Regression for python including plotting, reading from files, and more.
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make sure you have python and pip installed (not python3)

run pip install -r requirements.txt


Tool for regression of csv files.

Run python --help for info

With csv, you can get from google sheets, or most websites with large datasets will allow download.

You can see examples in ./data/ directory.

The first line should not contain data

It should contain the title of the column. For example Price, Date, and the like are used

For dates, use --dateformat option.

The default model is linear, a*x+b, see below for more.


say we have data.csv

Date,      Open,  High,   . . . 
2009-07-01,143.50,144.66, . . .

To find the relationship between Date and High, Run

python --xcolumn Date --ycolumn High --dateformat "%Y-%m-%d" --model "a*x+b"

The dateformat is exactly what is sounds like.

The Date column has the year (%Y) then a - followed by the month (%m), then another - and finally the day (%d).

If you had January 2009 01, use --dateformat "%B %Y %d"

If you had Jan 2009 01, use --dateformat "%b %Y %d"

For more info, check:


You can use any model you'd like with this.

use x for the data in xcolumn. a, b, c, and d are all variables you use.

For example, using a*x+b will pick a and b such that the sum of the squares between a*x+b and y dataset are minimized

For exponential fit, a*(x**b).

This just pulls an eval on the code.

If you need more than 4 parameters, just use --parameters $n.

Then, use p[i] for the ith parameter (a=p[0], b=p[1], c=p[2], d=p[3])

For example, --model "a*x+b" is the same as --model "p[0]*x+p[1]"


A window pops up with two windows.

One is the actual dataset (in blue), and the model (in red). The lower portion also has the residuals plotted in red dots.

The second is a histogram of the residuals, with some info at the top, such as mean, standard deviation, mean, and R^2 value.

The a, b, and all parameters which are used are printed in the terminal.