Plotting multiple groups from a dataframe with datashader as lines

I am trying to make plots with datashader. the data itself is a time series of points in polar coordiantes. i managed to transform them to cartesian coordianted(to have equal spaced pixles) and i can plot them with datashader.

the point where i am stuck is that if i just plot them with line() instead of points() it just connects the whole dataframe as a single line. i would like to plot the data of the dataframe group per group(the groups are the names in list_of_names ) onto the canvas as lines.

data can be found here

i get this kind of image with datashader

This is a zoomed in view of the plot generated with points() instead of line() the goal is to produce the same plot but with connected lines instead of points

import datashader as ds, pandas as pd, colorcet
import numby as np

df = pd.read_csv('file.csv')


print(df)

starlink_name = df.loc[:,'Name']
starlink_alt = df.loc[:,'starlink_alt']
starlink_az = df.loc[:,'starlink_az']

name = starlink_name.values
alt = starlink_alt.values
az = starlink_az.values
print(name)
print(df['Name'].nunique())
df['Date'] = pd.to_datetime(df['Date'])

for name, df_name in df.groupby('Name'):
    print(name)

df_grouped = df.groupby('Name')



list_of_names = list(df_grouped.groups)
print(len(list_of_names))
#########################################################################################

#i want this kind of plot with connected lines with datashader

#########################################################################################
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8], polar=True)
# ax.invert_yaxis()
ax.set_theta_zero_location('N')
ax.set_rlim(90, 60, 1)
# Note: you must set the end of arange to be slightly larger than 90 or it won't include 90
ax.set_yticks(np.arange(0, 91, 15))
ax.set_rlim(bottom=90, top=0)


for name in list_of_names:
    df2 = df_grouped.get_group(name)
    ax.plot(np.deg2rad(df2['starlink_az']), df2['starlink_alt'], linestyle='solid', marker='.',linewidth=0.5, markersize=0.1)
plt.show()


print(df)
#########################################################################################

#transformation to cartasian coordiantes

#########################################################################################
df['starlink_alt'] = 90 -  df['starlink_alt']

df['x'] = df.apply(lambda row: np.deg2rad(row.starlink_alt) * np.cos(np.deg2rad(row.starlink_az)), axis=1)
df['y'] = df.apply(lambda row: -1 * np.deg2rad(row.starlink_alt) * np.sin(np.deg2rad(row.starlink_az)), axis=1)

#########################################################################################

# this is what i want but as lines group per group

#########################################################################################

cvs = ds.Canvas(plot_width=2000, plot_height=2000)
agg = cvs.points(df, 'y', 'x')
img = ds.tf.shade(agg, cmap=colorcet.fire, how='eq_hist')


#########################################################################################

#here i am stuck

#########################################################################################


for name in list_of_names:
    df2 = df_grouped.get_group(name)
    cvs = ds.Canvas(plot_width=2000, plot_height=2000)
    agg = cvs.line(df2, 'y', 'x')
    img = ds.tf.shade(agg, cmap=colorcet.fire, how='eq_hist')
    #plt.imshow(img)
plt.show()

To do this, you have a couple options. One is inserting NaN rows as a breakpoint into your dataframe when using cvs.line. You need DataShader to "pick up the pen" as it were, by inserting a row of NaNs after each group. It's not the slickest, but that's a current recommended solution.

Really simple, hacky example:

In [17]: df = pd.DataFrame({
    ...:     'name': list('AABBCCDD'),
    ...:     'x': np.arange(8),
    ...:     'y': np.arange(10, 18),
    ...: })

In [18]: df
Out[18]:
  name  x   y
0    A  0  10
1    A  1  11
2    B  2  12
3    B  3  13
4    C  4  14
5    C  5  15
6    D  6  16
7    D  7  17

This block groups on the 'name' column, then reindexes each group to be one element longer than the original data:

In [20]: res = df.set_index('name').groupby('name').apply(
    ...:     lambda x: x.reset_index(drop=True).reindex(np.arange(len(x) + 1))
    ...: )

In [21]: res
Out[21]:
          x     y
name
A    0  0.0  10.0
     1  1.0  11.0
     2  NaN   NaN
B    0  2.0  12.0
     1  3.0  13.0
     2  NaN   NaN
C    0  4.0  14.0
     1  5.0  15.0
     2  NaN   NaN
D    0  6.0  16.0
     1  7.0  17.0
     2  NaN   NaN

You can plug this reindexed dataframe into datashader to have multiple disconnected lines in the result.

This is a still-open issue on the datashader repo, including additional examples and boilerplate code: https://github.com/holoviz/datashader/issues/257

Other options include restructuring your data to accommodate one of cvs.line's other formats. From the Canvas.line docstring:

def line(self, source, x=None, y=None, agg=None, axis=0, geometry=None,
         antialias=False):
    Parameters
    ----------
    source : pandas.DataFrame, dask.DataFrame, or xarray.DataArray/Dataset
        The input datasource.
    x, y : str or number or list or tuple or np.ndarray
        Specification of the x and y coordinates of each vertex
        * str or number: Column labels in source
        * list or tuple: List or tuple of column labels in source
        * np.ndarray: When axis=1, a literal array of the
          coordinates to be used for every row
    agg : Reduction, optional
        Reduction to compute. Default is ``any()``.
    axis : 0 or 1, default 0
        Axis in source to draw lines along
        * 0: Draw lines using data from the specified columns across
             all rows in source
        * 1: Draw one line per row in source using data from the
             specified columns

There are a number of additional examples in the cvs.line docstring. You can pass arrays as the x, y arguments giving multiple columns to use in forming lines when axis=1, or you can a dataframe with ragged array values.

See this pull request adding the line options (h/t to @James-a-bednar in the comments) for a discussion of their use.