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---------

Co-authored-by: jose-donato <zmcdonato@gmail.com>
Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com>
Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
2023-10-30 21:01:29 +00:00

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title, description, keywords
title description keywords
clean This documentation page provides information about how to clean a dataset by filling and dropping NaN values. It also offers examples and descriptions of different parameters.
data cleaning
filling NaN values
dropping NaN values
data cleaning parameters
dataset cleaning
data processing
usage examples

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

Clean a dataset by filling and dropping NaN values.

Usage

clean [-f {rfill,cfill,rbfill,cbfill,rffill,bffill}] [--drop {rdrop,cdrop}] [-d {}]

Parameters

Name Description Default Optional Choices
fill The method of filling NaNs. This has options to fill rows (rfill, rbfill, rffill) or fill columns (cfill, cbfill, cffill). Furthermore, it has the option to forward fill and backward fill (up to --limit) which refer to how many rows/columns can be set equal to the last non-NaN value True rfill, cfill, rbfill, cbfill, rffill, bffill
drop The method of dropping NaNs. This either has the option rdrop (drop rows that contain NaNs) or cdrop (drop columns that contain NaNs) True rdrop, cdrop
target_dataset The name of the dataset you want to select None True None

Examples

(🦋) /forecast/ $ show TSLA

TSLA dataset has shape (row, column): (611, 7)

                    Dataset TSLA | Showing 10 of 611 rows
┏━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓
┃   ┃ date       ┃ open   ┃ high   ┃ low    ┃ close  ┃ adj_close ┃ volume    ┃
┡━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩
│ 0 │ 2020-01-02 │ 84.90  │ 86.14  │ 84.34  │ 86.05  │ 86.05     │ 47660500  │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 1 │ 2020-01-03 │ 88.10  │ 90.80  │ 87.38  │ 88.60  │ 88.60     │ 88892500  │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 2 │ 2020-01-06 │ 88.09  │ 90.31  │ 88.00  │ 90.31  │ 90.31     │ 50665000  │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 3 │ 2020-01-07 │ 92.28  │ 94.33  │ 90.67  │ 93.81  │ 93.81     │ 89410500  │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 4 │ 2020-01-08 │ 94.74  │ 99.70  │ 93.65  │ 98.43  │ 98.43     │ 155721500 │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 5 │ 2020-01-09 │ 99.42  │ 99.76  │ 94.57  │ 96.27  │ 96.27     │ 142202000 │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 6 │ 2020-01-10 │ 96.36  │ 96.99  │ 94.74  │ 95.63  │ 95.63     │ 64797500  │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 7 │ 2020-01-13 │ 98.70  │ 105.13 │ 98.40  │ 104.97 │ 104.97    │ 132588000 │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 8 │ 2020-01-14 │ 108.85 │ 109.48 │ 104.98 │ 107.58 │ 107.58    │ 144981000 │
├───┼────────────┼────────┼────────┼────────┼────────┼───────────┼───────────┤
│ 9 │ 2020-01-15 │ 105.95 │ 107.57 │ 103.36 │ 103.70 │ 103.70    │ 86844000  │
└───┴────────────┴────────┴────────┴────────┴────────┴───────────┴───────────┘