工具系列：PandasAI介绍_快速入门

PandasAI

PandasAI是一个使数据分析变得富有对话性和有趣的库。它利用pandas数据框和最先进的LLMs的强大功能，让用户以对话方式进行数据分析。

与pandas所做的类似（10分钟入门pandas -> https://pandas.pydata.org/docs/user_guide/10min.html），我们希望创建最简单的方式来学习如何掌握PandasAI。

让我们开始吧！

设置

要开始使用，我们需要安装最新版本的PandasAI。

# 安装pandasai库
!pip install pandasai

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SmartDataframe

SmartDataframe是一个继承了pd.DataFrame的pandas（或polars）数据框，它除了具有pd.DataFrame的所有属性和方法外，还添加了对话功能。

# 导入pandasai库中的SmartDataframe类
from pandasai import SmartDataframe

您可以通过从多个不同的来源实例化一个数据框架（pandas或polars数据框架、csv、xlsx或Google Sheets）。

从pandas数据框导入

要从pandas dataframe导入数据，您需要先导入pandas库并创建一个dataframe。

# 导入pandas库
import pandas as pd

# 创建一个DataFrame对象，包含国家、GDP和幸福指数的数据
df = pd.DataFrame({
    "country": [
        "United States",  # 美国
        "United Kingdom",  # 英国
        "France",  # 法国
        "Germany",  # 德国
        "Italy",  # 意大利
        "Spain",  # 西班牙
        "Canada",  # 加拿大
        "Australia",  # 澳大利亚
        "Japan",  # 日本
        "China",  # 中国
    ],
    "gdp": [
        19294482071552,  # GDP数据
        2891615567872,
        2411255037952,
        3435817336832,
        1745433788416,
        1181205135360,
        1607402389504,
        1490967855104,
        4380756541440,
        14631844184064,
    ],
    "happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12],  # 幸福指数数据
})

由于PandasAI由LLM提供支持，您应该导入您想要用于您的用例的LLM。在这种情况下，我们将使用OpenAI。

要使用OpenAI，您需要一个API令牌。按照以下简单步骤生成您的API_TOKEN：
openai。

1. 访问https://openai.com/api/并使用您的电子邮件地址注册或连接您的Google帐户。
2. 在个人帐户设置的左侧，点击"View API Keys"。
3. 选择"Create new Secret key"。

访问openai的API是一个付费服务。在进行实验之前，请阅读Pricing信息。

# 导入OpenAI类
from pandasai.llm import OpenAI

# 创建一个OpenAI对象，并传入api_token参数
llm = OpenAI(api_token="YOUR TOKEN")

现在我们已经实例化了LLM，我们终于可以实例化SmartDataframe了。

# 创建一个SmartDataframe对象，并传入一个DataFrame对象df和一个配置参数config={"llm": llm}
sdf = SmartDataframe(df, config={"llm": llm})

一个SmartDataframe继承了原始数据框的所有方法和属性。例如：

# 使用条件筛选，返回country列为'United States'的行
result = sdf[sdf['country'] == 'United States']

# 打印结果
print(result)

但是您也可以用自然语言进行查询。

# 调用chat函数，参数为"Return the top 5 countries by GDP"
sdf.chat("Return the top 5 countries by GDP")

# 调用chat函数，并传入一个问题作为参数
sdf.chat("What's the sum of the gdp of the 2 unhappiest countries?")

# 打印出sdf对象的last_code_generated属性的值
print(sdf.last_code_generated)

def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df_combined = pd.concat(dfs)
    df_sorted = df_combined.sort_values('happiness_index')
    sum_gdp = df_sorted.head(2)['gdp'].sum()
    return {'type': 'number', 'value': sum_gdp}


result = analyze_data(dfs)

绘制图表

您还可以使用PandasAI轻松绘制图表

# 调用chat函数，传入参数"Plot a chart of the gdp by country"，并输出结果
sdf.chat("Plot a chart of the gdp by country")

您还可以提供额外的指示。例如，假设您想为每个柱状图使用不同的颜色。您只需要向PandasAI提出要求即可。

# 使用seaborn库中的chat函数绘制直方图
# 参数为gdp和country，表示按照国家绘制gdp的直方图
# 每个直方图的颜色不同
sdf.chat("Plot a histogram of the gdp by country, using a different color for each bar")

作为一种替代方法，您可以使用shortcuts。快捷方式是一种函数，可以避免您编写提示并在幕后为您执行"魔法"。

例如，您可以使用.plot_bar_chart()生成相同的图表，提供字段：

# 绘制柱状图
sdf.plot_bar_chart(x="country", y="gdp")

因此，例如，如果我们想要将其可视化为饼图，您可以调用plot_pie_chart快捷方式，传递我们想要用作标签的字段和我们想要用作值的字段。

# 绘制饼图
sdf.plot_pie_chart(labels="country", values="gdp")

智能数据湖

有时候，您可能希望同时处理多个数据框，让LLM来协调使用哪一个来回答您的查询。在这种情况下，您应该使用SmartDatalake而不是SmartDataframe。

这个概念与SmartDataframe非常相似，但是它可以接受多个数据框作为输入，而不仅仅是一个。

# 导入SmartDatalake模块
from pandasai import SmartDatalake

例如，在这个例子中，我们提供了两个不同的数据框。
在第一个数据框中，每个员工报告了员工编号、姓名和部门。
而在第二个数据框中，提供了员工编号和每个员工的薪水。

询问PandasAI，它将通过员工编号将这两个不同的数据框连接起来，并找出薪水最高的员工的姓名。

# 创建员工信息的数据框
employees_df = pd.DataFrame(
    {
        "EmployeeID": [1, 2, 3, 4, 5],
        "Name": ["John", "Emma", "Liam", "Olivia", "William"],
        "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],
    }
)

# 创建薪资信息的数据框
salaries_df = pd.DataFrame(
    {
        "EmployeeID": [1, 2, 3, 4, 5],
        "Salary": [5000, 6000, 4500, 7000, 5500],
    }
)

# 创建SmartDatalake对象，并将员工信息和薪资信息作为参数传入
lake = SmartDatalake(
    [employees_df, salaries_df],
    config={"llm": llm}
)

# 调用chat方法，传入问题"Who gets paid the most?"，返回结果
lake.chat("Who gets paid the most?")

这是一个生成的代码示例：

# 打印变量lake中存储的最后一次执行的代码
print(lake.last_code_executed)

def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
    4. Output: return a dictionary of:
    - type (possible values "text", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Example output: { "type": "text", "value": "The average loan amount is $15,000." }
    """
    merged_df = pd.merge(dfs[0], dfs[1], on='EmployeeID')
    max_salary_employee = merged_df.loc[merged_df['Salary'].idxmax()]
    employee_name = max_salary_employee['Name']
    return {'type': 'text', 'value': f'The employee who gets paid the most is {employee_name}.'}

好的，在这种情况下很容易：两个表都共享一个名为EmployeeID的公共值，对吗？

让我们试试更复杂的情况

# 创建一个包含用户信息的DataFrame
users_df = pd.DataFrame(
    {
        "id": [1, 2, 3, 4, 5],
        "name": ["John", "Emma", "Liam", "Olivia", "William"]
    }
)

# 创建一个名为"users"的SmartDataframe对象，用于处理用户信息
users = SmartDataframe(users_df, name="users")

# 创建一个包含照片信息的DataFrame
photos_df = pd.DataFrame(
    {
        "id": [31, 32, 33, 34, 35],
        "user_id": [1, 1, 2, 4, 5]
    }
)

# 创建一个名为"photos"的SmartDataframe对象，用于处理照片信息
photos = SmartDataframe(photos_df, name="photos")

# 创建一个SmartDatalake对象，将"users"和"photos"作为参数传入，并设置配置项
lake = SmartDatalake([users, photos], config={"llm": llm})

# 调用SmartDatalake对象的chat方法，向其提问"John上传了多少张照片？"
lake.chat("How many photos has been uploaded by John?")

在这种情况下，我们为每个数据框提供了一个表名，这样LLM就有了一些上下文，并且可以更好地执行连接操作。正如您在下面的示例中所看到的，它成功地找出了正确的连接方式。实际上，用户"John"实际上有2张照片。

# 打印lake变量中存储的最后一次执行的代码
print(lake.last_code_executed)

def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    users = dfs[0]
    photos = dfs[1]
    merged_df = pd.merge(users, photos, left_on='id', right_on='user_id')
    john_photos = merged_df[merged_df['name'] == 'John']
    num_photos = john_photos.shape[0]
    return {'type': 'number', 'value': num_photos}


result = analyze_data(dfs)

不同的LLM

尽管目前OpenAI GPT3.5和GPT4是推荐的模型，我们也支持其他模型，如Starcoder和Falcon。

您可以按照以下方式使用它们：

# 导入所需的库
from pandasai import SmartDataframe
from pandasai.llm import Starcoder, Falcon

# 创建一个Starcoder对象，并传入API令牌
starcoder_llm = Starcoder(api_token="YOUR TOKEN")

# 创建一个Falcon对象，并传入API令牌
falcon_llm = Falcon(api_token="YOUR TOKEN")

# 使用Starcoder对象创建一个SmartDataframe对象，并传入数据框和配置参数
df1 = SmartDataframe(df, config={"llm": starcoder_llm})

# 使用Falcon对象创建一个SmartDataframe对象，并传入数据框和配置参数
df2 = SmartDataframe(df, config={"llm": falcon_llm})

# 打印使用df1对象进行的聊天操作的结果
print(df1.chat("Which country has the highest GDP?"))

# 打印使用df2对象进行的聊天操作的结果
print(df2.chat("Which one is the unhappiest country?"))

LangChain LLMs

在某些情况下，您可能希望使用LangChain LLMs。

# 安装pandasai[langchain]模块
!pip install pandasai[langchain]

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然后您可以将它们用作PandasAI LLMs。

# 导入所需的库
from pandasai import SmartDataframe
from langchain.llms import OpenAI
# from langchain.llms import Anthropic
# from langchain.llms import LlamaCpp

# 创建一个OpenAI实例，传入你的API密钥和最大token数
langchain_llm = OpenAI(openai_api_key="YOUR TOKEN", max_tokens=1000)

# 创建一个SmartDataframe实例，传入数据框和配置参数
langchain_sdf = SmartDataframe(df, config={"llm": langchain_llm})

# 调用chat方法，向模型提问
langchain_sdf.chat("Which are the top 5 countries by GPD?")

连接器

PandasAI提供了许多连接器，允许您连接到不同的数据源。这些连接器被设计成易于使用，即使您对数据源或PandasAI不熟悉。

要使用连接器，您首先需要安装所需的依赖项。您可以通过运行以下命令来完成此操作：

# 安装pandasai[connectors]包
!pip install pandasai[connectors]

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# 导入MySQLConnector和PostgreSQLConnector类
from pandasai.connectors import MySQLConnector, PostgreSQLConnector

# 使用MySQL数据库
loan_connector = MySQLConnector(
    config={
        "host": "localhost", # 主机名
        "port": 3306, # 端口号
        "database": "mydb", # 数据库名
        "username": "root", # 用户名
        "password": "root", # 密码
        "table": "loans", # 表名
        "where": [
            # 这是可选的，用于过滤数据以减少数据框的大小
            ["loan_status", "=", "PAIDOFF"], # 过滤条件
        ],
    }
)

# 使用PostgreSQL数据库
payment_connector = PostgreSQLConnector(
    config={
        "host": "localhost", # 主机名
        "port": 5432, # 端口号
        "database": "mydb", # 数据库名
        "username": "root", # 用户名
        "password": "root", # 密码
        "table": "payments", # 表名
        "where": [
            # 这是可选的，用于过滤数据以减少数据框的大小
            ["payment_status", "=", "PAIDOFF"], # 过滤条件
        ],
    }
)

# 创建SmartDatalake对象，将MySQLConnector和PostgreSQLConnector对象作为参数传入
df_connector = SmartDatalake([loan_connector, payment_connector], config={"llm": llm})

# 调用chat方法，传入问题作为参数，返回答案
response = df_connector.chat("How many loans from the United states?")
print(response)

# 导入YahooFinanceConnector模块
from pandasai.connectors.yahoo_finance import YahooFinanceConnector

# 创建一个YahooFinanceConnector对象，参数为股票代码"MSFT"
yahoo_connector = YahooFinanceConnector("MSFT")

# 使用YahooFinanceConnector对象创建一个SmartDataframe对象，同时传入配置参数{"llm": llm}
df = SmartDataframe(yahoo_connector, config={"llm": llm})

# 使用SmartDataframe对象的chat方法进行对话，参数为询问昨天的收盘价
response = df.chat("What is the closing price for yesterday?")

# 打印返回的结果
print(response)

The closing price for yesterday was $319.53.

# 创建一个YahooFinanceConnector对象，传入参数为股票代码"TSLA"
yahoo_connector = YahooFinanceConnector("TSLA")

# 创建一个SmartDataframe对象，传入参数为yahoo_connector和配置参数{"llm": llm}
df_connector = SmartDataframe(yahoo_connector, config={"llm": llm})

# 调用df_connector的chat方法，传入参数为"Plot the chart of tesla over time"，返回结果赋值给response
response = df_connector.chat("Plot the chart of tesla over time")

您可以在此处找到有关连接器（以及更多连接器）的更多信息：https://docs.pandas-ai.com/en/latest/connectors/