Skip to main content

Functions for Data Processing

Project description

Datafuncs

Functions for Data Processing

Functions

.mean(data) - Returns the mean of a list

.setOutlier(data, low, high) - Removes the outliers from a list

.imputate(data, low, high) - Replaces outliers in a list with the mean of the filtered list.

.median(data) - Returns the median of a list

.mode(data) - Returns the mode of a list

.freq(data, value) - Returns the Amount of times that a value occurs in the list data

.Range(data) - Returns the range of the list

.replace(data, old, new) - Replaces every old value in the list with the new value

.gen(length, low, high, type) - Generates a list of random values of the type specified that is length long - Ignore the low and high values if you are not generating a list of integers

.stdDev(data) - Returns the standard deviation of the list to two decimal places

.variance(data) - Returns the variance of the list to two decimal places

.iqr(data) - Returns the Interquartile Range of the list data to two decimal places

.zScore(data, value) - Returns the z score of a value using the list data as a dataset

.percentile(zScore) - Returns the percentile of a value using the z score

.outlier(data, zScoreCutoff) - Automatically removes outliers outside a set z score

.merge(list1, list2) - Merges two lists into tupled lists

.covariance(list1, list2) - Returns the covariance of the two given lists to two decimal places

.correlate(list1, list2) - Returns the correlation coefficient of the two given lists to two decimal places

.slope(list1, list2) - Calculates the of a linear regression line using the two given lists

.regression(list1, list2, value) - Calculates the regression line for a given value between two lists

.all(list1, list2, list3, etc...) - Returns everything the script knows about a list and compares multiple lists

.Help() - Prints a list of the above functions and their explanations

Created by Fraser Woodward & Liam Leonard

Project details


Download files

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

Source Distribution

datafuncs-0.3.5.tar.gz (9.6 kB view details)

Uploaded Source

Built Distribution

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

datafuncs-0.3.5-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file datafuncs-0.3.5.tar.gz.

File metadata

  • Download URL: datafuncs-0.3.5.tar.gz
  • Upload date:
  • Size: 9.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for datafuncs-0.3.5.tar.gz
Algorithm Hash digest
SHA256 3c78b3a745ce83a8e81b650f403124f5105b16813b117bb6b63f34e863a54f69
MD5 1f832125461deb9cfe3f875ce2820af9
BLAKE2b-256 8ebe7968127c9efda2683153341895fe20e80d9194cb100aee65f2df5b6d2e3b

See more details on using hashes here.

File details

Details for the file datafuncs-0.3.5-py3-none-any.whl.

File metadata

  • Download URL: datafuncs-0.3.5-py3-none-any.whl
  • Upload date:
  • Size: 9.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for datafuncs-0.3.5-py3-none-any.whl
Algorithm Hash digest
SHA256 a8e04dc08a6c3c299d9364f67ab51ab78679c524a817a4852bd75a4f25c290b4
MD5 042f94ccad79e35a3380a1c1d8f91ca6
BLAKE2b-256 60367922bbcb674ac3bea9c575cecef5b17a8130ed95ddfe5f03960315b9d77a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page