python数据分析之足球运动员分析

足球运动员分析

背景信息

当前，足球运动是最受欢迎的运动之一（也可以说没有之一）。

任务说明

我们的任务，就是在众多的足球运动员中，发现统计一些关于足球运动员的共性，或某些潜在的规律。

数据集描述

数据集包含的是2017年所有活跃的足球运动员。

• Name 姓名
• Nationality 国籍
• National_Position 国家队位置
• National_Kit 国家队号码
• Club 所在俱乐部
• Club_Position 所在俱乐部位置
• Club_Kit 俱乐部号码
• Club_Joining 加入俱乐部时间
• Contract_Expiry 合同到期时间
• Rating 评分
• Height 身高
• Weight 体重
• Preffered_Foot 擅长左（右）脚
• Birth_Date 出生日期
• Age 年龄
• Preffered_Position 擅长位置
• Work_Rate 工作效率
• Weak_foot 非惯用脚使用频率
• Skill_Moves 技术等级
• Ball_Control 控球技术
• Dribbling 盘球（带球）能力
• Marking 盯人能力
• Sliding_Tackle 铲球
• Standing_Tackle 逼抢能力
• Aggression 攻击能力
• Reactions 反击
• Attacking_Position 攻击性跑位
• Interceptions 抢断
• Vision 视野
• Composure 镇静
• Crossing 下底传中
• Short_Pass 短传
• Long_Pass 长传
• Acceleration 加速度
• Speed 速度
• Stamina 体力
• Strength 强壮
• Balance 平衡
• Agility 敏捷度
• Jumping 跳跃
• Heading 投球
• Shot_Power 射门力量
• Finishing 射门
• Long_Shots 远射
• Curve 弧线
• Freekick_Accuracy 任意球精准度
• Penalties 点球
• Volleys 凌空能力
• GK_Positioning 门将位置感
• GK_Diving 扑救能力
• GK_Kicking 门将踢球能力
• GK_Handling 扑球脱手几率
• GK_Reflexes 门将反应度

程序实现

导入相关的库

导入需要的库，同时，进行一些初始化的设置。

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

 

# 支持中文显示。
mpl.rcParams["font.family"] = "SimHei"
mpl.rcParams["axes.unicode_minus"] = False

加载相关的数据集

• 加载相关的数据集（注意原数据集中是否存在标题），并查看数据的大致情况。
• 可以使用head / tail，也可以使用sample。
• 列没有显式完整，我们需要进行设置。（pd.set_option）

data = pd.read_csv("FullData.csv")
# data.head(10)
# data.sample(3)
pd.set_option("display.max_columns", 55)
data.head()

数据探索与清洗

缺失值处理

• 通过info查看缺失值信息（以及每列的类型信息）。
• 可以通过isnull, any, dropna，fillna等方法结合使用，对缺失值进行处理。

# data.info()
# 查看存在空值的记录。
# data[data["Club_Position"].isnull()]
# data.isnull().sum(axis=0)
# data["Club_Position"].notnull()
# 过滤空值记录。
# data = data[data["Club_Position"].notnull()]
# data.info()

异常值处理

• 通过describe查看数值信息。
• 可配合箱线图辅助。

# data.describe()
# data["Rating"].plot(kind="box")

重复值处理

• 使用duplicate检查重复值。可配合keep参数进行调整。
• 使用drop_duplicate删除重复值。

# np.sum(data.duplicated())
# data[data.duplicated()]
# data.drop_duplicates()

将身高与体重处理成数值类型，便于分析。

# 对数据列进行转换。可以使用apply或map。

# data["Height"] = 

# data["Height"] = data["Height"].apply(lambda item: item.replace("cm", "")).astype(np.int32)

# data["Weight"] = data["Weight"].apply(lambda item: item.replace("kg", "")).astype(np.int32)

# 也可以使用map实现同样的转换。

#data["Height"].map(lambda item: item.replace("cm", "")).astype(np.int32)

​
# 使用字符串的矢量化运算完成转换。
data["Height"] = data["Height"].str.replace("cm", "").astype(np.int32)
data["Weight"] = data["Weight"].str.replace("kg", "").astype(np.int32)

运动员的身高，体重，评分信息分布。

# data["Height"].plot(kind="kde")
# data["Weight"].plot(kind="kde")
# data["Rating"].plot(kind="kde")
data[["Height", "Weight", "Rating"]].plot(kind="kde")

左脚与右脚选手在数量上是否存在偏差？

# 第一种方式
# g = data.groupby("Preffered_Foot")
# g["Preffered_Foot"].count()
​
# 第二种方式
# g = data.groupby("Preffered_Foot")
# g.size()
​
# 第三种方式
data["Preffered_Foot"].value_counts().plot(kind="bar")

从球员平均评分上考虑，拥有top10评分能力的俱乐部 / 国家。【超过20人】

# 俱乐部
# g = data.groupby("Club")
# g["Rating"].mean().sort_values(ascending=False)
# t = g["Rating"].agg(["mean", "count"])
# t = t[t["count"] > 20]
# t = t.sort_values(by="mean", ascending=False).head(10)
# t["mean"].plot(kind="bar")
​
# 国家队
g = data.groupby("Nationality")
# g["Rating"].mean().sort_values(ascending=False)
t = g["Rating"].agg(["mean", "count"])
t = t[t["count"] > 20]
t = t.sort_values(by="mean", ascending=False).head(10)
t
# t["mean"].plot(kind="bar")

哪个俱乐部拥有更多忠心的球员（5年及以上）？

t = data["Club_Joining"].apply(lambda item: int(item.split("/")[-1]))
# 计算球员的效力时间。
year = 2017 - t
# 对数据集进行过滤，只保留效力时间达到或超过5年的球员。
t = data[(year >= 5) & (data["Club"] != "Free Agents")]
# t
t["Club"].value_counts()

足球运动员是否是出生年月相关？

1. 全体运动员
2. 知名运动员（80分及以上）

# 全体运行员
​
# expand 默认为False。如果设置为True，就是展开成为一个DataFrame。
t = data["Birth_Date"].str.split("/", expand=True)
# t[2].value_counts().plot(kind="bar")
# t[0].value_counts().plot(kind="bar")
# t[1].value_counts().plot(kind="bar")
​
# 80分以上的运动员
t = data[data["Rating"] >= 80]
t = t["Birth_Date"].str.split("/", expand=True)
# t[2].value_counts().plot(kind="bar")
# t[0].value_counts().plot(kind="bar")
# t[1].value_counts().plot(kind="bar")

Out[74]:

足球运动员号码是否与位置相关？

# 去掉替补与预备队的球员
t = data[(data["Club_Position"] != "Res") & (data["Club_Position"] != "Sub")]
# g = data.groupby(["Club_Position", "Club_Kit"])
g = t.groupby(["Club_Kit", "Club_Position"])
g.size()

Out[78]:

Club_Kit  Club_Position
1.0       GK               333
2.0       CAM                1
          CB                 3
          GK                 1
          LB                26
          LCB               36
          LCM                3
          LDM                3
          LM                 5
          LW                 1
          LWB                1
          RB               148
          RCB               41
          RCM                4
          RDM                3
          RM                 3
          RWB               11
3.0       CAM                1
          CB                 5
          CDM                1
          CM                 2
          LB               103
          LCB               58
          LCM                2
          LDM                6
          LM                 3
          LW                 1
          LWB               12
          RB                19
          RCB               75
                          ... 
93.0      LM                 2
          LS                 1
          LW                 1
          RCM                1
          ST                 1
94.0      CAM                2
          RB                 1
          RCB                1
          RCM                1
          RM                 1
          RS                 1
          RW                 1
95.0      LB                 1
          LM                 2
          RB                 1
          RM                 1
96.0      LB                 1
97.0      CAM                1
          CM                 1
          ST                 1
98.0      LB                 1
          LCM                1
          RDM                1
99.0      CAM                1
          GK                 4
          LM                 1
          LS                 4
          LW                 1
          RS                 2
          ST                 8
Length: 954, dtype: int64

身高与体重是否具有相关性？

# data.plot(kind="scatter", x="Height", y="Weight")
# data.plot(kind="scatter", x="Height", y="Rating")

Out[80]:

哪些指标对评分的影响最大？

data.corr()

Out[82]:

	National_Kit	Club_Kit	Contract_Expiry	Rating	Height	Weight	Age	Weak_foot	Skill_Moves	Ball_Control	Dribbling	Marking	Sliding_Tackle	Standing_Tackle	Aggression	Reactions	Attacking_Position	Interceptions	Vision	Composure	Crossing	Short_Pass	Long_Pass	Acceleration	Speed	Stamina	Strength	Balance	Agility	Jumping	Heading	Shot_Power	Finishing	Long_Shots	Curve	Freekick_Accuracy	Penalties	Volleys	GK_Positioning	GK_Diving	GK_Kicking	GK_Handling	GK_Reflexes
National_Kit	1.000000	0.055408	-0.027211	-0.084289	-0.101229	-0.093795	-0.103583	0.027268	0.105903	0.046644	0.093836	-0.162083	-0.152251	-0.148329	-0.073288	-0.058696	0.124641	-0.154804	0.076988	-0.016093	0.045986	0.036071	-0.006230	0.079866	0.056282	0.003828	-0.099086	0.089038	0.085691	-0.100935	-0.072020	0.083610	0.138914	0.109168	0.081888	0.106621	0.095289	0.117381	-0.031492	-0.012435	-0.015472	-0.024611	-0.018816
Club_Kit	0.055408	1.000000	0.077060	-0.172710	-0.028711	-0.072341	-0.198230	-0.037234	0.006378	-0.071067	-0.028584	-0.105522	-0.094920	-0.104446	-0.121709	-0.148887	-0.031746	-0.125007	-0.071177	-0.117220	-0.067383	-0.085123	-0.100525	-0.006315	-0.010092	-0.101258	-0.141140	0.010762	-0.036128	-0.106797	-0.076264	-0.051829	-0.007898	-0.040658	-0.053631	-0.062077	-0.016220	-0.020482	0.004684	0.011290	0.008788	0.006725	0.007480
Contract_Expiry	-0.027211	0.077060	1.000000	0.047430	-0.080641	-0.053049	-0.118370	0.004865	0.044511	0.035324	0.048664	0.015409	0.011482	0.008307	-0.010361	0.051309	0.043698	0.006995	0.028113	0.005840	0.012553	0.034513	0.016371	0.071003	0.079394	0.053830	-0.012495	0.046095	0.047667	0.002805	0.026235	0.021477	0.032341	0.028781	0.010840	0.007001	0.020878	0.025120	-0.027994	-0.026117	-0.023273	-0.023064	-0.024923
Rating	-0.084289	-0.172710	0.047430	1.000000	0.046937	0.139703	0.458098	0.226263	0.251926	0.463211	0.368565	0.236843	0.215385	0.249156	0.404422	0.828329	0.354501	0.319504	0.489277	0.613612	0.401851	0.496239	0.483217	0.206392	0.224253	0.355335	0.369045	0.087811	0.283309	0.289840	0.343265	0.441773	0.328576	0.419517	0.420796	0.399575	0.339898	0.386494	-0.018586	-0.027615	-0.031696	-0.021343	-0.022978
Height	-0.101229	-0.028711	-0.080641	0.046937	1.000000	0.758208	0.076727	-0.180431	-0.431177	-0.402596	-0.483545	-0.042485	-0.069602	-0.054282	-0.049009	-0.016407	-0.419544	-0.050358	-0.359610	-0.169460	-0.471327	-0.356768	-0.323575	-0.521688	-0.451128	-0.293564	0.537223	-0.799775	-0.611198	-0.063048	0.005367	-0.273413	-0.347154	-0.364182	-0.423115	-0.380217	-0.321246	-0.333576	0.358795	0.357683	0.356070	0.359746	0.360260
Weight	-0.093795	-0.072341	-0.053049	0.139703	0.758208	1.000000	0.223432	-0.135402	-0.375163	-0.338460	-0.412959	-0.030512	-0.062312	-0.047097	0.017366	0.079019	-0.344840	-0.028947	-0.283098	-0.083541	-0.389293	-0.299264	-0.261611	-0.465558	-0.404240	-0.241365	0.613829	-0.680791	-0.535404	0.005842	0.019235	-0.188739	-0.281775	-0.274446	-0.343380	-0.293956	-0.246189	-0.258932	0.342502	0.340775	0.339505	0.341829	0.341785
Age	-0.103583	-0.198230	-0.118370	0.458098	0.076727	0.223432	1.000000	0.086137	-0.016088	0.082875	0.004575	0.131425	0.096679	0.116847	0.259413	0.456724	0.073873	0.192667	0.189982	0.355588	0.134576	0.127537	0.179109	-0.175966	-0.168440	0.070685	0.329289	-0.100131	-0.019459	0.168694	0.137785	0.151174	0.064487	0.149635	0.139101	0.195338	0.131469	0.133950	0.122371	0.105158	0.109931	0.113216	0.106979
Weak_foot	0.027268	-0.037234	0.004865	0.226263	-0.180431	-0.135402	0.086137	1.000000	0.336905	0.367420	0.363398	0.026950	0.025865	0.043656	0.134120	0.207098	0.354699	0.058148	0.352235	0.316486	0.324117	0.338919	0.298103	0.257156	0.240919	0.228619	-0.004841	0.253466	0.306049	0.064655	0.190847	0.334897	0.361983	0.365637	0.359044	0.345745	0.340757	0.366610	-0.232109	-0.236372	-0.230924	-0.233080	-0.235275
Skill_Moves	0.105903	0.006378	0.044511	0.251926	-0.431177	-0.375163	-0.016088	0.336905	1.000000	0.727123	0.762623	0.032811	0.043037	0.070841	0.230424	0.223236	0.719577	0.067407	0.591623	0.490854	0.644761	0.628088	0.515631	0.619623	0.598727	0.469200	-0.117029	0.542743	0.637400	0.035166	0.391626	0.640183	0.715228	0.680434	0.689265	0.634867	0.662108	0.701017	-0.607676	-0.610500	-0.605746	-0.606713	-0.609282
Ball_Control	0.046644	-0.071067	0.035324	0.463211	-0.402596	-0.338460	0.082875	0.367420	0.727123	1.000000	0.931117	0.355429	0.357025	0.391689	0.543142	0.424991	0.855487	0.392803	0.732518	0.704726	0.838775	0.904398	0.793084	0.669026	0.665755	0.724758	0.079898	0.573786	0.695569	0.172802	0.658111	0.829902	0.781218	0.831846	0.832009	0.763315	0.767803	0.789287	-0.776117	-0.779922	-0.775690	-0.775632	-0.777668
Dribbling	0.093836	-0.028584	0.048664	0.368565	-0.483545	-0.412959	0.004575	0.363398	0.762623	0.931117	1.000000	0.228543	0.243277	0.270005	0.426393	0.349111	0.888391	0.264903	0.736197	0.627388	0.848864	0.833578	0.717857	0.740657	0.725716	0.676495	-0.046376	0.638638	0.754705	0.117374	0.548257	0.795016	0.817034	0.836080	0.840061	0.750014	0.765259	0.804633	-0.741793	-0.743848	-0.739995	-0.740616	-0.742065
Marking	-0.162083	-0.105522	0.015409	0.236843	-0.042485	-0.030512	0.131425	0.026950	0.032811	0.355429	0.228543	1.000000	0.959955	0.960272	0.708671	0.218235	0.089095	0.922118	0.097899	0.327939	0.384596	0.476693	0.535364	0.138092	0.163242	0.538772	0.327395	0.117471	0.085645	0.258669	0.546994	0.219159	-0.096066	0.116621	0.206493	0.233461	0.062951	0.013556	-0.492932	-0.496354	-0.495732	-0.496431	-0.497013
Sliding_Tackle	-0.152251	-0.094920	0.011482	0.215385	-0.069602	-0.062312	0.096679	0.025865	0.043037	0.357025	0.243277	0.959955	1.000000	0.971730	0.702852	0.200828	0.097474	0.915207	0.108223	0.314994	0.400583	0.479273	0.540317	0.159178	0.181316	0.538313	0.287903	0.145242	0.103459	0.254936	0.523946	0.217468	-0.098796	0.117163	0.216875	0.241188	0.059355	0.015133	-0.498386	-0.501643	-0.501019	-0.501429	-0.501738
Standing_Tackle	-0.148329	-0.104446	0.008307	0.249156	-0.054282	-0.047097	0.116847	0.043656	0.070841	0.391689	0.270005	0.960272	0.971730	1.000000	0.728240	0.230117	0.130957	0.930060	0.143787	0.353054	0.417381	0.514137	0.567442	0.158883	0.182390	0.561885	0.321261	0.139473	0.110567	0.255130	0.555447	0.256805	-0.058670	0.156849	0.247088	0.274715	0.096483	0.053435	-0.521127	-0.524291	-0.523387	-0.523648	-0.524598
Aggression	-0.073288	-0.121709	-0.010361	0.404422	-0.049009	0.017366	0.259413	0.134120	0.230424	0.543142	0.426393	0.708671	0.702852	0.728240	1.000000	0.390453	0.371589	0.736112	0.312152	0.534981	0.476202	0.597913	0.583940	0.260563	0.291840	0.643250	0.450040	0.172027	0.239329	0.352169	0.677640	0.496297	0.230695	0.386260	0.395877	0.402368	0.329841	0.318366	-0.561675	-0.567497	-0.563989	-0.564734	-0.566184
Reactions	-0.058696	-0.148887	0.051309	0.828329	-0.016407	0.079019	0.456724	0.207098	0.223236	0.424991	0.349111	0.218235	0.200828	0.230117	0.390453	1.000000	0.373846	0.318822	0.482080	0.589313	0.381557	0.457700	0.444277	0.183576	0.188701	0.346043	0.290929	0.130092	0.283719	0.260403	0.305042	0.404946	0.316802	0.408545	0.405543	0.390557	0.333713	0.382709	-0.039962	-0.048234	-0.051864	-0.044558	-0.045050
Attacking_Position	0.124641	-0.031746	0.043698	0.354501	-0.419544	-0.344840	0.073873	0.354699	0.719577	0.855487	0.888391	0.089095	0.097474	0.130957	0.371589	0.373846	1.000000	0.146484	0.738465	0.612847	0.771688	0.749769	0.612211	0.666267	0.656369	0.629698	-0.002622	0.565325	0.690857	0.116465	0.532394	0.795237	0.880495	0.850896	0.804621	0.727188	0.798127	0.841600	-0.665015	-0.668924	-0.663450	-0.664273	-0.666512
Interceptions	-0.154804	-0.125007	0.006995	0.319504	-0.050358	-0.028947	0.192667	0.058148	0.067407	0.392803	0.264903	0.922118	0.915207	0.930060	0.736112	0.318822	0.146484	1.000000	0.180741	0.386457	0.417610	0.516755	0.578525	0.152006	0.170350	0.566760	0.342730	0.138773	0.126500	0.279615	0.536809	0.264911	-0.044888	0.181337	0.260755	0.292869	0.104781	0.071093	-0.470613	-0.474997	-0.473268	-0.474025	-0.474850
Vision	0.076988	-0.071177	0.028113	0.489277	-0.359610	-0.283098	0.189982	0.352235	0.591623	0.732518	0.736197	0.097899	0.108223	0.143787	0.312152	0.482080	0.738465	0.180741	1.000000	0.648886	0.688579	0.728223	0.699200	0.457209	0.431337	0.482344	-0.035903	0.476798	0.594925	0.054090	0.305804	0.679237	0.695177	0.754865	0.750496	0.718754	0.647055	0.700223	-0.400721	-0.405353	-0.398335	-0.397728	-0.401582
Composure	-0.016093	-0.117220	0.005840	0.613612	-0.169460	-0.083541	0.355588	0.316486	0.490854	0.704726	0.627388	0.327939	0.314994	0.353054	0.534981	0.589313	0.612847	0.386457	0.648886	1.000000	0.594509	0.704532	0.659843	0.385533	0.391405	0.544327	0.258847	0.329439	0.463008	0.243641	0.542475	0.673363	0.562688	0.642917	0.641637	0.606544	0.590273	0.622623	-0.462405	-0.470743	-0.468175	-0.464087	-0.469048
Crossing	0.045986	-0.067383	0.012553	0.401851	-0.471327	-0.389293	0.134576	0.324117	0.644761	0.838775	0.848864	0.384596	0.400583	0.417381	0.476202	0.381557	0.771688	0.417610	0.688579	0.594509	1.000000	0.811867	0.764061	0.650463	0.638415	0.667137	-0.032359	0.593965	0.681712	0.117779	0.482777	0.704200	0.644380	0.740554	0.826872	0.761017	0.648078	0.684915	-0.658450	-0.662598	-0.658385	-0.658085	-0.661232
Short_Pass	0.036071	-0.085123	0.034513	0.496239	-0.356768	-0.299264	0.127537	0.338919	0.628088	0.904398	0.833578	0.476693	0.479273	0.514137	0.597913	0.457700	0.749769	0.516755	0.728223	0.704532	0.811867	1.000000	0.900749	0.558926	0.555353	0.708465	0.118579	0.515630	0.606182	0.174645	0.632108	0.771737	0.653929	0.761394	0.779465	0.742423	0.675478	0.691174	-0.714431	-0.717572	-0.713856	-0.714071	-0.716827
Long_Pass	-0.006230	-0.100525	0.016371	0.483217	-0.323575	-0.261611	0.179109	0.298103	0.515631	0.793084	0.717857	0.535364	0.540317	0.567442	0.583940	0.444277	0.612211	0.578525	0.699200	0.659843	0.764061	0.900749	1.000000	0.443544	0.435200	0.632998	0.108879	0.450968	0.522242	0.141914	0.518379	0.679311	0.507594	0.674009	0.716427	0.710268	0.550959	0.569566	-0.595199	-0.598739	-0.594084	-0.594416	-0.597275
Acceleration	0.079866	-0.006315	0.071003	0.206392	-0.521688	-0.465558	-0.175966	0.257156	0.619623	0.669026	0.740657	0.138092	0.159178	0.158883	0.260563	0.183576	0.666267	0.152006	0.457209	0.385533	0.650463	0.558926	0.443544	1.000000	0.922681	0.612846	-0.164064	0.681551	0.792586	0.209320	0.345955	0.538325	0.591831	0.571502	0.595826	0.486247	0.522338	0.563226	-0.589681	-0.589198	-0.589036	-0.589799	-0.588293
Speed	0.056282	-0.010092	0.079394	0.224253	-0.451128	-0.404240	-0.168440	0.240919	0.598727	0.665755	0.725716	0.163242	0.181316	0.182390	0.291840	0.188701	0.656369	0.170350	0.431337	0.391405	0.638415	0.555353	0.435200	0.922681	1.000000	0.627496	-0.085941	0.619552	0.748592	0.232382	0.398331	0.548975	0.582587	0.558026	0.576378	0.462375	0.515824	0.555322	-0.600348	-0.599891	-0.599152	-0.599809	-0.599522
Stamina	0.003828	-0.101258	0.053830	0.355335	-0.293564	-0.241365	0.070685	0.228619	0.469200	0.724758	0.676495	0.538772	0.538313	0.561885	0.643250	0.346043	0.629698	0.566760	0.482344	0.544327	0.667137	0.708465	0.632998	0.612846	0.627496	1.000000	0.230247	0.458269	0.557333	0.331521	0.640626	0.620266	0.495899	0.591168	0.586883	0.537091	0.516367	0.523170	-0.702177	-0.705673	-0.702016	-0.703375	-0.705185
Strength	-0.099086	-0.141140	-0.012495	0.369045	0.537223	0.613829	0.329289	-0.004841	-0.117029	0.079898	-0.046376	0.327395	0.287903	0.321261	0.450040	0.290929	-0.002622	0.342730	-0.035903	0.258847	-0.032359	0.118579	0.108879	-0.164064	-0.085941	0.230247	1.000000	-0.419046	-0.243772	0.268866	0.458370	0.172875	-0.012010	0.045462	-0.036263	-0.003688	0.052389	0.022775	-0.079647	-0.085167	-0.084113	-0.080986	-0.084108
Balance	0.089038	0.010762	0.046095	0.087811	-0.799775	-0.680791	-0.100131	0.253466	0.542743	0.573786	0.638638	0.117471	0.145242	0.139473	0.172027	0.130092	0.565325	0.138773	0.476798	0.329439	0.593965	0.515630	0.450968	0.681551	0.619552	0.458269	-0.419046	1.000000	0.748228	0.167705	0.157088	0.429301	0.486708	0.504732	0.558874	0.493454	0.453402	0.484856	-0.484823	-0.484486	-0.484118	-0.487582	-0.485658
Agility	0.085691	-0.036128	0.047667	0.283309	-0.611198	-0.535404	-0.019459	0.306049	0.637400	0.695569	0.754705	0.085645	0.103459	0.110567	0.239329	0.283719	0.690857	0.126500	0.594925	0.463008	0.681712	0.606182	0.522242	0.792586	0.748592	0.557333	-0.243772	0.748228	1.000000	0.208837	0.266705	0.563776	0.627152	0.637318	0.668396	0.577702	0.552681	0.616572	-0.507581	-0.508065	-0.508568	-0.506763	-0.507833
Jumping	-0.100935	-0.106797	0.002805	0.289840	-0.063048	0.005842	0.168694	0.064655	0.035166	0.172802	0.117374	0.258669	0.254936	0.255130	0.352169	0.260403	0.116465	0.279615	0.054090	0.243641	0.117779	0.174645	0.141914	0.209320	0.232382	0.331521	0.268866	0.167705	0.208837	1.000000	0.360225	0.170115	0.070301	0.111898	0.087956	0.063666	0.109438	0.102458	-0.158394	-0.158524	-0.161687	-0.158992	-0.157636
Heading	-0.072020	-0.076264	0.026235	0.343265	0.005367	0.019235	0.137785	0.190847	0.391626	0.658111	0.548257	0.546994	0.523946	0.555447	0.677640	0.305042	0.532394	0.536809	0.305804	0.542475	0.482777	0.632108	0.518379	0.345955	0.398331	0.640626	0.458370	0.157088	0.266705	0.360225	1.000000	0.619807	0.476265	0.506579	0.451545	0.425968	0.553426	0.502497	-0.747203	-0.751930	-0.747164	-0.749532	-0.750380
Shot_Power	0.083610	-0.051829	0.021477	0.441773	-0.273413	-0.188739	0.151174	0.334897	0.640183	0.829902	0.795016	0.219159	0.217468	0.256805	0.496297	0.404946	0.795237	0.264911	0.679237	0.673363	0.704200	0.771737	0.679311	0.538325	0.548975	0.620266	0.172875	0.429301	0.563776	0.170115	0.619807	1.000000	0.797201	0.880013	0.784935	0.749059	0.786514	0.817316	-0.650511	-0.652568	-0.648162	-0.649521	-0.651392
Finishing	0.138914	-0.007898	0.032341	0.328576	-0.347154	-0.281775	0.064487	0.361983	0.715228	0.781218	0.817034	-0.096066	-0.098796	-0.058670	0.230695	0.316802	0.880495	-0.044888	0.695177	0.562688	0.644380	0.653929	0.507594	0.591831	0.582587	0.495899	-0.012010	0.486708	0.627152	0.070301	0.476265	0.797201	1.000000	0.863186	0.750697	0.687716	0.834705	0.876136	-0.573674	-0.576028	-0.571200	-0.571792	-0.573409
Long_Shots	0.109168	-0.040658	0.028781	0.419517	-0.364182	-0.274446	0.149635	0.365637	0.680434	0.831846	0.836080	0.116621	0.117163	0.156849	0.386260	0.408545	0.850896	0.181337	0.754865	0.642917	0.740554	0.761394	0.674009	0.571502	0.558026	0.591168	0.045462	0.504732	0.637318	0.111898	0.506579	0.880013	0.863186	1.000000	0.830707	0.801342	0.806805	0.857302	-0.597866	-0.601603	-0.595864	-0.597474	-0.598692
Curve	0.081888	-0.053631	0.010840	0.420796	-0.423115	-0.343380	0.139101	0.359044	0.689265	0.832009	0.840061	0.206493	0.216875	0.247088	0.395877	0.405543	0.804621	0.260755	0.750496	0.641637	0.826872	0.779465	0.716427	0.595826	0.576378	0.586883	-0.036263	0.558874	0.668396	0.087956	0.451545	0.784935	0.750697	0.830707	1.000000	0.856065	0.750694	0.799441	-0.604127	-0.606164	-0.601059	-0.601801	-0.603812
Freekick_Accuracy	0.106621	-0.062077	0.007001	0.399575	-0.380217	-0.293956	0.195338	0.345745	0.634867	0.763315	0.750014	0.233461	0.241188	0.274715	0.402368	0.390557	0.727188	0.292869	0.718754	0.606544	0.761017	0.742423	0.710268	0.486247	0.462375	0.537091	-0.003688	0.493454	0.577702	0.063666	0.425968	0.749059	0.687716	0.801342	0.856065	1.000000	0.732686	0.737697	-0.557384	-0.559510	-0.554188	-0.554871	-0.557189
Penalties	0.095289	-0.016220	0.020878	0.339898	-0.321246	-0.246189	0.131469	0.340757	0.662108	0.767803	0.765259	0.062951	0.059355	0.096483	0.329841	0.333713	0.798127	0.104781	0.647055	0.590273	0.648078	0.675478	0.550959	0.522338	0.515824	0.516367	0.052389	0.453402	0.552681	0.109438	0.553426	0.786514	0.834705	0.806805	0.750694	0.732686	1.000000	0.816956	-0.612528	-0.614877	-0.609397	-0.611300	-0.613301
Volleys	0.117381	-0.020482	0.025120	0.386494	-0.333576	-0.258932	0.133950	0.366610	0.701017	0.789287	0.804633	0.013556	0.015133	0.053435	0.318366	0.382709	0.841600	0.071093	0.700223	0.622623	0.684915	0.691174	0.569566	0.563226	0.555322	0.523170	0.022775	0.484856	0.616572	0.102458	0.502497	0.817316	0.876136	0.857302	0.799441	0.737697	0.816956	1.000000	-0.576643	-0.579607	-0.574444	-0.574602	-0.576507
GK_Positioning	-0.031492	0.004684	-0.027994	-0.018586	0.358795	0.342502	0.122371	-0.232109	-0.607676	-0.776117	-0.741793	-0.492932	-0.498386	-0.521127	-0.561675	-0.039962	-0.665015	-0.470613	-0.400721	-0.462405	-0.658450	-0.714431	-0.595199	-0.589681	-0.600348	-0.702177	-0.079647	-0.484823	-0.507581	-0.158394	-0.747203	-0.650511	-0.573674	-0.597866	-0.604127	-0.557384	-0.612528	-0.576643	1.000000	0.968213	0.962937	0.968031	0.968550
GK_Diving	-0.012435	0.011290	-0.026117	-0.027615	0.357683	0.340775	0.105158	-0.236372	-0.610500	-0.779922	-0.743848	-0.496354	-0.501643	-0.524291	-0.567497	-0.048234	-0.668924	-0.474997	-0.405353	-0.470743	-0.662598	-0.717572	-0.598739	-0.589198	-0.599891	-0.705673	-0.085167	-0.484486	-0.508065	-0.158524	-0.751930	-0.652568	-0.576028	-0.601603	-0.606164	-0.559510	-0.614877	-0.579607	0.968213	1.000000	0.963512	0.968287	0.972519
GK_Kicking	-0.015472	0.008788	-0.023273	-0.031696	0.356070	0.339505	0.109931	-0.230924	-0.605746	-0.775690	-0.739995	-0.495732	-0.501019	-0.523387	-0.563989	-0.051864	-0.663450	-0.473268	-0.398335	-0.468175	-0.658385	-0.713856	-0.594084	-0.589036	-0.599152	-0.702016	-0.084113	-0.484118	-0.508568	-0.161687	-0.747164	-0.648162	-0.571200	-0.595864	-0.601059	-0.554188	-0.609397	-0.574444	0.962937	0.963512	1.000000	0.963264	0.964500
GK_Handling	-0.024611	0.006725	-0.023064	-0.021343	0.359746	0.341829	0.113216	-0.233080	-0.606713	-0.775632	-0.740616	-0.496431	-0.501429	-0.523648	-0.564734	-0.044558	-0.664273	-0.474025	-0.397728	-0.464087	-0.658085	-0.714071	-0.594416	-0.589799	-0.599809	-0.703375	-0.080986	-0.487582	-0.506763	-0.158992	-0.749532	-0.649521	-0.571792	-0.597474	-0.601801	-0.554871	-0.611300	-0.574602	0.968031	0.968287	0.963264	1.000000	0.968501
GK_Reflexes	-0.018816	0.007480	-0.024923	-0.022978	0.360260	0.341785	0.106979	-0.235275	-0.609282	-0.777668	-0.742065	-0.497013	-0.501738	-0.524598	-0.566184	-0.045050	-0.666512	-0.474850	-0.401582	-0.469048	-0.661232	-0.716827	-0.597275	-0.588293	-0.599522	-0.705185	-0.084108	-0.485658	-0.507833	-0.157636	-0.750380	-0.651392	-0.573409	-0.598692	-0.603812	-0.557189	-0.613301	-0.576507	0.968550	0.972519	0.964500	0.968501	1.000000

假设我们不清楚后2列的具体含义是什么，分析该标题可能的含义。

g = data.groupby("Club_Position")
g["GK_Handling"].mean().sort_values(ascending=False).plot(kind="bar")

Out[85]:

年龄与评分具有怎样的关系？

data["Age"].head(3)

Out[90]:

0    32
1    29
2    25
Name: Age, dtype: int64

t = data.copy()
# data.plot(kind="scatter", x="Age", y="Rating")
# 将连续值切分为离散值。bins指定区间的数量（桶的数量）。这里的区间界限与直方图不同。
# 直方图的区间界限是前闭后开，最后一个区间双闭，而cut产生的区间，是前开后闭的。
# t["Age"] = pd.cut(t["Age"], bins=3)
# bins如果提供一个整数，表示区间的数量，此时会根据数据范围，进行等分区间。如果需要不等分区间，
# 可以传递一个数组，显式指定区间范围。
​
# cut方法默认情况下，会使用区间来作为区分之后的值，该值可能不够友好，我们也可以通过labels参数
# 指定切分之后的显示内容。
t["Age"] = pd.cut(t["Age"], bins=[1, 20, 30, 40, 100], labels=["弱冠之年", "而立之年", "不惑之年", "垂暮之年"])
g = t.groupby("Age")
g["Rating"].mean().plot(kind="line", marker="o", xticks=[0, 1, 2, 3])

Out[101]:

总结