python pandas csv file conversion of integers to binary

Question

I have a csv file like that

Meme1, Meme2, Meme3, Meme4, Meme5, Meme6
Meme1, Meme2, Meme3, Meme99, Meme5, Meme6
Meme5, Meme2, Meme2, Meme4, Meme10, Meme6
Meme99, Meme3, Meme4, Meme4, Meme5, Meme6

I want like that

00000001, 00000010, 00000011, 00000100, 00000101, 00000110
00000001, 00000010, 01100011, 00000100, 00000101, 00000110
00000100, 00000010, 00000010, 00000100, 00001010, 00000110

means every integer should be converted to binary and word meme should be deleted

I am trying but cannot do:(
import pandas as pd
import csv
import numpy as np

dataset = pd.read_csv('datsetcoma.txt')
reader = csv.DictReader(dataset)
print (reader)
 # print back the headers
for row in reader:
    if row.is_integer:
      
       b=np.binary_repr(10, width=8)
       print (b)

Do you have only Meme or there are other words?

Hamzah Al-Qadasi
– Hamzah Al-Qadasi

2022-03-24 16:17:09 +00:00
Commented Mar 24, 2022 at 16:17 — Hamzah Al-Qadasi
– Hamzah Al-Qadasi, Commented Mar 24, 2022 at 16:17
only meme @Phoenix

Maya
– Maya

2022-03-24 16:17:52 +00:00
Commented Mar 24, 2022 at 16:17 — Maya
– Maya, Commented Mar 24, 2022 at 16:17
So what if there are strings like foo123bar456?

ifly6
– ifly6

2022-03-24 16:18:28 +00:00
Commented Mar 24, 2022 at 16:18 — ifly6
– ifly6, Commented Mar 24, 2022 at 16:18
only meme with different integers @ifly6

Maya
– Maya

2022-03-24 16:19:22 +00:00
Commented Mar 24, 2022 at 16:19 — Maya
– Maya, Commented Mar 24, 2022 at 16:19

Hamzah Al-Qadasi · Accepted Answer · 2022-03-24 17:05:08Z

1

You can also try this:

import pandas as pd
import numpy as np
import io

# example taken from @ifly6  
df = pd.read_csv(io.StringIO('''Meme1, Meme2, Meme3, Meme4, Meme5, Meme6
Meme1, Meme2, Meme3, Meme99, Meme5, Meme6
Meme5, Meme2, Meme2, Meme4, Meme10, Meme6
Meme99, Meme3, Meme4, Meme4, Meme5, Meme6'''), header=None)

df.apply(lambda x: x.apply(lambda y:  bin(int(y.replace('Meme', '')))[2:].zfill(8) ) ) 

#output

          0         1         2         3         4         5
0  00000001  00000010  00000011  00000100  00000101  00000110
1  00000001  00000010  00000011  01100011  00000101  00000110
2  00000101  00000010  00000010  00000100  00001010  00000110
3  01100011  00000011  00000100  00000100  00000101  00000110

edited Mar 24, 2022 at 17:05

answered Mar 24, 2022 at 16:51

Hamzah Al-Qadasi

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3 Comments

Maya Over a year ago

facing this error: AttributeError: 'int' object has no attribute 'replace'

Hamzah Al-Qadasi Over a year ago

I updated the answer, try to run the entire solution at once.

Maya Over a year ago

yeah, its working THANKS but how can I give full CSV FILE with pandas io LIKE df = pd.read_csv(io.my-file.csv) rather then stringio?

ifly6 · Accepted Answer · 2022-03-24 16:33:02Z

1

Loading the DF using import io with no headers, I extract the integers using a regular expression without expansion. Then cast to integer types. Because np.binary_repr is not vectorised, I have to "vectorise" it.

Because np methods do not retain indexing, I then reproduce the indicies (which is needed to retain row and column positions that are preserved in the multi-index) in the pd.Series constructor and unstack back to the original data frame shape.

df = pd.read_csv(io.StringIO('''Meme1, Meme2, Meme3, Meme4, Meme5, Meme6
Meme1, Meme2, Meme3, Meme99, Meme5, Meme6
Meme5, Meme2, Meme2, Meme4, Meme10, Meme6
Meme99, Meme3, Meme4, Meme4, Meme5, Meme6'''), header=None)

s = df.stack()
s = s.str.extract(r'(\d+)', expand=False).astype(int)
pd.Series(np.vectorize(np.binary_repr)(s, width=8), index=s.index).unstack()

The final output,

          0         1         2         3         4         5
0  00000001  00000010  00000011  00000100  00000101  00000110
1  00000001  00000010  00000011  01100011  00000101  00000110
2  00000101  00000010  00000010  00000100  00001010  00000110
3  01100011  00000011  00000100  00000100  00000101  00000110

Nb your binary conversions in the original post are not all accurate. Eg Meme5 is erroneously converted to 00000100 when it should be 00000101. The OP version also omits (probably for convenience) the final row.

Nb also that this will not work if there are multiple expansion groups. In a comment I posited the hypothetical example foo123bar456. This would result in two expansion groups which would disturb the indexing.

answered Mar 24, 2022 at 16:33

ifly6

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3 Comments

Maya Over a year ago

yeah, its working THANKS but how can I give full CSV FILE with pandas io LIKE df = pd.read_csv(io.my-file.csv) rather then stringio?

Maya Over a year ago

I mean you are giving string i wanna give full file

ifly6 Over a year ago

Just load the file directly like normal... pandas.pydata.org/docs/reference/api/pandas.read_csv.html

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