Excel Is Dead: AI Tools Revolutionizing Spreadsheets

Here are AI tools that make Excel seem like a toy: 👇

1. SheetAI App

• Type your request in plain English.
• Automates complex tasks in minutes.
• Perfect for large-scale analysis.
  🔗 https://www.sheetai.app

2. Arcwise

• Integrates AI customized to your business.
• Models built directly into spreadsheets.
• Boosts efficiency and personalization.
  🔗 https://arcwise.app

3. ChatCSV (acquired by Flatfile)

• Ask questions directly to your CSV files.
• Acts like a personal data analyst.
• Simplifies complex queries effortlessly.
  🔗 https://www.chatcsv.co

4. Numerous AI

• Integrates ChatGPT into Google Sheets.
• Simplifies data management and manipulation.
• Cost-effective and powerful.
  🔗 https://numerous.ai

5. Rows

• AI-driven data analysis, summaries, and transformations.
• Accelerates spreadsheet creation.
• Ideal for quick decision-making.
  🔗 https://rows.com/ai

6. Genius Sheets

• Connects to internal data using natural language.
• Runs instant analysis like never before.
• Perfect for real-time insights.
  🔗 https://lnkd.in/dVtyX7xb

7. Equals

• Start with a blank sheet and gain instant insights.
• Ideal for quick, AI-powered analytics.
• Reduces manual effort drastically.
  🔗 https://equals.com/ai

8. ChartPixel

• Creates AI-assisted charts and slides.
• Turns raw data into actionable insights.
• Saves hours of presentation preparation.
  🔗 https://chartpixel.com

9. Julius AI

• Chat with your data for immediate insights.
• Provides expert-level analytics in seconds.
• Easy to use and highly intuitive.
  🔗 https://julius.ai

Practice Verified Commands and Codes:

1. Automating CSV Analysis with Python:

import pandas as pd

<h1>Load CSV file</h1>

data = pd.read_csv('data.csv')

<h1>Perform basic analysis</h1>

print(data.describe())

<h1>Filter data</h1>

filtered_data = data[data['column_name'] > 50]

<h1>Save filtered data to a new CSV</h1>

filtered_data.to_csv('filtered_data.csv', index=False)

2. Google Sheets API Integration:

import gspread
from oauth2client.service_account import ServiceAccountCredentials

<h1>Authenticate and connect to Google Sheets</h1>

scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
client = gspread.authorize(creds)

<h1>Open a sheet by name</h1>

sheet = client.open("Sheet Name").sheet1

<h1>Read data</h1>

records = sheet.get_all_records()
print(records)

3. AI-Powered Data Analysis with R:

<h1>Load necessary libraries</h1>

library(dplyr)
library(ggplot2)

<h1>Load dataset</h1>

data <- read.csv('data.csv')

<h1>Perform AI-driven analysis</h1>

summary(data)

<h1>Visualize data</h1>

ggplot(data, aes(x=column1, y=column2)) + geom_point()

4. Automating Excel with VBA:

[vba]
Sub AutomateExcel()
Dim ws As Worksheet
Set ws = ThisWorkbook.Sheets(“Sheet1”)

‘ Perform operations
ws.Range(“A1”).Value = “Hello, AI!”
ws.Range(“A2”).Formula = “=SUM(B1:B10)”
End Sub
[/vba]

What Undercode Says:

The integration of AI into spreadsheet tools is revolutionizing how we handle data, making complex tasks simpler and more efficient. Tools like SheetAI, Arcwise, and ChatCSV are transforming traditional spreadsheets into powerful data analysis platforms. By leveraging AI, professionals can now automate repetitive tasks, reduce errors, and gain insights faster than ever before.

For those looking to dive deeper into AI and data analysis, here are some essential commands and tools to get started:

• Linux Commands for Data Processing:

  </li>
  </ul>
  
  <h1>Count lines in a CSV file</h1>
  
  wc -l data.csv
  
  <h1>Filter rows using awk</h1>
  
  awk -F, '$3 > 50' data.csv
  
  <h1>Sort data by a specific column</h1>
  
  sort -t, -k2 data.csv
  

  • Windows PowerShell for Data Management:

    </li>
    </ul>
    
    <h1>Import CSV file</h1>
    
    $data = Import-Csv -Path "data.csv"
    
    <h1>Filter data</h1>
    
    $filteredData = $data | Where-Object { $_.ColumnName -gt 50 }
    
    <h1>Export filtered data</h1>
    
    $filteredData | Export-Csv -Path "filtered_data.csv" -NoTypeInformation
    

    • AI and Machine Learning with Python:

      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression</li>
      </ul>
      
      <h1>Load dataset</h1>
      
      data = pd.read_csv('data.csv')
      
      <h1>Prepare data</h1>
      
      X = data[['feature1', 'feature2']]
      y = data['target']
      
      <h1>Train model</h1>
      
      model = LinearRegression()
      model.fit(X, y)
      
      <h1>Predict</h1>
      
      predictions = model.predict(X)
      

      For further learning, explore the following courses:

      • Google Data Analytics: https://lnkd.in/gv4whkFn
      • IBM Data Science: https://lnkd.in/guyY26Ye
      • Generative AI for Cybersecurity Professionals: https://lnkd.in/gyicuxb5

      The future of data analysis lies in AI-driven tools, and mastering these technologies will give you a significant edge in the evolving digital landscape.

      References:

      Hackers Feeds, Undercode AI