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In [ ]:
import pandas as pd
In [ ]:
# https://www.koreabaseball.com/Record/Player/HitterDetail/Daily.aspx?playerId=77532
url = 'https://www.koreabaseball.com/Record/Player/HitterDetail/Daily.aspx?playerId=77532'
df = pd.read_html(url)
df
Out[ ]:
[ 4월 상대 AVG1 PA AB R H 2B 3B HR RBI SB CS BB HBP SO \
0 04.01 삼성 0.200 5 5 0 1 1 0 0 1 0 0 0 0 0
1 04.02 삼성 0.500 4 4 2 2 0 1 0 0 0 0 0 0 1
2 04.04 두산 0.333 3 3 0 1 0 0 0 0 0 0 0 0 1
3 04.05 두산 0.167 6 6 1 1 0 0 0 1 0 0 0 0 0
4 04.06 두산 0.200 5 5 0 1 0 0 0 0 0 0 0 0 1
5 04.07 키움 0.000 3 3 0 0 0 0 0 0 0 0 0 0 1
6 04.08 키움 0.500 5 4 1 2 1 0 0 3 0 0 0 0 0
7 04.09 키움 0.000 4 3 0 0 0 0 0 0 0 0 1 0 2
8 04.11 KT 0.000 3 3 0 0 0 0 0 0 0 0 0 0 1
9 04.12 KT 0.250 4 4 0 1 0 1 0 0 0 0 0 0 0
10 04.13 KT 0.333 4 3 1 1 1 0 0 0 0 0 1 0 0
11 04.14 SSG 0.250 4 4 0 1 0 0 0 0 0 0 0 0 2
12 04.15 SSG 0.000 4 4 0 0 0 0 0 0 0 0 0 0 1
13 04.16 SSG 0.000 3 2 0 0 0 0 0 0 0 0 1 0 1
14 04.18 LG 0.750 5 4 1 3 1 0 0 1 0 0 1 0 0
15 04.19 LG 0.500 5 4 1 2 1 0 0 0 0 0 1 0 0
16 04.20 LG 0.400 5 5 1 2 1 0 0 2 0 0 0 0 1
17 04.21 롯데 0.000 4 3 0 0 0 0 0 2 0 0 0 0 0
18 04.22 롯데 0.200 5 5 1 1 0 0 0 0 0 0 0 0 2
19 04.23 롯데 0.250 4 4 0 1 0 0 0 0 1 0 0 0 0
20 04.25 KIA 0.250 5 4 1 1 0 0 0 1 0 0 0 0 1
21 04.26 KIA 0.500 4 4 0 2 0 0 0 0 0 0 0 0 0
22 04.27 KIA 0.250 4 4 0 1 0 0 0 0 0 0 0 0 1
23 04.28 한화 0.400 5 5 0 2 0 0 0 0 0 0 0 0 2
24 04.29 한화 0.750 5 4 0 3 1 0 0 1 0 0 0 1 0
25 04.30 한화 0.333 4 3 1 1 1 0 0 0 0 0 1 0 1
26 합계 합계 0.294 112 102 11 30 8 2 0 12 1 0 6 1 19
GDP AVG2
0 0 0.200
1 0 0.333
2 0 0.333
3 0 0.278
4 0 0.261
5 1 0.231
6 0 0.267
7 0 0.242
8 0 0.222
9 0 0.225
10 0 0.233
11 0 0.234
12 0 0.216
13 0 0.208
14 0 0.246
15 0 0.262
16 0 0.273
17 0 0.261
18 0 0.257
19 0 0.256
20 0 0.256
21 0 0.267
22 0 0.267
23 0 0.274
24 0 0.293
25 0 0.294
26 1 0.294 ,
5월 상대 AVG1 PA AB R H 2B 3B HR RBI SB CS BB HBP SO \
0 05.02 LG 0.500 4 4 0 2 0 0 0 0 0 0 0 0 0
1 05.03 LG 0.250 4 4 0 1 0 0 0 0 0 0 0 0 0
2 05.09 KT 0.500 6 6 3 3 1 0 0 1 1 0 0 0 1
3 05.10 KT 0.400 5 5 1 2 0 0 0 2 0 0 0 0 0
4 05.11 KT 0.800 5 5 1 4 0 0 0 0 1 0 0 0 0
5 05.12 키움 0.200 5 5 0 1 0 0 0 2 0 0 0 0 1
6 05.13 키움 0.000 3 3 0 0 0 0 0 0 0 0 0 0 2
7 05.14 키움 0.200 5 5 1 1 0 0 0 0 0 0 0 0 0
8 05.16 SSG 0.200 5 5 0 1 0 0 0 0 0 0 0 0 2
9 05.17 SSG 0.000 5 4 0 0 0 0 0 0 1 0 1 0 1
10 05.19 삼성 0.250 5 4 1 1 1 0 0 0 0 0 1 0 0
11 05.20 삼성 0.750 5 4 3 3 3 0 0 3 0 0 0 1 0
12 05.21 삼성 0.250 4 4 0 1 0 0 0 0 0 0 0 0 1
13 05.23 롯데 0.000 4 3 0 0 0 0 0 0 0 0 1 0 1
14 05.24 롯데 0.250 5 4 1 1 1 0 0 1 0 0 1 0 1
15 05.25 롯데 0.250 4 4 0 1 1 0 0 1 0 0 0 0 1
16 05.26 한화 0.250 5 4 0 1 0 0 0 2 0 0 1 0 0
17 05.27 한화 0.400 5 5 0 2 1 0 0 0 0 0 0 0 1
18 05.30 두산 0.667 5 3 1 2 0 0 0 2 0 0 2 0 0
19 합계 합계 0.333 89 81 12 27 8 0 0 14 3 0 7 1 12
GDP AVG2
0 0 0.302
1 0 0.300
2 0 0.310
3 0 0.314
4 0 0.333
5 0 0.328
6 1 0.321
7 0 0.317
8 0 0.313
9 0 0.304
10 0 0.303
11 0 0.314
12 0 0.313
13 0 0.307
14 0 0.305
15 0 0.304
16 0 0.303
17 1 0.306
18 0 0.311
19 2 0.311 ]In [ ]:
# 'AVG1', 'AB', 'H', '2B', '3B', 'HR' 컬럼만 남기고 제거하기
In [ ]:
# '루타' 파생변수를 생성하여 열 계산하기
# H:1, 2B:2, 3B:3, HR:4
# 4월, 5월의 합계를 제거 후 타율에 대한 날짜 라인 그래프를 생성
df1 = df[0]
df2 = df[1]
dic1 = {'H':1, '2B':2, '3B':3, 'HR':4}
In [ ]:
df1 = df1.loc[:25, ['4월', 'AVG1', 'AB', 'H', '2B', '3B', 'HR']]
df2 = df2.loc[:12, ['5월', 'AVG1', 'AB', 'H', '2B', '3B', 'HR']]
In [ ]:
df1.rename(columns={'4월':'날짜'}, inplace=True)
df2.rename(columns={'5월':'날짜'}, inplace=True)
In [ ]:
df_concat = pd.concat([df1, df2], sort=False)
df_concat.reset_index(drop=True)
Out[ ]:
| 날짜 | AVG1 | AB | H | 2B | 3B | HR | |
|---|---|---|---|---|---|---|---|
| 0 | 04.01 | 0.200 | 5 | 1 | 1 | 0 | 0 |
| 1 | 04.02 | 0.500 | 4 | 2 | 0 | 1 | 0 |
| 2 | 04.04 | 0.333 | 3 | 1 | 0 | 0 | 0 |
| 3 | 04.05 | 0.167 | 6 | 1 | 0 | 0 | 0 |
| 4 | 04.06 | 0.200 | 5 | 1 | 0 | 0 | 0 |
| 5 | 04.07 | 0.000 | 3 | 0 | 0 | 0 | 0 |
| 6 | 04.08 | 0.500 | 4 | 2 | 1 | 0 | 0 |
| 7 | 04.09 | 0.000 | 3 | 0 | 0 | 0 | 0 |
| 8 | 04.11 | 0.000 | 3 | 0 | 0 | 0 | 0 |
| 9 | 04.12 | 0.250 | 4 | 1 | 0 | 1 | 0 |
| 10 | 04.13 | 0.333 | 3 | 1 | 1 | 0 | 0 |
| 11 | 04.14 | 0.250 | 4 | 1 | 0 | 0 | 0 |
| 12 | 04.15 | 0.000 | 4 | 0 | 0 | 0 | 0 |
| 13 | 04.16 | 0.000 | 2 | 0 | 0 | 0 | 0 |
| 14 | 04.18 | 0.750 | 4 | 3 | 1 | 0 | 0 |
| 15 | 04.19 | 0.500 | 4 | 2 | 1 | 0 | 0 |
| 16 | 04.20 | 0.400 | 5 | 2 | 1 | 0 | 0 |
| 17 | 04.21 | 0.000 | 3 | 0 | 0 | 0 | 0 |
| 18 | 04.22 | 0.200 | 5 | 1 | 0 | 0 | 0 |
| 19 | 04.23 | 0.250 | 4 | 1 | 0 | 0 | 0 |
| 20 | 04.25 | 0.250 | 4 | 1 | 0 | 0 | 0 |
| 21 | 04.26 | 0.500 | 4 | 2 | 0 | 0 | 0 |
| 22 | 04.27 | 0.250 | 4 | 1 | 0 | 0 | 0 |
| 23 | 04.28 | 0.400 | 5 | 2 | 0 | 0 | 0 |
| 24 | 04.29 | 0.750 | 4 | 3 | 1 | 0 | 0 |
| 25 | 04.30 | 0.333 | 3 | 1 | 1 | 0 | 0 |
| 26 | 05.02 | 0.500 | 4 | 2 | 0 | 0 | 0 |
| 27 | 05.03 | 0.250 | 4 | 1 | 0 | 0 | 0 |
| 28 | 05.09 | 0.500 | 6 | 3 | 1 | 0 | 0 |
| 29 | 05.10 | 0.400 | 5 | 2 | 0 | 0 | 0 |
| 30 | 05.11 | 0.800 | 5 | 4 | 0 | 0 | 0 |
| 31 | 05.12 | 0.200 | 5 | 1 | 0 | 0 | 0 |
| 32 | 05.13 | 0.000 | 3 | 0 | 0 | 0 | 0 |
| 33 | 05.14 | 0.200 | 5 | 1 | 0 | 0 | 0 |
| 34 | 05.16 | 0.200 | 5 | 1 | 0 | 0 | 0 |
| 35 | 05.17 | 0.000 | 4 | 0 | 0 | 0 | 0 |
| 36 | 05.19 | 0.250 | 4 | 1 | 1 | 0 | 0 |
| 37 | 05.20 | 0.750 | 4 | 3 | 3 | 0 | 0 |
| 38 | 05.21 | 0.250 | 4 | 1 | 0 | 0 | 0 |
In [ ]:
import matplotlib.pyplot as plt
In [ ]:
url = 'https://www.koreabaseball.com/Record/Player/HitterDetail/Daily.aspx?playerId=77532'
df = pd.read_html(url)
df
Out[ ]:
[ 4월 상대 AVG1 PA AB R H 2B 3B HR RBI SB CS BB HBP SO \
0 04.01 삼성 0.200 5 5 0 1 1 0 0 1 0 0 0 0 0
1 04.02 삼성 0.500 4 4 2 2 0 1 0 0 0 0 0 0 1
2 04.04 두산 0.333 3 3 0 1 0 0 0 0 0 0 0 0 1
3 04.05 두산 0.167 6 6 1 1 0 0 0 1 0 0 0 0 0
4 04.06 두산 0.200 5 5 0 1 0 0 0 0 0 0 0 0 1
5 04.07 키움 0.000 3 3 0 0 0 0 0 0 0 0 0 0 1
6 04.08 키움 0.500 5 4 1 2 1 0 0 3 0 0 0 0 0
7 04.09 키움 0.000 4 3 0 0 0 0 0 0 0 0 1 0 2
8 04.11 KT 0.000 3 3 0 0 0 0 0 0 0 0 0 0 1
9 04.12 KT 0.250 4 4 0 1 0 1 0 0 0 0 0 0 0
10 04.13 KT 0.333 4 3 1 1 1 0 0 0 0 0 1 0 0
11 04.14 SSG 0.250 4 4 0 1 0 0 0 0 0 0 0 0 2
12 04.15 SSG 0.000 4 4 0 0 0 0 0 0 0 0 0 0 1
13 04.16 SSG 0.000 3 2 0 0 0 0 0 0 0 0 1 0 1
14 04.18 LG 0.750 5 4 1 3 1 0 0 1 0 0 1 0 0
15 04.19 LG 0.500 5 4 1 2 1 0 0 0 0 0 1 0 0
16 04.20 LG 0.400 5 5 1 2 1 0 0 2 0 0 0 0 1
17 04.21 롯데 0.000 4 3 0 0 0 0 0 2 0 0 0 0 0
18 04.22 롯데 0.200 5 5 1 1 0 0 0 0 0 0 0 0 2
19 04.23 롯데 0.250 4 4 0 1 0 0 0 0 1 0 0 0 0
20 04.25 KIA 0.250 5 4 1 1 0 0 0 1 0 0 0 0 1
21 04.26 KIA 0.500 4 4 0 2 0 0 0 0 0 0 0 0 0
22 04.27 KIA 0.250 4 4 0 1 0 0 0 0 0 0 0 0 1
23 04.28 한화 0.400 5 5 0 2 0 0 0 0 0 0 0 0 2
24 04.29 한화 0.750 5 4 0 3 1 0 0 1 0 0 0 1 0
25 04.30 한화 0.333 4 3 1 1 1 0 0 0 0 0 1 0 1
26 합계 합계 0.294 112 102 11 30 8 2 0 12 1 0 6 1 19
GDP AVG2
0 0 0.200
1 0 0.333
2 0 0.333
3 0 0.278
4 0 0.261
5 1 0.231
6 0 0.267
7 0 0.242
8 0 0.222
9 0 0.225
10 0 0.233
11 0 0.234
12 0 0.216
13 0 0.208
14 0 0.246
15 0 0.262
16 0 0.273
17 0 0.261
18 0 0.257
19 0 0.256
20 0 0.256
21 0 0.267
22 0 0.267
23 0 0.274
24 0 0.293
25 0 0.294
26 1 0.294 ,
5월 상대 AVG1 PA AB R H 2B 3B HR RBI SB CS BB HBP SO \
0 05.02 LG 0.500 4 4 0 2 0 0 0 0 0 0 0 0 0
1 05.03 LG 0.250 4 4 0 1 0 0 0 0 0 0 0 0 0
2 05.09 KT 0.500 6 6 3 3 1 0 0 1 1 0 0 0 1
3 05.10 KT 0.400 5 5 1 2 0 0 0 2 0 0 0 0 0
4 05.11 KT 0.800 5 5 1 4 0 0 0 0 1 0 0 0 0
5 05.12 키움 0.200 5 5 0 1 0 0 0 2 0 0 0 0 1
6 05.13 키움 0.000 3 3 0 0 0 0 0 0 0 0 0 0 2
7 05.14 키움 0.200 5 5 1 1 0 0 0 0 0 0 0 0 0
8 05.16 SSG 0.200 5 5 0 1 0 0 0 0 0 0 0 0 2
9 05.17 SSG 0.000 5 4 0 0 0 0 0 0 1 0 1 0 1
10 05.19 삼성 0.250 5 4 1 1 1 0 0 0 0 0 1 0 0
11 05.20 삼성 0.750 5 4 3 3 3 0 0 3 0 0 0 1 0
12 05.21 삼성 0.250 4 4 0 1 0 0 0 0 0 0 0 0 1
13 05.23 롯데 0.000 4 3 0 0 0 0 0 0 0 0 1 0 1
14 05.24 롯데 0.250 5 4 1 1 1 0 0 1 0 0 1 0 1
15 05.25 롯데 0.250 4 4 0 1 1 0 0 1 0 0 0 0 1
16 05.26 한화 0.250 5 4 0 1 0 0 0 2 0 0 1 0 0
17 05.27 한화 0.400 5 5 0 2 1 0 0 0 0 0 0 0 1
18 05.30 두산 0.667 5 3 1 2 0 0 0 2 0 0 2 0 0
19 합계 합계 0.333 89 81 12 27 8 0 0 14 3 0 7 1 12
GDP AVG2
0 0 0.302
1 0 0.300
2 0 0.310
3 0 0.314
4 0 0.333
5 0 0.328
6 1 0.321
7 0 0.317
8 0 0.313
9 0 0.304
10 0 0.303
11 0 0.314
12 0 0.313
13 0 0.307
14 0 0.305
15 0 0.304
16 0 0.303
17 1 0.306
18 0 0.311
19 2 0.311 ]In [ ]:
df1 = pd.concat(df)
df1
Out[ ]:
| 4월 | 상대 | AVG1 | PA | AB | R | H | 2B | 3B | HR | RBI | SB | CS | BB | HBP | SO | GDP | AVG2 | 5월 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 04.01 | 삼성 | 0.200 | 5 | 5 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.200 | NaN |
| 1 | 04.02 | 삼성 | 0.500 | 4 | 4 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.333 | NaN |
| 2 | 04.04 | 두산 | 0.333 | 3 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.333 | NaN |
| 3 | 04.05 | 두산 | 0.167 | 6 | 6 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.278 | NaN |
| 4 | 04.06 | 두산 | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.261 | NaN |
| 5 | 04.07 | 키움 | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.231 | NaN |
| 6 | 04.08 | 키움 | 0.500 | 5 | 4 | 1 | 2 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0.267 | NaN |
| 7 | 04.09 | 키움 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0.242 | NaN |
| 8 | 04.11 | KT | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.222 | NaN |
| 9 | 04.12 | KT | 0.250 | 4 | 4 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225 | NaN |
| 10 | 04.13 | KT | 0.333 | 4 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.233 | NaN |
| 11 | 04.14 | SSG | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.234 | NaN |
| 12 | 04.15 | SSG | 0.000 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.216 | NaN |
| 13 | 04.16 | SSG | 0.000 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.208 | NaN |
| 14 | 04.18 | LG | 0.750 | 5 | 4 | 1 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0.246 | NaN |
| 15 | 04.19 | LG | 0.500 | 5 | 4 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.262 | NaN |
| 16 | 04.20 | LG | 0.400 | 5 | 5 | 1 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0.273 | NaN |
| 17 | 04.21 | 롯데 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0.261 | NaN |
| 18 | 04.22 | 롯데 | 0.200 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.257 | NaN |
| 19 | 04.23 | 롯데 | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.256 | NaN |
| 20 | 04.25 | KIA | 0.250 | 5 | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0.256 | NaN |
| 21 | 04.26 | KIA | 0.500 | 4 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.267 | NaN |
| 22 | 04.27 | KIA | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.267 | NaN |
| 23 | 04.28 | 한화 | 0.400 | 5 | 5 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.274 | NaN |
| 24 | 04.29 | 한화 | 0.750 | 5 | 4 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0.293 | NaN |
| 25 | 04.30 | 한화 | 0.333 | 4 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.294 | NaN |
| 26 | 합계 | 합계 | 0.294 | 112 | 102 | 11 | 30 | 8 | 2 | 0 | 12 | 1 | 0 | 6 | 1 | 19 | 1 | 0.294 | NaN |
| 0 | NaN | LG | 0.500 | 4 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.302 | 05.02 |
| 1 | NaN | LG | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.300 | 05.03 |
| 2 | NaN | KT | 0.500 | 6 | 6 | 3 | 3 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0.310 | 05.09 |
| 3 | NaN | KT | 0.400 | 5 | 5 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0.314 | 05.10 |
| 4 | NaN | KT | 0.800 | 5 | 5 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.333 | 05.11 |
| 5 | NaN | 키움 | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0.328 | 05.12 |
| 6 | NaN | 키움 | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0.321 | 05.13 |
| 7 | NaN | 키움 | 0.200 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.317 | 05.14 |
| 8 | NaN | SSG | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.313 | 05.16 |
| 9 | NaN | SSG | 0.000 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0.304 | 05.17 |
| 10 | NaN | 삼성 | 0.250 | 5 | 4 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.303 | 05.19 |
| 11 | NaN | 삼성 | 0.750 | 5 | 4 | 3 | 3 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0.314 | 05.20 |
| 12 | NaN | 삼성 | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.313 | 05.21 |
| 13 | NaN | 롯데 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.307 | 05.23 |
| 14 | NaN | 롯데 | 0.250 | 5 | 4 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.305 | 05.24 |
| 15 | NaN | 롯데 | 0.250 | 4 | 4 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0.304 | 05.25 |
| 16 | NaN | 한화 | 0.250 | 5 | 4 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0.303 | 05.26 |
| 17 | NaN | 한화 | 0.400 | 5 | 5 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.306 | 05.27 |
| 18 | NaN | 두산 | 0.667 | 5 | 3 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0.311 | 05.30 |
| 19 | NaN | 합계 | 0.333 | 89 | 81 | 12 | 27 | 8 | 0 | 0 | 14 | 3 | 0 | 7 | 1 | 12 | 2 | 0.311 | 합계 |
In [ ]:
df1['루타'] = df1['H']+(df1['2B']*2)+(df1['3B']*3)+(df1['HR']*4)
In [ ]:
df1
Out[ ]:
| 4월 | 상대 | AVG1 | PA | AB | R | H | 2B | 3B | HR | RBI | SB | CS | BB | HBP | SO | GDP | AVG2 | 5월 | 루타 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 04.01 | 삼성 | 0.200 | 5 | 5 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.200 | NaN | 3 |
| 1 | 04.02 | 삼성 | 0.500 | 4 | 4 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.333 | NaN | 5 |
| 2 | 04.04 | 두산 | 0.333 | 3 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.333 | NaN | 1 |
| 3 | 04.05 | 두산 | 0.167 | 6 | 6 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.278 | NaN | 1 |
| 4 | 04.06 | 두산 | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.261 | NaN | 1 |
| 5 | 04.07 | 키움 | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.231 | NaN | 0 |
| 6 | 04.08 | 키움 | 0.500 | 5 | 4 | 1 | 2 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0.267 | NaN | 4 |
| 7 | 04.09 | 키움 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0.242 | NaN | 0 |
| 8 | 04.11 | KT | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.222 | NaN | 0 |
| 9 | 04.12 | KT | 0.250 | 4 | 4 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225 | NaN | 4 |
| 10 | 04.13 | KT | 0.333 | 4 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.233 | NaN | 3 |
| 11 | 04.14 | SSG | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.234 | NaN | 1 |
| 12 | 04.15 | SSG | 0.000 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.216 | NaN | 0 |
| 13 | 04.16 | SSG | 0.000 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.208 | NaN | 0 |
| 14 | 04.18 | LG | 0.750 | 5 | 4 | 1 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0.246 | NaN | 5 |
| 15 | 04.19 | LG | 0.500 | 5 | 4 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.262 | NaN | 4 |
| 16 | 04.20 | LG | 0.400 | 5 | 5 | 1 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0.273 | NaN | 4 |
| 17 | 04.21 | 롯데 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0.261 | NaN | 0 |
| 18 | 04.22 | 롯데 | 0.200 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.257 | NaN | 1 |
| 19 | 04.23 | 롯데 | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.256 | NaN | 1 |
| 20 | 04.25 | KIA | 0.250 | 5 | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0.256 | NaN | 1 |
| 21 | 04.26 | KIA | 0.500 | 4 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.267 | NaN | 2 |
| 22 | 04.27 | KIA | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.267 | NaN | 1 |
| 23 | 04.28 | 한화 | 0.400 | 5 | 5 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.274 | NaN | 2 |
| 24 | 04.29 | 한화 | 0.750 | 5 | 4 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0.293 | NaN | 5 |
| 25 | 04.30 | 한화 | 0.333 | 4 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.294 | NaN | 3 |
| 26 | 합계 | 합계 | 0.294 | 112 | 102 | 11 | 30 | 8 | 2 | 0 | 12 | 1 | 0 | 6 | 1 | 19 | 1 | 0.294 | NaN | 52 |
| 0 | NaN | LG | 0.500 | 4 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.302 | 05.02 | 2 |
| 1 | NaN | LG | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.300 | 05.03 | 1 |
| 2 | NaN | KT | 0.500 | 6 | 6 | 3 | 3 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0.310 | 05.09 | 5 |
| 3 | NaN | KT | 0.400 | 5 | 5 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0.314 | 05.10 | 2 |
| 4 | NaN | KT | 0.800 | 5 | 5 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.333 | 05.11 | 4 |
| 5 | NaN | 키움 | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0.328 | 05.12 | 1 |
| 6 | NaN | 키움 | 0.000 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0.321 | 05.13 | 0 |
| 7 | NaN | 키움 | 0.200 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.317 | 05.14 | 1 |
| 8 | NaN | SSG | 0.200 | 5 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0.313 | 05.16 | 1 |
| 9 | NaN | SSG | 0.000 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0.304 | 05.17 | 0 |
| 10 | NaN | 삼성 | 0.250 | 5 | 4 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.303 | 05.19 | 3 |
| 11 | NaN | 삼성 | 0.750 | 5 | 4 | 3 | 3 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0.314 | 05.20 | 9 |
| 12 | NaN | 삼성 | 0.250 | 4 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.313 | 05.21 | 1 |
| 13 | NaN | 롯데 | 0.000 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.307 | 05.23 | 0 |
| 14 | NaN | 롯데 | 0.250 | 5 | 4 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.305 | 05.24 | 3 |
| 15 | NaN | 롯데 | 0.250 | 4 | 4 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0.304 | 05.25 | 3 |
| 16 | NaN | 한화 | 0.250 | 5 | 4 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0.303 | 05.26 | 1 |
| 17 | NaN | 한화 | 0.400 | 5 | 5 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.306 | 05.27 | 4 |
| 18 | NaN | 두산 | 0.667 | 5 | 3 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0.311 | 05.30 | 2 |
| 19 | NaN | 합계 | 0.333 | 89 | 81 | 12 | 27 | 8 | 0 | 0 | 14 | 3 | 0 | 7 | 1 | 12 | 2 | 0.311 | 합계 | 43 |
In [ ]:
df1.plot(x='4월' ,y='AVG1')
Out[ ]:
<Axes: xlabel='4월'>/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 50900 (\N{HANGUL SYLLABLE WEOL}) missing from current font.
func(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50900 (\N{HANGUL SYLLABLE WEOL}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
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