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@stared
Last active September 4, 2015 19:12
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Python dla biologów i IPython Notebook (SKNN UW, 3 marca 2015)
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{
"metadata": {
"name": "",
"signature": "sha256:3d546dd3e4d16caee074661edbc99271b801ac0da6f7e23e512e6d622298a185"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Python dla biolog\u00f3w - IPython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Piotr Migda\u0142](http://migdal.wikidot.com/)\n",
"\n",
"(Nie kr\u0119powa\u0107 si\u0119\u00a0z mailowaniem mnie! :))\n",
"\n",
"[Studenckie Ko\u0142o Naukowe Neurobiologii Uniwersytetu Warszawskiego](http://www.biol.uw.edu.pl/sknn/), 3 marca 2015\n",
"\n",
"[Ten IPython Notebook jest online tu](http://nbviewer.ipython.org/gist/stared/0fb6257c0b14aac2b9fe)."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Dlaczego Python?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"\n",
"with plt.xkcd():\n",
"\n",
" fig = plt.figure()\n",
" ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
" \n",
" ax.spines['right'].set_color('none')\n",
" ax.spines['top'].set_color('none')\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" ax.set_xlim([0, 6])\n",
" ax.set_ylim([0, 6])\n",
" plt.title(\"Languages for data analysis\\n(very subjective)\")\n",
" plt.xlabel(u\"Code beauty\")\n",
" plt.ylabel(u\"Versatility\")\n",
" data = [(\"Python\", 5, 5),\n",
" (\"MATLAB\", 2, 2),\n",
" (\"R\", 2, 4),\n",
" (\"SAS\", 1, 1),\n",
" (\"Julia\", 4, 4),\n",
" (\"Mathematica\", 4, 3)]\n",
" for name, bea, ver in data:\n",
" plt.text(bea, ver, name,\n",
" horizontalalignment='center',\n",
" verticalalignment='center')\n",
" \n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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KdJG9ePEiHR0dC52sRCh9oqPTK6B27dpYtmwZEhISDJaXK1cOoaGhOHz4MLZv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\njDE2Sv0fJnYmxL/1Tf8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x106459750>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A powa\u017cniej:\n",
"\n",
"* dobrze zaprojektowany j\u0119zyk, \u0142adna sk\u0142adnia\n",
"* du\u017co narz\u0119dzi do przetwarzania danych\n",
"* bardzo popularny j\u0119zyk, i do wszystkiego (od danych do robienia stron)\n",
"* interpretowany (mo\u017cna linia po lini), i *IPython Notebook*\n",
"* darmowy i otwarty\n",
"\n",
"Cho\u0107\u00a0uczciwie:\n",
"\n",
"* niekt\u00f3re specjalistyczne narz\u0119dzia statystyczne s\u0105\u00a0tylko w *R*\n",
"* je\u015bli piszemy du\u017co numeryki, mo\u017ce warto przetesowa\u0107 nowy j\u0119zyk *Julia* (albo u\u017cywa\u0107 stare, dobre *C++*)\n",
"* ...ale bez b\u00f3lu ob\u0119dziemy si\u0119\u00a0bez *MATLAB* :)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Instalacja"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jest wiele sposob\u00f3w instalacji Pythona.\n",
"\n",
"Na zaj\u0119ciach b\u0119dziemu u\u017cywa\u0107 Pythona z pakietu [Anaconda](https://store.continuum.io/cshop/anaconda/), gdy\u017c:\n",
"\n",
"* od razu zawiera typowe bibioteki do numeryki i analizy danych,\n",
"* instaluje si\u0119\u00a0podobnie na r\u00f3\u017cnych systemach.\n",
"\n",
"Obecnie wersje Pythona to `2.7` i `3.4`. Na tych zaj\u0119ciach b\u0119dzi\u0119my u\u017cywa\u0107\u00a0`2.7`. \n",
"\n",
"Pythona mo\u017cna pisa\u0107 w plikach tekstowych. Ale tu b\u0119dziemy u\u017cywali *IPython Notebook*."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Kalkulator"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"2 + 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"4"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(6 * 7 + 8) * 2 "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"100"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"2**10"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"1024"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# uwaga - w Python 2 dostajemy:\n",
"5 / 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"2"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"5. / 2."
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"2.5"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"5. // 2."
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"2.0"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# to jest komentarz\n",
"liczba_zyjatek = 12\n",
"masa_zyjatka = 0.03\n",
"\n",
"masa_zyjatka * liczba_zyjatek"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"0.36"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(1 + 1j) * (3 - 2j)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"(5+1j)"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Teksty (czyli jedyne stringi jakie widzi programista ;))"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tekst = \"Bum!\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tekst = 'Bum!'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print('He\\'s a\\n nice boy') "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"He's a\n",
" nice boy\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(tekst)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Bum!\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"Co slychac? \" + tekst + \" No tak...\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"'Co slychac? Bum! No tak...'"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Teksty mo\u017cna tylko dodawa\u0107, ale i mno\u017cy\u0107:\n",
"tekst * 5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"'Bum!Bum!Bum!Bum!Bum!'"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"Jaki\u015b\u00a0tekst\"[-2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"'s'"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"Jakis tekst\"[1:5:2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"'ai'"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
"po_polsku = \"I \u2764 \u017b\u00f3\u0142\u0107 & ja\u017a\u0144\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"po_polsku"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"'I \\xe2\\x9d\\xa4 \\xc5\\xbb\\xc3\\xb3\\xc5\\x82\\xc4\\x87 & ja\\xc5\\xba\\xc5\\x84'"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(po_polsku)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"I \u2764 \u017b\u00f3\u0142\u0107 & ja\u017a\u0144\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(po_polsku.upper())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"I \u2764 \u017b\u00f3\u0142\u0107 & JA\u017a\u0144\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# u znaczy unicode, utf-8 - standard kodowania znakow tez nielacinskich\n",
"po_polsku = u\"I \u2764 \u017b\u00f3\u0142\u0107 & ja\u017a\u0144\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"po_polsku"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"u'I \\u2764 \\u017b\\xf3\\u0142\\u0107 & ja\\u017a\\u0144'"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(po_polsku.upper())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"I \u2764 \u017b\u00d3\u0141\u0106 & JA\u0179\u0143\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"\\u2764\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\\u2764\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(u\"\\u2764\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\u2764\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z = \"\"\"\n",
"Dlugi\n",
"tekst\n",
"na wiele linijek\\\\\n",
"\"\"\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"'\\nDlugi\\ntekst\\nna wiele linijek\\\\\\n'"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(z)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dlugi\n",
"tekst\n",
"na wiele linijek\\\n",
"\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l_kur = 5\n",
"\"To jest \" + str(l_kur) + \" kur\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"'To jest 5 kur'"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"luty = \"marzec\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(u\"{} miesi\u0105c to {}\".format(2, luty))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2 miesi\u0105c to marzec\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(u\"Liczba zakup\u00f3w wzros\u0142a o {:.1f}%\".format(15.123456))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Liczba zakup\u00f3w wzros\u0142a o 15.1%\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# komentarze w Pythonie piszemy tak"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"W *IPythonie* jest mo\u017cliwo\u015bc pisania w tzw. [Markdown](http://en.wikipedia.org/wiki/Markdown),\n",
"pozwalaj\u0105cy na pisanie tekstu z formatowaniem, np:\n",
"\n",
"* list,\n",
"* *kursywy*, **pogrubienia** i ~~przekre\u015blenia~~,\n",
"* [link\u00f3w](http://en.wikipedia.org/wiki/Markdown),\n",
"* wzor\u00f3w $\\tilde{x} = \\sum_{i=1}^{n} x_i$,\n",
"* kodu `x = 5`,\n",
"* itd."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"P\u0119tle i funkcje"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(10):\n",
" print(i**2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"1\n",
"4\n",
"9\n",
"16\n",
"25\n",
"36\n",
"49\n",
"64\n",
"81\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dni_tygodnia = [\"Poniedzialek\", \"Wtorek\", \"Sroda\", \"Czwartek\", \"Sobota\", \"Niedziela\"]\n",
"\n",
"for dzien in dni_tygodnia:\n",
" print(dzien)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Poniedzialek\n",
"Wtorek\n",
"Sroda\n",
"Czwartek\n",
"Sobota\n",
"Niedziela\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dni_tygodnia"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"['Poniedzialek', 'Wtorek', 'Sroda', 'Czwartek', 'Sobota', 'Niedziela']"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i, dzien in enumerate(dni_tygodnia):\n",
" print(\"{} dzien tygodnia to {}\".format(i + 1, dzien))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 dzien tygodnia to Poniedzialek\n",
"2 dzien tygodnia to Wtorek\n",
"3 dzien tygodnia to Sroda\n",
"4 dzien tygodnia to Czwartek\n",
"5 dzien tygodnia to Sobota\n",
"6 dzien tygodnia to Niedziela\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(enumerate(dni_tygodnia, 5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"[(5, 'Poniedzialek'),\n",
" (6, 'Wtorek'),\n",
" (7, 'Sroda'),\n",
" (8, 'Czwartek'),\n",
" (9, 'Sobota'),\n",
" (10, 'Niedziela')]"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 7"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = a + 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a == 7"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"False"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(0,9):\n",
" \n",
" print(i)\n",
"\n",
" if i == 4 and 5 == 5 or False:\n",
" print(\"Co?!\")\n",
" elif i % 2 == 0:\n",
" print(\"Aaa!\")\n",
" else:\n",
" print(\"S\u0142ysz\u0119...\")\n",
"\n",
"\n",
"print(\"XXX\") "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"Aaa!\n",
"1\n",
"S\u0142ysz\u0119...\n",
"2\n",
"Aaa!\n",
"3\n",
"S\u0142ysz\u0119...\n",
"4\n",
"Co?!\n",
"5\n",
"S\u0142ysz\u0119...\n",
"6\n",
"Aaa!\n",
"7\n",
"S\u0142ysz\u0119...\n",
"8\n",
"Aaa!\n",
"XXX\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"8 > 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"True"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"5 > 5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"False"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"5 >= 5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"True"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def dodaj_5(x):\n",
" return x + 5"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dodaj_5(13)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"18"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# n!\n",
"def silnia(n):\n",
" if n == 0:\n",
" return 1\n",
" else:\n",
" return n * silnia(n-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"max([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"3"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"silnia(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"120"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# w Pythonie licznik sie nie przekreca!\n",
"silnia(200)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"788657867364790503552363213932185062295135977687173263294742533244359449963403342920304284011984623904177212138919638830257642790242637105061926624952829931113462857270763317237396988943922445621451664240254033291864131227428294853277524242407573903240321257405579568660226031904170324062351700858796178922222789623703897374720000000000000000000000000000000000000000000000000L"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# opcjonalne argumenty\n",
"def drukuj(tekst, jak='wielkie'):\n",
" if jak == 'wielkie':\n",
" print(tekst.upper())\n",
" elif jak == 'male':\n",
" print(tekst.lower())\n",
" else:\n",
" print(tekst)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"drukuj(\"Ala ma kota\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ALA MA KOTA\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"drukuj(\"Ala ma kota\", jak='male')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ala ma kota\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Listy i s\u0142owniki"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"[0, 1, 2, 3, 4]"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(5) + range(5, 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista = [99, \"a\", \"v\"]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista.append(\"x\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"[99, 'a', 'v', 'x']"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista[1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"'a'"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista[-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 62,
"text": [
"'x'"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista[1:-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"text": [
"['a', 'v']"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lista[::2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"[99, 'v']"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kwadraty = [x**2 for x in range(10)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kwadraty"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
"[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"16 in kwadraty"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"True"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"18 in kwadraty"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"False"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sum(kwadraty)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"285"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# uwaga\n",
"kwadraty * 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
"[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[2*x for x in kwadraty]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
"[0, 2, 8, 18, 32, 50, 72, 98, 128, 162]"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kwadraty_bis = [x**2 for x in range(10, 20)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kwadraty_bis"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 73,
"text": [
"[100, 121, 144, 169, 196, 225, 256, 289, 324, 361]"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kwadraty + kwadraty_bis"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"[0,\n",
" 1,\n",
" 4,\n",
" 9,\n",
" 16,\n",
" 25,\n",
" 36,\n",
" 49,\n",
" 64,\n",
" 81,\n",
" 100,\n",
" 121,\n",
" 144,\n",
" 169,\n",
" 196,\n",
" 225,\n",
" 256,\n",
" 289,\n",
" 324,\n",
" 361]"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"krotka = (\"aaa\", 111, 33)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"krotka[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 76,
"text": [
"'aaa'"
]
}
],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a, b, c = krotka"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 78,
"text": [
"'aaa'"
]
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik = {\"pl\":\"polski\", \"en\": \"angielski\", \"es\": \"hiszpanski\"}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik[\"pl\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 80,
"text": [
"'polski'"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# - > wyskoczy blad\n",
"slownik[\"hu\"] "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik[\"hu\"] = \"wegierski\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 84,
"text": [
"{'en': 'angielski', 'es': 'hiszpanski', 'hu': 'wegierski', 'pl': 'polski'}"
]
}
],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tekst = u\"Ala ma kota a kot ma kota a kot tez kota\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tekst.split(\" \")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 86,
"text": [
"[u'Ala',\n",
" u'ma',\n",
" u'kota',\n",
" u'a',\n",
" u'kot',\n",
" u'ma',\n",
" u'kota',\n",
" u'a',\n",
" u'kot',\n",
" u'tez',\n",
" u'kota']"
]
}
],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"liczymy = {}\n",
"\n",
"for slowo in tekst.split(\" \"):\n",
" if slowo in liczymy:\n",
" liczymy[slowo] += 1\n",
" else:\n",
" liczymy[slowo] = 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"liczymy"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
"{u'Ala': 1, u'a': 2, u'kot': 2, u'kota': 3, u'ma': 2, u'tez': 1}"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"es\" in slownik"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 89,
"text": [
"True"
]
}
],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"hiszpanski\" in slownik.values()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"True"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 91,
"text": [
"['en', 'es', 'pl', 'hu']"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slownik.values()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 92,
"text": [
"['angielski', 'hiszpanski', 'polski', 'wegierski']"
]
}
],
"prompt_number": 92
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Matematyka"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# tez blad jesli nie importowalismy jeszcze \n",
"sqrt(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'sqrt' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-93-40e415486bd6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'sqrt' is not defined"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"math.sqrt(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
"1.4142135623730951"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"math.sin(15)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 96,
"text": [
"0.6502878401571169"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ale jak wiemy co robimy i koniecznie chcemy sie pozbyc \"math.\", to mozna\n",
"from math import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 97
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sqrt(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 98,
"text": [
"1.4142135623730951"
]
}
],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.sqrt(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 100,
"text": [
"1.4142135623730951"
]
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"zakres = np.arange(1, 3, 0.2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"zakres"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 102,
"text": [
"array([ 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8])"
]
}
],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = np.zeros((4, 4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 104,
"text": [
"array([[ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.]])"
]
}
],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X[1, 1] = 5"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 106,
"text": [
"array([[ 0., 0., 0., 0.],\n",
" [ 0., 5., 0., 0.],\n",
" [ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.]])"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.array([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 107,
"text": [
"array([1, 2, 3])"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# a jak chcemy siegnac po pomoc\n",
"np.log?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe = np.random.randn(10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 110,
"text": [
"array([-0.47347501, -0.66352997, 0.70129438, -0.33662391, -3.06306408,\n",
" -0.78218065, 1.5477451 , -0.65961002, -0.74979676, -0.58815037])"
]
}
],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe * zakres"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 111,
"text": [
"array([-0.47347501, -0.79623596, 0.98181213, -0.53859825, -5.51351534,\n",
" -1.56436129, 3.40503923, -1.58306405, -1.94947158, -1.64682103])"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe.dot(zakres)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 112,
"text": [
"-9.6786911502055339"
]
}
],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lub\n",
"np.dot(losowe, zakres)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 113,
"text": [
"-9.6786911502055339"
]
}
],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe_m = np.matrix(losowe)\n",
"zakres_m = np.matrix(zakres)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe_m.transpose() * zakres_m"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 115,
"text": [
"matrix([[-0.47347501, -0.56817002, -0.66286502, -0.75756002, -0.85225502,\n",
" -0.94695003, -1.04164503, -1.13634003, -1.23103503, -1.32573004],\n",
" [-0.66352997, -0.79623596, -0.92894196, -1.06164795, -1.19435394,\n",
" -1.32705994, -1.45976593, -1.59247192, -1.72517792, -1.85788391],\n",
" [ 0.70129438, 0.84155326, 0.98181213, 1.12207101, 1.26232988,\n",
" 1.40258876, 1.54284763, 1.68310651, 1.82336539, 1.96362426],\n",
" [-0.33662391, -0.40394869, -0.47127347, -0.53859825, -0.60592303,\n",
" -0.67324782, -0.7405726 , -0.80789738, -0.87522216, -0.94254694],\n",
" [-3.06306408, -3.67567689, -4.28828971, -4.90090252, -5.51351534,\n",
" -6.12612815, -6.73874097, -7.35135378, -7.9639666 , -8.57657941],\n",
" [-0.78218065, -0.93861678, -1.0950529 , -1.25148903, -1.40792516,\n",
" -1.56436129, -1.72079742, -1.87723355, -2.03366968, -2.19010581],\n",
" [ 1.5477451 , 1.85729413, 2.16684315, 2.47639217, 2.78594119,\n",
" 3.09549021, 3.40503923, 3.71458825, 4.02413727, 4.33368629],\n",
" [-0.65961002, -0.79153202, -0.92345403, -1.05537603, -1.18729804,\n",
" -1.31922004, -1.45114204, -1.58306405, -1.71498605, -1.84690806],\n",
" [-0.74979676, -0.89975611, -1.04971547, -1.19967482, -1.34963417,\n",
" -1.49959352, -1.64955287, -1.79951223, -1.94947158, -2.09943093],\n",
" [-0.58815037, -0.70578044, -0.82341051, -0.94104059, -1.05867066,\n",
" -1.17630073, -1.29393081, -1.41156088, -1.52919095, -1.64682103]])"
]
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe_m * zakres_m.transpose()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"matrix([[-9.67869115]])"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"losowe_m + 99"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 117,
"text": [
"matrix([[ 98.52652499, 98.33647003, 99.70129438, 98.66337609,\n",
" 95.93693592, 98.21781935, 100.5477451 , 98.34038998,\n",
" 98.25020324, 98.41184963]])"
]
}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 10**6\n",
"\n",
"lista1 = range(n)\n",
"lista2 = range(4*n, 5*n)\n",
"\n",
"lista1a = np.array(lista1)\n",
"lista2a = np.array(lista2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit\n",
"s = 0\n",
"for i in range(n):\n",
" s += lista1[i] * lista2[i]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 loops, best of 3: 236 ms per loop\n"
]
}
],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit\n",
"np.sum(lista1a * lista2a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 11.2 ms per loop\n"
]
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit\n",
"np.dot(lista1a, lista2a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 3.38 ms per loop\n"
]
}
],
"prompt_number": 121
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Regex"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import re\n",
"\n",
"tekst = \" Cos sie stalo w roku 2014, a cos innego w 2077\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 122
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"re.findall(r\"[\\d]+\", tekst)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 123,
"text": [
"['2014', '2077']"
]
}
],
"prompt_number": 123
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jak to jest problem, kt\u00f3ry chcesz rozwi\u0105za\u0107, to polecam:\n",
"\n",
"* [Sam Hughes, Learn regular expressions in about 55 minutes](http://qntm.org/files/re/re.html)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Wykresy"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(np.arange(1, 3, 0.2), np.random.randn(10));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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MKfD1dUeWiHyPGdx+u98f9/TT/U1ZDZk+HWbO9PvpSuk0WupxzgVYfAX4un6X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\nrQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10652b550>"
]
}
],
"prompt_number": 125
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# albo jak chcemy punkty\n",
"plt.plot(zakres, losowe, 'rs')\n",
"plt.xlabel(\"Jakis zakres\")\n",
"plt.ylabel(\"Jakis wynik\")\n",
"plt.xscale('log')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1065117d0>"
]
}
],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(losowe_m.transpose() * zakres_m,\n",
" interpolation='none');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nShcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108e02450>"
]
}
],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.hist(np.random.randn(10000), bins=50);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x108eec990>"
]
}
],
"prompt_number": 128
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.hist(np.random.rand(10000));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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hjj7UAP2oow81QD/q6EMN0I86+lDD/KVqqAn5wg+ULAH+FXgz8DiwE3hrVT06lgIkSf9v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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108dac190>"
]
}
],
"prompt_number": 129
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"przyk\u0142ad z:\n",
"http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter05_hpc/01_numba.ipynb"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def mandelbrot(size, iterations):\n",
" m = np.zeros((size, size))\n",
" for i in range(size):\n",
" for j in range(size):\n",
" c = -2 + 3./size*j + 1j*(1.5-3./size*i)\n",
" z = 0\n",
" for n in range(iterations):\n",
" if np.abs(z) <= 10:\n",
" z = z*z + c\n",
" m[i, j] = n\n",
" else:\n",
" break\n",
" return m"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 130
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wykres = mandelbrot(200, 100)\n",
"plt.imshow(np.log(wykres), cmap=plt.cm.hot,);\n",
"plt.xticks([]);\n",
"plt.yticks([]);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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S2EnSAFBnWGWnTcAgmElx8BayJmog84ztcoNfp9x6bGlHE9ifgJ8m4HUR2PQi\nIqR0NoZ0CkCyH8K/hCG/6PN0+uFIDCaSYtk0AvVIU+uDchfj2CZtHkWbBcecn7oLKVmspJdNZ9iV\nRWzvhPWMBDFEcvQYUkDdDL4RWeMGY7aZGidVKEGdWQq3B3znIZ3N/xnCo9A0h0BfCmENx88Gv8y0\njRFIuiHkRjIGzqNG2iyY7IVdP4ZfTckI+bVz4Z+PwpPjEDOh2Q2rXXjOj9F6UMbEPha2qVYxmNNT\n9yAFJpV+deooXIlRe4nmjJOpktoLzCSJei07NR6z9+NMY3MOBB4veFuRoXka+AMIfBciQ5As4cm7\nLZN4BtoDcwnwh5+HbhMe/1tYdiksuxexbaoxGFElaHgtrPsVtF0vMrN3Pw+fWA+tfWAOw43XwInl\n8JnvL/SZloSS+RZAkiYqTVh9dwsJVvvybOv2gK8JWXwvQbwLQYQkY+AxU5X3FZFpIbVLd9yFvAxb\nkCn8aaABfFFm7ON4zB4AssHjtY7nbHOnhP3HNtgUguB/gudqWPkJmLoXXI8BbyrgbpzFMLxQ1wBb\nmuGir4NnA/DHsOqALDQffgZOPTfzrCrlGViOvIuDZd5vSZyrR06okm0RWrG9tYV6bFPUHSxoYoUf\nax2qXqQp5N0fYUYT2HCWZqh7sc9yDLksj24A21a/EvgxMts2gJFAXIZxIaM7z93V/jlgnZNKWriB\nrnFYmYDk7wMfgqYGiL0TGSpryIv6Orj0Imh5PRCC+JfhtXfDodthbB+cizzHL1buFLxUJge+aNJq\n+WYlE+VakRhvMQagn9m1qGDfODdCqvgkePqxL6AVcQ82IZ4nbXIeQlyJJricBTNB7M7VHcANiLDY\nL7Cn+Jg1caqi+Qi5E1E15uMcnW6LwvMJuGIVrNtsbbcm732owYJnGbR8jpk8NM9l0HkATgQlvdFR\nU7nYaqGKIm0jkotZySynFutTTDxUY8O5OsbFgHBS+rp4xhFX7+OImVxvHTSJeCSiiCmxBvgOEgvS\nNLigtf0gsANZbL8eMbu8yEwcQQaDbmQQ2EPuZjGaL6ctPgJIUeZkAty/Ept+5TXI9FBDYWgCXun4\n/x0QewgOnIDfQHIcogOFeYrVvzFEdRC8YNIqYSsh16LdyyA1tloIlLCZwk3aJhLkZs/c8AQy+z2N\nrE291k66EadspB46W6D+hL0wGUBmS591sDGk7cQwsMKA12+DC/rh4T44FZGIzDmIs+sYdluOKJmf\nvI782k53Nb5hAAAgAElEQVSgAzHBE/fDcDOsvLyIu1JDCvZ+H0bvhcEH4eFe2AXmaYj2yyOdIjcZ\n3ditMhcNaeupXB6x6ictLeG7GibNFh92hmmcTeJIYIt7P2ftZMz6wgVA2xJYfhn8+l64cFQItR8Z\naoPIXVNBtQZgzAXXfBxi/wmnh8CMyA3r9sOIDzrGbSmDMTLHFlyOfbcAl2yFbZ3Q5IOWN1gnVkNJ\n+M3fwL6joi11DMx+iE/IOJpk/htozRV5SRtAJptyr2E1fU9lYYqFF7sXbLa/F+QVDFsbRxCJ+Cgi\nvdnyanhHEp64EyZMiMUhlBQyupGnvQHo9MHmreB5M/R/FVonxWSeBHz18N4WaJiEO5PQ7YETCWnf\nlp4+pXrejUhmyGVvhU0fBv9CNUY5g9C8FMKnYDwKE5CYhOlo9Qq35UNe0q6l/F5i7WZeqpyHdgPP\ndl5qzhRV0ziOEMYDNCwDPgK+d8D1R62dvgAHJuSGtNdBTz0kYtDRDv7tss2yEGzyids4lpTudX43\nvL0RDo3B34fg1hG4PyGCY05vtUoD1iOz8YP/BM1rYe2FyNBUW8+WjPe8Gw6egiPHZ9LjDBcYiyWb\nIg15SVuJsM4S5lZQ0EX2E3cjy8GiY2+qONeFY/puQ9QNgfrr4ZwnZBZc/RG49PPAY2B83bGTIGx1\nw9Y/g/AIJKeg8aMw9DFYswuig7Iw0iz0bDmPdcDvvR/OuR15y24u9mpqcOLur8HgCVl+LAVPEBoG\nwThd/hjqfGBe8+A0tltq7MpNbnkYL5mbdtWT27NMN5Io8ZqrYMtm8DlnNWtVvN6AwCqo+yR43g+u\nZuC1SMs6xTfBMw2EJJnZNMHYC4HDUg/7EvChDviPUdgfkeQOZ/mIqrm9E1j6djAutM6+VhgwJ3gS\n8AZTogUvIi/iaeCxyh+6E3msvWXcZ8VJ68FOQfRTugyHl9yeZfUipw8IKohuIOmCHj0JZw+SCcRc\n/e3z0oDq8stmH6D9a+COgWsldnfaIKnDgWN1PtPB6yII3gPbLFusYRo+cDvcfzc8dcIu5o0hM2wD\nEu4JfQY2/R9oelX2m1JDYbjmNnBHYPK/wbm/gaPAT+bn0Oq7Kfc+KwYfYqrmnOXyQCMgmqCUa5v0\n2K4S1oUsMz1ecHmtjTUTI4g4lSaB3hHYuwtCS+H81UiJ1/tkD94i+rKmIASuqxyxsnug+3p47Sbg\nZ7D9IVlPR63zCgJXfwXWtENgfYnHrCEFrZfKz+uuBBLw+Cm4b/+CntJcUBHS6oShS7dSEUTe9Wwe\nYlWJ8GFnTxnYnm6dVGcI60dkId8M3GttpB6tDsRMbhyGxH4kJrSS8pumy4GN0D0B1z8HrsfhmZh4\nr73WRZm7oemvwbeizMc+S7Hvf8LqzdBzBPpOw54J4gUmVlQjyq5cobkHcyGrktEpwZIOTUzS2VWT\nijTcqZghrG6wFngHYhKfQIjaZp3wOcDardB+E5IdsXEOV5ENVkoiA7BqK1y9AaK7Zb2rNYhasV1D\neRD/Jjy6Bh44CAdPkDwM0WN2YoWinFphlUTZSFuHvPulmsIz7yuSLZorjVF75MzItbhEqzvTsT0+\nqzpHe5ZOAjuRAngvcHEjrN4E4XYpll71RuBDJV5FMVgCvEmyqS75EZx6XN6YILD501BfaPVwDXnh\nA+56GHZA8ihEBiAclxwbpy7UGHbiWjVjTqTVoheYabZdFLTNhu6rndzGqLb9aMZ2aAWARi94m5F1\nahLxDOqQqeLCmtN7HPgmMsNeAWxtgWWfBu912FIR84U14P0YtK+AFY9LC3I/YLQxP+2tzxI8vw0a\nh6BznEQPTMcXr2kMJTpzlTwqM7OKwgnr7Iej6ZGdCOnzEdaD3RQcHCLTfiT34PeAt2BL9KsuaTNi\nCrgcH5ChdvsxGJoGM8CCFZY318GWLjnvZixbvhbmmTtMYBLe8XX4+Cvghrq593GpApQ00zZQuJJE\nOko1ob3M6hNuN6gCSSDdh8y06qnVjJegtaGKpdUhnOgH6tww8UFo/TZ431/CmZUB9a+BNXfCsUts\nTdcayoAw8EtIfge6vgg7ffDiz6oi6X8uKJq0IcTRWswBnINbsVN7HZkri0JYBfI+WbcSB5b74ZPt\n8ImT8uJrKd0y7AVMA2IavwvJwW94GALrwJ2pGnceoa3+JqlM5fRZiTiwE/7iWXj2TcSen2RsXJxP\nYVI1wBYTiuJQC6kqh9lQj2QDdiEEdzs+hU4iqh7ayOwm3kpYA+wkhjhwIAa3DkgrjbUIOVuQpKXL\nsb3ELsQZ9QzyH7fTDbZACHTAuR+GFtc8NyA9k1EH/DmcG4ChQcyxMEnTLtNcpKnHhb0e7Uh0MV9x\nuuo5aYMpX57tFW4kx8j50VaszgHCsI6hhPX4HJ3RTWA0CXujwr/rkHWuNqoNIrKRG62dGC64YC34\nl1FZ4ZwC4eqAug9Dh1EjbTkweQh2fBjMbtjhIdYPyRLjOTFE7KJazOq85nE74hvJtaF6c4OO7TRP\noBCocHc2aAmfOoHBEkXzODSWFAbC+mutk48jU36TG3ztMB2CXfshYELXGHjWUlmJ9ULhB2MtNLc6\nFORqKBnhEXjuNrg3CI8NkRyFaasiMsJs9Z9hsgvVF9Ksaz6Rl7QtzDaH0wmpJqzCh5jIDQbUe2xV\nwjDZTZJcBqobi9SGrZbo8VoaTPpH1WPyWgcZQ3SN698FjeuhwQX+VphoAt926PkOGB9ApuIq0Q82\nPOC7mJo86hwxfACevRX2TMHP/514H5gxyRTVNO+IY3PLx7xoWnDnfVudhHWavOnOIWf1jh+Zu+rc\nEGqEWASIghGHRBYbwwAaPdJLNL1TiWFIFMRwWR3RlyJDn8aP/NbvVJMphmg2XXAjtP21VS1joQHY\nfB00PA6eL1BdvVyDwAeojpl/kWLiJdj373DfV+FZYBDiYYiYi4eU+VDQFKPpgVqj7WL2WjVk7Uwd\nTj4DGnxgNEl/UE5D07i05jC1ak0lRA2RqvWFIDoMZtraw+W2xMR9iC1+EbLIGEeGSR/iot6E2D3d\niChb4AuphFX4m+H8mxH9GH+ht2EeEARuWuiTWNw48iD85l8l/LffXsdOsThSFAtB3rfViziXnGvO\nANmL2OsNCBjg9lqzYsgDmzphTw/e3iREZeZNxCUv2Kst0VuAVeB7gcytxjwIYRsQ26bNOpEE9qgS\nB651CynXRiGQzc+9BPgMcL914NrMtugxcQomRmFnH9wHHAFzXITmzzTkJe2KxkZaSfWv+sgu8ub1\nWevOBiRValsH/P2X4fN/DNtHYQC8sbSZWnWGN1j/n4m0OtWD3aIviuxIU6xWACtCcO5HESW2XLFX\nD/DGHH+vYVEgMQmJJNz/KbjnB7YAX7W4eiuAvKS9+PjxWbHVbLFWI/0fLsBjgDsIn9sHcTP7zVRh\ns1wLD2djKhezs4f0ePgQb1Qun3QNZwS2Xw/PHIAXp0UeYhzJjitnh+gqQ17S7r3qqllq/5oNqPxT\n3piA1yum8UzXrM1t8Nn/Dl/5a9g1LqNg+uLChXi2NgAHSHXtKdzYLut2ZA0bw55l3chMe1EjrLkZ\n8UT9V2RKruGMxdYfwbkRuO+v4YFfSpqTvpxjC3xuFULeptK/QLKaCl3T1rnAb4DbD54WYIUPrtoI\nT+yF43EIQ9yxpvVoBkULtrh3JvPYjZ34vwYh6hQyofoQS/g84AqPlLWtmoSW+8GzOcPOQEaO3wKX\nUuuofgZgYC8M9MBDd8H3vgLHwRyBsKWT6mjZxASpfDYRtaFsRl6pTaUV/UjpdjGYU1PpMKLhoBNd\nEDvW5YQqVSST8h1fGBpGwOOLwq93wQDEx+TvMx5kl7RcxQAjAt4IxAYyeI9djvxinfb7SPUeB61/\n/y4OHYdlvbs1luUKhxCRoFdRK4E7Q7Bkk3wiB6Wjw9Ng7ANfwgo5nkFr3IJiHeqAi2OromSKbqqv\n1gfUJaULXdM4xIeAMExE0+K0SWayLYwoNEVESjh98k+6rBBR1Drhl7CzvTVOOwgcluMwbZ3keV+D\nuk+BcX7qDqPDcPJbsOr9lmhUtSACPIj0oFngXOjFijWXw+tuhvD3YRrc+4S0AYS3Z0LYJy9ptYkc\nCB/C2G4eRT2paWHqaTYS4BqDmCXKPUXujKiUVpMOuJPS+9UwELtmr5jWRgA7xxHE7lliHaQNOH0L\nRP1Qt1nsdm8Ipurg5WfhyD5Y9i3wfRCZvqsBU0iF/iXUSFsimrfBls/AaBjit8MJcEchGIWoeZaQ\nVoX3nRHPKGJgKtTxqyFXtUbMpMyu+aA5xdnyO2fmQlPa5Oi/3VhE1nWtH7scbzUSYE98TXKPW9zg\n74KJFnh6D3gNSP434PoMV7hQiEH8KXnLajW1pWPJFnjFX8HgXeB5Ld47HyR5YhpPTEjrQd4pXeKp\nAss0i6PyJy9pVWRZJ7VMX1CVdu2t42Y2sXNBq3zSf+do98qo9f+aeaWzt9vreL9N68ADiI/pRWTy\nWo9M+6MnoO6EmNZLDBjrBu9RcK+hMv0Ai0EMkn0wOgAtiRpp5wpfHWy8Fv7wF3B6LcY9R6kfgaSV\nzmgiRQKKduAUqZ1aFCrfmymosRAoqJykF2k4NkzukWgYufBR7P7MhYxc2sTO+dFRL30JPIxt4sSi\n4oWeQdCAVW5h+QMIaY8iV5lEPNPPIaSdTMJjL8DUXqqihsMcgOit0Jc8c5JkFxKNG+Dq+8CYhtck\n8S0DV4nZql4kT6haaq+Kuowh5N3P1+VuHJsGHiQVuFiMIsvXOmbnNQ2TQbHRC2z2w98sgY8cl9Ej\nae3gSYQIw4ibuxnx9WwG6t+G2M8LjHAvHPg3mEguDhttUWAC+AI8PirP32kTL2IUNXiYCJkK6Uui\n6gBxJAamn0wh2Fz7mEKiO+mm9hhWqVVUPniAkxH47Cl7uteTfQExFbR3Tg/wPeDrwI9fD0fXQuxn\nRZxZBRA14VhC1ho10pYJHuAC+NJFcN8v8b7zDTQ1yDieK9eg2lG0wZBEZtIIsu7Ml2+U7mYfwh4p\nguTODtbvq5k9hL321cJk04R6E/ElrTUhGhfzd8TaqN7aiQtxVum/x7EkME6A7/+A55o8Z1JBTP0W\njn1covDprvka5gA/cCO0Xwa9X8K47Glce8C103aeLkaUZOUnkDWngUxaIAv5Qkq3ndZJglQPXqaO\ndwoTGSiGEaK7cKyZo0hAXavsNf1Fp2qNU+mXdM3oAy7qgtY1YPisLy1A3HZ0UHoIDWN5wHU1X/NG\nzQ0uYAnc90XYcQ/sG1x8bd8zYE6FpOnpnbkaPWdCHHsWNhyfTM209Hg6WGiQJgKYUfD1QiCD3e5J\nWjW7WsLXCbwJIfMQcDwMfAui90B7ELquBd5exFXMBSch9igM/kTMd5VWMIcRG6aaEj8WMVruhCOn\n4SC4p8HvgXg8s6d4MaBs1d9jpAr5F5vNa2IXZpjYiU6ZBoEpUhttxZIQSco86ex6oHB7wRVHZtwW\nJHvxdiyiDEPnXXKgcwxIPAOhaWi8EkmGrhSGgfuh9//C7t+JdaAT66Fb4fxPQXBVBY9/FqEJeOXF\nYJ7A1dBP8AgkXobJhLynmhhUjxhq1Z6AUVbJBrU8GrAzqUpJxVfyajvlTGa3lQeOD/siJnB0HbD+\npvpUeMEVRTK3H0GyBfVgR5CKpLAJo4/Dxik476+whabKaaYeRq5sEPp+C089C09j54cCDL4sGik1\nlAcTb4IbLofOX8CJnbieHsd3+yABK4tfSduEjOtnFWkV6qT1I8GUUqtaJ5FZt4nM5J20PkHshlyq\n6Yb1Oz9CXNO0/E67gEPYuRRT2N6wGOCph3O6raM9grSILydpd8mP4Sg80gePx+SEI9hF/p0fgsQg\nxAfB01bGY5+l2PZV+bnlCGyJQP1xvM8MUn+iuGhGtaCi4kgRJMyj/d88FP/6aztCLxIfzuTxm0bI\n7WzMBfaM7cdOwvAaYCirVQW9Cau8LwAbtsDWtyNNgRxI9kpWldFMcWJwYTD7JaQD4FsH49+H+38C\njx6RZmEJ5GbpzfnZO+DaTrjoW9D8piKOVUNGTJ8UVcGHtkP0V1KzXUjcskpRcUWzGDKxgaQD+ylt\n3ooh4ddlWb4fRvxK6f1+tHoviBDXNMEfRNaQGqhrQWpxX38lbLkEzPDsgwy9E+p7IfDfwbgJWzoj\niT2UaIKcc+h4AqZvhB3T8uutXXDLMLwQTs1C0ZMFSYfedhvUX0nNi1wGPHA11B2VrhL7sFOcDuX+\nWrlQ7qrAeZUhPI68ftqFoFgkkJm7i8wzbgxJgVyS9ns1s7Our3usnYd/Cye2w4Yb4Jw/Sd1mPzB1\nBFr/DFYegvZPI6lW3wa+b230IUg8Bq6PQHgMklNQfxOE18HePdI/6Ft98IwpM2yU1Ceq3LwNWPEz\nWPMNZMZ/Z467UkNeRICHkBn2NDJQDub8RtlwCgm/lxN5SRulfNLZGh49jcxPpWSkJJAMqUxxYU3k\nOI3EfF2O3+cc7RLIgzwehfYorNTU8GHg9+Wf4y/AoQSsGQHz6/DsL4Fx2OgVmRsATsHzPXDwyxBL\nQFcSNjwA8V542YC6ZvCMQTgpXjtn0FrbcpqIyXD39+DNX4RVqxHdhDUF36Ma0vDq34dd34DxU9AH\nidMwOT0/MlLOtIByIS9pjyIRw3JWd8YQOkwiTqp0k7aQ7w8hpE8PCWmhj+YnF5z1UoddyjHRB/xY\nYqg7H4RJE3aG7UqGg30Q6ZMmX311sPQd4P029I/Bnhgc6BUTLBSC6XF4cBReMOELk7A/KeupKKmZ\nT0lk8e5GTIkt74G2V1EVedGLHY/8FnpH5cWJSVw/GV+8dRkF1dP2IDkJ5dTij1of9d4VS9wIMlk1\nkdk7HSF1tZkVGlgOICTcANQNwNjd8Og98OKkjAQHkJGg39qpH6ufxBT474AN90MwCAMBODEF5yNl\nSHdPwCNxIWpvVG5opjRFNY3HEW/y0X3Q8iVoCEDTq6FtvhI+zkAcPQDGlAzMdeAKgj8JwVhV1HcV\njYLWtKPIO7WU8jfRiCNc0He2mNnRqUOdibhTyPl6kEE2qtupzrKKwfkRc6INudjB09D/JMRPi/PC\njZBuAluPqgFZpHsBErDz32HgFOw3ZdtOoH8anp+WFEv1WGfTK9IAs4nY948/BCMPyX42jELbVmRU\nqaFoXP4xWPsI9D8FvzmN62nwDYJ/UKy9Bmy/x2JAwY4olWSqBHG1nlahhe6FIBdxJ7GbfqT0I3Uj\nU/sFiPUZRsj3MuLpiozB8jEJ0R5BiKZ5zbpOWIrMpiuAFhO++SDsRTwPEWQ06kNGigly57xqn5Uw\nQtxJaz8BoOkSCGzCTiepoWhc8WngCoj+/+B+AKaHMEbA0wfBfXLLp8hOWo39Vwupi/IejyAn3k3l\nFIwGsPvQFkNcZyFPJviQGnmXFzn5OiS08igyM+7BzmHzIE/pSWTWcw7DUesE263vn0ZUMnYg2VbD\nyLTei5jUAcRTmcsO82CbBFPWz+XAOhdccjOs+y8F3okabEwBB5GiaQO4FnxH4PxDMDEk7VUHILkv\nf4KFPs5qQdEhn1Hk3V5F5Roy9iMc0RaahZykxmk7036vlUAmsiNvI3ab+hbsdeooqRnk2kKzR7Su\nZvrgxrAb8Q4jEZ+XkRFNK/dVUXKE3DAsjas4QmqnVNWHPXBxQIoHkj2AHxKj4G1n4aVxFgGSxyDy\nlxC8DaiT/58YAb8pjcXdwDMLfI4loqQ47TgSlz6PyklwqOxMM2KJFppekJ6KoFapC3CZlgmtYlM/\nJbVgPst6MzINvoDV63kKO9PjKeBixMU+jpDUyryKx6zi/Bzw+KQjAwYyuoxZF2wCsVaYboHYLeA6\nAtHVMPpDOOd/IgHfGnJiYhJ2PgVXbAfXFpi8Ce55UnwOHjf0uuDXlZWxSJdLKhdKTq4II4IQG6ls\nvo7O7IVI1kQQMyZbkCQRl6IenxuZkjsQEo4AlgJGPMNzNE2IOJKk3EfAO2kd7DKkScHzEJmQWVm/\nkw/xqOg8u9wyKMyIYAH8+QB0noaP/B10euCxz8OKi+Gct+XfcQ3ygu4chmfeAiE3vHk5HG8G3zDc\neA0cXQ6//n7e3cwFxylc3LAYlExajYe+gIgdVqry00Qsx5NICmM+6CS4lMxWgKlSGFOIqfCMHCQW\nTRVKT5DqO2oy7ZuVsBQfvf2Ip+u7EBmSzgnFwjQhkYBo2CKuavqYpnRC/vaXZRTyhIFdcOo66PoQ\n8D5q2shZMPEQHP5DGDDhwAT8/fnw9R54bExekJ3PQOdzcDlwsHKTTtXNtCAnFEaWdCupXI+6JOIL\n6iH/jGtii1YoJnHU2Wqf2y6EuOMQmxLCTpu2TKZyWzGOPQj4TRG/Th4EvgOcFMJOUFjAXn1OzpNO\nOomrC/E40NsnN7kDSYscPgpdG9P3UIMTgXo47yLwXQGf/RH8w0nYMwXDCUunaBL6pfPFKOInrBbP\ncCEoy5PXmbCLyrWyUm2qU8j7myuWq5q2zcgFxhEeuNzgbUKm4R4kn3hEsmOmTfFCZ6uldP4+CcTU\nBDho/z49ySkXVEtXnXmmaZnvYel6b0xbJ689ila64A1u6PQjsagaabPC0wkt74BkJ0z8GB4fn/E3\nJOKQsITKYlPyzHSgTs8srVaU7clrAkYACVZXwr+plqMLyS3OZZLr7Nrk2M7wSjaMOQ7x/TA1KjuN\nm3ZoFetnpgbiAWtfTpmcUqD+KdXIUkc1yEvl9WOnjBlI+Oc8oH4ptL6X7C29z3ZMQe9TMHwA1lwI\nL/0QRk0h7LQsa+IxiCTk1sZIjcRNMfu5akpsNaGsw7VGOBqxdZwqAc2gaiE3cSeQmd+L3PxJE/wT\nYI5CbNQOv4ZJNWvjZE5lUOncXNAOC4XASoWdqTPWPOpEzOqc4EFKljYD65MQ7wRuBn4EvA27aUut\nefZM3O7EI/DEz6H7TfDsv840ZEtYjsZIUgbkSYSk+VJWYuSP3GXCCJXrSFARG0trWFWkrRIYsvY/\nS7Q8DSkqLhEIZ7iT4xRmFk3l34RklvNxkjIdOkioOkcsKqa8EUQC4ppiuXQa+rZD7HPQ3Q19eyDa\nAq1XQ+NZric19jAMH4aXX4Idu+GO3TK6W887HoOoRdgIs2dZTXgrF/op7H0pBRVbGE0gLu81VG4e\nUAdCK9kvRD3AdditOiuJbOVeBsI7g8wKHgnruyniMlqYMITkQK9+ESY+Di+G4TX/CI/9FqZG4JVf\ngo1/wllrNscOwpHvwfY7pch9AhkFB4AxW5F2CruQxGlZabr3Yqn6qWh7kmnET1NJoawh7PrcbBhF\nHFgj2PWNlahzzAV9MQawR/VMHku9DlPranuQdMrvAU83wNYtcCwJ/3Q3PDEC0RC4d0L8MYg5+52f\nRRj6K+ApaO+AzkZoMWTA8wAuce4lHS/IFKnSv9mQkq9eRah4T6EokkdfSeKOUFhu6CSpLUpOsjCv\n+DBSS5C+nkpidwWJhiVrkXuBu5HR7/kQNF4GH0beul7gylvh3H+GFx6Ap29iRjjubELHD2HzcXjL\ncfjUl+BPfZI8sB5xfJT4lo9hi/FXEwwzR+qOYRjmW7L+tTj4gHVUzlRW4YdCEjCccFN4cL2V8qYz\n6DmnG7WaZem11Ns9XvCEgA4PrKiDxJjMwkFg2VIJAwVGoDkGF7TAllWw8otIRcPZFhoah+gg9OyB\nR98C/wLmEYiOw0jS0jjAnmkTyNiXaVJRC61Y7GfupX53AKZpZnw15+2JRpEkjBVUxjmlCRhHERIu\nozAvbjEm8jCzK+zczNakKhQqVqEysc7fjwMNJnhN8XqaY+CNxUUdQ1keA+iTN3EJMgL4NkHHl5Ha\n28XarSYfBoBbgP/K7LKVRhjZDbs/Ly/d1WBoLXQGpPeaKgfSpb/KjXkdhqcRc6OLyrhMktjx1V4k\nCaOc6ZWZTGkDWasqVI21UGhfJP2uQj3KdYDPhITlQfH6rS/VWz/jSP7zMiDohgs7IHBpEWewyDC1\nB/r/AVYvJeuKMzkIo7tkpXAEcea5KWqE1vqPasS8204TCKHSZ5dyYxzbU9tI5bJ0TVLXptrSBIS8\nhQgGaCp0emw7hh028JkSvzUAj1NacjkikbMxCIGroOOPSrqORYHk0zD0j7D3Lui4DILJzGubJhec\n74EdcVlGtIHHgGA/JDNlzWRAlMwJNrmgpnalHZwLsuBRQmkzu+YKHUfXLVoC66f8qhvpcMbmdFJ0\nF3Bcbd2pPYr0XUwnbjwuJX2AnTdpVSnRtgF4a+YDjNwL7npxZFWsErqCGHwKjv8rHP6pLBpbd8Hl\nibQVwFNABOraYeVl0P6EmHUXgvsZCDwpZZaVQhK7FrySWDAvxZj1CWKL/FcK49anDrmx6gCqNCLY\nfXyVhAGyOzO1C6GBfV9AiKsDXAoSSDB8H+CZhuik3cA3HSd/COFRWPI2aFoLzQvYj7dgPAQjg+BN\nwuQvYd9tEv6KA0tjsPnn0PB7YDTC5HYY+RfwR6H+RhjugvMboCMEK3rhxdnzn3M55USYysfz54IF\ndy1OI7pnaxBSVbI219liRKuFAhU+JqRqYLWTSshMUGdXvu1oQKbzU0BnC2zoyLLhMAxPw55fQOgX\ncNFboPEimHwBmrZRtR0Mwp+Dvb8DdwJaveDywMmElZc6Dbs/CMu9EFoFh/8O9t4DjfWw7FE4PA7X\ndoCxFr4zCH0JDI/kn6tzQqV40zFCYXFcJ9SpOB9YcNKCWHYHEJ20UtuGFIMY4mUGKSlUZ5Wbygeu\nTyPE9WO3EsoEZyaXijTOSMJqTuRyxGTwAqsugguuQ7RuwhAdku283RD+NfQdlIteDXinoPcheOlD\n8MqnwVjOwnuarTpJltq/OhWFPUlLvjYmI5/emB8gI++Wm0SEoNeAvQaMTIJ7Ui7n5EmInZSHfCl4\nPeB+DgmSlxlh5q3LSHWQFuSFfB5RwpgP4iqOOf7dTmoaYaXOQb3NzeRuj2IVIdGADGxjOLoybAH+\nBzyCG68AABWRSURBVBKH2gH0/Aaea4fL1sD0Q7DzY0LoDXvh0Vtg5w7xkpwC9j0ArQ9AtxuuvxaM\nJ4FlYCYQ4aqK59ykwkwCd4L5DTDutYSz3LBmCXgD0DctywDn9OdCXhofYi7d4IUpE74fk7d6DVJo\n8V3gPuRmdiI3vQKknU9UDWkV+7Gr0UppGzIXDGG7+f04un1UCCrLWZIj7jDwD8BHgPdijQR3QGIH\nTC+D31k7XvNhOLpfZtkBbCmQMNCSgP4eWBKXmen5P4Wmi2HFR+d6acXhxDdg51/B+BRcuB4uuAr4\nOoz2Q8DS+VERdyd2ICqatwNmDMZNWYs0IgPUg4ip0gf0Q+wlK3SWA6eYn3Yhc0HVkVYD3SeRgXQ+\nu7MmsSN/SSTEp+gie5XOXI43gfAo2wCl1T/qODOTEJkSqSPjCCJOdxkyq3jCcMfL8PIpMVvcwIHn\n4HBU3kb1uiSATavh3X8BY/8F2u6Awfth9+MQ+RVc9E04vx0iXXBwEC7+GQy9EZq+Ap7zS7jSY8AD\norwx9h1ougOO/i14n4O2T8LSN0HbKXjki3D8GLw0BJufh9b9kDDlJqj2dCbPUR+SrG1iKwssAz4E\n3APcB9FRGIvDaWt302Suk1XRkGIwQarFVmlUHWkVUaxnQfEtQ8qBJKl6uL3Yq75GSuv6lwkJ7Dhv\nJuKmFzYYhoR8jAAyom1C3pqngU0NsKoeXuyTJeIw8FJYzAdn7VkCOHQaHvoRvHkZuB+Dpl0wNAYH\nhqGtHja9Ck63ws4vwYVfgMeehK4/hmizLfbe8SoI/Xn2ixv9HvT/FIYmYKIHInEYOQhvjcLvXoLJ\nZ+HqpyEyAPfcIWvXsSRcMQbDu2Dp6yC0F8weqdYZh3gekWJXVMQWOYYMXPuAaZhICmFVEijJ7GSZ\nfkprMp0o8XulompJC3IjtOeVjxQXxbzDOcBrCMZL9ghLMVDian1wOiIIBxuASQPaPMA51sEPIVUG\nXmAwBnXTQiofthazU0dF2y0cn4DeJ2F7CBJPwOkR2G1Kypo5Aj3PQsQLhybgP/4DDk1DxyPi0r7g\nvRC6Bnx5Zl3fegi9Dkb2wN4HZRCZACY+B88dkosavFfM3idetN37DyDpbM+cgF1TcFjSrePT+cXz\nTCtu7TmIFFpYdZAxbHWSMJmzGjMpV+TDBKndMeYDVU1aELJMI++kgTzLhYbGX73YgfQ5FJMAdj2t\nZkU596UvXB2O+OEUQqAnEDOgBXgxAnURuML6m7YjmcKerj3INNMDPJeEZ4fBPSwLektonaE+2Ncn\n+zCAkydk/x5kZl91HXT8cf6LCl4uH88L0B+Bh26VAeb+/7TX1Sctsqp3bgz4FdKW98nnYT8kBiE+\nBdFEqp58JniSIroHiFPiSSH7ZNIm7BizZ8ZhSstBnqb48NBcUfWkVag0qpu5E6Rc0CbWYIdvZikt\nFgFNbtI1rDMIk0AI6zJhMgb+PZaJfASx607J/7vWIG/2UdlZQoWjFXEwIuAKA/dbvwsiM7Pzpk4g\nU7sPu8JjQxAuboD2IlNTWtvhqsth9Ha4b0zMCi21UbPFacqMAcchcRgYEJM4mpBN8pmhXsCVBHdS\n7tXUYzA2LVJRMexJwIlSi+ALkaupBBYNaUFuqlbxaEuearkAjSIsQSIQPkovVlANrDps4kaxxPNM\nGAxDU1iiIzMIS/g10Ii44B+RX8fCs4XTXW7r3KyYp2sCe5TQYLVegKZhrXfBq66HjvXAuUVe0QA0\nPQvXN8GuMfEyqikctj+maYu980OIRSCWlDFH/VBOZHIER5DvcARcX4JTYTEidA2bPpualJ4UMUJl\nxMjzoVre+aJwxPqp0kkLnRbghM68zciSUweYYjGEvFCaFeXGbgvahv2yOFMk/QlIHkbCQVlgACRS\nc3D9QTDGrfBoHTLqOGUbXMC7fND0EUi+BlzFFlduhOjHoPd22FYHB6ZhxBpJlLQO7WfrFAGZkJ0O\nI9WjVmGPTHm+fiCehGTYtrr13jlnWSXs8SKvBjIPAPOFRUlaxQnkvVqCvIzVYDIrRqxPHTK4lHJu\nw9anEVkSaMuffsc2LdjCAoXkzKrErcLAJrAvAG5tQaj9H/XAPwjDG2+G838KdW8s/mKC7XDxB2Hr\nDtj9OzideqaJOEStX2nvMqdVr+SMkH92i2DfI93HaWbfmzClERbEJTBY4nfnikVNWhAfTB8ys1Wj\nHuE08mLM5dw0lpspZq1rYJBZOV8oSp1oYOtHK1Qo3QN28HgdEt55E3DePVL+VxJcEPPBjiehM5oi\nYRKPiSkMMigNM3sG1WVwMRU0CWy5mGrUeioVi560+jC0d+7qhTuVjHC2TgGZdYs1l9WMyxSzNrFf\nZJUHdSJ9+eB86RPIrNVMFksgAkx44X+sgYOHIPE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duiD41iUeo4VSOeO6102TU5PyScvr\nFLPO1JMPV8m/H9BEQTutk/J4PKNJFbQej6d4zJW4wuM5Dfig9XhKhg9aj6dk+KD1eEqGD1qPp2T8\nH2bXh/UqodsgAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10911f4d0>"
]
}
],
"prompt_number": 131
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit\n",
"mandelbrot(200, 100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 loops, best of 3: 4.73 s per loop\n"
]
}
],
"prompt_number": 132
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Wczywanie i zapisywanie danych"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rozmiar = 100\n",
"df = pd.DataFrame({'plaski': np.random.rand(rozmiar),\n",
" 'normalny': 3 + 5 * np.random.randn(rozmiar),\n",
" 'liczby': np.random.randint(-5, 5, rozmiar),\n",
" 'nukleotyd': np.random.choice(list(\"ACTG\"), rozmiar)})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 134
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>liczby</th>\n",
" <th>normalny</th>\n",
" <th>nukleotyd</th>\n",
" <th>plaski</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2</td>\n",
" <td>-1.373925</td>\n",
" <td> A</td>\n",
" <td> 0.755093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-3</td>\n",
" <td> 6.195501</td>\n",
" <td> T</td>\n",
" <td> 0.440674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 2.062832</td>\n",
" <td> C</td>\n",
" <td> 0.597053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1</td>\n",
" <td> 7.210289</td>\n",
" <td> T</td>\n",
" <td> 0.913081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td>-2.430652</td>\n",
" <td> A</td>\n",
" <td> 0.941089</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 135,
"text": [
" liczby normalny nukleotyd plaski\n",
"0 2 -1.373925 A 0.755093\n",
"1 -3 6.195501 T 0.440674\n",
"2 1 2.062832 C 0.597053\n",
"3 -1 7.210289 T 0.913081\n",
"4 0 -2.430652 A 0.941089"
]
}
],
"prompt_number": 135
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.nukleotyd[df.normalny < 0] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"prompt_number": 136
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>liczby</th>\n",
" <th>normalny</th>\n",
" <th>nukleotyd</th>\n",
" <th>plaski</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2</td>\n",
" <td>-1.373925</td>\n",
" <td> NaN</td>\n",
" <td> 0.755093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-3</td>\n",
" <td> 6.195501</td>\n",
" <td> T</td>\n",
" <td> 0.440674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 2.062832</td>\n",
" <td> C</td>\n",
" <td> 0.597053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1</td>\n",
" <td> 7.210289</td>\n",
" <td> T</td>\n",
" <td> 0.913081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td>-2.430652</td>\n",
" <td> NaN</td>\n",
" <td> 0.941089</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 137,
"text": [
" liczby normalny nukleotyd plaski\n",
"0 2 -1.373925 NaN 0.755093\n",
"1 -3 6.195501 T 0.440674\n",
"2 1 2.062832 C 0.597053\n",
"3 -1 7.210289 T 0.913081\n",
"4 0 -2.430652 NaN 0.941089"
]
}
],
"prompt_number": 137
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>liczby</th>\n",
" <th>normalny</th>\n",
" <th>plaski</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 100.000000</td>\n",
" <td> 100.000000</td>\n",
" <td> 100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> -0.130000</td>\n",
" <td> 3.239471</td>\n",
" <td> 0.529069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 2.680476</td>\n",
" <td> 5.565459</td>\n",
" <td> 0.270838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> -5.000000</td>\n",
" <td> -9.754608</td>\n",
" <td> 0.023878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> -2.000000</td>\n",
" <td> -0.547144</td>\n",
" <td> 0.340004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.000000</td>\n",
" <td> 3.350655</td>\n",
" <td> 0.551427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 2.000000</td>\n",
" <td> 7.508838</td>\n",
" <td> 0.742801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 4.000000</td>\n",
" <td> 20.735975</td>\n",
" <td> 0.991182</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 138,
"text": [
" liczby normalny plaski\n",
"count 100.000000 100.000000 100.000000\n",
"mean -0.130000 3.239471 0.529069\n",
"std 2.680476 5.565459 0.270838\n",
"min -5.000000 -9.754608 0.023878\n",
"25% -2.000000 -0.547144 0.340004\n",
"50% 0.000000 3.350655 0.551427\n",
"75% 2.000000 7.508838 0.742801\n",
"max 4.000000 20.735975 0.991182"
]
}
],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.nukleotyd.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 139,
"text": [
"A 24\n",
"C 19\n",
"T 16\n",
"G 15\n",
"dtype: int64"
]
}
],
"prompt_number": 139
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.nukleotyd = df.nukleotyd.fillna(\"X\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>liczby</th>\n",
" <th>normalny</th>\n",
" <th>nukleotyd</th>\n",
" <th>plaski</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2</td>\n",
" <td>-1.373925</td>\n",
" <td> X</td>\n",
" <td> 0.755093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-3</td>\n",
" <td> 6.195501</td>\n",
" <td> T</td>\n",
" <td> 0.440674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 2.062832</td>\n",
" <td> C</td>\n",
" <td> 0.597053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1</td>\n",
" <td> 7.210289</td>\n",
" <td> T</td>\n",
" <td> 0.913081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td>-2.430652</td>\n",
" <td> X</td>\n",
" <td> 0.941089</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 141,
"text": [
" liczby normalny nukleotyd plaski\n",
"0 2 -1.373925 X 0.755093\n",
"1 -3 6.195501 T 0.440674\n",
"2 1 2.062832 C 0.597053\n",
"3 -1 7.210289 T 0.913081\n",
"4 0 -2.430652 X 0.941089"
]
}
],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.groupby('nukleotyd').mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>liczby</th>\n",
" <th>normalny</th>\n",
" <th>plaski</th>\n",
" </tr>\n",
" <tr>\n",
" <th>nukleotyd</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td> 0.500000</td>\n",
" <td> 6.537929</td>\n",
" <td> 0.386814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>-0.421053</td>\n",
" <td> 5.330686</td>\n",
" <td> 0.535696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>G</th>\n",
" <td>-0.200000</td>\n",
" <td> 5.415330</td>\n",
" <td> 0.655309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>T</th>\n",
" <td>-0.187500</td>\n",
" <td> 5.486809</td>\n",
" <td> 0.667745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>X</th>\n",
" <td>-0.423077</td>\n",
" <td>-3.971737</td>\n",
" <td> 0.497370</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 163,
"text": [
" liczby normalny plaski\n",
"nukleotyd \n",
"A 0.500000 6.537929 0.386814\n",
"C -0.421053 5.330686 0.535696\n",
"G -0.200000 5.415330 0.655309\n",
"T -0.187500 5.486809 0.667745\n",
"X -0.423077 -3.971737 0.497370"
]
}
],
"prompt_number": 163
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.plot(kind='scatter', style='.',\n",
" x='liczby', y='normalny', s=(100*df.plaski), c='plaski');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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s5ZUKe/fuZeLEiTg6OpKSkkJaWhppaWk4ODhga2uLSqUiISGBu3fvUrFixWLZ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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10a9f4590>"
]
}
],
"prompt_number": 152
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Wi\u0119cej o wykresach w Pandas](http://pandas.pydata.org/pandas-docs/stable/visualization.html)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pliki ladujemy tak\n",
"nasz_df = pd.read_csv(\"siezka/i/nazwa_pliku.csv\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Wykresy raz jeszcze"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# warto, nawet jesli tylko dla zmiany stylu wszystkihc innych wykresow\n",
"import seaborn as sns"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 153
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.plot(kind='scatter', style='.',\n",
" x='liczby', y='normalny', s=(100*df.plaski), c='plaski');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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55ZbbLCn7/vsf4umn/8jw4cOJjIwE3LO+bdq0iRkz7rGkzGDgj0m2uZq0KpVS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\nfq9nz36Slv5O5eTksGHDempqaprMfy2SLFIxyR4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RTU11s/tzc3PZv39fgqMRkeYk2z3n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\n",
"text": [
"<matplotlib.figure.Figure at 0x10e2ffa50>"
]
}
],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.jointplot(df.normalny, df.plaski, kind=\"hex\");"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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2oCAcDt8cCoWmASuA+eFweKg5xmmZ6dEa6WTt1mq2H0ztUedwrBaF804uZXKJ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\nR/nwrh5Wdbpx2mzFni290twYuzXF3u6fjFgswY1RPxcuXuX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TkBJ3ivO6rg+nvgKhD2gF\nXtN1Pf0lel8FHp91/2NzHjuk63oYGCf5rakAN4CW1NcsvAq8qpT6IvAcyRFVWvpL+X6p63owtZz+\n1GPfAlalAuoT3PrSOyFEBiSkxJ0iMutni+Q3vM7+RlcHs9rbuq5HZ/1bbM6yErN/UUr1Au8ATcD/\nAb7Jwt8WO7eG9H2+RTLk/hnwnaX/DCHEbBJS4k52VCnVkvr5M8AbOS7nAeCyrutfBo4DR8jueO43\ngX8B3NR1fTTHGoS4K0lIiTvBQt9yOgN8EfipUqqP5CjoP8y6/1KPnbvs1wGHUuo88P+AnwLrFljG\ngsvRdX2QZOvwm8v/KUKI2eSrOoQoMqXUKuAtYJuu6/EylyNERZGRlBBFpJR6BTgN/KEElBDZk5GU\nEEII25KRlBBCCNuSkBJCCGFbElJCCCFsS0JKCCGEbUlICSGEsK3/D86pzZdlvL/MAAAAAElFTkSu\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10a62df10>"
]
}
],
"prompt_number": 154
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.jointplot(df.normalny, df.plaski, kind=\"reg\");"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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WhJgQQoi0JSEmhBAibUmICSGESFsSYkIIIdKWNZk71zTtJ8ANQJuu6+ui/PxS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\n",
"text": [
"<matplotlib.figure.Figure at 0x10adc9890>"
]
}
],
"prompt_number": 155
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# klasyczny zbior irysow (uwaga: wymaga dostepu do Internetu)\n",
"df_iris = sns.load_dataset(\"iris\")\n",
"sns.pairplot(df_iris, hue=\"species\", size=2.5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 156,
"text": [
"<seaborn.axisgrid.PairGrid at 0x10c62be50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Jv/6N4UuuC3htSbsqIjGu9G5UomvIfGI94O7/96jd3jmDwa4ZrsZGCqaXe//t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1AAAg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\nEamdOiYiIiIiIlK76+00wHRm12eHAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10c50a1d0>"
]
}
],
"prompt_number": 156
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# macierz korelacji pomiedzy cechami\n",
"sns.heatmap(df_iris.corr());"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10c802c50>"
]
}
],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.clustermap(df_iris.corr());"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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0+bOny34jyZuq6r9ncmHG4sz+Zx8v5+P51uDbhZmcT3jhzLa7tn9pkrdW1dOSfDSTKeR0\n97VV9ZGq+kySP5t+Le1zTzXcwpbFxRXbL7ty+/btpoM3jl1DyovP3XLsHMtgb5yzuDNJ8vlr/32+\nhbAm97zTEUmSHR/83JwrYa22PeK4JMkbLvqHOVfCWj375GOS1U+jrqiqPpjJ1bqX7I/9HaxMBwMA\nDMh0MADAjO5+xN5sV1WnZ3LLllkXdvcL9rmoA0AIBADYD7r73CTnzrmMVTMdDAAwICEQAGBAQiAA\nwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEA\nBiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAw\nICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIAB\nCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGdOi8C2D9\nnLO4c94lsJfueacj5l0Ce2HbI46bdwnspWeffMy8S4ADzkggAMCAjAQO5PPX/vu8S2CNdo0APnfL\nsXOtg7XZNer+uu+433wLYc2ed90VSZK3H/W9c66EtXryNZfNu4QNx0ggAMCAhEAAgAEJgQAAAxIC\nAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAI\nADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAA\ngAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIA\nDEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABg\nQEIgAMCAhEAAgAEJgQAA+6CqHl5V562w/vSqes0B6PeZVXXXmec7q+qOq91eCAQAOLAWD9B+T09y\ntyX9bFntxofu72oAAA42VXW7JG9LcvckhyR5WZIrk7wiyRFJvpzk9O7+UlVdkOSTSR6WSVZ6dnf/\nVVWdkuRVSbYm+VqSZ3X336yxjrskeV2SY6aLXtjdH62q7dNl3z39/qrufs10m5ckeUaSf07yhSQX\nJ9mZ5OQkb6mqryY5dbq/F1TVE5IcluQp3d27q8VIIAAwgsckuaq7f6C7H5DkvUleneRJ3X1ykjcm\nefm07WKSw7v7hCQ/n+QN0+WXJ3lod5+Y5Mwkv74XdfxWkld29ylJnpzk92bW3TfJo5OckuTMqjqk\nqh6Y5CeSfF+Sx2YS/Ba7+0+SXJTk6d19YncvTPfxz919UiZB80UrFWIkcAPYsWNHFhYW9txwGdu3\nb9+/xQDAxvTpJGdX1Y4kf5rkuiT3T/L+qkomo4NXz7R/a5J094er6siqOjLJHZK8uaruk0lQPGwv\n6nhUku+Z9pkkt5+OUi4meXd335jk2qr6pyRHJzktyTu7+4YkNyxz7uHS6d93TL9fkkl43C0hcANY\nWFgQ5gBgH3T356rqhCSPS/JrST6Y5LLuPnXlLW/hZUk+0N1PrKp7JrlgL0rZkuRB01D3TdNQOLvs\n5kxy2tLz/JaGvqXnG359yfa7ZToYANj0plfRLnT3W5KcncmU652r6sHT9YdV1fEzmzx1uvwhSa7r\n7q8kOTLfGi181hq6nw1uf57kF2fq+v4VtltM8pEkT6iq21TVEZmE2F2un9a0V4wEAgAjeECS/1FV\n38hkxO15mYyWvbqq7pBJJnplks9O2y9U1SXT5c+eLvuNJG+qqv+e5N255SjcSlcAL86s/8Ukr62q\nT033/ReZnHe47D66+6Kqelcm09nXJPlMkn+brj43yTlLLgxZrs9lCYEAwKbX3X+eySjcUg/bzSa/\n393/95J9fDxJzSx6yXT5BVlhari735TkTdPH1yb5z8u0eemS5w+YeXp2d7+0qm6bSWi8eNrmHfnW\nOYDJ5MriXdtfnOSRu6spEQIBAA52vzudqt6a5Nzu/uT+2KkQCAAwo7sfsTfbVdXpSX5pyeILu/sF\n+1jPM/Zl+90RAgEA9oPuPjeT8/Q2BFcHAwAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJgQAA\nAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAY\nkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCA\nhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYk\nBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABHTrvAlg/97zTEfMugb10zuLOeZfAXnjedVfM\nuwT20pOvuWzeJcABZyQQAGBARgIHsuODn5t3CazRtkcclyR53Xfcb86VsBa7RgCfu+XYudbB2u0a\ndX/RYfeabyGs2dk3/t28S9hwjAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIB\nAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgA\nMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGNChe7PR1q1bs3379v1cCrtzxRVX\nzLsEAGCT2asQuG3btv1dBysQuAGA/c10MADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgA\nMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACA\nAQmBAAADEgIBAAYkBAIADEgIBABYoqqeWVV3XUW7c6vqSSusv6CqTtrPtd2hqp438/zhVXXeWvcj\nBAIAfLvTk9xtFe0Wp197u35vfGeSn9/XnRy6HwoBADioVdWxSd6b5KIkJya5LMlPJzk+ySuSHJHk\ny5mEv4ckOTnJW6rqq0lOTXJGkscnOTzJR7v7OTO737LKGh6dZHuS2yS5Msmzuvv/VNXOJOcmeUKS\nw5I8pbu7qu6S5H8luWuSjyX5kSQnJdmR5N5VdWmS85O8O8kRVfXHSe6f5OLu/qk91WMkEAAYxX2T\nvLa7j0/ylSTPT/LqJE/u7pOTvDHJy7v77ZmExad394ndvZDkNd19Snc/IMnhVfX4tXRcVXdO8uIk\nP9zdJyW5OMkvT1cvJvnn6fLXJXnRdPmZSd7f3fdP8vYkx0zb/mqSK7v7hO4+I5MQekKSX8ok1N6r\nqk7bU01GAgGAUXyhuz82ffwHmYSy+yc5v6qS5JAkV8+0nx3he2RV/UqS2ya5Y5K/TvKnq+x3S5IH\nZxLQPjrt69ZJPjrT5h3T75ck+Ynp49OS/Kck6e73VdW/LlPXLp/o7quTpKo+meTYJB9ZqSghcBPa\nsWNHFhYWkiTbt2+fbzEAcPCYPTdvSyajgZd196krta+qrUlem+Sk7r6qqs5MsnUv+j+/u5++m3Vf\nn36/ObfMZ6uaap7Zfrl9LMt08Ca0sLCQ7du3C4AAcEvHVNWDp4+fnuTjSe6ya1lVHVZVx0/XX5/k\nyOnjXYHv2qo6IslT1tjv4rSv06rq3tO+bldVx+1hu48k+clp+0dnckHIrtpuv8Yavo0QCACMopP8\nQlV9NskdMj0fMMlZ0ynUS5P84LTtuUnOqapLkiwkeX0mU8DvTfKXS/a7x6t/u3vXRSdvrapPZTIV\nXMs0nb2a+KVJHl1Vn5nW+aUk13f3tUk+UlWfqaqzsvwVyHusyXQwADCKm7r7/1qy7FNJHra0YXe/\nI986Ty9JXjL9WtruWSt12N2PmHn8wSSnLNPmu2ceX5zkkdOn/5bkP3b3zVX1g0lO7u4bp+2esWQ3\nfzGzjxesVNMuQiAAMIr9fb++A+2YJG+rqlsluSHJz+7PnQuBAMCm1907k3zfgdp/Vb0jyXcvWXxG\nd5+/t/vs7r/N5J6GB4QQCACwj7r7J/bc6uDiwhAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAw\nICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIAB\nCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxI\nCAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBC\nIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQIfOuwDWz7ZHHDfvEthLz7vu\ninmXwF44Z3HnvEtgL51949/NuwQ44ITADWDr1q3Zvn37qttfcYXAAACsTAjcALZt27am9rsLjG+4\n6B/2QzWsp2effEyS5O1Hfe+cK2EtnnzNZUmSFx12rzlXwlrtGgF87pZj51oHa2fkfe2cEwgAMCAh\nEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmB\nAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgE\nABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAA\nwICEQACAAQmBAAADEgIBAAYkBAIADEgIBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEA\nBiQEAgAMSAgEABiQEAgAMCAhEABgiap6ZlXddRXtzq2qJ+1DPy+tqh9eZvnDq+q86eOHVdUP7q8+\ndzl0X3cAALAJnZ7kr5P84x7aLU6/9kp3n7mKZo9Icn2Sj830uc+EQABg06uqY5O8N8lFSU5MclmS\nn05yfJJXJDkiyZczCX8PSXJykrdU1VeTnJrkjCSPT3J4ko9293Nmdr9lN30+MMm27n5SVf14krcm\nOTKT/HVZd9+7qs5Ncl53/0lVPSbJK5N8NcmF033cM8lzktxcVc9I8ovT3f9QVf1ykqOTnNHdf7LW\n18R0MAAwivsmeW13H5/kK0men+TVSZ7c3ScneWOSl3f32zMJi0/v7hO7eyHJa7r7lO5+QJLDq+rx\nq+jv0iQ/MH380CSfSXJKkgcl+fh0+WKSxaramuR3kzy+u0/KJNwtdvfnk5yT5DentVyYSeg8urtP\nyySY7tibF0MI3IS2bt2a7du3Z/v27fMuBQAOJl/o7l1Tqn+Q5D8muX+S86vq0iQvTnL3mfazI3yP\nrKqPV9WnkzwykxHEFXX3TUmurKr7JXlgkt9M8kOZjDR+eEk/90vy99195Ux9W5a02WUxyTunfVye\n5Kg91bIc08Gb0LZt2+ZdAgAcjGbPpduSyWjgZd196krtp6N0r01yUndfVVVnJtm6yj4/lORHk9yY\n5ANJ3pTJINyLVqhtV30ruWENbZdlJBAAGMUxVfXg6eOnZzIle5ddy6rqsKraNcJ3fSbn7yXfCnzX\nVtURSZ6yhj4/nOSFmZxH+OUkd0py3+6+bKbNYpIrkhxbVfeaLnvazPrrk9x+DX2uihAIAIyik/xC\nVX02yR0yPR8wyVlV9clMzuHbdSuWc5OcU1WXJFlI8vpMrhZ+b5K/XLLfla7W/USS/5DJiGCSfCqT\ncwNvWVj315P8XJJ3V9XFSa6Z2e95SZ5YVZdU1UOW6XOvrhbesri44nb75RJk5mrXEPHiGy76h7kW\nwto9++RjkiRvP+p751wJa/HkayZ/4L/osHvtoSUHm7Nv/LskyXO3HDvXOli7cxZ3JitMi06vDj5v\nemEHMRIIAIzD4NYMF4YAAJted+9M8n0Hav9V9Y4k371k8Rndff6B6nNfCYEAAPuou39i3jWslelg\nAIABCYFc2t7nAAAJOUlEQVQAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIgAMCAhEAAgAEJ\ngQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBAIADEgI\nBAAYkBAIADAgIRAAYEBCIADAgIRAAIABCYEAAAMSAgEABiQEAgAMSAgEABiQEAgAMCAhEABgQEIg\nAMCAhEAAgAEJgQAAAxICAQAGJAQCAAxICAQAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIB\nAAYkBAIADEgIBAAYkBAIADAgIRAAYEBbFhcXV1q/4ko2hC3T744lAJvdlj03YZdD97Dei7l5OJYA\nwDeZDgYAGJAQCAAwICEQAGBAQiAAwICEQACAAQmBAAADEgIBAAYkBLJhVNXDq+q8fdj+pKr6rd2s\n21lVd6yqO1TV8/ZXn6Pb0+tXVadX1WsOQL/PrKq7zjzfWVV33N/9bERLX5sV2p1bVU9aYf0FVXXS\nfq7N+28V9tcxXMX2L62qH15m+TePS1U9rKp+cH/1yfra082iYdPo7ouTXLyb1bs+UeU7k/x8ktet\nS1EcqE+yOT3JXyf5x5l+3DB94vTc8rXZncWsfHz2tH5veP+tzunZP8dwRd195iqaPSLJ9Uk+NtMn\nG4QQyH5VVbdL8rYkd09ySJKXJbkyySuSHJHky0lO7+4vVdUFST6Z5GGZ/F98dnf/VVWdkuRVSbYm\n+VqSZ3X336yi708neUgmP5C+nOSF3f37VfXmJG9OclOS/9LdT6iqOyV5a5K7ZfLDa8v0a0eSe1fV\npUnOT/LuJEdU1R8nuX+Si7v7p/bxZTqozPOYLanjLpn88j9muuiF3f3Rqto+Xfbd0++v6u7XTLd5\nSZJnJPnnJF/IJOTvTHJykrdU1VeTnDrd3wuq6glJDkvylO7utdR3sKqqY5O8N8lFSU5MclmSn05y\nfJYcw0zeH0tfmzOSPD7J4Uk+2t3Pmdn9qoJzVT06yfYkt8nk/86zuvv/VNXOJOcmucXrPj3W/yvJ\nXTN5//1IkpMy4Psvmc8xrKoHJtnW3U+qqh/P5OfhkZm8ry/r7ntX1blJzuvuP6mqxyR5ZZKvJrlw\nuo97JnlOkpur6hlJfnG6+x+qql9OcnSSM7r7T/bpBeKAMR3M/vaYJFd19w909wMy+cH26iRP6u6T\nk7wxycunbReTHN7dJ2Ty1/8bpssvT/LQ7j4xyZlJfn2VfX8kkx+Q35vJL6KHTJc/eLpu1plJPtTd\n90/yvzMJF4tJfjXJld19QnefkckP0BOS/FImP5DvVVWnrbKejWKex2zWbyV5ZXefkuTJSX5vZt19\nkzw6ySlJzqyqQ6a/xH4iyfcleWwmvxgXp79wLkry9O4+sbsXpvv45+4+KZOg+aK9qO9gdt8kr+3u\n45N8JcnzMzmGT549ht399nz7a/Oa7j5leuwPr6rHr6Xjqrpzkhcn+eHp63txkl+erl7M8q/7mUne\nP33/vT1jv/92We9jeGmSH5g+fmiSz2Ty/npQko9Ply8mWayqrUl+N8njp8fy6Ezea59Pck6S35zW\ncmEmx+zo7j4tk2C6Y19eFA4sI4Hsb59OcnZV7Ujyp0muy+Qv+PdXVTIZabp6pv1bk6S7P1xVR1bV\nkUnukOTNVXWfTH4IHbbKvj+c5IeSfD6TXzg/V1V3S/Kv3f21af+7PDTJE6d9/1lV/et0+XJ/NX+i\nu69Okqr6ZJJj8+2hciOb5zGb9agk3zNznG4/HaVcTPLu7r4xybVV9U+Z/BI6Lck7u/uGJDcsc+7Y\n0mP5jun3SzIJj5vJF7p713TcH2QSyu6f5PzdHMPZ1+aRVfUrSW6b5I6ZTDP+6Sr73ZLJH1nHJ/no\ntK9bJ/noTJvlXvfTkvynJOnu9w3+/ttlXY9hd99UVVdW1f2SPDDJb2by8/OQTH6WzvZzvyR/391X\nztT3c7upZTHJO6d9XF5VR61UB/MlBLJfdffnquqEJI9L8mtJPpjJ1MKpK295Cy9L8oHufuJ0uuGC\nVW73oUz+et6ZyQ/QJ2YyovSh3bRf7TliX595fHM22ftmzsds1pYkD5qGum+a/gKcXbbrGCw9z2/p\n8Vx6btKu47jpjmFu+W/dkslI0krHcDFJpiM8r01yUndfVVVnZjKlv1bnd/fTd7Nud6+7998tzeMY\nfijJjya5MckHkrwpkxnCpSPlS99Lezp2s+9X5+IexEwHs19Nr1hb6O63JDk7k+mFO1fVg6frD6uq\n42c2eep0+UOSXNfdX8nkvJRdf/E+a7V9d/cXk9w5yX26++8zOW/lRVk+BH4oydOnfT82kxPSk8n5\nhLdfbZ+bwTyPWW75C+LP861zilJV37/CdouZjAY9oapuU1VHZBJid7l+WtMojtl1vDL5f/3xJHfZ\nzTGcfW12hYVrp6/hU9bY7+K0r9Oq6t7Tvm5XVcftYbuPJPnJaftHZ+D334x5HMMPJ3lhJucRfjnJ\nnZLct7svm2mzmOSKJMdW1b2my542s37kY7bhCYHsbw9I8pfTE7tfMv16SpKzplM5lyb5wZn2C1V1\nSZL/N8nPTJf9RpL/Z7r8kNzyr9A9XXn28SS7Lki4MJMLPy6c2XbX9i/N5OTlv85kxPDzSdLd1yb5\nSFV9pqrOyvJX1222q9/mecxmX99fTHJyVX2qqi7L5ITz3e6juy9K8q5MprP/LJNzmv5tuvrcJOdU\n1SXTkZLd9blZdJJfqKrPZjI1/+pMRsGXO4bnZvraJFlI8vpMpg/fm+Qvl+x3j6/TNDycnuStVfWp\nTKaCa5mmS99/j66qz0zr/FKS6wd9/+0yj2P4iST/Id/6Q/lTmbyPbllY99czmf59d1VdnOSamf2e\nl+SJ0/farvOw1/Izmznasrjo+DAfVfXBTK7WvWTetbA6B9sxq6rbTa9CvW2Sv0jys939yXnXtZ6m\nV5aeN70oYEOoqlsnubm7b67JPeZeO72oaEgb8RiyOWzWcyuAMfzudIpsa5JzRwuAMzbaX/PHJHlb\nVd0qk/PHfnbO9RwMNtoxZBMwEsiGU1WnZ3LLiFkXdvcL5lAOq+CYbVxV9Y5M7tE464zuPn8e9bB2\njiG7IwQCAAzIhSEAAAMSAgEABiQEAgAMSAgEABiQEAgAMKD/H0Y1O+kdFKdkAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10af5be10>"
]
}
],
"prompt_number": 161
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Wi\u0119cej o pakiecie Seaborn](http://stanford.edu/~mwaskom/software/seaborn)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"R\u00f3\u017cne IPython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [San Francisco's Drug Geography](http://lmart999.github.io/2015/02/28/gis/)\n",
" * [notebook here](http://nbviewer.ipython.org/github/lmart999/GIS/blob/master/SF_GIS_Crime.ipynb)\n",
"* [Reproducable research](http://nbviewer.ipython.org/github/batterio/intro_ipython_notebook/blob/master/notebooks/example/analysis.ipynb) - i og\u00f3lnie dobre [wprowadzenie do IPython Notebook](http://nbviewer.ipython.org/github/batterio/intro_ipython_notebook/blob/master/notebooks/index.ipynb)\n",
"* [M\u00f3j wyk\u0142ad z uczenia maszynowego na przyk\u0142adzie irys\u00f3w](http://nbviewer.ipython.org/gist/stared/4738143)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Kilka s\u0142\u00f3w wi\u0119cej"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Pakiety istalujemy tak (z lini polece\u0144 lub w IPythonie poprzedzaj\u0105c `!`):\n",
" * `pip install jakis_pakiet`\n",
" * `conda install jakis_pakiet`\n",
"* [StackOverflow](stackoverflow.com) - tu warto pisa\u0107 pytania\n",
"* *Zen of Python* i [Style Guide](https://www.python.org/dev/peps/pep-0008/) \n",
"* Jest wolny, ale mo\u017cna go przyspiesza\u0107\n",
" * [numba](http://numba.pydata.org/), `numpy`, `cython`, wywo\u0142ywanie kodu w `C`\n",
"* [GitHub](https://github.com/) - tak naj\u0142atwiej dzili\u0107 si\u0119 kodem\n",
" * np. kod tego zeszytu jest tu: https://gist.github.com/stared/0fb6257c0b14aac2b9fe\n",
"* [nbviewer](http://nbviewer.ipython.org/) - pozazywaczka IPython Notebook\u00f3w\n",
" * np. to co widzimy jest na http://nbviewer.ipython.org/gist/stared/0fb6257c0b14aac2b9fe\n",
"\n",
"* Wprowadzenia\n",
" * [Software for scientists](https://gist.github.com/stared/9130888) - zbi\u00f3r link\u00f3w do program\u00f3w przydatnych naukowcom i tutoriali\n",
" * [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/gist/rpmuller/5920182)\n",
" * [Kr\u00f3tki wst\u0119p do Pandas](http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/)\n",
" * [Wykresy w Matplotlib i Pandas](http://nbviewer.ipython.org/gist/fonnesbeck/5850463)\n",
" * [Pandas Pivot Table Explained](http://pbpython.com/pandas-pivot-table-explained.html)\n",
" * Zaawansowane: [IPython Cookbook](http://ipython-books.github.io/cookbook/) - czytam, polecam, mog\u0119 po\u017cyczy\u0107\u00a0wersj\u0119 papierow\u0105 "
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Deser"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(\"http://imgs.xkcd.com/comics/python.png\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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5PpxcHfebUbb2gGj2+M8a/ftUDcGveCIFSJs0Pxc8u1wCCBk6V2OjB+kIwkuihxe9A9wr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chfus4XwLliVR9SZxd4Ah9LDU4icVAxe2By3STgXUqIEnx7FXuCtEu5N5zPfwbNFDE5lXuF16rw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\nERB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DrrlMqZmlvHNLLyth9xA+6HtEwEt3GzUVZWUxL1T9LLDJw58jJmFSreUg3esAdNKO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\nhWVYqt1Wf4tucN5A2pluztGzEGz1lKSQa66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"prompt_number": 165,
"text": [
"<IPython.core.display.Image at 0x10c47fe90>"
]
}
],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import this"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The Zen of Python, by Tim Peters\n",
"\n",
"Beautiful is better than ugly.\n",
"Explicit is better than implicit.\n",
"Simple is better than complex.\n",
"Complex is better than complicated.\n",
"Flat is better than nested.\n",
"Sparse is better than dense.\n",
"Readability counts.\n",
"Special cases aren't special enough to break the rules.\n",
"Although practicality beats purity.\n",
"Errors should never pass silently.\n",
"Unless explicitly silenced.\n",
"In the face of ambiguity, refuse the temptation to guess.\n",
"There should be one-- and preferably only one --obvious way to do it.\n",
"Although that way may not be obvious at first unless you're Dutch.\n",
"Now is better than never.\n",
"Although never is often better than *right* now.\n",
"If the implementation is hard to explain, it's a bad idea.\n",
"If the implementation is easy to explain, it may be a good idea.\n",
"Namespaces are one honking great idea -- let's do more of those!\n"
]
}
],
"prompt_number": 166
}
],
"metadata": {}
}
]
}
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