Last updated on 2019-7-23…
概率密度图
可见特征f1是要保留的，特征f20是要舍弃的
import matplotlib.pyplot as plt
import seaborn as sns
def plot_kde(train, test, col, values=True):
fig,ax =plt.subplots(1,2,figsize=(15,5))
sns.kdeplot(train[col][train['label']==0],color='g',ax=ax[0])
sns.kdeplot(train[col][train['label']==1],color='r',ax=ax[0])
sns.kdeplot(train[col][train['label']==-1],color='y',ax=ax[0])
sns.kdeplot(train[col],color='y',ax=ax[1])
sns.kdeplot(test[col],color='b',ax=ax[1])
plt.show()
del train, col,test
for one_col in train.columns:
plot_kde(train,test,one_col)
networkx社会网络图
import networkx as nx
#初始化
G=nx.Graph()
#添加点
G.add_node('a')#添加点a
G.add_node(1,1)#用坐标来添加点
#添加边
G.add_edge('x','y')#添加边,起点为x，终点为y
G.add_weight_edges_from([('x','y',1.0)]) #第三个输入量为权值
list = [[('a','b',5.0),('b','c',3.0),('a','c',1.0)]
G.add_weight_edges_from([(list)])
for index in node.index: #遍历添加边
G.add_edge(str(node.loc[index].values[0]),str(node.loc[index].values[1]))
#画图
import matplotlib.pyplot as plt
nx.draw(G,pos = nx.random_layout(G),node_color = 'r',edge_color = 'b',with_labels = True)
plt.show()
#保存
plt.savefig("data/links.jpg")
pyecharts地理地图
from pyecharts import Map, Page, Style
value = [1.3, 1.3, 1.3, 3.3, 1.3, 1.3, 1.3, 1.5, 1.5, 1.5, 1.5, 3.3, 3.5, 2.9]
attr = ["Pakistan","Afghanistan","Tajikistan","Bangladesh","Jordan", "Syria","Iraq","Nigeria","S.Sudan","Somalia","Uganda","MozamBique","Philippines","Colombia"]
chart = Map("级别分布", width=1200, height=600)
chart.add("", attr, value, maptype="world", is_visualmap=True,
visual_range=[1,5], visual_range_color=['#d94e5d', '#eac763', '#ffffff'],
is_map_symbol_show=False, visual_text_color='#000')
chart.render(path="data/级别分布.html")
