# Find row whose value is always zero pandas

I have multiple table like –

Table A –

Seller Date sales
Ron 1-Aug 0
Jon 1-Aug 2
Kon 1-Aug 3
Kat 1-Aug 2
Mat 1-Aug 4

Table B –

Seller Date sales
Ron 2-Aug 0
Jon 2-Aug 2
Kon 2-Aug 3
Kat 2-Aug 0
Mat 2-Aug 4

Table C –

Seller Date sales
Ron 3-Aug 0
Jon 3-Aug 2
Kon 3-Aug 3
Kat 3-Aug 0
Mat 3-Aug 4

Table D –

Seller Date sales
Ron 4-Aug 0
Jon 4-Aug 2
Kon 4-Aug 3
Kat 4-Aug 0
Mat 4-Aug 4

I need to find the seller list whose value is always zero like –

Ron – Always zero

Another finding is – Find the seller whose value is zero in last 3 dates like –

Kat – Zero in the last 3 dates. In Table A for date 1-Aug Kat had some value.

Create a list of sellers like that. I have many data in similar types of tables.

Note – Tables are not in order by dates.

### >Solution :

First is necessary create DataFrame with converting columns to datetimes and sorting:

``````L = [df1, df2, df3, df4]

df=pd.concat([x.set_index('Seller')['sales'].rename(x['Date'].iat[0]) for x in L], axis=1)
df.columns = pd.to_datetime('2023'+df.columns, format='%Y%d-%b')

df = df.sort_index(axis=1)
print (df)
2023-08-01  2023-08-02  2023-08-03  2023-08-04
Seller
Ron              0           0           0           0
Jon              2           2           2           2
Kon              3           3           3           3
Kat              2           0           0           0
Mat              4           4           4           4
``````

Then test `0` values and get list of names from indices if all values match:

``````m = df.eq(0)

out1 = df.index[m.all(axis=1)].tolist()
print (out1)
['Ron']
``````

And for test only last 3 zero values:

``````N = 3
out2 = df.index[m.iloc[:, -N:].all(axis=1)].difference(out1).tolist()
print (out2)
['Kat']
``````