Skip to main content

dexplo

A data analysis library comparible to pandas

Main Goals

  • A very minimal set of features

  • Be as explicit as possible

  • There should be one– and preferably only one –obvious way to do it.

Data Structures

  • Only DataFrames

  • No Series

Data Types

  • Only primitive types - int, float, boolean, numpy.unicode

  • No object data types

Row and Column Labels

  • No index, meaning no row labels

  • No hierarchical index

  • Column names must be strings

  • Column names must be unique

  • Columns stored in a numpy array

Subset Selection

  • Only one way to select data - [ ]

  • Subset selection will be explicit and necessitate both rows and columns

  • Rows will be selected only by integer location

  • Columns will be selected by either label or integer location. Since columns must be strings, this will not be amibguous

  • Column names cannot be duplicated

All selections and operations copy

  • All selections and operations provide new copies of the data

  • This will avoid any chained indexing confusion

Development

  • Must use type hints

  • Must use 3.6 - fstrings

  • Must have numpy, bottleneck, numexpr

Small feature set

  • Implement as few attributes and methods as possible

  • Focus on good idiomatic cookbook examples for doing more complex tasks

Only Scalar Data Types

No complex Python data types - [x] bool - always 8 bits, not-null - [x] int - always 64 bits, not-null - [x] float - always 64 bits, nulls allowed - [x] str - A python unicode object, nulls allowed - [ ] categorical - [ ] datetime - [ ] timedelta

Attributes to implement

  • [x] size

  • [x] shape

  • [x] values

  • [x] dtypes

May not implement any of the binary operators as methods (add, sub, mul, etc…)

Methods

Stats - [x] abs - [x] all - [x] any - [x] argmax - [x] argmin - [x] clip - [ ] corr - [x] count - [ ] cov - [x] cummax - [x] cummin - [ ] cumprod - [x] cumsum - [ ] describe - [x] max - [x] min - [x] median - [x] mean - [ ] mode - [ ] nlargest - [ ] nsmallest - [ ] quantile - [ ] rank - [x] std - [x] sum - [x] var - [ ] unique - [ ] nunique

Selection - [ ] drop - [ ] drop_duplicates - [x] head - [ ] isin - [ ] sample - [x] select_dtypes - [x] tail - [ ] where

Missing Data - [ ] isna - [ ] dropna - [ ] fillna - [ ] interpolate

Other - [ ] append - [ ] apply - [ ] assign - [x] astype - [ ] groupby - [ ] info - [ ] melt - [ ] memory_usage - [ ] merge - [ ] pivot - [ ] replace - [ ] rolling - [ ] sort_values

Functions - [ ] read_csv - [ ] read_sql - [ ] concat

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dexplo-0.0.10.tar.gz (1.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dexplo-0.0.10-cp36-cp36m-macosx_10_7_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.6mmacOS 10.7+ x86-64

File details

Details for the file dexplo-0.0.10.tar.gz.

File metadata

  • Download URL: dexplo-0.0.10.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for dexplo-0.0.10.tar.gz
Algorithm Hash digest
SHA256 cace25f0e785b2283228c4cf740619765e42583c912a40fbd9e5b3475647cfdc
MD5 9c98d7e48c975ebbdc7fad1a36633629
BLAKE2b-256 199d0954051c2e72ca5c67d80062da5be9d7bad8be0a0b2b944294f63d241310

See more details on using hashes here.

File details

Details for the file dexplo-0.0.10-cp36-cp36m-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for dexplo-0.0.10-cp36-cp36m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 09fe4fa94ed3c183b80fba04d157f46deb9b7f7710dd766904b6dccfce145913
MD5 2a189a6b383e808e6913fa12ffadaee5
BLAKE2b-256 f55a01a9d573233b2a99e8ecc7d803c65e9a341b7df6ac95f1813397242e6efd

See more details on using hashes here.

Release history Release notifications | RSS feed

0.0.13

2 files

0.0.12

2 files

0.0.11

2 files

This release

0.0.10 This release

2 files

0.0.9

1 file

0.0.8

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

1 file

0.0.4

1 file

0.0.3

2 files

0.0.2

2 files

0.0.1

1 file

0.0.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page