學習pandas數據框的繪圖，輕松搞定各種圖畫法。
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,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)
Parameters:
data : DataFrame
x : label or position, default None#指數據框列的標簽或位置參數
y : label or position, default None
Allows plotting of one column versus another
kind : str
- ‘line’ : line plot (default)#折線圖
- ‘bar’ : vertical bar plot#條形圖
- ‘barh’ : horizontal bar plot#橫向條形圖
- ‘hist’ : histogram#柱狀圖
- ‘box’ : boxplot#箱線圖
- ‘kde’ : Kernel Density Estimation plot#Kernel 的密度估計圖，主要對柱狀圖添加Kernel 概率密度線
- ‘density’ : same as ‘kde’
- ‘area’ : area plot#不了解此圖
- ‘pie’ : pie plot#餅圖
- ‘scatter’ : scatter plot#散點圖
- ‘hexbin’ : hexbin plot#不了解此圖
ax : matplotlib axes object, default None#一個圖片切成不同片段，子圖對象
subplots : boolean, default False#判斷圖片中是否有子圖
Make separate subplots for each column
sharex : boolean, default True if ax is None else False#如果有子圖，子圖共x軸刻度，標簽
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#如果有子圖，子圖共y軸刻度，標簽
In case subplots=True, share y axis and set some y axis labels to invisible
layout : tuple (optional)#子圖的行列布局
(rows, columns) for the layout of subplots
figsize : a tuple (width, height) in inches#圖片尺寸大小
use_index : boolean, default True#默認用索引做x軸
Use index as ticks for x axis
title : string#圖片的標題用字符串
Title to use for the plot
grid : boolean, default None (matlab style default)#圖片是否有網格
Axis grid lines
legend : False/True/’reverse’#子圖的圖例
Place legend on axis subplots
style : list or dict#對每列折線圖設置線的類型
matplotlib line style per column
logx : boolean, default False#設置x軸刻度是否取對數
Use log scaling on x axis
logy : boolean, default False
Use log scaling on y axis
loglog : boolean, default False#同時設置x，y軸刻度是否取對數
Use log scaling on both x and y axes
xticks : sequence#設置x軸刻度值，序列形式（比如列表）
Values to use for the xticks
yticks : sequence#設置y軸刻度，序列形式（比如列表）
Values to use for the yticks
xlim : 2-tuple/list#設置坐標軸的范圍，列表或元組形式
ylim : 2-tuple/list
rot : int, default None#設置軸標簽（軸刻度）的顯示旋轉度數
Rotation for ticks (xticks for vertical, yticks for horizontal plots)
fontsize : int, default None#設置軸刻度的字體大小
Font size for xticks and yticks
colormap : str or matplotlib colormap object, default None#設置圖的區域顏色
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 Plotting with Error Bars 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*
下面從http://pandas.pydata.org/pandas-docs/version/0.13.1/visualization.html的實例分析
%matplotlib inlineimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltplt.rc('figure', figsize=(5, 3))#設置圖片大小ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))ts = ts.cumsum()ts.plot()
***[圖片上傳失敗...(image-a3978f-1566875905618)]
***
*** plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()#創建個新圖片，在新圖片上畫ts的折線圖，并添加圖例***
***[圖片上傳失敗...(image-aeb8e3-1566875905618)]
***
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- df =pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
- df = df.cumsum()
- plt.figure(); df.plot(); plt.legend(loc='best')
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- df.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')#對數據框相同索引分列分別作圖
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- 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) #對y軸進行log(y)放縮，圖中y軸刻度依然是y的真實值，而不是log(y)
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- 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分別設置x軸，y軸的列標簽或列的位置
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- plt.figure()
- df.A.plot()
- df.B.plot(secondary_y=True, style='g')#設置第二個y軸（右y軸）
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- plt.figure()
- ax = df.plot(secondary_y=['A', 'B'])#設置2個列軸，分別對各個列軸畫折線圖。ax（axes）可以理解為子圖，也可以理解成對黑板進行切分，每一個板塊就是一個axes
- ax.set_ylabel('CD scale')
- ax.right_ax.set_ylabel('AB scale')
- ax.legend(loc=2)#設置圖例的位置
- plt.legend(loc=1)
摘要：主要作用是設置畫的圖的分辨率，大小等信息
plt.rcParams['figure.figsize'] = (8.0, 4.0) # 設置figure_size尺寸
plt.rcParams['image.interpolation'] = 'nearest' # 設置 interpolation style
plt.rcParams['image.cmap'] = 'gray' # 設置 顏色 style
figsize(12.5, 4) # 設置 figsize
plt.rcParams['savefig.dpi'] = 300 #圖片像素
plt.rcParams['figure.dpi'] = 300 #分辨率
默認的像素：[6.0,4.0]，分辨率為100，圖片尺寸為 600&400
指定dpi=200，圖片尺寸為 1200800
指定dpi=300，圖片尺寸為 18001200
設置figsize可以在不改變分辨率情況下改變比例
原文鏈接：https://blog.csdn.net/NockinOnHeavensDoor/article/details/80565764
