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# Comparison with R / R libraries

Since `pandas` aims to provide a lot of the data manipulation and analysis functionality that people use R for, this page was started to provide a more detailed look at the R language and its many third party libraries as they relate to `pandas`. In comparisons with R and CRAN libraries, we care about the following things:

> - Functionality / flexibility: what can/cannot be done with each tool
> - Performance: how fast are operations. Hard numbers/benchmarks are preferable
> - Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of this, given side-by-side code comparisons)

This page is also here to offer a bit of a translation guide for users of these R packages.

## Base R

### Slicing with R’s `c`

R makes it easy to access `data.frame` columns by name

    df <- data.frame(a=rnorm(5), b=rnorm(5), c=rnorm(5), d=rnorm(5), e=rnorm(5))
    df[, c("a", "c", "e")]

or by integer location

    df <- data.frame(matrix(rnorm(1000), ncol=100))
    df[, c(1:10, 25:30, 40, 50:100)]

Selecting multiple columns by name in `pandas` is straightforward

    In [1]: df = DataFrame(np.random.randn(10, 3), columns=list('abc'))

    In [2]: df[['a', 'c']]
    Out[2]:
              a         c
    0 -0.010277  1.754450
    1 -1.979042  0.026731
    2 -0.171905 -0.668032
    3  0.156823 -0.287102
    4 -0.654693  2.486931
    5  0.314941 -0.209642
    6 -0.482069  0.713264
    7  1.524014 -0.483850
    8  1.615149  0.673194
    9  1.512817 -0.017685

    In [3]: df.loc[:, ['a', 'c']]
    Out[3]:
              a         c
    0 -0.010277  1.754450
    1 -1.979042  0.026731
    2 -0.171905 -0.668032
    3  0.156823 -0.287102
    4 -0.654693  2.486931
    5  0.314941 -0.209642
    6 -0.482069  0.713264
    7  1.524014 -0.483850
    8  1.615149  0.673194
    9  1.512817 -0.017685

Selecting multiple noncontiguous columns by integer location can be achieved with a combination of the `iloc` indexer attribute and `numpy.r_`.

    In [4]: named = list('abcdefg')

    In [5]: n = 30

    In [6]: columns = named + np.arange(len(named), n).tolist()

    In [7]: df = DataFrame(np.random.randn(n, n), columns=columns)

    In [8]: df.iloc[:, np.r_[:10, 24:30]]
    Out[8]:
               a         b         c         d         e         f         g  \
    0  -0.182877  1.556201 -1.717420 -3.017047  1.201081  0.980077 -0.026234
    1  -1.679855 -0.912033  0.895000  0.759727  1.053398 -0.854995  0.514409
    2  -0.242666 -0.153091  0.571129  1.049663 -0.200188  0.169303  0.127031
    3   0.442832 -1.344020 -0.497400 -1.255580 -0.000235  2.493078 -1.483518
    4   0.939187 -2.739487 -0.573693 -1.233017 -0.803782 -1.527202  0.680366
    5   0.398306 -1.886066 -0.488051  1.022238 -1.097735  0.182293  0.166052
    6  -1.621497 -1.229428  0.340857 -0.240188 -0.640714 -0.620492  1.395629
    ..       ...       ...       ...       ...       ...       ...       ...
    23 -0.636174 -0.236723 -0.542805  0.475213  1.683208 -0.759966  1.525081
    24  1.093811 -1.589366 -0.402728  0.333326 -1.036511  0.756512 -1.622032
    25 -0.634240  0.286129  0.835316  0.826629 -0.735065 -0.285695 -1.094918
    26 -0.235821 -1.058125 -1.137497  1.768844  1.973471  0.747723 -0.532274
    27 -0.056739 -1.497562  0.697053  1.246539 -0.369645  1.788288 -0.425494
    28  0.951542  1.199311  0.361042 -1.004705  0.124648 -0.564100 -1.675530
    29 -0.210387 -0.236078  1.614966  0.897880 -0.840843 -0.403887 -0.559663

               7         8         9        24        25        26        27  \
    0  -0.170669  0.013884  0.027505  1.366784  1.864420 -1.119262  2.489142
    1  -1.659617 -0.430681 -0.158656  0.014234 -1.392410 -0.049204  1.503745
    2   0.272596  0.604549 -2.485841 -0.477770  1.454589 -0.392196 -1.705802
    3  -2.641568  0.667512  1.527341 -0.766140 -0.199585  1.569370  0.628867
    4  -0.373935 -2.253353 -1.046336  1.421232  1.055904 -0.429448  0.023700
    5   1.008944  0.979035 -0.525153 -0.058021 -0.173119 -1.375517  0.270460
    6   0.306483  0.433323 -0.522057 -0.245659  0.240485  0.962699 -0.172859
    ..       ...       ...       ...       ...       ...       ...       ...
    23  0.251470 -0.137568 -0.556620 -1.226969 -0.459633 -0.733977 -1.221608
    24 -0.468504 -0.656569  1.187661  0.714776 -0.459475 -2.880218  0.629157
    25  0.782185  0.026271 -0.671403  0.185990  1.271593 -0.722660  1.232652
    26 -1.982213  0.189767  0.952486  0.220958 -0.345484 -0.615855  0.513608
    27  1.867720  0.456395 -0.673380 -1.201060  0.133686  1.563471  0.532838
    28  1.351386 -0.415076  1.449936  0.045704  0.079924 -0.846139  1.179317
    29 -1.589124  1.188404  0.137041  0.522771  1.131500 -0.863309 -1.412090

              28        29
    0   1.115041 -0.078156
    1   1.088485 -0.119904
    2   0.091318  1.444209
    3  -1.485142  0.583219
    4  -0.177887  1.346323
    5  -0.042217  0.923272
    6  -0.323793 -0.704276
    ..       ...       ...
    23 -0.348523 -1.829344
    24 -0.090673  0.283814
    25  0.964679 -0.327307
    26  0.109942 -0.346066
    27 -1.326245  0.520447
    28 -0.812521  0.290519
    29  1.344344 -1.151327

    [30 rows x 16 columns]

### `aggregate`

In R you may want to split data into subsets and compute the mean for each. Using a data.frame called `df` and splitting it into groups `by1` and `by2`:

    df <- data.frame(
      v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9),
      v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99),
      by1 = c("red", "blue", 1, 2, NA, "big", 1, 2, "red", 1, NA, 12),
      by2 = c("wet", "dry", 99, 95, NA, "damp", 95, 99, "red", 99, NA, NA))
    aggregate(x=df[, c("v1", "v2")], by=list(mydf2$by1, mydf2$by2), FUN = mean)

The `groupby()` method is similar to base R `aggregate` function.

    In [9]: from pandas import DataFrame

    In [10]: df = DataFrame({
       ....:   'v1': [1,3,5,7,8,3,5,np.nan,4,5,7,9],
       ....:   'v2': [11,33,55,77,88,33,55,np.nan,44,55,77,99],
       ....:   'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
       ....:   'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
       ....:           np.nan]
       ....: })
       ....:

    In [11]: g = df.groupby(['by1','by2'])

    In [12]: g[['v1','v2']].mean()
    Out[12]:
               v1  v2
    by1  by2
    1    95     5  55
         99     5  55
    2    95     7  77
         99   NaN NaN
    big  damp   3  33
    blue dry    3  33
    red  red    4  44
         wet    1  11

For more details and examples see the groupby documentation.

### `match` / `%in%`

A common way to select data in R is using `%in%` which is defined using the function `match`. The operator `%in%` is used to return a logical vector indicating if there is a match or not:

    s <- 0:4
    s %in% c(2,4)

The `isin()` method is similar to R `%in%` operator:

    In [13]: s = pd.Series(np.arange(5),dtype=np.float32)

    In [14]: s.isin([2, 4])
    Out[14]:
    0    False
    1    False
    2     True
    3    False
    4     True
    dtype: bool

The `match` function returns a vector of the positions of matches of its first argument in its second:

    s <- 0:4
    match(s, c(2,4))

The `apply()` method can be used to replicate this:

    In [15]: s = pd.Series(np.arange(5),dtype=np.float32)

    In [16]: pd.Series(pd.match(s,[2,4],np.nan))
    Out[16]:
    0   NaN
    1   NaN
    2     0
    3   NaN
    4     1
    dtype: float64

For more details and examples see the reshaping documentation.

### `tapply`

`tapply` is similar to `aggregate`, but data can be in a ragged array, since the subclass sizes are possibly irregular. Using a data.frame called `baseball`, and retrieving information based on the array `team`:

    baseball <-
      data.frame(team = gl(5, 5,
                 labels = paste("Team", LETTERS[1:5])),
                 player = sample(letters, 25),
                 batting.average = runif(25, .200, .400))

    tapply(baseball$batting.average, baseball.example$team,
           max)

In `pandas` we may use `pivot_table()` method to handle this:

    In [17]: import random

    In [18]: import string

    In [19]: baseball = DataFrame({
       ....:    'team': ["team %d" % (x+1) for x in range(5)]*5,
       ....:    'player': random.sample(list(string.ascii_lowercase),25),
       ....:    'batting avg': np.random.uniform(.200, .400, 25)
       ....:    })
       ....:

    In [20]: baseball.pivot_table(values='batting avg', columns='team', aggfunc=np.max)
    Out[20]:
    team
    team 1    0.382841
    team 2    0.395048
    team 3    0.387240
    team 4    0.383183
    team 5    0.364851
    Name: batting avg, dtype: float64

For more details and examples see the reshaping documentation.

### `subset`

New in version 0.13.

The `query()` method is similar to the base R `subset` function. In R you might want to get the rows of a `data.frame` where one column’s values are less than another column’s values:

    df <- data.frame(a=rnorm(10), b=rnorm(10))
    subset(df, a <= b)
    df[df$a <= df$b,]  # note the comma

In `pandas`, there are a few ways to perform subsetting. You can use `query()` or pass an expression as if it were an index/slice as well as standard boolean indexing:

    In [21]: df = DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

    In [22]: df.query('a <= b')
    Out[22]:
              a         b
    0 -0.869907  0.118969
    1 -1.243405  1.059719
    2 -2.103633 -0.585222
    3 -0.160015  0.476629
    4 -1.361355  1.317180
    8 -0.945780 -0.571590
    9 -0.761339 -0.073684

    In [23]: df[df.a <= df.b]
    Out[23]:
              a         b
    0 -0.869907  0.118969
    1 -1.243405  1.059719
    2 -2.103633 -0.585222
    3 -0.160015  0.476629
    4 -1.361355  1.317180
    8 -0.945780 -0.571590
    9 -0.761339 -0.073684

    In [24]: df.loc[df.a <= df.b]
    Out[24]:
              a         b
    0 -0.869907  0.118969
    1 -1.243405  1.059719
    2 -2.103633 -0.585222
    3 -0.160015  0.476629
    4 -1.361355  1.317180
    8 -0.945780 -0.571590
    9 -0.761339 -0.073684

For more details and examples see the query documentation.

### `with`

New in version 0.13.

An expression using a data.frame called `df` in R with the columns `a` and `b` would be evaluated using `with` like so:

    df <- data.frame(a=rnorm(10), b=rnorm(10))
    with(df, a + b)
    df$a + df$b  # same as the previous expression

In `pandas` the equivalent expression, using the `eval()` method, would be:

    In [25]: df = DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

    In [26]: df.eval('a + b')
    Out[26]:
    0   -0.251791
    1   -0.061720
    2   -1.395907
    3   -0.095439
    4    0.277944
    5   -0.445539
    6    1.199896
    7   -0.524619
    8   -0.657901
    9    2.069409
    dtype: float64

    In [27]: df.a + df.b  # same as the previous expression
    Out[27]:
    0   -0.251791
    1   -0.061720
    2   -1.395907
    3   -0.095439
    4    0.277944
    5   -0.445539
    6    1.199896
    7   -0.524619
    8   -0.657901
    9    2.069409
    dtype: float64

In certain cases `eval()` will be much faster than evaluation in pure Python. For more details and examples see the eval documentation.

## zoo

## xts

## plyr

`plyr` is an R library for the split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, `a` for `arrays`, `l` for `lists`, and `d` for `data.frame`. The table below shows how these data structures could be mapped in Python.

R | Python
--- | ---
array | list
lists | dictionary or list of objects
data.frame | dataframe

### `ddply`

An expression using a data.frame called `df` in R where you want to summarize `x` by `month`:

    require(plyr)
    df <- data.frame(
      x = runif(120, 1, 168),
      y = runif(120, 7, 334),
      z = runif(120, 1.7, 20.7),
      month = rep(c(5,6,7,8),30),
      week = sample(1:4, 120, TRUE)
    )

    ddply(df, .(month, week), summarize,
          mean = round(mean(x), 2),
          sd = round(sd(x), 2))

In `pandas` the equivalent expression, using the `groupby()` method, would be:

    In [28]: df = DataFrame({
       ....:     'x': np.random.uniform(1., 168., 120),
       ....:     'y': np.random.uniform(7., 334., 120),
       ....:     'z': np.random.uniform(1.7, 20.7, 120),
       ....:     'month': [5,6,7,8]*30,
       ....:     'week': np.random.randint(1,4, 120)
       ....: })
       ....:

    In [29]: grouped = df.groupby(['month','week'])

    In [30]: print grouped['x'].agg([np.mean, np.std])
                     mean        std
    month week
    5     1     77.357995  49.531314
          2     85.764198  58.738812
          3     89.386008  57.720890
    6     1     91.738228  62.007638
          2     91.100447  46.713644
          3     76.136174  56.952827
    7     1     83.534178  52.964956
          2     92.955649  49.004203
          3     83.708346  46.338509
    8     1     84.729214  53.924477
          2     72.545882  55.905134
          3     71.868187  50.813235

For more details and examples see the groupby documentation.

## reshape / reshape2

### `melt.array`

An expression using a 3 dimensional array called `a` in R where you want to melt it into a data.frame:

    a <- array(c(1:23, NA), c(2,3,4))
    data.frame(melt(a))

In Python, since `a` is a list, you can simply use list comprehension.

    In [31]: a = np.array(list(range(1,24))+[np.NAN]).reshape(2,3,4)

    In [32]: DataFrame([tuple(list(x)+[val]) for x, val in np.ndenumerate(a)])
    Out[32]:
        0  1  2   3
    0   0  0  0   1
    1   0  0  1   2
    2   0  0  2   3
    3   0  0  3   4
    4   0  1  0   5
    5   0  1  1   6
    6   0  1  2   7
    .. .. .. ..  ..
    17  1  1  1  18
    18  1  1  2  19
    19  1  1  3  20
    20  1  2  0  21
    21  1  2  1  22
    22  1  2  2  23
    23  1  2  3 NaN

    [24 rows x 4 columns]

### `melt.list`

An expression using a list called `a` in R where you want to melt it into a data.frame:

    a <- as.list(c(1:4, NA))
    data.frame(melt(a))

In Python, this list would be a list of tuples, so `DataFrame()` method would convert it to a dataframe as required.

    In [33]: a = list(enumerate(list(range(1,5))+[np.NAN]))

    In [34]: DataFrame(a)
    Out[34]:
       0   1
    0  0   1
    1  1   2
    2  2   3
    3  3   4
    4  4 NaN

For more details and examples see the Into to Data Structures documentation.

### `melt.data.frame`

An expression using a data.frame called `cheese` in R where you want to reshape the data.frame:

    cheese <- data.frame(
      first = c('John', 'Mary'),
      last = c('Doe', 'Bo'),
      height = c(5.5, 6.0),
      weight = c(130, 150)
    )
    melt(cheese, id=c("first", "last"))

In Python, the `melt()` method is the R equivalent:

    In [35]: cheese = DataFrame({'first' : ['John', 'Mary'],
       ....:                     'last' : ['Doe', 'Bo'],
       ....:                     'height' : [5.5, 6.0],
       ....:                     'weight' : [130, 150]})
       ....:

    In [36]: pd.melt(cheese, id_vars=['first', 'last'])
    Out[36]:
      first last variable  value
    0  John  Doe   height    5.5
    1  Mary   Bo   height    6.0
    2  John  Doe   weight  130.0
    3  Mary   Bo   weight  150.0

    In [37]: cheese.set_index(['first', 'last']).stack() # alternative way
    Out[37]:
    first  last
    John   Doe   height      5.5
                 weight    130.0
    Mary   Bo    height      6.0
                 weight    150.0
    dtype: float64

For more details and examples see the reshaping documentation.

### `cast`

In R `acast` is an expression using a data.frame called `df` in R to cast into a higher dimensional array:

    df <- data.frame(
      x = runif(12, 1, 168),
      y = runif(12, 7, 334),
      z = runif(12, 1.7, 20.7),
      month = rep(c(5,6,7),4),
      week = rep(c(1,2), 6)
    )

    mdf <- melt(df, id=c("month", "week"))
    acast(mdf, week ~ month ~ variable, mean)

In Python the best way is to make use of `pivot_table()`:

    In [38]: df = DataFrame({
       ....:      'x': np.random.uniform(1., 168., 12),
       ....:      'y': np.random.uniform(7., 334., 12),
       ....:      'z': np.random.uniform(1.7, 20.7, 12),
       ....:      'month': [5,6,7]*4,
       ....:      'week': [1,2]*6
       ....: })
       ....:

    In [39]: mdf = pd.melt(df, id_vars=['month', 'week'])

    In [40]: pd.pivot_table(mdf, values='value', index=['variable','week'],
       ....:                  columns=['month'], aggfunc=np.mean)
       ....:
    Out[40]:
    month                   5           6           7
    variable week
    x        1      58.488427   32.594687  149.838258
             2      88.972028  109.131941   59.435615
    y        1      34.774928  173.914293  167.835338
             2     126.265859   70.692387  123.789140
    z        1       8.408572    3.194041    9.885935
             2      18.922270    9.083850    7.874963

Similarly for `dcast` which uses a data.frame called `df` in R to aggregate information based on `Animal` and `FeedType`:

    df <- data.frame(
      Animal = c('Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
                 'Animal2', 'Animal3'),
      FeedType = c('A', 'B', 'A', 'A', 'B', 'B', 'A'),
      Amount = c(10, 7, 4, 2, 5, 6, 2)
    )

    dcast(df, Animal ~ FeedType, sum, fill=NaN)
    # Alternative method using base R
    with(df, tapply(Amount, list(Animal, FeedType), sum))

Python can approach this in two different ways. Firstly, similar to above using `pivot_table()`:

    In [41]: df = DataFrame({
       ....:     'Animal': ['Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
       ....:                'Animal2', 'Animal3'],
       ....:     'FeedType': ['A', 'B', 'A', 'A', 'B', 'B', 'A'],
       ....:     'Amount': [10, 7, 4, 2, 5, 6, 2],
       ....: })
       ....:

    In [42]: df.pivot_table(values='Amount', index='Animal', columns='FeedType', aggfunc='sum')
    Out[42]:
    FeedType   A   B
    Animal
    Animal1   10   5
    Animal2    2  13
    Animal3    6 NaN

The second approach is to use the `groupby()` method:

    In [43]: df.groupby(['Animal','FeedType'])['Amount'].sum()
    Out[43]:
    Animal   FeedType
    Animal1  A           10
             B            5
    Animal2  A            2
             B           13
    Animal3  A            6
    Name: Amount, dtype: int64

For more details and examples see the reshaping documentation or the groupby documentation.

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