I am trying to manipulate data from some video tracking experiments using python pandas. I placed a number of point markers on a structure, and tracked the points' XY coordinates over time. Together these data describe the shape of the structure over the course of the test. I am having trouble arranging my data into a hierarchical/nested DataFrame object.
My tracking method outputs each point's X,Y coordinates (and time) for each frame of video. This data is stored in csv files with a column for each variable, and a row for each video frame:
t,x,y
0.000000000E0,-4.866015168E2,-2.116143012E0
1.000000000E-1,-4.866045511E2,-2.123012558E0
2.000000000E-1,-4.866092436E2,-2.129722560E0
using pandas.read_csv I am able to read these csv files into DataFrames, with the same columns/rows format:
In [1]: pd.read_csv(point_a.csv)
Out[17]:
t x y
0 0.0 -486.601517 -2.116143
1 0.1 -486.604551 -2.123013
2 0.2 -486.609244 -2.129723
No problem so far.
I would like to merge several of the above DataFrames (one for each point), and create a large DataFrame with hierarchical columns, where all variables share one index (video frames). See the below columns point_a, point_b etc, with subcolumns for x, y, t. The shape column represents useful vectors for plotting the shape of the structure.
| point_a | point_b | point_c | shape
frames | x y t | x y t | x y t | x y
-----------------------------------------------------------------------------------
0 | xa0 ya0 ta0 | xb0 yb0 tb0 | xc0 yc0 tc0 | [xa0,xb0,xc0] [ya0,yb0,yc0]
1 | xa1 ya1 ta1 | xb1 yb1 tb1 | xc1 yc1 tc1 | [xa1,xb1,xc1] [ya1,yb1,yc1]
2 | xa2 ya2 ta2 | xb2 yb2 tb2 | xc2 yc2 tc2 | [xa2,xb2,xc2] [ya2,yb2,yc2]
3 | xa3 ya3 ta3 | xb3 yb3 tb3 | xc3 yc3 tc3 | [xa3,xb3,xc3] [ya3,yb3,yc3]
I would like to specify a video frame, and be able to grab a variable's value for that frame, e.g. df[1].point_b.y = yb1
dicts as inputMy previous approach to handling this kind of thing is to use nested dicts:
nested_dicts = {
"point_a": {
"x": [xa0, xa1, xa2],
"y": [ya0, ya1, ya2],
"t": [ta0, ta1, ta2],
},
"point_b": {
"x": [xb0, xb1, xb2],
"y": [yb0, yb1, yb2],
"t": [tb0, tb1, tb2],
},
"point_c": {
"x": [xc0, xc1, xc2],
"y": [yc0, yc1, yc2],
"t": [tc0, tc1, tc2],
},
}
This does everything I need except for slicing the data by frame number. When I try to use this nested dict as an input to a DataFrame, I get the following:
In [1]: pd.DataFrame(nested_dicts)
Out[2]:
point_a point_b point_c
t [ta0, ta1, ta2] [tb0, tb1, tb2] [tc0, tc1, tc2]
x [xa0, xa1, xa2] [xb0, xb1, xb2] [xc0, xc1, xc2]
y [ya0, ya1, ya2] [yb0, yb1, yb2] [yc0, yc1, yc2]
Problem: there is no shared frames index. The DataFrame has taken t,x,y as the index.
If I try to specify an index:
In [1]: pd.DataFrame(nested_dicts, index=range(number_of_frames))
Then I get a DataFrame with the correct number of rows, but no subcolumns, and full of NaNs:
Out[2]:
point_a point_b point_c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
DataFrame individuallyIf I create a DataFrame for each point:
point_a = point_b =
t x y t x y
0 ta0 xa0 ya0 0 tb0 xb0 yb0
1 ta1 xa1 ya1 1 tb1 xb1 yb1
2 ta2 xa2 ya2 2 tb2 xb2 yb2
and pass these to a DataFrame, indicating the index to be shared, as follows:
In [1]: pd.DataFrame({"point_a":point_a,"point_b":point_b},index=point_a.index)
then I get the following, which just contains x,y,t as strings:
Out[2]:
point_a point_b
0 (t,) (t,)
1 (x,) (x,)
2 (y,) (y,)
I think you can use dict comprehension with concat and then reshape DataFrame by stack and unstack:
df = pd.concat({key:pd.DataFrame(nested_dicts[key]) for key in nested_dicts.keys()})
.stack()
.unstack([0,2])
print (df)
point_a point_b point_c
t x y t x y t x y
0 ta0 xa0 ya0 tb0 xb0 yb0 tc0 xc0 yc0
1 ta1 xa1 ya1 tb1 xb1 yb1 tc1 xc1 yc1
2 ta2 xa2 ya2 tb2 xb2 yb2 tc2 xc2 yc2
Another solution with swaplevel and sort first level in MultiIndex in columns by sort_index:
df = pd.concat({key:pd.DataFrame(nested_dicts[key]) for key in nested_dicts.keys()})
.unstack(0)
df.columns = df.columns.swaplevel(0,1)
df = df.sort_index(level=0, axis=1)
print (df)
point_a point_b point_c
t x y t x y t x y
0 ta0 xa0 ya0 tb0 xb0 yb0 tc0 xc0 yc0
1 ta1 xa1 ya1 tb1 xb1 yb1 tc1 xc1 yc1
2 ta2 xa2 ya2 tb2 xb2 yb2 tc2 xc2 yc2
Or you can use Panel with transpose and to_frame:
df = pd.Panel(nested_dicts).transpose(0,1,2).to_frame().unstack()
print (df)
point_a point_b point_c
minor t x y t x y t x y
major
0 ta0 xa0 ya0 tb0 xb0 yb0 tc0 xc0 yc0
1 ta1 xa1 ya1 tb1 xb1 yb1 tc1 xc1 yc1
2 ta2 xa2 ya2 tb2 xb2 yb2 tc2 xc2 yc2