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-0.3.3.tar.gz (8.2 kB view details)

Uploaded Source

Built Distribution

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

datafuncs-0.3.3-py3-none-any.whl (2.6 kB view details)

Uploaded Python 3

File details

Details for the file datafuncs-0.3.3.tar.gz.

File metadata

  • Download URL: datafuncs-0.3.3.tar.gz
  • Upload date:
  • Size: 8.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for datafuncs-0.3.3.tar.gz
Algorithm Hash digest
SHA256 78e4a121dffc1d08544219258ad86a0ff97fad51b321cd70d6da987c2d0f05fb
MD5 b9b5dfc04d9cf66f49008af92fa726bb
BLAKE2b-256 a700b8688143cb00f1d5deed36158d191861ae6510f06f96332f8df85412ed5c

See more details on using hashes here.

File details

Details for the file datafuncs-0.3.3-py3-none-any.whl.

File metadata

  • Download URL: datafuncs-0.3.3-py3-none-any.whl
  • Upload date:
  • Size: 2.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for datafuncs-0.3.3-py3-none-any.whl
Algorithm Hash digest
SHA256 22e3fcfe27a9dcc3844c7d7fd1a1977bfa7d35f20e37bfe66c79c4dc54a1f2af
MD5 f7c4f5feabd58edfba49bbc22a1a5ae4
BLAKE2b-256 6b5c3dadfab8f8cdd5a55e14a46ce73f5f7c27801ae90ded87aecd62b3b77117

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