4种更快更简单实现Python数据可视化的方法

热力图

```# Importing libs
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create a random dataset
data = pd.DataFrame(np.random.random((10,6)), columns=["Iron Man","Captain America","Black Widow","Thor","Hulk", "Hawkeye"])

print(data)

# Plot the heatmap
heatmap_plot = sns.heatmap(data, center=0, cmap='gist_ncar')

plt.show()```

二维密度图

```# Importing libs
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import skewnorm

# Create the data
speed = skewnorm.rvs(4, size=50)
size = skewnorm.rvs(4, size=50)

# Create and shor the 2D Density plot
ax = sns.kdeplot(speed, size, cmap="Reds", shade=False, bw=.15, cbar=True)
ax.set(xlabel='speed', ylabel='size')
plt.show()```

蜘蛛网图

```# Import libs
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# Get the data
print(df)

"""
#             Name  Attack  Defense  Speed  Range  Health
0  1         Iron Man      83       80     75     70      70
1  2  Captain America      60       62     63     80      80
2  3             Thor      80       82     83    100     100
3  3             Hulk      80      100     67     44      92
4  4      Black Widow      52       43     60     50      65
5  5          Hawkeye      58       64     58     80      65

"""

# Get the data for Iron Man
labels=np.array(["Attack","Defense","Speed","Range","Health"])
stats=df.loc[0,labels].values

# Make some calculations for the plot
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))

# Plot stuff
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)
ax.set_thetagrids(angles * 180/np.pi, labels)
ax.set_title([df.loc[0,"Name"]])
ax.grid(True)

plt.show()```

树状图

```# Import libs
import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster import hierarchy
import numpy as np

# Read in the dataset
# Drop any fields that are strings
# Only get the first 40 because this dataset is big
df = df.set_index('Name')
del df.index.name
df = df.drop(["Type 1", "Type 2", "Legendary"], axis=1)

# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')

# Orientation our tree
hierarchy.dendrogram(Z, orientation="left", labels=df.index)

plt.show()```