pandas convert row values to binary format

Question

I am looking for a way to convert column values of my dataframe to boolean values. In my dataframe below, I have columns x,y,z.

I prepared a reference dict where I got all the unique values from each column, sorted and separated by colons. Finally, I concatenated my dict with the dataframe: Here is what my dict looks like: {'x':'0:1:10','y':'5:10','z':'0:10:300'}.

Now taking these sorted dict values as a reference, I want to change my column values into binary code (as below in desired output).

My dataframe:

_______________________________
 | x | y | z |

 | 0:1:10 | 5:10 | 0:10:300 |
_______________________________
A | 0,1 | 10 | 10,300 |

B | 1 | 5 | 300,0 |

C | 10,0,1 |  | 300,10 |

________________________________

This is what my desired dataframe with binary coding looks like.

Desired output:

______________________________
 | x | y | Z |

 | 0:1:10 | 5:10 | 0:10:300 |
______________________________
A | 110 | 01 | 011 |

B | 010 | 10 | 101 |

C | 111 | 00 | 011 |
_______________________________

Thanks,Rtut

Fortunato · Accepted Answer · 2016-07-12 21:06:02Z

1

not sure how scalable this is, but here's one idea:

df = pd.DataFrame([["0,1", "10", "10,300"], ["1", "5", "300,0"], ["10,1,0", "", "300,10"]], columns = ["x", "y", "z"])

bin_dict_x = {'0': 100, '1': 10, '10': 1}
bin_dict_y = {'5': 10, '10': 1}
bin_dict_z = {'0': 100, '10': 10, '300': 1}

def to_bin(dct, entry):
    out = 0
    for i in entry.split(','):
       if len(i) > 0:
           out+= dct[i]  
    return str(out).zfill(len(dct))

df['x_bin'] = df.apply(lambda x: to_bin(bin_dict_x, x['x']), axis = 1)
df['y_bin'] = df.apply(lambda x: to_bin(bin_dict_y, x['y']), axis = 1)
df['z_bin'] = df.apply(lambda x: to_bin(bin_dict_y, x['z']), axis = 1)

df[['x_bin','y_bin','z_bin']]

  x_bin y_bin z_bin
0   110    01   011
1   010    10   101
2   111    00   011

answered Jul 12, 2016 at 21:06

Fortunato

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