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# 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` or `copy=False` keyword argument available for some methods:

    df.replace(5, inplace=True)

There is an active discussion about deprecating and removing `inplace` and `copy` for most methods (e.g. `dropna`) except for a very small subset of methods (including `replace`). Both keywords won’t be necessary anymore in the context of Copy-on-Write. The proposal can be found here.

## 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)

    In [5]: tips
    Out[5]:
         total_bill   tip     sex smoker   day    time  size
    0         16.99  1.01  Female     No   Sun  Dinner     2
    1         10.34  1.66    Male     No   Sun  Dinner     3
    2         21.01  3.50    Male     No   Sun  Dinner     3
    3         23.68  3.31    Male     No   Sun  Dinner     2
    4         24.59  3.61  Female     No   Sun  Dinner     4
    ..          ...   ...     ...    ...   ...     ...   ...
    239       29.03  5.92    Male     No   Sat  Dinner     3
    240       27.18  2.00  Female    Yes   Sat  Dinner     2
    241       22.67  2.00    Male    Yes   Sat  Dinner     2
    242       17.82  1.75    Male     No   Sat  Dinner     2
    243       18.78  3.00  Female     No  Thur  Dinner     2

    [244 rows x 7 columns]

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 `DataFrame`s 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)
    Out[1]:
       total_bill   tip     sex smoker  day    time  size
    0       16.99  1.01  Female     No  Sun  Dinner     2
    1       10.34  1.66    Male     No  Sun  Dinner     3
    2       21.01  3.50    Male     No  Sun  Dinner     3
    3       23.68  3.31    Male     No  Sun  Dinner     2
    4       24.59  3.61  Female     No  Sun  Dinner     4

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

    In [2]: tips["new_bill"] = tips["total_bill"] / 2

    In [3]: tips
    Out[3]:
         total_bill   tip     sex smoker   day    time  size  new_bill
    0         14.99  1.01  Female     No   Sun  Dinner     2     7.495
    1          8.34  1.66    Male     No   Sun  Dinner     3     4.170
    2         19.01  3.50    Male     No   Sun  Dinner     3     9.505
    3         21.68  3.31    Male     No   Sun  Dinner     2    10.840
    4         22.59  3.61  Female     No   Sun  Dinner     4    11.295
    ..          ...   ...     ...    ...   ...     ...   ...       ...
    239       27.03  5.92    Male     No   Sat  Dinner     3    13.515
    240       25.18  2.00  Female    Yes   Sat  Dinner     2    12.590
    241       20.67  2.00    Male    Yes   Sat  Dinner     2    10.335
    242       15.82  1.75    Male     No   Sat  Dinner     2     7.910
    243       16.78  3.00  Female     No  Thur  Dinner     2     8.390

    [244 rows x 8 columns]

    In [4]: tips = tips.drop("new_bill", axis=1)

### 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]
    Out[1]:
         total_bill   tip     sex smoker   day    time  size
    0         14.99  1.01  Female     No   Sun  Dinner     2
    2         19.01  3.50    Male     No   Sun  Dinner     3
    3         21.68  3.31    Male     No   Sun  Dinner     2
    4         22.59  3.61  Female     No   Sun  Dinner     4
    5         23.29  4.71    Male     No   Sun  Dinner     4
    ..          ...   ...     ...    ...   ...     ...   ...
    239       27.03  5.92    Male     No   Sat  Dinner     3
    240       25.18  2.00  Female    Yes   Sat  Dinner     2
    241       20.67  2.00    Male    Yes   Sat  Dinner     2
    242       15.82  1.75    Male     No   Sat  Dinner     2
    243       16.78  3.00  Female     No  Thur  Dinner     2

    [204 rows x 7 columns]

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"

    In [3]: is_dinner
    Out[3]:
    0      True
    1      True
    2      True
    3      True
    4      True
           ...
    239    True
    240    True
    241    True
    242    True
    243    True
    Name: time, Length: 244, dtype: bool

    In [4]: is_dinner.value_counts()
    Out[4]:
    time
    True     176
    False     68
    Name: count, dtype: int64

    In [5]: tips[is_dinner]
    Out[5]:
         total_bill   tip     sex smoker   day    time  size
    0         14.99  1.01  Female     No   Sun  Dinner     2
    1          8.34  1.66    Male     No   Sun  Dinner     3
    2         19.01  3.50    Male     No   Sun  Dinner     3
    3         21.68  3.31    Male     No   Sun  Dinner     2
    4         22.59  3.61  Female     No   Sun  Dinner     4
    ..          ...   ...     ...    ...   ...     ...   ...
    239       27.03  5.92    Male     No   Sat  Dinner     3
    240       25.18  2.00  Female    Yes   Sat  Dinner     2
    241       20.67  2.00    Male    Yes   Sat  Dinner     2
    242       15.82  1.75    Male     No   Sat  Dinner     2
    243       16.78  3.00  Female     No  Thur  Dinner     2

    [176 rows x 7 columns]

### 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")

    In [2]: tips
    Out[2]:
         total_bill   tip     sex smoker   day    time  size bucket
    0         14.99  1.01  Female     No   Sun  Dinner     2   high
    1          8.34  1.66    Male     No   Sun  Dinner     3    low
    2         19.01  3.50    Male     No   Sun  Dinner     3   high
    3         21.68  3.31    Male     No   Sun  Dinner     2   high
    4         22.59  3.61  Female     No   Sun  Dinner     4   high
    ..          ...   ...     ...    ...   ...     ...   ...    ...
    239       27.03  5.92    Male     No   Sat  Dinner     3   high
    240       25.18  2.00  Female    Yes   Sat  Dinner     2   high
    241       20.67  2.00    Male    Yes   Sat  Dinner     2   high
    242       15.82  1.75    Male     No   Sat  Dinner     2   high
    243       16.78  3.00  Female     No  Thur  Dinner     2   high

    [244 rows x 8 columns]

### 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")

    In [2]: tips["date2"] = pd.Timestamp("2015-02-15")

    In [3]: tips["date1_year"] = tips["date1"].dt.year

    In [4]: tips["date2_month"] = tips["date2"].dt.month

    In [5]: tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin()

    In [6]: tips["months_between"] = tips["date2"].dt.to_period("M") - tips[
       ...:     "date1"
       ...: ].dt.to_period("M")
       ...:

    In [7]: tips[
       ...:     ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"]
       ...: ]
       ...:
    Out[7]:
             date1      date2  date1_year  date2_month date1_next    months_between
    0   2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    1   2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    2   2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    3   2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    4   2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    ..         ...        ...         ...          ...        ...               ...
    239 2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    240 2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    241 2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    242 2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>
    243 2013-01-15 2015-02-15        2013            2 2013-02-01  <25 * MonthEnds>

    [244 rows x 6 columns]

### 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"]]
    Out[1]:
            sex  total_bill   tip
    0    Female       14.99  1.01
    1      Male        8.34  1.66
    2      Male       19.01  3.50
    3      Male       21.68  3.31
    4    Female       22.59  3.61
    ..      ...         ...   ...
    239    Male       27.03  5.92
    240  Female       25.18  2.00
    241    Male       20.67  2.00
    242    Male       15.82  1.75
    243  Female       16.78  3.00

    [244 rows x 3 columns]

#### Drop a column

    In [2]: tips.drop("sex", axis=1)
    Out[2]:
         total_bill   tip smoker   day    time  size
    0         14.99  1.01     No   Sun  Dinner     2
    1          8.34  1.66     No   Sun  Dinner     3
    2         19.01  3.50     No   Sun  Dinner     3
    3         21.68  3.31     No   Sun  Dinner     2
    4         22.59  3.61     No   Sun  Dinner     4
    ..          ...   ...    ...   ...     ...   ...
    239       27.03  5.92     No   Sat  Dinner     3
    240       25.18  2.00    Yes   Sat  Dinner     2
    241       20.67  2.00    Yes   Sat  Dinner     2
    242       15.82  1.75     No   Sat  Dinner     2
    243       16.78  3.00     No  Thur  Dinner     2

    [244 rows x 6 columns]

#### Rename a column

    In [3]: tips.rename(columns={"total_bill": "total_bill_2"})
    Out[3]:
         total_bill_2   tip     sex smoker   day    time  size
    0           14.99  1.01  Female     No   Sun  Dinner     2
    1            8.34  1.66    Male     No   Sun  Dinner     3
    2           19.01  3.50    Male     No   Sun  Dinner     3
    3           21.68  3.31    Male     No   Sun  Dinner     2
    4           22.59  3.61  Female     No   Sun  Dinner     4
    ..            ...   ...     ...    ...   ...     ...   ...
    239         27.03  5.92    Male     No   Sat  Dinner     3
    240         25.18  2.00  Female    Yes   Sat  Dinner     2
    241         20.67  2.00    Male    Yes   Sat  Dinner     2
    242         15.82  1.75    Male     No   Sat  Dinner     2
    243         16.78  3.00  Female     No  Thur  Dinner     2

    [244 rows x 7 columns]

### 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"])

    In [2]: tips
    Out[2]:
         total_bill    tip     sex smoker   day    time  size
    67         1.07   1.00  Female    Yes   Sat  Dinner     1
    92         3.75   1.00  Female    Yes   Fri  Dinner     2
    111        5.25   1.00  Female     No   Sat  Dinner     1
    145        6.35   1.50  Female     No  Thur   Lunch     2
    135        6.51   1.25  Female     No  Thur   Lunch     2
    ..          ...    ...     ...    ...   ...     ...   ...
    182       43.35   3.50    Male    Yes   Sun  Dinner     3
    156       46.17   5.00    Male     No   Sun  Dinner     6
    59        46.27   6.73    Male     No   Sat  Dinner     4
    212       46.33   9.00    Male     No   Sat  Dinner     4
    170       48.81  10.00    Male    Yes   Sat  Dinner     3

    [244 rows x 7 columns]

## 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()
    Out[1]:
    67     6
    92     6
    111    6
    145    5
    135    5
          ..
    182    6
    156    6
    59     6
    212    6
    170    6
    Name: time, Length: 244, dtype: int64

    In [2]: tips["time"].str.rstrip().str.len()
    Out[2]:
    67     6
    92     6
    111    6
    145    5
    135    5
          ..
    182    6
    156    6
    59     6
    212    6
    170    6
    Name: time, Length: 244, dtype: int64

### 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")
    Out[1]:
    67     3
    92     3
    111    3
    145    3
    135    3
          ..
    182    1
    156    1
    59     1
    212    1
    170    1
    Name: sex, Length: 244, dtype: int64

### 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]
    Out[1]:
    67     F
    92     F
    111    F
    145    F
    135    F
          ..
    182    M
    156    M
    59     M
    212    M
    170    M
    Name: sex, Length: 244, dtype: str

### 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]: np.float64(-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.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()

    In [2]: tips_summed
    Out[2]:
                   total_bill     tip
    sex    smoker
    Female No          869.68  149.77
           Yes         527.27   96.74
    Male   No         1725.75  302.00
           Yes        1217.07  183.07

### 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"]

    In [2]: tips["adj_total_bill"] = tips["total_bill"] - gb.transform("mean")

    In [3]: tips
    Out[3]:
         total_bill    tip     sex smoker   day    time  size  adj_total_bill
    67         1.07   1.00  Female    Yes   Sat  Dinner     1      -17.686344
    92         3.75   1.00  Female    Yes   Fri  Dinner     2      -15.006344
    111        5.25   1.00  Female     No   Sat  Dinner     1      -11.938278
    145        6.35   1.50  Female     No  Thur   Lunch     2      -10.838278
    135        6.51   1.25  Female     No  Thur   Lunch     2      -10.678278
    ..          ...    ...     ...    ...   ...     ...   ...             ...
    182       43.35   3.50    Male    Yes   Sun  Dinner     3       24.593656
    156       46.17   5.00    Male     No   Sun  Dinner     6       28.981722
    59        46.27   6.73    Male     No   Sat  Dinner     4       29.081722
    212       46.33   9.00    Male     No   Sat  Dinner     4       29.141722
    170       48.81  10.00    Male    Yes   Sat  Dinner     3       30.053656

    [244 rows x 8 columns]

### 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()
    Out[4]:
                   total_bill   tip   day    time  size  adj_total_bill
    sex    smoker
    Female No            5.25  1.00   Sat  Dinner     1      -11.938278
           Yes           1.07  1.00   Sat  Dinner     1      -17.686344
    Male   No            5.51  2.00  Thur   Lunch     2      -11.678278
           Yes           5.25  5.15   Sun  Dinner     2      -13.506344

## 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