how to group uneven data frame by condition

Question

I am wondering is there a thing with a unique or other function/method for pandas to slice a table where I have a pattern:

1.0, 2.0, 2.1,
1.0, 2.2, 2.0,
1.0, 2.3, 1.9,
1.5, 2.1, 2.0
1.5, 1.8, 1.9,
...

uneven table with 1st column as an indicator of where to group/slice lines.

Loop/ group it for array/list/whatever that contains values of 1.0, 1.5 grouped together I was trying to get

array1 as:

 1.0, 2.0, 2.1
 1.0, 2.2, 2.0
 1.0, 2.3, 1.9

array_2

 1.5, 2.1, 2.0
 1.5, 1.8, 1.9

I tried first_col = df.iloc[:, 0].tolist() but it is not what I would need.

Answer 1

If need loop by groups by first column use:

for i, g in df.groupby(df.columns[0]):
     print (g.mean().to_numpy())

If need list of DataFrames:

L = [g for i, g in df.groupby(df.columns[0])]

If need dictionary of DataFrames:

d = dict(tuple(df.groupby(df.columns[0])))

EDIT:

Simpliest is new column:

df['avg'] = df.mean(axis=1)

For arrays per groups:

for i, g in df.groupby(df.columns[0]):
     print (g.mean().to_numpy())