{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73bd3476",
   "metadata": {},
   "source": [
    "# Convert `dtype`\n",
    "\n",
    "Sometimes the pandas data types do not fit really well. This can be due to serialisation formats that do not contain type information, for example. However, sometimes you should also change the type to achieve better performance – either more manipulation possibilities or less memory requirements. In the following examples, we will make different conversions of a `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69abe2d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:31.791981Z",
     "iopub.status.busy": "2026-05-16T14:17:31.791817Z",
     "iopub.status.idle": "2026-05-16T14:17:32.142849Z",
     "shell.execute_reply": "2026-05-16T14:17:32.142551Z",
     "shell.execute_reply.started": "2026-05-16T14:17:31.791963Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2a35bac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.143343Z",
     "iopub.status.busy": "2026-05-16T14:17:32.143181Z",
     "iopub.status.idle": "2026-05-16T14:17:32.145742Z",
     "shell.execute_reply": "2026-05-16T14:17:32.145440Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.143328Z"
    }
   },
   "outputs": [],
   "source": [
    "rng = np.random.default_rng()\n",
    "s = pd.Series(rng.normal(size=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98ffcd09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.146360Z",
     "iopub.status.busy": "2026-05-16T14:17:32.146256Z",
     "iopub.status.idle": "2026-05-16T14:17:32.151148Z",
     "shell.execute_reply": "2026-05-16T14:17:32.150812Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.146352Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.247177\n",
       "1   -0.541495\n",
       "2    0.627878\n",
       "3   -0.160339\n",
       "4    0.798170\n",
       "5   -0.075034\n",
       "6   -0.371027\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab32810f",
   "metadata": {},
   "source": [
    "## Automatic conversion\n",
    "\n",
    "[pandas.Series.convert_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.Series.convert_dtypes.html) tries to convert a `Series` to a type that supports `NA`. In the case of our `Series`, the type is changed from `float64` to `Float64`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9738255",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.152090Z",
     "iopub.status.busy": "2026-05-16T14:17:32.151728Z",
     "iopub.status.idle": "2026-05-16T14:17:32.154967Z",
     "shell.execute_reply": "2026-05-16T14:17:32.154677Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.152067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.247177\n",
       "1   -0.541495\n",
       "2    0.627878\n",
       "3   -0.160339\n",
       "4     0.79817\n",
       "5   -0.075034\n",
       "6   -0.371027\n",
       "dtype: Float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.convert_dtypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ef14c8",
   "metadata": {},
   "source": [
    "Unfortunately, however, with `convert_dtypes` I have little control over what data type is converted to. Therefore, I prefer [pandas.Series.astype](https://pandas.pydata.org/docs/reference/api/pandas.Series.astype.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c6de2eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.155447Z",
     "iopub.status.busy": "2026-05-16T14:17:32.155334Z",
     "iopub.status.idle": "2026-05-16T14:17:32.158599Z",
     "shell.execute_reply": "2026-05-16T14:17:32.158350Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.155438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.247177\n",
       "1   -0.541495\n",
       "2    0.627878\n",
       "3   -0.160339\n",
       "4     0.79817\n",
       "5   -0.075034\n",
       "6   -0.371027\n",
       "dtype: Float32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(\"Float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cc3259-4e93-4b05-a826-24db437bdecd",
   "metadata": {},
   "source": [
    "However, if non-convertible values are included, an error will be returned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29f5c8d4-de3e-420b-ae01-1b2c3d6abb5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.161135Z",
     "iopub.status.busy": "2026-05-16T14:17:32.161003Z",
     "iopub.status.idle": "2026-05-16T14:17:32.164192Z",
     "shell.execute_reply": "2026-05-16T14:17:32.163830Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.161124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65.0\n",
       "1     NaN\n",
       "2    40.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng()\n",
    "n = pd.Series([rng.integers(127), np.nan, rng.integers(127)])\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72500da6-7057-4298-a18a-704b9d0e8eea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:32.164841Z",
     "iopub.status.busy": "2026-05-16T14:17:32.164626Z",
     "iopub.status.idle": "2026-05-16T14:17:32.467206Z",
     "shell.execute_reply": "2026-05-16T14:17:32.466230Z",
     "shell.execute_reply.started": "2026-05-16T14:17:32.164831Z"
    }
   },
   "outputs": [
    {
     "ename": "IntCastingNaNError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIntCastingNaNError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mint8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   6637\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   6638\u001b[0m         ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m   6639\u001b[0m     ]\n\u001b[1;32m   6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6642\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6643\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6644\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m   6645\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/internals/managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m    428\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/internals/managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    361\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 363\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    364\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m    366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/internals/blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[1;32m    755\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    756\u001b[0m     values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :]  \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    234\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    179\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[1;32m     97\u001b[0m         arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[1;32m    100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_astype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[1;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[0;32m~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 145\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[1;32m    146\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    147\u001b[0m     )\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[0;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ]
    }
   ],
   "source": [
    "n.astype(\"int8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c1c73f-c268-4a95-9691-08c4f21ae43e",
   "metadata": {},
   "source": [
    "Errors such as IntCastingNaNError can be avoided by retaining the original data type using `errors = \"ignore\"` if necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8efe298d-25bb-43ea-8641-f200148826b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:38.201676Z",
     "iopub.status.busy": "2026-05-16T14:17:38.201385Z",
     "iopub.status.idle": "2026-05-16T14:17:38.206241Z",
     "shell.execute_reply": "2026-05-16T14:17:38.205824Z",
     "shell.execute_reply.started": "2026-05-16T14:17:38.201658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65.0\n",
       "1     NaN\n",
       "2    40.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.astype(\"int8\", errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b725610a",
   "metadata": {},
   "source": [
    "Using the correct type can save memory. The usual data type is 8 bytes wide, for example `int64` or `float64`. If you can use a narrower type, this will significantly reduce memory consumption, allowing you to process more data. You can use NumPy to check the limits of integer and float types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46c513b8-06b2-42e4-bd5b-39ca17832003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:39.173396Z",
     "iopub.status.busy": "2026-05-16T14:17:39.173113Z",
     "iopub.status.idle": "2026-05-16T14:17:39.177039Z",
     "shell.execute_reply": "2026-05-16T14:17:39.176536Z",
     "shell.execute_reply.started": "2026-05-16T14:17:39.173377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iinfo(min=-128, max=127, dtype=int8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(\"int8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9e051ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:39.534356Z",
     "iopub.status.busy": "2026-05-16T14:17:39.534000Z",
     "iopub.status.idle": "2026-05-16T14:17:39.538113Z",
     "shell.execute_reply": "2026-05-16T14:17:39.537619Z",
     "shell.execute_reply.started": "2026-05-16T14:17:39.534334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iinfo(min=-9223372036854775808, max=9223372036854775807, dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "add9af88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:39.930385Z",
     "iopub.status.busy": "2026-05-16T14:17:39.930098Z",
     "iopub.status.idle": "2026-05-16T14:17:39.933844Z",
     "shell.execute_reply": "2026-05-16T14:17:39.933306Z",
     "shell.execute_reply.started": "2026-05-16T14:17:39.930367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finfo(resolution=1e-06, min=-3.4028235e+38, max=3.4028235e+38, dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a5e1e44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:40.296176Z",
     "iopub.status.busy": "2026-05-16T14:17:40.295894Z",
     "iopub.status.idle": "2026-05-16T14:17:40.299460Z",
     "shell.execute_reply": "2026-05-16T14:17:40.298952Z",
     "shell.execute_reply.started": "2026-05-16T14:17:40.296159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finfo(resolution=1e-15, min=-1.7976931348623157e+308, max=1.7976931348623157e+308, dtype=float64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo(\"float64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea015364",
   "metadata": {},
   "source": [
    "## Memory usage\n",
    "\n",
    "To calculate the memory consumption of the `Series`, you can use [pandas.Series.nbytes](https://pandas.pydata.org/docs/reference/api/pandas.Series.nbytes.html) to determine the memory used by the data. [pandas.Series.memory_usage](https://pandas.pydata.org/docs/reference/api/pandas.Series.memory_usage.html) also records the index memory and the data type. With `deep=True` you can also determine the memory consumption at system level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e17aa411",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:41.220601Z",
     "iopub.status.busy": "2026-05-16T14:17:41.220022Z",
     "iopub.status.idle": "2026-05-16T14:17:41.224294Z",
     "shell.execute_reply": "2026-05-16T14:17:41.223878Z",
     "shell.execute_reply.started": "2026-05-16T14:17:41.220558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a73df20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:41.610843Z",
     "iopub.status.busy": "2026-05-16T14:17:41.610281Z",
     "iopub.status.idle": "2026-05-16T14:17:41.614875Z",
     "shell.execute_reply": "2026-05-16T14:17:41.614476Z",
     "shell.execute_reply.started": "2026-05-16T14:17:41.610801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(\"Float32\").nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b3c53d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:42.110510Z",
     "iopub.status.busy": "2026-05-16T14:17:42.110215Z",
     "iopub.status.idle": "2026-05-16T14:17:42.114023Z",
     "shell.execute_reply": "2026-05-16T14:17:42.113570Z",
     "shell.execute_reply.started": "2026-05-16T14:17:42.110491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1add8c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:42.951863Z",
     "iopub.status.busy": "2026-05-16T14:17:42.951568Z",
     "iopub.status.idle": "2026-05-16T14:17:42.956756Z",
     "shell.execute_reply": "2026-05-16T14:17:42.955472Z",
     "shell.execute_reply.started": "2026-05-16T14:17:42.951845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(\"Float32\").memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91108fce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:43.370306Z",
     "iopub.status.busy": "2026-05-16T14:17:43.370006Z",
     "iopub.status.idle": "2026-05-16T14:17:43.373619Z",
     "shell.execute_reply": "2026-05-16T14:17:43.373167Z",
     "shell.execute_reply.started": "2026-05-16T14:17:43.370287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7859b4e2",
   "metadata": {},
   "source": [
    "## String and category types\n",
    "\n",
    "The [pandas.Series.astype](https://pandas.pydata.org/docs/reference/api/pandas.Series.astype.html) method can also convert numeric series into strings if you pass `str`. Note the `dtype` in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98fff9a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:44.389987Z",
     "iopub.status.busy": "2026-05-16T14:17:44.389437Z",
     "iopub.status.idle": "2026-05-16T14:17:44.396777Z",
     "shell.execute_reply": "2026-05-16T14:17:44.396315Z",
     "shell.execute_reply.started": "2026-05-16T14:17:44.389946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.2471774174803347\n",
       "1     -0.5414946268558698\n",
       "2      0.6278777749662021\n",
       "3    -0.16033855275793557\n",
       "4      0.7981700700350348\n",
       "5    -0.07503397423765741\n",
       "6     -0.3710273398593862\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c4a2c37",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-05-16T14:17:44.728147Z",
     "iopub.status.idle": "2026-05-16T14:17:44.731900Z",
     "shell.execute_reply": "2026-05-16T14:17:44.731476Z",
     "shell.execute_reply.started": "2026-05-16T14:17:44.728410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str).memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f72c5dec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:45.170373Z",
     "iopub.status.busy": "2026-05-16T14:17:45.170034Z",
     "iopub.status.idle": "2026-05-16T14:17:45.174292Z",
     "shell.execute_reply": "2026-05-16T14:17:45.173860Z",
     "shell.execute_reply.started": "2026-05-16T14:17:45.170354Z"
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   "outputs": [
    {
     "data": {
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       "607"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str).memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee2d501",
   "metadata": {},
   "source": [
    "To convert to a categorical type, you can pass `\"category\"` as the type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1dcfcfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:46.068389Z",
     "iopub.status.busy": "2026-05-16T14:17:46.068106Z",
     "iopub.status.idle": "2026-05-16T14:17:46.073320Z",
     "shell.execute_reply": "2026-05-16T14:17:46.072849Z",
     "shell.execute_reply.started": "2026-05-16T14:17:46.068372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.2471774174803347\n",
       "1     -0.5414946268558698\n",
       "2      0.6278777749662021\n",
       "3    -0.16033855275793557\n",
       "4      0.7981700700350348\n",
       "5    -0.07503397423765741\n",
       "6     -0.3710273398593862\n",
       "dtype: category\n",
       "Categories (7, object): ['-0.07503397423765741', '-0.16033855275793557', '-0.3710273398593862', '-0.5414946268558698', '0.2471774174803347', '0.6278777749662021', '0.7981700700350348']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str).astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332aecb0",
   "metadata": {},
   "source": [
    "A categorical `Series` is useful for string data and can lead to large memory savings. This is because when converting to categorical data, pandas no longer uses Python strings for each value, but repeating values are not duplicated. You still have all the features of the `str` attribute, but you save a lot of memory when you have a lot of duplicate values and you increase performance because you don’t have to do as many string operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65a2aecb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:46.658887Z",
     "iopub.status.busy": "2026-05-16T14:17:46.658612Z",
     "iopub.status.idle": "2026-05-16T14:17:46.662728Z",
     "shell.execute_reply": "2026-05-16T14:17:46.662196Z",
     "shell.execute_reply.started": "2026-05-16T14:17:46.658869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(\"category\").memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ebb412",
   "metadata": {},
   "source": [
    "## Ordered categories\n",
    "\n",
    "To create ordered categories, you need to define your own [pandas.CategoricalDtype](https://pandas.pydata.org/docs/reference/api/pandas.CategoricalDtype.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2aec8c09-f0f5-431c-bdb4-f7518c9ab2c1",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-05-16T14:17:47.817409Z",
     "iopub.status.idle": "2026-05-16T14:17:47.824999Z",
     "shell.execute_reply": "2026-05-16T14:17:47.824598Z",
     "shell.execute_reply.started": "2026-05-16T14:17:47.817943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.247177\n",
       "1   -0.541495\n",
       "2    0.627878\n",
       "3   -0.160339\n",
       "4    0.798170\n",
       "5   -0.075034\n",
       "6   -0.371027\n",
       "dtype: category\n",
       "Categories (7, float64): [-0.541495 < -0.371027 < -0.160339 < -0.075034 < 0.247177 < 0.627878 < 0.798170]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "\n",
    "\n",
    "s_sorted = pd.Series(sorted(set(s)))\n",
    "cat_dtype = CategoricalDtype(categories=s_sorted, ordered=True)\n",
    "\n",
    "s.astype(cat_dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac5641a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-16T14:17:48.454293Z",
     "iopub.status.busy": "2026-05-16T14:17:48.453990Z",
     "iopub.status.idle": "2026-05-16T14:17:48.459583Z",
     "shell.execute_reply": "2026-05-16T14:17:48.459082Z",
     "shell.execute_reply.started": "2026-05-16T14:17:48.454273Z"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "495"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(cat_dtype).memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac146b8",
   "metadata": {},
   "source": [
    "The following table lists the types you can pass to `astype`.\n",
    "\n",
    "Data type | Description\n",
    ":-------- | :----------\n",
    "`str`, `'str'` | convert to Python string\n",
    "`'string'` | convert to Pandas string with `pandas.NA`\n",
    "`int`, `'int'`, `'int64'` | convert to NumPy `int64`\n",
    "`'int32'`, `'uint32'` | convert to NumPy `int32`\n",
    "`'Int64'` | convert to pandas `Int64` with `pandas.NA`\n",
    "`float`, `'float'`, `'float64'` | convert to floats\n",
    "`'category'` | convert to `CategoricalDtype` with `pandas.NA`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bab7d2e",
   "metadata": {},
   "source": [
    "## Conversion to other data types\n",
    "\n",
    "The [pandas.Series.to_numpy](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_numpy.html) method or the [pandas.Series.values](https://pandas.pydata.org/docs/reference/api/pandas.Series.values.html) property gives us a NumPy array of values, and [pandas.Series.to_list](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_list.html) returns a Python list of values. Why would you want to do this? pandas objects are usually much more user-friendly and the code is easier to read. Also, python lists will be much slower to process. With [pandas.Series.to_frame](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_frame.html) you can create a DataFrame with a single column, if necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de6e2fa8",
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    "execution": {
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       "4  0.798170\n",
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    "s.to_frame()"
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  },
  {
   "cell_type": "markdown",
   "id": "eaff05be",
   "metadata": {},
   "source": [
    "The function [pandas.to_datetime](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html) can also be useful to convert values in pandas to date and time."
   ]
  }
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