convert multiple columns - values to single column binary value(python, pandas)-CodePudding

I'd like to add a column with binary value that mirrors the - from multiple columns. I've found several posts that convert integers/strings to binary value, however, they are separated into multiple columns instead of a single column, eg. 01100 in a column. Below is a sample df.:

original df:

 -------- -------- -------- -------- -------- 
| te1_pl | te2_pl | te3_pl | te4_pl | te5_pl |
 -------- -------- -------- -------- -------- 
|    -10 | -32    | 305.3  | -69    | -44    |
|     -7 | 69     | -14    | 41     | 41     |
|     47 | 16     | -38    | -9     | 82     |
|      1 | 18     | 37     | 0      | 42     |
|      9 | 36     | 47     | -33    | 6      |
|    -18 | 4.3    | 16.5   | 28.1   | -9.9   |
 -------- -------- -------- -------- --------

output hope to get:

 -------- -------- -------- -------- -------- ------- 
| te1_pl | te2_pl | te3_pl | te4_pl | te5_pl |  bin  |
 -------- -------- -------- -------- -------- ------- 
|    -10 | -32    | 305.3  | -69    | -44    | 00100 |
|     -7 | 69     | -14    | 41     | 41     | 01011 |
|     47 | 16     | -38    | -9     | 82     | 11001 |
|      1 | 18     | 37     | 0      | 42     | 11101 |
|      9 | 36     | 47     | -33    | 6      | 11101 |
|    -18 | 4.3    | 16.5   | 28.1   | -9.9   | 01110 |
 -------- -------- -------- -------- -------- -------

data = {
       "te1_pl": [-10,-7,47,1,9,-18,],
       "te2_pl": [-32,69,16,18,36,4.3,],
       "te3_pl": [305.3,-14,-38,37,47,16.5,],
       "te4_pl": [-69,41,-9,0,-33,28.1,],
       "te5_pl": [-44,41,82,42,6,-9.9,],}
df = pd.DataFrame(data)

kindly advise

CodePudding user response：

bin_all = df.apply(lambda x: x.apply(lambda y: "1" if y > 0 else "0"))
df["bin"] = bin_all.T.apply(lambda x: "".join(x))
df
#   te1_pl  te2_pl  te3_pl  te4_pl  te5_pl    bin
#0     -10   -32.0   305.3   -69.0   -44.0  00100
#1      -7    69.0   -14.0    41.0    41.0  01011
#2      47    16.0   -38.0    -9.0    82.0  11001
#3       1    18.0    37.0     0.0    42.0  11101
#4       9    36.0    47.0   -33.0     6.0  11101
#5     -18     4.3    16.5    28.1    -9.9  01110