# Dataset basics

# Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from eelbrain import *
import numpy


A dataset can be constructed column by column, by adding one variable after another:

# initialize an empty Dataset:
ds = Dataset()
# numeric values are added as Var object:
ds['y'] = Var(numpy.random.normal(0, 1, 6))
# categorical data as represented in Factors:
ds['a'] = Factor(['a', 'b', 'c'], repeat=2)
# A variable that's equal in all cases can be assigned quickly:
ds[:, 'z'] = 0.
# check the result:
ds


For larger datasets it can be more convenient to print only the first few cases…

ds.head()


… or a summary of variables:

ds.summary()


An alternative way of constructing a dataset is case by case (i.e., row by row):

rows = []
for i in range(6):
subject = f'S{i}'
y = numpy.random.normal(0, 1)
a = 'abc'[i % 3]
rows.append([subject, y, a])
ds = Dataset.from_caselist(['subject', 'y', 'a'], rows, random='subject')
ds


# Example

Below is a simple example using data objects (for more, see the Examples):

y = numpy.empty(21)
y[:14] = numpy.random.normal(0, 1, 14)
y[14:] = numpy.random.normal(2, 1, 7)
ds = Dataset({
'a': Factor('abc', 'A', repeat=7),
'y': Var(y, 'Y'),
})
ds


table.frequencies('a', data=ds)


test.ANOVA('y', 'a', data=ds)


test.pairwise('y', 'a', data=ds, corr='Hochberg')


t = test.pairwise('y', 'a', data=ds, corr='Hochberg')
print(t.get_tex())

\begin{center}
\begin{tabular}{lll}
\toprule
& b & c \\
\midrule
a & $t_{12} = 0.75^{ \ \ \ }$\\
$p = .466$\\
$p_{c} = .466$ & $t_{12} = -3.09^{* \ \ }$\\
$p = .009$\\
$p_{c} = .028$ \\
b &  & $t_{12} = -3.97^{** \ }$\\
$p = .002$\\
$p_{c} = .009$ \\
\bottomrule
\end{tabular}
\end{center}

p = plot.Boxplot('y', 'a', data=ds, title="My Boxplot", ylabel="value", corr='Hochberg')


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