PandasAI 
is a Python library that enhances pandas functionalities through the integration of natural language processing (NLP) capabilities. It utilizes a large language model (LLM) to automatically generate Python code for tasks such as answering data-related questions, conducting data analysis, and creating visualizations. In this answer, we will learn how to use PandasAI for data analysis with a CSV file.




# Reading a CSV file
First, we'll read the CSV file using the `read_csv` function of pandas and then see how our data looks using `data.head()`. 


import pandas as pd

#Loading the "data.csv" file
data = pd.read_csv("/data.csv")
a = data.head()

print(a)

pand

# Steps
Here are the steps to use PandasAI with a CSV file. 

1. The first step to use PandasAI with a CSV file is to install the PandasAI library. Once we have installed it, we can import it into our Python code as follows:

``` python
from pandasai import SmartDataframe
```

2. Now, we can create a `SmartDataframe` object of our CSV file. The `SmartDataframe` object is what we'll use to interact with our data in a natural language way.

``` python
data = SmartDataframe("csv_file.csv")
```

3. Once we have created a `SmartDataframe` object, we can start asking it questions about our data. For example, we can ask it the following question:

``` python
answer = df.chat('Which movie has the best IMDb rating but a shorter runtime?')
```

# Implementation
Now, we'll see the complete implementation of how to use PandasAI with a CSV file using an example.

Movie Title,Year,IMDb Rating,Runtime (minutes)
The Shawshank Redemption,1994,9.3,142
The Godfather,1972,9.2,175
Pulp Fiction,1994,8.9,154
The Dark Knight,2008,9.0,152
Forrest Gump,1994,8.8,142
Inception,2010,8.8,148
Schindler's List,1993,8.9,195
The Matrix,1999,8.7,136
Fight Club,1999,8.8,139
The Lord of the Rings: The Fellowship of the Ring,2001,8.8,178

from pandasai import SmartDataframe
from pandasai.llm import OpenAI
llm = OpenAI(api_token="OPENAI_API_KEY")

# You can instantiate a SmartDataframe with a path to a CSV file
df = SmartDataframe("/usercode/data.csv", config={"llm": llm})

answer = df.chat('Which movie has the best IMDb rating but a shorter runtime?')
print(answer)

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


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React

Dart

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Typescript React

Vue.js

> **Note:** Make sure to replace `OPENAI_API_KEY` with your actual OpenAI API key. For further details on how to get an OpenAI API key, check out our Answer on [How to get API Key of GPT-3](https://www.educative.io/answers/how-to-get-api-key-of-gpt-3).

# Code explanation
**Line 1:** We import `SmartDataframe` from `pandasai` to answer our questions from a CSV file. 


**Lines 2--3:** We import and initialize the `OpenAI` language model (referred to as `llm` here) from the `pandasai.llm` module. 



**Line 6:** We instantiated a `SmartDataframe` object to interact with it in natural language to answer questions about our `data.csv` file.


> **Note:** The above example shows how to use PandasAI to get insights from a CSV file quickly and easily. We can use PandasAI to answer various questions about our data and generate reports and visualizations.




pandcsv.tar.gz

How to use PandasAI with a CSV file

PandasAI leverages NLP with a large language model to automate data analysis from CSV files using natural language queries.