Does PandasAI support matching descriptive text in tables?

[Yekai97]

Does PandasAI support matching descriptive text in tables?

I have a table like this, the first column is the file name, the second column is the descriptive text,

can I talk to PandasAI and let it help me find the photographic lens in the table

[dosubot[bot]]

Yes, PandasAI supports text matching within table columns. It has built-in support for operators like "contains", "notContains", "startsWith", and "endsWith" to search for specific text patterns in your data. You can use these capabilities to find entries like "photographic lens" in your table's descriptive text column ^[1].

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[dosubot[bot]]

Certainly! Here's an example of how you can use these operators with PandasAI to find entries in your table:

Assuming you have a DataFrame df with a column named descriptive_text, you can use the following code to find rows containing the term "photographic lens":

import pandas as pd

# Sample DataFrame
data = {
    'file_name': ['file1', 'file2', 'file3'],
    'descriptive_text': ['photographic lens', 'telephoto lens', 'endoscopic lens']
}
df = pd.DataFrame(data)

# Use the 'contains' operator to find rows with 'photographic lens'
photographic_lens_rows = df[df['descriptive_text'].str.contains('photographic lens', case=False)]

print(photographic_lens_rows)

This code will filter the DataFrame to show only the rows where the descriptive_text column contains the phrase "photographic lens". You can adjust the case parameter to True if you want the search to be case-sensitive. Similarly, you can use notContains, startsWith, and endsWith in a similar manner by replacing str.contains with the appropriate method.

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[dosubot[bot]]

To use PandasAI for text matching in a DataFrame, you can integrate it with the SmartDataframe class, which allows you to perform natural language queries. Here's a complete example, including the necessary imports and setup:

import os
import pandas as pd
from pandasai import SmartDataframe

# Sample DataFrame
data = {
    'file_name': ['file1', 'file2', 'file3'],
    'descriptive_text': ['photographic lens', 'telephoto lens', 'endoscopic lens']
}
df = pd.DataFrame(data)

# Set up the API key for BambooLLM (or another LLM you are using)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"

# Initialize SmartDataframe with the DataFrame
sdf = SmartDataframe(df)

# Use the SmartDataframe to perform a natural language query
response = sdf.chat('Find the file with a photographic lens in the descriptive text.')
print(response)

In this example, the SmartDataframe is used to wrap the pandas DataFrame, allowing you to query it using natural language. Make sure to replace "YOUR_API_KEY" with your actual API key for the language model you are using. This setup enables you to interact with your data conversationally, leveraging the capabilities of PandasAI ^[1][2].

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[dosubot[bot]]

To use operators like "contains", "notContains", "startsWith", and "endsWith" with PandasAI, you can leverage the SmartDataframe class to perform natural language queries. Here's a complete example for each operator:

import os
import pandas as pd
from pandasai import SmartDataframe

# Sample DataFrame
data = {
    'file_name': ['file1', 'file2', 'file3', 'file4'],
    'descriptive_text': ['photographic lens', 'telephoto lens', 'endoscopic lens', 'corrected lens']
}
df = pd.DataFrame(data)

# Set up the API key for BambooLLM (or another LLM you are using)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"

# Initialize SmartDataframe with the DataFrame
sdf = SmartDataframe(df)

# Example using 'contains'
response_contains = sdf.chat('Find the files with a photographic lens in the descriptive text.')
print("Contains:", response_contains)

# Example using 'notContains'
response_not_contains = sdf.chat('Find the files without a photographic lens in the descriptive text.')
print("Not Contains:", response_not_contains)

# Example using 'startsWith'
response_starts_with = sdf.chat('Find the files where the descriptive text starts with "telephoto".')
print("Starts With:", response_starts_with)

# Example using 'endsWith'
response_ends_with = sdf.chat('Find the files where the descriptive text ends with "lens".')
print("Ends With:", response_ends_with)

In this example, the SmartDataframe is used to wrap the pandas DataFrame, allowing you to query it using natural language. Replace "YOUR_API_KEY" with your actual API key for the language model you are using. This setup enables you to interact with your data conversationally, leveraging the capabilities of PandasAI.

To continue talking to Dosu, mention @dosu.

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[dosubot[bot]]

To use the built-in operators like "contains", "notContains", "startsWith", and "endsWith" in PandasAI, you should use the SmartDataframe class, which allows you to perform these operations through natural language queries. Here's how you can set it up:

import os
import pandas as pd
from pandasai import SmartDataframe

# Sample DataFrame
data = {
    'file_name': ['file1', 'file2', 'file3'],
    'descriptive_text': ['photographic lens', 'telephoto lens', 'endoscopic lens']
}
df = pd.DataFrame(data)

# Set up the API key for BambooLLM (or another LLM you are using)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"

# Initialize SmartDataframe with the DataFrame
sdf = SmartDataframe(df)

# Use the SmartDataframe to perform a natural language query
response = sdf.chat('Find the files with a photographic lens in the descriptive text.')
print(response)

In this setup, the SmartDataframe is used to wrap the pandas DataFrame, enabling you to query it using natural language. Replace "YOUR_API_KEY" with your actual API key for the language model you are using. This approach allows you to leverage PandasAI's capabilities for text matching and other operations.

To continue talking to Dosu, mention @dosu.

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[dosubot[bot]]

PandasAI uses Large Language Models (LLMs) to interpret natural language queries and generate executable code, such as Python or SQL, to perform operations on dataframes. This process involves the LLM understanding the query context and generating the necessary code to execute the query, which may include determining parameter ranges or conditions based on the query's intent. This is different from direct text matching, where specific string operations like "contains" or "startsWith" are used to filter data based on exact text patterns ^[1].

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