df.plot 参数解释以及使用

df.plot 参数解释以及使用

df.plot

df.plot(x, y, kind, figsize, title, grid, legend, style)

x 只有dataframe对象时，x可用。横坐标     y 同上，纵坐标变量     kind 可视化图的种类，如下：          |      - '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          |      - 'hexbin' : hexbin plot.     figsize 画布尺寸     title 标题     grid 是否显示格子线条     legend 是否显示图例     style 图的风格

查看plot参数可以使用help：

import pandas as pdhelp(pd.DataFrame.plot)

查看参考文档：

Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]Type 'copyright', 'credits' or 'license' for more informationIPython 7.22.0 -- An enhanced Interactive Python. Type '?' for help.PyDev console: using IPython 7.22.0Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)] on win32runfile('D:/LXFWorkSpace/PycharmProjects/pythonProject/tttttample.py', wdir='D:/LXFWorkSpace/PycharmProjects/pythonProject')Help on class PlotAccessor in module pandas.plotting._core:class PlotAccessor(pandas.core.base.PandasObject) | PlotAccessor(data) | | 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 | - 'hexbin' : hexbin plot. | ax : matplotlib axes object, default None | An axes of the current figure. | subplots : bool, default False | Make separate subplots for each column. | 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. | .. versionchanged:: 0.25.0 | | logy : bool or 'sym' default False | Use log scaling or symlog scaling on y axis. | .. versionchanged:: 0.25.0 | | loglog : bool or 'sym', default False | Use log scaling or symlog scaling on both x and y axes. | .. versionchanged:: 0.25.0 | | 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. | | .. versionadded:: 1.1.0 | | .. versionchanged:: 1.2.0 | | Now applicable to planar plots (`scatter`, `hexbin`). | | 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. | | .. versionadded:: 1.1.0 | | .. versionchanged:: 1.2.0 | | Now applicable to planar plots (`scatter`, `hexbin`). | | rot : int, default None | Rotation for ticks (xticks for vertical, yticks for horizontal | plots). | fontsize : int, 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. | sort_columns : bool, default False | Sort column names to determine plot ordering. | 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``. | | .. versionadded:: 1.0.0 | | **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) | | Method resolution order: | PlotAccessor | pandas.core.base.PandasObject | pandas.core.accessor.DirNamesMixin | builtins.object | | Methods defined here: | | __call__(self, *args, **kwargs) | 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 | - 'hexbin' : hexbin plot. | ax : matplotlib axes object, default None | An axes of the current figure. | subplots : bool, default False | Make separate subplots for each column. | 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. | .. versionchanged:: 0.25.0 | | logy : bool or 'sym' default False | Use log scaling or symlog scaling on y axis. | .. versionchanged:: 0.25.0 | | loglog : bool or 'sym', default False | Use log scaling or symlog scaling on both x and y axes. | .. versionchanged:: 0.25.0 | | 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. | | .. versionadded:: 1.1.0 | | .. versionchanged:: 1.2.0 | | Now applicable to planar plots (`scatter`, `hexbin`). | | 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. | | .. versionadded:: 1.1.0 | | .. versionchanged:: 1.2.0 | | Now applicable to planar plots (`scatter`, `hexbin`). | | rot : int, default None | Rotation for ticks (xticks for vertical, yticks for horizontal | plots). | fontsize : int, 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. | sort_columns : bool, default False | Sort column names to determine plot ordering. | 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``. | | .. versionadded:: 1.0.0 | | **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) | | __init__(self, data) | Initialize self. See help(type(self)) for accurate signature. | | area(self, x=None, y=None, **kwargs) | 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='M')) | >>> 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') | | bar(self, x=None, y=None, **kwargs) | 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. | | 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 bar will be filled in | green or yellow, alternatively. | | - 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 bars for | column `a` in green and bars for column `b` in red. | | .. versionadded:: 1.1.0 | | **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``. | | 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) | | barh(self, x=None, y=None, **kwargs) | 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. | | 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 bar will be filled in | green or yellow, alternatively. | | - 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 bars for | column `a` in green and bars for column `b` in red. | | .. versionadded:: 1.1.0 | | **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``. | | 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') | | box(self, by=None, **kwargs) | 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. | **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() | | density = kde(self, bw_method=None, ind=None, **kwargs) | | hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None, **kwargs) | 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") | | hist(self, by=None, bins=10, **kwargs) | 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. | 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 draw a dice 6000 times, we expect to get each value around 1000 | times. But when we draw two dices 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) | | kde(self, bw_method=None, ind=None, **kwargs) | Generate Kernel Density Estimate plot using Gaussian kernels. | | In statistics, `kernel density estimation`_ (KDE) is a non-parametric | way to estimate the probability density function (PDF) of a random | variable. This function uses Gaussian kernels and includes automatic | bandwidth determination. | | .. _kernel density estimation: | | | Parameters | ---------- | bw_method : str, scalar or callable, optional | The method used to calculate the estimator bandwidth. This can be | 'scott', 'silverman', a scalar constant or a callable. | If None (default), 'scott' is used. | See :class:`scipy.stats.gaussian_kde` for more information. | ind : NumPy array or int, optional | Evaluation points for the estimated PDF. If None (default), | 1000 equally spaced points are used. If `ind` is a NumPy array, the | KDE is evaluated at the points passed. If `ind` is an integer, | `ind` number of equally spaced points are used. | **kwargs | Additional keyword arguments are documented in | :meth:`pandas.%(this-datatype)s.plot`. | | Returns | ------- | matplotlib.axes.Axes or numpy.ndarray of them | | See Also | -------- | scipy.stats.gaussian_kde : Representation of a kernel-density | estimate using Gaussian kernels. This is the function used | internally to estimate the PDF. | | Examples | -------- | Given a Series of points randomly sampled from an unknown | distribution, estimate its PDF using KDE with automatic | bandwidth determination and plot the results, evaluating them at | 1000 equally spaced points (default): | | .. plot:: | :context: close-figs | | >>> 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]) | | line(self, x=None, y=None, **kwargs) | Plot Series or DataFrame as lines. | | This function is useful to plot lines using DataFrame's values | as coordinates. | | 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 line will be filled in | green or yellow, alternatively. | | - 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 lines for | column `a` in green and lines for column `b` in red. | | .. versionadded:: 1.1.0 | | **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``. | | 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() | | .. 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') | | pie(self, **kwargs) | 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)) | | scatter(self, x, y, s=None, c=None, **kwargs) | 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. | | .. versionchanged:: 1.1.0 | | 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') | | ---------------------------------------------------------------------- | Methods inherited from pandas.core.base.PandasObject: | | __repr__(self) -> str | Return a string representation for a particular object. | | __sizeof__(self) | Generates the total memory usage for an object that returns | either a value or Series of values | | ---------------------------------------------------------------------- | Data and other attributes inherited from pandas.core.base.PandasObject: | | __annotations__ = {'_cache': typing.Dict[str, typing.Any]} | | ---------------------------------------------------------------------- | Methods inherited from pandas.core.accessor.DirNamesMixin: | | __dir__(self) -> List[str] | Provide method name lookup and completion. | | Notes | ----- | Only provide 'public' methods. | | ---------------------------------------------------------------------- | Data descriptors inherited from pandas.core.accessor.DirNamesMixin: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)

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