相关数据下载，请关注公众号"一行数据"，回复"pubg"可得

既然学习一段时间python了，那么得拿些好玩的东西练练手，这里通过加载几万局的吃鸡数据，来对吃鸡胜率进行可视化分析。

通过绘制击杀地图和被击杀地图查找LYB的藏身之地

下面贴上代码，和分析。

#这个代码，是通过展示地图击杀和死亡最多的地方，让我们可以挑选出有哪些好玩的地方#加载模块import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport seaborn as snsfrom scipy.misc.pilutil import imreadf = open(r'F:\spyder\kernel项目\pubg\PUBG_MatchData_Flattened.tsv')#添加路径df = pd.read_csv(f,sep = '\t')#edf和mdf是两个地图，下面把两张地图分开进行处理edf = df.loc[df['map_id'] == 'ERANGEL']mdf = df.loc[df['map_id'] == 'MIRAMAR']#print(edf.head())def killer_victim_df_maker(df):#挑出地图中击杀和被杀玩家的坐标df = edfvictim_x_df = df.filter(regex = 'victim_position_x')victim_y_df = df.filter(regex = 'victim_position_y')killer_x_df = df.filter(regex = 'killer_position_x')killer_y_df = df.filter(regex = 'killer_position_y')#ravel()将多维矩阵变成一维victim_x_s = pd.Series(victim_x_df.values.ravel('F'))victim_y_s = pd.Series(victim_y_df.values.ravel('F'))killer_x_s = pd.Series(killer_x_df.values.ravel('F'))killer_y_s = pd.Series(killer_y_df.values.ravel('F'))vdata = {'x':victim_x_s, 'y':victim_y_s}kdata = {'x':killer_x_s, 'y':killer_y_s}#dropna(how = 'any')删除带nan的行#再留下坐标等于0（在边界上的异常数据）剔除victim_df = pd.DataFrame(data = vdata).dropna(how = 'any')victim_df = victim_df[victim_df['x'] > 0]killer_df = pd.DataFrame(data = kdata).dropna(how = 'any')killer_df = killer_df[killer_df['x'] > 0]return killer_df, victim_dfekdf,evdf = killer_victim_df_maker(edf)mkdf,mvdf = killer_victim_df_maker(mdf)# print(ekdf.head())#在森林击杀的坐标数据# print(evdf.head())#在森林被杀的坐标数据# print(mkdf.head())# print(mvdf.head())# print(len(ekdf), len(evdf), len(mkdf), len(mvdf))#将dataframe转换成numpy arrayplot_data_ev = evdf[['x','y']].valuesplot_data_ek = ekdf[['x','y']].valuesplot_data_mv = mvdf[['x','y']].valuesplot_data_mk = mkdf[['x','y']].values#将获得的坐标数据与地图上的坐标数据进行匹配plot_data_ev = plot_data_ev * 4040 /800000plot_data_ek = plot_data_ek * 4040 /800000plot_data_mv = plot_data_mv * 976 /800000plot_data_mk = plot_data_mk * 976 /800000#加载模块from scipy.ndimage.filters import gaussian_filterimport matplotlib.cm as cmfrom matplotlib.colors import Normalize#热力图函数def heatmap(x, y, s, bins = 100):# x = plot_data_ev[:,0]# y = plot_data_ev[:,1]# s = 1.5# bins = 800#np.histogram2d()将两列数值转为矩阵heatmap, xedges, yedges = np.histogram2d(x, y, bins = bins)#高斯锐化模糊对象heatmap = gaussian_filter(heatmap, sigma = s)extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]return heatmap.T, extent#读取森林地图底图#Normalize归一化#np.clip(x,a,b)将x中小于a的值设为a，大于b的值设为b#cm.bwr 蓝白红bg = imread('erangel.jpg')hmap, extent = heatmap(plot_data_ev[:,0], plot_data_ev[:,1], 1.5, bins =800)alphas = np.clip(Normalize(0, hmap.max()/100, clip=True)(hmap)*1.5,0.0,1.)colors = Normalize(hmap.max()/100, hmap.max()/20, clip=True)(hmap)colors = cm.bwr(colors)colors[..., -1] = alphashmap2, extent2 = heatmap(plot_data_ek[:,0],plot_data_ek[:,1],1.5, bins = 800)alphas2 = np.clip(Normalize(0, hmap2.max()/100, clip = True)(hmap2)*1.5, 0.0, 1.)colors2 = Normalize(hmap2.max()/100, hmap2.max()/20, clip=True)(hmap2)colors2 = cm.RdBu(colors2)colors2[...,-1] = alphas2#'森林死亡率图'fig, ax = plt.subplots(figsize = (24,24))ax.set_xlim(0, 4096);ax.set_ylim(0, 4096)ax.imshow(bg)ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.bwr, alpha = 1)#ax.imshow(colors2, extent = extent2, origin = 'lower', cmap = cm.RdBu, alpha = 0.5)plt.gca().invert_yaxis()plt.title('森林地图死亡率图')

从图中可以看出来，港口，军事基地，p城，学校等这些红色的地方伤亡人数最大，而且很明显能看出下岛的两座桥上网率也很大，想来钢枪还是躲开人群都可以借鉴

#森林击杀图fig, ax = plt.subplots(figsize = (24,24))ax.set_xlim(0, 4096); ax.set_ylim(0, 4096)ax.imshow(bg)ax.imshow(colors2, extent = extent2, origin = 'lower', cmap = cm.RdBu, alpha = 1)plt.gca().invert_yaxis()plt.colorbar()plt.title('森林地图击杀率图')

这是森林击杀图，和森林死亡图基本重合，有人在的被击杀的地方就肯定有人击杀，不过要是仔细看的话，还有一点的差异，这便是LYB的藏身之地。

#沙漠地图bg = imread('miramar.jpg')hmap, extent = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 1.5, bins = 800)alphas = np.clip(Normalize(0, hmap.max()/200, clip=True)(hmap2)*1.5, 0.0, 1.)colors2 = Normalize(hmap2.max()/100, hmap2.max()/20, clip=True)(hmap2)colors2 = cm.rainbow(colors2)colors2[..., -1] = alphas2hmap2, extent2 = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 1.5, bins = 800)alphas2 = np.clip(Normalize(0, hmap2.max()/200, clip=True)(hmap2)*1.5, 0.0, 1.)colors = Normalize(hmap.max()/100, hmap.max()/20, clip=True)(hmap)colors = cm.rainbow(colors)colors[..., -1] = alphasa = colors2[...,-1]colors3 = colors2colors3[...,-1] = np.clip(abs(colors2[...,-1]-colors[...,-1]),0.0,1.)np.mean(colors2[...,-1]-colors[...,-1])

#沙漠LYB图fig, ax = plt.subplots(figsize = (24,24))ax.set_xlim(0, 1000);ax.set_ylim(0, 1000)ax.imshow(bg)#ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.Blues, alpha = 0.5)ax.imshow(colors3, extent = extent2, origin = 'lower', cmap = cm.Reds, alpha = 0.5)plt.gca().invert_yaxis()plt.title('沙漠地图击杀率图')#通过对比击杀率和死亡率，寻找lyb藏身之地#color3,击杀率大于死亡率的地方

'''在查看上面的热图时，重要的是要记住蓝圈对整体死亡的影响。地图中心区域的许多紫色斑点可能是由于区域向内推动玩家！ 同样，通过查看时间片可以稍微减轻这种情况。 我将创建另一个内核来执行此操作，因为它需要更改此内核的第一个代码块。在此之前，让我们尝试查看每个垃圾箱中的杀死率。首先，让我们定义一个除法函数，这样我们就不会除以0。'''def divbutnotbyzero(a, b):c = np.zeros(a.shape)for i, row in enumerate(b):for j, el in enumerate(row):if el == 0:#如果击杀率等于0的话c[i][j] = a[i][j]#c值就等于死亡率的值else:c[i][j] = a[i][j]/el#击杀/死亡return cbg = imread('erangel.jpg')hmap, extent = heatmap(plot_data_ev[:,0], plot_data_ev[:,1], 0, bins = 800)hmap2, extent2 = heatmap(plot_data_ek[:,0], plot_data_ek[:,1], 0, bins = 800)hmap3 = divbutnotbyzero(hmap, hmap2)alphas = np.clip(Normalize(0, hmap3.max()/100, clip=True)(hmap)*1.5, 0.0,1.)colors = Normalize(hmap3.max()/100, hmap3.max()/20, clip=True)(hmap)colors = cm.rainbow(colors)colors[...,-1] = alphasfig, ax = plt.subplots(figsize = (24, 24))ax.set_xlim(0,4096); ax.set_ylim(0, 4096)ax.imshow(bg)ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.rainbow, alpha = 0.5)plt.gca().invert_yaxis()'''Pretty cool! Notably, the typical "hot zones" arentthe only places for getting a good kill/death ratio.Anywhere that you are seeing red is a pretty good spot to land. Let's print the k/d mean:太酷了！ 值得注意的是，典型的“热区”并不是获得良好杀伤/死亡率的唯一场所。 你看到红色的任何地方都是降落的好地方。 让我们打印k / d的意思是：'''print(hmap3.mean())bg = imread('miramar.jpg')hmap, extent = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 0, bins=800)hmap2, extent2 = heatmap(plot_data_mk[:,0], plot_data_mk[:,1], 0, bins=800)hmap3 = divbutnotbyzero(hmap,hmap2)alphas = np.clip(Normalize(0, hmap3.max()/100, clip=True)(hmap)*1.5, 0.0, 1.)colors = Normalize(hmap3.max()/100, hmap3.max()/20, clip=True)(hmap)colors = cm.rainbow(colors)colors[..., -1] = alphasfig, ax = plt.subplots(figsize=(24,24))ax.set_xlim(0, 1000); ax.set_ylim(0, 1000)ax.imshow(bg)ax.imshow(colors, extent=extent, origin='lower', cmap=cm.rainbow, alpha=0.5)plt.gca().invert_yaxis()

如果觉得《利用python进行吃鸡(绝地求生)可视化分析》对你有帮助，请点赞、收藏，并留下你的观点哦！