22-10-2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    "For data visualisation in 2D array,jhon hunter in 2002\n",
    "several plot:line,bar,scatter,histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotTool',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_auto_draw_if_interactive',\n",
       " '_backend_mod',\n",
       " '_get_running_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setp',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_show',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cbook',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'dedent',\n",
       " 'delaxes',\n",
       " 'deprecated',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'functools',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_docs',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotfile',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'pylab_setup',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'set_loglevel',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'silent_list',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'warn_deprecated',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot=pl.plot(axis(x,y))\n",
    "#pl.show(variable)\n",
    "#pl.color('hot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using the\n",
      "    'axes.prop_cycle' rcParam.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to `range(len(y))`.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
      "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            `plot('n', 'o', '', data=obj)`.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, optional, default: True\n",
      "        These parameters determined if the view limits are adapted to\n",
      "        the data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot command, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "      agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "      alpha: float\n",
      "      animated: bool\n",
      "      antialiased or aa: bool\n",
      "      clip_box: `.Bbox`\n",
      "      clip_on: bool\n",
      "      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]\n",
      "      color or c: color\n",
      "      contains: callable\n",
      "      dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "      dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "      dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "      drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "      figure: `.Figure`\n",
      "      fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "      gid: str\n",
      "      in_layout: bool\n",
      "      label: object\n",
      "      linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "      linewidth or lw: float\n",
      "      marker: marker style\n",
      "      markeredgecolor or mec: color\n",
      "      markeredgewidth or mew: float\n",
      "      markerfacecolor or mfc: color\n",
      "      markerfacecoloralt or mfcalt: color\n",
      "      markersize or ms: float\n",
      "      markevery: None or int or (int, int) or slice or List[int] or float or (float, float)\n",
      "      path_effects: `.AbstractPathEffect`\n",
      "      picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
      "      pickradius: float\n",
      "      rasterized: bool or None\n",
      "      sketch_params: (scale: float, length: float, randomness: float)\n",
      "      snap: bool or None\n",
      "      solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "      solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "      transform: `matplotlib.transforms.Transform`\n",
      "      url: str\n",
      "      visible: bool\n",
      "      xdata: 1D array\n",
      "      ydata: 1D array\n",
      "      zorder: float\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    lines\n",
      "        A list of `.Line2D` objects representing the plotted data.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pl.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot\n",
    "import matplotlib.pyplot as p\n",
    "x=[1,2,3,4,5]\n",
    "y=[4,6,2,9,10]\n",
    "p.plot(x,y)\n",
    "p.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7, 9]\n",
      "[20, 22, 24, 26, 28]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by using using range display lineplot\n",
    "import matplotlib.pyplot as p_l\n",
    "x=list(range(1,10,2)) #[expression loop condition]\n",
    "y=list(range(20,30,2))\n",
    "print(x)\n",
    "print(y)\n",
    "p_l.plot(x,y,'rs')\n",
    "p_l.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "#x=(1,10,2)\n",
    "#y=(20,30,2)\n",
    "for i in range(1,10,2):\n",
    "    print((i))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list comphrehension\n",
    "l=[i for i in range(1,10,2)]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "[1.0, 1.4142135623730951, 1.7320508075688772, 2.0, 2.23606797749979, 2.449489742783178, 2.6457513110645907, 2.8284271247461903, 3.0, 3.1622776601683795]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lineplot by using list comphrension for 1-10 inters sqrt\n",
    "import math as m\n",
    "import matplotlib.pyplot as f\n",
    "x=[i for i in range(1,11)]\n",
    "print(x)\n",
    "sqrt=[m.sqrt(i) for i in range(1,11)]\n",
    "print(sqrt)\n",
    "f.plot(x,sqrt,'ys')\n",
    "f.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### label names and title for a graph\n",
    "#lib.xlable('x-axis')\n",
    "#lib.ylable('y-axis')\n",
    "#lib.title('sqrt of 1-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as h\n",
    "x=[2,4]\n",
    "y=[2,0]\n",
    "h.plot(x,y)\n",
    "h.xlabel('X-Data')\n",
    "h.ylabel('y-Axis')\n",
    "h.title('Line plot')\n",
    "h.grid()\n",
    "h.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 7]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar graph\n",
    "import matplotlib.pyplot as pl\n",
    "x=list(range(1,10,3))\n",
    "print(x)\n",
    "y=[3,5,8]\n",
    "pl.bar(x,y)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scatter plot\n",
    "#histogram plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function scatter in module matplotlib.pyplot:\n",
      "\n",
      "scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs)\n",
      "    A scatter plot of *y* vs *x* with varying marker size and/or color.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array_like, shape (n, )\n",
      "        The data positions.\n",
      "    \n",
      "    s : scalar or array_like, shape (n, ), optional\n",
      "        The marker size in points**2.\n",
      "        Default is ``rcParams['lines.markersize'] ** 2``.\n",
      "    \n",
      "    c : color, sequence, or sequence of color, optional\n",
      "        The marker color. Possible values:\n",
      "    \n",
      "        - A single color format string.\n",
      "        - A sequence of color specifications of length n.\n",
      "        - A sequence of n numbers to be mapped to colors using *cmap* and\n",
      "          *norm*.\n",
      "        - A 2-D array in which the rows are RGB or RGBA.\n",
      "    \n",
      "        Note that *c* should not be a single numeric RGB or RGBA sequence\n",
      "        because that is indistinguishable from an array of values to be\n",
      "        colormapped. If you want to specify the same RGB or RGBA value for\n",
      "        all points, use a 2-D array with a single row.  Otherwise, value-\n",
      "        matching will have precedence in case of a size matching with *x*\n",
      "        and *y*.\n",
      "    \n",
      "        Defaults to ``None``. In that case the marker color is determined\n",
      "        by the value of ``color``, ``facecolor`` or ``facecolors``. In case\n",
      "        those are not specified or ``None``, the marker color is determined\n",
      "        by the next color of the ``Axes``' current \"shape and fill\" color\n",
      "        cycle. This cycle defaults to :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    marker : `~matplotlib.markers.MarkerStyle`, optional\n",
      "        The marker style. *marker* can be either an instance of the class\n",
      "        or the text shorthand for a particular marker.\n",
      "        Defaults to ``None``, in which case it takes the value of\n",
      "        :rc:`scatter.marker` = 'o'.\n",
      "        See `~matplotlib.markers` for more information about marker styles.\n",
      "    \n",
      "    cmap : `~matplotlib.colors.Colormap`, optional, default: None\n",
      "        A `.Colormap` instance or registered colormap name. *cmap* is only\n",
      "        used if *c* is an array of floats. If ``None``, defaults to rc\n",
      "        ``image.cmap``.\n",
      "    \n",
      "    norm : `~matplotlib.colors.Normalize`, optional, default: None\n",
      "        A `.Normalize` instance is used to scale luminance data to 0, 1.\n",
      "        *norm* is only used if *c* is an array of floats. If *None*, use\n",
      "        the default `.colors.Normalize`.\n",
      "    \n",
      "    vmin, vmax : scalar, optional, default: None\n",
      "        *vmin* and *vmax* are used in conjunction with *norm* to normalize\n",
      "        luminance data. If None, the respective min and max of the color\n",
      "        array is used. *vmin* and *vmax* are ignored if you pass a *norm*\n",
      "        instance.\n",
      "    \n",
      "    alpha : scalar, optional, default: None\n",
      "        The alpha blending value, between 0 (transparent) and 1 (opaque).\n",
      "    \n",
      "    linewidths : scalar or array_like, optional, default: None\n",
      "        The linewidth of the marker edges. Note: The default *edgecolors*\n",
      "        is 'face'. You may want to change this as well.\n",
      "        If *None*, defaults to rcParams ``lines.linewidth``.\n",
      "    \n",
      "    edgecolors : {'face', 'none', *None*} or color or sequence of color, optional.\n",
      "        The edge color of the marker. Possible values:\n",
      "    \n",
      "        - 'face': The edge color will always be the same as the face color.\n",
      "        - 'none': No patch boundary will be drawn.\n",
      "        - A Matplotlib color or sequence of color.\n",
      "    \n",
      "        Defaults to ``None``, in which case it takes the value of\n",
      "        :rc:`scatter.edgecolors` = 'face'.\n",
      "    \n",
      "        For non-filled markers, the *edgecolors* kwarg is ignored and\n",
      "        forced to 'face' internally.\n",
      "    \n",
      "    plotnonfinite : boolean, optional, default: False\n",
      "        Set to plot points with nonfinite *c*, in conjunction with\n",
      "        `~matplotlib.colors.Colormap.set_bad`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    paths : `~matplotlib.collections.PathCollection`\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs : `~matplotlib.collections.Collection` properties\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    plot : To plot scatter plots when markers are identical in size and\n",
      "        color.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    \n",
      "    * The `.plot` function will be faster for scatterplots where markers\n",
      "      don't vary in size or color.\n",
      "    \n",
      "    * Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which\n",
      "      case all masks will be combined and only unmasked points will be\n",
      "      plotted.\n",
      "    \n",
      "    * Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c*\n",
      "      may be input as 2-D arrays, but within scatter they will be\n",
      "      flattened. The exception is *c*, which will be flattened only if its\n",
      "      size matches the size of *x* and *y*.\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.\n",
      "    \n",
      "        Objects passed as **data** must support item access (``data[<arg>]``) and\n",
      "        membership test (``<arg> in data``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pl.scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe82b0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.scatter(x,y,cmap='hot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 0., 0., 0., 1., 0., 0., 0., 0., 1.]),\n",
       " array([3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.hist(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8390eb8>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a plot for time and distance by using list comphrension\n",
    "  #distance=2*time\n",
    "import matplotlib.pyplot as pl\n",
    "time=[i for i in range(20,30)]\n",
    "distance=[i *2 for i in range(10,20) ]\n",
    "pl.plot(time,distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting images by using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x96b4710>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "import matplotlib.image as m\n",
    "image=m.imread('pspk.jpg')\n",
    "pl.imshow(image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0xa188ac8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply Psudo color to an image\n",
    "import matplotlib.pyplot as pl\n",
    "import matplotlib.image as m\n",
    "x=m.imread('pspk.jpg')\n",
    "c=x[:,:,0]# Appy psudo color\n",
    "pl.imshow(c,cmap='hot')\n",
    "pl.colorbar()\n",
    "#pl.grid()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### matplotlib ticks\n",
    "    ## ticks is values within a coordinates\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([<matplotlib.axis.XTick object at 0x000000000860F470>, <matplotlib.axis.XTick object at 0x0000000009756518>, <matplotlib.axis.XTick object at 0x00000000097564E0>, <matplotlib.axis.XTick object at 0x00000000096FB6D8>, <matplotlib.axis.XTick object at 0x00000000096FB470>, <matplotlib.axis.XTick object at 0x00000000096FB550>, <matplotlib.axis.XTick object at 0x0000000008609F98>, <matplotlib.axis.XTick object at 0x00000000096FBBE0>, <matplotlib.axis.XTick object at 0x0000000008609748>, <matplotlib.axis.XTick object at 0x000000000A5353C8>], <a list of 10 Text xticklabel objects>)\n",
      "[5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "y=[5,10,15,20,25,30,35,40,45,50]\n",
    "print(x)\n",
    "x=[9,8,5,6,3,2,1,4,7,10]\n",
    "print(y)\n",
    "pl.plot(x,y)\n",
    "pl.title('A Graph a withaexample to Ticks')\n",
    "pl.xlabel('Natural Numbers')\n",
    "pl.ylabel('Values with ++5 up to 50')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 8, 5, 6, 3, 2, 1, 4, 7, 10]\n",
      "[5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "import numpy as np\n",
    "pl.title(\"Matplotlib-Ticks\")\n",
    "pl.xlabel('10 Natural Numbers')\n",
    "pl.ylabel('5-50 values with 5+')\n",
    "pl.xticks(np.arange(1,10,1))\n",
    "pl.yticks(np.arange(5,51,5))\n",
    "pl.plot(x,y)\n",
    "print(x)\n",
    "print(y)\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representing a sine wave\n",
    "A=sine(wt)\n",
    "A=Amplitude\n",
    "w=frequency\n",
    "t=time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "Time=np.arange(1,10,0.1)\n",
    "Amplitude=np.sin(Time)\n",
    "pl.grid()\n",
    "pl.plot(Time,Amplitude)\n",
    "pl.title(\"Sine wave A=sin(Wt)\")\n",
    "pl.xlabel('Time')\n",
    "pl.ylabel('Amplitude')\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>62993</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "0  04000US01     Alabama  37150  37952  42212  44476  39980  40933  42590   \n",
       "1  04000US02      Alaska  55891  56418  62993  63989  61604  57848  57431   \n",
       "2  04000US04     Arizona  45245  46657  62993  46914  45739  46896  48621   \n",
       "3  04000US05    Arkansas  36658  37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755  55319  55734  57014  56134  54283  53367   \n",
       "\n",
       "    2012   2013  \n",
       "0  43464  41381  \n",
       "1  63648  61137  \n",
       "2  47044  50602  \n",
       "3  39018  39919  \n",
       "4  57020  57528  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('income.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "years=df.columns[2::]\n",
    "Alaska_income=df.iloc[1,2:]\n",
    "pl.plot(years,Alaska_income)\n",
    "pl.title(\"Income of Alaska from 2005-2013\")\n",
    "pl.xlabel(\"years\")\n",
    "pl.ylabel(\"Income of Alaska\")\n",
    "pl.savefig(\"Income of Alaska from 2005-2013.jpg\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotTool',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_auto_draw_if_interactive',\n",
       " '_backend_mod',\n",
       " '_get_running_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setp',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_show',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cbook',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'dedent',\n",
       " 'delaxes',\n",
       " 'deprecated',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'functools',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_docs',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotfile',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'pylab_setup',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'set_loglevel',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'silent_list',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'warn_deprecated',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function savefig in module matplotlib.pyplot:\n",
      "\n",
      "savefig(*args, **kwargs)\n",
      "    Save the current figure.\n",
      "    \n",
      "    Call signature::\n",
      "    \n",
      "      savefig(fname, dpi=None, facecolor='w', edgecolor='w',\n",
      "              orientation='portrait', papertype=None, format=None,\n",
      "              transparent=False, bbox_inches=None, pad_inches=0.1,\n",
      "              frameon=None, metadata=None)\n",
      "    \n",
      "    The output formats available depend on the backend being used.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    \n",
      "    fname : str or PathLike or file-like object\n",
      "        A path, or a Python file-like object, or\n",
      "        possibly some backend-dependent object such as\n",
      "        `matplotlib.backends.backend_pdf.PdfPages`.\n",
      "    \n",
      "        If *format* is not set, then the output format is inferred from\n",
      "        the extension of *fname*, if any, and from :rc:`savefig.format`\n",
      "        otherwise.  If *format* is set, it determines the output format.\n",
      "    \n",
      "        Hence, if *fname* is not a path or has no extension, remember to\n",
      "        specify *format* to ensure that the correct backend is used.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    \n",
      "    dpi : [ *None* | scalar > 0 | 'figure' ]\n",
      "        The resolution in dots per inch.  If *None*, defaults to\n",
      "        :rc:`savefig.dpi`.  If 'figure', uses the figure's dpi value.\n",
      "    \n",
      "    quality : [ *None* | 1 <= scalar <= 100 ]\n",
      "        The image quality, on a scale from 1 (worst) to 95 (best).\n",
      "        Applicable only if *format* is jpg or jpeg, ignored otherwise.\n",
      "        If *None*, defaults to :rc:`savefig.jpeg_quality` (95 by default).\n",
      "        Values above 95 should be avoided; 100 completely disables the\n",
      "        JPEG quantization stage.\n",
      "    \n",
      "    optimize : bool\n",
      "        If *True*, indicates that the JPEG encoder should make an extra\n",
      "        pass over the image in order to select optimal encoder settings.\n",
      "        Applicable only if *format* is jpg or jpeg, ignored otherwise.\n",
      "        Is *False* by default.\n",
      "    \n",
      "    progressive : bool\n",
      "        If *True*, indicates that this image should be stored as a\n",
      "        progressive JPEG file. Applicable only if *format* is jpg or\n",
      "        jpeg, ignored otherwise. Is *False* by default.\n",
      "    \n",
      "    facecolor : color spec or None, optional\n",
      "        The facecolor of the figure; if *None*, defaults to\n",
      "        :rc:`savefig.facecolor`.\n",
      "    \n",
      "    edgecolor : color spec or None, optional\n",
      "        The edgecolor of the figure; if *None*, defaults to\n",
      "        :rc:`savefig.edgecolor`\n",
      "    \n",
      "    orientation : {'landscape', 'portrait'}\n",
      "        Currently only supported by the postscript backend.\n",
      "    \n",
      "    papertype : str\n",
      "        One of 'letter', 'legal', 'executive', 'ledger', 'a0' through\n",
      "        'a10', 'b0' through 'b10'. Only supported for postscript\n",
      "        output.\n",
      "    \n",
      "    format : str\n",
      "        The file format, e.g. 'png', 'pdf', 'svg', ... The behavior when\n",
      "        this is unset is documented under *fname*.\n",
      "    \n",
      "    transparent : bool\n",
      "        If *True*, the axes patches will all be transparent; the\n",
      "        figure patch will also be transparent unless facecolor\n",
      "        and/or edgecolor are specified via kwargs.\n",
      "        This is useful, for example, for displaying\n",
      "        a plot on top of a colored background on a web page.  The\n",
      "        transparency of these patches will be restored to their\n",
      "        original values upon exit of this function.\n",
      "    \n",
      "    bbox_inches : str or `~matplotlib.transforms.Bbox`, optional\n",
      "        Bbox in inches. Only the given portion of the figure is\n",
      "        saved. If 'tight', try to figure out the tight bbox of\n",
      "        the figure. If None, use savefig.bbox\n",
      "    \n",
      "    pad_inches : scalar, optional\n",
      "        Amount of padding around the figure when bbox_inches is\n",
      "        'tight'. If None, use savefig.pad_inches\n",
      "    \n",
      "    bbox_extra_artists : list of `~matplotlib.artist.Artist`, optional\n",
      "        A list of extra artists that will be considered when the\n",
      "        tight bbox is calculated.\n",
      "    \n",
      "    metadata : dict, optional\n",
      "        Key/value pairs to store in the image metadata. The supported keys\n",
      "        and defaults depend on the image format and backend:\n",
      "    \n",
      "        - 'png' with Agg backend: See the parameter ``metadata`` of\n",
      "          `~.FigureCanvasAgg.print_png`.\n",
      "        - 'pdf' with pdf backend: See the parameter ``metadata`` of\n",
      "          `~.backend_pdf.PdfPages`.\n",
      "        - 'eps' and 'ps' with PS backend: Only 'Creator' is supported.\n",
      "    \n",
      "    pil_kwargs : dict, optional\n",
      "        Additional keyword arguments that are passed to `PIL.Image.save`\n",
      "        when saving the figure.  Only applicable for formats that are saved\n",
      "        using Pillow, i.e. JPEG, TIFF, and (if the keyword is set to a\n",
      "        non-None value) PNG.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pl.savefig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for all states income from 2005-2013\n",
    "## x-axis=years\n",
    "## y-axis=All states incomes\n",
    "import matplotlib.pyplot as pl\n",
    "import pandas as pd\n",
    "years=df.columns[-5::]\n",
    "allstate_income=df.iloc[0:,2:]\n",
    "allstate_income\n",
    "pl.plot(years,allstate_income)\n",
    "pl.title(\"All State income from 2009-2013\")\n",
    "pl.xlabel(\"years\")\n",
    "pl.ylabel(\"Income\")\n",
    "pl.savefig('All State income from 2009-2013.jpg')\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function xlabel in module matplotlib.pyplot:\n",
      "\n",
      "xlabel(xlabel, fontdict=None, labelpad=None, **kwargs)\n",
      "    Set the label for the x-axis.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    xlabel : str\n",
      "        The label text.\n",
      "    \n",
      "    labelpad : scalar, optional, default: None\n",
      "        Spacing in points from the axes bounding box including ticks\n",
      "        and tick labels.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs : `.Text` properties\n",
      "        `.Text` properties control the appearance of the label.\n",
      "    \n",
      "    See also\n",
      "    --------\n",
      "    text : for information on how override and the optional args work\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pl.xlabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}