Use Pandas to read multiple separated files
If the first line of your text file is separated by commas, the rest are separated by tab
You need to use the read_csv function in Pandas and specify multiple delimiters using regular expressions.
1,2,3,4,5,6 a b c d e f z x c v b n
Here is the code for how to read the file using Pandas:
import pandas as pd
# Read text file, specify multiple delimiters using regular expressions, and use the first row as the column namedf = pd.read_csv('', sep=r'[,\t]', engine='python', header=0)
# Print data frameprint(df)The output result should be:
1 2 3 4 5 6
0 a b c d e f
1 z x c v b n
The sep parameter here uses regular expressions[,\t], means that the delimiter can be a comma or a tab.
The engine parameter specifies the resolver's engine, here we have selected the resolver that comes with Python.
Finally, the header=0 parameter tells Pandas to use the first row as the column name.
Pandas reads TXT, data in txt is separated by spaces
You can use pandas' read_csv function to read data in TXT files.
When calling the read_csv function, you can use the sep parameter to specify the separator between the data.
For example:
If the data in the TXT file is spaced, you can call the read_csv function using sep=' '.
Here is an example
import pandas as pd
# Read data in TXT filedf = pd.read_csv('', sep=' ')
# Display the first 5 rows of data()Summarize
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