list =[{'ticket_code': '16000065019810'}] 如何只取列表中16000065019810
时间: 2023-12-19 12:05:06 浏览: 14
你可以使用列表索引的方式来访问列表中的元素,因为只有一个元素,所以可以使用索引0来访问该元素,例如:
```
list = [{'ticket_code': '16000065019810'}]
code = list[0]['ticket_code']
print(code)
```
这样就可以得到 `16000065019810`。由于列表中只有一个元素,所以使用索引0来访问该元素,然后再使用 `['ticket_code']` 来访问该元素中的 `ticket_code` 键对应的值。
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将https://www.kaggle.com/code/startupsci/titanic-data-science-solutions的python代码转换成R语言实现数据处理、可视化与建模过程
好的,我可以为您提供将该项目的Python代码转换为R语言代码的实现过程,以下是详细步骤:
1. 导入数据:
Python代码:
```python
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
combine = [train_df, test_df]
```
R语言代码:
```R
train_df <- read.csv("../input/titanic/train.csv", header = TRUE)
test_df <- read.csv("../input/titanic/test.csv", header = TRUE)
combine <- list(train_df, test_df)
```
2. 数据清洗和特征工程:
Python代码:
```python
# 填充缺失值
for dataset in combine:
dataset['Age'].fillna(dataset['Age'].median(), inplace=True)
dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace=True)
dataset['Fare'].fillna(dataset['Fare'].median(), inplace=True)
# 将分类变量转换为数值变量
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2}).astype(int)
# 创建新特征
for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
# 删除无用特征
drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp', 'Parch', 'FamilySize']
train_df = train_df.drop(drop_elements, axis=1)
test_df = test_df.drop(drop_elements, axis=1)
```
R语言代码:
```R
# 填充缺失值
for (dataset in combine) {
dataset$Age[is.na(dataset$Age)] <- median(dataset$Age, na.rm = TRUE)
dataset$Embarked[is.na(dataset$Embarked)] <- names(which.max(table(dataset$Embarked)))
dataset$Fare[is.na(dataset$Fare)] <- median(dataset$Fare, na.rm = TRUE)
}
# 将分类变量转换为数值变量
for (dataset in combine) {
dataset$Sex <- as.integer(factor(dataset$Sex, levels = c("male", "female")))
dataset$Embarked <- as.integer(factor(dataset$Embarked, levels = c("S", "C", "Q")))
}
# 创建新特征
for (dataset in combine) {
dataset$FamilySize <- dataset$SibSp + dataset$Parch + 1
dataset$IsAlone <- 0
dataset$IsAlone[dataset$FamilySize == 1] <- 1
}
# 删除无用特征
drop_elements <- c("PassengerId", "Name", "Ticket", "Cabin", "SibSp", "Parch", "FamilySize")
train_df <- train_df[, !(names(train_df) %in% drop_elements)]
test_df <- test_df[, !(names(test_df) %in% drop_elements)]
```
3. 数据可视化:
Python代码:
```python
import matplotlib.pyplot as plt
import seaborn as sns
# 绘制直方图
sns.histplot(train_df['Age'], kde=False)
plt.show()
# 绘制条形图
sns.barplot(x='Sex', y='Survived', data=train_df)
plt.show()
# 绘制热力图
corr = train_df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()
```
R语言代码:
```R
library(ggplot2)
library(reshape2)
# 绘制直方图
ggplot(train_df, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "lightblue", col = "black") +
labs(title = "Age Distribution", x = "Age", y = "Count")
# 绘制条形图
ggplot(train_df, aes(x = Sex, y = Survived, fill = factor(Sex))) +
geom_bar(stat = "summary", fun = mean, position = "dodge") +
scale_fill_manual(values = c("lightblue", "pink"), name = "Sex") +
labs(title = "Survival Rate by Sex", x = "Sex", y = "Survival Rate")
# 绘制热力图
cor_matrix <- cor(train_df)
melted_cor_matrix <- melt(cor_matrix)
ggplot(melted_cor_matrix, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "lightblue", mid = "white", high = "pink") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Correlation Matrix")
```
4. 建立模型:
Python代码:
```python
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
# 数据预处理
X_train = train_df.drop('Survived', axis=1)
y_train = train_df['Survived']
X_test = test_df.drop('Survived', axis=1)
# 逻辑回归模型
logreg = LogisticRegression()
logreg_scores = cross_val_score(logreg, X_train, y_train, cv=10)
print('Logistic Regression Accuracy: {:.2f}%'.format(logreg_scores.mean()*100))
# 随机森林模型
rf = RandomForestClassifier(n_estimators=100)
rf_scores = cross_val_score(rf, X_train, y_train, cv=10)
print('Random Forest Accuracy: {:.2f}%'.format(rf_scores.mean()*100))
```
R语言代码:
```R
library(caret)
# 数据预处理
X_train <- train_df[, !(names(train_df) %in% c("Survived"))]
y_train <- train_df$Survived
X_test <- test_df[, !(names(test_df) %in% c("Survived"))]
# 逻辑回归模型
logreg_model <- train(x = X_train, y = y_train, method = "glm", family = "binomial")
logreg_scores <- logreg_model$results$Accuracy
print(paste0("Logistic Regression Accuracy: ", round(mean(logreg_scores)*100, 2), "%"))
# 随机森林模型
rf_model <- train(x = X_train, y = y_train, method = "rf", ntree = 100)
rf_scores <- rf_model$results$Accuracy
print(paste0("Random Forest Accuracy: ", round(mean(rf_scores)*100, 2), "%"))
```
以上是将该项目的Python代码转换为R语言代码的过程,您可以在Kaggle的R语言环境中运行这段代码,完成数据处理、可视化和建模的过程。
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