Question: I want to delete the row that is column 'revenue' = 0 or 'Budget' = 0 from the movie table
so I tried this code.

```
movies[(movies['revenue'] == 0) or (movies['Budget'] == 0)]

```

Then, I got a value error

So I tried this code as well

```
columns = ['budget', 'revenue']
df = movies.replace(0, pd.np.nan).dropna(axis=0, how='any', subset = columns).fillna(0).astype(int)

```

also I got an Future warning " The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead.
plus, value error that traceback

Answer: 
Try this, and use `|` bitwise operator (means `or`):

```
new = movies[(movies['revenue'] != 0) | (movies['budget'] != 0)]  

```

The code will create a new `df` with the rows that are not 0 in column or not 0 in budget.

Note that `!=` means not equal. For extra information on operators, I find this link clear: <https://www.w3schools.com/python/python_operators.asp>
You can't use the `or` operator there, you have to use the logical or `|` sign:

```
movies = movies[(movies['revenue'] == 0) & (movies['Budget'] == 0)]

```

**Edit:**

If you want to remove the rows with `0`, try using the `~` sign:

```
movies = movies[~(movies['revenue'] == 0) & ~(movies['Budget'] == 0)]

```

**I use `&` since OP said in the comments that he needs `&`**