Styjun

I realise this is quite a lengthy ask, but I have been trying to solve this for days now with no success and wondered if anyone might have some ideas.

Consider a spreadsheet like so:

 apple1 grape1 apple2 grape2 apple3 grape3
1 0 4 -0.2 2 0 4
2 0 4 0 6 0 3
3 -0.1 2 0 4 0 4
4 -0.5 5 0 6 -0.2 5
5 -0.4 4 0 5 0 2
6 0 6 -0.1 5 0 3

I would like to search my dataframe for any cell with a value less than -0.1, and write the value, column header, row number, and neighbouring value out.

At the start, I though it might be as simple as something along the lines of:

Newlist()

if df >= -0.1:
 Newlist.append(cell.value)
 Newlist.append(row.value)
 Newlist.append(column.value)
 Newlist.append(cell.value.shift(1))

I fully realise the above makes no sense, but I hope it conveys the idea of what I've been trying to do.

Next, I could convert the df to a list and work from there( using an ifnot >= -0.1 to delete objects?), but this seems inelegant and far from ideal. I am however open to this if anyone can get it to work.

I must have looked at every stack exchange question ever posted on this without managing anything so apologies if I've overlooked something very obvious.

Thanks!

First, to filter your dataframe you can use boolean indexing like this :

df[df >= -0.1]

This way, all the data that is not superior to -0.1 will be displayed as nan, you can then use Pandas.isnull() to identify them.

To get the row and columns of the data you want, you could turn your dataframe into an array with df.to_numpy() and iterate over the rows and columns with enumerate to keep the id of row/column you are currently iterating through :

my_data = df[df >= -0.1].to_numpy()
for idrow, row in enumerate(my_data):
 for idcol, col in enumerate(row):
 if not pd.isnull(col):
 print("Value :"+str(col)+" column:"+str(idcol)+" row:"+str(idrow))

This will result in something like this :

Value :0.0 column:0 row:0
Value :4.0 column:1 row:0
Value :2.0 column:3 row:0

You can get columns name by using this in the loop :

df.columns[idcol]

Once you got those ids, you can get the neighbouring values by direct access ie.

my_data[x][y]

Just remember to set a condition to not access value that are not in the array !