https://github.com/dexplo/dexplo

Data exploration library with a pandas-like API
https://github.com/dexplo/dexplo

Last synced: 4 days ago
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Data exploration library with a pandas-like API

• Host: GitHub
• URL: https://github.com/dexplo/dexplo
• Owner: dexplo
• License: bsd-3-clause
• Created: 2018-01-25T19:08:59.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-06-16T12:57:14.000Z (over 4 years ago)
• Last Synced: 2024-09-19T01:39:45.594Z (about 2 months ago)
• Language: Python
• Size: 1.02 MB
• Stars: 74
• Watchers: 8
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-dataframes - dexplo - Data exploration library with a pandas-like API. (Libraries)

README

        
# dexplo

[![](https://img.shields.io/pypi/v/dexplo)](https://pypi.org/project/dexplo)

[![Build Status](https://travis-ci.org/dexplo/dexplo.svg?branch=master)](https://travis-ci.org/dexplo/dexplo)

[![PyPI - License](https://img.shields.io/pypi/l/dexplo)](LICENSE)

A data analysis library comparable to pandas

# Installation

You must have cython installed. Run `python setup.py build_ext --use-cython -i`

# Main Goals

* A 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

### Only Scalar Data Types

All data types allow nulls

- [x] bool - always 8 bits

- [x] int

- [x] float

- [x] str - stored as a categorical

- [x] datetime

- [x] timedelta

### Column Labels

* No hierarchical index

* Column names must be strings

* Column names must be unique

### Row Labels

* No row labels for now

* Only a number display on the output

### 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

* Slice notation is also OK

### Development

* Must use type hints

* Must use 3.6+ - fstrings

* numpy

### Advantages over pandas

* Easier to write idiomatically

* String processing will be much faster

* Nulls allowed in each data type

* Nearly all operations will be faster

## API

#### Attributes

- [x] size

- [x] shape

- [x] values

- [x] dtypes

#### Methods

**Stats**

- [x] abs

- [x] all

- [x] any

- [x] argmax

- [x] argmin

- [x] clip

- [x] corr

- [x] count

- [x] cov

- [x] cummax

- [x] cummin

- [x] cumprod

- [x] cumsum

- [x] describe

- [x] max

- [x] min

- [x] median

- [x] mean

- [x] mode

- [x] nlargest

- [x] nsmallest

- [x] prod

- [x] quantile

- [x] rank

- [x] round

- [x] std

- [x] streak

- [x] sum

- [x] var

- [x] unique

- [x] nunique

- [x] value_counts

**Selection**

- [x] drop

- [x] head

- [x] isin

- [x] rename

- [x] sample

- [x] select_dtypes

- [x] tail

- [x] where

**Missing Data**

- [x] isna

- [x] dropna

- [x] fillna

- [ ] interpolate

**Other**

- [x] append

- [x] astype

- [x] factorize

- [x] groupby

- [x] iterrows

- [ ] join

- [x] melt

- [x] pivot

- [x] replace

- [x] rolling

- [x] sort_values

- [x] to_csv

**Other (after 0.1 release)**

- [ ] cut

- [ ] plot

- [ ] profile

**Functions**

- [x] read_csv

- [ ] read_sql

- [ ] concat

**Group By** - specifically with `groupby` method

- [x] agg

- [x] all

- [x] apply

- [x] any

- [x] corr

- [x] count

- [x] cov

- [x] cumcount

- [x] cummax

- [x] cummin

- [x] cumsum

- [x] cumprod

- [x] head

- [x] first

- [ ] fillna

- [x] filter

- [x] last

- [x] max

- [x] median

- [x] min

- [x] ngroups

- [x] nunique

- [x] prod

- [ ] quantile

- [ ] rank

- [ ] rolling

- [x] size

- [x] sum

- [x] tail

- [x] var

**str** - `df.str.`

- [x] capitalize

- [x] cat

- [x] center

- [x] contains      

- [x] count         

- [x] endswith      

- [x] find         

- [x] findall

- [x] get           

- [x] get_dummies

- [x] isalnum

- [x] isalpha

- [x] isdecimal

- [x] isdigit

- [x] islower

- [x] isnumeric

- [x] isspace

- [x] istitle

- [x] isupper

- [x] join

- [x] len

- [x] ljust

- [x] lower         

- [x] lstrip

- [x] partition

- [x] repeat

- [x] replace

- [x] rfind

- [x] rjust

- [x] rpartition

- [x] rsplit

- [x] rstrip

- [x] slice

- [x] slice_replace

- [x] split

- [x] startswith

- [x] strip

- [x] swapcase

- [x] title

- [x] translate

- [x] upper         

- [x] wrap

- [x] zfill

**dt** - `df.dt.`

- [x] ceil

- [x] day

- [x] day_of_week

- [x] day_of_year

- [x] days_in_month

- [x] floor

- [ ] freq

- [x] hour

- [x] is_leap_year

- [x] is_month_end

- [x] is_month_start

- [x] is_quarter_end

- [x] is_quarter_start

- [x] is_year_end

- [x] is_year_start

- [x] microsecond

- [x] millisecond

- [x] minute

- [x] month

- [x] nanosecond

- [x] quarter

- [x] round

- [x] second

- [x] strftime

- [x] to_pydatetime

- [x] to_pytime

- [ ] tz

- [ ] tz_convert

- [ ] tz_localize

- [x] weekday_name

- [x] week_of_year

- [x] year

**td** - `df.td.`

- [ ] ceil

- [ ] components

- [x] days

- [ ] floor

- [ ] freq

- [x] microseconds

- [x] milliseconds

- [x] nanoseconds

- [ ] round

- [x] seconds

- [ ] to_pytimedelta