Question: I'd like to return the rows which qualify to a certain condition. I can do this for a single row, but I need this for multiple rows combined. For example 'light green' qualifies to 'XYZ' being positive and 'total' > 10, where 'Red' does not. When I combine a neighbouring row or rows, it does => 'dark green'. Can I achieve this going over all the rows and not return duplicate rows?

```
N = 1000

np.random.seed(0)

df = pd.DataFrame(
    {'X':np.random.uniform(-3,10,N),
     'Y':np.random.uniform(-3,10,N),
     'Z':np.random.uniform(-3,10,N),
    })

df['total'] = df.X + df.Y + df.Z

df.head(10)

```

[![enter image description here](https://i.stack.imgur.com/9kL6Z.png)](https://i.stack.imgur.com/9kL6Z.png)

EDIT;

Desired output is 'XYZ'> 0 and 'total' > 10

Answer: 
Here's a try. You would maybe want to use `rolling` or `expanding` (for speed and elegance) instead of explicitly looping with `range`, but I did it that way so as to be able to print out the rows being used to calculate each boolean.

```
df = df[['X','Y','Z']]    # remove the "total" column in order
                          # to make the syntax a little cleaner

df = df.head(4)           # keep the example more manageable

for i in range(len(df)):
    for k in range( i+1, len(df)+1 ):
        df_sum = df[i:k].sum()
        print( "rows", i, "to", k, (df_sum>0).all() & (df_sum.sum()>10) )

rows 0 to 1 True
rows 0 to 2 True
rows 0 to 3 True
rows 0 to 4 True
rows 1 to 2 False
rows 1 to 3 True
rows 1 to 4 True
rows 2 to 3 True
rows 2 to 4 True
rows 3 to 4 True

```
I am not too sure if I understood your question correctly, but if you are looking to put multiple conditions within a dataframe, you can consider this approach:

`new_df = df[(df["X"] > 0) & (df["Y"] < 0)]`

The `&` condition is for AND, while replacing that with `|` is for OR condition. Do remember to put the different conditions in `()`. 

Lastly, if you want to remove duplicates, you can use this

`new_df.drop_duplicates()`

You can find more information about this function at here: <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html>

Hope my answer is useful to you.