Multiple values in single column of a pandas DataFrame

Question

I have some data that I\'m parsing from XML to a pandas DataFrame. The XML data roughly looks like this:

Answer 1

Assuming you have enough memory, your task will be more easily accomplished if your DataFrame held one variant per row:

track_name     variants  time     route_id  stop_id  serial
"trackname1"   1         "21:23"         5      103       1
"trackname1"   2         "21:23"         5      103       1
"trackname1"   3         "21:23"         5      103       1
"trackname1"   1         "21:26"         5       17       2
"trackname1"   2         "21:26"         5       17       2
"trackname1"   3         "21:26"         5       17       2
...
"trackname1"   4         "21:20"         5      103       1
"trackname1"   5         "21:20"         5      103       1
...
"trackname2"   1         "20:59"         3       45       1

Then you could find "all rows for variant 3 on route_id 5 with

df.loc[(df['variants']==3) & (df['route_id']==5)]

If you pack many variants into one row, such as

"trackname1"   "1,2,3"   "21:23"  "5"       "103"    "1"

then you could find such rows using

df.loc[(df['variants'].str.contains("3")) & (df['route_id']=="5")]

assuming that the variants are always single digits. If there are also 2-digit variants like "13" or "30", then you would need to pass a more complicated regex pattern to str.contains.

Alternatively, you could use apply to split each variant on commas:

df['variants'].apply(lambda x: "3" in x.split(','))

but this is very inefficent since you would now be calling a Python function once for every row, and doing string splitting and a test for membership in a list compared to a vectorized integer comparision.

Thus, to avoid possibly complicated regex or a relatively slow call to apply, I think your best bet is to build the DataFrame with one integer variant per row.