pandans_数据过滤与排序
2023-01-15 本文已影响0人
敬子v
数据源：链接: https://pan.baidu.com/s/1EFqJFXf70t2Rubkh6D19aw 提取码: syqg
探索2012欧洲杯数据
数据源示例
步骤1 - 导入必要的库
import pandas as pd
步骤2 - 从以下地址导入数据集
path1='pandas_exercise\exercise_data\Euro2012_stats.csv'
步骤3 - 将数据集命名为euro12
euro12=pd.read_csv(path1)
print(euro12.head())
步骤4 只选取 Goals 这一列
print(euro12.Goals)
步骤5 有多少球队参与了2012欧洲杯？
print(euro12.shape[0])
步骤6 该数据集中一共有多少列(columns)?
print(euro12.info())
步骤7 将数据集中的列Team, Yellow Cards和Red Cards单独存为一个名叫discipline的数据框
discipline=euro12[['Team','Yellow Cards','Red Cards']]
print(discipline)
步骤8 对数据框discipline按照先Red Cards再Yellow Cards进行排序
discipline.sort_values(['Red Cards','Yellow Cards'],ascending=False,inplace=True)
print(discipline)
步骤9 计算每个球队拿到的黄牌数的平均值
print(discipline['Yellow Cards'].mean())
步骤10 找到进球数Goals超过6的球队数据
print(euro12[euro12.Goals>6])
步骤11 选取以字母G开头的球队数据 .str.startswith
print(euro12[euro12.Team.str.startswith('G')])
步骤12 选取前7列 取值 .iloc[:,0:7]
print(euro12.iloc[:,0:7])
步骤13 选取除了最后3列之外的全部列 .iloc[:,:-3]
print(euro12.iloc[:,:-3])
步骤14 找到英格兰(England)、意大利(Italy)和俄罗斯(Russia)的射正率(Shooting Accuracy)
loc(条件，所取得列）
print(euro12.loc[euro12.Team.isin(['England','Italy','Russia']),['Team','Shooting Accuracy']])
# 步骤3
Team Goals Shots on target ... Subs on Subs off Players Used
0 Croatia 4 13 ... 9 9 16
1 Czech Republic 4 13 ... 11 11 19
2 Denmark 4 10 ... 7 7 15
3 England 5 11 ... 11 11 16
4 France 3 22 ... 11 11 19
[5 rows x 35 columns]
# 步骤5
0 4
1 4
2 4
3 5
4 3
5 10
6 5
7 6
8 2
9 2
10 6
11 1
12 5
13 12
14 5
15 2
Name: Goals, dtype: int64
# 步骤6
16
# 步骤7
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 35 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Team 16 non-null object
1 Goals 16 non-null int64
2 Shots on target 16 non-null int64
3 Shots off target 16 non-null int64
4 Shooting Accuracy 16 non-null object
5 % Goals-to-shots 16 non-null object
6 Total shots (inc. Blocked) 16 non-null int64
7 Hit Woodwork 16 non-null int64
8 Penalty goals 16 non-null int64
9 Penalties not scored 16 non-null int64
10 Headed goals 16 non-null int64
11 Passes 16 non-null int64
12 Passes completed 16 non-null int64
13 Passing Accuracy 16 non-null object
14 Touches 16 non-null int64
15 Crosses 16 non-null int64
16 Dribbles 16 non-null int64
17 Corners Taken 16 non-null int64
18 Tackles 16 non-null int64
19 Clearances 16 non-null int64
20 Interceptions 16 non-null int64
21 Clearances off line 15 non-null float64
22 Clean Sheets 16 non-null int64
23 Blocks 16 non-null int64
24 Goals conceded 16 non-null int64
25 Saves made 16 non-null int64
26 Saves-to-shots ratio 16 non-null object
27 Fouls Won 16 non-null int64
28 Fouls Conceded 16 non-null int64
29 Offsides 16 non-null int64
30 Yellow Cards 16 non-null int64
31 Red Cards 16 non-null int64
32 Subs on 16 non-null int64
33 Subs off 16 non-null int64
34 Players Used 16 non-null int64
dtypes: float64(1), int64(29), object(5)
memory usage: 4.5+ KB
None
# 步骤8
Team Yellow Cards Red Cards
6 Greece 9 1
9 Poland 7 1
11 Republic of Ireland 6 1
7 Italy 16 0
10 Portugal 12 0
13 Spain 11 0
0 Croatia 9 0
1 Czech Republic 7 0
14 Sweden 7 0
4 France 6 0
12 Russia 6 0
3 England 5 0
8 Netherlands 5 0
15 Ukraine 5 0
2 Denmark 4 0
5 Germany 4 0
# 步骤9
7.4375
# 步骤10
Team Goals Shots on target ... Subs on Subs off Players Used
5 Germany 10 32 ... 15 15 17
13 Spain 12 42 ... 17 17 18
[2 rows x 35 columns]
# 步骤11
Team Goals Shots on target ... Subs on Subs off Players Used
5 Germany 10 32 ... 15 15 17
6 Greece 5 8 ... 12 12 20
[2 rows x 35 columns]
# 步骤12
Team Goals ... % Goals-to-shots Total shots (inc. Blocked)
0 Croatia 4 ... 16.0% 32
1 Czech Republic 4 ... 12.9% 39
2 Denmark 4 ... 20.0% 27
3 England 5 ... 17.2% 40
4 France 3 ... 6.5% 65
5 Germany 10 ... 15.6% 80
6 Greece 5 ... 19.2% 32
7 Italy 6 ... 7.5% 110
8 Netherlands 2 ... 4.1% 60
9 Poland 2 ... 5.2% 48
10 Portugal 6 ... 9.3% 82
11 Republic of Ireland 1 ... 5.2% 28
12 Russia 5 ... 12.5% 59
13 Spain 12 ... 16.0% 100
14 Sweden 5 ... 13.8% 39
15 Ukraine 2 ... 6.0% 38
[16 rows x 7 columns]
# 步骤13
Team Goals ... Yellow Cards Red Cards
0 Croatia 4 ... 9 0
1 Czech Republic 4 ... 7 0
2 Denmark 4 ... 4 0
3 England 5 ... 5 0
4 France 3 ... 6 0
5 Germany 10 ... 4 0
6 Greece 5 ... 9 1
7 Italy 6 ... 16 0
8 Netherlands 2 ... 5 0
9 Poland 2 ... 7 1
10 Portugal 6 ... 12 0
11 Republic of Ireland 1 ... 6 1
12 Russia 5 ... 6 0
13 Spain 12 ... 11 0
14 Sweden 5 ... 7 0
15 Ukraine 2 ... 5 0
[16 rows x 32 columns]
# 步骤14
Team Shooting Accuracy
3 England 50.0%
7 Italy 43.0%
12 Russia 22.5%
