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tabsight

A DataFrame in.
A dashboard you can trust out.

tabsight reads your data the way a careful analyst would, explains every guess it makes, and keeps the numbers exact. One offline file, no network.

Read the quickstart See how it reads data Sponsor

pip install tabsight

Home

Most chart tools take your columns at face value. They sum discounts, treat zip codes as numbers, and average averages when you filter. tabsight starts one step earlier: it works out what each column actually is, tells you why it thinks so, and only then does the maths.

Here it is on a small sales file from the repository:

import tabsight as ts

orders = ts.load("examples/superstore_sample.csv")
print(ts.profile(orders))
Output
Profile: 1,200 rows, 18 columns

Column        Role       Unit       Agg     Why
Row_ID        id         none       countd  name says it is an ID/code; never summed
Order_ID      id         none       countd  name says it is an ID/code
Order_Date    time       none       none    text that reads as dates; 100% parse as %m/%d/%Y
Ship_Date     time       none       none    text that reads as dates; 100% parse as %m/%d/%Y
Ship_Mode     dimension  none       none    categories
Customer_ID   id         none       countd  name says it is an ID/code
Segment       dimension  none       none    categories
Country       dimension  none       none    categories
City          dimension  none       none    categories
State         dimension  none       none    categories
Postal_Code   id         none       countd  name says it is an ID/code
Region        dimension  none       none    categories
Category      dimension  none       none    categories
Sub_Category  dimension  none       none    categories
Sales         measure    currency   sum     name suggests money
Quantity      measure    count      sum     name suggests a count
Discount      measure    percent    mean    name suggests a percentage; values stored as fractions (0.15 = 15%); averaged, because totals of rates/prices/scores are meaningless
Profit        measure    currency   sum     name suggests money

Hierarchies:
  Category -> Sub_Category
  Region -> City
  Region -> State

Nothing was configured. The IDs will never be added up, the discount will be averaged rather than summed, and the dates were read as month-first because a value like 01/15 settles it. Every one of those decisions can be changed with one argument, and none of them touched orders itself.

The numbers stay right when people filter

Dashboards get filtered. Re-adding sums after a filter is easy; re-averaging an average is not, and it is where hand-built dashboards quietly go wrong. tabsight keeps every aggregation in a form that can be recombined exactly:

by_region = ts.aggregate(orders, "Region", ["Sales", "Discount"])
print(by_region.values)
Output
    Region     Sales  Discount
0     East  87642.84      0.17
1     West  79286.84      0.15
2  Central  64667.90      0.15
3    South  43661.26      0.17

It formats numbers the way a finance team would

print(ts.format_number(2_297_200.86, "currency", "compact"))
print(ts.format_number(1234.5, "currency", locale="de_DE", currency="EUR", decimals=2))
print(ts.format_number(1_230_000, "currency", "compact", locale="en_IN", currency="INR"))
Output
$2.3M
1.234,50 €
₹12.3L

One line, or one command

# not run
ts.auto(orders, "Sales").to_html("sales.html")  # KPIs, a trend, a map, bars, filters and insights

tabsight auto sales.csv          # the same from a terminal
tabsight serve sales.csv         # server mode: no size limit, every tile follows the filters

Dashboards cross-filter in one offline file, and server mode puts them in FastAPI, Flask, Streamlit or Dash apps.

Where to start

If you have five minutes, the quickstart walks through one file from loading to a chart. If you want to know why tabsight behaves the way it does, read how tabsight thinks. The user guide covers each feature in depth.

Install it with pip install tabsight (installation).