Pure Pandas approach to converting data in a text file into a table-CodePudding

I am looking to convert data in a textile into a table (data frame) using just methods from Pandas.

Textfile

Table/Dataframe format

    0  1  2  3  4
0   0  0  1  0  0
1   1  1  1  1  0
2   1  0  1  1  0
3   1  0  1  1  1
4   1  0  1  0  1
5   0  1  1  1  1
6   0  0  1  1  1
7   1  1  1  0  0
8   1  0  0  0  0
9   1  1  0  0  1
10  0  0  0  1  0
11  0  1  0  1  0

My approach

The only way I could think of doing it was to use some Python code to read the file into a 2D list of characters and then convert that to a data frame:

with open("data.txt") as f:
        # Removes newline character and splits binary string into individual character bits
        binary = [list(line.strip()) for line in f]

df = pd.DataFrame(binary, dtype="object")  # 2D list into pd dataframe

Although this works, I would like to know if this could have been done using Pandas with the read_csv() method

CodePudding user response：

Below code can help. Will also work with txt file

df = pd.read_csv('Book2.csv',header=None, dtype='str') #read file
df = df[0].astype('str').str.split('',expand=True) #split column
df[df.columns[1:-1]] #print df after removing first & last empty column

Output will look like this

CodePudding user response：

This should work in your case:

df = pd.read_fwf('untitled.txt', widths=[1,1,1,1,1])
df.columns = range(df.shape[1])
print(df)

Result:

    0  1  2  3  4
0   1  1  1  1  0
1   1  0  1  1  0
2   1  0  1  1  1
3   1  0  1  0  1
4   0  1  1  1  1
5   0  0  1  1  1
6   1  1  1  0  0
7   1  0  0  0  0
8   1  1  0  0  1
9   0  0  0  1  0
10  0  1  0  1  0