109 CZ100RADIO x názvy tracků.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ce629f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85e2eaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join(\"data\",\"CZ_RADIO_TOP_100_tydenni.csv\"))\n",
    "wikid = pd.read_json(os.path.join(\"wikidata\",\"wikidata.json\"))\n",
    "df = df.merge(wikid, on=\"interpret_hlavni\", how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a87964ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16a0eddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ohol(text):\n",
    "    try:\n",
    "        oholenost = text.replace(\".\",\"\").replace(\",\",\"\").replace(\"?\",\"\").replace(\"!\",\"\").replace(\"-\",\"\").replace(\"(\",\"\").replace(\")\",\"\").lower()\n",
    "    except:\n",
    "        oholenost = text\n",
    "    return oholenost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afe9eca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['track_oholeny'] = df['track'].apply(lambda x: ohol(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9931ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['slova'] = df['track_oholeny'].apply(lambda x: x.split(\" \") if isinstance(x,str) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b1701c",
   "metadata": {},
   "source": [
    "## Žebříčky slov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7225aa48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slova\n",
       "you      259\n",
       "the      226\n",
       "me       213\n",
       "feat     183\n",
       "love     178\n",
       "i        151\n",
       "to       132\n",
       "my       117\n",
       "it       109\n",
       "a        102\n",
       "on        89\n",
       "of        86\n",
       "in        83\n",
       "up        64\n",
       "your      57\n",
       "all       55\n",
       "&         54\n",
       "like      52\n",
       "for       50\n",
       "is        48\n",
       "we        46\n",
       "be        45\n",
       "don't     40\n",
       "no        40\n",
       "this      40\n",
       "what      40\n",
       "one       39\n",
       "do        36\n",
       "life      36\n",
       "and       34\n",
       "night     34\n",
       "go        33\n",
       "v         32\n",
       "with      32\n",
       "down      31\n",
       "out       31\n",
       "heart     30\n",
       "know      29\n",
       "se        29\n",
       "time      29\n",
       "good      28\n",
       "now       27\n",
       "so        27\n",
       "world     27\n",
       "let       26\n",
       "never     26\n",
       "song      26\n",
       "back      25\n",
       "na        25\n",
       "are       24\n",
       "Name: track, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.explode('slova').groupby('slova')['track'].nunique().nlargest(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "437bdbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slova\n",
       "v          27\n",
       "feat       26\n",
       "to         25\n",
       "a          23\n",
       "se         23\n",
       "na         18\n",
       "the        15\n",
       "you        15\n",
       "je         14\n",
       "já         14\n",
       "co         13\n",
       "do         11\n",
       "love       10\n",
       "láska      10\n",
       "my         10\n",
       "o          10\n",
       "tě         10\n",
       "i           9\n",
       "mám         9\n",
       "tak         9\n",
       "z           9\n",
       "jsem        8\n",
       "mi          8\n",
       "mě          8\n",
       "on          8\n",
       "si          8\n",
       "noc         7\n",
       "of          7\n",
       "ráno        7\n",
       "s           7\n",
       "all         6\n",
       "chci        6\n",
       "jsi         6\n",
       "me          6\n",
       "nad         6\n",
       "nebe        6\n",
       "srdce       6\n",
       "vánoční     6\n",
       "čas         6\n",
       "život       6\n",
       "is          5\n",
       "it          5\n",
       "jako        5\n",
       "jednou      5\n",
       "nech        5\n",
       "no          5\n",
       "ty          5\n",
       "vánoce      5\n",
       "by          4\n",
       "dál         4\n",
       "Name: track, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['zeme_puvodu'] == 'Česko'].explode('slova').groupby('slova')['track'].nunique().nlargest(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec16b4af",
   "metadata": {},
   "source": [
    "## Vývoj slov (ošemetné: je silně ovlivněno dramaturgií domácí/zahraniční)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3173e565",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pomer(s):\n",
    "    podil = df[df['track'].str.lower().str.contains(s, na=False)].groupby('rok').size() / df.groupby('rok').size()\n",
    "    podil.plot.bar()\n",
    "    return podil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16b524ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rok\n",
       "2006.0    0.039608\n",
       "2007.0    0.039020\n",
       "2008.0    0.051346\n",
       "2009.0    0.033529\n",
       "2010.0    0.075385\n",
       "2011.0    0.031400\n",
       "2012.0    0.047451\n",
       "2013.0    0.058235\n",
       "2014.0    0.063800\n",
       "2015.0    0.074808\n",
       "2016.0    0.064706\n",
       "2017.0    0.039615\n",
       "2018.0    0.034800\n",
       "2019.0    0.040196\n",
       "2020.0    0.051569\n",
       "2021.0    0.066078\n",
       "2022.0    0.048824\n",
       "2023.0    0.042857\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lrRtj/nxJ19Tzb5K0tGfeH9bt2yT9eou5R0REA+MWeklzgcuBM4AVwLmSVvR1Ox/Ya/t44DJgfb3sCuAc4ARgNfC+Ol5EREyTJlv0q4BR29ttPwJsAtb09VkDXFlPXwucJkl1+ybbP7J9HzBax4uIiGki20/cQToTWG37jfX71wMn276gp8+ddZ+d9ft7gZOBi4EbbX+wbt8IfNL2tX0/Yy2wtn77XGBbg9yPAb7boN90xWkzVhdzajNWcpr+WMlp+mNNd07Psj001ox5LSUxKbY3ABsGWUbSiO2Vk/3ZbcUpPac2YyWn6Y+VnKY/VpdyarLr5gFgcc/7RXXbmH0kzQMWALsbLhsREVOoSaG/GVguaZmkI6gOrg739RkGzqunzwRucLVPaBg4pz4rZxmwHPhyO6lHREQT4+66sb1f0gXAFmAucIXtrZIuAUZsDwMbgasljQJ7qL4MqPt9GLgL2A/8Z9uPtpT7QLt6piFOm7G6mFObsZLT9MdKTtMfqzM5jXswNiIiZrdcGRsRUbgU+oiIwqXQR0QUblYVeklPl/T0rsVqS+nja1Nb4yt9PbWpi+uqizm1qa3xdb7QS1oiaZOkXcBNwJcl/UvdtnQGYy2QdKmkeyTtkbRb0t1129EzlFNrsep4z5B0Uv16xqDLtx2rrfG1vZ7qmK2sq1LXeek5tRlrKj6f2O70C/gScDYwt6dtLtUpnDfOYKwtwIXAsT1tx9Zt18/m8QEnAjcCdwOfrl/31G0nDZhTm7HaGl+b67yV8R0G67z0nDo3voNiTmSh6XwBX5/IvGmItW0i82bD+IDbqe5n1N/+EuArA+bUZqy2xtfmOm9lfIfBOi89p86Nr/fViXvdjOMWSe+jujvmjrptMdWVuLfNYKxvSHoHcKXt70D1ZxvwH3piT3dObcV6qu2b+htt3yjpqQPm1GastsbX5jpva3ylr/PSc+ri+H6i8xdMqbrtwvlUtzw+rm7eCfwdsNH2j2Yo1s8A6+pYP1s3f4fqtg/rbe+ZgZxaiSXpvcCzgas4+IP2BuA+99y5dJpjtTW+Ntd5K+M7DNZ56Tl1bnwHxex6oY+ZIekMDv6gPQAM2948k7G6qK3xlb7OS8+pi+M7YFYXekmvsv33HYx1ku1bO5ZTa7G6qK3xlb6e2tTFddXFnNo00fF1/vTKcby4o7H+U0txOjc+VQ+JaUWbsWhvXbW2ztsaX+nrvPScujC+WV3obb+zo7He1FKcLo5PLcVpNVZb42tzndPe+Ipe5xSeU5uxJjq+zu+6kbQE+Bfb+ySJ6qyWk6huffx+2/sHiPVqqnPc97WU28uA79jeJumlwC8Bd9u+bsA4R1E9PH0x8CjwtTrPxyaQ0/MYez/h3YPG6iJJqwDbvlnVw+dXA/dMdj+opKtsv6GVJAuix59B8S3bn5b0OuCXqc4X32D7xzOUVz7ng8SbBYX+TmCV7Yclrac6sv0J4BUAtv/jALF+CPwA+CTwIWCLJ3h/fEl/RvWg83lUF0+dVsd9OXCb7T9oGOe1wNuBO4BfBb5I9ZfW84F/b/uOAXK6EDiX6gHuO+vmRVS/qJtsX9o01hP8jN+x/VcDLvM8ql/Im2w/1NO+2vanBojzTuAMqnX+f6meS/wZ4HSq/8s/aRin/8E5olr3NwDYfnXTnMaIfQrV5+JO29cPsNzJVBsJ35f0FKozug5s0Pyp7QcHiPVW4OO2BznN91Cx/oZqfR8JfA84CvgY1eddts879NI/FevngX/HwRs0f2v7+wPmlM/5oAY56X4mXsBdPdO3AHN63g96IcJtwM8AbwL+gep0yL8AXj6BvLZSFYgjgb3AkXX7k6h+yZvGuaNn2WPq/0iAFwBfHDCnrwFPGqP9CCZ4ocUYsb45YP+3Uj3s/RPA/cCannm3Dhjrq1RXCB4JfB94Wt3+FOCOAeLcCnwQOJXqi/lU4Nv19ECfBeDLPdNvorpw5p3AF4B1A36e5tXTG4A/A06pY31swJweBL4FfB54CzA0if/vO+p/59W/L3Pr9xpwnb8VuB74r1QbM5cDf0L1RXbqgDnlcz7gazZcMLVD0its30C1AhdTXay0cAKxbHsv8H7g/ZKOBV4LXCppke3FT7z4T8WypAO7Vw78afQYgx37EPDDevoH1Ofk275D0tMGiHPgZz8T+EZf+8/V85olJB3qrwgBg96/403Ai2w/VN+n41pJS22/h8H3Xe539RfYw5Ludb0laPuHPf8PTawE3gb8F+APbN8u6Ye2PztgPlB9sR+wFjjd9i5J76K6/L3p1uUcP74bcqXtk+rpf5J0+4A5bQdeBPwbqkvp/5ukW6j+iv2Y7f83QKw59e6bp1IVngVUT5Gbz8FjH8+bgBNtPyrp3cBm26dK+kvg/wC/OECsfM4HNBsK/RuBqyRdTLWlcnv9wT8a+P0BYx20wm3/M/Be4L2SnjVgrOskfR54MvAB4MOSbqTaKvzcAHE2A5+S9Dmq/XAfgequdf35NvB7wD9I+jqPX7SxBDgeaHzBBtWH/Nep/lLpJaqtsUHMcf1nrO37JZ1K9UvwLAYf3yOSjrT9MFUhq5KSFjDAL7irYx+XSfpI/e93mPjvwpz64rk5VLsydtU/4weSGh8/Au7s2V3wFUkrbY9Ieg4w6H5w12O8Hrhe0pOodgWcC7wLGBog1kaqe7bMpfpi/Iik7VSX9m8aMK95VLts5lPtAsL2N+v8BvF75HM+mDb+zJmOF/ALVAdfXkO1z2rOBGKc2nJOvwS8pJ5+NtW+9tcOmhvwG/Wyp/e0zQHmTyCnOVS/hK+pXy+h5+ZIDWNsBE45xLy/HTDWDVRbcr1t86iuIHx0wFhjrg+qXV7Pn8T/4yup9oNPZNn7qbag76v//bm6/Sjg9gHiLAD+GriX6o6FP67jfRZ44YA53fYE846cwBifCTyznj4aOJPquNkgMd5GtZvy/VRfHL9Ttw8Bn5tATvmcD/Dq/MHYA1TdR+YnR9hd31+mlFht5nSI+Ee55wDRdJG0iOpP0X8eY95LbX+hpZ/TyvhajHMk8Azb9w243NOAZVRFYudEPgeSnmP7a4MuN4GfM9C6knQC1QbbnbbvmbrMpt90fc4nqvOFXtKJVAdMF1CdQgXVEfbvAW/xAFegdjFWmzmN83O+aXtJC3Fa+8JoOVZb42slTh2rU18+UxBrRj5Tkl5AdcD6OKoz3S50dewNSV+2vWqyOc2kqRjfbNhH/9fA77rvznCSXgL8FfDCWR6rtZwkHeqYhaj3ibbgLqr9odMeq63xTdN6gvbW1axf5+MYdHzvAy6mOtj9RqoD1q+2fS8DHCBus6BKej7Vbqk2inMr4+s1Gwp9V28l2sXb0/4p8D+BsQ4CNj4TqM1f7pYLRSvjazFOJ798urjOW87pX/nx89LfVZ9R9ClJr+fxs9+aaLOg/u8WY7U1vp+YDYX+k5KuY+zbfza+CKHDsdrM6VbgE7Zv6Z8h6Y0DxGmtELYcq63xtRUHOvjl03KsLn6mkLTA9UVktj8j6TXAR4FBnq/aZkFttTi3NL7HTeQI7nS/qE4N+wuq+zH/XT39G6XEajHOcznExTFUBwabxvki1TnBY83bMWBObcZqa3ytxGlzfIfBOm8zp9dRn+3W176E6rYoTeN8BVjQ1/YC4OvA7gFzajNWK+PrfXX+YGxMP0nPBfa4Pie8b94zPMCZIG3G6qK2xlf6Ou9oTq8Dttu+sa99CfBHHuDmhG3GmhIT+XaYzhfV2SiXUt1EaQ+wu56+FDh6tseaopzumWysLr7aGl/p66mL67z0nLo+vtlwm+IPU1259qu2n257IdUNqL5Xz5vtsaYip1P7Yu0dJJakBZIulXSPpD2Sdku6u247epCE2oxFS+NrMU5r4yt9nZeeUxfHd5CZ/vZq8O22bSLzZkusjua0BbgQOLan7di67foBc2ozVhfXeSvjOwzWeek5dW58By03kYWm80V1v4530HPgh+oeFRcCn57tsTqaU+e+fDq8zrv45dPFdV56Tp0bX+9rNuy6ORtYCHxW0l5Je4B/pDrN6LUFxOpiTt+Q9A5Vt2UAqgNmqu4DPug9ztuM1cV13tb4Sl/npefUxfE9biLfDtP9Ap5HdcvVo/raV5cQq2s5Ud2zfz3VwaC9VAeE7q7bnj5gPq3F6uI6b2t8pa/z0nPq4vgOWm4iC03ni3Zv6N+5WF3Mqe0PWluxurjOWx5fseu89Jy6Or6fLDeRhabzRfW0laPq6aXACPC2+v1tsz1WR3Pq6pdPF9d55758OrrOS8+pc+Prfc2GWyC0eUP/LsbqYk5tPi2nzVhdXOdtja/0dV56Tl0c3+MBJ7LQNPuOqlv5AlCvgFdR34S/gFhdzOmgDxrVM1XPUPUIuEl9aCcZq4vrvK3xlb7OS8+pi+N73ET+DJjOF9W92Y89xLyXzvZYHc2pzafltBmri+u8lfEdBuu89Jw6N77eV+51Ez9FLT4tp81YXdTW+Epf56Xn1MXxHZRDCn1ERNlmwz76iIiYhBT6iIjCpdBHTICkUyX9/UznEdFECn3EE1Alvycxq+UDHNFH0lJJ2yRdBdwJbJR0p6SvSjp7jP4vlnSbpGdPf7YR45sNV8ZGzITlwHnAccCbgRdSXbBys6TPHegk6ZeB/0V1yfs3ZyLRiPFkiz5ibN9w9fzPU4AP2X7U1XNNPwu8uO7zC8AG4DdT5KPLUugjxvaDBn2+DewDfnGKc4mYlBT6iCf2eeBsSXMlDQEvA75cz/se8Ergv9c3noropBT6iCf2ceAO4CtU9zN5R+9l7vXunFcBl0s6eWZSjHhiuQVCREThskUfEVG4FPqIiMKl0EdEFC6FPiKicCn0ERGFS6GPiChcCn1EROH+P6x1UOa++iOuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pomer('love')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02fc84b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}