Comparison with SAS¶

For potential users coming from SAS this page is meant to demonstrate how different SAS operations would be performed in pandas.

If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library.

As is customary, we import pandas and NumPy as follows:

In [1]: import pandas as pd

In [2]: import numpy as np

Data structures¶

General terminology translation¶

pandas	SAS
`DataFrame`	data set
column	variable
row	observation
groupby	BY-group
`NaN`	`.`

`DataFrame`¶

A DataFrame in pandas is analogous to a SAS data set - a two-dimensional data source with labeled columns that can be of different types. As will be shown in this document, almost any operation that can be applied to a data set using SAS’s DATA step, can also be accomplished in pandas.

`Series`¶

A Series is the data structure that represents one column of a DataFrame. SAS doesn’t have a separate data structure for a single column, but in general, working with a Series is analogous to referencing a column in the DATA step.

`Index`¶

Every DataFrame and Series has an Index - which are labels on the rows of the data. SAS does not have an exactly analogous concept. A data set’s rows are essentially unlabeled, other than an implicit integer index that can be accessed during the DATA step (_N_).

In pandas, if no index is specified, an integer index is also used by default (first row = 0, second row = 1, and so on). While using a labeled Index or MultiIndex can enable sophisticated analyses and is ultimately an important part of pandas to understand, for this comparison we will essentially ignore the Index and just treat the DataFrame as a collection of columns. Please see the indexing documentation for much more on how to use an Index effectively.

Copies vs. in place operations¶

Most pandas operations return copies of the Series/DataFrame. To make the changes “stick”, you’ll need to either assign to a new variable:

sorted_df = df.sort_values("col1")

or overwrite the original one:

df = df.sort_values("col1")

Note

You will see an inplace=True keyword argument available for some methods:

df.sort_values("col1", inplace=True)

Its use is discouraged. More information.

Data input / output¶

Constructing a DataFrame from values¶

A SAS data set can be built from specified values by placing the data after a datalines statement and specifying the column names.

data df;
    input x y;
    datalines;
    1 2
    3 4
    5 6
    ;
run;

A pandas DataFrame can be constructed in many different ways, but for a small number of values, it is often convenient to specify it as a Python dictionary, where the keys are the column names and the values are the data.

In [1]: df = pd.DataFrame({"x": [1, 3, 5], "y": [2, 4, 6]})

In [2]: df
Out[2]: 
   x  y
0  1  2
1  3  4
2  5  6

Reading external data¶

Like SAS, pandas provides utilities for reading in data from many formats. The tips dataset, found within the pandas tests (csv) will be used in many of the following examples.

SAS provides PROC IMPORT to read csv data into a data set.

proc import datafile='tips.csv' dbms=csv out=tips replace;
    getnames=yes;
run;

The pandas method is read_csv(), which works similarly.

In [3]: url = (
   ...:     "https://raw.githubusercontent.com/pandas-dev/"
   ...:     "pandas/main/pandas/tests/io/data/csv/tips.csv"
   ...: )
   ...: 

In [4]: tips = pd.read_csv(url)
---------------------------------------------------------------------------
ConnectionRefusedError                    Traceback (most recent call last)
File /usr/lib/python3.11/urllib/request.py:1348, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1347 try:
-> 1348     h.request(req.get_method(), req.selector, req.data, headers,
   1349               encode_chunked=req.has_header('Transfer-encoding'))
   1350 except OSError as err: # timeout error

File /usr/lib/python3.11/http/client.py:1282, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
   1281 """Send a complete request to the server."""
-> 1282 self._send_request(method, url, body, headers, encode_chunked)

File /usr/lib/python3.11/http/client.py:1328, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
   1327     body = _encode(body, 'body')
-> 1328 self.endheaders(body, encode_chunked=encode_chunked)

File /usr/lib/python3.11/http/client.py:1277, in HTTPConnection.endheaders(self, message_body, encode_chunked)
   1276     raise CannotSendHeader()
-> 1277 self._send_output(message_body, encode_chunked=encode_chunked)

File /usr/lib/python3.11/http/client.py:1037, in HTTPConnection._send_output(self, message_body, encode_chunked)
   1036 del self._buffer[:]
-> 1037 self.send(msg)
   1039 if message_body is not None:
   1040 
   1041     # create a consistent interface to message_body

File /usr/lib/python3.11/http/client.py:975, in HTTPConnection.send(self, data)
    974 if self.auto_open:
--> 975     self.connect()
    976 else:

File /usr/lib/python3.11/http/client.py:1447, in HTTPSConnection.connect(self)
   1445 "Connect to a host on a given (SSL) port."
-> 1447 super().connect()
   1449 if self._tunnel_host:

File /usr/lib/python3.11/http/client.py:941, in HTTPConnection.connect(self)
    940 sys.audit("http.client.connect", self, self.host, self.port)
--> 941 self.sock = self._create_connection(
    942     (self.host,self.port), self.timeout, self.source_address)
    943 # Might fail in OSs that don't implement TCP_NODELAY

File /usr/lib/python3.11/socket.py:851, in create_connection(address, timeout, source_address, all_errors)
    850 if not all_errors:
--> 851     raise exceptions[0]
    852 raise ExceptionGroup("create_connection failed", exceptions)

File /usr/lib/python3.11/socket.py:836, in create_connection(address, timeout, source_address, all_errors)
    835     sock.bind(source_address)
--> 836 sock.connect(sa)
    837 # Break explicitly a reference cycle

ConnectionRefusedError: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
Cell In [4], line 1
----> 1 tips = pd.read_csv(url)

File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:211, in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper(*args, **kwargs)
    209     else:
    210         kwargs[new_arg_name] = new_arg_value
--> 211 return func(*args, **kwargs)

File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:950, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    935 kwds_defaults = _refine_defaults_read(
    936     dialect,
    937     delimiter,
   (...)
    946     defaults={"delimiter": ","},
    947 )
    948 kwds.update(kwds_defaults)
--> 950 return _read(filepath_or_buffer, kwds)

File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:605, in _read(filepath_or_buffer, kwds)
    602 _validate_names(kwds.get("names", None))
    604 # Create the parser.
--> 605 parser = TextFileReader(filepath_or_buffer, **kwds)
    607 if chunksize or iterator:
    608     return parser

File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1442, in TextFileReader.__init__(self, f, engine, **kwds)
   1439     self.options["has_index_names"] = kwds["has_index_names"]
   1441 self.handles: IOHandles | None = None
-> 1442 self._engine = self._make_engine(f, self.engine)

File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1735, in TextFileReader._make_engine(self, f, engine)
   1733     if "b" not in mode:
   1734         mode += "b"
-> 1735 self.handles = get_handle(
   1736     f,
   1737     mode,
   1738     encoding=self.options.get("encoding", None),
   1739     compression=self.options.get("compression", None),
   1740     memory_map=self.options.get("memory_map", False),
   1741     is_text=is_text,
   1742     errors=self.options.get("encoding_errors", "strict"),
   1743     storage_options=self.options.get("storage_options", None),
   1744 )
   1745 assert self.handles is not None
   1746 f = self.handles.handle

File /usr/lib/python3/dist-packages/pandas/io/common.py:713, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    710     codecs.lookup_error(errors)
    712 # open URLs
--> 713 ioargs = _get_filepath_or_buffer(
    714     path_or_buf,
    715     encoding=encoding,
    716     compression=compression,
    717     mode=mode,
    718     storage_options=storage_options,
    719 )
    721 handle = ioargs.filepath_or_buffer
    722 handles: list[BaseBuffer]

File /usr/lib/python3/dist-packages/pandas/io/common.py:363, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
    361 # assuming storage_options is to be interpreted as headers
    362 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
--> 363 with urlopen(req_info) as req:
    364     content_encoding = req.headers.get("Content-Encoding", None)
    365     if content_encoding == "gzip":
    366         # Override compression based on Content-Encoding header

File /usr/lib/python3/dist-packages/pandas/io/common.py:265, in urlopen(*args, **kwargs)
    259 """
    260 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
    261 the stdlib.
    262 """
    263 import urllib.request
--> 265 return urllib.request.urlopen(*args, **kwargs)

File /usr/lib/python3.11/urllib/request.py:216, in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    214 else:
    215     opener = _opener
--> 216 return opener.open(url, data, timeout)

File /usr/lib/python3.11/urllib/request.py:519, in OpenerDirector.open(self, fullurl, data, timeout)
    516     req = meth(req)
    518 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 519 response = self._open(req, data)
    521 # post-process response
    522 meth_name = protocol+"_response"

File /usr/lib/python3.11/urllib/request.py:536, in OpenerDirector._open(self, req, data)
    533     return result
    535 protocol = req.type
--> 536 result = self._call_chain(self.handle_open, protocol, protocol +
    537                           '_open', req)
    538 if result:
    539     return result

File /usr/lib/python3.11/urllib/request.py:496, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
    494 for handler in handlers:
    495     func = getattr(handler, meth_name)
--> 496     result = func(*args)
    497     if result is not None:
    498         return result

File /usr/lib/python3.11/urllib/request.py:1391, in HTTPSHandler.https_open(self, req)
   1390 def https_open(self, req):
-> 1391     return self.do_open(http.client.HTTPSConnection, req,
   1392         context=self._context, check_hostname=self._check_hostname)

File /usr/lib/python3.11/urllib/request.py:1351, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1348         h.request(req.get_method(), req.selector, req.data, headers,
   1349                   encode_chunked=req.has_header('Transfer-encoding'))
   1350     except OSError as err: # timeout error
-> 1351         raise URLError(err)
   1352     r = h.getresponse()
   1353 except:

URLError: <urlopen error [Errno 111] Connection refused>

In [5]: tips
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [5], line 1
----> 1 tips

NameError: name 'tips' is not defined

Like PROC IMPORT, read_csv can take a number of parameters to specify how the data should be parsed. For example, if the data was instead tab delimited, and did not have column names, the pandas command would be:

tips = pd.read_csv("tips.csv", sep="\t", header=None)

# alternatively, read_table is an alias to read_csv with tab delimiter
tips = pd.read_table("tips.csv", header=None)

In addition to text/csv, pandas supports a variety of other data formats such as Excel, HDF5, and SQL databases. These are all read via a pd.read_* function. See the IO documentation for more details.

Limiting output¶

By default, pandas will truncate output of large DataFrames to show the first and last rows. This can be overridden by changing the pandas options, or using DataFrame.head() or DataFrame.tail().

In [1]: tips.head(5)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips.head(5)

NameError: name 'tips' is not defined

The equivalent in SAS would be:

proc print data=df(obs=5);
run;

Exporting data¶

The inverse of PROC IMPORT in SAS is PROC EXPORT

proc export data=tips outfile='tips2.csv' dbms=csv;
run;

Similarly in pandas, the opposite of read_csv is to_csv(), and other data formats follow a similar api.

tips.to_csv("tips2.csv")

Data operations¶

Operations on columns¶

In the DATA step, arbitrary math expressions can be used on new or existing columns.

data tips;
    set tips;
    total_bill = total_bill - 2;
    new_bill = total_bill / 2;
run;

pandas provides vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in the same way. The DataFrame.drop() method drops a column from the DataFrame.

In [1]: tips["total_bill"] = tips["total_bill"] - 2
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["total_bill"] = tips["total_bill"] - 2

NameError: name 'tips' is not defined

In [2]: tips["new_bill"] = tips["total_bill"] / 2
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips["new_bill"] = tips["total_bill"] / 2

NameError: name 'tips' is not defined

In [3]: tips
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 tips

NameError: name 'tips' is not defined

In [4]: tips = tips.drop("new_bill", axis=1)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [4], line 1
----> 1 tips = tips.drop("new_bill", axis=1)

NameError: name 'tips' is not defined

Filtering¶

Filtering in SAS is done with an if or where statement, on one or more columns.

data tips;
    set tips;
    if total_bill > 10;
run;

data tips;
    set tips;
    where total_bill > 10;
    /* equivalent in this case - where happens before the
       DATA step begins and can also be used in PROC statements */
run;

DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing.

In [1]: tips[tips["total_bill"] > 10]
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips[tips["total_bill"] > 10]

NameError: name 'tips' is not defined

The above statement is simply passing a Series of True/False objects to the DataFrame, returning all rows with True.

In [2]: is_dinner = tips["time"] == "Dinner"
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 is_dinner = tips["time"] == "Dinner"

NameError: name 'tips' is not defined

In [3]: is_dinner
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 is_dinner

NameError: name 'is_dinner' is not defined

In [4]: is_dinner.value_counts()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [4], line 1
----> 1 is_dinner.value_counts()

NameError: name 'is_dinner' is not defined

In [5]: tips[is_dinner]
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [5], line 1
----> 1 tips[is_dinner]

NameError: name 'tips' is not defined

If/then logic¶

In SAS, if/then logic can be used to create new columns.

data tips;
    set tips;
    format bucket $4.;

    if total_bill < 10 then bucket = 'low';
    else bucket = 'high';
run;

The same operation in pandas can be accomplished using the where method from numpy.

In [1]: tips["bucket"] = np.where(tips["total_bill"] < 10, "low", "high")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["bucket"] = np.where(tips["total_bill"] < 10, "low", "high")

NameError: name 'tips' is not defined

In [2]: tips
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips

NameError: name 'tips' is not defined

Date functionality¶

SAS provides a variety of functions to do operations on date/datetime columns.

data tips;
    set tips;
    format date1 date2 date1_plusmonth mmddyy10.;
    date1 = mdy(1, 15, 2013);
    date2 = mdy(2, 15, 2015);
    date1_year = year(date1);
    date2_month = month(date2);
    * shift date to beginning of next interval;
    date1_next = intnx('MONTH', date1, 1);
    * count intervals between dates;
    months_between = intck('MONTH', date1, date2);
run;

The equivalent pandas operations are shown below. In addition to these functions pandas supports other Time Series features not available in Base SAS (such as resampling and custom offsets) - see the timeseries documentation for more details.

In [1]: tips["date1"] = pd.Timestamp("2013-01-15")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["date1"] = pd.Timestamp("2013-01-15")

NameError: name 'tips' is not defined

In [2]: tips["date2"] = pd.Timestamp("2015-02-15")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips["date2"] = pd.Timestamp("2015-02-15")

NameError: name 'tips' is not defined

In [3]: tips["date1_year"] = tips["date1"].dt.year
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 tips["date1_year"] = tips["date1"].dt.year

NameError: name 'tips' is not defined

In [4]: tips["date2_month"] = tips["date2"].dt.month
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [4], line 1
----> 1 tips["date2_month"] = tips["date2"].dt.month

NameError: name 'tips' is not defined

In [5]: tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [5], line 1
----> 1 tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin()

NameError: name 'tips' is not defined

In [6]: tips["months_between"] = tips["date2"].dt.to_period("M") - tips[
   ...:     "date1"
   ...: ].dt.to_period("M")
   ...: 
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [6], line 1
----> 1 tips["months_between"] = tips["date2"].dt.to_period("M") - tips[
      2     "date1"
      3 ].dt.to_period("M")

NameError: name 'tips' is not defined

In [7]: tips[
   ...:     ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"]
   ...: ]
   ...: 
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [7], line 1
----> 1 tips[
      2     ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"]
      3 ]

NameError: name 'tips' is not defined

Selection of columns¶

SAS provides keywords in the DATA step to select, drop, and rename columns.

data tips;
    set tips;
    keep sex total_bill tip;
run;

data tips;
    set tips;
    drop sex;
run;

data tips;
    set tips;
    rename total_bill=total_bill_2;
run;

The same operations are expressed in pandas below.

Keep certain columns¶

In [1]: tips[["sex", "total_bill", "tip"]]
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips[["sex", "total_bill", "tip"]]

NameError: name 'tips' is not defined

Drop a column¶

In [2]: tips.drop("sex", axis=1)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips.drop("sex", axis=1)

NameError: name 'tips' is not defined

Rename a column¶

In [3]: tips.rename(columns={"total_bill": "total_bill_2"})
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 tips.rename(columns={"total_bill": "total_bill_2"})

NameError: name 'tips' is not defined

Sorting by values¶

Sorting in SAS is accomplished via PROC SORT

proc sort data=tips;
    by sex total_bill;
run;

pandas has a DataFrame.sort_values() method, which takes a list of columns to sort by.

In [1]: tips = tips.sort_values(["sex", "total_bill"])
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips = tips.sort_values(["sex", "total_bill"])

NameError: name 'tips' is not defined

In [2]: tips
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips

NameError: name 'tips' is not defined

String processing¶

Finding length of string¶

SAS determines the length of a character string with the LENGTHN and LENGTHC functions. LENGTHN excludes trailing blanks and LENGTHC includes trailing blanks.

data _null_;
set tips;
put(LENGTHN(time));
put(LENGTHC(time));
run;

You can find the length of a character string with Series.str.len(). In Python 3, all strings are Unicode strings. len includes trailing blanks. Use len and rstrip to exclude trailing blanks.

In [1]: tips["time"].str.len()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["time"].str.len()

NameError: name 'tips' is not defined

In [2]: tips["time"].str.rstrip().str.len()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips["time"].str.rstrip().str.len()

NameError: name 'tips' is not defined

Finding position of substring¶

SAS determines the position of a character in a string with the FINDW function. FINDW takes the string defined by the first argument and searches for the first position of the substring you supply as the second argument.

data _null_;
set tips;
put(FINDW(sex,'ale'));
run;

You can find the position of a character in a column of strings with the Series.str.find() method. find searches for the first position of the substring. If the substring is found, the method returns its position. If not found, it returns -1. Keep in mind that Python indexes are zero-based.

In [1]: tips["sex"].str.find("ale")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["sex"].str.find("ale")

NameError: name 'tips' is not defined

Extracting substring by position¶

SAS extracts a substring from a string based on its position with the SUBSTR function.

data _null_;
set tips;
put(substr(sex,1,1));
run;

With pandas you can use [] notation to extract a substring from a string by position locations. Keep in mind that Python indexes are zero-based.

In [1]: tips["sex"].str[0:1]
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips["sex"].str[0:1]

NameError: name 'tips' is not defined

Extracting nth word¶

The SAS SCAN function returns the nth word from a string. The first argument is the string you want to parse and the second argument specifies which word you want to extract.

data firstlast;
input String $60.;
First_Name = scan(string, 1);
Last_Name = scan(string, -1);
datalines2;
John Smith;
Jane Cook;
;;;
run;

The simplest way to extract words in pandas is to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them.

In [1]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]})

In [2]: firstlast["First_Name"] = firstlast["String"].str.split(" ", expand=True)[0]

In [3]: firstlast["Last_Name"] = firstlast["String"].str.rsplit(" ", expand=True)[1]

In [4]: firstlast
Out[4]: 
       String First_Name Last_Name
0  John Smith       John     Smith
1   Jane Cook       Jane      Cook

Changing case¶

The SAS UPCASE LOWCASE and PROPCASE functions change the case of the argument.

data firstlast;
input String $60.;
string_up = UPCASE(string);
string_low = LOWCASE(string);
string_prop = PROPCASE(string);
datalines2;
John Smith;
Jane Cook;
;;;
run;

The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title().

In [1]: firstlast = pd.DataFrame({"string": ["John Smith", "Jane Cook"]})

In [2]: firstlast["upper"] = firstlast["string"].str.upper()

In [3]: firstlast["lower"] = firstlast["string"].str.lower()

In [4]: firstlast["title"] = firstlast["string"].str.title()

In [5]: firstlast
Out[5]: 
       string       upper       lower       title
0  John Smith  JOHN SMITH  john smith  John Smith
1   Jane Cook   JANE COOK   jane cook   Jane Cook

Merging¶

The following tables will be used in the merge examples:

In [1]: df1 = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)})

In [2]: df1
Out[2]: 
  key     value
0   A  0.469112
1   B -0.282863
2   C -1.509059
3   D -1.135632

In [3]: df2 = pd.DataFrame({"key": ["B", "D", "D", "E"], "value": np.random.randn(4)})

In [4]: df2
Out[4]: 
  key     value
0   B  1.212112
1   D -0.173215
2   D  0.119209
3   E -1.044236

In SAS, data must be explicitly sorted before merging. Different types of joins are accomplished using the in= dummy variables to track whether a match was found in one or both input frames.

proc sort data=df1;
    by key;
run;

proc sort data=df2;
    by key;
run;

data left_join inner_join right_join outer_join;
    merge df1(in=a) df2(in=b);

    if a and b then output inner_join;
    if a then output left_join;
    if b then output right_join;
    if a or b then output outer_join;
run;

pandas DataFrames have a merge() method, which provides similar functionality. The data does not have to be sorted ahead of time, and different join types are accomplished via the how keyword.

In [1]: inner_join = df1.merge(df2, on=["key"], how="inner")

In [2]: inner_join
Out[2]: 
  key   value_x   value_y
0   B -0.282863  1.212112
1   D -1.135632 -0.173215
2   D -1.135632  0.119209

In [3]: left_join = df1.merge(df2, on=["key"], how="left")

In [4]: left_join
Out[4]: 
  key   value_x   value_y
0   A  0.469112       NaN
1   B -0.282863  1.212112
2   C -1.509059       NaN
3   D -1.135632 -0.173215
4   D -1.135632  0.119209

In [5]: right_join = df1.merge(df2, on=["key"], how="right")

In [6]: right_join
Out[6]: 
  key   value_x   value_y
0   B -0.282863  1.212112
1   D -1.135632 -0.173215
2   D -1.135632  0.119209
3   E       NaN -1.044236

In [7]: outer_join = df1.merge(df2, on=["key"], how="outer")

In [8]: outer_join
Out[8]: 
  key   value_x   value_y
0   A  0.469112       NaN
1   B -0.282863  1.212112
2   C -1.509059       NaN
3   D -1.135632 -0.173215
4   D -1.135632  0.119209
5   E       NaN -1.044236

Missing data¶

Both pandas and SAS have a representation for missing data.

pandas represents missing data with the special float value NaN (not a number). Many of the semantics are the same; for example missing data propagates through numeric operations, and is ignored by default for aggregations.

In [1]: outer_join
Out[1]: 
  key   value_x   value_y
0   A  0.469112       NaN
1   B -0.282863  1.212112
2   C -1.509059       NaN
3   D -1.135632 -0.173215
4   D -1.135632  0.119209
5   E       NaN -1.044236

In [2]: outer_join["value_x"] + outer_join["value_y"]
Out[2]: 
0         NaN
1    0.929249
2         NaN
3   -1.308847
4   -1.016424
5         NaN
dtype: float64

In [3]: outer_join["value_x"].sum()
Out[3]: -3.5940742896293765

One difference is that missing data cannot be compared to its sentinel value. For example, in SAS you could do this to filter missing values.

data outer_join_nulls;
    set outer_join;
    if value_x = .;
run;

data outer_join_no_nulls;
    set outer_join;
    if value_x ^= .;
run;

In pandas, Series.isna() and Series.notna() can be used to filter the rows.

In [1]: outer_join[outer_join["value_x"].isna()]
Out[1]: 
  key  value_x   value_y
5   E      NaN -1.044236

In [2]: outer_join[outer_join["value_x"].notna()]
Out[2]: 
  key   value_x   value_y
0   A  0.469112       NaN
1   B -0.282863  1.212112
2   C -1.509059       NaN
3   D -1.135632 -0.173215
4   D -1.135632  0.119209

pandas provides a variety of methods to work with missing data. Here are some examples:

Drop rows with missing values¶

In [3]: outer_join.dropna()
Out[3]: 
  key   value_x   value_y
1   B -0.282863  1.212112
3   D -1.135632 -0.173215
4   D -1.135632  0.119209

Forward fill from previous rows¶

In [4]: outer_join.fillna(method="ffill")
Out[4]: 
  key   value_x   value_y
0   A  0.469112       NaN
1   B -0.282863  1.212112
2   C -1.509059  1.212112
3   D -1.135632 -0.173215
4   D -1.135632  0.119209
5   E -1.135632 -1.044236

Replace missing values with a specified value¶

Using the mean:

In [5]: outer_join["value_x"].fillna(outer_join["value_x"].mean())
Out[5]: 
0    0.469112
1   -0.282863
2   -1.509059
3   -1.135632
4   -1.135632
5   -0.718815
Name: value_x, dtype: float64

GroupBy¶

Aggregation¶

SAS’s PROC SUMMARY can be used to group by one or more key variables and compute aggregations on numeric columns.

proc summary data=tips nway;
    class sex smoker;
    var total_bill tip;
    output out=tips_summed sum=;
run;

pandas provides a flexible groupby mechanism that allows similar aggregations. See the groupby documentation for more details and examples.

In [1]: tips_summed = tips.groupby(["sex", "smoker"])[["total_bill", "tip"]].sum()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 tips_summed = tips.groupby(["sex", "smoker"])[["total_bill", "tip"]].sum()

NameError: name 'tips' is not defined

In [2]: tips_summed
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips_summed

NameError: name 'tips_summed' is not defined

Transformation¶

In SAS, if the group aggregations need to be used with the original frame, it must be merged back together. For example, to subtract the mean for each observation by smoker group.

proc summary data=tips missing nway;
    class smoker;
    var total_bill;
    output out=smoker_means mean(total_bill)=group_bill;
run;

proc sort data=tips;
    by smoker;
run;

data tips;
    merge tips(in=a) smoker_means(in=b);
    by smoker;
    adj_total_bill = total_bill - group_bill;
    if a and b;
run;

pandas provides a Transformation mechanism that allows these type of operations to be succinctly expressed in one operation.

In [1]: gb = tips.groupby("smoker")["total_bill"]
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [1], line 1
----> 1 gb = tips.groupby("smoker")["total_bill"]

NameError: name 'tips' is not defined

In [2]: tips["adj_total_bill"] = tips["total_bill"] - gb.transform("mean")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [2], line 1
----> 1 tips["adj_total_bill"] = tips["total_bill"] - gb.transform("mean")

NameError: name 'tips' is not defined

In [3]: tips
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 tips

NameError: name 'tips' is not defined

By group processing¶

In addition to aggregation, pandas groupby can be used to replicate most other by group processing from SAS. For example, this DATA step reads the data by sex/smoker group and filters to the first entry for each.

proc sort data=tips;
   by sex smoker;
run;

data tips_first;
    set tips;
    by sex smoker;
    if FIRST.sex or FIRST.smoker then output;
run;

In pandas this would be written as:

In [4]: tips.groupby(["sex", "smoker"]).first()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [4], line 1
----> 1 tips.groupby(["sex", "smoker"]).first()

NameError: name 'tips' is not defined

Other considerations¶

Disk vs memory¶

pandas operates exclusively in memory, where a SAS data set exists on disk. This means that the size of data able to be loaded in pandas is limited by your machine’s memory, but also that the operations on that data may be faster.

If out of core processing is needed, one possibility is the dask.dataframe library (currently in development) which provides a subset of pandas functionality for an on-disk DataFrame

Data interop¶

pandas provides a read_sas() method that can read SAS data saved in the XPORT or SAS7BDAT binary format.

libname xportout xport 'transport-file.xpt';
data xportout.tips;
    set tips(rename=(total_bill=tbill));
    * xport variable names limited to 6 characters;
run;

df = pd.read_sas("transport-file.xpt")
df = pd.read_sas("binary-file.sas7bdat")

You can also specify the file format directly. By default, pandas will try to infer the file format based on its extension.

df = pd.read_sas("transport-file.xpt", format="xport")
df = pd.read_sas("binary-file.sas7bdat", format="sas7bdat")

XPORT is a relatively limited format and the parsing of it is not as optimized as some of the other pandas readers. An alternative way to interop data between SAS and pandas is to serialize to csv.

# version 0.17, 10M rows

In [8]: %time df = pd.read_sas('big.xpt')
Wall time: 14.6 s

In [9]: %time df = pd.read_csv('big.csv')
Wall time: 4.86 s

Comparison with spreadsheets

Comparison with Stata

Comparison with SAS¶

Data structures¶

General terminology translation¶

DataFrame¶

Series¶

Index¶

Copies vs. in place operations¶

Data input / output¶

Constructing a DataFrame from values¶

Reading external data¶

Limiting output¶

Exporting data¶

Data operations¶

Operations on columns¶

Filtering¶

If/then logic¶

Date functionality¶

Selection of columns¶

Keep certain columns¶

Drop a column¶

Rename a column¶

Sorting by values¶

String processing¶

Finding length of string¶

Finding position of substring¶

Extracting substring by position¶

Extracting nth word¶

Changing case¶

Merging¶

Missing data¶

Drop rows with missing values¶

Forward fill from previous rows¶

Replace missing values with a specified value¶

GroupBy¶

Aggregation¶

Transformation¶

By group processing¶

Other considerations¶

Disk vs memory¶

Data interop¶

`DataFrame`¶

`Series`¶

`Index`¶