Comments (2)
Good point. As things evolve, should we make sure to write the docs in a way we don't assume everyone has a ML/Pandas background.
from explorer.
This post (on a blog about a new Unix shell, of all things) actually seems to be trying to answer this very question:
From that post:
What Is a Data Frame? (In Python, R, and SQL)
A data frame is a table-like data structure available in languages like R and Python. Statisticians, scientists, and programmers use them in data analysis code.
...
In the last post, I analyzed Oil's code with data frames.
When writing the praise in that section, I looked for a friendly intro to the idea, but the best hit was from an inactive Clojure project. Surprisingly, Wikipedia doesn't have a page on data frames! This post aims to fill that gap.
...
Typical Operations
Before I show source code, here's some more color on how data frames are used. You may:
- Create a data frame from the results of a SQL query, or from a CSV file. The columns have types like string, number, and date.
- Filter a data frame down to the rows and columns of interest.
- Clean its values with arithmetic and string operations.
- Summarize groups of rows.
- Compute new columns based on existing columns.
- Join a data frame with others, for further analysis.
- Plot one column vs. another (in many different ways)
- Mathematically model one column as a function of another — e.g. with linear regression.
Logically, data frames look like SQL tables, but:
- They're manipulated directly in the memory of your program. They don't live on a remote server.
- They more suited to complex, "offline" analysis with statistics, visualization, and mathematical modeling.
The first item in that last list doesn't seem to be entirely true – or at least not according to Databricks about their Spark DataFrames:
... a Spark DataFrame can span thousands of computers
So SQL tables are kinda 'dataframes' but typically a 'dataframe' 'lives' closer to the 'processing', e.g. 'in-memory' versus accessible over the network or (for Elixir) outside the BEAM VM (?).
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Related Issues (20)
- [Feature request] Add support for Decimal type HOT 12
- Should we always raise when a column is missing? HOT 3
- Split string column into multiple columns (feature request / use case) HOT 4
- Seeing `:nif_not_loaded` error for `Series.split/2` when mutating a dataframe HOT 1
- [Feature request] Add support for read_database in Polars backend. HOT 1
- Using `sort_by` with a grouped data frame doesn't respect `nils:` option HOT 1
- `{:datetime, :second}` dtype support HOT 2
- Add :streaming option to DataFrame.to_csv/3 HOT 1
- Exporting to CSV with a duration column returns an error
- Regression in `DataFrame.concat_rows/2` in v0.8.2 HOT 1
- Filter throwing undefined variable error HOT 1
- Error using is_finite and is_infinite within mutate HOT 1
- Explorer NIF broken on FreeBSD HOT 12
- Support Elixir built in Duration struct HOT 1
- Bug: Rounding Error in Tests HOT 1
- exposing the `fold` expressions from Polars HOT 7
- :nif_panicked "Chunk require all its arrays to have an equal number of rows" HOT 1
- Sorting an empty DataFrame results in a runtime Polars error HOT 1
- Performance of `DataFrame.new/2` on dataframes containing list columns HOT 7
- `Series.filter` should work inside `DataFrame.summarise` HOT 5
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