学习pandas下的dataframe画图参数——转载

学习pandas数据框的绘图，轻松搞定各种图画法。

DataFrame.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

data : DataFrame

x : label or position, default None#指数据框列的标签或位置参数y : label or position, default None

Allows plotting of one column versus another

kind : str

‘line’ : line plot (default)#折线图‘bar’ : vertical bar plot#条形图

‘barh’ : horizontal bar plot#横向条形图‘hist’ : histogram#柱状图‘box’ : boxplot#箱线图

‘kde’ : Kernel Density Estimation plot#Kernel 的密度估计图，主要对柱状图添加Kernel 概率密度线‘density’ : same as ‘kde’

‘area’ : area plot#不了解此图‘pie’ : pie plot#饼图

‘scatter’ : scatter plot#散点图‘hexbin’ : hexbin plot#不了解此图ax : matplotlib axes object, default None#⼀个图⽚切成不同⽚段，⼦图对象subplots : boolean, default False#判断图⽚中是否有⼦图

Make separate subplots for each column

sharex : boolean, default True if ax is None else False#如果有⼦图，⼦图共x轴刻度，标签

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 andsharex=True will alter all x axis labels for all axis in a figure!sharey : boolean, default False#如果有⼦图，⼦图共y轴刻度，标签In case subplots=True, share y axis and set some y axis labels to invisiblelayout : tuple (optional)#⼦图的⾏列布局

(rows, columns) for the layout of subplotsfigsize : a tuple (width, height) in inches#图⽚尺⼨⼤⼩use_index : boolean, default True#默认⽤索引做x轴

Use index as ticks for x axistitle : string#图⽚的标题⽤字符串

Title to use for the plot

grid : boolean, default None (matlab style default)#图⽚是否有⽹格

Axis grid lineslegend : False/True/’reverse’#⼦图的图例

Place legend on axis subplotsstyle : list or dict#对每列折线图设置线的类型

matplotlib line style per column

logx : boolean, default False#设置x轴刻度是否取对数

Use log scaling on x axislogy : boolean, default False

Use log scaling on y axis

loglog : boolean, default False#同时设置x，y轴刻度是否取对数Use log scaling on both x and y axesxticks : sequence#设置x轴刻度值，序列形式（⽐如列表）

Values to use for the xticksyticks : sequence#设置y轴刻度，序列形式（⽐如列表）Values to use for the yticks

xlim : 2-tuple/list#设置坐标轴的范围，列表或元组形式ylim : 2-tuple/list

rot : int, default None#设置轴标签（轴刻度）的显⽰旋转度数

Rotation for ticks (xticks for vertical, yticks for horizontal plots)fontsize : int, default None#设置轴刻度的字体⼤⼩

Font size for xticks and yticks

colormap : str or matplotlib colormap object, default None#设置图的区域颜⾊

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 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-axismark_right : boolean, default True

When using a secondary_y axis, automatically mark the column labels with “(right)” in the legendkwds : keywords

Options to pass to matplotlib plotting methodaxes : matplotlib.AxesSubplot or np.array of them

下⾯从http://pandas.pydata.org/pandas-docs/version/0.13.1/visualization.html的实例分析%matplotlib inlineimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltplt.rc('figure', figsize=(5, 3))#设置图⽚⼤⼩ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))ts = ts. plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()#创建个新图⽚，在新图⽚上画ts的折线图，并添加图例 [python]

1. df =pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD')) 2. df = df.cumsum()

3. plt.figure(); df.plot(); plt.legend(loc='best') [python]

1. df.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')#对数据框相同索引分列分别作图

[python]

1. 2. 3. 4.

plt.figure();

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) ts = np.exp(ts.cumsum())

ts.plot(logy=True) #对y轴进⾏log(y)放缩，图中y轴刻度依然是y的真实值，⽽不是log(y)

[python]

1. 2. 3. 4.

plt.figure()

df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum() df3['A'] = pd.Series(list(range(len(df))))

df3.plot(x='A', y='B')#x,y分别设置x轴，y轴的列标签或列的位置

[python]

1. plt.figure() 2. df.A.plot()

3. df.B.plot(secondary_y=True, style='g')#设置第⼆个y轴（右y轴） [python]

1. 2. 3. 4. 5. 6.

plt.figure()

ax = df.plot(secondary_y=['A', 'B'])#设置2个列轴，分别对各个列轴画折线图。ax（axes）可以理解为⼦图，也可以理解成对⿊板进⾏切分，每⼀个板块就是⼀个axes ax.set_ylabel('CD scale')

ax.right_ax.set_ylabel('AB scale') ax.legend(loc=2)#设置图例的位置 plt.legend(loc=1)