I am using Python 3.6 and I have the output of minisom package in below format
defaultdict(list,{(9,1):[array([0.1,0.3,0.5,0.9]),array([0.2,0.6,0.8,0.9])],(3,2):[array([1,3,5,9]),array([2,6,8,9])] })
and I would like to have my output(Pandas DataFrame) as shown below
X Y V1 V2 V3 V4
9 1 0.1 0.3 0.5 0.9
9 1 0.2 0.6 0.8 0.9
3 2 1 3 5 9
3 2 2 6 8 9
I appreciate your help on this.
I would try something like this:
>>> x
defaultdict(<class 'list'>, {(9, 1): [array([0.1, 0.3, 0.5, 0.9]), array([0.2, 0.6, 0.8, 0.9])], (3, 2): [array([1, 3, 5, 9]), array([2, 6, 8, 9])]})
>>> df=pd.DataFrame()
>>> df[["X", "Y", "V1", "V2", "V3", "V4"]]=pd.DataFrame(pd.DataFrame.from_dict(x, orient="index").stack().reset_index().drop("level_1", axis=1).rename(columns={0: "val"}, inplace=False).apply(lambda x: [el_inner for el in x.values for el_inner in el], axis=1).to_list())
>>> df
X Y V1 V2 V3 V4
0 9 1 0.1 0.3 0.5 0.9
1 9 1 0.2 0.6 0.8 0.9
2 3 2 1.0 3.0 5.0 9.0
3 3 2 2.0 6.0 8.0 9.0
>>> df.dtypes
X int64
Y int64
V1 float64
V2 float64
V3 float64
V4 float64
dtype: object
Alternatively:
>>> df=pd.DataFrame.from_dict(x, orient="index").stack().reset_index().drop("level_1", axis=1).rename(columns={0: "val"}, inplace=False).apply(lambda x: pd.Series({"x": x.level_0[0], "y": x.level_0[1], "v1": x.val[0], "v2": x.val[1], "v3": x.val[2], "v4": x.val[3]}), axis=1)
>>> df
x y v1 v2 v3 v4
0 9.0 1.0 0.1 0.3 0.5 0.9
1 9.0 1.0 0.2 0.6 0.8 0.9
2 3.0 2.0 1.0 3.0 5.0 9.0
3 3.0 2.0 2.0 6.0 8.0 9.0
>>> df.dtypes
x float64
y float64
v1 float64
v2 float64
v3 float64
v4 float64
dtype: object
If you want to convert x and y to int:
>>> df[["x", "y"]]=df[["x", "y"]].astype(int)
>>> df
x y v1 v2 v3 v4
0 9 1 0.1 0.3 0.5 0.9
1 9 1 0.2 0.6 0.8 0.9
2 3 2 1.0 3.0 5.0 9.0
3 3 2 2.0 6.0 8.0 9.0
>>> df.dtypes
x int32
y int32
v1 float64
v2 float64
v3 float64
v4 float64
dtype: object