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As much as we love SAS, we cannot deny the trend that Python is replacing SAS in a very fast pace. In this article we will discuss how to use Python to read data. The Python package used here is pandas.

The first thing of data analysis is usually to read data. There are tons of scenarios regarding how to read data in SAS depending on the data formats. The two methods commonly used in SAS are proc import and the combination of infile and input statements. Proc import can be used for well formatted data such as CSV file, Excel file while infile and input can be more flexible. The commonly used function in Python is read_csv in the package Pandas.


One of the most common data files is the one with delimiters separated data. The delimiters are usually comma, space, tab or other characters. The following data are separated by comma.


It can be easily read into SAS data set by using either Proc import or infile and input.

/* SAS Version */
Proc import datafile = 'to_be_read_file.txt' 
    out = employee
    dbms = csv

Here option dbms specifies the type of the file to be read. csv means the input file is a file with comma separated values. The possible values of this option are DLM (delimited files), EXCEL (EXCEL files). Please refer to SAS documentation for details.

The Python script to read such file is even easier with Pandas read_csv function. Please note that this function cannot read Excel files. Use function read_excel for Excel files.

# Python Version
import pandas as pd
employee = pd.read_csv('to_be_read_file.txt', sep=',')

Delimiters The sep=',' option here is similar to the dbms option in Proc import. Depending on the data file, one can choose space (sep = ' '), tab (sep = r'\t'), multiple spaces (sep = r'\s+'), or a combination of space and tab (sep = r'\s+|\t+) or any other characters. It can be a regular string or a regular expression.

Reading Columns It is not necessary to read all the columns in the data file. One can use usecols option to pick desired columns. This option takes value of a list of integers (column numbers) or strings (column names). If we just want to read the first and the third columns of the above data example, we can use the following command. Note that the indexing starts with 0 instead of 1.

# read the first and third columns
employee = pd.read_csv('to_be_read_file.txt', sep=',', usecols=[0, 2])

Column Names If the column names are included in the first row of the raw file as in the example above, read_csv function reads them as column names by default. If there are no column names in the raw data file, one can specify the column names using option names in the function. Let’s assume data example without the first line, the following command can read the file and assign names to the columns.

# specify the column names
employee = pd.read_csv('to_be_read_file.txt', sep=',', names=['id', 'name', 'age'])

Data Type In SAS proc import infers the data type of one column from the raw data. One can specify the number of rows used to determined the data type by using the statement guessingrows=100;. Here 100 is the number of lines one can specify. In the Python read_csv function, one can actually specify the data type for individual columns by using the option dtype which takes a dictionary with column name as a key and data type as the corresponding value. The following example specifies the data type of ‘int32’ for the column id and ‘int16’ for the column age.

# specify the date types
employee = pd.read_csv('to_be_read_file.txt', sep=',', dtype={'id': 'int32', 'age': 'int16'})

When one need more flexibility in handling data, one would switch to the combination of input and infile statements in SAS. One example is reading a string or a number to a date. With proc import one can read the data first and then convert it to a date variable. However this can be achieved by using input and infile in SAS. It can also be easily done in Python using the option parse_dates in the function read_csv. Let’s add one more column ‘birthday’ to the above example.


The code below reads the birthday into a date column. The value of parse_dates can be a list of column numbers or column names.

# read birthday to a date
employee = pd.read_csv('to_be_read_file.txt', sep=',', parse_dates=['birthday'])


Some raw data files values are not separated, but each column starts from fixed position and has fixed length. In SAS we use @n to specify the starting position of one column in the input statement. In Python one can use function read_fwf in pandas. The main syntax is as follows.

# read_fwf
pd.read_fwf('to_be_read_file.txt', colspecs=[(0, 5), (10, 14)])

The parameter colspecs is a list of tuples and each tuple includes the starting and ending position of each column.


  1. read_csv reads delimited data. One can specify the columns to be read, set the column names, specify the delimiters, specify the data type and a lot more.
  2. read_fwf reads data with fixed starting and ending positions.

There are lots of other complicated data files that cannot be read either by read_csv or read_fwf such as unformatted data file. In those cases, one would need to use open() to read file line by line and handle them case by case.