用python画箱体图-python学习笔记21

函数：d.plot()

Type:           FramePlotMethods

String form:    <pandas.tools.plotting.FramePlotMethods object at 0x08FC8510>

File:           c:\program files (x86)\python35-32\lib\site-packages\pandas\tools\plotting.py

Signature:      d.plot(x=None, y=None, kind='line', ax=None, subplots=False, sharex=None, sharey=False, layout=None, figsize=None, use_index=True, title=None, grid=None, legend=True, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None,
xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, secondary_y=False, sort_columns=False, **kwds)

Docstring:     

DataFrame plotting accessor and method

Examples

--------

df.plot.line()

df.plot.scatter('x', 'y')

df.plot.hexbin()

These plotting methods can also be accessed by calling the accessor as a

method with the ``kind`` argument:

``df.plot(kind='line')`` is equivalent to ``df.plot.line()``

Call docstring:

Make plots of DataFrame using matplotlib / pylab.

*New in version 0.17.0:* Each plot kind has a corresponding method on the

``DataFrame.plot`` accessor:

``df.plot(kind='line')`` is equivalent to

``df.plot.line()``.

Parameters

----------

data : DataFrame

x : label or position, default None

y : label or position, default None

    Allows plotting of one column versus another

kind : str

    - '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

subplots : boolean, default False

    Make separate subplots for each column

sharex : boolean, 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 : boolean, 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

use_index : boolean, default True

    Use index as ticks for x axis

title : string

    Title to use for the plot

grid : boolean, default None (matlab style default)

    Axis grid lines

legend : False/True/'reverse'

    Place legend on axis subplots

style : list or dict

    matplotlib line style per column

logx : boolean, default False

    Use log scaling on x axis

logy : boolean, default False

    Use log scaling on y axis

loglog : boolean, default False

    Use log 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

ylim : 2-tuple/list

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 : boolean, 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)

layout : tuple (optional)

    (rows, columns) for the layout of the plot

table : boolean, 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 <visualization.errorbars>` for

    detail.

xerr : same types as yerr.

stacked : boolean, default False in line and

    bar plots, and True in area plot. If True, create stacked plot.

sort_columns : boolean, default False

    Sort column names to determine plot ordering

secondary_y : boolean 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 : boolean, default True

    When using a secondary_y axis, automatically mark the column

    labels with "(right)" in the legend

kwds : keywords

    Options to pass to matplotlib plotting method

Returns

-------

axes : matplotlib.AxesSubplot or np.array of them

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)

- If `kind` = 'scatter' and the argument `c` is the name of a dataframe

  column, the values of that column are used to color each point.

- If `kind` = 'hexbin', you can control the size of the bins with the

  `gridsize` argument. By default, a histogram of the counts around each

  `(x, y)` point is computed. You can specify alternative aggregations

  by passing values to the `C` and `reduce_C_function` arguments.

  `C` specifies the value at each `(x, y)` point and `reduce_C_function`

  is a function of one argument that reduces all the values in a bin to

  a single number (e.g. `mean`, `max`, `sum`, `std`).

代码：

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

plt.rcParams['font.sans-serif']=['SimHei']

plt.rcParams['axes.unicode_minus']=False

plt.figure(figsize=(8,6))#以上为模板

x=np.random.randn(1000)

d=pd.DataFrame([x,x+1]).T

d.plot(kind='box')

图形：