# How to drop row where all columns BUT the timestamp are zeros in python [closed]

I have a dataset that like any other has zeros and i need to get rid of them. The problem is that I want to delete rows where all the columns values but the timestamp are zeros

You can use difference to not look at the Timestamp column. Then sum over the horizontal axis looking for non-zero sum rows:

df.loc[df[df.columns.difference(['Timestamp'])].sum(axis=1) != 0]

If you want to remove rows which have only timestamp as null, you can use something like this

df.drop(df[df['Timestamp'].isnull()].index,inplace=True)

• I actually want to delete the rows that all their columns are zeros. But the problem is that i have a timestamp column which does not have zeros ( it has normal values ) – MOHAMED KOUBAA Sep 12 '19 at 12:03

If you want to actually remove the rows, you can do that as follows:

# get the columns, you want to check
cols_to_check= [col for col in df.columns if col != 'Timestamp']
# you can also use TitoOrt's method like:
# cols_to_check= df.columns.difference(['Timestamp'])

# build an indexer with one boolean per row that is
# true if all cols_to_check contain zero
all_zero_indexer= (df[cols] == 0).all(axis='columns')

# if it is now sufficient to just work with the
# subset of the dataframe, you can use
df.loc[~all_zero_indexer]
# in place of df to only see the rows where not all
# columns are zero (just reasign it to variable df)

# if you actually want to delete the zero-rows,
# you can do that with the following line
df.drop(labels=all_zero_indexer.index[all_zero_indexer], inplace=True)