Performance difference in pandas read_table vs. read_csv vs. from_csv vs. read_excel?
read_table is read_csv with sep=',' replaced by sep='\t', they are two thin wrappers around the same function so the performance will be identical.  read_excel uses the xlrd package to read xls and xlsx files into a DataFrame, it doesn't handle csv files.
from_csv calls read_table, so no.
I tend to import .csv files into pandas, but sometimes I may get data in other formats to make DataFrame objects.
Today, I just found out about read_table as a "generic" importer for other formats, and wondered if there were significant performance differences between the various methods in pandas for reading .csv files, e.g. read_table, from_csv, read_excel.

Do these other methods have better performance than read_csv? 
Is read_csv much different than from_csv for creating a DataFrame?