Get count of non zero values per row in Pandas DataFrame

Question

I know this is a simple question, but I'm very new to Pandas. I want to compare for each row the cells to see if any of the cells in the columns are more or less than 0.00.

              GOOG    AAPL     XOM     IBM       Value
2011-01-10     0.0     0.0     0.0     0.0       0.00
2011-01-13     0.0 -1500.0     0.0  4000.0  -61900.00

I know that pandas have built in the iterrows. However, with the following piece of code I'm receiving an error

for index, row in dataFrame.iterrows():
    for i in range(0, len(of_columns)):
        print dataFrame[index][i]

Error

return self._engine.get_loc(self._maybe_cast_indexer(key)) File "pandas\index.pyx", line 132, in pandas.index.IndexEngine.get_loc (pandas\index.c:4433) File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:4279) File "pandas\src\hashtable_class_helper.pxi", line 732, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:13742) File "pandas\src\hashtable_class_helper.pxi", line 740, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:13696)

Intended action If the cell contains 0, do nothing (continue). If the cell contains other than zero, then give count of non zero values per row

Answer 1

Compare by gt (>), lt (<) or le, ge, ne, eq first and then sum Trues, there are processing like 1:

Bad -> check all previous columns:

df['> zero'] = df.gt(0).sum(axis=1)
df['< zero'] = df.lt(0).sum(axis=1)
df['== zero'] = df.eq(0).sum(axis=1)
print (df)
            GOOG    AAPL  XOM     IBM    Value  > zero  < zero  == zero
2011-01-10   0.0     0.0  0.0     0.0      0.0       0       0        7
2011-01-13   0.0 -1500.0  0.0  4000.0 -61900.0       1       2        2

Correct - select columns for check:

cols = df.columns
df['> zero'] = df[cols].gt(0).sum(axis=1)
df['< zero'] = df[cols].lt(0).sum(axis=1)
df['== zero'] = df[cols].eq(0).sum(axis=1)
print (df)
            GOOG    AAPL  XOM     IBM    Value  > zero  < zero  == zero
2011-01-10   0.0     0.0  0.0     0.0      0.0       0       0        5
2011-01-13   0.0 -1500.0  0.0  4000.0 -61900.0       1       2        2

Detail:

print (df.gt(0))
             GOOG   AAPL    XOM    IBM  Value
2011-01-10  False  False  False  False  False
2011-01-13  False  False  False   True  False

EDIT:

To remove some columns from the 'cols' use difference:

cols = df.columns.difference(['Value'])
print (cols)
Index(['AAPL', 'GOOG', 'IBM', 'XOM'], dtype='object')

df['> zero'] = df[cols].gt(0).sum(axis=1)
df['< zero'] = df[cols].lt(0).sum(axis=1)
df['== zero'] = df[cols].eq(0).sum(axis=1)
print (df)
            GOOG    AAPL  XOM     IBM    Value  > zero  < zero  == zero
2011-01-10   0.0     0.0  0.0     0.0      0.0       0       0        4
2011-01-13   0.0 -1500.0  0.0  4000.0 -61900.0       1       1        2