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  • Python画热图之seaborn

    2020-07-06 16:56:11
    df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"]) # 计算每对变量之间的相关性 corr_matrix=df.corr() # 半个矩阵 mask = np.zeros_like(corr_matrix) mask[np.triu_indices_from...
    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    from scipy.stats import kde
    
    my_dpi=96
    plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
    x = np.random.normal(size=500)
    y = x * 3 + np.random.normal(size=500)
    
    nbins=300
    k = kde.gaussian_kde([x,y])
    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
    zi = k(np.vstack([xi.flatten(), yi.flatten()]))
    plt.pcolormesh(xi, yi, zi.reshape(xi.shape))
    plt.show()

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    from scipy.stats import kde
    
    my_dpi=96
    plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
    x = np.random.normal(size=500)
    y = x * 3 + np.random.normal(size=500)
    
    nbins=300
    k = kde.gaussian_kde([x,y])
    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
    zi = k(np.vstack([xi.flatten(), yi.flatten()]))
    plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    from scipy.stats import kde
    
    my_dpi=96
    plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
    x = np.random.normal(size=500)
    y = x * 3 + np.random.normal(size=500)
    
    nbins=300
    k = kde.gaussian_kde([x,y])
    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
    zi = k(np.vstack([xi.flatten(), yi.flatten()]))
    plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
    plt.colorbar()
    plt.show()

     

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((5,5)), columns=["a","b","c","d","e"])
    p1 = sns.heatmap(df)
    plt.show()

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])
    # 计算每对变量之间的相关性
    corr_matrix=df.corr()
    p1 = sns.heatmap(corr_matrix, cmap='PuOr')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])
    # 计算每对变量之间的相关性
    corr_matrix=df.corr()
    # 画半个矩阵
    mask = np.zeros_like(corr_matrix)
    mask[np.triu_indices_from(mask)] = True
    with sns.axes_style("white"):
        p2 = sns.heatmap(corr_matrix, mask=mask, square=True)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    people=np.repeat(("A","B","C","D","E"),5)
    feature=list(range(1,6))*5
    value=np.random.random(25)
    df=pd.DataFrame({'feature': feature, 'people': people, 'value': value })
    df_wide=df.pivot_table( index='people', columns='feature', values='value' )
    p2=sns.heatmap( df_wide )
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p1 = sns.heatmap(df, linewidths=2, linecolor='yellow')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p2 = sns.heatmap(df, annot=True, annot_kws={"size": 7})
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p3 = sns.heatmap(df, cbar=False)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p4 = sns.heatmap(df, yticklabels=False)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p5 = sns.heatmap(df, xticklabels=4)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p1 = sns.heatmap(df, cmap="YlGnBu")
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p1 = sns.heatmap(df, cmap="Blues")
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p1 = sns.heatmap(df, vmin=0, vmax=0.5)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
    p1 = sns.heatmap(df, vmin=0.5, vmax=0.7)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.randn(6, 6))
    
    # 离散分布
    df_q = pd.DataFrame()
    for col in df:
        df_q[col] = pd.to_numeric( pd.qcut(df[col], 3, labels=list(range(3))) )
    
    p1 = sns.heatmap(df_q)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = np.random.randn(30, 30)
    p1 = sns.heatmap(df, cmap="PiYG")
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = np.random.randn(30, 30)
    p1 = sns.heatmap(df, center=1)
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
    df[1]=df[1]+40
    p1 = sns.heatmap(df, cmap='viridis')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
    df[1]=df[1]+40
    
    df_norm_col=(df-df.mean())/df.std()
    p2 = sns.heatmap(df_norm_col, cmap='viridis')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
    df.iloc[2]=df.iloc[2]+40
    
    p3 = sns.heatmap(df, cmap='viridis')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    np.random.seed(0)
    
    df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
    df.iloc[2]=df.iloc[2]+40
    # 减去均值
    df_norm_row=df.sub(df.mean(axis=1), axis=0)
    
    df_norm_row=df_norm_row.div( df.std(axis=1), axis=0 )
    
    p4 = sns.heatmap(df_norm_row, cmap='viridis')
    plt.show()

     

    import matplotlib.pylab as plt
    import seaborn as sns
    import numpy as np
    import pandas as pd
    from mpl_toolkits.mplot3d import Axes3D
    
    my_dpi=96
    plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
    
    # 获得网页数据
    url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
    data = pd.read_csv(url)
    
    sns.heatmap(data, cmap="viridis")
    # 坐标轴和标题
    plt.tick_params(labelbottom='off', labelleft='off')
    plt.xlabel('Latitude')
    plt.ylabel('Longitude')
    plt.title('Altitude on the volcano area', loc='left' )
    plt.show()

     

    本博主新开公众号, 希望大家能扫码关注一下,十分感谢大家。

     

    本文来自:https://github.com/holtzy/The-Python-Graph-Gallery/blob/master/PGG_notebook.py  

    展开全文
  • python 画热图方式

    2020-10-07 19:53:35
    im = plt.imshow(data, cmap=plt.cm.jet) plt.colorbar(im) plt.show() 最后热图的排序和data排序一致
    im = plt.imshow(data, cmap=plt.cm.jet)
    plt.colorbar(im)
    plt.show()
    

    最后热图的排序和data排序一致

    展开全文
  • python绘制热力图heatmap

    2021-01-20 04:12:09
    本文实例为大家分享了python绘制热力图的具体代码,供大家参考,具体内容如下 python的热力图是用皮尔逊相关系数来查看两者之间的关联性。 #encoding:utf-8 import numpy as np import pandas as pd from ...
  • Python-Seaborn热图绘制

    万次阅读 多人点赞 2017-11-19 11:08:20
    python-3.6 Seaborn-0.8热图import numpy as np import seaborn as sns import matplotlib.pyplot as plt sns.set() np.random.seed(0) uniform_data = np.random.rand(10, 12) ax = sns.heatmap(unif

    制图环境:
    pycharm
    python-3.6
    Seaborn-0.8

    热图

    import numpy as np
    import seaborn as sns
    import matplotlib.pyplot as plt
    sns.set()
    np.random.seed(0)
    uniform_data = np.random.rand(10, 12)
    ax = sns.heatmap(uniform_data)
    plt.show()
    

    这里写图片描述

    # 改变颜色映射的值范围
    ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
    plt.show()

    这里写图片描述

    uniform_data = np.random.randn(10, 12)
    #为以0为中心的数据绘制一张热图
    ax = sns.heatmap(uniform_data, center=0)
    plt.show()
    

    这里写图片描述

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    #用行和列标签绘制
    flights_long = sns.load_dataset("flights")
    flights = flights_long.pivot("month", "year", "passengers")
    # 绘制x-y-z的热力图,比如 年-月-销量 的热力图
    f, ax = plt.subplots(figsize=(9, 6))
    sns.heatmap(flights, ax=ax)
    #设置坐标字体方向
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='right')
    label_x = ax.get_xticklabels()
    plt.setp(label_x, rotation=45, horizontalalignment='right')
    plt.show()
    

    这里写图片描述

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    flights_long = sns.load_dataset("flights")
    flights = flights_long.pivot("month", "year", "passengers")
    # 绘制x-y-z的热力图,比如 年-月-销量 的热力图
    f, ax = plt.subplots(figsize=(9, 6))
    #使用不同的颜色
    sns.heatmap(flights, fmt="d",cmap='YlGnBu', ax=ax)
    #设置坐标字体方向
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='right')
    label_x = ax.get_xticklabels()
    plt.setp(label_x, rotation=45, horizontalalignment='right')
    plt.show()
    

    这里写图片描述

    注释热图

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    flights_long = sns.load_dataset("flights")
    flights = flights_long.pivot("month", "year", "passengers")
    # 绘制x-y-z的热力图,比如 年-月-销量 的热力图
    f, ax = plt.subplots(figsize=(9, 6))
    #绘制热力图,还要将数值写到热力图上
    sns.heatmap(flights, annot=True, fmt="d", ax=ax)
    #设置坐标字体方向
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='right')
    label_x = ax.get_xticklabels()
    plt.setp(label_x, rotation=45, horizontalalignment='right')
    plt.show()
    

    这里写图片描述

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    flights_long = sns.load_dataset("flights")
    flights = flights_long.pivot("month", "year", "passengers")
    # 绘制x-y-z的热力图,比如 年-月-销量 的热力图
    f, ax = plt.subplots(figsize=(9, 6))
    #绘制热力图,还要将数值写到热力图上
    #每个网格上用线隔开
    sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)
    #设置坐标字体方向
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='right')
    label_x = ax.get_xticklabels()
    plt.setp(label_x, rotation=45, horizontalalignment='right')
    plt.show()
    

    这里写图片描述

    聚类热图

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    flights_long = sns.load_dataset("flights")
    flights = flights_long.pivot("month", "year", "passengers")
    # 绘制x-y-z的热力图,比如 年-月-销量 的聚类热图
    g= sns.clustermap(flights, fmt="d",cmap='YlGnBu')
    ax = g.ax_heatmap
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='left')
    plt.show()
    

    这里写图片描述

    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set(color_codes=True)
    iris = sns.load_dataset("iris")
    species = iris.pop("species")
    #设置图片大小
    g= sns.clustermap(iris, fmt="d",cmap='YlGnBu',figsize=(6,9))
    ax = g.ax_heatmap
    label_y = ax.get_yticklabels()
    plt.setp(label_y, rotation=360, horizontalalignment='left')
    #设置图片名称,分辨率,并保存
    plt.savefig('cluster.tif',dpi = 300)
    plt.show()
    

    这里写图片描述

    注:更多参数的用法请查阅官方文档

    展开全文
  • python数据可视化绘制热图

    万次阅读 2018-07-31 21:12:20
    最近要处理的数据需要更直观的显示出来,也就是需要数据的可视化操作,需要用到pyheatmap,具体安装,直接pip install pyheatmap 即可 ...Python-Seaborn热图绘制 Python可视化:Seaborn库热力图使用进阶...

    最近要处理的数据需要更直观的显示出来,也就是需要数据的可视化操作,需要用到pyheatmap,具体安装,直接pip install pyheatmap 即可

    由于我要绘制的数据是二维的,行是样本,列是代谢物,或者pathway 也就是不同的特征,绘制heatmap时 参考了下面的资料,觉得很有用呀,嘻嘻
    Python-Seaborn热图绘制

    Python可视化:Seaborn库热力图使用进阶

    相关系数矩阵与热力图heatmap(Python高级可视化库seaborn)

    上手也很快,主要是简单,有时间再来详细介绍功能

    展开全文
  • python绘制简单的热图

    万次阅读 2017-04-06 16:57:00
    有时候图像需要用热图也就是heatmap来进行可视化下面是我的代码 # coding=utf-8 import numpy as np from PIL import Image import matplotlib.pyplot as plt import urllib from pyheatmap.heatmap import HeatMap...
  • 这是一个生成热图的小程序,基于 Python 和 PIL 开发。 程序截图: 点击图 热图 安装: 通过 pip 安装: pip install pyheatmap 通过 easy_install 安装: easy_install pyheatmap 通过源码安装: ...
  • from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable import numpy as np x1 = np.arange(-1, 0, 0.01) x2 = np.arange(-1, 1, 0.01) x3 = np.arange(0, 1, 0.01) ...
  • python 美国地图

    千次阅读 2020-04-24 09:47:29
    我想用python 中国和美国的地图,刚刚学会了美国地图,特做一个记录。 当然首先是用google 找网上资料。 找到下面的链接:...
  • 小弟是python小白,想用python画一个类似热图但是其中标有数字并有colorbar的图,论坛的大神能不能给个思路啊 ![图片说明](https://img-ask.csdn.net/upload/201804/02/1522661147_944281.png) 图片具体就是上面...
  • 一、python可视化——热力图

    万次阅读 多人点赞 2018-04-04 10:41:57
    :设置作图的坐标轴,一般多个子图时需要修改不同的子图的该值 **kwargs :All other keyword arguments are passed to ax.pcolormesh   热力图矩阵块颜色参数 #cmap(颜色) import matplotlib.pyplot ...
  • 我们在做诸如人群密集度等可视化的时候,可能会考虑使用热力图,在Python中能很方便地绘制热力图。 下面以识别图片中的行人,并绘制热力图为例进行讲解。 步骤1:首先识别图像中的人,得到bounding box的中心坐标。...
  • Python画散点图

    千次阅读 2020-07-19 18:12:42
    import matplotlib.pyplot as plt import numpy as np import pandas as pd my_dpi=96 plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi) df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100...
  • python画热力图(相关系数矩阵图)

    万次阅读 多人点赞 2018-05-08 16:40:00
    使用热力图的形式展示包括相关系数矩阵...如果想试一下,可以参考https://zhuanlan.zhihu.com/p/26100511所以研究了一下第二种方法,就是用seaborn包。参考了https://blog.csdn.net/a19990412/article/details/793...
  • Python绘制热力图示例

    2020-09-18 14:47:17
    主要介绍了Python绘制热力图,结合实例形式分析了Python使用pyheatmap及matplotlib模块进行数值计算与图形绘制相关操作技巧,需要的朋友可以参考下
  • 使用plotly三维立体高逼格图,代码传送门: import plotly.graph_objects as go import numpy as np np.random.seed(1) N = 70 fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)), y=(55*np.random....
  • 混淆矩阵热力图如下所示: 代码如下: import seaborn as sn #画图模块 from sklearn.metrics import confusion_matrix def plot_matrix(y_true, y_pred,title_name): cm = confusion_matrix(y_true, y_pred)#...
  • # 定义热图的横纵坐标 xLabel = ['A', 'B', 'C', 'D', 'E'] yLabel = ['1', '2', '3', '4', '5'] # 准备数据阶段,利用random生成二维数据(5*5) data = [] for i in range(5): temp = [] for j in range(5...
  • Python代码: import plotly.graph_objects as go import numpy as np import pandas as pd def get_data(size): R = np.linspace(0, 2 * np.pi, size) x = np.linspace(start=0, stop=size, num=size, dtype...
  • 主要介绍了详解python 利用echarts地图(热力图)(世界地图,省市地图,区县地图),文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧
  • pyHeatMap一个用于绘制热力图的Python库。 依赖于Pillow,Python 2/3兼容。
  • 利用python画中热力地图

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  • Python生物信息学⑥绘制热图及火山图

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  • 'weight' : 'normal', 'size' : 20, } #横纵轴的名称 plt.xlabel('round',font1) plt.ylabel('value',font1) #热力图名称 ax.set_title('DX model score',font1) #图的输出 #将文件保存至文件中并且出图 ...
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    2021-05-13 16:31:46
    点上方蓝字人工智能算法与Python大数据获取更多干货在右上方···设为星标★,第一时间获取资源仅做学术分享,如有侵权,联系删除转载于 :作者丨Drazen Zaric来源 |专...
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  • xticklabels(ax.get_xticklabels(), rotation=20) cmap的参数如下,参考【Python】绘制热力图seaborn.heatmap,cmap设置颜色的参数: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r,...
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