text data into dataframe the options for these functions fall into few categoriesindexingcan treat one or more columns as the returned dataframeand whether to get column names from the filethe useror not at all type inference and data conversionthis includes the user-defined value conversions and custom list of missing value markers datetime parsingincludes combining capabilityincluding combining date and time information spread over multiple columns into single column in the result iteratingsupport for iterating over chunks of very large files unclean data issuesskipping rows or footercommentsor other minor things like numeric data with thousands separated by commas type inference is one of the more important features of these functionsthat means you don' have to specify which columns are numericintegerbooleanor string handling dates and other custom types requires bit more effortthough let' start with small comma-separated (csvtext filein [ ]!cat ch /ex csv , , , ,message , , , ,hello , , , ,world , , , ,foo since this is comma-delimitedwe can use read_csv to read it into dataframein [ ]df pd read_csv('ch /ex csv'in [ ]df out[ ] message hello world foo we could also have used read_table and specifying the delimiterin [ ]pd read_table('ch /ex csv'sep=','out[ ] message hello world foo here used the unix cat shell command to print the raw contents of the file to the screen if you're on windowsyou can use type instead of cat to achieve the same effect data loadingstorageand file formats www it-ebooks info