\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"\n",
"Örneğin bu interactive kodları girilmeden aşağıdaki kod çalışıtırılırsa sadece 2 sonucunu alırken\n",
"\n",
"a=1\n",
"b=2\n",
"a\n",
"b\n",
"\n",
"\n",
"Yukarıdaki iki satırlık kod girilirse hem 1 hem 2 sonucu görünür."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-15T15:32:54.059281Z",
"start_time": "2021-05-15T15:32:54.055292Z"
}
},
"outputs": [],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a=1\n",
"b=2\n",
"a\n",
"b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Çeşitli operatörler"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Volume in drive E is MYHDD\n",
" Volume Serial Number is 9E4D-C087\n",
"\n",
" Directory of E:\\OneDrive\\Uygulama GeliŸtirme\n",
"\n",
"09.01.2020 00:23 \n", "Yukarıdaki code hücresiyidi, bu ise markdown hücresidir
\n", "$$ ile yazım formülü ortalar
\n", "\\$ ile yazımda ise sola dayalı ve küçük
\n", "\n", "$$\\Pi p(n)$$\n", "\n", "$\\frac \\pi 2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Diğer ifadeler\n", "\n", "\\frac ifadesi ile kesirler
\n", "_ karakteri ile indis(subscript)
\n", "\\pi, \\sum, \\bar ve \\hat gibi özel ifadeler
\n", "\n", "$$\\sum p(n)$$\n", "\n", "$$y_i, \\bar{y}_i, \\hat{y}_i$$\n", "\n", "$$TSS=\\sum_{i=1}^n(y_i-\\bar{y}_i)^2$$\n", "\n", "$$P(c|x)=\\frac {P(x|c).P(c)}{P(x)}$$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$" ], "text/plain": [ "
\n", "**iki yıldız ile bold yapma**
\n", "*tek yıldız ile italik yapma*
\n", "`sağa dönük tek tırnak ile` vurgulu yapma. Bu karakteri Alt+96 ile yazabilirsiniz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Paragraf, satır geçme ve html kullanımı" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Üst paragraf\n", "\n", "İki kere entera basarak paragraf açabilirsiniz(bu satırda olduğu gibi. hücreye çift tıklayın ve görün)
\n", "veya bi satırın sonunda \"br\" tagi ekleyerek bir alt satıra geçebilrsiniz. (bu satırda olduğu gibi. hücreye çift tıklayın ve görün)\n", "\n", "NOT:Bu kolaylıkları öğrenene/keşfedene kadar ben bu paragraf ve bi alt satır işlerini Markdown değil de Raw NB Convert hücre tipi ile yapıyordum. Bunu öğrendikten sonra Raw NB Covnert tipli hücrelere pek ihtiyaç duymadım." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Naming convention" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Pep 008 python\" araması yapın ve tüm detayları görün. aşağıda sadece \"_\" kullanımını koydum" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ad\n", "soyad\n" ] } ], "source": [ "_privatevariable=3\n", "#_privatemethod()\n", "list_=[1,2,3] #rezerv keylerin sonuna _ gelir. list diye bir değişken adı kullanamayız, list_ olabilir\n", "dict_={\"ad\":\"ali\",\"soyad\":\"yılmaz\"}\n", "for x,_ in dict_.items(): #ilgilenmediğimiz değerler için \"_\"\n", " print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bununu dışında sklearn(Machine Learning kütüphanesi)de bazı propertylerin _ ile bittiğini görürsünüz. bunların anlamı da, ilgili propertyle ulaşmak için öncelikle modelin eğitilmesi(fit edilmesi) gerekmekte, eğitilmemiş modelde bu bilgiye ulaşamak anlamsızdır demek." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modül, Package, Class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bu 3 kavram hiyerarşik olarak şöyle sıralanır. Package>module>class.\n", "\n", "Yani her sınıf bir modül içindedir. modüller py uzantılı dosylardır. birkaç modül biraraya gelerek bir paket oluşturur. Ör: DataScience çalışmalarında pandas paketi kullanılır, bu anaconda sürümü ile birlikte gelir.\n", "\n", "Bir de kütüphane kavramı var. Python'daki kütüphane(library) kavramı C/C# gibi dillerdeki dll dosyalarından farklı bir anlama sahiptir. Burada daha çok belirli modüllerin veya package'ların biraraya gelerek kavramsal bir topluluk oluşturumasından bahsediyoruz. Ör:Makine öğrenme kütüphanesi gibi. Bu arada ille bir fiziksel karşılık aranacaksa package'lar gibi düşünülebilirler." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**DİKKAT:Kafa karışıtırıcı bir paragraf, isterseniz şimdilik atlayın ma sonra mutlaka glin ve özellikle alttaki linki okuyun**\n", "\n", "Yeni bir paket kurmak istediğinizde;\n", "\n", "conda install paketadı demeniz yeterlidir. Eğer bu yeterli gelmezse;
\n", "pip install paketadı diyebilirsiniz. Bunu bazen başında ! olacak şekilde yapmak gerekebiliyor. ! varsa aslında komut satırı komutu gibi çalışmış oluyor, ! yoksa da % işareti varmış gibi çalışıyor. % işareti olması automagic olarak çalışması anlmamına gleiyor. automagic konusunu araştırrsanız anlarsınız.\n", "\n", "Ama şimdi bu yukarıdaki iki yöntemi de unutun ve aşağıdaki şu linke bakın. kurulumları nasıl yapmanız gerektiğini göreceksiniz.\n", "\n", "Daha detaylı bilgiyi aşağıdaki linkten edinebilirsiniz:
\n", "https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-10-06T20:09:52.718668Z", "start_time": "2020-10-06T20:09:52.712650Z" } }, "outputs": [], "source": [ "#örnek olarak şimdi DeepLearning paketi olan kerası kuruyorum\n", "#conda install keras" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Package Version \n", "---------------------------------- -------------------\n", "-umpy 1.16.2 \n", "absl-py 0.9.0 \n", "alabaster 0.7.12 \n", "anaconda-client 1.7.2 \n", "anaconda-navigator 1.9.7 \n", "anaconda-project 0.8.3 \n", "argh 0.26.2 \n", "asn1crypto 1.3.0 \n", "astor 0.8.0 \n", "astroid 2.3.3 \n", "astropy 4.0 \n", "atomicwrites 1.3.0 \n", "attrs 19.3.0 \n", "autopep8 1.4.4 \n", "Babel 2.8.0 \n", "backcall 0.1.0 \n", "backports.os 0.1.1 \n", "backports.shutil-get-terminal-size 1.0.0 \n", "bcrypt 3.1.7 \n", "beautifulsoup4 4.8.2 \n", "bitarray 1.2.1 \n", "bkcharts 0.2 \n", "bleach 3.1.0 \n", "blinker 1.4 \n", "bokeh 1.4.0 \n", "boto 2.49.0 \n", "Bottleneck 1.3.2 \n", "cachetools 3.1.1 \n", "certifi 2019.11.28 \n", "cffi 1.14.0 \n", "chardet 3.0.4 \n", "Click 7.0 \n", "cloudpickle 1.3.0 \n", "clyent 1.2.2 \n", "colorama 0.4.3 \n", "comtypes 1.1.7 \n", "conda 4.8.2 \n", "conda-build 3.17.8 \n", "conda-package-handling 1.6.0 \n", "conda-verify 3.1.1 \n", "confuse 1.0.0 \n", "contextlib2 0.6.0.post1 \n", "cryptography 2.8 \n", "cycler 0.10.0 \n", "Cython 0.29.15 \n", "cytoolz 0.10.1 \n", "dask 2.11.0 \n", "decorator 4.4.1 \n", "defusedxml 0.6.0 \n", "diff-match-patch 20181111 \n", "distributed 2.11.0 \n", "docutils 0.16 \n", "entrypoints 0.3 \n", "et-xmlfile 1.0.1 \n", "fastcache 1.1.0 \n", "filelock 3.0.12 \n", "flake8 3.7.9 \n", "Flask 1.1.1 \n", "fsspec 0.6.2 \n", "future 0.18.2 \n", "gast 0.2.2 \n", "gevent 1.4.0 \n", "glob2 0.7 \n", "google-auth 1.11.2 \n", "google-auth-oauthlib 0.4.1 \n", "google-pasta 0.1.8 \n", "graphviz 0.10.1 \n", "greenlet 0.4.15 \n", "grpcio 1.27.2 \n", "h5py 2.10.0 \n", "HeapDict 1.0.1 \n", "html5lib 1.0.1 \n", "htmlmin 0.1.12 \n", "hypothesis 5.5.4 \n", "idna 2.8 \n", "imageio 2.6.1 \n", "imagesize 1.2.0 \n", "importlib-metadata 1.5.0 \n", "intervaltree 3.0.2 \n", "ipykernel 5.1.4 \n", "ipython 7.12.0 \n", "ipython-genutils 0.2.0 \n", "ipywidgets 7.5.1 \n", "isort 4.3.21 \n", "itsdangerous 1.1.0 \n", "jdcal 1.4.1 \n", "jedi 0.14.1 \n", "Jinja2 2.11.1 \n", "joblib 0.14.1 \n", "json5 0.9.1 \n", "jsonschema 3.2.0 \n", "jupyter 1.0.0 \n", "jupyter-client 5.3.4 \n", "jupyter-console 6.1.0 \n", "jupyter-contrib-core 0.3.3 \n", "jupyter-contrib-nbextensions 0.5.1 \n", "jupyter-core 4.6.1 \n", "jupyter-helpers 0.1.1 \n", "jupyter-highlight-selected-word 0.2.0 \n", "jupyter-latex-envs 1.4.4 \n", "jupyter-nbextensions-configurator 0.4.1 \n", "jupyterlab 1.2.6 \n", "jupyterlab-server 1.0.6 \n", "Keras 2.3.0 \n", "Keras-Applications 1.0.8 \n", "Keras-Preprocessing 1.1.0 \n", "keyring 21.1.0 \n", "kiwisolver 1.1.0 \n", "lazy-object-proxy 1.4.3 \n", "libarchive-c 2.8 \n", "llvmlite 0.31.0 \n", "locket 0.2.0 \n", "lxml 4.5.0 \n", "Markdown 3.1.1 \n", "MarkupSafe 1.1.1 \n", "matplotlib 3.1.3 \n", "mccabe 0.6.1 \n", "menuinst 1.4.16 \n", "missingno 0.4.2 \n", "mistune 0.8.4 \n", "mkl-fft 1.0.15 \n", "mkl-random 1.1.0 \n", "mkl-service 2.3.0 \n", "mock 4.0.1 \n", "more-itertools 8.2.0 \n", "mpmath 1.1.0 \n", "msgpack 0.6.1 \n", "multipledispatch 0.6.0 \n", "navigator-updater 0.2.1 \n", "nbconvert 5.6.1 \n", "nbformat 5.0.4 \n", "networkx 2.4 \n", "nltk 3.4.5 \n", "nose 1.3.7 \n", "notebook 6.0.3 \n", "numba 0.48.0 \n", "numexpr 2.7.1 \n", "numpy 1.18.1 \n", "numpydoc 0.9.2 \n", "oauthlib 3.1.0 \n", "olefile 0.46 \n", "openpyxl 3.0.3 \n", "opt-einsum 3.1.0 \n", "packaging 20.1 \n", "pandas 1.0.1 \n", "pandas-profiling 2.3.0 \n", "pandocfilters 1.4.2 \n", "paramiko 2.7.1 \n", "parso 0.5.2 \n", "partd 1.1.0 \n", "path 13.1.0 \n", "pathlib2 2.3.5 \n", "pathtools 0.1.2 \n", "patsy 0.5.1 \n", "pep8 1.7.1 \n", "pexpect 4.8.0 \n", "phik 0.9.8 \n", "pickleshare 0.7.5 \n", "Pillow 7.0.0 \n", "pip 20.0.2 \n", "pkginfo 1.5.0.1 \n", "plotly 4.2.1 \n", "pluggy 0.13.1 \n", "ply 3.11 \n", "prometheus-client 0.7.1 \n", "prompt-toolkit 3.0.3 \n", "protobuf 3.11.4 \n", "psutil 5.6.7 \n", "ptvsd 4.3.2 \n", "py 1.8.1 \n", "pyasn1 0.4.8 \n", "pyasn1-modules 0.2.7 \n", "pycodestyle 2.5.0 \n", "pycosat 0.6.3 \n", "pycparser 2.19 \n", "pycrypto 2.6.1 \n", "pycurl 7.43.0.5 \n", "pydocstyle 4.0.1 \n", "pydotplus 2.0.2 \n", "pyflakes 2.1.1 \n", "Pygments 2.5.2 \n", "PyJWT 1.7.1 \n", "pylint 2.4.4 \n", "PyNaCl 1.3.0 \n", "pyodbc 4.0.0-unsupported \n", "pyOpenSSL 19.1.0 \n", "pyparsing 2.4.6 \n", "pyreadline 2.1 \n", "pyrsistent 0.15.7 \n", "PySocks 1.7.1 \n", "pytest 5.3.5 \n", "pytest-arraydiff 0.3 \n", "pytest-astropy 0.8.0 \n", "pytest-astropy-header 0.1.2 \n", "pytest-doctestplus 0.5.0 \n", "pytest-openfiles 0.4.0 \n", "pytest-pylint 0.14.1 \n", "pytest-remotedata 0.3.2 \n", "python-dateutil 2.8.1 \n", "python-jsonrpc-server 0.3.4 \n", "python-language-server 0.31.7 \n", "pytz 2019.3 \n", "PyWavelets 1.1.1 \n", "pywin32 227 \n", "pywin32-ctypes 0.2.0 \n", "pywinpty 0.5.7 \n", "PyYAML 5.3 \n", "pyzmq 18.1.1 \n", "QDarkStyle 2.8 \n", "QtAwesome 0.6.1 \n", "qtconsole 4.6.0 \n", "QtPy 1.9.0 \n", "requests 2.22.0 \n", "requests-oauthlib 1.3.0 \n", "retrying 1.3.3 \n", "rope 0.16.0 \n", "rsa 4.0 \n", "Rtree 0.9.3 \n", "ruamel-yaml 0.15.87 \n", "scikit-image 0.16.2 \n", "scikit-learn 0.22.1 \n", "scipy 1.4.1 \n", "seaborn 0.10.0 \n", "Send2Trash 1.5.0 \n", "setuptools 45.2.0.post20200210\n", "simplegeneric 0.8.1 \n", "singledispatch 3.4.0.3 \n", "six 1.14.0 \n", "snowballstemmer 2.0.0 \n", "sortedcollections 1.1.2 \n", "sortedcontainers 2.1.0 \n", "soupsieve 1.9.5 \n", "Sphinx 2.4.0 \n", "sphinxcontrib-applehelp 1.0.1 \n", "sphinxcontrib-devhelp 1.0.1 \n", "sphinxcontrib-htmlhelp 1.0.2 \n", "sphinxcontrib-jsmath 1.0.1 \n", "sphinxcontrib-qthelp 1.0.2 \n", "sphinxcontrib-serializinghtml 1.1.3 \n", "sphinxcontrib-websupport 1.2.0 \n", "spotipy 2.4.4 \n", "spyder 4.0.1 \n", "spyder-kernels 1.8.1 \n", "SQLAlchemy 1.3.13 \n", "statsmodels 0.9.0 \n", "sympy 1.5.1 \n", "tables 3.6.1 \n", "tblib 1.6.0 \n", "tensorboard 2.1.0 \n", "tensorflow 1.15.0 \n", "tensorflow-estimator 1.15.1 \n", "termcolor 1.1.0 \n", "terminado 0.8.3 \n", "testpath 0.4.4 \n", "toolz 0.10.0 \n", "tornado 6.0.3 \n", "tqdm 4.42.1 \n", "traitlets 4.3.3 \n", "typed-ast 1.4.0 \n", "ujson 1.35 \n", "unicodecsv 0.14.1 \n", "urllib3 1.25.8 \n", "watchdog 0.10.2 \n", "wcwidth 0.1.8 \n", "webencodings 0.5.1 \n", "Werkzeug 0.16.1 \n", "wheel 0.34.2 \n", "widgetsnbextension 3.5.1 \n", "win-inet-pton 1.1.0 \n", "win-unicode-console 0.5 \n", "wincertstore 0.2 \n", "wordcloud 1.5.0 \n", "wrapt 1.11.2 \n", "xlrd 1.2.0 \n", "XlsxWriter 1.2.7 \n", "xlwings 0.17.1 \n", "xlwt 1.3.0 \n", "yapf 0.28.0 \n", "zict 1.0.0 \n", "zipp 2.2.0 \n" ] } ], "source": [ "#mevcut packageların listesi\n", "!pip list" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#bi kapet hakkında bilgi\n", "!pip show numpy" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "django-matplotlib (0.1) - Matplotlib field for Django\n", "matplotlib-stream (1.0.0) - GCPDS: matplotlib stream\n", "hangar-matplotlib (0.0.3) - Matplotlib plugin for hangar\n", "matplotlib-helpers (0.1.post6) - Helper functions, etc. for matplotlib\n", "matplotlib-terminal (0.1a2) - Render matplotlib plots in terminal.\n", "matplotlib-colorbar (0.4.0) - Artist for matplotlib to display a color bar\n", "matplotlib-scalebar (0.6.2) - Artist for matplotlib to display a scale bar\n", "jirafs-matplotlib (1.0.3) - Render matplotlib charts in your JIRA issues\n", "matplotlib-pgfutils (1.3.1) - Utilities for generating PGF figures from Matplotlib\n", "matplotlib-subsets (1.0) - Functions for plotting area-proportional hierarchical subset diagrams in matplotlib.\n", "matplotlib-venn (0.11.5) - Functions for plotting area-proportional two- and three-way Venn diagrams in matplotlib.\n", "matplotlib (3.2.1) - Python plotting package\n", " INSTALLED: 3.2.1 (latest)\n", "matplotlib-label-lines (0.3.8) - Label lines in matplotlib.\n", "matplotlib-backend-qtquick (0.0.6) - A QtQuick backend for matplotlib\n", "fc-simesh-matplotlib (0.0.3) - Matplotlib add-on to the fc_simesh package\n", "japanize-matplotlib (1.1.2) - matplotlibのフォント設定を自動で日本語化する\n", "eddington-matplotlib (0.0.4) - A laboratory utility library for fitting and presenting data\n", "timeseriesql-matplotlib (0.0.2) - A plotting backend for the TimeSeriesQL library\n", "matplotlib-hep (0.1.0) - Module for making data analysis in High Energy Physics with Python more convenient\n", "ing-theme-matplotlib (0.1.5) - ING styles for common plotting libraries\n", "matplotlib-venn-wordcloud (0.2.5) - Create a Venn diagram with word clouds corresponding to each subset.\n", "mplstereonet (0.5) - Stereonets for matplotlib\n", "prettyfigure (0.1.1) - Styles for matplotlib\n", "PlotDraw (0.0.1) - interface of Matplotlib\n", "pyplottr (0.1.0) - Matplotlib for humans\n", "mpl-tools (0.1.10) - Tools for Matplotlib\n", "mplutils (0.3.0) - Tools for matplotlib\n", "imatplotlib (0.0.1) - innovata-matplotlib\n", "mobobob (0.5.1) - An alias for matplotlib\n", "mpltools (0.2.0) - Tools for Matplotlib\n", "mplstyle (0.1.2) - A simple API for setting matplotlib styles in matplotlib.\n", "matplotlibXtns (20.5) - matplotlib extension functions.\n", "kwplot (0.4.5) - A wrapper around matplotlib\n", "plopy (1.5.5) - GUI for matplotlib graphing\n", "celluloid (0.2.0) - Easy matplotlib animation.\n", "contours (0.0.2) - Contour calculation with Matplotlib.\n", "mpl-font (1.0.1) - Font selector for matplotlib\n", "plotcsv (0.1.2) - Plot CSV with matplotlib\n", "ipympl (0.5.6) - Matplotlib Jupyter Extension\n", "mpl-toolkits.clifford (0.0.3) - Matplotlib tools for clifford\n", "pycbc-mpld3 (0.3.dev0) - D3 Viewer for Matplotlib\n", "matplotlylib (0.1.0) - Matplotlib to Plotly Converter\n", "ficus (0.5) - context managers for matplotlib\n", "figurefirst (0.0.6) - Matplotlib plotting stuff\n", "mplhep (0.1.21) - Matplotlib styles for HEP\n", "mpld3 (0.3) - D3 Viewer for Matplotlib\n", "animateimages (0.2.4) - Animation of matplotlib images\n", "oscplotlib (0.1.0) - OSC1337 wrapper for Matplotlib\n", "matplotlibaux (20.2.23.0) - matplotlib auxiliary stuff\n", "mpl_style_gallery (0.1) - Gallery for Matplotlib stylesheets\n", "matplotvideo (0.0.2) - Syncing matplotlib and video\n", "mplsvds (0.1.7) - matplotlib styling for svds\n", "chainplot (0.2.1) - A(nother) matplotlib wrapper\n", "plotmark (1.0.0) - Plot markers for Matplotlib\n", "dufte (0.2.5) - Clean matplotlib plots\n", "mpl4qt (2.2.0) - Matplotlib widget for PyQt\n", "mpt-multiplot (0.0.2) - Convenient matplotlib subplot grids\n", "psynlig (0.0.6.dev0) - A package for creating plots with matplotlib.\n", "wraplot (0.1.1) - A simple python wrapper of matplotlib\n", "latex-plot-utils (1.0.0) - Helpers for Matplotlib plots in LaTeX\n", "mpl-tune (0.1) - A collection of matplotlib tuning scripts\n", "GooseMPL (0.4.1) - Style and extension functions for matplotlib\n", "panel-plots (0.1.0) - Organize matplotlib plots in panels\n", "mpltern (0.3.0) - Ternary plots as projections of Matplotlib\n", "mpls (0.2.0) - An open library of matplotlib styles\n", "fishbowl (0.3.1) - Customizable matplotlib style extension\n", "mpl-sns-viz (0.1.1) - Creates matplotlib and seaborn visuals\n", "mpl-finance (0.10.1) - Finance plots using matplotlib\n", "hpcplot (0.1) - A matplotlib wrapper for HPC Plots\n", "nc-time-axis (1.2.0) - cftime support for matplotlib axis\n", "figplotter (0.0.3) - A figure plotter using matplotlib\n", "MatPlotTheme (0.1.2) - MatPlotTheme is a theming library for MatPlotLib.\n", "feynman (2.0.2) - Feynman diagrams with python-matplotlib.\n", "plot_max (0.24) - high level to use matplotlib\n", "vapeplot (0.0.8) - matplotlib extension for vaporwave aesthetics\n", "grid-strategy (0.0.1) - A package for organizing matplotlib plots.\n", "quick-plot (0.0.2) - convenience wrapper around matplotlib\n", "plotje (1.1) - Simple matplotlib styling function.\n", "ezplot (0.1.0a4) - Remote Procedure interface to Matplotlib\n", "plotformatter (0.1.0) - Creates matplotlib and seaborn visuals\n", "hipsterplot (0.1) - because matplotlib is too mainstream\n", "atlas-mpl-style (0.11.0) - ATLAS style for Matplotlib 3.0+\n", "blume (0.0.1) - Better looking tables for matplotlib\n", "umPlot (0.0.1) - with matplotlib and basemap plot data\n", "python-controlchart (0.2.0) - Creation of control charts with matplotlib\n", "tgext.matplotrender (0.0.1) - Renderer to expose matplotlib figures\n", "svgpath2mpl (0.2.1) - SVG path parser for matplotlib" ] } ], "source": [ "# bir paketi sorgulama, var mı yok mu, hangi versiyonu kurulu v.s\n", "!pip search matplotlib" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already up-to-date: numpy in c:\\users\\volka\\anaconda3\\lib\\site-packages (1.18.1)\n" ] } ], "source": [ "#eğer, daha üst sürümü varsa ona upgrade etmek için\n", "!pip install numpy --upgrade \n", "#veya conda update numpy" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "#yeni sürümle çalışıtırma sıkıntısı yaşarsanız eski sürüme dönebilirsiniz\n", "#!pip install --upgrade paketadı=versiyonno #(Ör:pip install --upgrade werkzeug==0.12.2)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "#paketi komple kaldırmak için\n", "#!pip uninstall paketadı" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.7.3\n" ] } ], "source": [ "#python versiyonu öğrenmek\n", "!python --version" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "#python'ın versiyonunu yükseltmek\n", "#!conda update python" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['C:\\\\Users\\\\volka\\\\Documents\\\\GitHub\\\\PythonRocks',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\python37.zip',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\DLLs',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3',\n", " '',\n", " 'C:\\\\Users\\\\volka\\\\AppData\\\\Roaming\\\\Python\\\\Python37\\\\site-packages',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages\\\\Pythonwin',\n", " 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n", " 'C:\\\\Users\\\\volka\\\\.ipython']" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#packageler hangi klasörlere kuruluyor\n", "import sys\n", "sys.path" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['C:\\\\Users\\\\volka\\\\Anaconda3', 'C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages']\n" ] } ], "source": [ "import site\n", "print(site.getsitepackages())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Özel kurulum şekillleri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In IPython (jupyter) 7.3 and later, there is a magic %pip and %conda command that will install into the current kernel (rather than into the instance of Python that launched the notebook).\n", "\n", "%pip install geocoder\n", "\n", "! pip install --user
\n", "* Modüldeki herşeyi dahil etme: from x import * , kullanımı: falanca(...)
\n", "* Tek birşeyi dahil etme: from x import falanca. falanca doğrudan kullanılabilir, x.falanca demeye gerek yok(üsstekinden farkı daha az şey importladık)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOT: Performans açısından mümkün mertebe az şey import etmeye çalışın." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from math import sqrt #math modülünden sqrt fonksiyonu\n", "kök=sqrt(16)\n", "kök" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Volume in drive E is MYHDD\n", " Volume Serial Number is 9E4D-C087\n", "\n", " Directory of E:\\OneDrive\\Uygulama GeliŸtirme\n", "\n", "16.01.2020 20:42
\n", "for, foreach şeklindedir, klasik for yok. onun yerine range fonksiyonundan yararlanılabilir." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "apple\n", "banana\n", "cherry\n" ] } ], "source": [ "fruits = [\"apple\", \"banana\", \"cherry\"]\n", "for x in fruits:\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "apple\n", "banana\n", "cherry\n" ] } ], "source": [ "#klasik for için range kullanımı.\n", "for i in range(len(fruits)):\n", " print(fruits[i])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v-o-l-k-a-n-" ] } ], "source": [ "#metinler de loop ile dolaşılabilir\n", "isim=\"volkan\"\n", "for i in isim:\n", " print(i,end=\"-\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "apple\n", "banana\n" ] } ], "source": [ "#döngüden çıkış\n", "fruits = [\"apple\", \"banana\", \"cherry\"]\n", "for x in fruits:\n", " print(x)\n", " if x == \"banana\":\n", " break" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "#while ile bir şart gerçekleş(me)tiği sürece döngüde kalırız\n", "i = 1\n", "while i < 6:\n", " print(i)\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "#belirli bir ara şart gerçekleşirse döngüden çıkabiliriz\n", "i = 1\n", "while i < 6:\n", " print(i)\n", " if i == 3:\n", " break\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " #\n", " ##\n", " ###\n", " ####\n", " #####\n", "######\n" ] } ], "source": [ "#hackerrank sitesindeki bir ödev\n", "def staircase(n):\n", " for i in range(n):\n", " print((n-i-1)*\" \"+\"#\"*(i+1))\n", "staircase(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Döngü içinde \"else\" kullanımı" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### for döngülerinde" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tüm liste bittiğinde son olarak bu kısım yürütülür" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "bitti\n" ] } ], "source": [ "for i in range(4):\n", " print(i)\n", "else:\n", " print(\"bitti\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 equals 2*5.0\n", "11 bir asal sayıdır\n", "12 equals 2*6.0\n", "13 bir asal sayıdır\n", "14 equals 2*7.0\n", "15 equals 3*5.0\n", "16 equals 2*8.0\n", "17 bir asal sayıdır\n", "18 equals 2*9.0\n", "19 bir asal sayıdır\n" ] } ], "source": [ "for num in range(10,20):\n", " for i in range(2,num):\n", " if num%i==0:\n", " j=num/i\n", " print(\"{} equals {}*{}\".format(num,i,j))\n", " break\n", " else:\n", " print(num,\" bir asal sayıdır\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### while döngülerinde" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "koşul sağlanmadığında yürütülür" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "4\n", "3\n", "2\n", "1\n", "artık sağlanmıyor\n" ] } ], "source": [ "n=5\n", "while n!=0:\n", " print(n)\n", " n-=1\n", "else:\n", " print(\"artık sağlanmıyor\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## içiçe döngülerden çıkış" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "içiçe döngü varsa, break ifadesi en içteki döngüden çıkar ve o bloktan sonraki ilk satırdan devam eder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### iç döngüden çıkış, dış döngüye devam" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T20:42:53.102424Z", "start_time": "2020-11-22T20:42:53.093449Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b ali\n", "b dursun\n", "b ıtır\n", "b emel\n", "b cemil\n", "b için iç döngüde eşleşme bulunamadı\n", "\n", "a ali\n", "ali ekleniyor ve iç döngüden çıkış yapılacak\n", "\n", "c ali\n", "c dursun\n", "c ıtır\n", "c emel\n", "c cemil\n", "cemil ekleniyor ve iç döngüden çıkış yapılacak\n", "\n", "d ali\n", "d dursun\n", "dursun ekleniyor ve iç döngüden çıkış yapılacak\n", "\n", "e ali\n", "e dursun\n", "e ıtır\n", "e emel\n", "emel ekleniyor ve iç döngüden çıkış yapılacak\n", "\n", "['ali', 'cemil', 'dursun', 'emel']\n" ] } ], "source": [ "liste=[]\n", "for x in list(\"bacde\"):\n", " for z in [\"ali\",\"dursun\",\"ıtır\",\"emel\",\"cemil\"]:\n", " print(x,z) #kontrol için\n", " if x in z:\n", " print(f\"{z} ekleniyor ve iç döngüden çıkış yapılacak\\n\")\n", " liste.append(z)\n", " break #bir kez ekledikten sonra çıkıyorum, o yüzden mükerrer ekleme olmuyor, comment/uncomment\n", " else:\n", " print(f\"{x} için iç döngüde eşleşme bulunamadı\\n\")\n", "print(liste)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### tüm döngüden çıkış" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T20:44:32.757763Z", "start_time": "2020-11-22T20:44:32.751738Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b ali\n", "b dursun\n", "b ıtır\n", "b emel\n", "b cemil\n", "dış döngüdeki b için tur tamamlandı,sonraki için devam\n", "\n", "k ali\n", "k dursun\n", "k ıtır\n", "k emel\n", "k cemil\n", "dış döngüdeki k için tur tamamlandı,sonraki için devam\n", "\n", "c ali\n", "c dursun\n", "c ıtır\n", "c emel\n", "c cemil\n", "cemil ekleniyor ve tüm döngüden çıkış yapılacak\n", "\n", "['cemil']\n" ] } ], "source": [ "#herhangi birinin olması yeterliyse, ilk gördüğümü ekleyip çıkayım\n", "liste=[]\n", "for x in list(\"bkcde\"): #a'yı k yapalım\n", " for z in [\"ali\",\"dursun\",\"ıtır\",\"emel\",\"cemil\"]:\n", " print(x,z) #kontrol için\n", " if x in z:\n", " print(f\"{z} ekleniyor ve tüm döngüden çıkış yapılacak\\n\")\n", " liste.append(z)\n", " break\n", " else:\n", " print(f\"dış döngüdeki {x} için tur tamamlandı,sonraki için devam\\n\")\n", " continue \n", " break #iç döngüden çıkıldığında buraya gelinir\n", " \n", "print(liste)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(11, 15, 26)]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#iki dizidekilerin toplamı 20den büyük olduğunda çık\n", "dizi=[[11,21,3],[5,15,6]]\n", "records=[]\n", "\n", "for j in dizi[0]:\n", " for i in dizi[1]: \n", " if j+i>20:\n", " records.append((j,i,j+i))\n", " break\n", " else:\n", " continue\n", " break\n", "\n", "records" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(11, 15, 26)]\n" ] } ], "source": [ "#2.yöntem. bi fonk içinte return kullanmak\n", "records=[]\n", "def myfonk():\n", " dizi=[[11,21,3],[5,15,6]] \n", " for j in dizi[0]:\n", " for i in dizi[1]: \n", " if j+i>20:\n", " records.append((j,i,j+i))\n", " return\n", "\n", "myfonk()\n", "print(records)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(11, 15, 26)]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#3.yöntem: exception\n", "records=[]\n", "try:\n", " dizi=[[11,21,3],[5,15,6]] \n", " for j in dizi[0]:\n", " for i in dizi[1]: \n", " if j+i>20:\n", " records.append((j,i,j+i))\n", " raise StopIteration\n", "except StopIteration: pass\n", "records" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Structures(Veri yapıları)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1]\n", "True\n" ] } ], "source": [ "liste=[0,1,2,3,4,5]\n", "liste.append(6)\n", "print(liste[:2]) #stringler gibi slicing yapılır\n", "print(3 in liste) #üyelik kontrolü" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "[0, 1, 2, 3, 4, 5]\n" ] } ], "source": [ "son=liste.pop() #son elemanı çıkarıp buna atar\n", "print(son)\n", "print(liste)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75, 78, 81, 84, 87, 90, 93, 96, 99]\n" ] } ], "source": [ "rangelist=list(range(0,100,3))\n", "print(rangelist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sıralama" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sort metodu bir listeyi kendi üstünde sıralar, sonuç olarak birşey döndürmez. yani sıralanmış listeyi bir değişkene atayamayız. sıralanmış halini başka bir değişkene atamak istersek sorted fonkisyonunu kullanırız." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 ----------\n", "1\n", " \n", "3 ----------\n", "2\n", " \n", "5 ----------\n", "['armut', 'elma', 'muz', 'muz', 'portakal', 'çilek', 'üzüm']\n", " \n", "7 ----------\n", "['üzüm', 'çilek', 'portakal', 'muz', 'muz', 'elma', 'armut']\n", " \n" ] } ], "source": [ "meyveler=[\"elma\",\"muz\",\"portakal\",\"çilek\",\"üzüm\",\"armut\",\"muz\"]\n", "printy(meyveler.index(\"muz\")) #ilk gördüğün indeksi\n", "printy(meyveler.count(\"muz\"))\n", "meyveler.sort()\n", "printy(meyveler)\n", "meyveler.reverse()\n", "printy(meyveler)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['üzüm', 'çilek', 'portakal', 'muz', 'muz', 'elma', 'armut']\n" ] } ], "source": [ "siralimeyveler=sorted(meyveler,reverse=True) #ayrıca tersten sırala demiş olduk. bu parametre normal sort metodunda da var\n", "print(siralimeyveler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuple" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List gibi ama değişmez yapılardır yani eleman eklenip çıkarılamaz. [] yerine () veya parantezsiz" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "
\n", "[x if ... else y for x in datastruct]" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['üzüm', 'muz', 'muz', 'elma']" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kısaisimlimeyveler=[x for x in meyveler if len(x)<5]\n", "kısaisimlimeyveler" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 3, 5, 7, 9]\n", "[1, '', 3, '', 5, '', 7, '', 9]\n" ] } ], "source": [ "liste=range(1,10)\n", "sadecetekler=[sayı for sayı in liste if sayı % 2 !=0] #tek if\n", "tekler=[sayı if sayı%2!=0 else \"\" for sayı in liste] #if-else\n", "print(sadecetekler)\n", "print(tekler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### içiçe(nested) list comprehension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "****syntax:[x for iç in dış for x in iç]****" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2 boyutlu bir matrisi düzleştirmek istiyorum
\n", "matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
\n", "Beklediğimiz çıktı: flatten_matrix = [1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n" ] } ], "source": [ "# 2-D List \n", "matrix = [[1, 2, 3], [4, 5], [6, 7, 8, 9]] \n", "\n", "flatten_matrix = [] \n", "\n", "for sublist in matrix: \n", "\tfor val in sublist: \n", "\t\tflatten_matrix.append(val) \n", "\t\t\n", "print(flatten_matrix) \n" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n" ] } ], "source": [ "# 2-D List \n", "matrix = [[1, 2, 3], [4, 5], [6, 7, 8, 9]] \n", "\n", "# Nested List Comprehension to flatten a given 2-D matrix \n", "flatten_matrix = [val for sublist in matrix for val in sublist] \n", "\n", "print(flatten_matrix) " ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Venus', 'Earth', 'Mars', 'Pluto']\n" ] } ], "source": [ "# 2-D List of planets \n", "planets = [['Mercury', 'Venus', 'Earth'], ['Mars', 'Jupiter', 'Saturn'], ['Uranus', 'Neptune', 'Pluto']] \n", "\n", "flatten_planets = [] \n", "\n", "for sublist in planets: \n", "\tfor planet in sublist: \n", "\t\t\n", "\t\tif len(planet) < 6: \n", "\t\t\tflatten_planets.append(planet) \n", "\t\t\n", "print(flatten_planets) " ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Venus', 'Earth', 'Mars', 'Pluto']\n" ] } ], "source": [ "flatten_planets = [planet for sublist in planets for planet in sublist if len(planet) < 6] \n", "print(flatten_planets)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Venus', 'Earth', 'Mars', 'Pluto']" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kısalar=[p for iç in planets for p in iç if len(p)<6]\n", "kısalar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Daha genel bir gösterim için https://stackoverflow.com/questions/18072759/list-comprehension-on-a-nested-list sayfasındaki gif animasyonlu açıklamaya bakınız" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matrisler ve matrislerde comprehension" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "7 ----------\n", "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", " \n", "8 ----------\n", "[1, 4, 7]\n", " \n", "9 ----------\n", "[[1, 4, 7], [2, 5, 8], [3, 6, 9]]\n", " \n", "10 ----------\n", "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n", " \n" ] } ], "source": [ "matris=[\n", " [1,2,3],\n", " [4,5,6],\n", " [7,8,9]\n", "]\n", "print(len(matris))\n", "printy([satır for satır in matris]) #satır satır\n", "printy([satır[0] for satır in matris]) #ilk sütun\n", "printy([[satır[i] for satır in matris] for i in range(3)]) #sütun sütun, transpozesi\n", "printy([x for iç in matris for x in iç]) #nested" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "amaç aşağıdakini elde etmek olsun\n", "
\n",
"matrix = [[0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4],\n",
" [0, 1, 2, 3, 4]]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]\n"
]
}
],
"source": [
"matrix = [] \n",
"\n",
"for i in range(5): \n",
"\t\n",
"\t# Append an empty sublist inside the list \n",
"\tmatrix.append([]) \n",
"\t\n",
"\tfor j in range(5): \n",
"\t\tmatrix[i].append(j) \n",
"\t\t\n",
"print(matrix) \n"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]\n"
]
}
],
"source": [
"# Nested list comprehension \n",
"matrix = [[j for j in range(5)] for i in range(5)] \n",
"\n",
"print(matrix) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalde böyle bi sınıf yok. list'i stack gibi kullanırız. append ve pop sayesinde. ilk giren ilk çıkar"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stack=[1,2,3]\n",
"stack.append(4)\n",
"stack.pop()\n",
"stack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Queue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bunu da istersek listten yaparız, ilk giren son çıkar. ama bunun için collections modülünde bi sınıf var"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"deque([2, 3, 4])\n"
]
}
],
"source": [
"from collections import deque\n",
"kuyruk=deque([1,2,3])\n",
"kuyruk.append(4)\n",
"sıradaki=kuyruk.popleft()\n",
"print(sıradaki)\n",
"print(kuyruk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dictionary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Key-value ikililerini tutarlar. Sırasızdırlar(EDIT:Python 3.7den itibaren girdilen sırayı korur), indeksle ulaşamayız. key'lerle valuelara ulaşırız veya döngü içinde dolanarak ikisine birden tek seferde de ulaşabiliriz."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Yaratım"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Klasik"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 ----------\n",
"dict_keys(['one', 'two'])\n",
" \n",
"6 ----------\n",
"dict_values(['bir', 'zwei'])\n",
" \n",
"7 ----------\n",
"dict_items([('one', 'bir'), ('two', 'zwei')])\n",
" \n",
"bir\n",
"N/A\n"
]
}
],
"source": [
"dict_={}\n",
"dict_[\"one\"]=\"bir\" #add,append, insert gibi bir metodu yok, direkt atanıyor\n",
"dict_[\"two\"]=\"iki\"\n",
"dict_[\"two\"]=\"zwei\"\n",
"printy(dict_.keys())\n",
"printy(dict_.values())\n",
"printy(dict_.items())\n",
"print(dict_[\"one\"])\n",
"#print(dict_[\"three\"]) # hata alır, almaması için get kullan\n",
"print(dict_.get(\"three\",\"N/A\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### dict metodu ile ikili elemanlardan oluşan bir yapıdan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bu ikili yapılar genelde zip veya enumerate olacaktır. bakınız ilgili fonksiyonar."
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n", "Reduce: elemanları sırayla gönderir, bir eritme mantığı var, her bir önceki elamnını sonucyla bir sonraki eleman işleme girer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Map" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 4, 9, 16, 25]" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "items=[1,2,3,4,5]\n", "def kareal(sayı):\n", " return sayı**2\n", "\n", "kareler=map(kareal,items) #lambdalı da olur. map(lambda x: x**2, items)\n", "list(kareler) #yazdırmak için liste çevir\n" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "6\n", "15\n", "28\n", "45\n", "66\n", "91\n", "120\n", "153\n" ] } ], "source": [ "#birden fazla veri yapısı da girebilir işleme\n", "range1=range(1,10)\n", "range2=range(1,20,2)\n", "\n", "mymap=map(lambda x,y:x*y,range1,range2)\n", "for i in mymap:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 6, 15, 28, 45, 66, 91, 120, 153]" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#comprehensionla da yapılabilir.\n", "çarpım=[x*y for x,y in zip(range1,range2)]\n", "çarpım" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o']" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "harfler=map(chr,range(97,112))\n", "list(harfler)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o']" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "harfler2=[chr(x) for x in range(97,112)]\n", "harfler2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reduce" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "362880" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from functools import reduce\n", "def faktoriyel(sayı1,sayı2):\n", " return sayı1*sayı2\n", "\n", "sayılar=range(1,10)\n", "fakt=reduce(faktoriyel,sayılar)\n", "fakt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Filter" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-12-04T17:50:33.788733Z", "start_time": "2020-12-04T17:50:33.783746Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18\n", "24\n", "32\n" ] } ], "source": [ "ages = [5, 12, 17, 18, 24, 32]\n", "\n", "def myFunc(x):\n", " if x < 18:\n", " return False\n", " else:\n", " return True\n", "\n", "adults = filter(myFunc, ages)\n", "\n", "for x in adults:\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-12-04T17:51:27.064547Z", "start_time": "2020-12-04T17:51:27.060518Z" } }, "outputs": [ { "data": { "text/plain": [ "[18, 24, 32]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#veya lambda ile\n", "list(filter(lambda x:x>=18,ages))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Enumerate" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 'Ocak'), (2, 'Şubat'), (3, 'Mart')]\n", "1 Ocak\n", "2 Şubat\n", "3 Mart\n" ] } ], "source": [ "aylar=[\"Ocak\",\"Şubat\",\"Mart\"]\n", "print(list(enumerate(aylar,start=1)))\n", "dict_=dict(enumerate(aylar,start=1))\n", "for k,v in dict_.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### All ve Any" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n", "True\n" ] } ], "source": [ "liste1=[True,True,False]\n", "liste2=[True,True,True]\n", "print(all(liste1))\n", "print(any(liste1))\n", "print(all(liste2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Date Time işlemleri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modüller ve üyeleri" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "import datetime as dt\n", "import time as t\n", "import timeit as tit\n", "import dateutil as du\n", "#import timer as tr #bu threadlerle ilgili kullanma, settimer ve killtimer var" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### datetime" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['MAXYEAR', 'MINYEAR', 'date', 'datetime', 'datetime_CAPI', 'sys', 'time', 'timedelta', 'timezone', 'tzinfo']\n" ] } ], "source": [ "print([i for i in dir(dt) if not \"__\" in i])" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-01-16 20:44:06.031080\n", "20\n", "2020-01-16\n", "2020\n", "2019-04-03\n" ] } ], "source": [ "print(dt.datetime.now())\n", "print(dt.datetime.now().hour) #saliseden yıla kadar hepsi elde edilebilir\n", "print(dt.date.today())\n", "print(dt.date.today().year)\n", "print(dt.date(2019,4,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### time (süre ölçümlerinde bunu kullancaz, bundaki time metodunu)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['_STRUCT_TM_ITEMS', 'altzone', 'asctime', 'clock', 'ctime', 'daylight', 'get_clock_info', 'gmtime', 'localtime', 'mktime', 'monotonic', 'monotonic_ns', 'perf_counter', 'perf_counter_ns', 'process_time', 'process_time_ns', 'sleep', 'strftime', 'strptime', 'struct_time', 'thread_time', 'thread_time_ns', 'time', 'time_ns', 'timezone', 'tzname']\n" ] } ], "source": [ "print([i for i in dir(t) if not \"__\" in i])" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Timer', '_globals', 'default_number', 'default_repeat', 'default_timer', 'dummy_src_name', 'gc', 'itertools', 'main', 'reindent', 'repeat', 'sys', 'template', 'time', 'timeit']\n" ] } ], "source": [ "print([i for i in dir(tit) if not \"__\" in i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### dateutil" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['_common', '_version', 'parser', 'relativedelta', 'tz']\n" ] } ], "source": [ "print([i for i in dir(du) if not \"__\" in i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Süre ölçümü" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performans amaçlı süre ölçümü(sonuçtan bağımsız)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `%'li` olanı bir fonksiyon takip eder, `%%'li` kullanımda ise alt satırdan yazarsın. \n", "- `Time` olan tek seferlik run'ın süresini verirken `timeit` onlarca kez çalıştırıp ortalama süre verir" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "78.9 ms ± 3.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%%timeit\n", "x=sum(range(1000000))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:59:00.443453Z", "start_time": "2020-09-02T18:59:00.277897Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 154 ms\n" ] } ], "source": [ "%%time\n", "x=sum(range(1000000))" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "75.6 ms ± 1.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "def hesapla(): \n", " y=sum(range(1000000))\n", " \n", "%timeit hesapla()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Süreyle birlikte sonuç görme" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [], "source": [ "#tit.Timer(hesapla).timeit() uzun sürüyo ve bitmiyo ??" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hey\n", "süre:0.07879209518432617\n" ] } ], "source": [ "bas=t.time()\n", "print(\"hey\")\n", "hesapla()\n", "bit=t.time()\n", "print(\"süre:{}\".format(bit-bas))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### jupyter nbextensions" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nbextensionsı hala kurmadıysanız alın size bir sebep daha. bu süre ölçümü kodlarına gerek yok. yukarıdaki eklentiyle her hücrenin çalışma süresini veriyor. Bir örnek:" ] }, { "attachments": { "image.png": { "image/png": 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rommjcZixo7SBhfoh+3i7dlzcMUq+pbaVsZC4+vmsOdYsV0Pw5EG+jEHLptbx+iWDEgYJbiZIC7yJfy5I8/HEWI+Yn9yGSMKx2Y8NbCalXdkN31ynTgxsHoZAaQSEXNvNdVqfG4u9FmqE694Kpsgnkg2ltqbzfYHjpb7Ilr1qYRzaCnVK30EQrFIQfKx2Z/onkO+rx7RAO16LlwzM4o6dfC04lR2mJtx0iyJ1kG1CE9FtcD9R2w/ydfKOyGYMuPNppyKdgyWHXJmHOm7azvLit0HXnVlm+sq3zdvj7wmTdK4s5pILcXm6pENYx3bn8JD9yM8UmHm/1t60L7JFvtryPHgG+2TZ8NngKvDMY6nr5DAMbbWP1wRBvm1pi+VBCFp6jLxMbjz2jJSw7ZifTLCXWLb7sbi09g+01qkJ/ltxq6IRCWJttwRBY0yW9zGuMW498+ywzYoIqj89r6fcRwBBsDpBcJgY0Au6KAjMWZ9YDBXnxx2QdBRLEQTGUUZp/9iBWEdElwri13GCwJ1JiKCS4hI77tzxWkCxDnirtjfjsxhqxzo0nooO783ZYlb0ZQIzBRifKqZAoh1avjzZbYNxR8DRaeJbcSo765AdB+P0OzlW+ZoPqIRP/jU/njK/W320jtc44AKFxz3M38ACjMRNjsscz7Xhz/Y75ifLlRjvdj+2fM/Za7wW43WaXE6gywabuFxbuNfnJsu/ooioteXwpTlgcxfmSsx11XfU+urHOPQdz6P8HoJgVYLgcDGgJ74oCLIOoEzEssPUZMs72Zh45bZ5uewi3fddMuDt6PfyTMR8lmnH3Nl968yDAmh24ceLr46bLlvGxThg4zicwxH9HdZ2e67sHJAoKJXndjv7IieX1ksDihSkMXaETTFjkOWwbCP9HHANx8pY8nGG8oFrreNtQcBT/aHd0JexV8w9L5cbDz9O6zPGUcxPB5btfpzNHXsIuH1ynWYzBAetUzFG0YblueBgEYf2PNsxubVQma8peI09BDME8SmbnOfHjS4mBjRBi4LABaKQbrWE12dmOQdVdpjaCdQXnl0ofQsqu0gPFAR7mWZ01yb5HgLumPz7zDVMf4ycSua6elKmSziUcYkdsJsf7miM8+LX7UMQsbY45+QDtfs81OqkqXw7JyQQbB9xkJL9ZDhRdLTS5vhzjEF8rM7JMp8lx+znOHsR5rI8P7ZM63hNELisCbuGHfoNY42xDt/7sm5TXG7d2jId8+PWML+e7tuPOJ/3D1HZsXcZyHVqBPmgoqwd2cBfO9ZpDTvJA/JjsXAivNvzTBjUeWnbO5zXur69y2CqZzwgQzClEnBtzSII5EOJTvUeAvvf+yyCsiAgx09pSu3wrePwjsUFHJ++pZSYT0XSYrH1grig7/lrZUEV++lwPsxB5FKNiVNxTiS6E2EjzhJ8diFcVkhFRM7JuvEWxxOCqnEIHk+ag8HffqmdS+I0fLvMXv8dayMKMBZ3mkM9t7pdPlc5WzwHHL4+aJLNXJhQMPHCQ+OQciJpw7Xl+8qNJeqH8yltnxyyfQ3j9u1zrujLFzQWbbcOqqyvHCamfDRGbU+F1xw71nZsJ4kCZo8QbMaWSn3TnhMFfkx6bL5Ojqt5/JJx+zYc9tV+wvzU9hF0rVMnCqJ1msnuxZce4g3DBhfHqRwHcnzk60LXT/DgnDHzG3gWsGfrU5eZlNd7tR+ZgZF8k58hCNYiCJgDk5PY+7kqCCZov9eOK1nOOMcQ5K/kGKfiSAEr65SFA+3o89B6mKMQtGfFYmyWoGPOD7W3JKiWyKG2TdM+g0BjCkEAQeCfQ3DoIkM961jbC/hIDnhGhzrJXGcFgTtzlWehrbFk2wLOk8xTC/sxxxf+WwaLW7tNXuO3DGYI3fM0OcslgzGLr1AWGYL5A4U+A5GpxsU55wI/jmlnNv2apOBb82VFRC4FfMyxoK/WPNnj5o6DiR6gczHMdWo/zuYtSxB08FoLrMrzHQ7FBxmCGTQBBEGfgziUtKgHfMEBcAAcmJ4DEAQQBLhksIAzZji36Z0bMAWm4MA4DkAQQBBAEEAQgAPgADgADmBT4Qx6QOGSwThVChUPvMABcAAcuHwOIEMwgyKAIOgn9vo34aUblODY+ucfWAErcGA5HIAggCAwqcL4oR7iOeEzpdK6d/aaW3DS+9Nt/daufnc7Gz1EROwuNs5IPixk5K1v3eOYCUc41OU4VMwF5mLNHIAgWI0gsD93TE8nNK8vPx513a/rtkP56NC5gljzPlvmWC4gCEoPIgmL1j5drHarYI/waPfDxjMXpmh31HoIHMDcAAtwQHMAgmA1gkAS1gqE3scW68lejiBwZ+29950XBEHbidlgX71Pvda2yxyc3N2qjXiEbNJ35bGoSVkEbgRucAAcWCAHIAhWKwj26vHLp+p0RJZgMYJgTHZAL5pa0K4uqosIAi1a6OEl7SyCDvrIEkjRis8Qg+DAmjgAQbBWQfDRTp2dnqr7j/oX3MUFwU5t/O+R6x/74df1z9X2jv1BkegHS3I/QnL3hP3YSs5+G4DDD4ToH3thfRmBEH4ARmYAKMUf13flTVZC7isIbek6sj360ZraZQWz6A8WLjkM8N2aHClsBV+vAgcgCFYlCOJ9BGMuF2iyXkwQWDHAf+nPbkSkQG0Fgf5FslAmraPtMGfSxcsFmbPxYqBtZQBax3uzDxmbcpkJc9mAsgpwkFfBQWIM4PF14gAEwaoEAV+cH6vd2ak6Pdupj3PBKfPdRQSBCf7J2b4N+PZnhZ0g2O7YtcGQNQiLqr5/wJzdy13+qxIEuQwDnze8D1wAFsACHFgSByAIVisI9mpvLhucqd1HfYvqwoIgulygLxnY/3GCoJ4hyGYPViUIkCFYkoODLX2+ATgBJ80BCII1C4JH99Xp6X31OJMNyC3wCwuCJEPAF1FvhmCvslkAN4bssbUIgqKdHCe8z3ET34EX4MDlcwCCYLWCwO0nONZdBub5BHx/gCRvvyCwdw0UzqRNUGXHzGexqdALoNYegdbxafcQZLMb3laJFz4jAIAD4MCyOABBsBZB4O4q4A8mGnOHgV545QyBC+aZSwJhg+Be7Z0ooEsF9lVsKmzuIdALoGMfAT1ZUO8nMJv1qB93yYGO89dko+LFBIEJ8Lx9ei/3OJig39EXxAHbX7IsR4jAhPkAB3DJYAY5oPDjRj2BT2YCeuosuIwRD1mhAEcLRwsOgAPr4AAyBDNIAvy4UR/57fMCwpn/Wp3GVRnHWvGH3X3rDTgBpxYHIAggCC41javPrJsP/FlwZsA+tIjteVi0rXCILYeI4+DIdeYABAEEwaUKguu8+DB2BB9wABxYEgcgCCAIIAhwVg8OgAPgADiA5xDMoAewqRALC84VHAAHwIHVcQAZghkUATYVIg24pDQgbAEfwQFwoIcDEAQQBKtTsT3ERhk4QHAAHAAHxnEAggCCAIIAqU1wABwAB8AB7CGYQQ9gDwEWFpwrOAAOgAOr4wAyBDMoAuwhGJemQloPeIED4AA4cPkcgCCYSRBoUYB/YAAOgAPgADiwFg5AEMwkCKB2L1/tYg4wB+AAOAAO9HMAggCCYHXXubDA+xc4sAJW4AA40MsBCAIIAggCbH4CB8ABcAAcwF0GM+iBxd5l0KsS6+V2ajMMavNAqu5ztb154A8VmZ9CHtQw6P9j/vqhtdn0+zd/q06Gjh8pMrY2bOwp0+V8Slhz7O0Y5HzYX2B0mPb8LLO2WZYbPS95WzyfGriYn5A2HNB2y7mwWOQ4Eo014Wa5nrfLz0WlbIRFzj4+J/Re4sHbd3NzdM6TbXhN5x+YIEMwgyLQG0iWTjbjfG/tDrDTOjUZgPZ76/xG/3Lhe1t1wpy4ce4yMHmHPfGC5QHK2CGDUKY/XqdkV0+ZUt3o+xLWZJfFXAfJaD5M/2Eszbk25YdYEIh52esy1Xkp2OLHQ8EwL6bkvNsgH8ryMfD3xi4uIitjj+p5uwjLveLH+Xu9lo09o9ZLCw/d74FrJmP70v0N7As8WzIWEAQQBCNFQStIjSP+eEc7rv3q4msGuQP7MkEpBLOqDVXnXsHaBezh1ibJ2Ohg1ivMTOAbTtTm1kkU8GWAtsEriIxoTBVbqJxu7+TmSX8GKBJoAgdzrIQvL8vf79W+u15adgymtp9B5eaG8NCvKcYH8q3KIbTJMcf7Oh8gCC5DEJADNelCcrLujIKdhRmH4c9+rHOzKVNxRrjPHUvPPuisx7brUpZ01pO1yZHHBDgqnwYgu8hYf6YtF2QoBUz9RM6L1Ym+t/1ae7fmUkRIFfOxhqBgA5uzkWFYdAB8TDo9/ZrOVLi5cAF9cyukdX2A5cE+wozNiSuzvasDoG3D19fjjOrR/I/BWrexU7v3dB0R9JLPrl2Hr5l7Nhe7BzZLJIOT/ExnszYToftkdhdtcX3r8eo54dhl5juaK8ehrR4jf+/xY/1HbTE8xtSrlrU8jbIwUZ8xxuW54eWYnbW2cGzkCQvHGO+jNdXBJQiCowsC4QgiJ8kCZOSg2Pd6UruOiTr7Wkq0ZlN8jMRE6hxZf8Y+fYbkLkm4z6U6mwe2jxD07UK2QZ4cv20/7DGwdUyg1RgyEaDrRQG4tBA49hxT8z1rw3x2dvg61h4/JlPfCZRafRmwfXt6zL1Yc0cX17FtnKjtA3spxmDKsCk5iEQAuDH48ckxZTGVtoR5NO1EY+VjSN+buSe7ObamX90P8SKua/np5mFEPbumgsD0ODLRReJOv3bxS8wnxz7BO4tnPDZeH++BzRwcgCA4tiDQTpEcnXECucAizioTx2aDu3FK5ljOObIA7ZyNcbIuSPP36fVhZlPixPNOn84g8zbV64Qg70SLwyeyUQiaqD8XrHzw6nWufGwcR/69bit7zGIUz6VzUq36pfmX9SoBJTgDia39HOxydpI4K2CTC1A2uLosyS2bqakHQmnLXhluUd/J+PJO3fbLOJ3wX/fDjtOYTPssU9Nbz89xQRCYdmS77DP1n7xm8DBl0rUZ5jOPCY4Dl2NwAILgyIIgcrKUThdnHNHZkXYgztHxMxTzXjvaopNNnQ4PsPx9zSZzrBTAIgfI+uMB1JQZ4RhZXW6jXgzxZ9ZfBqMuccCxY/0mmBaPWRv8vBBOvF1tG6s/DdbcOUps5WfiDw92vL59n86zLJNpN5p/XV6W0fiwwC1xSeq76+oy2DP8jFOUn9n8R/Muy7HP8Txs1I4dK/bB7CUuJu2wMikehKnGieES1aEyeD1GAEQfMc8gCI4sCEywocCRcwTGaeoNWCwtaZxVwaFLR+bbFAFTBFRyaGZB6D5LNiVOXDp9IhTrL7GpVIdlOshuNtbIRmF/lCGguu7VOukCXrwsHxu3mX+vy9eO+fbsGM0ZdKv+hbEmzPWrxNbOQxQYtT2lPjlmtTIcAz9mbkfGFlMn7MPwwqkQDMvzpsfIAqhpl82vwZsd9/Y16vlyZDtrQ/YRlc3wVhy3gUbOjcNL8iNbV2KLzwje83MAguDYgkA6cOc0rQNnDsR8Tw6KBVvjPGw5m77NH9PtmYDqnbytQ9f142DL+tXtZ2zyqWLjzHLpUmZHZLsmsWifO0DhHLld/L12BvHn0J8JJH6c7oyYf+b98fe8b24z/97j4ebCH5NjsvaYefRl3ALmbUssDsKaOwZph9y9HnCqOdQEQ2MXBV3bBnGn3E5qS1RW4pLMBfGdj8++53PP38dcHVGP9+3e83b5+yRjZMZRtjWMOY+HwZouo2TsCPXT8eAYMJmTAxAERxcEIeDSGZMNttZ5cKcbnzG543SZIXIo8TEfvF2wsf1slNn1TvWMU2P3nkdlWXZCOyx+7OZGbW5OKAh0+2SLHhsL5JFTrggCvUBMWcKGn03qtlmb0WIy/bqgZ8Yog75zPqVjHBfdd4QtBVPCjwUQUc/PVzfW3ClWgg7hQXbRrW7sM+GRCAIqS21EGOo+2Xh8UMvbQn0kgdXXk/MXsgoh08E5HrC1a55sXSQAAAUvSURBVCSUj9eUxilfz9vEbKiW5RyVz32I2mjPjeR13hbeDt4Do+NwAILgMgRB0YEcZ9Kv1+I6V9tbW3UOzHH7FjgADoADVQ5AEEAQVAmyevHw3lZt7p5f7THCyWF+wQFwYAIOQBBAEGAhTbCQVi+cgAHWAThw7TkAQQBBcO0XAYI5LlWBA+AAOLDHrx3OoAeu+K8dwnHAcYAD4AA4cBU5gAzBDIpgDb92eBXJjDHBSYMD4AA4cDgHIAhmEARvv/22+tOf/oRUPK5JggPgADgADqyGAxAEMwiCL7/8UmlRoDMF+AcG4AA4AA6AA2vgAATBDIIATQIBIAAEgAAQWBsCEARrmzHYCwSAABAAAkBgBgQgCGYAFU0CASAABIAAEFgbAhAEa5sx2AsEgAAQAAJAYAYEIAhmABVNAgEgAASAABBYGwIQBGubMdgLBIAAEAACQGAGBCAIZgAVTQIBIAAEgAAQWBsCEARrmzHYCwSAABAAAkBgBgQgCGYAFU0CASAABIAAEFgbAgsVBL9VHzz5FfUf9P/sL9VfZkD2L/eeDn08+RX17r0/ztALmgQCQAAIAAEgsHwExgmCT36p3qUgrV8PDNQmEFfrWkHwwe/qAFJATwO5EBTO5rQctf9H9eGzEASEBl6BABAAAkDg+iHQLwh+9wNzNh2CtA2ih4iCCwsCJ0zevfdLk0lIA32foAjTfQmC4MMzdXMY1PCtM/VJMEQp+l4fk//Pvx5KynL8WCiFd0AACAABIAAEuhDoFASlgDk28FqbLiYItC1Pqw9NFLX9r0sQfKLOvmWFwNnzGUGQnbbX1e1hUDdfctLBiYHbb1Bh1yZEAQGCVyAABIAAEBiJQJ8gcGfkITtAvbgswQ9/q5Ti7+PjFLA/+yHbF8AvPTz5FRW33Ss0jiMIyO7YRhrjuNdPXrrpA/vrnYJA1xmG24ryA6aeDP5GJIQy46xCaSAABIAAELjuCIwQBHRWHkNmgmWnIKCaF8sQUCv6tS4I/KbE5n6HUgbE9jWlIODW9wkCkR1QSmUFgdLlbqqzD3kPeA8EgAAQAAJAoA+BKyoI5OCtcCjvd6gLAtnaVJ97BIHMDpi+37itBhH8TVvDoMJlhKmsRDtAAAgAASBwHRAYIQhkWl/DwwNp+5IBATp/hoB6Yq9mU2Q+yxGPg9WZ+W1bEKTZATLJCoWw8fD2S3aTIgQBIYRXIAAEgAAQGINAnyCIAj9rPtpbsGxBYETIkz9QnzHzw1subMK3c79rCgKTCejcF4A9BHNPF9oHAkAACFxpBDoFgVKqdNuh2T9gMTLX2v3zBZxAyD3wp3q2rtu66KZCMWf+NsXSg4fqguBy9hCMu3PAiAu50VDAgI9AAAgAASAABEoI9AsC3YITBX6zHhMDtgN3rZ49CEgHU7rLgBtBQZbainfw1wWBrEtt+D0CTgD4758sXSogixYoCDL7BMha+Wr3D3RmEmRlfAYCQAAIAAEgoJQaJwiOBlldEExvRl0QTNqfCfTh2n94+BC/Q8DuHRiKZ/zuOD24qFhuUsvRGBAAAkAACFxhBCAIzOQeURBcYTJhaEAACAABILBeBBYtCHzK3+9LmBZou9EwPCwpd2lj2h7RGhAAAkAACACBZSKwUEGwTLBgFRAAAkAACACBq4oABMFVnVmMCwgAASAABIDACAQgCEaAhaJAAAgAASAABK4qAhAEV3VmMS4gAASAABAAAiMQgCAYARaKAgEgAASAABC4qghAEFzVmcW4gAAQAAJAAAiMQOD/AWXAJcNuhDpJAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Önemli bazı modüller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aşağıda önemli olduğunu düşündüğüm bazı modülleri bulacaksınız. Tabiki bunların dışında da başak kütüphaneler/modüller var, bunları da zamanla öğreneceksiniz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## os " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "İşletim sisteminden bağımsız çalışacak bir koda(dosyalama sistemi v.s ile ilgili) ihtiyacımız olduğunda os modülünü kullanırız." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Klasör ve dosya işlemleri " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'E:\\\\OneDrive\\\\Dökümanlar\\\\GitHub\\\\PythonRocks'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "'.'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "os.getcwd() #o anki aktif folder\n", "os.curdir #mevcut dizini temsil eden karakter, genelde bu . oluyor. Bunu daha çok prefix path olarak kullanırız" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "os.chdir(os.curdir + \"\\.git\") #aktif klasörü değiştiriyoruz" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'..'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.pardir #parent klasörü temsil eden karakter" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['COMMIT_EDITMSG',\n", " 'config',\n", " 'description',\n", " 'FETCH_HEAD',\n", " 'HEAD',\n", " 'hooks',\n", " 'index',\n", " 'info',\n", " 'logs',\n", " 'objects',\n", " 'ORIG_HEAD',\n", " 'refs']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir() #o anki aktif folderdaki dosyaları lsiteler" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['.git',\n", " '.gitattributes',\n", " '.gitignore',\n", " '.ipynb_checkpoints',\n", " '.vscode',\n", " 'abc.txt',\n", " 'AutoML.py',\n", " 'cheatsheet.png',\n", " 'Classification Examples.ipynb',\n", " 'EDA with Python.ipynb',\n", " 'EDA.py',\n", " 'LICENSE',\n", " 'Machine Learning with Spotify.ipynb',\n", " 'Numpy Pandas challenge.ipynb',\n", " 'Python Challenges.ipynb',\n", " 'Python Genel.ipynb',\n", " 'Python ile Machine Learning.ipynb',\n", " 'Python ile Veri Analizi.ipynb',\n", " 'README.md',\n", " 'Regression Examples.ipynb',\n", " 'test.cs',\n", " 'test.txt',\n", " 'Untitled.ipynb',\n", " 'Untitled1.ipynb',\n", " 'utility.py',\n", " '__pycache__']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir(os.pardir)" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.path.exists('Python Genel.ipynb') #bir dosya/klasör mevcut mu" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.path.isfile('olmayandosya.txt') #parametredeki bir dosya mı? veya bir dosya mevcut mu anlamında da kullanılabilir." ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.path.isdir(\"klasor\")" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['.gitattributes', '.gitignore', 'abc.txt', 'cheatsheet.png', 'LICENSE', 'Python Genel.ipynb', 'Python ile Veri Analizi.ipynb', 'test.txt', 'utility.py']\n" ] } ], "source": [ "# list all files in a directory in Python.\n", "from os import listdir\n", "from os.path import isfile, join\n", "p=os.getcwd()\n", "files_list = [f for f in listdir(p) if isfile(join(p, f))]\n", "print(files_list);" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a11\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a12\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a2\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\Yeni klasör\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b1\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\c\n" ] } ], "source": [ "dizin= r\"E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\"\n", "for kökdizin, altdizinler, dosyalar in os.walk(dizin):\n", " print(kökdizin)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\mevcut klasör yeni.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\roo1.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\roo2.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a_roo1.accdb\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a_roo2.pptx\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\mevcutyeni2.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a1_roo1.pub\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a11\\a11_1.bmp\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a11\\a11_2.xlsx\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a1\\a12\\a12_1.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a2\\a2_.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a2\\a2_2.accdb\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\a2\\a2_3.docx\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\a\\Yeni klasör\\yeniklasörde.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b_roo1.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b_roo2.xlsx\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b1\\b1_1.bmp\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b1\\b1_2.txt\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\b\\b1\\b1_3.pptx\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\c\\c_1.bmp\n", "E:\\OneDrive\\udemy dosyaları\\vba2\\FSO Örnek\\c\\c_2.txt\n" ] } ], "source": [ "for kökdizin, altdizinler, dosyalar in os.walk(dizin):\n", " for dosya in dosyalar:\n", " print(os.sep.join([kökdizin, dosya]))" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last modified: 1579164058.249096\n", "Created: Thu Jan 16 11:40:54 2020\n" ] } ], "source": [ "import os.path, time, datetime\n", "print(\"Last modified: %s\" % os.path.getmtime(\"abc.txt\")) #formatlanmamış\n", "print(\"Created: %s\" % time.ctime(os.path.getctime(\"abc.txt\")))" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": [ "os.startfile('calc.exe') #dosya açar, program başaltır, internet sitesine gider v.s" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "os.startfile(r'E:\\OneDrive\\Uygulama Geliştirme\\PDF dokümantasyon\\AI & DS\\Pandas & EDA & ML\\pandas.pdf')" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "os.startfile(\"www.volkanyurtseven.com\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sistem ve Environment bilgileri" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nt\n", "Windows\n", "10\n" ] } ], "source": [ "import platform, os\n", "print(os.name)\n", "print(platform.system())\n", "print(platform.release())" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ALLUSERSPROFILE': 'C:\\\\ProgramData',\n", " 'APPDATA': 'C:\\\\Users\\\\volka\\\\AppData\\\\Roaming',\n", " 'COMMONPROGRAMFILES': 'C:\\\\Program Files\\\\Common Files',\n", " 'COMMONPROGRAMFILES(X86)': 'C:\\\\Program Files (x86)\\\\Common Files',\n", " 'COMMONPROGRAMW6432': 'C:\\\\Program Files\\\\Common Files',\n", " 'COMPUTERNAME': 'YENILENOVO',\n", " 'COMSPEC': 'C:\\\\Windows\\\\system32\\\\cmd.exe',\n", " 'DRIVERDATA': 'C:\\\\Windows\\\\System32\\\\Drivers\\\\DriverData',\n", " 'FPS_BROWSER_APP_PROFILE_STRING': 'Internet Explorer',\n", " 'FPS_BROWSER_USER_PROFILE_STRING': 'Default',\n", " 'HOMEDRIVE': 'C:',\n", " 'HOMEPATH': '\\\\Users\\\\volka',\n", " 'LOCALAPPDATA': 'C:\\\\Users\\\\volka\\\\AppData\\\\Local',\n", " 'LOGONSERVER': '\\\\\\\\YENILENOVO',\n", " 'NUMBER_OF_PROCESSORS': '8',\n", " 'ONEDRIVE': 'E:\\\\OneDrive',\n", " 'ONEDRIVECONSUMER': 'E:\\\\OneDrive',\n", " 'OS': 'Windows_NT',\n", " 'PATH': 'C:\\\\Users\\\\volka\\\\Anaconda3;C:\\\\Users\\\\volka\\\\Anaconda3\\\\Library\\\\mingw-w64\\\\bin;C:\\\\Users\\\\volka\\\\Anaconda3\\\\Library\\\\usr\\\\bin;C:\\\\Users\\\\volka\\\\Anaconda3\\\\Library\\\\bin;C:\\\\Users\\\\volka\\\\Anaconda3\\\\Scripts;C:\\\\Windows\\\\system32;C:\\\\Windows;C:\\\\Windows\\\\System32\\\\Wbem;C:\\\\Windows\\\\System32\\\\WindowsPowerShell\\\\v1.0\\\\;C:\\\\Windows\\\\System32\\\\OpenSSH\\\\;C:\\\\Program Files\\\\dotnet\\\\;C:\\\\Program Files\\\\Microsoft SQL Server\\\\130\\\\Tools\\\\Binn\\\\;C:\\\\Program Files\\\\Microsoft SQL Server\\\\Client SDK\\\\ODBC\\\\170\\\\Tools\\\\Binn\\\\;C:\\\\Program Files (x86)\\\\LINQPad5;C:\\\\Program Files (x86)\\\\Microsoft SQL Server\\\\150\\\\DTS\\\\Binn\\\\;E:\\\\Program Files\\\\Brackets\\\\command;C:\\\\Users\\\\volka\\\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps;C:\\\\Users\\\\volka\\\\AppData\\\\Local\\\\GitHubDesktop\\\\bin;C:\\\\Users\\\\volka\\\\AppData\\\\Local\\\\Programs\\\\Microsoft VS Code\\\\bin;C:\\\\Users\\\\volka\\\\.dotnet\\\\tools;C:\\\\Users\\\\volka\\\\Anaconda3\\\\lib\\\\site-packages\\\\numpy\\\\.libs',\n", " 'PATHEXT': '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC',\n", " 'PROCESSOR_ARCHITECTURE': 'AMD64',\n", " 'PROCESSOR_IDENTIFIER': 'Intel64 Family 6 Model 142 Stepping 10, GenuineIntel',\n", " 'PROCESSOR_LEVEL': '6',\n", " 'PROCESSOR_REVISION': '8e0a',\n", " 'PROGRAMDATA': 'C:\\\\ProgramData',\n", " 'PROGRAMFILES': 'C:\\\\Program Files',\n", " 'PROGRAMFILES(X86)': 'C:\\\\Program Files (x86)',\n", " 'PROGRAMW6432': 'C:\\\\Program Files',\n", " 'PSMODULEPATH': 'C:\\\\Program Files\\\\WindowsPowerShell\\\\Modules;C:\\\\Windows\\\\system32\\\\WindowsPowerShell\\\\v1.0\\\\Modules',\n", " 'PUBLIC': 'C:\\\\Users\\\\Public',\n", " 'SESSIONNAME': 'Console',\n", " 'SYSTEMDRIVE': 'C:',\n", " 'SYSTEMROOT': 'C:\\\\Windows',\n", " 'TEMP': 'C:\\\\Users\\\\volka\\\\AppData\\\\Local\\\\Temp',\n", " 'TMP': 'C:\\\\Users\\\\volka\\\\AppData\\\\Local\\\\Temp',\n", " 'USERDOMAIN': 'YENILENOVO',\n", " 'USERDOMAIN_ROAMINGPROFILE': 'YENILENOVO',\n", " 'USERNAME': 'volka',\n", " 'USERPROFILE': 'C:\\\\Users\\\\volka',\n", " 'WINDIR': 'C:\\\\Windows',\n", " 'CONDA_PREFIX': 'C:\\\\Users\\\\volka\\\\Anaconda3',\n", " 'JPY_INTERRUPT_EVENT': '2692',\n", " 'IPY_INTERRUPT_EVENT': '2692',\n", " 'JPY_PARENT_PID': '2460',\n", " 'TERM': 'xterm-color',\n", " 'CLICOLOR': '1',\n", " 'PAGER': 'cat',\n", " 'GIT_PAGER': 'cat',\n", " 'MPLBACKEND': 'module://ipykernel.pylab.backend_inline'}" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict(os.environ)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\Users\\volka\n", "volka\n" ] } ], "source": [ "print(dict(os.environ)[\"HOMEPATH\"])\n", "print(dict(os.environ)[\"USERNAME\"])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\volka'" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.path.expanduser('~')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## random " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T15:44:49.592635Z", "start_time": "2020-11-22T15:44:49.579668Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "13\n", "8\n", "[3, 1, 3]\n", "0.37961522332372777\n", "2.6298644191144316\n", "[3, 3]\n" ] } ], "source": [ "import random\n", "dizi=[3,5,8,3,2,1,9]\n", "random.seed(1)\n", "print(random.randint(2,48)) #2 ile 48 arasında\n", "print(random.randrange(1,100,3))\n", "print(random.choice(dizi))\n", "print(random.choices(dizi,k=3)) #çektiğini geri koyar, sampledan farkı bu, o yüzden aynısı çıkabilir\n", "print(random.random()) #0-1 arası\n", "print(random.uniform(2,5)) #2-5 arası küsurlu sayı\n", "print(random.sample(dizi,2)) #çektiğini geri koymaz, choicestan farkı bu" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[8, 1, 2, 9, 3, 5, 3]" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#liste karıştırmak\n", "random.shuffle(dizi)\n", "dizi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "listin hemen hemen aynısı olup önemli bir farkı, içine sadece belli tipte eleman almasıdır. Bu yüzden memory performansı açısından liste göre daha iyidir.ancak list'ler daha hızlıdır. Ayrıca sayısal bir dizi kullanacaksanız numpy kullanmanızı tavsiye ederim. Bunun dışıdna çok büyük metinsel diziler oluşturacaksanız array kullanabilirsinz." ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "76 88\n" ] } ], "source": [ "from array import array\n", "import sys\n", "arr=array('i',[1,2,3])\n", "lst=[1,2,3]\n", "print(sys.getsizeof(arr),sys.getsizeof(lst)) #arr'ın memory kullanımı daha düşüktür" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## collections" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T12:42:13.976560Z", "start_time": "2020-11-22T12:42:13.970578Z" } }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_list = [\"a\",\"b\",\"c\",\"c\",\"e\",\"c\",\"b\",\"b\",\"a\"]\n", "my_list.count(\"a\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T12:42:42.671252Z", "start_time": "2020-11-22T12:42:42.668256Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({'b': 3, 'c': 3, 'a': 2, 'e': 1})\n", "2\n" ] } ], "source": [ "from collections import Counter\n", "\n", "cn = Counter(my_list) #dictionary benzeri bir yapı. tüm dictionary metodları kullanılabilir\n", "print(cn)\n", "print(cn[\"a\"]) #yukarda list ile yaptığımızın aynısı" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'sen': 3, 'seni': 2, 'bil': 1, 'bilmezsen': 1, 'kendini': 1})" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str_ = \"sen seni bil sen seni sen bilmezsen kendini\"\n", "cn = Counter(str_.split(' '))\n", "cn" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('sen', 3), ('seni', 2)]\n", "['sen', 'seni', 'bil', 'bilmezsen', 'kendini']\n", "{'sen': 3, 'seni': 2, 'bil': 1, 'bilmezsen': 1, 'kendini': 1}\n" ] } ], "source": [ "print(cn.most_common(2)) #en çok kullanılan 2 kelime\n", "print(list(cn)) #key değerlerinin listeye çevrilmiş hali\n", "print(dict(cn)) #key-value değerlerinin sözlüğe çevrilmiş hali" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 4\n", "c 3\n", "b 2\n", "----\n", "a 4\n", "c 3\n", "b 2\n" ] } ], "source": [ "#OrderDict'e artık ihtiyaç yoktur. Python 3.7den itibaren de dictioanryye girilen sırayı korumaktadır\n", "#aşağıdaki örnekten de görebiliyoruz\n", "from collections import OrderedDict \n", "liste = [\"a\",\"c\",\"c\",\"a\",\"b\",\"a\",\"a\",\"b\",\"c\"]\n", "cnt = Counter(liste)\n", "od = OrderedDict(cnt.most_common())\n", "d=dict(cnt.most_common())\n", "for key, value in od.items():\n", " print(key, value)\n", "print(\"----\")\n", "for key, value in d.items():\n", " print(key, value)" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "John\n" ] } ], "source": [ "from collections import namedtuple\n", "\n", "Student = namedtuple('Student', 'fname, lname, age') #class yapısına benzer bir kullanım. indeks ezberlemeye son!\n", "s1 = Student('John', 'Clarke', '13')\n", "print(s1.fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## itertools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Permütasyon" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('A', 'B', 'C'), ('A', 'C', 'B'), ('B', 'A', 'C'), ('B', 'C', 'A'), ('C', 'A', 'B'), ('C', 'B', 'A')]\n", "[('A', 'B'), ('A', 'C'), ('B', 'A'), ('B', 'C'), ('C', 'A'), ('C', 'B')]\n" ] } ], "source": [ "from itertools import permutations #sadece tekrarsızlar yapılabiliyor. n!/(n-r)!\n", "liste=[\"A\",\"B\",\"C\"]\n", "per1=list(permutations(liste))\n", "per2=list(permutations(liste,2))\n", "print(per1)\n", "print(per2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kombinasyon" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('A', 'B'), ('A', 'C'), ('B', 'C')]" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from itertools import combinations #n!/(r!*(n-r)!)\n", "com=list(combinations(liste,2))\n", "com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kartezyen çarpım" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('A', 2000),\n", " ('A', 2001),\n", " ('A', 2002),\n", " ('A', 2003),\n", " ('B', 2000),\n", " ('B', 2001),\n", " ('B', 2002),\n", " ('B', 2003),\n", " ('C', 2000),\n", " ('C', 2001),\n", " ('C', 2002),\n", " ('C', 2003)]" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from itertools import product\n", "list1=[\"A\",\"B\",\"C\"]\n", "list2=[2000,2001,2002,2003]\n", "krt=list(product(list1,list2))\n", "krt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regex (Düzenli ifadeler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pythona özgü değildir, hemen her dilde implementasyonu vardır. Başlı başına büyük bi konudur. Burada özet vermeye çalışıcam. İleride Text mining, NLP v.s çalışacaksanız iyi öğrenmenizde fayda var" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Isınma " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-11-22T14:55:50.942122Z", "start_time": "2020-11-22T14:55:50.939128Z" } }, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [], "source": [ "a=\"benim adım volkan\"" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [], "source": [ "#match: başında var mı diye kontrol eder\n", "re.match(\"volkan\",a) #eşleşme yok, sonuç dönmez" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
\\r\\n \\r\\n
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"execution_count": 29,
"metadata": {},
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],
"source": [
"response = requests.get(httpget1)\n",
"response.content #byte olarak"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:02.611257Z",
"start_time": "2021-04-12T11:50:02.604275Z"
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]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response.text #string olarak"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-12T11:50:07.692493Z",
"start_time": "2021-04-12T11:50:07.689491Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"\\r\\n
\\r\\n\\r\\n \\r\\n \\r\\n
\\r\\n Dump View
\\r\\n\\r\\n \\r\\n
\\r\\n \\r\\n
\\r\\nWhat is this?
\\r\\nSome Rules
\\r\\nHow it works
\\r\\nWhat\\'s in a dump
\\r\\nContact
\\r\\n
\\r\\n\\r\\n \\r\\nWhat is this?
\\r\\nSome Rules
\\r\\nHow it works
\\r\\nWhat\\'s in a dump
\\r\\nContact
\\r\\n
\\r\\n
\\r\\n\\r\\n\\r\\n \\r\\n
\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n
\\r\\n View this as: json or raw text
\\r\\n See what\\'s in a dump for info on how these fields are populated.
\\r\\n\\r\\n
\\r\\n Source: 177.23.244.132
\\r\\n\\r\\n\\r\\n \\r\\n
\\r\\n\\r\\n
\\r\\n Dump 1
\\r\\n Parent Toilet\\r\\n View this as: json or raw text
\\r\\n See what\\'s in a dump for info on how these fields are populated.
\\r\\n\\r\\n
Details
\\r\\n Posted: 2018-05-17 16:55:50 UTC\\r\\n Method: GET\\r\\n Source: 177.23.244.132
\\r\\n\\r\\n
Headers
\\r\\n| Header | \\r\\nValues | \\r\\n
|---|---|
| Accept | \\r\\n\\r\\n \\r\\n text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 \\r\\n | \\r\\n
| Accept-Language | \\r\\n\\r\\n \\r\\n en-GB,en;q=0.5 \\r\\n | \\r\\n
| Host | \\r\\n\\r\\n \\r\\n ptsv2.com \\r\\n | \\r\\n
| Referer | \\r\\n\\r\\n \\r\\n http://ptsv2.com/t/trump \\r\\n | \\r\\n
| Upgrade-Insecure-Requests | \\r\\n\\r\\n \\r\\n 1 \\r\\n | \\r\\n
| User-Agent | \\r\\n\\r\\n \\r\\n Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:60.0) Gecko/20100101 Firefox/60.0 \\r\\n | \\r\\n
| X-Cloud-Trace-Context | \\r\\n\\r\\n \\r\\n 7a8644a414c1c3d2b54c9c795d1bdcc1/5295215977831438294 \\r\\n | \\r\\n
| X-Google-Apps-Metadata | \\r\\n\\r\\n \\r\\n domain=gmail.com,host=ptsv2.com \\r\\n | \\r\\n
Parameters
\\r\\n \\r\\n No Parameters.\\r\\n \\r\\n\\r\\nPost Body
\\r\\n \\r\\n No Body.\\r\\n \\r\\n\\r\\nMultipart Files
\\r\\n \\r\\n No Files\\r\\n \\r\\n\\r\\nMultipart Values
\\r\\n \\r\\n No Multipart Values\\r\\n \\r\\n\\r\\n\\r\\n\\r\\nsonraki dizin : /httpapiservice/ResponseveRequestTarget.aspx
Full adres : https://www.excelinefendisi.com/httpapiservice/ResponseveRequestTarget.aspx?Anakonu=VBAMakro&Altkonu=Temeller
Portlu Full adres : https://www.excelinefendisi.com:443/httpapiservice/ResponseveRequestTarget.aspx?Anakonu=VBAMakro&Altkonu=Temeller
Portu : 443
Query :?Anakonu=VBAMakro&Altkonu=Temeller
http : https
\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n
| Amaç | \n", "Kısayol | \n", "
|---|---|
| Sayfalar arasında dolaşmak | \n", "CTRL + PgUp/PgDn | \n", "
| Bugünün Tarihini yazmak | \n", "CTRL + SHIFT +, | \n", "
| Tüm açık dosyalarda calculation yapmak | \n", "F9 | \n", "
| Seçili kısmın değerini hesaplayıp göstermek | \n", "Hücre içindeki formül seçilip F9 | \n", "
| Aktif sayfada calculation yapmak | \n", "SHIFT+F9 | \n", "
| Sadece belli range için calculation yapmak | \n", "VBA ile yapılır. Burdan bakın. | \n", "
| Bulunduğun hücrenin CurrentRegion'ını seçme | \n", "CTRL+ A | \n", "
| Bulunduğun hücreden CurrentRegion'ın uç noktlarına gitmek | \n", "CTRL+ Ok tuşları | \n", "
| Bulunduğun hücreden itibaren belli bir yöne doğru seçim yapmak | \n", "SHIFT+Ok tuşları | \n", "
| Bulunduğun hücreden itibaren CurrentRegion bir ucuna doğru toplu seçim yapmak | \n", "CTRL+SHIFT+Ok tuşları | \n", "
| Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına gitmek | \n", "CTRL+END | \n", "
| Bulunduğun hücreden CurrentRegion'ın Sağ Aşağı uç noktlasına kadar seçmek | \n", "CTRL+SHIFT+END | \n", "
| Bulunduğun hücreden A1 hücresine kadar olan alanı(sol yukarı) seçmek | \n", "CTRL+SHIFT+HOME | \n", "
| Bir hücre içinde veri girerken, aynı hücre içinde yeni bir satır açıp oradan devam etmek | \n", "ALT+ENTER | \n", "
| Veri/Formül girişi yaptığınız hücrede alt hücreye geçmeden giriş tamamlamak | \n", "CTRL+ENTER | \n", "
| Ekranda bir sayfa sağa kaymak. | \n", "ALT+PGE DOWN | \n", "
| AutoFilter'ı aktif/pasif hale getirmek | \n", "CTRL+SHIFT+L | \n", "
| Bulunduğunuz hücrenin satır ve sütununa aynı anda freeze uygulamak/kaldırmak | \n", "Alt+W+FF | \n", "
| VBA editörünü açmak | \n", "Alt+F11 | \n", "
| Ribbonu küçültüp/büyütmek | \n", "CTRL+F1 | \n", "
| Üst hücrelerdeki tüm rakamların toplamını almak | \n", "ALT+= | \n", "
| Flash Fill uygulamak | \n", "CTRL+E | \n", "
| Sadece görünen hücreleri seçmek | \n", "ALT+; | \n", "
\n",
"utf-8 mi cp1254 mü?
\n",
"\n",
"https://python-istihza.yazbel.com/karakter_kodlama.html"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T13:53:00.576889Z",
"start_time": "2020-11-22T13:53:00.572903Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'cp1254'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import locale\n",
"locale.getpreferredencoding()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-11-22T13:53:07.023663Z",
"start_time": "2020-11-22T13:53:07.017681Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bu birinci satır\n",
"bu ikinci\n",
"bu da üçüncü\n"
]
}
],
"source": [
"with open(r\"E:\\OneDrive\\Dökümanlar\\GitHub\\PythonRocks\\abc.txt\", \"r\", encoding='cp1254') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Yazma"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yenidosya=io.open(\"writetest.txt\",\"w\") #x\n",
"yenidosya.write(\"merhaba\\n\")\n",
"lines=[\"satır1\\n\",\"satır2\\n\"]\n",
"yenidosya.writelines(lines)\n",
"yenidosya.close()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#varolana yaz, sonuna ekleme\n",
"yenidosya=io.open(\"writetest.txt\",\"a\")\n",
"yenidosya.write(\"\\nselam\")\n",
"yenidosya.flush() #hemen yazsın. bunu kullanmazsak yaptığımız değişiklikleri hemen görmeyiz\n",
"yenidosya.close()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = io.open(r\"E:\\OneDrive\\Dökümanlar\\GitHub\\PythonRocks\\abc.txt\", \"a\", encoding='utf-8')\n",
"f.write(\"yeni satır ekleniyor\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Veritabanı işlemleri"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"import sqlite3 as sql #python içinde otomatikman gelir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"sqlite sayfasından chinook databaseini indirin"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"vt = sql.connect(r'C:\\Users\\volka\\Downloads\\chinook\\chinook.db')"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"cur=vt.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"NOT:sqlite3 çok basit bir veritabanı olup, oracle veya sql server gibi güçlü veritabanlarını sorgulamak için sqlalchemy veya pyodbc gibi modülleri kullanırız ve buradan aldığmız datayı pandas ile işleyebiliriz. Bunun için benim github repomdaki Python Veri Analizi notebookuna bakmanızı tavsiye ederim.
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://sqlitebrowser.org/. sitesi de incelenebilir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classlar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python nesne yönelimli(oo) bir dildir ve tüm oo dillerde olduğu gibi sınıflar yaratılabilir. Örnek bir sınıf yaratımı aşağıdaki gibi olup detaylar için googlelamanızı rica ederim." ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yeni araç hazır\n", "yeni araç hazır\n", "yeni araç hazır\n", "<__main__.Araba object at 0x0000018A63391860>\n", "çalışıyor\n", "durdu\n", "Mekanik\n", "Mekanik\n" ] } ], "source": [ "class Araba:\n", " aractipi=\"Mekanik\" #class seviyesinde, tüm Arabalar tarafından paylaşılan bir değer\n", " def __init__(self,model,marka,km):\n", " self.model=model\n", " self.marka=marka\n", " self.km=km\n", " print(\"yeni araç hazır\")\n", " def run(self):\n", " print(\"çalışıyor\")\n", " def stop(self):\n", " print(\"durdu\")\n", "\n", "bmw0=Araba(2011,\"bmw\",0)\n", "bmw1=Araba(2014,\"bmw\",0)\n", "audi=Araba(2011,\"audi\",0)\n", "print(bmw0)\n", "bmw0.run()\n", "bmw0.stop()\n", "print(bmw0.aractipi)\n", "print(audi.aractipi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paralelleştirme" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Özellikle analitik model kurma sırasında çok işimize yarayan bir olgudur. Bunun için ayrı bir notebook'um olacak. Önden araştırmak isteyenler şu kavramları araştırabilir. Multi-threading, multiprocessing, conccurency ve parallelism" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Verimlilik ve Diğer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## debugging" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "--Return--\n", ">Falanca konu(detay görmek için tıklayın)
\n", "Buraya çeşitli bilgiler yazılabilir.
\n", "Burası bir markdown hücresi olup içeriği görmek için çift tıklayın.
\n", "Gördüğünüz gibi burada details elementi içinde summary elementi kullanıyoruz
\n", "