MetaGPT数据分析和可视化
概述
数据可视化是通过图表、图形和其他视觉元素将数据转化为可理解和易于分析的形式。它帮助我们发现数据中的模式、趋势和关联，以及提供洞察力和见解。通过数据可视化，我们可以更好地理解数据的含义，传达和解释数据的结果，并支持数据驱动的决策和沟通。
示例：Iris数据集可视化
任务
使用DataInterpreter对sklearn Iris数据集进行简单的数据分析并绘制可视化图表。
代码
bash
python examples/di/data_visualization.pypython examples/di/data_visualization.pyexamples/di/data_visualization.py 文件中的代码具体为：
python
import asyncio
from metagpt.logs import logger
from metagpt.roles.di.data_interpreter import DataInterpreter
from metagpt.utils.recovery_util import save_history
async def main(requirement: str = ""):
di = DataInterpreter()
rsp = await di.run(requirement)
logger.info(rsp)
save_history(role=di)
if __name__ == "__main__":
requirement = "Run data analysis on sklearn Iris dataset, include a plot"
asyncio.run(main(requirement))import asyncio
from metagpt.logs import logger
from metagpt.roles.di.data_interpreter import DataInterpreter
from metagpt.utils.recovery_util import save_history
async def main(requirement: str = ""):
di = DataInterpreter()
rsp = await di.run(requirement)
logger.info(rsp)
save_history(role=di)
if __name__ == "__main__":
requirement = "Run data analysis on sklearn Iris dataset, include a plot"
asyncio.run(main(requirement))执行上面的代码，生成的plan和code会分别保存在 dada/output/当前时间/plan.json和 dada/output/当前时间/code.ipynb中。
运行结果
DataInterpreter提出的task如下:
json
[
{
"task_id": "1",
"dependent_task_ids": [],
"instruction": "Load the Iris dataset from sklearn."
},
{
"task_id": "2",
"dependent_task_ids": ["1"],
"instruction": "Perform exploratory data analysis on the Iris dataset."
},
{
"task_id": "3",
"dependent_task_ids": ["2"],
"instruction": "Create a plot visualizing the Iris dataset features."
}
][
{
"task_id": "1",
"dependent_task_ids": [],
"instruction": "Load the Iris dataset from sklearn."
},
{
"task_id": "2",
"dependent_task_ids": ["1"],
"instruction": "Perform exploratory data analysis on the Iris dataset."
},
{
"task_id": "3",
"dependent_task_ids": ["2"],
"instruction": "Create a plot visualizing the Iris dataset features."
}
]DataInterpreter 能够把任务分解为合理的tasks, 并按照加载数据、分析数据和绘制图表的步骤运行。
DataInterpreter写的代码如下:
python
# ----------------------------------task1------------------------------------
from sklearn.datasets import load_iris
iris_data = load_iris()
iris_data.keys()
!pip install scikit-learn
from sklearn.datasets import load_iris
iris_data = load_iris()
iris_data.keys()
# ----------------------------------task2------------------------------------
import pandas as pd
# Create a DataFrame from the iris dataset
iris_df = pd.DataFrame(iris_data['data'], columns=iris_data['feature_names'])
iris_df['species'] = pd.Categorical.from_codes(iris_data['target'], iris_data['target_names'])
# Summary statistics
summary_statistics = iris_df.describe()
# Check for missing values
missing_values = iris_df.isnull().sum()
(summary_statistics, missing_values)
# ----------------------------------task3------------------------------------
import matplotlib.pyplot as plt
import seaborn as sns
# Use seaborn's pairplot to visualize the dataset features
sns.set(style='whitegrid', context='notebook')
iris_pairplot = sns.pairplot(iris_df, hue='species', height=2.5)
plt.show()# ----------------------------------task1------------------------------------
from sklearn.datasets import load_iris
iris_data = load_iris()
iris_data.keys()
!pip install scikit-learn
from sklearn.datasets import load_iris
iris_data = load_iris()
iris_data.keys()
# ----------------------------------task2------------------------------------
import pandas as pd
# Create a DataFrame from the iris dataset
iris_df = pd.DataFrame(iris_data['data'], columns=iris_data['feature_names'])
iris_df['species'] = pd.Categorical.from_codes(iris_data['target'], iris_data['target_names'])
# Summary statistics
summary_statistics = iris_df.describe()
# Check for missing values
missing_values = iris_df.isnull().sum()
(summary_statistics, missing_values)
# ----------------------------------task3------------------------------------
import matplotlib.pyplot as plt
import seaborn as sns
# Use seaborn's pairplot to visualize the dataset features
sns.set(style='whitegrid', context='notebook')
iris_pairplot = sns.pairplot(iris_df, hue='species', height=2.5)
plt.show()在完成task1时，由于环境中没有安装scikit-learn导致第一次执行报错，但DataInterpreter可以分析并通过安装scikit-learn来解决这个问题。在task3中DataInterpreter使用seaborn的pairplot函数绘制一个散点图矩阵，用于可视化数据集中不同特征之间的关系，并通过颜色区分不同种类的数据点，最后使用plt.show()将图表显示出来。
下面是DataInterpreter运行代码绘制出的图，可以看出代码成功执行并绘制了精美的可视化图表，帮助我们更好地对数据集特征进行分析。
