I have dataframe eg. like below

Event['EVENT_ID'] = [ 4162, 4161, 4160, 4159,4158, 4157, 4156, 4155, 4154]

need to convert each row word to binary.

Event['b']=bin(Event['EVENT_ID']) doesn't work 

TypeError: cannot convert the series to <class 'int'>

expected new column with binary, remove 0b and split the column to 16 separate column

bin(4162) = '0b1000001000010'

You can also work with numpy, much faster than pandas.

Edit: faster numpy using view Couple of tricks here:

• Work only with the column of interest
• Convert the underlaying array to uint16, to ensure compatibility with any integer input
• Swapbytes to have a proper H,L order (at least on my architecture)
• Split H,L without actually moving any data with view
• Run unpackbits and reshape accordingly

My machine requires a byteswap to have the bytes of the uint16 in the proper place. Note that this aproach requires to have the data as int16/uint16, while the other one would work for int64 as well.

import pandas as pd
import numpy as npdf = pd.DataFrame({'EVENT_ID': [ 4162, 4161, 4160, 4159,4158, 4157, 4156, 4155, 4154]}, dtype='uint16')zz=np.unpackbits(df.EVENT_ID.values.astype('uint16').byteswap().view('uint8')).reshape(-1,16)
df3 = pd.concat([df,pd.DataFrame(zz)],axis=1)print(f"{df3 =}")df3 =   EVENT_ID  0  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15
0      4162  0  0  0  1  0  0  0  0  0  1   0   0   0   0   1   0
1      4161  0  0  0  1  0  0  0  0  0  1   0   0   0   0   0   1
2      4160  0  0  0  1  0  0  0  0  0  1   0   0   0   0   0   0
3      4159  0  0  0  1  0  0  0  0  0  0   1   1   1   1   1   1
4      4158  0  0  0  1  0  0  0  0  0  0   1   1   1   1   1   0
5      4157  0  0  0  1  0  0  0  0  0  0   1   1   1   1   0   1
6      4156  0  0  0  1  0  0  0  0  0  0   1   1   1   1   0   0
7      4155  0  0  0  1  0  0  0  0  0  0   1   1   1   0   1   1
8      4154  0  0  0  1  0  0  0  0  0  0   1   1   1   0   1   0

older proposed method:

lh = np.unpackbits((df.values & 0xFF).astype('uint8')).reshape(-1,8)
uh = np.unpackbits((df.values >> 8).astype('uint8')).reshape(-1,8)df2 = pd.concat([df, pd.DataFrame(np.concatenate([uh,lh],axis=1),index=df.index)],axis=1)

Benchmark: numpy is orders of magnitude faster than pandas" For 1M points:

• numpy view: 35ms for 1million uint64 points
• numpy low/high: 50ms
• pandas list bin: 1.78s
• pandas apply format + list: 1.97s
• pandas apply lambda: 6.08s

df = pd.DataFrame({'EVENT_ID': (np.random.random(int(1e6))*65000).astype('uint16')})

pandas apply format list

In [13]: %timeit df2 = df.join(pd.DataFrame(df['EVENT_ID'].apply('{0:b}'.format).apply(list).tolist()))
1.97 s ± 42.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

pandas list bin

In [10]: %%timeit...: binary_values = pd.DataFrame([list(bin(x)[2:]) for x in df['EVENT_ID']])...: df2 = df.join(binary_values)...:...:
1.78 s ± 53.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

pandas3 apply lambda

In [5]: %%timeit...: for i in range(16):...:     df[f"bit{i}"] = df["EVENT_ID"].apply(lambda x: x & 1 << i).astype(bool).astype(int)...:
6.08 s ± 65.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

numpy

In [14]: %%timeit...: lh = np.unpackbits((df.values & 0xFF).astype('uint8')).reshape(-1,8)...: uh = np.unpackbits((df.values >> 8).astype('uint8')).reshape(-1,8)...: df3=pd.concat([df, pd.DataFrame(np.concatenate([uh,lh],axis=1),index=df.index)],axis=1)...:...:
49.9 ms ± 232 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)