CODEWITHSUNDEEP
pythonpandas
swifty
swifty

Reputation: 1282

Pandas - Remove leading and trailing zeroes from each row

I would like to remove preceding and trialing zero values row-wise in my df and then have them shift to be 'aligned'.

Probably best demonstrated with the below example.

Initial df:

index c1 c2 c3 c4 c5 c6 c7 c8
1      0  0  1  2  3  4  5  0
2      0  0  0  1  2  3  4  5
3      0  1  2  3  0  0  0  0
4      0  0  1  2  3  4  0  0
5      1  2  3  4  5  6  7  0
6      0  0  0  1  0  0  4  0

Output:

index c1 c2 c3 c4 c5 c6 c7
1      1  2  3  4  5  
2      1  2  3  4  5
3      1  2  3  
4      1  2  3  4  
5      1  2  3  4  5  6  7  
6      1  0  0  4

Note that there is potential to be zeroes within the "string" of true values so need to stop at the first / reverse first occurrence. Is this possible? Thanks.

Upvotes: 2

Views: 2536

Answers (2)

Scott Boston
Scott Boston

Reputation: 153560

You can use this:

df_out = df.apply(lambda x: pd.Series(x.loc[x.mask(x == 0).first_valid_index():
                                      x.mask(x == 0).last_valid_index()].tolist()), 
                  axis=1)
df_out.set_axis(df.columns[df_out.columns], axis=1, inplace=False)

Output:

        c1   c2   c3   c4   c5   c6   c7
index                                   
1      1.0  2.0  3.0  4.0  5.0  NaN  NaN
2      1.0  2.0  3.0  4.0  5.0  NaN  NaN
3      1.0  2.0  3.0  NaN  NaN  NaN  NaN
4      1.0  2.0  3.0  4.0  NaN  NaN  NaN
5      1.0  2.0  3.0  4.0  5.0  6.0  7.0
6      1.0  0.0  0.0  4.0  NaN  NaN  NaN

N

Upvotes: 3

user3483203
user3483203

Reputation: 51185

Using np.trim_zeros:

Trim the leading and/or trailing zeros from a 1-D array or sequence.

out = pd.DataFrame([np.trim_zeros(i) for i in df.values], index=df.index)
out.columns = df.columns[:len(out.columns)]

       c1  c2  c3   c4   c5   c6   c7
index
1       1   2   3  4.0  5.0  NaN  NaN
2       1   2   3  4.0  5.0  NaN  NaN
3       1   2   3  NaN  NaN  NaN  NaN
4       1   2   3  4.0  NaN  NaN  NaN
5       1   2   3  4.0  5.0  6.0  7.0
6       1   0   0  4.0  NaN  NaN  NaN

Upvotes: 6

Related Questions