4i$a SSKJr SSKJr SSKJrJr SSKJr SSK J r \(a,SSK J r J r SSKJr SSKJr SS KJr SS KJr SS KrSS KJrJr \"S 5SSj5r\"S 5SSj5r\"S 5SSj5r\"S 5SSSjj5r\"S 5SSSjj5r\"S 5S S!Sjj5r \"S 5S"S#Sjj5r!\"S 5S$S%Sjj5r"\"S 5S&S'Sjj5r#\"S 5S(S)Sjj5r$"SS\%5r&\&"5r'S \'l(g )*) annotations)contextmanager) TYPE_CHECKINGAny) set_module)_get_plot_backend) GeneratorMapping)Axes)Colormap)Figure)TableN) DataFrameSerieszpandas.plottingc D[S5nUR"SXSSS.UD6$)a Helper function to convert DataFrame and Series to matplotlib.table. This method provides an easy way to visualize tabular data within a Matplotlib figure. It automatically extracts index and column labels from the DataFrame or Series, unless explicitly specified. This function is particularly useful when displaying summary tables alongside other plots or when creating static reports. It utilizes the `matplotlib.pyplot.table` backend and allows customization through various styling options available in Matplotlib. Parameters ---------- ax : Matplotlib axes object The axes on which to draw the table. data : DataFrame or Series Data for table contents. **kwargs Keyword arguments to be passed to matplotlib.table.table. If `rowLabels` or `colLabels` is not specified, data index or column names will be used. Returns ------- matplotlib table object The created table as a matplotlib Table object. See Also -------- DataFrame.plot : Make plots of DataFrame using matplotlib. matplotlib.pyplot.table : Create a table from data in a Matplotlib plot. Examples -------- .. plot:: :context: close-figs >>> import matplotlib.pyplot as plt >>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4]}) >>> fig, ax = plt.subplots() >>> ax.axis("off") (np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0)) >>> table = pd.plotting.table( ... ax, df, loc="center", cellLoc="center", colWidths=[0.2, 0.2] ... ) matplotlibN)axdata rowLabels colLabels)rtable)rrkwargs plot_backends W/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/pandas/plotting/_misc.pyrrs6`%\2L     DD >> pd.plotting.register_matplotlib_converters() >>> df = pd.DataFrame( ... {"ts": pd.period_range("2020", periods=2, freq="M"), "y": [1, 2]} ... ) >>> plot = df.plot.line(x="ts", y="y") Unsetting the register manually an error will be raised: >>> pd.set_option( ... "plotting.matplotlib.register_converters", False ... ) # doctest: +SKIP >>> df.plot.line(x="ts", y="y") # doctest: +SKIP Traceback (most recent call last): TypeError: float() argument must be a string or a real number, not 'Period' rN)rregisterrs rrrUsV%\2Lrc:[S5nUR5 g)a Remove pandas formatters and converters. Removes the custom converters added by :func:`register`. This attempts to set the state of the registry back to the state before pandas registered its own units. Converters for pandas' own types like Timestamp and Period are removed completely. Converters for types pandas overwrites, like ``datetime.datetime``, are restored to their original value. See Also -------- register_matplotlib_converters : Register pandas formatters and converters with matplotlib. Examples -------- .. plot:: :context: close-figs The following line is done automatically by pandas so the plot can be rendered: >>> pd.plotting.register_matplotlib_converters() >>> df = pd.DataFrame( ... {"ts": pd.period_range("2020", periods=2, freq="M"), "y": [1, 2]} ... ) >>> plot = df.plot.line(x="ts", y="y") Unsetting the register manually an error will be raised: >>> pd.set_option( ... "plotting.matplotlib.register_converters", False ... ) # doctest: +SKIP >>> df.plot.line(x="ts", y="y") # doctest: +SKIP Traceback (most recent call last): TypeError: float() argument must be a string or a real number, not 'Period' rN)r deregisterrs rr!r!sR%\2Lrc  R[S5n U R"SUUUUUUUUUU S. U D6$)a Draw a matrix of scatter plots. Each pair of numeric columns in the DataFrame is plotted against each other, resulting in a matrix of scatter plots. The diagonal plots can display either histograms or Kernel Density Estimation (KDE) plots for each variable. Parameters ---------- frame : DataFrame The data to be plotted. alpha : float, optional Amount of transparency applied. figsize : (float,float), optional A tuple (width, height) in inches. ax : Matplotlib axis object, optional An existing Matplotlib axis object for the plots. If None, a new axis is created. grid : bool, optional Setting this to True will show the grid. diagonal : {'hist', 'kde'} Pick between 'kde' and 'hist' for either Kernel Density Estimation or Histogram plot in the diagonal. marker : str, optional Matplotlib marker type, default '.'. density_kwds : keywords Keyword arguments to be passed to kernel density estimate plot. hist_kwds : keywords Keyword arguments to be passed to hist function. range_padding : float, default 0.05 Relative extension of axis range in x and y with respect to (x_max - x_min) or (y_max - y_min). **kwargs Keyword arguments to be passed to scatter function. Returns ------- numpy.ndarray A matrix of scatter plots. See Also -------- plotting.parallel_coordinates : Plots parallel coordinates for multivariate data. plotting.andrews_curves : Generates Andrews curves for visualizing clusters of multivariate data. plotting.radviz : Creates a RadViz visualization. plotting.bootstrap_plot : Visualizes uncertainty in data via bootstrap sampling. Examples -------- .. plot:: :context: close-figs >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=["A", "B", "C", "D"]) >>> pd.plotting.scatter_matrix(df, alpha=0.2) array([[, , , ], [, , , ], [, , , ], [, , , ]], dtype=object) r) framealphafigsizergriddiagonalmarker density_kwds hist_kwds range_paddingr)rscatter_matrix) r#r$r%rr&r'r(r)r*r+rrs rr,r,sO`%\2L  & &  !#    rc H[S5nUR"SUUUUUS.UD6$)a Plot a multidimensional dataset in 2D. Each Series in the DataFrame is represented as an evenly distributed slice on a circle. Each data point is rendered in the circle according to the value on each Series. Highly correlated `Series` in the `DataFrame` are placed closer on the unit circle. RadViz allow to project an N-dimensional data set into a 2D space where the influence of each dimension can be interpreted as a balance between the influence of all dimensions. More info available at the `original article `_ describing RadViz. Parameters ---------- frame : `DataFrame` Object holding the data. class_column : str Column name containing the name of the data point category. ax : :class:`matplotlib.axes.Axes`, optional A plot instance to which to add the information. color : list[str] or tuple[str], optional Assign a color to each category. Example: ['blue', 'green']. colormap : str or :class:`matplotlib.colors.Colormap`, default None Colormap to select colors from. If string, load colormap with that name from matplotlib. **kwds Options to pass to matplotlib scatter plotting method. Returns ------- :class:`matplotlib.axes.Axes` The Axes object from Matplotlib. See Also -------- plotting.andrews_curves : Plot clustering visualization. Examples -------- .. plot:: :context: close-figs >>> df = pd.DataFrame( ... { ... "SepalLength": [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6], ... "SepalWidth": [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6], ... "PetalLength": [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0], ... "PetalWidth": [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2], ... "Category": [ ... "virginica", ... "virginica", ... "setosa", ... "virginica", ... "virginica", ... "versicolor", ... "versicolor", ... "setosa", ... "virginica", ... "setosa", ... ], ... } ... ) >>> pd.plotting.radviz(df, "Category") # doctest: +SKIP r)r# class_columnrcolorcolormapr)rradviz)r#r.rr/r0kwdsrs rr1r1s@\%\2L    !      rc J[S5nUR"SUUUUUUS.UD6$)aF Generate a matplotlib plot for visualizing clusters of multivariate data. Andrews curves have the functional form: .. math:: f(t) = \frac{x_1}{\sqrt{2}} + x_2 \sin(t) + x_3 \cos(t) + x_4 \sin(2t) + x_5 \cos(2t) + \cdots Where :math:`x` coefficients correspond to the values of each dimension and :math:`t` is linearly spaced between :math:`-\pi` and :math:`+\pi`. Each row of frame then corresponds to a single curve. Parameters ---------- frame : DataFrame Data to be plotted, preferably normalized to (0.0, 1.0). class_column : label Name of the column containing class names. ax : axes object, default None Axes to use. samples : int Number of points to plot in each curve. color : str, list[str] or tuple[str], optional Colors to use for the different classes. Colors can be strings or 3-element floating point RGB values. colormap : str or matplotlib colormap object, default None Colormap to select colors from. If a string, load colormap with that name from matplotlib. **kwargs Options to pass to matplotlib plotting method. Returns ------- :class:`matplotlib.axes.Axes` The matplotlib Axes object with the plot. See Also -------- plotting.parallel_coordinates : Plot parallel coordinates chart. DataFrame.plot : Make plots of Series or DataFrame. Examples -------- .. plot:: :context: close-figs >>> df = pd.read_csv( ... "https://raw.githubusercontent.com/pandas-dev/" ... "pandas/main/pandas/tests/io/data/csv/iris.csv" ... ) # doctest: +SKIP >>> pd.plotting.andrews_curves(df, "Name") # doctest: +SKIP r)r#r.rsamplesr/r0r)randrews_curves)r#r.rr4r/r0rrs rr5r5jsC@%\2L  & & !     rc B[S5nUR"SXX#S.UD6$)a Bootstrap plot on mean, median and mid-range statistics. The bootstrap plot is used to estimate the uncertainty of a statistic by relying on random sampling with replacement [1]_. This function will generate bootstrapping plots for mean, median and mid-range statistics for the given number of samples of the given size. .. [1] "Bootstrapping (statistics)" in https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29 Parameters ---------- series : pandas.Series Series from where to get the samplings for the bootstrapping. fig : matplotlib.figure.Figure, default None If given, it will use the `fig` reference for plotting instead of creating a new one with default parameters. size : int, default 50 Number of data points to consider during each sampling. It must be less than or equal to the length of the `series`. samples : int, default 500 Number of times the bootstrap procedure is performed. **kwds Options to pass to matplotlib plotting method. Returns ------- matplotlib.figure.Figure Matplotlib figure. See Also -------- DataFrame.plot : Basic plotting for DataFrame objects. Series.plot : Basic plotting for Series objects. Examples -------- This example draws a basic bootstrap plot for a Series. .. plot:: :context: close-figs >>> s = pd.Series(np.random.uniform(size=100)) >>> pd.plotting.bootstrap_plot(s) # doctest: +SKIP
r)seriesfigsizer4r)rbootstrap_plot)r7r8r9r4r2rs rr:r:s4n%\2L  & & T >B rc  T[S5n U R"SUUUUUUUUUU U S. U D6$)a) Parallel coordinates plotting. Parameters ---------- frame : DataFrame The DataFrame to be plotted. class_column : str Column name containing class names. cols : list, optional A list of column names to use. ax : matplotlib.axis, optional Matplotlib axis object. color : list or tuple, optional Colors to use for the different classes. use_columns : bool, optional If true, columns will be used as xticks. xticks : list or tuple, optional A list of values to use for xticks. colormap : str or matplotlib colormap, default None Colormap to use for line colors. axvlines : bool, optional If true, vertical lines will be added at each xtick. axvlines_kwds : keywords, optional Options to be passed to axvline method for vertical lines. sort_labels : bool, default False Sort class_column labels, useful when assigning colors. **kwargs Options to pass to matplotlib plotting method. Returns ------- matplotlib.axes.Axes The matplotlib axes containing the parallel coordinates plot. See Also -------- plotting.andrews_curves : Generate a matplotlib plot for visualizing clusters of multivariate data. plotting.radviz : Plot a multidimensional dataset in 2D. Examples -------- .. plot:: :context: close-figs >>> df = pd.read_csv( ... "https://raw.githubusercontent.com/pandas-dev/" ... "pandas/main/pandas/tests/io/data/csv/iris.csv" ... ) # doctest: +SKIP >>> pd.plotting.parallel_coordinates( ... df, "Name", color=("#556270", "#4ECDC4", "#C7F464") ... ) # doctest: +SKIP r) r#r.colsrr/ use_columnsxticksr0axvlines axvlines_kwds sort_labelsr)rparallel_coordinates) r#r.r<rr/r=r>r0r?r@rArrs rrBrBsRL%\2L  , , !  #    rc B[S5nUR"SXUS.UD6$)a Lag plot for time series. A lag plot is a scatter plot of a time series against a lag of itself. It helps in visualizing the temporal dependence between observations by plotting the values at time `t` on the x-axis and the values at time `t + lag` on the y-axis. Parameters ---------- series : Series The time series to visualize. lag : int, default 1 Lag length of the scatter plot. ax : Matplotlib axis object, optional The matplotlib axis object to use. **kwds Matplotlib scatter method keyword arguments. Returns ------- matplotlib.axes.Axes The matplotlib Axes object containing the lag plot. See Also -------- plotting.autocorrelation_plot : Autocorrelation plot for time series. matplotlib.pyplot.scatter : A scatter plot of y vs. x with varying marker size and/or color in Matplotlib. Examples -------- Lag plots are most commonly used to look for patterns in time series data. Given the following time series .. plot:: :context: close-figs >>> np.random.seed(5) >>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50)) >>> s = pd.Series(x) >>> s.plot() # doctest: +SKIP A lag plot with ``lag=1`` returns .. plot:: :context: close-figs >>> _ = pd.plotting.lag_plot(s, lag=1) r)r7lagrr)rlag_plot)r7rDrr2rs rrErEJs*h%\2L  GB G$ GGrc @[S5nUR"SXS.UD6$)as Autocorrelation plot for time series. This method generates an autocorrelation plot for a given time series, which helps to identify any periodic structure or correlation within the data across various lags. It shows the correlation of a time series with a delayed copy of itself as a function of delay. Autocorrelation plots are useful for checking randomness in a data set. If the data are random, the autocorrelations should be near zero for any and all time-lag separations. If the data are not random, then one or more of the autocorrelations will be significantly non-zero. Parameters ---------- series : Series The time series to visualize. ax : Matplotlib axis object, optional The matplotlib axis object to use. **kwargs Options to pass to matplotlib plotting method. Returns ------- matplotlib.axes.Axes The matplotlib axes containing the autocorrelation plot. See Also -------- Series.autocorr : Compute the lag-N autocorrelation for a Series. plotting.lag_plot : Lag plot for time series. Examples -------- The horizontal lines in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band. .. plot:: :context: close-figs >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000) >>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing)) >>> pd.plotting.autocorrelation_plot(s) # doctest: +SKIP r)r7rr)rautocorrelation_plot)r7rrrs rrGrGs(\%\2L  , , LF LV LLrc^\rSrSrSrSS0rS/rSU4SjjrU4SjrSU4Sjjr SU4Sjjr SU4S jjr SS jr SS jr \SS j5rS rU=r$)_Optionsia Stores pandas plotting options. Allows for parameter aliasing so you can just use parameter names that are the same as the plot function parameters, but is stored in a canonical format that makes it easy to breakdown into groups later. See Also -------- plotting.register_matplotlib_converters : Register pandas formatters and converters with matplotlib. plotting.bootstrap_plot : Bootstrap plot on mean, median and mid-range statistics. plotting.autocorrelation_plot : Autocorrelation plot for time series. plotting.lag_plot : Lag plot for time series. Examples -------- .. plot:: :context: close-figs >>> np.random.seed(42) >>> df = pd.DataFrame( ... {"A": np.random.randn(10), "B": np.random.randn(10)}, ... index=pd.date_range("1/1/2000", freq="4MS", periods=10), ... ) >>> with pd.plotting.plot_params.use("x_compat", True): ... _ = df["A"].plot(color="r") ... _ = df["B"].plot(color="g") x_compat xaxis.compatc&>[TU]SS5 g)NrKF)super __setitem__)self __class__s r__init___Options.__init__s NE2rcj>URU5nX;a[US35e[TU] U5$)Nz& is not a valid pandas plotting option)_get_canonical_key ValueErrorrM __getitem__rOkeyrPs rrV_Options.__getitem__s;%%c* ?u$JKL Lw"3''rcF>URU5n[TU] X5 gN)rTrMrN)rOrXvaluerPs rrN_Options.__setitem__s %%c* C'rc>URU5nXR;a[SU35e[TU]U5 g)Nz Cannot remove default parameter )rT _DEFAULT_KEYSrUrM __delitem__rWs rr`_Options.__delitem__s?%%c* $$ $?uEF F C rcD>URU5n[TU] U5$r[)rTrM __contains__rWs rrc_Options.__contains__s#%%c*w#C((rc$UR5 g)zC Reset the option store to its initial state Returns ------- None N)rQ)rOs rreset_Options.resets  rc8URRX5$r[)_ALIASESget)rOrXs rrT_Options._get_canonical_keys}}  **rc#@# XnX U'Uv X0U'g!X0U'f=f7f)zO Temporarily set a parameter value using the with statement. 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U $ U UUp 04&* H HH H H . H $ H HHV  9 9 9 9 9  99x "04"&&*.2S SS S  S . S  S S$SS,SS SSl 4H4Hn .M.MbT"tT"nj * r