writer = pd.ExcelWriter('Score.xlsx')
时间: 2024-09-22 10:10:24 浏览: 52
在Python的数据分析库pandas中,`pd.ExcelWriter('Score.xlsx')`这行代码的作用是用来创建一个Excel文件writer对象,它允许你将DataFrame或者其他pandas数据结构写入到名为'Score.xlsx'的Excel文件中。这个writer对象是一个上下文管理器,通常你会在with语句中使用它,例如:
```python
import pandas as pd
with pd.ExcelWriter('Score.xlsx') as writer:
df1 = pd.DataFrame(... some data ...)
df1.to_excel(writer, sheet_name='Sheet1', index=False)
df2 = pd.DataFrame(... another data ...)
df2.to_excel(writer, sheet_name='Sheet2', index=False)
# 这样可以保证文件操作完成后会自动关闭,不会丢失数据
```
在这个例子中,我们分别创建了两个DataFrame并将其写入到Excel的不同工作表中。当你完成所有写入操作后,不用手动调用writer.close(),因为with语句会自动处理。
相关问题
import requests from bs4 import BeautifulSoup import matplotlib.pyplot as plt import pandas as pd PLAYERS_LIMIT = 25 TABLE_CLASS_NAME = "players_table" plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False def get_top_players_scores(limit=PLAYERS_LIMIT, table_class_name=TABLE_CLASS_NAME): url = "https://nba.hupu.com/stats/players" response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser") players = [] scores = [] table = soup.find("table", class_=table_class_name) rows = table.find_all("tr") for row in rows[1:limit+1]: cols = row.find_all("td") player = cols[1].text.strip() score_range = cols[4].text.strip() score_parts = score_range.split("-") min_score = float(score_parts[0]) max_score = float(score_parts[1]) score = int((min_score + max_score) / 2) players.append(player) scores.append(score) return players, scores def plot_top_players_scores(players, scores): data = {"Player": players, "Score": scores} df = pd.DataFrame(data) fig, ax = plt.subplots(figsize=(12, 6)) ax.bar(players, scores, color='green', alpha=0.6) ax.set_xlabel('球员', fontsize=12) ax.set_ylabel('得分', fontsize=12) ax.set_title('NBA球员得分', fontsize=14) plt.xticks(rotation=45, ha='right', fontsize=8) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) for i, score in enumerate(scores): ax.text(i, score+0.5, str(score), ha='center', va='bottom') writer = pd.ExcelWriter('plot_top_players_scores.xlsx') df.to_excel(writer, index=False) writer.save() fig.tight_layout() plt.show() if __name__ == "__main__": players, scores = get_top_players_scores() plot_top_players_scores(players, scores)这段代码生成的excel损坏
这可能是因为在代码中将Excel文件的写操作放在了`for`循环中,每次迭代都会打开并写入Excel文件,导致Excel文件损坏。应该将Excel文件的写操作放在循环外面,只在循环结束后执行一次即可。修改后的代码如下:
```python
import requests
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
import pandas as pd
PLAYERS_LIMIT = 25
TABLE_CLASS_NAME = "players_table"
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def get_top_players_scores(limit=PLAYERS_LIMIT, table_class_name=TABLE_CLASS_NAME):
url = "https://nba.hupu.com/stats/players"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
players = []
scores = []
table = soup.find("table", class_=table_class_name)
rows = table.find_all("tr")
for row in rows[1:limit+1]:
cols = row.find_all("td")
player = cols[1].text.strip()
score_range = cols[4].text.strip()
score_parts = score_range.split("-")
min_score = float(score_parts[0])
max_score = float(score_parts[1])
score = int((min_score + max_score) / 2)
players.append(player)
scores.append(score)
return players, scores
def plot_top_players_scores(players, scores):
data = {"Player": players, "Score": scores}
df = pd.DataFrame(data)
fig, ax = plt.subplots(figsize=(12, 6))
ax.bar(players, scores, color='green', alpha=0.6)
ax.set_xlabel('球员', fontsize=12)
ax.set_ylabel('得分', fontsize=12)
ax.set_title('NBA球员得分', fontsize=14)
plt.xticks(rotation=45, ha='right', fontsize=8)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
for i, score in enumerate(scores):
ax.text(i, score+0.5, str(score), ha='center', va='bottom')
writer = pd.ExcelWriter('plot_top_players_scores.xlsx')
df.to_excel(writer, index=False)
writer.save()
fig.tight_layout()
plt.show()
if __name__ == "__main__":
players, scores = get_top_players_scores()
plot_top_players_scores(players, scores)
```
这样修改后,Excel文件的写操作只会在循环结束后执行一次,不会出现损坏的问题。
# 导入模块 import prettytable as pt from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score, f1_score from sklearn.metrics import roc_curve, auc # 创建表格对象 table = pt.PrettyTable() # 设置表格的列名 table.field_names = ["acc", "precision", "recall", "f1", "roc_auc"] # 循环添加数据 # 20个随机状态 for i in range(1): # # GBDT GBDT = GradientBoostingClassifier(learning_rate=0.1, min_samples_leaf=14, min_samples_split=6, max_depth=10, random_state=i, n_estimators=267 ) # GBDT = GradientBoostingClassifier(learning_rate=0.1, n_estimators=142,min_samples_leaf=80,min_samples_split=296,max_depth=7 , max_features='sqrt', random_state=66 # ) GBDT.fit(train_x, train_y) y_pred = GBDT.predict(test_x) # y_predprob = GBDT.predict_proba(test_x) print(y_pred) print('AUC Score:%.4g' % metrics.roc_auc_score(test_y.values, y_pred)) # print('AUC Score (test): %f' %metrics.roc_auc_score(test_y.values,y_predprob[:,1])) accuracy = GBDT.score(val_x, val_y) accuracy1 = GBDT.score(test_x, test_y) print("GBDT最终精确度:{},{}".format(accuracy, accuracy1)) y_predict3 = GBDT.predict(test_x) get_score(test_y, y_predict3, model_name='GBDT') acc = accuracy_score(test_y, y_predict3) # 准确率 prec = precision_score(test_y, y_predict3) # 精确率 recall = recall_score(test_y, y_predict3) # 召回率 f1 = f1_score(test_y, y_predict3) # F1 fpr, tpr, thersholds = roc_curve(test_y, y_predict3) roc_auc = auc(fpr, tpr) data1 = acc data2 = prec data3 = recall data4 = f1 data5 = roc_auc # 将数据添加到表格中 table.add_row([data1, data2, data3, data4, data5]) print(table) import pandas as pd # 将数据转换为DataFrame格式 df = pd.DataFrame(list(table), columns=["acc","prec","recall","f1","roc_auc"]) # 将DataFrame写入Excel文件 writer = pd.ExcelWriter('output.xlsx') df.to_excel(writer, index=False) writer.save(),出现上面的错误怎样更正
根据错误提示可以看出是因为缺少了sklearn库中的metrics模块,需要在开头添加如下代码:
```python
from sklearn import metrics
```
另外,在代码中出现了get_score函数的调用,但是并没有定义该函数,需要先定义该函数再进行调用。
阅读全文