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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.py
python examples/di/data_visualization.py

运行结果

  1. 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。

  1. DataInterpreter写的代码如下:
python
# ----------------------------------task3------------------------------------
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()
# ----------------------------------task3------------------------------------
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task3DataInterpreter使用seabornpairplot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。

Released under the MIT License.