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跳哪里代码：
1import numpy as np
2import matplotlib.pyplot as plt
3import pandas as pd
4import seaborn as sns
5from scipy.misc.pilutil import imread
6import matplotlib.cm as cm
7
8#导入部分数据
9deaths1 = pd.read_csv("deaths/kill_match_stats_final_0.csv")
10deaths2 = pd.read_csv("deaths/kill_match_stats_final_1.csv")
11
12deaths = pd.concat([deaths1, deaths2])
13
14#打印前5列，理解变量
15print (deaths.head(),'\n',len(deaths))
16
17#两种地图
18miramar = deaths[deaths["map"] == "MIRAMAR"]
19erangel = deaths[deaths["map"] == "ERANGEL"]
20
21#开局前100秒死亡热力图
22position_data = ["killer_position_x","killer_position_y","victim_position_x","victim_position_y"]
23for position in position_data:
24 miramar[position] = miramar[position].apply(lambda x: x*1000/800000)
25 miramar = miramar[miramar[position] != 0]
26
27 erangel[position] = erangel[position].apply(lambda x: x*4096/800000)
28 erangel = erangel[erangel[position] != 0]
29
30n = 50000
31mira_sample = miramar[miramar["time"] < 100].sample(n)
32eran_sample = erangel[erangel["time"] < 100].sample(n)
33
34# miramar热力图
35bg = imread("miramar.jpg")
36fig, ax = plt.subplots(1,1,figsize=(15,15))
37ax.imshow(bg)
38sns.kdeplot(mira_sample["victim_position_x"], mira_sample["victim_position_y"],n_levels=100, cmap=cm.Reds, alpha=0.9)
39
40# erangel热力图
41bg = imread("erangel.jpg")
42fig, ax = plt.subplots(1,1,figsize=(15,15))
43ax.imshow(bg)
44sns.kdeplot(eran_sample["victim_position_x"], eran_sample["victim_position_y"], n_levels=100,cmap=cm.Reds, alpha=0.9)
一苟到底还是冲出去干？
1library(dplyr)
2library(tidyverse)
3library(data.table)
4library(ggplot2)
5pubg_full <- fread("../agg_match_stats.csv")
6# 吃鸡团队平均击杀敌人的数量
7attach(pubg_full)
8pubg_winner <- pubg_full %>% filter(team_placement==1&party_size<4&game_size>90)
9detach(pubg_full)
10team_killed <- aggregate(pubg_winner$player_kills, by=list(pubg_winner$match_id,pubg_winner$team_id), FUN="mean")
11team_killed$death_num <- ceiling(team_killed$x)
12ggplot(data = team_killed) + geom_bar(mapping = aes(x = death_num, y = ..count..), color="steelblue") +
13 xlim(0,70) + labs(title = "Number of Death that PUBG Winner team Killed", x="Number of death")
14
15# 吃鸡团队最后存活的玩家击杀数量
16pubg_winner <- pubg_full %>% filter(pubg_full$team_placement==1) %>% group_by(match_id,team_id)
17attach(pubg_winner)
18team_leader <- aggregate(player_survive_time~player_kills, data = pubg_winner, FUN="max")
19detach(pubg_winner)
20
21# 吃鸡团队中击杀敌人最多的数量
22pubg_winner <- pubg_full %>% filter(pubg_full$team_placement==1&pubg_full$party_size>1)
23attach(pubg_winner)
24team_leader <- aggregate(player_kills, by=list(match_id,team_id), FUN="max")
25detach(pubg_winner)
26ggplot(data = team_leader) + geom_bar(mapping = aes(x = x, y = ..count..), color="steelblue") +
27 xlim(0,70) + labs(title = "Number of Death that PUBG Winner Killed", x="Number of death")
吃鸡武器排名
1#杀人武器排名
2death_causes = deaths['killed_by'].value_counts()
3
4sns.set_context('talk')
5fig = plt.figure(figsize=(30, 10))
6ax = sns.barplot(x=death_causes.index, y=[v / sum(death_causes) for v in death_causes.values])
7ax.set_title('Rate of Death Causes')
8ax.set_xticklabels(death_causes.index, rotation=90)
9
10#排名前20的武器
11rank = 20
12fig = plt.figure(figsize=(20, 10))
13ax = sns.barplot(x=death_causes[:rank].index, y=[v / sum(death_causes) for v in death_causes[:rank].values])
14ax.set_title('Rate of Death Causes')
15ax.set_xticklabels(death_causes.index, rotation=90)
16
17#两个地图分开取
18f, axes = plt.subplots(1, 2, figsize=(30, 10))
19axes[0].set_title('Death Causes Rate: Erangel (Top {})'.format(rank))
20axes[1].set_title('Death Causes Rate: Miramar (Top {})'.format(rank))
21
22counts_er = erangel['killed_by'].value_counts()
23counts_mr = miramar['killed_by'].value_counts()
24
25sns.barplot(x=counts_er[:rank].index, y=[v / sum(counts_er) for v in counts_er.values][:rank], ax=axes[0] )
26sns.barplot(x=counts_mr[:rank].index, y=[v / sum(counts_mr) for v in counts_mr.values][:rank], ax=axes[1] )
27axes[0].set_ylim((0, 0.20))
28axes[0].set_xticklabels(counts_er.index, rotation=90)
29axes[1].set_ylim((0, 0.20))
30axes[1].set_xticklabels(counts_mr.index, rotation=90)
31
32#吃鸡和武器的关系
33win = deaths[deaths["killer_placement"] == 1.0]
34win_causes = win['killed_by'].value_counts()
35
36sns.set_context('talk')
37fig = plt.figure(figsize=(20, 10))
38ax = sns.barplot(x=win_causes[:20].index, y=[v / sum(win_causes) for v in win_causes[:20].values])
39ax.set_title('Rate of Death Causes of Win')
40ax.set_xticklabels(win_causes.index, rotation=90)
单排吃鸡容易还是组队容易？
1library(dplyr)
2library(tidyverse)
3library(data.table)
4library(ggplot2)
5pubg_full <- fread("E:/aggregate/agg_match_stats_0.csv")
6attach(pubg_full)
7pubg_winner <- pubg_full %>% filter(team_placement==1)
8detach(pubg_full)
9ggplot(data = pubg_winner) + geom_bar(mapping = aes(x = player_assists, y = ..count..), fill="#E69F00") +
10 xlim(0,10) + labs(title = "Number of Player assisted", x="Number of death")
11ggplot(data = pubg_winner) + geom_bar(mapping = aes(x = player_assists, y = ..prop..), fill="#56B4E9") +
12 xlim(0,10) + labs(title = "Number of Player assisted", x="Number of death")
敌人离我越近越危险？
1# python代码：杀人和距离的关系
2import math
3def get_dist(df): #距离函数
4 dist = []
5 for row in df.itertuples():
6 subset = (row.killer_position_x - row.victim_position_x)**2 + (row.killer_position_y - row.victim_position_y)**2
7 if subset > 0:
8 dist.append(math.sqrt(subset) / 100)
9 else:
10 dist.append(0)
11 return dist
12
13df_dist = pd.DataFrame.from_dict({'dist(m)': get_dist(erangel)})
14df_dist.index = erangel.index
15
16erangel_dist = pd.concat([erangel,df_dist], axis=1)
17
18df_dist = pd.DataFrame.from_dict({'dist(m)': get_dist(miramar)})
19df_dist.index = miramar.index
20
21miramar_dist = pd.concat([miramar,df_dist], axis=1)
22
23f, axes = plt.subplots(1, 2, figsize=(30, 10))
24plot_dist = 150
25
26axes[0].set_title('Engagement Dist. : Erangel')
27axes[1].set_title('Engagement Dist.: Miramar')
28
29plot_dist_er = erangel_dist[erangel_dist['dist(m)'] <= plot_dist]
30plot_dist_mr = miramar_dist[miramar_dist['dist(m)'] <= plot_dist]
31
32sns.distplot(plot_dist_er['dist(m)'], ax=axes[0])
33sns.distplot(plot_dist_mr['dist(m)'], ax=axes[1])
岛上人越多我活得越久？
1# R语言代码如下：
2library(magrittr)
3library(dplyr)
4library(survival)
5library(tidyverse)
6library(data.table)
7library(ggplot2)
8library(survminer)
9pubg_full <- fread("../agg_match_stats.csv")
10# 数据预处理，将连续变量划为分类变量
11pubg_sub <- pubg_full %>%
12 filter(player_survive_time<2100) %>%
13 mutate(drive = ifelse(player_dist_ride>0, 1, 0)) %>%
14 mutate(size = ifelse(game_size<33, 1,ifelse(game_size>=33 &game_size<66,2,3)))
15# 创建生存对象
16surv_object <- Surv(time = pubg_sub$player_survive_time)
17fit1 <- survfit(surv_object~party_size,data = pubg_sub)
18# 可视化生存率
19ggsurvplot(fit1, data = pubg_sub, pval = TRUE, xlab="Playing time [s]", surv.median.line="hv",
20 legend.labs=c("SOLO","DUO","SQUAD"), ggtheme = theme_light(),risk.table="percentage")
21fit2 <- survfit(surv_object~drive,data=pubg_sub)
22ggsurvplot(fit2, data = pubg_sub, pval = TRUE, xlab="Playing time [s]", surv.median.line="hv",
23 legend.labs=c("walk","walk&drive"), ggtheme = theme_light(),risk.table="percentage")
24fit3 <- survfit(surv_object~size,data=pubg_sub)
25ggsurvplot(fit3, data = pubg_sub, pval = TRUE, xlab="Playing time [s]", surv.median.line="hv",
26 legend.labs=c("small","medium","big"), ggtheme = theme_light(),risk.table="percentage")
我的天命圈在哪儿?
1#最后毒圈位置
2import matplotlib.pyplot as plt
3import pandas as pd
4import seaborn as sns
5from scipy.misc.pilutil import imread
6import matplotlib.cm as cm
7
8#导入部分数据
9deaths = pd.read_csv("deaths/kill_match_stats_final_0.csv")
10#导入aggregate数据
11aggregate = pd.read_csv("aggregate/agg_match_stats_0.csv")
12print(aggregate.head())
13#找出最后三人死亡的位置
14
15team_win = aggregate[aggregate["team_placement"]==1] #排名第一的队伍
16#找出每次比赛第一名队伍活的最久的那个player
17grouped = team_win.groupby('match_id').apply(lambda t: t[t.player_survive_time==t.player_survive_time.max()])
18
19deaths_solo = deaths[deaths['match_id'].isin(grouped['match_id'].values)]
20deaths_solo_er = deaths_solo[deaths_solo['map'] == 'ERANGEL']
21deaths_solo_mr = deaths_solo[deaths_solo['map'] == 'MIRAMAR']
22
23df_second_er = deaths_solo_er[(deaths_solo_er['victim_placement'] == 2)].dropna()
24df_second_mr = deaths_solo_mr[(deaths_solo_mr['victim_placement'] == 2)].dropna()
25print (df_second_er)
26
27position_data = ["killer_position_x","killer_position_y","victim_position_x","victim_position_y"]
28for position in position_data:
29 df_second_mr[position] = df_second_mr[position].apply(lambda x: x*1000/800000)
30 df_second_mr = df_second_mr[df_second_mr[position] != 0]
31
32 df_second_er[position] = df_second_er[position].apply(lambda x: x*4096/800000)
33 df_second_er = df_second_er[df_second_er[position] != 0]
34
35df_second_er=df_second_er
36# erangel热力图
37sns.set_context('talk')
38bg = imread("erangel.jpg")
39fig, ax = plt.subplots(1,1,figsize=(15,15))
40ax.imshow(bg)
41sns.kdeplot(df_second_er["victim_position_x"], df_second_er["victim_position_y"], cmap=cm.Blues, alpha=0.7,shade=True)
42
43# miramar热力图
44bg = imread("miramar.jpg")
45fig, ax = plt.subplots(1,1,figsize=(15,15))
46ax.imshow(bg)
47sns.kdeplot(df_second_mr["victim_position_x"], df_second_mr["victim_position_y"], cmap=cm.Blues,alpha=0.8,shade=True)
