# 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

Note

Throughout this tutorial, the pandas `DataFrame` will be displayed by calling `df.head()`, which displays the first N (default 5) rows of the `DataFrame`. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be:

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

## Data structures

### General terminology translation

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

### `DataFrame` / `Series`

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.

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.

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

    In [4]: df
    Out[4]:
       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 [5]: url = ('https://raw.github.com/pandas-dev/'
       ...:        'pandas/master/pandas/tests/data/tips.csv')
       ...:

    In [6]: tips = pd.read_csv(url)
    ---------------------------------------------------------------------------
    ConnectionRefusedError                    Traceback (most recent call last)
    /usr/lib/python3.8/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
       1318             try:
    -> 1319                 h.request(req.get_method(), req.selector, req.data, headers,
       1320                           encode_chunked=req.has_header('Transfer-encoding'))

    /usr/lib/python3.8/http/client.py in request(self, method, url, body, headers, encode_chunked)
       1229         """Send a complete request to the server."""
    -> 1230         self._send_request(method, url, body, headers, encode_chunked)
       1231

    /usr/lib/python3.8/http/client.py in _send_request(self, method, url, body, headers, encode_chunked)
       1275             body = _encode(body, 'body')
    -> 1276         self.endheaders(body, encode_chunked=encode_chunked)
       1277

    /usr/lib/python3.8/http/client.py in endheaders(self, message_body, encode_chunked)
       1224             raise CannotSendHeader()
    -> 1225         self._send_output(message_body, encode_chunked=encode_chunked)
       1226

    /usr/lib/python3.8/http/client.py in _send_output(self, message_body, encode_chunked)
       1003         del self._buffer[:]
    -> 1004         self.send(msg)
       1005

    /usr/lib/python3.8/http/client.py in send(self, data)
        943             if self.auto_open:
    --> 944                 self.connect()
        945             else:

    /usr/lib/python3.8/http/client.py in connect(self)
       1391
    -> 1392             super().connect()
       1393

    /usr/lib/python3.8/http/client.py in connect(self)
        914         """Connect to the host and port specified in __init__."""
    --> 915         self.sock = self._create_connection(
        916             (self.host,self.port), self.timeout, self.source_address)

    /usr/lib/python3.8/socket.py in create_connection(address, timeout, source_address)
        807         try:
    --> 808             raise err
        809         finally:

    /usr/lib/python3.8/socket.py in create_connection(address, timeout, source_address)
        795                 sock.bind(source_address)
    --> 796             sock.connect(sa)
        797             # 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)
    <ipython-input-6-8ab2297b7141> in <module>
    ----> 1 tips = pd.read_csv(url)

    /usr/lib/python3/dist-packages/pandas/io/parsers.py in parser_f(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, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
        683         )
        684
    --> 685         return _read(filepath_or_buffer, kwds)
        686
        687     parser_f.__name__ = name

    /usr/lib/python3/dist-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
        437     # though mypy handling of conditional imports is difficult.
        438     # See https://github.com/python/mypy/issues/1297
    --> 439     fp_or_buf, _, compression, should_close = get_filepath_or_buffer(
        440         filepath_or_buffer, encoding, compression
        441     )

    /usr/lib/python3/dist-packages/pandas/io/common.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
        194
        195     if _is_url(filepath_or_buffer):
    --> 196         req = urlopen(filepath_or_buffer)
        197         content_encoding = req.headers.get("Content-Encoding", None)
        198         if content_encoding == "gzip":

    /usr/lib/python3.8/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
        220     else:
        221         opener = _opener
    --> 222     return opener.open(url, data, timeout)
        223
        224 def install_opener(opener):

    /usr/lib/python3.8/urllib/request.py in open(self, fullurl, data, timeout)
        523
        524         sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
    --> 525         response = self._open(req, data)
        526
        527         # post-process response

    /usr/lib/python3.8/urllib/request.py in _open(self, req, data)
        540
        541         protocol = req.type
    --> 542         result = self._call_chain(self.handle_open, protocol, protocol +
        543                                   '_open', req)
        544         if result:

    /usr/lib/python3.8/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
        500         for handler in handlers:
        501             func = getattr(handler, meth_name)
    --> 502             result = func(*args)
        503             if result is not None:
        504                 return result

    /usr/lib/python3.8/urllib/request.py in https_open(self, req)
       1360
       1361         def https_open(self, req):
    -> 1362             return self.do_open(http.client.HTTPSConnection, req,
       1363                 context=self._context, check_hostname=self._check_hostname)
       1364

    /usr/lib/python3.8/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
       1320                           encode_chunked=req.has_header('Transfer-encoding'))
       1321             except OSError as err: # timeout error
    -> 1322                 raise URLError(err)
       1323             r = h.getresponse()
       1324         except:

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

    In [7]: tips.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-7-53a0cf752a4f> in <module>
    ----> 1 tips.head()

    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.

### 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 similar vectorized operations by specifying the individual `Series` in the `DataFrame`. New columns can be assigned in the same way.

    In [8]: tips['total_bill'] = tips['total_bill'] - 2
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-8-c2b722dfbb7a> in <module>
    ----> 1 tips['total_bill'] = tips['total_bill'] - 2

    NameError: name 'tips' is not defined

    In [9]: tips['new_bill'] = tips['total_bill'] / 2.0
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-9-148d24098b60> in <module>
    ----> 1 tips['new_bill'] = tips['total_bill'] / 2.0

    NameError: name 'tips' is not defined

    In [10]: tips.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-10-53a0cf752a4f> in <module>
    ----> 1 tips.head()

    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 [11]: tips[tips['total_bill'] > 10].head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-11-0d501c078554> in <module>
    ----> 1 tips[tips['total_bill'] > 10].head()

    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 [12]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high')
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-12-dc36aec0e1e5> in <module>
    ----> 1 tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high')

    NameError: name 'tips' is not defined

    In [13]: tips.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-13-53a0cf752a4f> in <module>
    ----> 1 tips.head()

    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 [14]: tips['date1'] = pd.Timestamp('2013-01-15')
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-14-4218712dcdec> in <module>
    ----> 1 tips['date1'] = pd.Timestamp('2013-01-15')

    NameError: name 'tips' is not defined

    In [15]: tips['date2'] = pd.Timestamp('2015-02-15')
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-15-c2ca0884c4e4> in <module>
    ----> 1 tips['date2'] = pd.Timestamp('2015-02-15')

    NameError: name 'tips' is not defined

    In [16]: tips['date1_year'] = tips['date1'].dt.year
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-16-ce0e7e4dded0> in <module>
    ----> 1 tips['date1_year'] = tips['date1'].dt.year

    NameError: name 'tips' is not defined

    In [17]: tips['date2_month'] = tips['date2'].dt.month
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-17-45457f2388b5> in <module>
    ----> 1 tips['date2_month'] = tips['date2'].dt.month

    NameError: name 'tips' is not defined

    In [18]: tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-18-a8fce403598f> in <module>
    ----> 1 tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin()

    NameError: name 'tips' is not defined

    In [19]: tips['months_between'] = (
       ....:     tips['date2'].dt.to_period('M') - tips['date1'].dt.to_period('M'))
       ....:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-19-226e3a1f12df> in <module>
          1 tips['months_between'] = (
    ----> 2     tips['date2'].dt.to_period('M') - tips['date1'].dt.to_period('M'))

    NameError: name 'tips' is not defined

    In [20]: tips[['date1', 'date2', 'date1_year', 'date2_month',
       ....:       'date1_next', 'months_between']].head()
       ....:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-20-c75197eb9fd0> in <module>
    ----> 1 tips[['date1', 'date2', 'date1_year', 'date2_month',
          2       'date1_next', 'months_between']].head()

    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
    In [21]: tips[['sex', 'total_bill', 'tip']].head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-21-3fd2a7fe5ed5> in <module>
    ----> 1 tips[['sex', 'total_bill', 'tip']].head()

    NameError: name 'tips' is not defined

    # drop
    In [22]: tips.drop('sex', axis=1).head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-22-0aa2dc1f187e> in <module>
    ----> 1 tips.drop('sex', axis=1).head()

    NameError: name 'tips' is not defined

    # rename
    In [23]: tips.rename(columns={'total_bill': 'total_bill_2'}).head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-23-bea02207d15a> in <module>
    ----> 1 tips.rename(columns={'total_bill': 'total_bill_2'}).head()

    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 objects have a `sort_values()` method, which takes a list of columns to sort by.

    In [24]: tips = tips.sort_values(['sex', 'total_bill'])
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-24-5419d1fabdcc> in <module>
    ----> 1 tips = tips.sort_values(['sex', 'total_bill'])

    NameError: name 'tips' is not defined

    In [25]: tips.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-25-53a0cf752a4f> in <module>
    ----> 1 tips.head()

    NameError: name 'tips' is not defined

## String processing

### Length

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;

Python determines the length of a character string with the `len` function. `len` includes trailing blanks. Use `len` and `rstrip` to exclude trailing blanks.

    In [26]: tips['time'].str.len().head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-26-d7f5e5528e52> in <module>
    ----> 1 tips['time'].str.len().head()

    NameError: name 'tips' is not defined

    In [27]: tips['time'].str.rstrip().str.len().head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-27-61f535a15695> in <module>
    ----> 1 tips['time'].str.rstrip().str.len().head()

    NameError: name 'tips' is not defined

### Find

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;

Python determines the position of a character in a string with the `find` function. `find` searches for the first position of the substring. If the substring is found, the function returns its position. Keep in mind that Python indexes are zero-based and the function will return -1 if it fails to find the substring.

    In [28]: tips['sex'].str.find("ale").head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-28-059d9d8e0006> in <module>
    ----> 1 tips['sex'].str.find("ale").head()

    NameError: name 'tips' is not defined

### Substring

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 [29]: tips['sex'].str[0:1].head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-29-560ab9d35724> in <module>
    ----> 1 tips['sex'].str[0:1].head()

    NameError: name 'tips' is not defined

### Scan

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;

Python extracts a substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach.

    In [30]: firstlast = pd.DataFrame({'String': ['John Smith', 'Jane Cook']})

    In [31]: firstlast['First_Name'] = firstlast['String'].str.split(" ", expand=True)[0]

    In [32]: firstlast['Last_Name'] = firstlast['String'].str.rsplit(" ", expand=True)[0]

    In [33]: firstlast
    Out[33]:
           String First_Name Last_Name
    0  John Smith       John      John
    1   Jane Cook       Jane      Jane

### Upcase, lowcase, and propcase

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 Python functions are `upper`, `lower`, and `title`.

    In [34]: firstlast = pd.DataFrame({'String': ['John Smith', 'Jane Cook']})

    In [35]: firstlast['string_up'] = firstlast['String'].str.upper()

    In [36]: firstlast['string_low'] = firstlast['String'].str.lower()

    In [37]: firstlast['string_prop'] = firstlast['String'].str.title()

    In [38]: firstlast
    Out[38]:
           String   string_up  string_low string_prop
    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 [39]: df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
       ....:                     'value': np.random.randn(4)})
       ....:

    In [40]: df1
    Out[40]:
      key     value
    0   A  0.469112
    1   B -0.282863
    2   C -1.509059
    3   D -1.135632

    In [41]: df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
       ....:                     'value': np.random.randn(4)})
       ....:

    In [42]: df2
    Out[42]:
      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. Note that the data does not have to be sorted ahead of time, and different join types are accomplished via the `how` keyword.

    In [43]: inner_join = df1.merge(df2, on=['key'], how='inner')

    In [44]: inner_join
    Out[44]:
      key   value_x   value_y
    0   B -0.282863  1.212112
    1   D -1.135632 -0.173215
    2   D -1.135632  0.119209

    In [45]: left_join = df1.merge(df2, on=['key'], how='left')

    In [46]: left_join
    Out[46]:
      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 [47]: right_join = df1.merge(df2, on=['key'], how='right')

    In [48]: right_join
    Out[48]:
      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 [49]: outer_join = df1.merge(df2, on=['key'], how='outer')

    In [50]: outer_join
    Out[50]:
      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

Like SAS, pandas has a representation for missing data - which is 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 [51]: outer_join
    Out[51]:
      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 [52]: outer_join['value_x'] + outer_join['value_y']
    Out[52]:
    0         NaN
    1    0.929249
    2         NaN
    3   -1.308847
    4   -1.016424
    5         NaN
    dtype: float64

    In [53]: outer_join['value_x'].sum()
    Out[53]: -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;

Which doesn’t work in pandas. Instead, the `pd.isna` or `pd.notna` functions should be used for comparisons.

    In [54]: outer_join[pd.isna(outer_join['value_x'])]
    Out[54]:
      key  value_x   value_y
    5   E      NaN -1.044236

    In [55]: outer_join[pd.notna(outer_join['value_x'])]
    Out[55]:
      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 also provides a variety of methods to work with missing data - some of which would be challenging to express in SAS. For example, there are methods to drop all rows with any missing values, replacing missing values with a specified value, like the mean, or forward filling from previous rows. See the missing data documentation for more.

    In [56]: outer_join.dropna()
    Out[56]:
      key   value_x   value_y
    1   B -0.282863  1.212112
    3   D -1.135632 -0.173215
    4   D -1.135632  0.119209

    In [57]: outer_join.fillna(method='ffill')
    Out[57]:
      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

    In [58]: outer_join['value_x'].fillna(outer_join['value_x'].mean())
    Out[58]:
    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 [59]: tips_summed = tips.groupby(['sex', 'smoker'])['total_bill', 'tip'].sum()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-59-26485120ebb1> in <module>
    ----> 1 tips_summed = tips.groupby(['sex', 'smoker'])['total_bill', 'tip'].sum()

    NameError: name 'tips' is not defined

    In [60]: tips_summed.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-60-3d0a2a6150c9> in <module>
    ----> 1 tips_summed.head()

    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 `groupby` provides a `transform` mechanism that allows these type of operations to be succinctly expressed in one operation.

    In [61]: gb = tips.groupby('smoker')['total_bill']
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-61-5366e81198e8> in <module>
    ----> 1 gb = tips.groupby('smoker')['total_bill']

    NameError: name 'tips' is not defined

    In [62]: tips['adj_total_bill'] = tips['total_bill'] - gb.transform('mean')
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-62-4af8aafc38e1> in <module>
    ----> 1 tips['adj_total_bill'] = tips['total_bill'] - gb.transform('mean')

    NameError: name 'tips' is not defined

    In [63]: tips.head()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-63-53a0cf752a4f> in <module>
    ----> 1 tips.head()

    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 [64]: tips.groupby(['sex', 'smoker']).first()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-64-255e03c34a49> in <module>
    ----> 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

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