Python画热图之seaborn

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()

 

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

本文地址：https://blog.csdn.net/weixin_41869644/article/details/107160919