560006 , 0.8847805 ],\n",
       "       [0.33568636, 0.6303831 , 0.3406715 ],\n",
       "       [0.66093606, 0.06444538, 0.9164912 ],\n",
       "       [0.20784943, 0.8299043 , 0.5469985 ],\n",
       "       [0.24248955, 0.39786914, 0.8835176 ]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_float32 = data.astype(np.float32)\n",
    "data_float32"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.13 Kernel",
   "language": "python",
   "name": "python313"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}