python爬取电竞《绝地求生》比赛数据集分析

python爬取电竞《绝地求⽣》⽐赛数据集分析
python爬取电竞《绝地求⽣》⽐赛数据集分析
⼀，选题背景
电⼦竞技（Electronic Sports）是电⼦游戏⽐赛达到“竞技”层⾯的体育项⽬。

电⼦竞技就是利⽤电⼦设备作为运动器械进⾏的、⼈与⼈之间的智⼒和体⼒结合的⽐拼。

通过电⼦竞技，可以锻炼和提⾼参与者的思维能⼒、反应能⼒、四肢协调能⼒和意志⼒，培养团队精神，并且职业电竞对体⼒也有较⾼要求。

电⼦竞技也是⼀种职业，和棋艺等⾮电⼦游戏⽐赛类似，2003年11⽉18⽇，国家体育局正式批准，将电⼦竞技列为第99个正式体育竞赛项⽬。

2008年，国家体育总局将电⼦竞技改批为第78号正式体育竞赛项⽬。

2018年雅加达亚运会将电⼦竞技纳为表演项⽬。

数据来源：20G绝地求⽣⽐赛数据集。

⼆，设计⽅案
1，爬⾍名称：python爬取电竞《绝地求⽣》⽐赛数据集分析
2，爬⾍爬取的内容与数据特征分析
主要分成两部分，⼀部分是玩家⽐赛的统计数据，以agg_match_stats开头，⼀部分是玩家被击杀的数据，以kill_match_stats开头本次分析选取其中的两个数据集进⾏分析
3，设计⽅案：
1. 飞机嗡嗡地，我到底跳哪⾥⽐较安全？
2. 我是该苟着不动，还是应该出去猛⼲？
3. 是该单打独⽃还是跟队友⼀起配合？
4. 毒来了我跑不过毒怎么办啊？
5. 什么武器最有⽤？
6. 近战适合使⽤什么武器，狙击适合使⽤什么武器呢？
7. 最后的毒圈⼀般会在哪⾥呢？
三，结果特征分析
1，页⾯的结构与特征分析
四，程序设计
1，# 使⽤pandas读取数据
1import pandas as pd
2import numpy as np
3import matplotlib.pyplot as plt
4import seaborn as sns
5 %matplotlib inline
6 plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
7 plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显⽰为⽅块的问题
8
9# 使⽤pandas读取数据
10 agg1 = pd.read_csv('/Users/apple/Desktop/pubg/aggregate/agg_match_stats_1.csv')
2，# 探索数据结构并数据清洗
1 agg1.head()
# 总共有13844275⾏玩家数据，15列
1 agg1.shape
1 agg1.columns
1 ()
3，数据分析与可视化
# 丢弃重复数据
1 agg1.drop_duplicates(inplace=True)
2
3 agg1.loc[1]
# 添加是否成功吃鸡列
1 agg1['won'] = agg1['team_placement'] == 1
# 添加是否搭乘过车辆列
1 agg1['drove'] = agg1['player_dist_ride'] != 0
我是该苟着不动，还是应该出去猛⼲
1 agg1.loc[agg1['player_kills'] < 40, ['player_kills', 'won']].groupby('player_kills').won.mean().plot.bar(figsize=(15,6), rot=0)
2 plt.xlabel('击杀⼈数', fontsize=14)
3 plt.ylabel("吃鸡概率", fontsize=14)
4 plt.title('击杀⼈数与吃鸡概率的关系', fontsize=14)
不同模式下的平均击杀⼈数：
1 agg1.groupby('party_size').player_kills.mean()
1 g = sns.FacetGrid(agg1.loc[agg1['player_kills']<=10, ['party_size', 'player_kills']], row="party_size", size=4, aspect=2)
2 g = g.map(sns.countplot, "player_kills")
3
4
5
6 party_size=1
1 party_size=2
1 party_size=4
是该单打独⽃还是跟队友⼀起配合？
1 agg1.loc[agg1['party_size']!=1, ['player_assists', 'won']].groupby('player_assists').won.mean().plot.bar(figsize=(15,6), rot=0)
2 plt.xlabel('助攻次数', fontsize=14)
3 plt.ylabel("吃鸡概率", fontsize=14)
4 plt.title('助攻次数与吃鸡概率的关系', fontsize=14)
“毒来了我跑不过毒怎么办啊”之车辆到底有多重要？
1 agg1.groupby('drove').won.mean().plot.barh(figsize=(6,3))
2 plt.xlabel("吃鸡概率", fontsize=14)
3 plt.ylabel("是否搭乘过车辆", fontsize=14)
4 plt.title('搭乘车辆与吃鸡概率的关系', fontsize=14)
5 plt.yticks([1,0],['是','否'])
1 dist_ride = agg1.loc[agg1['player_dist_ride']<12000, ['player_dist_ride', 'won']]
2
3 labels=["0-1k", "1-2k", "2-3k", "3-4k","4-5k", "5-6k", "6-7k", "7-8k", "8-9k", "9-10k", "10-11k", "11-12k"]
4 dist_ride['drove_cut'] = pd.cut(dist_ride['player_dist_ride'], 12, labels=labels)
5
6 dist_ride.groupby('drove_cut').won.mean().plot.bar(rot=60, figsize=(8,4))
7 plt.xlabel("搭乘车辆⾥程", fontsize=14)
8 plt.ylabel("吃鸡概率", fontsize=14)
9 plt.title('搭乘车辆⾥程与吃鸡概率的关系', fontsize=14)
match_unique = agg1.loc[agg1['party_size'] == 1, 'match_id'].unique()
match_unique = agg1.loc[agg1['party_size'] == 1, 'match_id'].unique()
把玩家被击杀的数据导⼊进来
1# 先把玩家被击杀的数据导⼊进来并探索数据
2 death1 = pd.read_csv('/Users/apple/Desktop/pubg/deaths/kill_match_stats_final_1.csv')
3
4 death1.head()
1 ()
2
3 death1.shape
4
5 death1_solo = death1[death1['match_id'].isin(match_unique)]
6
7 death1_()
飞机嗡嗡地，我到底跳哪⾥⽐较安全
1# 只统计单⼈模式，筛选存活不超过180秒的玩家数据
2 death_180_seconds_erg = death1_solo.loc[(death1_solo['map'] == 'ERANGEL')&(death1_solo['time'] < 180)&(death1_solo['victim_position_x']>0), :].dropna()
3 death_180_seconds_mrm = death1_solo.loc[(death1_solo['map'] == 'MIRAMAR')&(death1_solo['time'] < 180)&(death1_solo['victim_position_x']>0), :].dropna() 4
5 death_180_seconds_erg.shape
6
7 death_180_seconds_mrm.shape
1# 选择存活不过180秒的玩家死亡位置
2 data_erg = death_180_seconds_erg[['victim_position_x', 'victim_position_y']].values
3 data_mrm = death_180_seconds_mrm[['victim_position_x', 'victim_position_y']].values
4
5# 重新scale玩家位置
6 data_erg = data_erg*4096/800000
7 data_mrm = data_mrm*1000/800000
8
9from scipy.ndimage.filters import gaussian_filter
10import matplotlib.cm as cm
11from matplotlib.colors import Normalize
12from scipy.misc.pilutil import imread
13
14from scipy.ndimage.filters import gaussian_filter
15import matplotlib.cm as cm
16from matplotlib.colors import Normalize
17
18def heatmap(x, y, s, bins=100):
19 heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins)
20 heatmap = gaussian_filter(heatmap, sigma=s)
21
22 extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
23return heatmap.T, extent
24
25 bg = imread('/Users/apple/Desktop/pubg/erangel.jpg')
26 hmap, extent = heatmap(data_erg[:,0], data_erg[:,1], 4.5)
27 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*4.5, 0.0, 1.)
28 colors = Normalize(0, hmap.max(), clip=True)(hmap)
29 colors = cm.Reds(colors)
30 colors[..., -1] = alphas
31
32 fig, ax = plt.subplots(figsize=(24,24))
33 ax.set_xlim(0, 4096); ax.set_ylim(0, 4096)
34 ax.imshow(bg)
35 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
36 plt.gca().invert_yaxis()
1 bg = imread('/Users/apple/Desktop/pubg/miramar.jpg')
2 hmap, extent = heatmap(data_mrm[:,0], data_mrm[:,1], 4)
3 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*4, 0.0, 1.)
4 colors = Normalize(0, hmap.max(), clip=True)(hmap)
5 colors = cm.Reds(colors)
6 colors[..., -1] = alphas
7
8 fig, ax = plt.subplots(figsize=(24,24))
9 ax.set_xlim(0, 1000); ax.set_ylim(0, 1000)
10 ax.imshow(bg)
11 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
12#plt.scatter(plot_data_mr[:,0], plot_data_mr[:,1])
13 plt.gca().invert_yaxis()
最后的毒圈⼀般会在哪⾥呢？
这⾥选取每场⽐赛第⼀名和第⼆名的位置数据，因为第⼀名和第⼆名所在的位置基本上就是最后的毒圈所在的位置
1 death_final_circle_erg = death1_solo.loc[(death1_solo['map'] == 'ERANGEL')&(death1_solo['victim_placement'] == 2)&(death1_solo['victim_position_x']>0)&(death1_solo['killer_position_x']>0), :].dropna()
2 death_final_circle_mrm = death1_solo.loc[(death1_solo['map'] == 'MIRAMAR')&(death1_solo['victim_placement'] == 2)&(death1_solo['victim_position_x']>0)&(death1_solo['killer_position_x']>0), :].dropna() 3
4print(death_final_circle_erg.shape)
5print(death_final_circle_mrm.shape)
6
7 final_circle_erg = np.vstack([death_final_circle_erg[['victim_position_x', 'victim_position_y']].values,
8 death_final_circle_erg[['killer_position_x', 'killer_position_y']].values])*4096/800000
9 final_circle_mrm = np.vstack([death_final_circle_mrm[['victim_position_x', 'victim_position_y']].values,
10 death_final_circle_mrm[['killer_position_x', 'killer_position_y']].values])*1000/800000
1 bg = imread('/Users/apple/Desktop/pubg/erangel.jpg')
2 hmap, extent = heatmap(final_circle_erg[:,0], final_circle_erg[:,1], 1.5)
3 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*1.5, 0.0, 1.)
4 colors = Normalize(0, hmap.max(), clip=True)(hmap)
5 colors = cm.Reds(colors)
6 colors[..., -1] = alphas
7
8 fig, ax = plt.subplots(figsize=(24,24))
9 ax.set_xlim(0, 4096); ax.set_ylim(0, 4096)
10 ax.imshow(bg)
11 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
12#plt.scatter(plot_data_er[:,0], plot_data_er[:,1])
13
14 plt.gca().invert_yaxis()
1 bg = imread('/Users/apple/Desktop/pubg/miramar.jpg')
2 hmap, extent = heatmap(final_circle_mrm[:,0], final_circle_mrm[:,1], 1.5)
3 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*1.5, 0.0, 1.)
4 colors = Normalize(0, hmap.max(), clip=True)(hmap)
5 colors = cm.Reds(colors)
6 colors[..., -1] = alphas
7
8 fig, ax = plt.subplots(figsize=(24,24))
9 ax.set_xlim(0, 1000); ax.set_ylim(0, 1000)
10 ax.imshow(bg)
11 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
12#plt.scatter(plot_data_mr[:,0], plot_data_mr[:,1])
13 plt.gca().invert_yaxis()
什么武器最有⽤？
1 erg_died_of = death1.loc[(death1['map']=='ERANGEL')&(death1['killer_position_x']>0)&(death1['victim_position_x']>0)&(death1['killed_by']!='Down and Out'),:]
2 mrm_died_of = death1.loc[(death1['map']=='MIRAMAR')&(death1['killer_position_x']>0)&(death1['victim_position_x']>0)&(death1['killed_by']!='Down and Out'),:]
3
4print(erg_died_of.shape)
5print(mrm_died_of.shape)
1 erg_died_of['killed_by'].value_counts()[:10].plot.barh(figsize=(10,5))
2 plt.xlabel("被击杀⼈数", fontsize=14)
3 plt.ylabel("击杀的武器", fontsize=14)
4 plt.title('武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
5 plt.yticks(fontsize=12)
狙击适合使⽤什么武器？
1# 把位置信息转换成距离，以“⽶”为单位
2 erg_distance = np.sqrt(((erg_died_of['killer_position_x']-erg_died_of['victim_position_x'])/100)**2 + ((erg_died_of['killer_position_y']-erg_died_of['victim_position_y'])/100)**2)
3
4
5
6 mrm_distance = np.sqrt(((mrm_died_of['killer_position_x']-mrm_died_of['victim_position_x'])/100)**2 + ((mrm_died_of['killer_position_y']-mrm_died_of['victim_position_y'])/100)**2) 7
8
9
10 sns.distplot(erg_distance.loc[erg_distance<400])
1 erg_died_of.loc[(erg_distance > 800)&(erg_distance < 1500), 'killed_by'].value_counts()[:10].plot.bar(rot=30)
2 plt.xlabel("狙击的武器", fontsize=14)
3 plt.ylabel("被狙击的⼈数", fontsize=14)
4 plt.title('狙击武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
5 plt.yticks(fontsize=12)
1 mrm_died_of.loc[(mrm_distance > 800)&(mrm_distance < 1000), 'killed_by'].value_counts()[:10].plot.bar(rot=30)
2 plt.xlabel("狙击的武器", fontsize=14)
3 plt.ylabel("被狙击的⼈数", fontsize=14)
4 plt.title('狙击武器跟击杀⼈数的统计(热情沙漠⽶拉玛)', fontsize=14)
5 plt.yticks(fontsize=12)
近战适合使⽤什么武器？
1 erg_died_of.loc[erg_distance<10, 'killed_by'].value_counts()[:10].plot.bar(rot=30)
2 plt.xlabel("近战武器", fontsize=14)
3 plt.ylabel("被击杀的⼈数", fontsize=14)
4 plt.title('近战武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
5 plt.yticks(fontsize=12)
1 mrm_died_of.loc[mrm_distance<10, 'killed_by'].value_counts()[:10].plot.bar(rot=30)
2 plt.xlabel("近战武器", fontsize=14)
3 plt.ylabel("被击杀的⼈数", fontsize=14)
4 plt.title('近战武器武器跟击杀⼈数的统计(热情沙漠⽶拉玛)', fontsize=14)
5 plt.yticks(fontsize=12)
Top10武器在各距离下的击杀百分⽐
1 erg_died_of['erg_dist'] = erg_distance
2 erg_died_of = erg_died_of.loc[erg_died_of['erg_dist']<800, :]
3 top_weapons_erg = list(erg_died_of['killed_by'].value_counts()[:10].index)
4 top_weapon_kills = erg_died_of[np.in1d(erg_died_of['killed_by'], top_weapons_erg)].copy()
5 top_weapon_kills['bin'] = pd.cut(top_weapon_kills['erg_dist'], np.arange(0, 800, 10), include_lowest=True, labels=False)
6 top_weapon_kills_wide = top_weapon_kills.groupby(['killed_by', 'bin']).size().unstack(fill_value=0).transpose()
7
8
10 top_weapon_kills_wide = top_weapon_kills_wide.div(top_weapon_kills_wide.sum(axis=1), axis=0)
11
12
13
14from bokeh.models.tools import HoverTool
15from bokeh.palettes import brewer
16from bokeh.plotting import figure, show, output_notebook
17from bokeh.models.sources import ColumnDataSource
18
19def stacked(df):
20 df_top = df.cumsum(axis=1)
21 df_bottom = df_top.shift(axis=1).fillna(0)[::-1]
22 df_stack = pd.concat([df_bottom, df_top], ignore_index=True)
23return df_stack
24
25 hover = HoverTool(
26 tooltips=[
27 ("index", "$index"),
28 ("weapon", "@weapon"),
29 ("(x,y)", "($x, $y)")
30 ],
31 point_policy='follow_mouse'
32 )
33
34 areas = stacked(top_weapon_kills_wide)
35
36 colors = brewer['Spectral'][areas.shape[1]]
37 x2 = np.hstack((top_weapon_kills_wide.index[::-1],
38 top_weapon_kills_wide.index)) /0.095
39
40 TOOLS="pan,wheel_zoom,box_zoom,reset,previewsave"
41 output_notebook()
42 p = figure(x_range=(1, 800), y_range=(0, 1), tools=[TOOLS, hover], plot_width=800)
43 p.grid.minor_grid_line_color = '#eeeeee'
44
45 source = ColumnDataSource(data={
46'x': [x2] * areas.shape[1],
47'y': [areas[c].values for c in areas],
48'weapon': list(top_weapon_kills_wide.columns),
49'color': colors
50 })
51
52 p.patches('x', 'y', source=source, legend="weapon",
53 color='color', alpha=0.8, line_color=None)
54 p.title.text = "Top10武器在各距离下的击杀百分⽐（绝地海岛艾伦格）"
55 p.xaxis.axis_label = "击杀距离（0-800⽶）"
56 p.yaxis.axis_label = "百分⽐"
57
58 show(p)
59
60
61
62 mrm_died_of['erg_dist'] = mrm_distance
63 mrm_died_of = mrm_died_of.loc[mrm_died_of['erg_dist']<800, :]
64 top_weapons_erg = list(mrm_died_of['killed_by'].value_counts()[:10].index)
65 top_weapon_kills = mrm_died_of[np.in1d(mrm_died_of['killed_by'], top_weapons_erg)].copy()
66 top_weapon_kills['bin'] = pd.cut(top_weapon_kills['erg_dist'], np.arange(0, 800, 10), include_lowest=True, labels=False)
67 top_weapon_kills_wide = top_weapon_kills.groupby(['killed_by', 'bin']).size().unstack(fill_value=0).transpose()
68
69 top_weapon_kills_wide = top_weapon_kills_wide.div(top_weapon_kills_wide.sum(axis=1), axis=0)
70
71
72
73def stacked(df):
74 df_top = df.cumsum(axis=1)
75 df_bottom = df_top.shift(axis=1).fillna(0)[::-1]
76 df_stack = pd.concat([df_bottom, df_top], ignore_index=True)
77return df_stack
78
79 hover = HoverTool(
80 tooltips=[
81 ("index", "$index"),
82 ("weapon", "@weapon"),
83 ("(x,y)", "($x, $y)")
84 ],
85 point_policy='follow_mouse'
86 )
87
88 areas = stacked(top_weapon_kills_wide)
89
90 colors = brewer['Spectral'][areas.shape[1]]
91 x2 = np.hstack((top_weapon_kills_wide.index[::-1],
92 top_weapon_kills_wide.index)) /0.095
93
94 TOOLS="pan,wheel_zoom,box_zoom,reset,previewsave"
95 output_notebook()
96 p = figure(x_range=(1, 800), y_range=(0, 1), tools=[TOOLS, hover], plot_width=800)
97 p.grid.minor_grid_line_color = '#eeeeee'
98
99 source = ColumnDataSource(data={
100'x': [x2] * areas.shape[1],
101'y': [areas[c].values for c in areas],
102'weapon': list(top_weapon_kills_wide.columns),
103'color': colors
104 })
105
106 p.patches('x', 'y', source=source, legend="weapon",
107 color='color', alpha=0.8, line_color=None)
108 p.title.text = "Top10武器在各距离下的击杀百分⽐（热情沙漠⽶拉玛）"
109 p.xaxis.axis_label = "击杀距离（0-800⽶）"
110 p.yaxis.axis_label = "击杀百分⽐"
112 show(p)
⾃⼰把⾃⼰⼲倒的⽅式与⼈数
1 death1.head()
1 kill_by_self = death1.loc[death1['killer_name']==death1['victim_name'], "killed_by"]
2
3 kill_by_self.value_counts()[:10].plot.barh()
4 plt.xlabel("⾃毙的⼈数", fontsize=14)
5 plt.ylabel("⾃毙的⽅式", fontsize=14)
6 plt.title('⾃⼰把⾃⼰⼲倒的⽅式与⼈数', fontsize=14)
7 plt.yticks(fontsize=12)
1# ⾃⼰把⾃⼰⼲倒的⼈数场均百分⽐
2 kill_by_self.shape[0]/death1.shape[0]*100
完整代码：
1import pandas as pd
2import numpy as np
3import matplotlib.pyplot as plt
4import seaborn as sns
5 %matplotlib inline
6 plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
7 plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显⽰为⽅块的问题
8
9# 使⽤pandas读取数据
10 agg1 = pd.read_csv('/Users/apple/Desktop/pubg/aggregate/agg_match_stats_1.csv')
11
12# 探索数据结构
13 agg1.head()
14
15# 总共有13844275⾏玩家数据，15列
16 agg1.shape
17
18 agg1.columns
19
20 ()
21
22# 丢弃重复数据
23 agg1.drop_duplicates(inplace=True)
24
25 agg1.loc[1]
26
27# 添加是否成功吃鸡列
28 agg1['won'] = agg1['team_placement'] == 1
29
30# 添加是否搭乘过车辆列
31 agg1['drove'] = agg1['player_dist_ride'] != 0
32
33 agg1.loc[agg1['player_kills'] < 40, ['player_kills', 'won']].groupby('player_kills').won.mean().plot.bar(figsize=(15,6), rot=0)
34 plt.xlabel('击杀⼈数', fontsize=14)
35 plt.ylabel("吃鸡概率", fontsize=14)
36 plt.title('击杀⼈数与吃鸡概率的关系', fontsize=14)
37
38 agg1.groupby('party_size').player_kills.mean()
39
40 g = sns.FacetGrid(agg1.loc[agg1['player_kills']<=10, ['party_size', 'player_kills']], row="party_size", size=4, aspect=2)
41 g = g.map(sns.countplot, "player_kills")
42
43 agg1.loc[agg1['party_size']!=1, ['player_assists', 'won']].groupby('player_assists').won.mean().plot.bar(figsize=(15,6), rot=0)
44 plt.xlabel('助攻次数', fontsize=14)
45 plt.ylabel("吃鸡概率", fontsize=14)
46 plt.title('助攻次数与吃鸡概率的关系', fontsize=14)
47
48 agg1.groupby('drove').won.mean().plot.barh(figsize=(6,3))
49 plt.xlabel("吃鸡概率", fontsize=14)
50 plt.ylabel("是否搭乘过车辆", fontsize=14)
51 plt.title('搭乘车辆与吃鸡概率的关系', fontsize=14)
52 plt.yticks([1,0],['是','否'])
53
54 dist_ride = agg1.loc[agg1['player_dist_ride']<12000, ['player_dist_ride', 'won']]
55
56 labels=["0-1k", "1-2k", "2-3k", "3-4k","4-5k", "5-6k", "6-7k", "7-8k", "8-9k", "9-10k", "10-11k", "11-12k"]
57 dist_ride['drove_cut'] = pd.cut(dist_ride['player_dist_ride'], 12, labels=labels)
58
59 dist_ride.groupby('drove_cut').won.mean().plot.bar(rot=60, figsize=(8,4))
60 plt.xlabel("搭乘车辆⾥程", fontsize=14)
61 plt.ylabel("吃鸡概率", fontsize=14)
62 plt.title('搭乘车辆⾥程与吃鸡概率的关系', fontsize=14)
63
64 match_unique = agg1.loc[agg1['party_size'] == 1, 'match_id'].unique()
65
66# 先把玩家被击杀的数据导⼊进来并探索数据
67 death1 = pd.read_csv('/Users/apple/Desktop/pubg/deaths/kill_match_stats_final_1.csv')
68
69 death1.head()
70
71 ()
72
73 death1.shape
74
75 death1_solo = death1[death1['match_id'].isin(match_unique)]
76
77 death1_()
78
79# 只统计单⼈模式，筛选存活不超过180秒的玩家数据
80 death_180_seconds_erg = death1_solo.loc[(death1_solo['map'] == 'ERANGEL')&(death1_solo['time'] < 180)&(death1_solo['victim_position_x']>0), :].dropna()
81 death_180_seconds_mrm = death1_solo.loc[(death1_solo['map'] == 'MIRAMAR')&(death1_solo['time'] < 180)&(death1_solo['victim_position_x']>0), :].dropna()
82
83 death_180_seconds_erg.shape
84
85 death_180_seconds_mrm.shape
86
87# 选择存活不过180秒的玩家死亡位置
88 data_erg = death_180_seconds_erg[['victim_position_x', 'victim_position_y']].values
89 data_mrm = death_180_seconds_mrm[['victim_position_x', 'victim_position_y']].values
90
91# 重新scale玩家位置
92 data_erg = data_erg*4096/800000
93 data_mrm = data_mrm*1000/800000
94
95from scipy.ndimage.filters import gaussian_filter
96import matplotlib.cm as cm
97from matplotlib.colors import Normalize
98from scipy.misc.pilutil import imread
99
100from scipy.ndimage.filters import gaussian_filter
101import matplotlib.cm as cm
102from matplotlib.colors import Normalize
103
104def heatmap(x, y, s, bins=100):
105 heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins)
106 heatmap = gaussian_filter(heatmap, sigma=s)
107
108 extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
109return heatmap.T, extent
110
111 bg = imread('/Users/apple/Desktop/pubg/erangel.jpg')
112 hmap, extent = heatmap(data_erg[:,0], data_erg[:,1], 4.5)
113 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*4.5, 0.0, 1.)
114 colors = Normalize(0, hmap.max(), clip=True)(hmap)
115 colors = cm.Reds(colors)
116 colors[..., -1] = alphas
117
118 fig, ax = plt.subplots(figsize=(24,24))
119 ax.set_xlim(0, 4096); ax.set_ylim(0, 4096)
120 ax.imshow(bg)
121 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
122 plt.gca().invert_yaxis()
123
124 bg = imread('/Users/apple/Desktop/pubg/miramar.jpg')
125 hmap, extent = heatmap(data_mrm[:,0], data_mrm[:,1], 4)
126 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*4, 0.0, 1.)
127 colors = Normalize(0, hmap.max(), clip=True)(hmap)
128 colors = cm.Reds(colors)
129 colors[..., -1] = alphas
130
131 fig, ax = plt.subplots(figsize=(24,24))
132 ax.set_xlim(0, 1000); ax.set_ylim(0, 1000)
133 ax.imshow(bg)
134 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
135#plt.scatter(plot_data_mr[:,0], plot_data_mr[:,1])
136 plt.gca().invert_yaxis()
137
138 death_final_circle_erg = death1_solo.loc[(death1_solo['map'] == 'ERANGEL')&(death1_solo['victim_placement'] == 2)&(death1_solo['victim_position_x']>0)&(death1_solo['killer_position_x']>0), :].dropna() 139 death_final_circle_mrm = death1_solo.loc[(death1_solo['map'] == 'MIRAMAR')&(death1_solo['victim_placement'] == 2)&(death1_solo['victim_position_x']>0)&(death1_solo['killer_position_x']>0), :].dropna() 140
141print(death_final_circle_erg.shape)
142print(death_final_circle_mrm.shape)
143
144 final_circle_erg = np.vstack([death_final_circle_erg[['victim_position_x', 'victim_position_y']].values,
145 death_final_circle_erg[['killer_position_x', 'killer_position_y']].values])*4096/800000
146 final_circle_mrm = np.vstack([death_final_circle_mrm[['victim_position_x', 'victim_position_y']].values,
147 death_final_circle_mrm[['killer_position_x', 'killer_position_y']].values])*1000/800000
148
149 bg = imread('/Users/apple/Desktop/pubg/erangel.jpg')
150 hmap, extent = heatmap(final_circle_erg[:,0], final_circle_erg[:,1], 1.5)
151 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*1.5, 0.0, 1.)
152 colors = Normalize(0, hmap.max(), clip=True)(hmap)
153 colors = cm.Reds(colors)
154 colors[..., -1] = alphas
155
156 fig, ax = plt.subplots(figsize=(24,24))
157 ax.set_xlim(0, 4096); ax.set_ylim(0, 4096)
158 ax.imshow(bg)
159 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
160#plt.scatter(plot_data_er[:,0], plot_data_er[:,1])
161 plt.gca().invert_yaxis()
162
163 bg = imread('/Users/apple/Desktop/pubg/miramar.jpg')
164 hmap, extent = heatmap(final_circle_mrm[:,0], final_circle_mrm[:,1], 1.5)
165 alphas = np.clip(Normalize(0, hmap.max(), clip=True)(hmap)*1.5, 0.0, 1.)
166 colors = Normalize(0, hmap.max(), clip=True)(hmap)
167 colors = cm.Reds(colors)
168 colors[..., -1] = alphas
169
170 fig, ax = plt.subplots(figsize=(24,24))
171 ax.set_xlim(0, 1000); ax.set_ylim(0, 1000)
172 ax.imshow(bg)
173 ax.imshow(colors, extent=extent, origin='lower', cmap=cm.Reds, alpha=0.9)
174#plt.scatter(plot_data_mr[:,0], plot_data_mr[:,1])
175 plt.gca().invert_yaxis()
176
177 erg_died_of = death1.loc[(death1['map']=='ERANGEL')&(death1['killer_position_x']>0)&(death1['victim_position_x']>0)&(death1['killed_by']!='Down and Out'),:]
178 mrm_died_of = death1.loc[(death1['map']=='MIRAMAR')&(death1['killer_position_x']>0)&(death1['victim_position_x']>0)&(death1['killed_by']!='Down and Out'),:]
179
180print(erg_died_of.shape)
181print(mrm_died_of.shape)
182
183 erg_died_of['killed_by'].value_counts()[:10].plot.barh(figsize=(10,5))
184 plt.xlabel("被击杀⼈数", fontsize=14)
185 plt.ylabel("击杀的武器", fontsize=14)
186 plt.title('武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
187 plt.yticks(fontsize=12)
188
189 mrm_died_of['killed_by'].value_counts()[:10].plot.barh(figsize=(10,5))
190 plt.xlabel("被击杀⼈数", fontsize=14)
191 plt.ylabel("击杀的武器", fontsize=14)
192 plt.title('武器跟击杀⼈数的统计(热情沙漠⽶拉玛)', fontsize=14)
193 plt.yticks(fontsize=12)
194
195# 把位置信息转换成距离，以“⽶”为单位
196 erg_distance = np.sqrt(((erg_died_of['killer_position_x']-erg_died_of['victim_position_x'])/100)**2 + ((erg_died_of['killer_position_y']-erg_died_of['victim_position_y'])/100)**2)
197
198 mrm_distance = np.sqrt(((mrm_died_of['killer_position_x']-mrm_died_of['victim_position_x'])/100)**2 + ((mrm_died_of['killer_position_y']-mrm_died_of['victim_position_y'])/100)**2) 199
200 sns.distplot(erg_distance.loc[erg_distance<400])
201
202 erg_died_of.loc[(erg_distance > 800)&(erg_distance < 1500), 'killed_by'].value_counts()[:10].plot.bar(rot=30)
203 plt.xlabel("狙击的武器", fontsize=14)
204 plt.ylabel("被狙击的⼈数", fontsize=14)
205 plt.title('狙击武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
206 plt.yticks(fontsize=12)
207
208 mrm_died_of.loc[(mrm_distance > 800)&(mrm_distance < 1000), 'killed_by'].value_counts()[:10].plot.bar(rot=30)
209 plt.xlabel("狙击的武器", fontsize=14)
210 plt.ylabel("被狙击的⼈数", fontsize=14)
211 plt.title('狙击武器跟击杀⼈数的统计(热情沙漠⽶拉玛)', fontsize=14)
212 plt.yticks(fontsize=12)
213
214 erg_died_of.loc[erg_distance<10, 'killed_by'].value_counts()[:10].plot.bar(rot=30)
215 plt.xlabel("近战武器", fontsize=14)
216 plt.ylabel("被击杀的⼈数", fontsize=14)
217 plt.title('近战武器跟击杀⼈数的统计(绝地海岛艾伦格)', fontsize=14)
218 plt.yticks(fontsize=12)
219
220 mrm_died_of.loc[mrm_distance<10, 'killed_by'].value_counts()[:10].plot.bar(rot=30)
221 plt.xlabel("近战武器", fontsize=14)
222 plt.ylabel("被击杀的⼈数", fontsize=14)
223 plt.title('近战武器武器跟击杀⼈数的统计(热情沙漠⽶拉玛)', fontsize=14)
224 plt.yticks(fontsize=12)
225
226 erg_died_of['erg_dist'] = erg_distance
227 erg_died_of = erg_died_of.loc[erg_died_of['erg_dist']<800, :]
228 top_weapons_erg = list(erg_died_of['killed_by'].value_counts()[:10].index)
229 top_weapon_kills = erg_died_of[np.in1d(erg_died_of['killed_by'], top_weapons_erg)].copy()
230 top_weapon_kills['bin'] = pd.cut(top_weapon_kills['erg_dist'], np.arange(0, 800, 10), include_lowest=True, labels=False)
231 top_weapon_kills_wide = top_weapon_kills.groupby(['killed_by', 'bin']).size().unstack(fill_value=0).transpose()
232
233 top_weapon_kills_wide = top_weapon_kills_wide.div(top_weapon_kills_wide.sum(axis=1), axis=0)
234
235from bokeh.models.tools import HoverTool
236from bokeh.palettes import brewer
237from bokeh.plotting import figure, show, output_notebook
238from bokeh.models.sources import ColumnDataSource
239
240def stacked(df):
241 df_top = df.cumsum(axis=1)
242 df_bottom = df_top.shift(axis=1).fillna(0)[::-1]
243 df_stack = pd.concat([df_bottom, df_top], ignore_index=True)
244return df_stack
245
246 hover = HoverTool(
247 tooltips=[
248 ("index", "$index"),
249 ("weapon", "@weapon"),
250 ("(x,y)", "($x, $y)")
251 ],
252 point_policy='follow_mouse'
253 )
254
255 areas = stacked(top_weapon_kills_wide)。