Quickly Analyze Excel Sheet Data with AI Conversation Tools

Ask:

How can I get AI to better analyze my structured data (Excel/CSV)? The content is long it feels like it's been dropped some

Answer:

Due to the limitations of the context window, AI can't process very long content at once. In addition, the large language model itself is not good at mathematical operations, so it alsoCan't do calculations based directly on the structured data you provide. The best way to use it at this point is to use it in conjunction with the code.

For example, tools like ChatGPT, Claude can automatically help you toGenerate code to analyze the data, and can even batch process the data for you. You can also write your own code to implement the analysis if you wish.

rationale

1. Provide the structure of the data, don't give the full data: in the prompt to the AI, just describe the structure of your data (e.g. column names and data types) or give a small example data. You don't need to paste the full data directly, so that you don't lose part of the information if the input is too long.
2. Clarify your needs: clearly tell the AI what you want to do with the data or analyze it, such as generating statistical tables, graphing, or calculating certain metrics.
3. Let AI generate code: Based on your needs, let AI write code to read and analyze data. This way, the code can process your complete data and generate accurate results without worrying about contextual window limitations.

typical example

For example, let's say I export the access data on my X to a csv/excel file containing the number of visits for each day, and then I submit that data to ChatGPT (Claude is fine) to help me: "Analyze my tweet data for the year, which month had the most visits? Which week had the most visits? Which day had the two biggest visits?"

ChatGPT The first thing that will be done is to sample my data to get an idea of what my data structure looks like.

Then go ahead and generate the code as per the requirement and finally run the code to get the result.

Claude It's similar, the only difference is that ChatGPT uses Python while Claude uses JavaScript.

Claude returns the result:

Let me help you analyze this data. I'll use an analytics tool to process the CSV files and look at the visits (Profile visits) data for different time dimensions.

// Read and parse the CSV file
const fileContent = await window.fs.readFile('account_overview_analytics 2.csv', { encoding: 'utf8' });
import Papa from 'papaparse';

const parsedData = Papa.parse(fileContent, {
  header: true, dynamicTyping: true, {
  dynamicTyping: true,
  skipEmptyLines: true
});

const data = parsedData.data;

// Group by month
const monthlyVisits = {};
data.forEach(row => {
  const date = new Date(row.)
  const monthKey = `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, '0')}`;
  if (!monthlyVisits[monthKey]) {
    monthlyVisits[monthKey] = 0;
  }
  monthlyVisits[monthKey] += row['Profile visits'] || 0; }
}).

// Find the month with the most visits
const maxMonthVisits = Object.entries(monthlyVisits)
  .sort((a, b) => b[1] - a[1])[0]; // Find the month with the most visits.
console.log('Highest monthly visits:', maxMonthVisits);

// Group by week
const weeklyVisits = {};
data.forEach(row => {
  const date = new Date(row.Date);
  const weekNumber = getWeekNumber(date);
  const weekKey = `${date.getFullYear()}-W${String(weekNumber).padStart(2, '0')}`;
  if (!weeklyVisits[weekKey]) {
    weeklyVisits[weekKey] = 0;
  }
  weeklyVisits[weekKey] += row['Profile visits'] || 0;
}).

// Find the week with the most visits
const maxWeekVisits = Object.entries(weeklyVisits)
  .sort((a, b) => b[1] - a[1])[0]; // Find the week with the most visits.
console.log('Highest weekly visits:', maxWeekVisits);

// Find the day with the most visits
const dailyVisits = data.map(row => ({
  date: row.
  visits: row['Profile visits'] || 0
Date, visits: row['Profile visits'] || 0)); const
const maxDayVisits = dailyVisits.sort((a, b) => b.visits - a.visits)[0]; console.log
console.log('Highest number of daily visits:', maxDayVisits);

// helper function: get the number of weeks the date is in.
function getWeekNumber(date) {
  const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
  const dayNum = d.getUTCDay() || 7; d.setUTCDate(d.date.getFullYear(), date.getMonth())
  const dayNum = d.getUTCDay() | 7; d.setUTCDate(d.getUTCDate() + 4 - dayNum);
  const yearStart = new Date(Date.UTC(d.getUTCFullYear(),0,1)); return Math.
  return Math.ceil((((d - yearStart) / 86400000) + 1)/7);
}

According to the analysis:

1. the most visited month is February 2024, with a total of 25,317 visits
2. the most visited week was the 7th week of 2024 (mid-February) with 9,537 visits
3. the highest number of visits on a single day was Friday, February 16, 2024, with 4,203 visits
It looks like February was the best month for your account, especially the week of mid-February. Do you remember posting any special content on February 16th that resulted in a spike in visits? This may shed some light on your future content strategy.
If you'd like more details, such as specific content type analysis or other metrics (e.g., interaction rates, follow rates, etc.), I can also analyze this further for you.

ChatGPT.

import pandas as pd

# Load the CSV file to analyze the data
file_path = '/mnt/data/account_overview_analytics (2).csv'
data = pd.read_csv(file_path)

# Display the first few rows to understand the structure of the data
data.head()

Result.
Result
                Date Impressions Likes Engagements Bookmarks Shares
0 Fri, Dec 27, 2024 17955 65 405 29 1
1 Thu, Dec 26, 2024 1126219 2870 21818 1386 65
2 Wed, Dec 25, 2024 1657311 4312 19589 1544 78
3 Tue, Dec 24, 2024 401326 1424 12266 749 41
4 Mon, Dec 23, 2024 467837 1528 16703 782 43

New follows Unfollows Replies Reposts Profile visits Create Post
0 4 0 10 10 35 0
1 206 39 198 427 1099 1
2 361 23 179 667 1700 3
3 141 15 153 272 517 5
4 96 27 217 260 559 2

Video views Media views
0 242 346
1 22707 88041
2 12096 23161
3 17368 42005
4 34821 77995

The dataset includes columns such as Date, Impressions, Likes, and others. To answer your questions regarding.

1. Month with the maximum impressions: I'll group the data by month and calculate total impressions. 2. Week with the maximum impressions: I'll group the data by month and calculate total impressions.
2. Week with the maximum impressions: I'll group the data by week and calculate total impressions. 3. Day with the maximum impressions: I'll group the data by week and calculate total impressions.
3. Day with the maximum impressions: I'll find the single date with the highest impressions.
Let me proceed with the calculations.

# Convert the 'Date' column to a datetime format for easier grouping
data['Date'] = pd.to_datetime(data['Date'], format='%a, %b %d, %Y')

# Add helper columns for month and week
data['Month'] = data['Date'].dt.to_period('M')
data['Week'] = data['Date'].dt.isocalendar().week

# Group by month and sum the impressions
monthly_impressions = data.groupby('Month')['Impressions'].sum()

# Group by week and sum the impressions
weekly_impressions = data.groupby('Week')['Impressions'].sum()

# Find the day with the maximum impressions
max_day = data.loc[data['Impressions'].idxmax()]

# Extract the results
max_month = monthly_impressions.idxmax()
max_month_value = monthly_impressions.max()

max_week = weekly_impressions.idxmax()
max_week_value = weekly_impressions.max()

max_day_date = max_day['Date']
max_day_value = max_day['Impressions']

(max_month, max_month_value, max_week, max_week_value, max_day_date, max_day_value)

Result

(Period('2024-02', 'M'),
 22484999,
 7,
 7480737,
 Timestamp('2024-02-16 00:00:00'),
 2753640)

Here is the analysis of your tweet impressions.

1. Month with the maximum impressions: February 2024, with a total of 22,484,999 impressions. 2. Week with the maximum impressions: Week 7 (mid-February 2024), with a total of 22,484,999 impressions.
2. Week with the maximum impressions: Week 7 (mid-February 2024), with a total of 7,480,737 impressions. 3.
3. Day with the maximum impressions: February 16, 2024, with 2,753,640 impressions.
Let me know if you'd like a more detailed visualization or further breakdown!

If you can't access the above AI tools, try using a similar product in your country:lit. record wisdom and say clearly consult with the Shang Tang dynasty (16th century BC) , both of which have AI dialog tools geared toward data analysis of EXCEL files.