{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3714bcce",
   "metadata": {},
   "source": [
    "# `dtype` konvertieren\n",
    "\n",
    "Manchmal passen die pandas-Datentypen nicht wirklich gut. Dies kann z.B. auf Serialisierungsformate zurückzuführen sein, die keine Typinformationen enthalten. Manchmal solltet ihr jedoch den Typ auch ändern, um eine bessere Performance zu erzielen – entweder  mehr Manipulationsmöglichkeiten oder weniger Speicherbedarf. In den folgenden Beispielen werden wir verschiedene Konvertierungen einer `Series` vornehmen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6bc0dcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.219373Z",
     "iopub.status.busy": "2026-05-21T14:25:58.219159Z",
     "iopub.status.idle": "2026-05-21T14:25:58.446137Z",
     "shell.execute_reply": "2026-05-21T14:25:58.445838Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.219349Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebae66af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.446624Z",
     "iopub.status.busy": "2026-05-21T14:25:58.446501Z",
     "iopub.status.idle": "2026-05-21T14:25:58.449016Z",
     "shell.execute_reply": "2026-05-21T14:25:58.448723Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.446615Z"
    }
   },
   "outputs": [],
   "source": [
    "rng = np.random.default_rng()\n",
    "s = pd.Series(rng.normal(size=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93f01178",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.449534Z",
     "iopub.status.busy": "2026-05-21T14:25:58.449455Z",
     "iopub.status.idle": "2026-05-21T14:25:58.453401Z",
     "shell.execute_reply": "2026-05-21T14:25:58.452821Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.449527Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.663472\n",
       "1    0.205134\n",
       "2    0.333356\n",
       "3   -0.639533\n",
       "4    1.519715\n",
       "5    0.331444\n",
       "6    1.551766\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0016ea",
   "metadata": {},
   "source": [
    "## Automatische Konvertierung\n",
    "\n",
    "[pandas.Series.convert_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.Series.convert_dtypes.html) versucht, eine `Series` in einen Typ zu konvertieren, der `NA` unterstützt. Im Fall unserer `Series` wird der Typ von `float64` in `Float64` geändert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70b57e04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.453993Z",
     "iopub.status.busy": "2026-05-21T14:25:58.453888Z",
     "iopub.status.idle": "2026-05-21T14:25:58.456900Z",
     "shell.execute_reply": "2026-05-21T14:25:58.456682Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.453983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.663472\n",
       "1    0.205134\n",
       "2    0.333356\n",
       "3   -0.639533\n",
       "4    1.519715\n",
       "5    0.331444\n",
       "6    1.551766\n",
       "dtype: Float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.convert_dtypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9463886",
   "metadata": {},
   "source": [
    "Bedauerlicherweise habe ich jedoch mit `convert_dtypes` kaum Kontrolle darüber, in welchen Datentyp konvertiert wird. Daher bevorzuge ich [pandas.Series.astype](https://pandas.pydata.org/docs/reference/api/pandas.Series.astype.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87b1c7e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.457277Z",
     "iopub.status.busy": "2026-05-21T14:25:58.457209Z",
     "iopub.status.idle": "2026-05-21T14:25:58.460227Z",
     "shell.execute_reply": "2026-05-21T14:25:58.460004Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.457269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.663472\n",
       "1    0.205134\n",
       "2    0.333356\n",
       "3   -0.639533\n",
       "4    1.519715\n",
       "5    0.331444\n",
       "6    1.551766\n",
       "dtype: float32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b9f452-619c-4667-98f6-7fc4d2639722",
   "metadata": {},
   "source": [
    "Sofern jedoch nicht konvertierbare Werte enthalten sind, wird ein Fehler ausgegeben:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b95d2906-b5f8-4e8c-854b-229d8afd1b61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.462067Z",
     "iopub.status.busy": "2026-05-21T14:25:58.461980Z",
     "iopub.status.idle": "2026-05-21T14:25:58.464763Z",
     "shell.execute_reply": "2026-05-21T14:25:58.464573Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.462060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90.0\n",
       "1     NaN\n",
       "2     1.0\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": "de312b5b-c7fc-4540-be84-8078be862b3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:25:58.465219Z",
     "iopub.status.busy": "2026-05-21T14:25:58.465092Z",
     "iopub.status.idle": "2026-05-21T14:25:58.770495Z",
     "shell.execute_reply": "2026-05-21T14:25:58.769548Z",
     "shell.execute_reply.started": "2026-05-21T14:25:58.465212Z"
    }
   },
   "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": "7edecbd2-841a-44fe-85f2-776522d16c1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-09T11:29:03.883303Z",
     "iopub.status.busy": "2026-03-09T11:29:03.882980Z",
     "iopub.status.idle": "2026-03-09T11:29:03.888305Z",
     "shell.execute_reply": "2026-03-09T11:29:03.887725Z",
     "shell.execute_reply.started": "2026-03-09T11:29:03.883281Z"
    }
   },
   "source": [
    "Fehler, wie dieser `IntCastingNaNError` können vermieden werden, indem durch `errors = \"ignore\"` ggf. der ursprüngliche Datentyp beibehalten wird:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31623ec6-337e-4689-afa8-ab4259f29984",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.865776Z",
     "iopub.status.busy": "2026-05-21T14:26:05.865476Z",
     "iopub.status.idle": "2026-05-21T14:26:05.871279Z",
     "shell.execute_reply": "2026-05-21T14:26:05.870665Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.865757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90.0\n",
       "1     NaN\n",
       "2     1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.astype(\"int8\", errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d36b3e4-c88f-4ee0-96b2-a72d2f22f144",
   "metadata": {},
   "source": [
    "Die Verwendung des richtigen Typs kann Speicherplatz einsparen. Üblich ist ein 8 Byte breiter Datentyp, also `int64` oder `float64`. Wenn ihr einen schmaleren Typ verwenden könnt, reduziert dies den Speicherverbrauch deutlich, sodass ihr mehr Daten verarbeiten könnt. Ihr könnt NumPy verwenden, um die Grenzen von Integer- und Float-Typen zu überprüfen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a98e0506-97e9-474a-b7bd-00480c663503",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.872698Z",
     "iopub.status.busy": "2026-05-21T14:26:05.872421Z",
     "iopub.status.idle": "2026-05-21T14:26:05.876450Z",
     "shell.execute_reply": "2026-05-21T14:26:05.876048Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.872660Z"
    }
   },
   "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": "ab52e51d-6b61-45eb-b030-ea396d87a785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.877160Z",
     "iopub.status.busy": "2026-05-21T14:26:05.877006Z",
     "iopub.status.idle": "2026-05-21T14:26:05.880612Z",
     "shell.execute_reply": "2026-05-21T14:26:05.880144Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.877146Z"
    }
   },
   "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": "c2134c5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.882231Z",
     "iopub.status.busy": "2026-05-21T14:26:05.882040Z",
     "iopub.status.idle": "2026-05-21T14:26:05.885571Z",
     "shell.execute_reply": "2026-05-21T14:26:05.885143Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.882215Z"
    }
   },
   "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": "80abda31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.886348Z",
     "iopub.status.busy": "2026-05-21T14:26:05.886092Z",
     "iopub.status.idle": "2026-05-21T14:26:05.889914Z",
     "shell.execute_reply": "2026-05-21T14:26:05.889458Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.886329Z"
    }
   },
   "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": "31113679",
   "metadata": {},
   "source": [
    "## Speicherverbrauch\n",
    "\n",
    "Um den Speicherverbrauch der `Series` zu berechnen, könnt ihr [pandas.Series.nbytes](https://pandas.pydata.org/docs/reference/api/pandas.Series.nbytes.html) verwenden um den Speicher, der von den Daten verwendet wird, zu ermitteln. [pandas.Series.memory_usage](https://pandas.pydata.org/docs/reference/api/pandas.Series.memory_usage.html) erfasst darüberhinaus auch den Indexspeicher und den Datentyp. Mit `deep=True` lässt sich auch der Speicherverbrauch auf Systemebene ermitteln."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f433772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.890677Z",
     "iopub.status.busy": "2026-05-21T14:26:05.890543Z",
     "iopub.status.idle": "2026-05-21T14:26:05.894181Z",
     "shell.execute_reply": "2026-05-21T14:26:05.893808Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.890663Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e32acea2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.894974Z",
     "iopub.status.busy": "2026-05-21T14:26:05.894812Z",
     "iopub.status.idle": "2026-05-21T14:26:05.897909Z",
     "shell.execute_reply": "2026-05-21T14:26:05.897555Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.894964Z"
    }
   },
   "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": "794f2ddc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.898546Z",
     "iopub.status.busy": "2026-05-21T14:26:05.898445Z",
     "iopub.status.idle": "2026-05-21T14:26:05.900801Z",
     "shell.execute_reply": "2026-05-21T14:26:05.900497Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.898537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "febe7656",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.901283Z",
     "iopub.status.busy": "2026-05-21T14:26:05.901123Z",
     "iopub.status.idle": "2026-05-21T14:26:05.903534Z",
     "shell.execute_reply": "2026-05-21T14:26:05.903171Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.901274Z"
    }
   },
   "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": "55459cf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.905159Z",
     "iopub.status.busy": "2026-05-21T14:26:05.905065Z",
     "iopub.status.idle": "2026-05-21T14:26:05.907359Z",
     "shell.execute_reply": "2026-05-21T14:26:05.907152Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.905150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c34f8a2",
   "metadata": {},
   "source": [
    "## String- und Kategorietypen\n",
    "\n",
    "Die Methode [pandas.Series.astype](https://pandas.pydata.org/docs/reference/api/pandas.Series.astype.html) kann auch numerische Reihen in Zeichenketten umwandeln, wenn ihr `str` übergebt. Beachtet den `dtype` im folgenden Beispiel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c771b5ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.907782Z",
     "iopub.status.busy": "2026-05-21T14:26:05.907690Z",
     "iopub.status.idle": "2026-05-21T14:26:05.910626Z",
     "shell.execute_reply": "2026-05-21T14:26:05.910417Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.907774Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -1.6634723613898739\n",
       "1    0.20513361124745808\n",
       "2     0.3333563040239043\n",
       "3    -0.6395333353979279\n",
       "4     1.5197153715827265\n",
       "5    0.33144403280572465\n",
       "6     1.5517663730128375\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01724d65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.911255Z",
     "iopub.status.busy": "2026-05-21T14:26:05.911141Z",
     "iopub.status.idle": "2026-05-21T14:26:05.913589Z",
     "shell.execute_reply": "2026-05-21T14:26:05.913330Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.911244Z"
    }
   },
   "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": "47e7f593",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.914134Z",
     "iopub.status.busy": "2026-05-21T14:26:05.914050Z",
     "iopub.status.idle": "2026-05-21T14:26:05.916759Z",
     "shell.execute_reply": "2026-05-21T14:26:05.916534Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.914126Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "605"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str).memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca82137",
   "metadata": {},
   "source": [
    "Zur Konvertierung in einen kategorialen Typ könnt ihr `'category'` als Typ übergeben:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19ce6064",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.917257Z",
     "iopub.status.busy": "2026-05-21T14:26:05.917179Z",
     "iopub.status.idle": "2026-05-21T14:26:05.920410Z",
     "shell.execute_reply": "2026-05-21T14:26:05.920197Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.917250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -1.6634723613898739\n",
       "1    0.20513361124745808\n",
       "2     0.3333563040239043\n",
       "3    -0.6395333353979279\n",
       "4     1.5197153715827265\n",
       "5    0.33144403280572465\n",
       "6     1.5517663730128375\n",
       "dtype: category\n",
       "Categories (7, object): ['-0.6395333353979279', '-1.6634723613898739', '0.20513361124745808', '0.33144403280572465', '0.3333563040239043', '1.5197153715827265', '1.5517663730128375']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(str).astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffcc4fe",
   "metadata": {},
   "source": [
    "Eine kategoriale `Series` ist nützlich für String-Daten und kann zu großen Speichereinsparungen führen. Das liegt daran, dass pandas bei der Konvertierung in kategoriale Daten nicht länger Python-Strings für jeden Wert verwendet, sondern sich wiederholende Werte nicht dupliziert werden. Ihr habt immer noch alle Funktionen des `str`-Attributs, aber ihr spart viel Speicherplatz wenn ihr viele doppelte Werte habt und steigert die Leistung, da ihr nicht so viele String-Operationen durchführen müsst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df605fdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.920857Z",
     "iopub.status.busy": "2026-05-21T14:26:05.920780Z",
     "iopub.status.idle": "2026-05-21T14:26:05.923310Z",
     "shell.execute_reply": "2026-05-21T14:26:05.923043Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.920851Z"
    }
   },
   "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": "211777e8",
   "metadata": {},
   "source": [
    "## Geordnete Kategorien\n",
    "\n",
    "Um geordnete Kategorien zu erstellen, müsst ihr einen eigenen [pandas.CategoricalDtype](https://pandas.pydata.org/docs/reference/api/pandas.CategoricalDtype.html) definieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcdbd52d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.923722Z",
     "iopub.status.busy": "2026-05-21T14:26:05.923650Z",
     "iopub.status.idle": "2026-05-21T14:26:05.926633Z",
     "shell.execute_reply": "2026-05-21T14:26:05.926348Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.923714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.663472\n",
       "1    0.205134\n",
       "2    0.333356\n",
       "3   -0.639533\n",
       "4    1.519715\n",
       "5    0.331444\n",
       "6    1.551766\n",
       "dtype: category\n",
       "Categories (7, float64): [-1.663472 < -0.639533 < 0.205134 < 0.331444 < 0.333356 < 1.519715 < 1.551766]"
      ]
     },
     "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": "2894c502",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.927012Z",
     "iopub.status.busy": "2026-05-21T14:26:05.926950Z",
     "iopub.status.idle": "2026-05-21T14:26:05.929460Z",
     "shell.execute_reply": "2026-05-21T14:26:05.929200Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.927005Z"
    }
   },
   "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": "8ad2da49",
   "metadata": {},
   "source": [
    "In der folgenden Tabelle sind die Typen aufgeführt, die ihr an `astype` übergeben könnt.\n",
    "\n",
    "Datentyp | Beschreibung\n",
    ":------- | :-----------\n",
    "`str`, `'str'` | in Python-String konvertieren\n",
    "`'string'` | in Pandas-String konvertieren mit `pandas.NA`\n",
    "`int`, `'int'`, `'int64'` | in NumPy `int64` konvertieren\n",
    "`'int32'`, `'uint32'` | in NumPy `int32` konvertieren\n",
    "`'Int64'` | in pandas `Int64` konvertieren mit `pandas.NA`\n",
    "`float`, `'float'`, `'float64'` | in Floats konvertieren\n",
    "`'category'` | in `CategoricalDtype` konvertieren mit `pandas.NA`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e02ba5",
   "metadata": {},
   "source": [
    "## Umwandlung in andere Datentypen\n",
    "\n",
    "Die Methode [pandas.Series.to_numpy](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_numpy.html) oder die Eigenschaft [pandas.Series.values](https://pandas.pydata.org/docs/reference/api/pandas.Series.values.html) liefert uns ein NumPy-Array mit Werten, und [pandas.Series.to_list](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_list.html) gibt eine Python-Liste mit Werten zurück. Warum solltet ihr das wollen? pandas-Objekte sind meist viel benutzerfreundlicher und der Code lässt sich leichter lesen. Zudem werden Python-Listen sehr viel langsamer verarbeitet werden können. Mit [pandas.Series.to_frame](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_frame.html) könnt ihr ggf. einen DataFrame mit einer einzigen Spalte erzeugen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "081a32ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T14:26:05.929912Z",
     "iopub.status.busy": "2026-05-21T14:26:05.929831Z",
     "iopub.status.idle": "2026-05-21T14:26:05.933941Z",
     "shell.execute_reply": "2026-05-21T14:26:05.933716Z",
     "shell.execute_reply.started": "2026-05-21T14:26:05.929905Z"
    }
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "4  1.519715\n",
       "5  0.331444\n",
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     },
     "execution_count": 25,
     "metadata": {},
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   ],
   "source": [
    "s.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa64c3a",
   "metadata": {},
   "source": [
    "Auch die Funktion [pandas.to_datetime](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html) kann hilfreich sein um in pandas um Werte in Datum und Uhrzeit zu konvertieren."
   ]
  }
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