Looping through rows and columns in dataframe

Question

I would like to loop through the rows and columns in a dataframe. My dataframe looks like this:

My code looks like this:

for index,j in usd_margin_data.iterrows():
    print(index,j)

But I actually want is loop rows and column in the data.

Something like this:

for row in usd_margin_data.iterrows():
     for column in list(usd_margin_data):

What is the best way to loop through rows and columns, where I need the index for each row and column?

The expected output

10 CME 1728005
10 HKEX 0
10 Nissan 1397464.22
...

Answer 1

For loop is possible use:

for r,j in usd_margin_data.iterrows():
    for c, val in j.items():
        print(r,c,val)

If want 3 columns DataFrame with remove missing values:

df = usd_margin_data.stack().rename_axis(('row','col')).reset_index(name='val')

And then for loop is possible use itertuples:

for x in df.itertuples():
    print (x.row, x.col, x.val)

Answer 2

Try:

val = pd.melt(df, id_vars='acct')
val[val.value.notnull()]