Mh%SSKJr SSKrSSKJrJrJr SSKJr SSK J r J r SSK J r Jr SSKJrJr SSKJr \(a*SS KJrJr SSKrSS KJr SSKrSS KJr SS KJrJ r SS K!J"r" SS Sjjr#S!S"Sjjr$Sr%Sr&Sr'\ "SSS9\ "\%5S#S$Sjj55r(\ "S\&S9\ "\%5S%S&Sjj55r)S'S(Sjjr*"SS\5r+0r,S\-S'S)Sjr.S*S+Sjjr/g),) annotationsN) TYPE_CHECKINGCallableLiteral) get_option)Appender Substitution) is_integer is_list_like) ABCDataFrame ABCSeries) PandasObject)HashableSequence)Axes) IndexLabel) DataFrameSeries)DataFrameGroupByc  T[U 5n U R"U4UUUUUUUUU U S. U D6$)a< Draw histogram of the input series using matplotlib. Parameters ---------- by : object, optional If passed, then used to form histograms for separate groups. ax : matplotlib axis object If not passed, uses gca(). grid : bool, default True Whether to show axis grid lines. xlabelsize : int, default None If specified changes the x-axis label size. xrot : float, default None Rotation of x axis labels. ylabelsize : int, default None If specified changes the y-axis label size. yrot : float, default None Rotation of y axis labels. figsize : tuple, default None Figure size in inches by default. bins : int or sequence, default 10 Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified. backend : str, default None Backend to use instead of the backend specified in the option ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to specify the ``plotting.backend`` for the whole session, set ``pd.options.plotting.backend``. legend : bool, default False Whether to show the legend. **kwargs To be passed to the actual plotting function. Returns ------- matplotlib.AxesSubplot A histogram plot. See Also -------- matplotlib.axes.Axes.hist : Plot a histogram using matplotlib. Examples -------- For Series: .. plot:: :context: close-figs >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst) >>> hist = ser.hist() For Groupby: .. plot:: :context: close-figs >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst) >>> hist = ser.groupby(level=0).hist() ) byaxgrid xlabelsizexrot ylabelsizeyrotfigsizebinslegend)_get_plot_backend hist_series)selfrrrrrrrrrbackendr kwargs plot_backends G/var/www/html/env/lib/python3.13/site-packages/pandas/plotting/_core.pyr"r"/sRb%W-L  # #          c \[U5nUR"U4UUUUUUUUU U U U UU S.UD6$)a Make a histogram of the DataFrame's columns. A `histogram`_ is a representation of the distribution of data. This function calls :meth:`matplotlib.pyplot.hist`, on each series in the DataFrame, resulting in one histogram per column. .. _histogram: https://en.wikipedia.org/wiki/Histogram Parameters ---------- data : DataFrame The pandas object holding the data. column : str or sequence, optional If passed, will be used to limit data to a subset of columns. by : object, optional If passed, then used to form histograms for separate groups. grid : bool, default True Whether to show axis grid lines. xlabelsize : int, default None If specified changes the x-axis label size. xrot : float, default None Rotation of x axis labels. For example, a value of 90 displays the x labels rotated 90 degrees clockwise. ylabelsize : int, default None If specified changes the y-axis label size. yrot : float, default None Rotation of y axis labels. For example, a value of 90 displays the y labels rotated 90 degrees clockwise. ax : Matplotlib axes object, default None The axes to plot the histogram on. sharex : bool, default True if ax is None else False In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure. sharey : bool, default False In case subplots=True, share y axis and set some y axis labels to invisible. figsize : tuple, optional The size in inches of the figure to create. Uses the value in `matplotlib.rcParams` by default. layout : tuple, optional Tuple of (rows, columns) for the layout of the histograms. bins : int or sequence, default 10 Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified. backend : str, default None Backend to use instead of the backend specified in the option ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to specify the ``plotting.backend`` for the whole session, set ``pd.options.plotting.backend``. legend : bool, default False Whether to show the legend. **kwargs All other plotting keyword arguments to be passed to :meth:`matplotlib.pyplot.hist`. Returns ------- matplotlib.AxesSubplot or numpy.ndarray of them See Also -------- matplotlib.pyplot.hist : Plot a histogram using matplotlib. Examples -------- This example draws a histogram based on the length and width of some animals, displayed in three bins .. plot:: :context: close-figs >>> data = {'length': [1.5, 0.5, 1.2, 0.9, 3], ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]} >>> index = ['pig', 'rabbit', 'duck', 'chicken', 'horse'] >>> df = pd.DataFrame(data, index=index) >>> hist = df.hist(bins=3) )columnrrrrrrrsharexshareyrlayoutr r)r! hist_frame)datar*rrrrrrrr+r,rr-rr$r r%r&s r'r.r.s^R%W-L  " "         ! r(a Make a box plot from DataFrame columns. Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than `1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots. For further details see Wikipedia's entry for `boxplot `_. Parameters ---------- %(data)scolumn : str or list of str, optional Column name or list of names, or vector. Can be any valid input to :meth:`pandas.DataFrame.groupby`. by : str or array-like, optional Column in the DataFrame to :meth:`pandas.DataFrame.groupby`. One box-plot will be done per value of columns in `by`. ax : object of class matplotlib.axes.Axes, optional The matplotlib axes to be used by boxplot. fontsize : float or str Tick label font size in points or as a string (e.g., `large`). rot : float, default 0 The rotation angle of labels (in degrees) with respect to the screen coordinate system. grid : bool, default True Setting this to True will show the grid. figsize : A tuple (width, height) in inches The size of the figure to create in matplotlib. layout : tuple (rows, columns), optional For example, (3, 5) will display the subplots using 3 rows and 5 columns, starting from the top-left. return_type : {'axes', 'dict', 'both'} or None, default 'axes' The kind of object to return. The default is ``axes``. * 'axes' returns the matplotlib axes the boxplot is drawn on. * 'dict' returns a dictionary whose values are the matplotlib Lines of the boxplot. * 'both' returns a namedtuple with the axes and dict. * when grouping with ``by``, a Series mapping columns to ``return_type`` is returned. If ``return_type`` is `None`, a NumPy array of axes with the same shape as ``layout`` is returned. %(backend)s **kwargs All other plotting keyword arguments to be passed to :func:`matplotlib.pyplot.boxplot`. Returns ------- result See Notes. See Also -------- pandas.Series.plot.hist: Make a histogram. matplotlib.pyplot.boxplot : Matplotlib equivalent plot. Notes ----- The return type depends on the `return_type` parameter: * 'axes' : object of class matplotlib.axes.Axes * 'dict' : dict of matplotlib.lines.Line2D objects * 'both' : a namedtuple with structure (ax, lines) For data grouped with ``by``, return a Series of the above or a numpy array: * :class:`~pandas.Series` * :class:`~numpy.array` (for ``return_type = None``) Use ``return_type='dict'`` when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned. Examples -------- Boxplots can be created for every column in the dataframe by ``df.boxplot()`` or indicating the columns to be used: .. plot:: :context: close-figs >>> np.random.seed(1234) >>> df = pd.DataFrame(np.random.randn(10, 4), ... columns=['Col1', 'Col2', 'Col3', 'Col4']) >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3']) # doctest: +SKIP Boxplots of variables distributions grouped by the values of a third variable can be created using the option ``by``. For instance: .. plot:: :context: close-figs >>> df = pd.DataFrame(np.random.randn(10, 2), ... columns=['Col1', 'Col2']) >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', ... 'B', 'B', 'B', 'B', 'B']) >>> boxplot = df.boxplot(by='X') A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot in order to group the data by combination of the variables in the x-axis: .. plot:: :context: close-figs >>> df = pd.DataFrame(np.random.randn(10, 3), ... columns=['Col1', 'Col2', 'Col3']) >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', ... 'B', 'B', 'B', 'B', 'B']) >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', ... 'B', 'A', 'B', 'A', 'B']) >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y']) The layout of boxplot can be adjusted giving a tuple to ``layout``: .. plot:: :context: close-figs >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... layout=(2, 1)) Additional formatting can be done to the boxplot, like suppressing the grid (``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``) or changing the fontsize (i.e. ``fontsize=15``): .. plot:: :context: close-figs >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15) # doctest: +SKIP The parameter ``return_type`` can be used to select the type of element returned by `boxplot`. When ``return_type='axes'`` is selected, the matplotlib axes on which the boxplot is drawn are returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes') >>> type(boxplot) When grouping with ``by``, a Series mapping columns to ``return_type`` is returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... return_type='axes') >>> type(boxplot) If ``return_type`` is `None`, a NumPy array of axes with the same shape as ``layout`` is returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... return_type=None) >>> type(boxplot) a backend : str, default None Backend to use instead of the backend specified in the option ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to specify the ``plotting.backend`` for the whole session, set ``pd.options.plotting.backend``. a Parameters ---------- x : label or position, optional Allows plotting of one column versus another. If not specified, the index of the DataFrame is used. y : label or position, optional Allows plotting of one column versus another. If not specified, all numerical columns are used. color : str, array-like, or dict, optional The color for each of the DataFrame's columns. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each column recursively. For instance ['green','yellow'] each column's %(kind)s will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used. - A dict of the form {column name : color}, so that each column will be colored accordingly. For example, if your columns are called `a` and `b`, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for column `a` in green and %(kind)ss for column `b` in red. **kwargs Additional keyword arguments are documented in :meth:`DataFrame.plot`. Returns ------- matplotlib.axes.Axes or np.ndarray of them An ndarray is returned with one :class:`matplotlib.axes.Axes` per column when ``subplots=True``. z,data : DataFrame The data to visualize. )r/r$c R[S5n U R"U4UUUUUUUUU S. U D6$)N matplotlib r*rrfontsizerotrrr- return_type)r!boxplot) r/r*rrr4r5rrr-r6r%r&s r'r7r7sN%\2L            r(c R[U 5n U R"U4UUUUUUUUU S. U D6$)Nr3)r! boxplot_frame) r#r*rrr4r5rrr-r6r$r%r&s r'r9r9sN %W-L  % %         r(c  T[U 5n U R"U4UUUUUUUUU U S. U D6$)a Make box plots from DataFrameGroupBy data. Parameters ---------- grouped : Grouped DataFrame subplots : bool * ``False`` - no subplots will be used * ``True`` - create a subplot for each group. column : column name or list of names, or vector Can be any valid input to groupby. fontsize : float or str rot : label rotation angle grid : Setting this to True will show the grid ax : Matplotlib axis object, default None figsize : A tuple (width, height) in inches layout : tuple (optional) The layout of the plot: (rows, columns). sharex : bool, default False Whether x-axes will be shared among subplots. sharey : bool, default True Whether y-axes will be shared among subplots. backend : str, default None Backend to use instead of the backend specified in the option ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to specify the ``plotting.backend`` for the whole session, set ``pd.options.plotting.backend``. **kwargs All other plotting keyword arguments to be passed to matplotlib's boxplot function. Returns ------- dict of key/value = group key/DataFrame.boxplot return value or DataFrame.boxplot return value in case subplots=figures=False Examples -------- You can create boxplots for grouped data and show them as separate subplots: .. plot:: :context: close-figs >>> import itertools >>> tuples = [t for t in itertools.product(range(1000), range(4))] >>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1']) >>> data = np.random.randn(len(index), 4) >>> df = pd.DataFrame(data, columns=list('ABCD'), index=index) >>> grouped = df.groupby(level='lvl1') >>> grouped.boxplot(rot=45, fontsize=12, figsize=(8, 10)) # doctest: +SKIP The ``subplots=False`` option shows the boxplots in a single figure. .. plot:: :context: close-figs >>> grouped.boxplot(subplots=False, rot=45, fontsize=12) # doctest: +SKIP ) subplotsr*r4r5rrrr-r+r,)r!boxplot_frame_groupby)groupedr;r*r4r5rrrr-r+r,r$r%r&s r'r<r<$sRT%W-L  - -        r(c8\rSrSrSrSrSrSrSS0r\\-\-r SSjr \ SS j5r S r \\ l\"S 5\"S S 9\"\5SS Sjj555r\"S5\"SS 9\"\5SS Sjj555r\"S5\"SS 9\"\5SS Sjj555rS!S"SjjrS#S$SjjrSS%Sjjr\rS&S'SjjrS(SjrSS)SjjrS*S+SjjrSrg), PlotAccessoriaz Make plots of Series or DataFrame. Uses the backend specified by the option ``plotting.backend``. By default, matplotlib is used. Parameters ---------- data : Series or DataFrame The object for which the method is called. x : label or position, default None Only used if data is a DataFrame. y : label, position or list of label, positions, default None Allows plotting of one column versus another. Only used if data is a DataFrame. kind : str The kind of plot to produce: - 'line' : line plot (default) - 'bar' : vertical bar plot - 'barh' : horizontal bar plot - 'hist' : histogram - 'box' : boxplot - 'kde' : Kernel Density Estimation plot - 'density' : same as 'kde' - 'area' : area plot - 'pie' : pie plot - 'scatter' : scatter plot (DataFrame only) - 'hexbin' : hexbin plot (DataFrame only) ax : matplotlib axes object, default None An axes of the current figure. subplots : bool or sequence of iterables, default False Whether to group columns into subplots: - ``False`` : No subplots will be used - ``True`` : Make separate subplots for each column. - sequence of iterables of column labels: Create a subplot for each group of columns. For example `[('a', 'c'), ('b', 'd')]` will create 2 subplots: one with columns 'a' and 'c', and one with columns 'b' and 'd'. Remaining columns that aren't specified will be plotted in additional subplots (one per column). .. versionadded:: 1.5.0 sharex : bool, default True if ax is None else False In case ``subplots=True``, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and ``sharex=True`` will alter all x axis labels for all axis in a figure. sharey : bool, default False In case ``subplots=True``, share y axis and set some y axis labels to invisible. layout : tuple, optional (rows, columns) for the layout of subplots. figsize : a tuple (width, height) in inches Size of a figure object. use_index : bool, default True Use index as ticks for x axis. title : str or list Title to use for the plot. If a string is passed, print the string at the top of the figure. If a list is passed and `subplots` is True, print each item in the list above the corresponding subplot. grid : bool, default None (matlab style default) Axis grid lines. legend : bool or {'reverse'} Place legend on axis subplots. style : list or dict The matplotlib line style per column. logx : bool or 'sym', default False Use log scaling or symlog scaling on x axis. logy : bool or 'sym' default False Use log scaling or symlog scaling on y axis. loglog : bool or 'sym', default False Use log scaling or symlog scaling on both x and y axes. xticks : sequence Values to use for the xticks. yticks : sequence Values to use for the yticks. xlim : 2-tuple/list Set the x limits of the current axes. ylim : 2-tuple/list Set the y limits of the current axes. xlabel : label, optional Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the x-column name for planar plots. .. versionchanged:: 2.0.0 Now applicable to histograms. ylabel : label, optional Name to use for the ylabel on y-axis. Default will show no ylabel, or the y-column name for planar plots. .. versionchanged:: 2.0.0 Now applicable to histograms. rot : float, default None Rotation for ticks (xticks for vertical, yticks for horizontal plots). fontsize : float, default None Font size for xticks and yticks. colormap : str or matplotlib colormap object, default None Colormap to select colors from. If string, load colormap with that name from matplotlib. colorbar : bool, optional If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots). position : float Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center). table : bool, Series or DataFrame, default False If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib's default layout. If a Series or DataFrame is passed, use passed data to draw a table. yerr : DataFrame, Series, array-like, dict and str See :ref:`Plotting with Error Bars ` for detail. xerr : DataFrame, Series, array-like, dict and str Equivalent to yerr. stacked : bool, default False in line and bar plots, and True in area plot If True, create stacked plot. secondary_y : bool or sequence, default False Whether to plot on the secondary y-axis if a list/tuple, which columns to plot on secondary y-axis. mark_right : bool, default True When using a secondary_y axis, automatically mark the column labels with "(right)" in the legend. include_bool : bool, default is False If True, boolean values can be plotted. backend : str, default None Backend to use instead of the backend specified in the option ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to specify the ``plotting.backend`` for the whole session, set ``pd.options.plotting.backend``. **kwargs Options to pass to matplotlib plotting method. Returns ------- :class:`matplotlib.axes.Axes` or numpy.ndarray of them If the backend is not the default matplotlib one, the return value will be the object returned by the backend. Notes ----- - See matplotlib documentation online for more on this subject - If `kind` = 'bar' or 'barh', you can specify relative alignments for bar plot layout by `position` keyword. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) Examples -------- For Series: .. plot:: :context: close-figs >>> ser = pd.Series([1, 2, 3, 3]) >>> plot = ser.plot(kind='hist', title="My plot") For DataFrame: .. plot:: :context: close-figs >>> df = pd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3], ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]}, ... index=['pig', 'rabbit', 'duck', 'chicken', 'horse']) >>> plot = df.plot(title="DataFrame Plot") For SeriesGroupBy: .. plot:: :context: close-figs >>> lst = [-1, -2, -3, 1, 2, 3] >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst) >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot") For DataFrameGroupBy: .. plot:: :context: close-figs >>> df = pd.DataFrame({"col1" : [1, 2, 3, 4], ... "col2" : ["A", "B", "A", "B"]}) >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot") )linebarbarhkdedensityareahistbox)pie)scatterhexbinrDrCcXlgN_parent)r#r/s r'__init__PlotAccessor.__init__Js r(c [U[5(a/SQnO<[U[5(a/SQnO"[S[ U5R S35eU(ax[U[5(ac[ U5SSnSR[XB5VVVs/sHuupgoS[U53PM snnn5n S U S US 3n [U 5e[XB5VVVs0sH uupgoU_M n nnnUS :Xa[U40U DUD6nO [U 40UD6nURS S5n URSS5n URSS5nXX4$s snnnfs snnnf)a This function makes calls to this accessor `__call__` method compatible with the previous `SeriesPlotMethods.__call__` and `DataFramePlotMethods.__call__`. Those had slightly different signatures, since `DataFramePlotMethods` accepted `x` and `y` parameters. )kindr@rNrN use_indexTtitleNrN)r FstyleNlogxFlogyFloglogFxticksNyticksNxlimNylimNr5Nr4NcolormapNtableFyerrNxerrN)labelN secondary_yFxlabelNylabelN))xN)yNrRrT)r;F)r+N)r,F)r-NrUrVrXrZ)r Tr[r]r_rarcrergrirkrlrmrorqrsrvrxrzzCalled plot accessor for type z, expected Series or DataFramez, =z`Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(z)` instead of `Series.plot(z)`.pandas.plotting._matplotlibr|Nr}rSr@) isinstancer r TypeErrortype__name__strjoinzipreprdictpop) backend_namer/argsr%arg_defpositional_argsname_value keyword_argsmsgpos_argsr|r}rSs r'_get_call_argsPlotAccessor._get_call_argsMs| dI & &G6l + +GB0d1D1D0EF//  JtY//!$i"oO99ADWASTAS-=Yd64;-(ASTL$%1>2 /0 5 C. 8;G8JK8J$4ITu%K8JK 8 8'8X88F(-f-F JJsD ! JJsD !zz&&)T!!)ULs E %Ec[URSS55nURURURX5upEpbUR R Xf5nURS:wa UR"UR4XEUS.UD6$X`R;a[USUR35eURR5n[U[5(aSUS'X`R;a:[U[5(aUR"U4XEUS.UD6$[SUS35eX`R;a[U[5(aUc"UR S 5S La[US 35eUb`[!U5(a.UR"R%5(dUR"UnXuR5nXWR&lGO[U[5(GaUR"nUbg[!U5(a$UR"R%5(dXnO"[Xt[5(d [S 5eUR+U5nUGbJ[-U5=(a [/S U55n [!U5=(d U n U (a#UR"R%5(dXnSU;aUSOS n SH?n X;dM [X,[05(d[!X,5(dM5XrU X,'MA XuR5n[U[5(aU =(d Un XlO][-U 5=(a [9U 5[9U5:HnU (aU(d [S5eU =(d UR"n XlUR"U4SU0UD6$![2[4[64a GMf=f)Nr$r)r|r}rSz, is not a valid plot kind Valid plot kinds: T reuse_plotz plot kind z! can only be used for data framesr;Fz, requires either y column or 'subplots=True'zx must be a label or positionc38# UHn[U5v M g7frL)r ).0cs r' (PlotAccessor.__call__..s3M1aJqMM1sru)rtrrz.label should be list-like and same length as yrS)r!rrrrN _kind_aliasesgetplot _all_kinds ValueErrorcopyrr _dataframe_kindsr _series_kindsr columns_holds_integerindexr set_indexr allr IndexErrorKeyErrorrlen)r#rr%r&r|r}rSr/ data_cols int_ylist int_y_arglabel_kwkw label_namematchs r'__call__PlotAccessor.__call__s\(It)DE !00  ! !4<< d!!%%d1  $A A$$T\\QQ$Q&Q Q  &&%%)__$57 ||  " dI & &#'F< (( ($ --#((MdMfMM :dV3T!UVV '' '$ --9J!75!@$& LM=!!}}T\\-H-H-J-J LLO7<<>D&'JJO l + + I}a==)D)D)F)F! A#DGY77$%DEE~~a(}(OM3M13M0M &qM6Y T\\%@%@%B%B! A.5.?6'?U*B|"6:s33z&*7M7M!)-Rj)9FJ +w||~dI..!)QJ *I(2Ns8}A7NE(L"*!9T\\J#-L  ;D;F;;%!+Hi@! !s OO.-O.a See Also -------- matplotlib.pyplot.plot : Plot y versus x as lines and/or markers. Examples -------- .. plot:: :context: close-figs >>> s = pd.Series([1, 3, 2]) >>> s.plot.line() # doctest: +SKIP .. plot:: :context: close-figs The following example shows the populations for some animals over the years. >>> df = pd.DataFrame({ ... 'pig': [20, 18, 489, 675, 1776], ... 'horse': [4, 25, 281, 600, 1900] ... }, index=[1990, 1997, 2003, 2009, 2014]) >>> lines = df.plot.line() .. plot:: :context: close-figs An example with subplots, so an array of axes is returned. >>> axes = df.plot.line(subplots=True) >>> type(axes) .. plot:: :context: close-figs Let's repeat the same example, but specifying colors for each column (in this case, for each animal). >>> axes = df.plot.line( ... subplots=True, color={"pig": "pink", "horse": "#742802"} ... ) .. plot:: :context: close-figs The following example shows the relationship between both populations. >>> lines = df.plot.line(x='pig', y='horse') r@)rSNc U"SSXS.UD6$)zt Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame's values as coordinates. r@rSr|r}rr#r|r}r%s r'r@PlotAccessor.line sD414V44r(a See Also -------- DataFrame.plot.barh : Horizontal bar plot. DataFrame.plot : Make plots of a DataFrame. matplotlib.pyplot.bar : Make a bar plot with matplotlib. Examples -------- Basic plot. .. plot:: :context: close-figs >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]}) >>> ax = df.plot.bar(x='lab', y='val', rot=0) Plot a whole dataframe to a bar plot. Each column is assigned a distinct color, and each row is nested in a group along the horizontal axis. .. plot:: :context: close-figs >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] >>> index = ['snail', 'pig', 'elephant', ... 'rabbit', 'giraffe', 'coyote', 'horse'] >>> df = pd.DataFrame({'speed': speed, ... 'lifespan': lifespan}, index=index) >>> ax = df.plot.bar(rot=0) Plot stacked bar charts for the DataFrame .. plot:: :context: close-figs >>> ax = df.plot.bar(stacked=True) Instead of nesting, the figure can be split by column with ``subplots=True``. In this case, a :class:`numpy.ndarray` of :class:`matplotlib.axes.Axes` are returned. .. plot:: :context: close-figs >>> axes = df.plot.bar(rot=0, subplots=True) >>> axes[1].legend(loc=2) # doctest: +SKIP If you don't like the default colours, you can specify how you'd like each column to be colored. .. plot:: :context: close-figs >>> axes = df.plot.bar( ... rot=0, subplots=True, color={"speed": "red", "lifespan": "green"} ... ) >>> axes[1].legend(loc=2) # doctest: +SKIP Plot a single column. .. plot:: :context: close-figs >>> ax = df.plot.bar(y='speed', rot=0) Plot only selected categories for the DataFrame. .. plot:: :context: close-figs >>> ax = df.plot.bar(x='lifespan', rot=0) rAc U"SSXS.UD6$)aG Vertical bar plot. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value. rArrrrs r'rAPlotAccessor.barNst3!3F33r(a See Also -------- DataFrame.plot.bar: Vertical bar plot. DataFrame.plot : Make plots of DataFrame using matplotlib. matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib. Examples -------- Basic example .. plot:: :context: close-figs >>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]}) >>> ax = df.plot.barh(x='lab', y='val') Plot a whole DataFrame to a horizontal bar plot .. plot:: :context: close-figs >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] >>> index = ['snail', 'pig', 'elephant', ... 'rabbit', 'giraffe', 'coyote', 'horse'] >>> df = pd.DataFrame({'speed': speed, ... 'lifespan': lifespan}, index=index) >>> ax = df.plot.barh() Plot stacked barh charts for the DataFrame .. plot:: :context: close-figs >>> ax = df.plot.barh(stacked=True) We can specify colors for each column .. plot:: :context: close-figs >>> ax = df.plot.barh(color={"speed": "red", "lifespan": "green"}) Plot a column of the DataFrame to a horizontal bar plot .. plot:: :context: close-figs >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] >>> index = ['snail', 'pig', 'elephant', ... 'rabbit', 'giraffe', 'coyote', 'horse'] >>> df = pd.DataFrame({'speed': speed, ... 'lifespan': lifespan}, index=index) >>> ax = df.plot.barh(y='speed') Plot DataFrame versus the desired column .. plot:: :context: close-figs >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] >>> index = ['snail', 'pig', 'elephant', ... 'rabbit', 'giraffe', 'coyote', 'horse'] >>> df = pd.DataFrame({'speed': speed, ... 'lifespan': lifespan}, index=index) >>> ax = df.plot.barh(x='lifespan') c U"SSXS.UD6$)a\ Make a horizontal bar plot. A horizontal bar plot is a plot that presents quantitative data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value. rBrrrrs r'rBPlotAccessor.barhsl414V44r(c U"SSUS.UD6$)aq Make a box plot of the DataFrame columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. The position of the whiskers is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the box. Outlier points are those past the end of the whiskers. For further details see Wikipedia's entry for `boxplot `__. A consideration when using this chart is that the box and the whiskers can overlap, which is very common when plotting small sets of data. Parameters ---------- by : str or sequence Column in the DataFrame to group by. .. versionchanged:: 1.4.0 Previously, `by` is silently ignore and makes no groupings **kwargs Additional keywords are documented in :meth:`DataFrame.plot`. Returns ------- :class:`matplotlib.axes.Axes` or numpy.ndarray of them See Also -------- DataFrame.boxplot: Another method to draw a box plot. Series.plot.box: Draw a box plot from a Series object. matplotlib.pyplot.boxplot: Draw a box plot in matplotlib. Examples -------- Draw a box plot from a DataFrame with four columns of randomly generated data. .. plot:: :context: close-figs >>> data = np.random.randn(25, 4) >>> df = pd.DataFrame(data, columns=list('ABCD')) >>> ax = df.plot.box() You can also generate groupings if you specify the `by` parameter (which can take a column name, or a list or tuple of column names): .. versionchanged:: 1.4.0 .. plot:: :context: close-figs >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85] >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list}) >>> ax = df.plot.box(column="age", by="gender", figsize=(10, 8)) rG)rSrrr)r#rr%s r'rGPlotAccessor.boxsB02000r(c U"SSXS.UD6$)a Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. .. versionchanged:: 1.4.0 Previously, `by` is silently ignore and makes no groupings bins : int, default 10 Number of histogram bins to be used. **kwargs Additional keyword arguments are documented in :meth:`DataFrame.plot`. Returns ------- class:`matplotlib.AxesSubplot` Return a histogram plot. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we roll a die 6000 times, we expect to get each value around 1000 times. But when we roll two dice and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) A grouped histogram can be generated by providing the parameter `by` (which can be a column name, or a list of column names): .. plot:: :context: close-figs >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85] >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list}) >>> ax = df.plot.hist(column=["age"], by="gender", figsize=(10, 8)) rF)rSrrrr)r#rrr%s r'rFPlotAccessor.histEsx<B>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5]) >>> ax = s.plot.kde() A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting: .. plot:: :context: close-figs >>> ax = s.plot.kde(bw_method=0.3) .. plot:: :context: close-figs >>> ax = s.plot.kde(bw_method=3) Finally, the `ind` parameter determines the evaluation points for the plot of the estimated PDF: .. plot:: :context: close-figs >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5]) For DataFrame, it works in the same way: .. plot:: :context: close-figs >>> df = pd.DataFrame({ ... 'x': [1, 2, 2.5, 3, 3.5, 4, 5], ... 'y': [4, 4, 4.5, 5, 5.5, 6, 6], ... }) >>> ax = df.plot.kde() A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting: .. plot:: :context: close-figs >>> ax = df.plot.kde(bw_method=0.3) .. plot:: :context: close-figs >>> ax = df.plot.kde(bw_method=3) Finally, the `ind` parameter determines the evaluation points for the plot of the estimated PDF: .. plot:: :context: close-figs >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6]) rC)rS bw_methodindrr)r#rrr%s r'rCPlotAccessor.kdes^G)GGGr(c U"SSXUS.UD6$)av Draw a stacked area plot. An area plot displays quantitative data visually. This function wraps the matplotlib area function. Parameters ---------- x : label or position, optional Coordinates for the X axis. By default uses the index. y : label or position, optional Column to plot. By default uses all columns. stacked : bool, default True Area plots are stacked by default. Set to False to create a unstacked plot. **kwargs Additional keyword arguments are documented in :meth:`DataFrame.plot`. Returns ------- matplotlib.axes.Axes or numpy.ndarray Area plot, or array of area plots if subplots is True. See Also -------- DataFrame.plot : Make plots of DataFrame using matplotlib / pylab. Examples -------- Draw an area plot based on basic business metrics: .. plot:: :context: close-figs >>> df = pd.DataFrame({ ... 'sales': [3, 2, 3, 9, 10, 6], ... 'signups': [5, 5, 6, 12, 14, 13], ... 'visits': [20, 42, 28, 62, 81, 50], ... }, index=pd.date_range(start='2018/01/01', end='2018/07/01', ... freq='ME')) >>> ax = df.plot.area() Area plots are stacked by default. To produce an unstacked plot, pass ``stacked=False``: .. plot:: :context: close-figs >>> ax = df.plot.area(stacked=False) Draw an area plot for a single column: .. plot:: :context: close-figs >>> ax = df.plot.area(y='sales') Draw with a different `x`: .. plot:: :context: close-figs >>> df = pd.DataFrame({ ... 'sales': [3, 2, 3], ... 'visits': [20, 42, 28], ... 'day': [1, 2, 3], ... }) >>> ax = df.plot.area(x='day') rE)rSr|r}stackedrr)r#r|r}rr%s r'rEPlotAccessor.areasZE17EfEEr(c [UR[5(a5URSS5c"URSS5(d [ S5eU"SSS0UD6$) a Generate a pie plot. A pie plot is a proportional representation of the numerical data in a column. This function wraps :meth:`matplotlib.pyplot.pie` for the specified column. If no column reference is passed and ``subplots=True`` a pie plot is drawn for each numerical column independently. Parameters ---------- y : int or label, optional Label or position of the column to plot. If not provided, ``subplots=True`` argument must be passed. **kwargs Keyword arguments to pass on to :meth:`DataFrame.plot`. Returns ------- matplotlib.axes.Axes or np.ndarray of them A NumPy array is returned when `subplots` is True. See Also -------- Series.plot.pie : Generate a pie plot for a Series. DataFrame.plot : Make plots of a DataFrame. Examples -------- In the example below we have a DataFrame with the information about planet's mass and radius. We pass the 'mass' column to the pie function to get a pie plot. .. plot:: :context: close-figs >>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97], ... 'radius': [2439.7, 6051.8, 6378.1]}, ... index=['Mercury', 'Venus', 'Earth']) >>> plot = df.plot.pie(y='mass', figsize=(5, 5)) .. plot:: :context: close-figs >>> plot = df.plot.pie(subplots=True, figsize=(11, 6)) r}Nr;Fz/pie requires either y column or 'subplots=True'rSrHr)rrNr rr)r#r%s r'rHPlotAccessor.pieEsW` t||\ 2 2 3%-JJz511NO O))&))r(c U"SSXX4S.UD6$)a8 Create a scatter plot with varying marker point size and color. The coordinates of each point are defined by two dataframe columns and filled circles are used to represent each point. This kind of plot is useful to see complex correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters ---------- x : int or str The column name or column position to be used as horizontal coordinates for each point. y : int or str The column name or column position to be used as vertical coordinates for each point. s : str, scalar or array-like, optional The size of each point. Possible values are: - A string with the name of the column to be used for marker's size. - A single scalar so all points have the same size. - A sequence of scalars, which will be used for each point's size recursively. For instance, when passing [2,14] all points size will be either 2 or 14, alternatively. c : str, int or array-like, optional The color of each point. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each point's color recursively. For instance ['green','yellow'] all points will be filled in green or yellow, alternatively. - A column name or position whose values will be used to color the marker points according to a colormap. **kwargs Keyword arguments to pass on to :meth:`DataFrame.plot`. Returns ------- :class:`matplotlib.axes.Axes` or numpy.ndarray of them See Also -------- matplotlib.pyplot.scatter : Scatter plot using multiple input data formats. Examples -------- Let's see how to draw a scatter plot using coordinates from the values in a DataFrame's columns. .. plot:: :context: close-figs >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], ... [6.4, 3.2, 1], [5.9, 3.0, 2]], ... columns=['length', 'width', 'species']) >>> ax1 = df.plot.scatter(x='length', ... y='width', ... c='DarkBlue') And now with the color determined by a column as well. .. plot:: :context: close-figs >>> ax2 = df.plot.scatter(x='length', ... y='width', ... c='species', ... colormap='viridis') rI)rSr|r}srrr)r#r|r}rrr%s r'rIPlotAccessor.scatter|spAaA&AAr(c 6UbXFS'UbXVS'U"SSXUS.UD6$)a Generate a hexagonal binning plot. Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None` (the default), this is a histogram of the number of occurrences of the observations at ``(x[i], y[i])``. If `C` is specified, specifies values at given coordinates ``(x[i], y[i])``. These values are accumulated for each hexagonal bin and then reduced according to `reduce_C_function`, having as default the NumPy's mean function (:meth:`numpy.mean`). (If `C` is specified, it must also be a 1-D sequence of the same length as `x` and `y`, or a column label.) Parameters ---------- x : int or str The column label or position for x points. y : int or str The column label or position for y points. C : int or str, optional The column label or position for the value of `(x, y)` point. reduce_C_function : callable, default `np.mean` Function of one argument that reduces all the values in a bin to a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`). gridsize : int or tuple of (int, int), default 100 The number of hexagons in the x-direction. The corresponding number of hexagons in the y-direction is chosen in a way that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the x-direction and the y-direction. **kwargs Additional keyword arguments are documented in :meth:`DataFrame.plot`. Returns ------- matplotlib.AxesSubplot The matplotlib ``Axes`` on which the hexbin is plotted. See Also -------- DataFrame.plot : Make plots of a DataFrame. matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib, the matplotlib function that is used under the hood. Examples -------- The following examples are generated with random data from a normal distribution. .. plot:: :context: close-figs >>> n = 10000 >>> df = pd.DataFrame({'x': np.random.randn(n), ... 'y': np.random.randn(n)}) >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20) The next example uses `C` and `np.sum` as `reduce_C_function`. Note that `'observations'` values ranges from 1 to 5 but the result plot shows values up to more than 25. This is because of the `reduce_C_function`. .. plot:: :context: close-figs >>> n = 500 >>> df = pd.DataFrame({ ... 'coord_x': np.random.uniform(-3, 3, size=n), ... 'coord_y': np.random.uniform(30, 50, size=n), ... 'observations': np.random.randint(1,5, size=n) ... }) >>> ax = df.plot.hexbin(x='coord_x', ... y='coord_y', ... C='observations', ... reduce_C_function=np.sum, ... gridsize=10, ... cmap="viridis") reduce_C_functiongridsizerJ)rSr|r}Crr)r#r|r}rrrr%s r'rJPlotAccessor.hexbins9t  (*;& '  !): ;Qq;F;;r(rM)r/Series | DataFramereturnNone)rrr/r)NN)r|Hashable | Noner}rrr?rL)rIndexLabel | Nonerr?)N )rrrintrr?)rz7Literal['scott', 'silverman'] | float | Callable | Nonerznp.ndarray | int | Nonerr?)NNT)r|rr}rrboolrr?)rr?) r|rr}rr$Hashable | Sequence[Hashable] | Nonerrrr?)NNN) r|rr}rrrrzCallable | Nonerzint | tuple[int, int] | Nonerr?)r __module__ __qualname____firstlineno____doc__ _common_kindsrrrrrO staticmethodrrrr _bar_or_line_docr@rArBrGrFrCrDrErHrIrJ__static_attributes__rr(r'r?r?sBHUMM,&M.1AAJb"b"HTB 5  5,; 5  5 o6r 5I KXu >B 4  4,; 4  4 YK\ 4E GPu >B 5  5,; 5  5 QGT 5A1H9;<=#<=25<= <=@NR'+oHJoH%oH  oHbG"! MF MF MF MF  MF^5*v3726 XB XB XB 0 XB 0 XB XB|"-115 _< _< _<  _< + _< / _< _<_rs" &  *$)=   !!&* "_ _  _  _  __ _$_ __ _H!% !! &*%) "!{ { {  {  {  {{ { { {${ #{ {{ !{|d L $NCRP ,&*!%#'*.%)"  "    !   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