Python Statistics.ipynb

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      "Dataset : Python_popularity.csv \n",
      "       Rank           Name   Share Last month's Last year's\n",
      "0      NaN            NaN     NaN        share       share\n",
      "1      1.0              C  17.67%       15.87%      16.83%\n",
      "2      2.0           Java  14.72%       15.45%      20.38%\n",
      "3      3.0    Objective-C   8.23%        8.52%       9.22%\n",
      "4      4.0            C++   6.77%        7.54%       7.91%\n",
      "..     ...            ...     ...          ...         ...\n",
      "119  119.0   Informix/4GL   0.01%        0.00%       0.01%\n",
      "120  120.0    TeX / LaTeX   0.00%        0.00%       0.01%\n",
      "121  121.0         Scilab   0.00%        0.00%       0.01%\n",
      "122  122.0        Harbour   0.00%        0.00%       0.00%\n",
      "123  123.0  JavaFX Script   0.00%        0.30%       0.01%\n",
      "\n",
      "[124 rows x 5 columns] \n",
      "\n",
      "\n",
      "\n",
      "Dataset : Python Version Usage Statistics 2022.csv \n",
      "    Series      Versions  measured % metrics\n",
      "0     1.0   Version 3.3                 0.1\n",
      "1     2.0   Version 3.4                 2.3\n",
      "2     3.0   Version 3.5                 7.1\n",
      "3     4.0   Version 3.6                45.0\n",
      "4     5.0   Version 3.7                20.0\n",
      "5     6.0   Version 3.8                19.0\n",
      "6     7.0   Version 3.9                 4.6\n",
      "7     8.0  Version 3.10                 1.9\n",
      "8     NaN           NaN                 NaN \n",
      "\n",
      "\n",
      "\n",
      "Dataset : Python developer perference.csv \n",
      "    Year Python 2.7 Python 3.7\n",
      "0  2013     78.00%        22%\n",
      "1  2014        66%        34%\n",
      "2  2016        60%        40%\n",
      "3  2017        25%        75%\n",
      "4  2018        16%        84%\n",
      "5  2019        13%        87% \n",
      "\n",
      "\n",
      "\n",
      "Dataset : Package across plateform.csv \n",
      "     Package Python 2 Python 3\n",
      "0    django   19.96%   78.84%\n",
      "1     flask   17.75%   81.76%\n",
      "2    pandas   23.34%   76.01%\n",
      "3  requests   39.38%   59.92% \n",
      "\n",
      "\n",
      "\n",
      "Dataset : Cython Downloads statistics.csv \n",
      "   Version    Downloads Downloads%\n",
      "0   3.7.x  1,55,19,728     58.36%\n",
      "1   3.6.x    56,16,969     21.12%\n",
      "2   3.5.x    11,87,840      4.47%\n",
      "3   3.8.x     1,56,111      0.59%\n",
      "4   2.7.x    41,12,428     15.46%\n",
      "5     NaN          NaN        NaN \n",
      "\n",
      "\n",
      "\n"
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   "source": [
    "# pip install pandas \n",
    "import pandas as pd\n",
    "\n",
    "# Read the csv file\n",
    "data = pd.read_csv('Python_popularity.csv')\n",
    "print('Dataset : Python_popularity.csv',\"\\n\",data,\"\\n\\n\\n\")\n",
    "data = pd.read_csv('Python Version Usage Statistics 2022.csv')\n",
    "print('Dataset : Python Version Usage Statistics 2022.csv',\"\\n\",data,\"\\n\\n\\n\")\n",
    "data = pd.read_csv('Python developer perference.csv')\n",
    "print('Dataset : Python developer perference.csv',\"\\n\",data,\"\\n\\n\\n\")\n",
    "data = pd.read_csv('Package across plateform.csv')\n",
    "print('Dataset : Package across plateform.csv',\"\\n\",data,\"\\n\\n\\n\")\n",
    "data = pd.read_csv('Cython Downloads statistics.csv')\n",
    "print('Dataset : Cython Downloads statistics.csv',\"\\n\",data,\"\\n\\n\\n\")\n",
    "\n",
    "# Print it out if you want\n",
    "#print(data)"
   ]
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    "# Python_popularity.csv\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Python_popularity.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "fig = px.line(df, y = 'Last year\\'s', x = 'Name', title='Python Popularity : popularity(2020) - Languages')\n",
    "fig.show()"
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   "source": [
    "# Python_popularity.csv\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Python_popularity.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "fig = px.line(df, y = 'Last month\\'s', x = 'Name', title='Python Popularity : April - Languages')\n",
    "fig.show()"
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  {
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   "source": [
    "# Python_popularity.csv\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Python_popularity.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "fig = px.line(df, x = 'Name', y = 'Share', title='Python Popularity : Popularity Share - Languages')\n",
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   "source": [
    "#Statista_Programming Popularity.csv\n",
    "\n",
    "df = pd.read_csv('Statista_Programming Popularity.csv')\n",
    "\n",
    "fig = px.pie(df, values='online survey scores', names='Programming languages',title=\"Pie chart distribution : \")\n",
    "\n",
    "fig.update_layout(\n",
    "    font_family=\"Times New Roman\",\n",
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    "    title_font_family=\"Times New Roman\",\n",
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    "    legend_title_font_color=\"green\",\n",
    "    legend_title=\"Legend : Programming Languages\",\n",
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  {
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   "metadata": {},
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     "metadata": {},
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   ],
   "source": [
    "# Python Version Usage Statistics 2022.csv\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Python Version Usage Statistics 2022.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "fig = px.pie(df, values='measured % metrics', names='Versions',title=\"Pie chart distribution : \")\n",
    "\n",
    "fig.update_layout(\n",
    "    font_family=\"Times New Roman\",\n",
    "    font_color=\"blue\",\n",
    "    title_font_family=\"Times New Roman\",\n",
    "    title_font_color=\"Black\",\n",
    "    legend_title_font_color=\"green\",\n",
    "    legend_title=\"Legend : Python Version Series\",\n",
    ")\n",
    "fig.update_xaxes(title_font_family=\"Times New Roman\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "                            \n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Python developer perference.csv\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Python developer perference.csv')\n",
    "\n",
    "fig = px.scatter_3d(df, x='Python 2.7', y='Python 3.7', z='Year')\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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         "y": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('af04f706-498b-4e72-b532-c5717775c782');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cython Downloads statistics.csv\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('Cython Downloads statistics.csv')\n",
    "\n",
    "fig = px.scatter_3d(df, x='Downloads', y='Downloads%', z='Version')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(Visual) FoxPro: FoxPro, Fox Pro, VFP, VFPA',\n",
       " '1C:Enterprise script',\n",
       " '4th Dimension/4D: 4D, 4th Dimension',\n",
       " 'ABAP',\n",
       " 'ABC: ABC (exceptions: -tv -channel)',\n",
       " 'ActionScript: ActionScript, AS1, AS2, AS3',\n",
       " 'Ada',\n",
       " 'Agilent VEE',\n",
       " 'Algol',\n",
       " 'Alice: Alice (confidence: 90%)',\n",
       " 'Angelscript',\n",
       " 'Apex',\n",
       " 'APL',\n",
       " 'Applescript',\n",
       " 'Arc',\n",
       " 'AspectJ',\n",
       " 'Assembly language: Assembly, Assembly language',\n",
       " 'ATLAS',\n",
       " 'AutoHotkey: AutoHotkey, AHK',\n",
       " 'AutoIt',\n",
       " 'AutoLISP',\n",
       " 'Automator',\n",
       " 'Avenue',\n",
       " 'Awk: Awk, Mawk, Gawk, Nawk',\n",
       " 'B4X',\n",
       " 'Ballerina',\n",
       " 'Bash',\n",
       " 'Basic: Basic (confidence: 0%)',\n",
       " 'BBC BASIC',\n",
       " 'bc',\n",
       " 'BCPL',\n",
       " 'BETA: BETA (confidence: 10%)',\n",
       " 'BlitzMax: BlitzMax, BlitzBasic, Blitz Basic',\n",
       " 'Boo',\n",
       " 'Bourne shell: Bourne shell, sh',\n",
       " 'Brainfuck',\n",
       " 'C shell: Csh, C shell (confidence: 90%)',\n",
       " 'C#: C#, C-Sharp, C Sharp, CSharp, CSharp.NET, C#.NET',\n",
       " 'C++',\n",
       " 'C++/CLI',\n",
       " 'C-Omega',\n",
       " 'C: C (exceptions: -\"Objective-C\")',\n",
       " 'Caml',\n",
       " 'Ceylon',\n",
       " 'CFML: CFML, ColdFusion',\n",
       " 'cg: cg (confidence: 80%, exceptions: -\"computer game\" -\"computer graphics\")',\n",
       " 'Ch: Ch (exceptions: +ChScite)',\n",
       " 'Chapel: Chapel (exceptions: -christ)',\n",
       " 'CHILL',\n",
       " 'CIL',\n",
       " 'CL (OS/400): CL (exceptions: -Lisp), CLLE',\n",
       " 'Clarion',\n",
       " 'Classic Visual Basic: Visual Basic (confidence: 50%), VB (confidence: 50%), VBA, VB6',\n",
       " 'Clean: Clean (confidence: 43%)',\n",
       " 'Clipper',\n",
       " 'CLIPS',\n",
       " 'Clojure: Clojure, ClojureScript',\n",
       " 'CLU',\n",
       " 'COBOL',\n",
       " 'Cobra',\n",
       " 'CoffeeScript',\n",
       " 'COMAL',\n",
       " 'Common Lisp',\n",
       " 'Crystal: Crystal (confidence: 61%, exceptions: -healing), crystallang',\n",
       " 'cT',\n",
       " 'Curl',\n",
       " 'D: D (confidence: 90%, exceptions: -\"3-D programming\" -\"DTrace\"), dlang',\n",
       " 'Dart',\n",
       " 'DCL',\n",
       " 'Delphi/Object Pascal: DwScript, Object Pascal, Delphi, Delphi.NET, Pascal (confidence: 95%)',\n",
       " 'DiBOL: DBL, Synergy/DE, DIBOL',\n",
       " 'Dylan',\n",
       " 'E: E (exceptions: +specman)',\n",
       " 'ECMAScript',\n",
       " 'EGL',\n",
       " 'Eiffel',\n",
       " 'Elixir',\n",
       " 'Elm',\n",
       " 'Emacs Lisp: Emacs Lisp, Elisp',\n",
       " 'Emerald',\n",
       " 'Erlang',\n",
       " 'Etoys',\n",
       " 'Euphoria',\n",
       " 'EXEC',\n",
       " 'F#: F#, F-Sharp, FSharp, F Sharp',\n",
       " 'Factor',\n",
       " 'Falcon',\n",
       " 'Fantom',\n",
       " 'Felix: Felix (confidence: 86%)',\n",
       " 'Forth',\n",
       " 'Fortran',\n",
       " 'Fortress',\n",
       " 'FreeBASIC',\n",
       " 'Gambas',\n",
       " 'GAMS',\n",
       " 'GLSL',\n",
       " 'GML: GML, GameMaker Language',\n",
       " 'GNU Octave',\n",
       " 'Go: Go, Golang',\n",
       " 'Gosu',\n",
       " 'Groovy: Groovy, GPATH, GSQL, Groovy++',\n",
       " 'Hack',\n",
       " 'Harbour',\n",
       " 'Haskell',\n",
       " 'Haxe',\n",
       " 'Heron',\n",
       " 'HPL',\n",
       " 'HyperTalk',\n",
       " 'Icon: Icon (confidence: 90%)',\n",
       " 'IDL: IDL (exceptions: -corba -interface)',\n",
       " 'Idris',\n",
       " 'Inform',\n",
       " 'Informix-4GL',\n",
       " 'INTERCAL',\n",
       " 'Io',\n",
       " 'Ioke',\n",
       " 'J#',\n",
       " 'J: J (confidence: 50%)',\n",
       " 'JADE',\n",
       " 'Java',\n",
       " 'JavaFX Script',\n",
       " 'JavaScript: JavaScript, JS, SSJS',\n",
       " 'JScript',\n",
       " 'JScript.NET',\n",
       " 'Julia: Julia, Julialang, julia-lang',\n",
       " 'Korn shell: Korn shell, ksh',\n",
       " 'Kotlin',\n",
       " 'LabVIEW',\n",
       " 'Ladder Logic',\n",
       " 'Lasso',\n",
       " 'Limbo',\n",
       " 'Lingo',\n",
       " 'Lisp',\n",
       " 'LiveCode: Revolution, LiveCode',\n",
       " 'Logo: Logo (confidence: 90%, exceptions: -tv)',\n",
       " 'LotusScript',\n",
       " 'LPC',\n",
       " 'Lua: Lua, LuaJIT',\n",
       " 'Lustre',\n",
       " 'M4',\n",
       " 'MAD: MAD (confidence: 50%)',\n",
       " 'Magic: Magic (confidence: 50%)',\n",
       " 'Magik',\n",
       " 'Malbolge',\n",
       " 'MANTIS',\n",
       " 'Maple',\n",
       " 'MATLAB',\n",
       " 'Max/MSP',\n",
       " 'MAXScript',\n",
       " 'MDX',\n",
       " 'MEL',\n",
       " 'Mercury',\n",
       " 'Miva',\n",
       " 'ML',\n",
       " 'Modula-2',\n",
       " 'Modula-3',\n",
       " 'Monkey',\n",
       " 'MOO',\n",
       " 'Moto',\n",
       " 'MQL4: MQL4, MQL5',\n",
       " 'MS-DOS batch',\n",
       " 'MUMPS',\n",
       " 'NATURAL',\n",
       " 'Nemerle',\n",
       " 'NetLogo',\n",
       " 'Nim: Nim, Nimrod',\n",
       " 'NQC',\n",
       " 'NSIS',\n",
       " 'NXT-G',\n",
       " 'Oberon',\n",
       " 'Object Rexx',\n",
       " 'Objective-C: Objective-C, objc, obj-c',\n",
       " 'OCaml: Objective Caml, OCaml',\n",
       " 'Occam',\n",
       " 'OpenCL',\n",
       " 'OpenEdge ABL: Progress, Progress 4GL, ABL, Advanced Business Language, OpenEdge',\n",
       " 'OPL',\n",
       " 'Oxygene',\n",
       " 'Oz',\n",
       " 'Paradox',\n",
       " 'Pascal: Pascal (confidence: 5%)',\n",
       " 'Perl',\n",
       " 'PHP',\n",
       " 'Pike',\n",
       " 'PILOT: PILOT (confidence: 50%, exceptions: -\"Palm Pilot programming\")',\n",
       " 'PL/I: PL/1, PL/I',\n",
       " 'PL/SQL',\n",
       " 'Pliant',\n",
       " 'Pony',\n",
       " 'PostScript: PostScript, PS',\n",
       " 'POV-Ray',\n",
       " 'PowerBasic',\n",
       " 'PowerScript',\n",
       " 'PowerShell',\n",
       " 'Processing: Processing (exceptions: +\"sketchbook\")',\n",
       " 'Programming Without Coding Technology: Programming Without Coding Technology, PWCT',\n",
       " 'Prolog',\n",
       " 'Pure Data: Pure Data, PD',\n",
       " 'PureBasic',\n",
       " 'Python',\n",
       " 'Q',\n",
       " 'R: R (confidence: 90%, exceptions: +\"statistical\")',\n",
       " 'Racket',\n",
       " 'Raku: Perl 6, Raku',\n",
       " 'REBOL',\n",
       " 'Red: Red (confidence: 20%)',\n",
       " 'REXX',\n",
       " 'Ring',\n",
       " 'RPG: RPG (confidence: 80%, exceptions: -role), RPGLE, ILERPG, RPGIV, RPGIII, RPG400, RPGII, RPG4',\n",
       " 'Ruby',\n",
       " 'Rust: Rust, Rustlang',\n",
       " 'S-PLUS: S-PLUS (exceptions: +statistical)',\n",
       " 'S: S (exceptions: +statistical)',\n",
       " 'SAS',\n",
       " 'Sather',\n",
       " 'Scala',\n",
       " 'Scheme: Scheme (exceptions: -tv -channel)',\n",
       " 'Scratch',\n",
       " 'sed',\n",
       " 'Seed7',\n",
       " 'SIGNAL: SIGNAL (confidence: 10%)',\n",
       " 'Simula',\n",
       " 'Simulink',\n",
       " 'Slate: Slate (confidence: 57%)',\n",
       " 'Small Basic',\n",
       " 'Smalltalk',\n",
       " 'Smarty',\n",
       " 'Snap!',\n",
       " 'SNOBOL',\n",
       " 'Solidity',\n",
       " 'SPARK',\n",
       " 'SPSS',\n",
       " 'SQL',\n",
       " 'SQR',\n",
       " 'Squeak',\n",
       " 'Squirrel',\n",
       " 'Standard ML: Standard ML, SML',\n",
       " 'Stata',\n",
       " 'Structured Text',\n",
       " 'Suneido',\n",
       " 'SuperCollider: SuperCollider (confidence: 80%)',\n",
       " 'Swift',\n",
       " 'TACL',\n",
       " 'Tcl: Tcl/Tk, Tcl',\n",
       " 'tcsh',\n",
       " 'Tex',\n",
       " 'thinBasic',\n",
       " 'TOM: TOM (confidence: 50%)',\n",
       " 'Transact-SQL: T-SQL, Transact-SQL, TSQL',\n",
       " 'TypeScript: TypeScript, TS',\n",
       " 'Uniface',\n",
       " 'Vala/Genie: Vala, Genie',\n",
       " 'VBScript',\n",
       " 'Verilog',\n",
       " 'VHDL',\n",
       " 'Visual Basic: Visual Basic .NET, VB.NET, Visual Basic.NET, Visual Basic (confidence: 50%), VB (confidence: 50%)',\n",
       " 'WebAssembly: WASM`, WebAssembly',\n",
       " 'WebDNA',\n",
       " 'Whitespace',\n",
       " 'Wolfram',\n",
       " 'X10',\n",
       " 'xBase',\n",
       " 'XBase++',\n",
       " 'XC',\n",
       " 'Xen',\n",
       " 'Xojo: REALbasic, Xojo',\n",
       " 'XPL',\n",
       " 'XQuery',\n",
       " 'XSLT',\n",
       " 'Xtend',\n",
       " 'yacc',\n",
       " 'Yorick',\n",
       " 'Z shell: Z shell, zsh',\n",
       " 'Zig: Zig, zlang']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# THIOBE index programming languages\n",
    "\n",
    "with open (\"Programming languages on THIOBE.txt\", \"r\") as myfile:\n",
    "    data = myfile.read().splitlines()\n",
    "    \n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Total programs included in thiobe index :  274\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for x in data:\n",
    "    count+=1\n",
    "print(\" Total programs included in thiobe index : \",count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#List of websites used for Thiobe Index.csv\n",
    "\n",
    "# some of these metrics have been derieved from usage statisitcs sourced from Alexa website retings hosted by Amazon\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('List of websites used for Thiobe Index.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "fig = px.pie(df, values='Percent', names='Engines',title=\"Pie chart distribution : \")\n",
    "\n",
    "fig.update_layout(\n",
    "    font_family=\"Times New Roman\",\n",
    "    font_color=\"blue\",\n",
    "    title_font_family=\"Times New Roman\",\n",
    "    title_font_color=\"Black\",\n",
    "    legend_title_font_color=\"green\",\n",
    "    legend_title=\"Legend : Websites Choosen By Thiobe calculating metrics\",\n",
    ")\n",
    "fig.update_xaxes(title_font_family=\"Times New Roman\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Website</th>\n",
       "      <th>Location</th>\n",
       "      <th>Reasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>fr:</td>\n",
       "      <td>UNKNOWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>it:</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bing</td>\n",
       "      <td>com:</td>\n",
       "      <td>NO_RESULTS_AT_ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Msn</td>\n",
       "      <td>com:</td>\n",
       "      <td>NO_RESULTS_AT_ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yahoo</td>\n",
       "      <td>jp:</td>\n",
       "      <td>NOT_ALLOWED_TO_ACCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>yahoo</td>\n",
       "      <td>com:</td>\n",
       "      <td>SOURCES_NOT_PARSABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>yandex</td>\n",
       "      <td>ru:</td>\n",
       "      <td>NO_COUNTERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>youtube</td>\n",
       "      <td>com:</td>\n",
       "      <td>NO_COUNTERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>zillow</td>\n",
       "      <td>com:</td>\n",
       "      <td>NO_SEARCH_FIELD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>zoom</td>\n",
       "      <td>us:</td>\n",
       "      <td>NO_SEARCH_FIELD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Website Location                Reasons\n",
       "0    Amazon      fr:                UNKNOWN\n",
       "1    Amazon      it:              CANDIDATE\n",
       "2      Bing     com:      NO_RESULTS_AT_ALL\n",
       "3       Msn     com:      NO_RESULTS_AT_ALL\n",
       "4     Yahoo      jp:  NOT_ALLOWED_TO_ACCESS\n",
       "..      ...      ...                    ...\n",
       "71    yahoo     com:   SOURCES_NOT_PARSABLE\n",
       "72   yandex      ru:            NO_COUNTERS\n",
       "73  youtube     com:            NO_COUNTERS\n",
       "74   zillow     com:        NO_SEARCH_FIELD\n",
       "75     zoom      us:        NO_SEARCH_FIELD\n",
       "\n",
       "[76 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Python Version Usage Statistics 2022.csv\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('website rejected by THIOBE.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "df\n",
    "\n",
    "#fig = px.pie(df, values='Percent', names='Engines')\n",
    "#fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   UNKNOWN\n",
       "1                 CANDIDATE\n",
       "2         NO_RESULTS_AT_ALL\n",
       "3         NO_RESULTS_AT_ALL\n",
       "4     NOT_ALLOWED_TO_ACCESS\n",
       "              ...          \n",
       "71     SOURCES_NOT_PARSABLE\n",
       "72              NO_COUNTERS\n",
       "73              NO_COUNTERS\n",
       "74          NO_SEARCH_FIELD\n",
       "75          NO_SEARCH_FIELD\n",
       "Name: Reasons, Length: 76, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reasons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create a new set:\n"
     ]
    }
   ],
   "source": [
    "print(\"Create a new set:\")\n",
    "x = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CANDIDATE', 'NO_SEARCH_FIELD', 'UNKNOWN', 'SOURCES_NOT_PARSABLE', 'NO_WEBSITE', 'NO_RESULTS_AT_ALL', 'NO_COUNTERS', 'PORN_SITE', 'NOT_ALLOWED_TO_ACCESS'}\n"
     ]
    }
   ],
   "source": [
    "for i in df['Reasons']:\n",
    "    #print(i)\n",
    "    x.add(i)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([Website     13\n",
      "Location    13\n",
      "Reasons     13\n",
      "dtype: int64, Website     1\n",
      "Location    1\n",
      "Reasons     1\n",
      "dtype: int64, Website     16\n",
      "Location    16\n",
      "Reasons     16\n",
      "dtype: int64, Website     1\n",
      "Location    1\n",
      "Reasons     1\n",
      "dtype: int64, Website     17\n",
      "Location    17\n",
      "Reasons     17\n",
      "dtype: int64, Website     5\n",
      "Location    5\n",
      "Reasons     5\n",
      "dtype: int64, Website     18\n",
      "Location    18\n",
      "Reasons     18\n",
      "dtype: int64, Website     3\n",
      "Location    3\n",
      "Reasons     3\n",
      "dtype: int64, Website     2\n",
      "Location    2\n",
      "Reasons     2\n",
      "dtype: int64])\n"
     ]
    }
   ],
   "source": [
    "dictionary={}\n",
    "a=0\n",
    "for i in df['Reasons']:\n",
    "    a=df[df.Reasons == i].count()\n",
    "    dictionary[i]=a\n",
    "print(dictionary.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "Reasons=%{label}<br>Counts=%{value}<extra></extra>",
         "labels": [
          "NOT_ALLOWED_TO_ACCESS",
          "NO_RESULTS_AT_ALL",
          "NO_SEARCH_FIELD",
          "UNKNOWN",
          "NO_WEBSITE",
          "SOURCES_NOT_PARSABLE",
          "CANDIDATE",
          "NO_COUNTERS"
         ],
         "legendgroup": "",
         "name": "",
         "showlegend": true,
         "type": "pie",
         "values": [
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       "layout": {
        "font": {
         "color": "black",
         "family": "Times New Roman"
        },
        "legend": {
         "title": {
          "text": "Legend : Reasons why some websites were excluded"
         },
         "tracegroupgap": 0
        },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rejectreasoncounts.csv'\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('rejectreasoncounts.csv')\n",
    "# Rank Name Share\n",
    "\n",
    "df\n",
    "\n",
    "fig = px.pie(df, values='Counts', names='Reasons',title=\"Pie chart distribution : \",color_discrete_sequence=px.colors.sequential.RdBu)\n",
    "\n",
    "fig.update_layout(\n",
    "    font_family=\"Times New Roman\",\n",
    "    font_color=\"black\",\n",
    "    title_font_family=\"Times New Roman\",\n",
    "    title_font_color=\"Black\",\n",
    "    legend_title_font_color=\"green\",\n",
    "    legend_title=\"Legend : Reasons why some websites were excluded\",\n",
    ")\n",
    "fig.update_xaxes(title_font_family=\"Times New Roman\")\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Use Case Purpose</th>\n",
       "      <th>Imperative</th>\n",
       "      <th>Object oriented</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Procedural</th>\n",
       "      <th>Other Paradigms</th>\n",
       "      <th>Standardized</th>\n",
       "      <th>Standardizations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>Application, system, general purpose, low-leve...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Nan</td>\n",
       "      <td>True</td>\n",
       "      <td>1998, ISO/IEC 2003, ISO/IEC 2011,ISO/IEC 2014,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Java</td>\n",
       "      <td>Application, business, client-side, general, m...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Concurrent</td>\n",
       "      <td>True</td>\n",
       "      <td>De facto standard via Java Language Specification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Python</td>\n",
       "      <td>Application, general, web, scripting, artifici...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Aspect-oriented</td>\n",
       "      <td>True</td>\n",
       "      <td>De facto standard via Python Enhancement Propo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language                                   Use Case Purpose  Imperative  \\\n",
       "0        C  Application, system, general purpose, low-leve...        True   \n",
       "1     Java  Application, business, client-side, general, m...        True   \n",
       "2   Python  Application, general, web, scripting, artifici...        True   \n",
       "\n",
       "   Object oriented  Functional  Procedural  Other Paradigms  Standardized  \\\n",
       "0            False       False        True              Nan          True   \n",
       "1             True        True        True       Concurrent          True   \n",
       "2             True        True        True  Aspect-oriented          True   \n",
       "\n",
       "                                    Standardizations  \n",
       "0  1998, ISO/IEC 2003, ISO/IEC 2011,ISO/IEC 2014,...  \n",
       "1  De facto standard via Java Language Specification  \n",
       "2  De facto standard via Python Enhancement Propo...  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data= pd.read_csv(\"PythonvCvJava1.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xab in position 9: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xab in position 9: invalid start byte",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-d7b433675e1e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PythonvCvJava-parameterics.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    684\u001b[0m     )\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m         \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1194\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nrows\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1196\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1198\u001b[0m         \u001b[1;31m# May alter columns / col_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   2153\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2154\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2156\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2157\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xab in position 9: invalid start byte"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data= pd.read_csv(\"PythonvCvJava-parameterics.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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