吃鸡--危险地带分布图

多图显示，吃鸡不同时间下的危险地带分布情况。

#多图显示
# coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy.misc.pilutil import imread
import matplotlib.cm as cm
from random import randint,sample
#导入部分数据
f1=open(".../deaths/kill_match_stats_final_0.csv")
deaths1 = pd.read_csv(f1)
#print(deaths1.columns.values.tolist()) #列名，理解变量
#地图
miramar = deaths1[deaths1["map"] == "MIRAMAR"]
#开局前100秒死亡热力图
position_data = ["killer_position_x","killer_position_y","victim_position_x","victim_position_y"]
for position in position_data:
    miramar[position] = miramar[position].apply(lambda x:x*1000/800000)
    miramar = miramar[miramar[position] != 0]
#print(miramar)
n=5000
mira_sample = miramar[miramar["time"]<100].sample(n,replace=False)  #抽取数集是否取代原有数集
mira_sample2 = miramar[(miramar["time"]>100)&(miramar["time"<200)].sample(n,replace=False)
print(mira_sample)
# miramar热力图
bg = imread("...\miramar.jpg")
fig,(ax11,ax12) = plt.subplots(1,2,figsize=(8,8),sharey="all)
ax11.imshow(bg)
sns.kdeplot(mira_sample["victim_position_x"], mira_sample["victim_position_y"],n_levels=100, cmap="Reds",ax=ax11)
ax12.imshow(bg)
sns.kdeplot(mira_sample["victim_position_x"], mira_sample["victim_position_y"],n_levels=100, cmap="Reds",ax=ax12)
plt.show()

 

如图，红色区域即为危险地带集中区域，一目了然。