目录
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一、参数详解
seaborn.heatmap（）
seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt=‘.2g’, annotkws=None, linewidths=0, linecolor=‘white’, cbar=True, cbarkws=None, cbar_ax=None, square=False, ax=None, xticklabels=True, yticklabels=True, mask=None, **kwargs)
data：矩阵数据集，可以使numpy的数组（array），如果是pandas的dataframe，则df的index/column信息会分别对应到heatmap的columns和rowsvmax,vmin, 图例中最大值和最小值的显示值，没有该参数时默认不显示linewidths,热力图矩阵之间的间隔大小cmap，热力图颜色ax，绘制图的坐标轴，否则使用当前活动的坐标轴。annot，annotate的缩写，annot默认为False，当annot为True时，在heatmap中每个方格写入数据。annot_kws，当annot为True时，可设置各个参数，包括大小，颜色，加粗，斜体字等：sns.heatmap(x, annot=True, ax=ax2, annot_kws={'size':9,'weight':'bold', 'color':'blue'}) fmt，格式设置，决定annot注释的数字格式，小数点后几位等；cbar : 是否画一个颜色条cbar_kws : 颜色条的参数，关键字同 fig.colorbar，可以参考：matplotlib自定义colorbar颜色条-以及matplotlib中的内置色条。mask，遮罩- 先用sns.set(font_scale)修改字体比例:
sns.set(font_scale=1.5)- 再用plt.rc对全图字体进行统一修改:
plt.rc('font',family='Times New Roman',size=12)二、颜色参数-cmap
cmap的参数如下：
Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu(绿到蓝), GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd（橘色到红色）, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia（蓝绿黄）, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd（红橙黄）, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm（蓝到红）, coolwarm_r, copper（铜色）, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r（红黄）, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer (黄到绿), summer_r (绿到黄), tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r,vlag, vlag_r, winter, winter_r
示范如下：
cmap=“hot”：黄色到红色，数字越大，颜色越浅
cmap=“OrRd”：深红色到浅红色，类似“Oranges”。
cmap=“greys”：灰色
cmap=“gist_rainbow”：彩虹色
将colormap置于特定值的中心（参考链接）：
>>> ax = sns.heatmap(flights, center=flights.loc["January", 1955])
使用遮罩绘制矩阵中的一部分
>>> corr = np.corrcoef(np.random.randn(10, 200))>>> mask = np.zeros_like(corr)>>> mask[np.triu_indices_from(mask)] = True>>> with sns.axes_style("white"):... ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)np.zeros_like() 返回一个零数组，其形状和类型与给定的数组相同；
np.triu_indices_from(mask) 返回数组上三角形的索引。
以下是一些网络上发现的配色好看的图:
# cmap用cubehelix map颜色cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)sns.heatmap(pt, linewidths = 0.05, ax = ax1, vmax=900, vmin=0, cmap=cmap)
# cmap用matplotlib colormapsns.heatmap(pt, linewidths = 0.05, ax = ax2, vmax=900, vmin=0, cmap='rainbow')
#center的用法(颜色)f, (ax1,ax2) = plt.subplots(figsize = (6, 4),nrows=2)cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)sns.heatmap(pt, linewidths = 0.05, ax = ax1, cmap=cmap, center=None )
#设置x轴图例为空值ax1.set_ylabel('kind')# 当center设置小于数据的均值时，生成的图片颜色要向0值代表的颜色一段偏移sns.heatmap(pt, linewidths = 0.05, ax = ax2, cmap=cmap, center=200) 
#robust的用法(颜色)f, (ax1,ax2) = plt.subplots(figsize = (6,4),nrows=2)cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)sns.heatmap(pt, linewidths = 0.05, ax = ax1, cmap=cmap, center=None, robust=False )sns.heatmap(pt, linewidths = 0.05, ax = ax2, cmap=cmap, center=None, robust=True ) #mask对某些矩阵块的显示进行覆盖p2 = sns.heatmap(pt, ax=ax2, cmap=cmap, xticklabels=True, mask=(pt<800)) #mask对pt进行布尔型转化,结果为True的位置用白色覆盖
用mask实现：突出显示某些数据
sns.heatmap(x, mask=x < 1, ax=ax2, annot=True, annot_kws={“weight”: “bold”}) #把小于1的区域覆盖掉
三、个性化设置
将x轴刻度放置在top位置的几种方法
# 将x轴刻度放置在top位置的几种方法# ax.xaxis.set_ticks_position(‘top‘)ax.xaxis.tick_top()# ax.tick_params(axis=‘x‘,labelsize=6, colors=‘b‘, labeltop=True, labelbottom=False)设置坐标轴刻度参数，”axis”不写的时候，默认是x轴和y轴的参数同时调整。
# 设置坐标轴刻度的字体大小# matplotlib.axes.Axes.tick_paramsax.tick_params(axis=‘y‘,labelsize=8) # y轴旋转轴刻度上文字方向的两种方法
# 旋转轴刻度上文字方向的两种方法ax.set_xticklabels(ax.get_xticklabels(), rotation=-90)# ax.set_xticklabels(corr.index, rotation=90)四、代码
'''深入挖掘'''font = {'family': 'Times New Roman', 'size': 12, }sns.set(font_scale=1.2)plt.rc('font',family='Times New Roman') fig = plt.figure(figsize = (16, 12))ax1=fig.add_subplot(2,1,1)cor = SubShowFeatures01.corr()mask = np.zeros_like(cor)for i in range(len(mask)): for j in range(i+1, len(mask[0])): mask[i][j] = True sns.heatmap(cor,linewidths = 0.05, ax=ax1, mask=mask, annot=True, annot_kws=font, vmax=1.0, vmin=-1.0, cmap='YlGnBu', center=0.5, cbar=True, robust=False)ax1.set_title('User metric features', fontdict=font) ax2=fig.add_subplot(2,1,2)SubShowFeatures02 = pd.DataFrame({"Label":FerFeatures.Label, "PEM":FerFeatures.PEM, "PVAL":FerFeatures.PVAL, "NVAL":FerFeatures.NVAL, "NEM":FerFeatures.NEM, "DEP":FerFeatures.DEP, "AGR":FerFeatures.AGR, "CONV":FerFeatures.CONV,"Cold":FerFeatures.Cold, "Warm":FerFeatures.Warm, "Pleasure":FerFeatures.Pleasure, "Arousal":FerFeatures.Arousal, "Dominance":FerFeatures.Dominance })cor = SubShowFeatures02.corr()mask = np.zeros_like(cor)for i in range(len(mask)): for j in range(i+1, len(mask[0])): mask[i][j] = Truesns.heatmap(cor,linewidths = 0.05, ax=ax2, mask=mask, annot=True, annot_kws=font, vmax=1.0, vmin=-1.0, cmap='YlOrRd', center=0)ax2.set_title('User personality features', fontdict=font) plt.show()
来源地址：https://blog.csdn.net/weixin_45841831/article/details/130558352
