工具系列：PandasAI介绍_快速入门_pandasai文档

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
1
2

Collecting pandasai
  Downloading pandasai-1.2.7-py3-none-any.whl (73 kB)
[?25l     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m0.0/73.2 kB[0m [31m?[0m eta [36m-:--:--[0m
[2K     [91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m[90m╺[0m [32m71.7/73.2 kB[0m [31m2.2 MB/s[0m eta [36m0:00:01[0m
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m73.2/73.2 kB[0m [31m1.8 MB/s[0m eta [36m0:00:00[0m
[?25hCollecting astor<0.9.0,>=0.8.1 (from pandasai)
  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)
Requirement already satisfied: duckdb<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pandasai) (0.8.1)
Collecting ipython<9.0.0,>=8.13.1 (from pandasai)
  Downloading ipython-8.15.0-py3-none-any.whl (806 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m806.6/806.6 kB[0m [31m15.7 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: matplotlib<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from pandasai) (3.7.1)
Collecting openai<0.28.0,>=0.27.5 (from pandasai)
  Downloading openai-0.27.10-py3-none-any.whl (76 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m76.5/76.5 kB[0m [31m8.6 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: pandas==1.5.3 in /usr/local/lib/python3.10/dist-packages (from pandasai) (1.5.3)
Requirement already satisfied: pydantic<2,>=1 in /usr/local/lib/python3.10/dist-packages (from pandasai) (1.10.12)
Collecting python-dotenv<2.0.0,>=1.0.0 (from pandasai)
  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)
Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from pandasai) (1.11.2)
Collecting sqlalchemy<2.0.0,>=1.4.49 (from pandasai)
  Downloading SQLAlchemy-1.4.49-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.6/1.6 MB[0m [31m43.8 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai) (2023.3.post1)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai) (1.23.5)
Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (0.2.0)
Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (4.4.2)
Collecting jedi>=0.16 (from ipython<9.0.0,>=8.13.1->pandasai)
  Downloading jedi-0.19.0-py2.py3-none-any.whl (1.6 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.6/1.6 MB[0m [31m63.3 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (0.1.6)
Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (0.7.5)
Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (3.0.39)
Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (2.16.1)
Collecting stack-data (from ipython<9.0.0,>=8.13.1->pandasai)
  Downloading stack_data-0.6.2-py3-none-any.whl (24 kB)
Requirement already satisfied: traitlets>=5 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (5.7.1)
Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (1.1.3)
Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai) (4.8.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (4.42.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (1.4.5)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai) (3.1.1)
Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai) (2.31.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai) (4.66.1)
Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai) (3.8.5)
Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2,>=1->pandasai) (4.5.0)
Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<2.0.0,>=1.4.49->pandasai) (2.0.2)
Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<9.0.0,>=8.13.1->pandasai) (0.8.3)
Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<9.0.0,>=8.13.1->pandasai) (0.7.0)
Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython<9.0.0,>=8.13.1->pandasai) (0.2.6)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas==1.5.3->pandasai) (1.16.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai) (3.2.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai) (2.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai) (2023.7.22)
Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (23.1.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (6.0.4)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (4.0.3)
Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (1.9.2)
Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (1.4.0)
Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai) (1.3.1)
Collecting executing>=1.2.0 (from stack-data->ipython<9.0.0,>=8.13.1->pandasai)
  Downloading executing-1.2.0-py2.py3-none-any.whl (24 kB)
Collecting asttokens>=2.1.0 (from stack-data->ipython<9.0.0,>=8.13.1->pandasai)
  Downloading asttokens-2.4.0-py2.py3-none-any.whl (27 kB)
Collecting pure-eval (from stack-data->ipython<9.0.0,>=8.13.1->pandasai)
  Downloading pure_eval-0.2.2-py3-none-any.whl (11 kB)
Installing collected packages: pure-eval, executing, sqlalchemy, python-dotenv, jedi, asttokens, astor, stack-data, openai, ipython, pandasai
  Attempting uninstall: sqlalchemy
    Found existing installation: SQLAlchemy 2.0.20
    Uninstalling SQLAlchemy-2.0.20:
      Successfully uninstalled SQLAlchemy-2.0.20
  Attempting uninstall: ipython
    Found existing installation: ipython 7.34.0
    Uninstalling ipython-7.34.0:
      Successfully uninstalled ipython-7.34.0
[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.15.0 which is incompatible.
ipython-sql 0.5.0 requires sqlalchemy>=2.0, but you have sqlalchemy 1.4.49 which is incompatible.[0m[31m
[0mSuccessfully installed astor-0.8.1 asttokens-2.4.0 executing-1.2.0 ipython-8.15.0 jedi-0.19.0 openai-0.27.10 pandasai-1.2.7 pure-eval-0.2.2 python-dotenv-1.0.0 sqlalchemy-1.4.49 stack-data-0.6.2

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

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
1
2

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
1
2

# 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
})


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

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")
1
2
3
4
5

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})
1
2

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)
1
2
3
4
5

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")
1
2
3

# 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?")
1
2
3

# 打印出sdf对象的last_code_generated属性的值
print(sdf.last_code_generated)
1
2

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)

1
2
3
4
5
6
7
8
9

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")
1
2
3

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")
1
2
3
4
5

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")
1
2
3
4

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")
1
2
3
4

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
1
2

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?")
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

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)
1
2
3

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}.'}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

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?")
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

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)
1
2
3
4

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)

1
2
3
4
5
6
7
8
9
10
11

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?"))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

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]
1
2

Requirement already satisfied: pandasai[langchain] in /usr/local/lib/python3.10/dist-packages (1.1.1)
Requirement already satisfied: astor<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (0.8.1)
Requirement already satisfied: ipython<9.0.0,>=8.13.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (8.15.0)
Requirement already satisfied: matplotlib<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (3.7.1)
Requirement already satisfied: openai<0.28.0,>=0.27.5 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (0.27.10)
Requirement already satisfied: pandas==1.5.3 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (1.5.3)
Requirement already satisfied: pydantic<2,>=1 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (1.10.12)
Requirement already satisfied: python-dotenv<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (1.0.0)
Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from pandasai[langchain]) (1.10.1)
Collecting langchain<0.0.200,>=0.0.199 (from pandasai[langchain])
  Downloading langchain-0.0.199-py3-none-any.whl (1.0 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.0/1.0 MB[0m [31m6.7 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[langchain]) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[langchain]) (2023.3)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[langchain]) (1.23.5)
Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.2.0)
Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (4.4.2)
Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.19.0)
Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.1.6)
Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.7.5)
Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (3.0.39)
Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (2.16.1)
Requirement already satisfied: stack-data in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.6.2)
Requirement already satisfied: traitlets>=5 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (5.7.1)
Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (1.1.3)
Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[langchain]) (4.8.0)
Requirement already satisfied: PyYAML>=5.4.1 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (6.0.1)
Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2.0.20)
Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (3.8.5)
Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (4.0.3)
Collecting dataclasses-json<0.6.0,>=0.5.7 (from langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading dataclasses_json-0.5.14-py3-none-any.whl (26 kB)
Collecting langchainplus-sdk>=0.0.9 (from langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading langchainplus_sdk-0.0.20-py3-none-any.whl (25 kB)
Requirement already satisfied: numexpr<3.0.0,>=2.8.4 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2.8.5)
Collecting openapi-schema-pydantic<2.0,>=1.2 (from langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading openapi_schema_pydantic-1.2.4-py3-none-any.whl (90 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m90.0/90.0 kB[0m [31m11.3 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2.31.0)
Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain<0.0.200,>=0.0.199->pandasai[langchain]) (8.2.3)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (4.42.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (1.4.4)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[langchain]) (3.1.1)
Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai[langchain]) (4.66.1)
Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2,>=1->pandasai[langchain]) (4.7.1)
Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (23.1.0)
Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (3.2.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (6.0.4)
Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (1.9.2)
Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (1.4.0)
Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (1.3.1)
Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.6.0,>=0.5.7->langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading marshmallow-3.20.1-py3-none-any.whl (49 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m49.4/49.4 kB[0m [31m6.8 MB/s[0m eta [36m0:00:00[0m
[?25hCollecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.6.0,>=0.5.7->langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)
Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.8.3)
Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.7.0)
Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.2.6)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas==1.5.3->pandasai[langchain]) (1.16.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2023.7.22)
Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain<0.0.200,>=0.0.199->pandasai[langchain]) (2.0.2)
Requirement already satisfied: executing>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (1.2.0)
Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (2.3.0)
Requirement already satisfied: pure-eval in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[langchain]) (0.2.2)
Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.6.0,>=0.5.7->langchain<0.0.200,>=0.0.199->pandasai[langchain])
  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)
Installing collected packages: mypy-extensions, marshmallow, typing-inspect, openapi-schema-pydantic, langchainplus-sdk, dataclasses-json, langchain
Successfully installed dataclasses-json-0.5.14 langchain-0.0.199 langchainplus-sdk-0.0.20 marshmallow-3.20.1 mypy-extensions-1.0.0 openapi-schema-pydantic-1.2.4 typing-inspect-0.9.0

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76

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?")
1
2
3
4
5
6
7
8
9
10
11
12
13
14

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]
1
2

Requirement already satisfied: pandasai[connectors] in /usr/local/lib/python3.10/dist-packages (1.2.7)
Requirement already satisfied: astor<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (0.8.1)
Requirement already satisfied: duckdb<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (0.8.1)
Requirement already satisfied: ipython<9.0.0,>=8.13.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (8.15.0)
Requirement already satisfied: matplotlib<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (3.7.1)
Requirement already satisfied: openai<0.28.0,>=0.27.5 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (0.27.10)
Requirement already satisfied: pandas==1.5.3 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (1.5.3)
Requirement already satisfied: pydantic<2,>=1 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (1.10.12)
Requirement already satisfied: python-dotenv<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (1.0.0)
Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (1.11.2)
Requirement already satisfied: sqlalchemy<2.0.0,>=1.4.49 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (1.4.49)
Requirement already satisfied: psycopg2<3.0.0,>=2.9.7 in /usr/local/lib/python3.10/dist-packages (from pandasai[connectors]) (2.9.7)
Collecting pymysql<2.0.0,>=1.1.0 (from pandasai[connectors])
  Downloading PyMySQL-1.1.0-py3-none-any.whl (44 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m44.8/44.8 kB[0m [31m709.3 kB/s[0m eta [36m0:00:00[0m
[?25hCollecting snowflake-sqlalchemy<2.0.0,>=1.5.0 (from pandasai[connectors])
  Downloading snowflake_sqlalchemy-1.5.0-py2.py3-none-any.whl (33 kB)
Collecting sqlalchemy-databricks<0.3.0,>=0.2.0 (from pandasai[connectors])
  Downloading sqlalchemy_databricks-0.2.0-py3-none-any.whl (4.3 kB)
Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[connectors]) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[connectors]) (2023.3.post1)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->pandasai[connectors]) (1.23.5)
Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.2.0)
Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (4.4.2)
Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.19.0)
Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.1.6)
Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.7.5)
Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (3.0.39)
Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (2.16.1)
Requirement already satisfied: stack-data in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.6.2)
Requirement already satisfied: traitlets>=5 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (5.7.1)
Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (1.1.3)
Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython<9.0.0,>=8.13.1->pandasai[connectors]) (4.8.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (4.42.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (1.4.5)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.1->pandasai[connectors]) (3.1.1)
Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai[connectors]) (2.31.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai[connectors]) (4.66.1)
Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.5->pandasai[connectors]) (3.8.5)
Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2,>=1->pandasai[connectors]) (4.5.0)
Collecting snowflake-connector-python<4.0.0 (from snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading snowflake_connector_python-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (24.6 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m24.6/24.6 MB[0m [31m34.3 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<2.0.0,>=1.4.49->pandasai[connectors]) (2.0.2)
Collecting PyHive<1,>=0 (from sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading PyHive-0.7.0.tar.gz (46 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m46.5/46.5 kB[0m [31m4.8 MB/s[0m eta [36m0:00:00[0m
[?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
Collecting databricks-sql-connector<3,>=2 (from sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading databricks_sql_connector-2.9.3-py3-none-any.whl (297 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m297.3/297.3 kB[0m [31m25.2 MB/s[0m eta [36m0:00:00[0m
[?25hCollecting alembic<2.0.0,>=1.0.11 (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading alembic-1.12.0-py3-none-any.whl (226 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m226.0/226.0 kB[0m [31m22.3 MB/s[0m eta [36m0:00:00[0m
[?25hCollecting lz4<5.0.0,>=4.0.2 (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading lz4-4.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.3/1.3 MB[0m [31m56.0 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: oauthlib<4.0.0,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (3.2.2)
Requirement already satisfied: openpyxl<4.0.0,>=3.0.10 in /usr/local/lib/python3.10/dist-packages (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (3.1.2)
Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.10/dist-packages (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (9.0.0)
Collecting thrift<0.17.0,>=0.16.0 (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading thrift-0.16.0.tar.gz (59 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m59.6/59.6 kB[0m [31m6.7 MB/s[0m eta [36m0:00:00[0m
[?25h  Preparing metadata (setup.py) ... [?25l[?25hdone
Requirement already satisfied: urllib3>=1.0 in /usr/local/lib/python3.10/dist-packages (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (2.0.4)
Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.8.3)
Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.7.0)
Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.2.6)
Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from PyHive<1,>=0->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (0.18.3)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas==1.5.3->pandasai[connectors]) (1.16.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai[connectors]) (3.2.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai[connectors]) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.5->pandasai[connectors]) (2023.7.22)
Collecting asn1crypto<2.0.0,>0.24.0 (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading asn1crypto-1.5.1-py2.py3-none-any.whl (105 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m105.0/105.0 kB[0m [31m11.4 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: cffi<2.0.0,>=1.9 in /usr/local/lib/python3.10/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (1.15.1)
Requirement already satisfied: cryptography<42.0.0,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (41.0.3)
Collecting oscrypto<2.0.0 (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading oscrypto-1.3.0-py2.py3-none-any.whl (194 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m194.6/194.6 kB[0m [31m18.8 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: pyOpenSSL<24.0.0,>=16.2.0 in /usr/local/lib/python3.10/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (23.2.0)
Collecting pycryptodomex!=3.5.0,<4.0.0,>=3.2 (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading pycryptodomex-3.19.0-cp35-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m2.1/2.1 MB[0m [31m43.4 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: pyjwt<3.0.0 in /usr/lib/python3/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (2.3.0)
Collecting urllib3>=1.0 (from databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m143.1/143.1 kB[0m [31m14.7 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: filelock<4,>=3.5 in /usr/local/lib/python3.10/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (3.12.2)
Requirement already satisfied: sortedcontainers>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (2.4.0)
Collecting platformdirs<3.9.0,>=2.6.0 (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading platformdirs-3.8.1-py3-none-any.whl (16 kB)
Collecting tomlkit (from snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors])
  Downloading tomlkit-0.12.1-py3-none-any.whl (37 kB)
Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (23.1.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (6.0.4)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (4.0.3)
Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (1.9.2)
Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (1.4.0)
Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.5->pandasai[connectors]) (1.3.1)
Requirement already satisfied: executing>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (1.2.0)
Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (2.4.0)
Requirement already satisfied: pure-eval in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython<9.0.0,>=8.13.1->pandasai[connectors]) (0.2.2)
Collecting Mako (from alembic<2.0.0,>=1.0.11->databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors])
  Downloading Mako-1.2.4-py3-none-any.whl (78 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m78.7/78.7 kB[0m [31m8.9 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi<2.0.0,>=1.9->snowflake-connector-python<4.0.0->snowflake-sqlalchemy<2.0.0,>=1.5.0->pandasai[connectors]) (2.21)
Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl<4.0.0,>=3.0.10->databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (1.1.0)
Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic<2.0.0,>=1.0.11->databricks-sql-connector<3,>=2->sqlalchemy-databricks<0.3.0,>=0.2.0->pandasai[connectors]) (2.1.3)
Building wheels for collected packages: PyHive, thrift
  Building wheel for PyHive (setup.py) ... [?25l[?25hdone
  Created wheel for PyHive: filename=PyHive-0.7.0-py3-none-any.whl size=53872 sha256=1d2a90767825eb44f25f15a386a3191b47df6b0fe2ee1c8b3718ad3c9e9c3592
  Stored in directory: /root/.cache/pip/wheels/d3/fc/31/6974270c69ccc5bf8f848e2e41b527d0e8f5b9b973696a29a9
  Building wheel for thrift (setup.py) ... [?25l[?25hdone
  Created wheel for thrift: filename=thrift-0.16.0-cp310-cp310-linux_x86_64.whl size=373871 sha256=9b6ad9cfb506732a6582e3d6f87721c28d3255de8eeeadf47ed54445c360ad89
  Stored in directory: /root/.cache/pip/wheels/52/f8/d2/acfd995e8247eb0cad372fa6a640a5fcf279ab2ed7c5c4490e
Successfully built PyHive thrift
Installing collected packages: asn1crypto, urllib3, tomlkit, thrift, pymysql, pycryptodomex, platformdirs, oscrypto, Mako, lz4, PyHive, alembic, databricks-sql-connector, sqlalchemy-databricks, snowflake-connector-python, snowflake-sqlalchemy
  Attempting uninstall: urllib3
    Found existing installation: urllib3 2.0.4
    Uninstalling urllib3-2.0.4:
      Successfully uninstalled urllib3-2.0.4
  Attempting uninstall: platformdirs
    Found existing installation: platformdirs 3.10.0
    Uninstalling platformdirs-3.10.0:
      Successfully uninstalled platformdirs-3.10.0
Successfully installed Mako-1.2.4 PyHive-0.7.0 alembic-1.12.0 asn1crypto-1.5.1 databricks-sql-connector-2.9.3 lz4-4.3.2 oscrypto-1.3.0 platformdirs-3.8.1 pycryptodomex-3.19.0 pymysql-1.1.0 snowflake-connector-python-3.2.0 snowflake-sqlalchemy-1.5.0 sqlalchemy-databricks-0.2.0 thrift-0.16.0 tomlkit-0.12.1 urllib3-1.26.16

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

# 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)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

# 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)
1
2
3
4
5
6
7
8
9
10
11
12
13
14

The closing price for yesterday was $319.53.

1
2

# 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")
1
2
3
4
5
6
7
8
9

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/