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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.

## Quick reference

We’ll start off with a quick reference guide pairing some common R operations using dplyr with pandas equivalents.

### Querying, filtering, sampling

R | pandas
--- | ---
`dim(df)` | `df.shape`
`head(df)` | `df.head()`
`slice(df, 1:10)` | `df.iloc[:9]`
`filter(df, col1 == 1, col2 == 1)` | `df.query('col1 == 1 & col2 == 1')`
`df[df$col1 == 1 & df$col2 == 1,]` | `df[(df.col1 == 1) & (df.col2 == 1)]`
`select(df, col1, col2)` | `df[['col1', 'col2']]`
`select(df, col1:col3)` | `df.loc[:, 'col1':'col3']`
`select(df, -(col1:col3))` | `df.drop(cols_to_drop, axis=1)` but see [1]
`distinct(select(df, col1))` | `df[['col1']].drop_duplicates()`
`distinct(select(df, col1, col2))` | `df[['col1', 'col2']].drop_duplicates()`
`sample_n(df, 10)` | `df.sample(n=10)`
`sample_frac(df, 0.01)` | `df.sample(frac=0.01)`

[1]

R’s shorthand for a subrange of columns (`select(df, col1:col3)`) can be approached cleanly in pandas, if you have the list of columns, for example `df[cols[1:3]]` or `df.drop(cols[1:3])`, but doing this by column name is a bit messy.

### Sorting

R | pandas
--- | ---
`arrange(df, col1, col2)` | `df.sort_values(['col1', 'col2'])`
`arrange(df, desc(col1))` | `df.sort_values('col1', ascending=False)`

### Transforming

R | pandas
--- | ---
`select(df, col_one = col1)` | `df.rename(columns={'col1': 'col_one'})['col_one']`
`rename(df, col_one = col1)` | `df.rename(columns={'col1': 'col_one'})`
`mutate(df, c=a-b)` | `df.assign(c=df['a']-df['b'])`

### Grouping and summarizing

R | pandas
--- | ---
`summary(df)` | `df.describe()`
`gdf <- group_by(df, col1)` | `gdf = df.groupby('col1')`
`summarise(gdf, avg=mean(col1, na.rm=TRUE))` | `df.groupby('col1').agg({'col1': 'mean'})`
`summarise(gdf, total=sum(col1))` | `df.groupby('col1').sum()`

## 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 = pd.DataFrame(np.random.randn(10, 3), columns=list("abc"))

    In [2]: df[["a", "c"]]
    Out[2]:
              a         c
    0  0.469112 -1.509059
    1 -1.135632 -0.173215
    2  0.119209 -0.861849
    3 -2.104569  1.071804
    4  0.721555 -1.039575
    5  0.271860  0.567020
    6  0.276232 -0.673690
    7  0.113648  0.524988
    8  0.404705 -1.715002
    9 -1.039268 -1.157892

    In [3]: df.loc[:, ["a", "c"]]
    Out[3]:
              a         c
    0  0.469112 -1.509059
    1 -1.135632 -0.173215
    2  0.119209 -0.861849
    3 -2.104569  1.071804
    4  0.721555 -1.039575
    5  0.271860  0.567020
    6  0.276232 -0.673690
    7  0.113648  0.524988
    8  0.404705 -1.715002
    9 -1.039268 -1.157892

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 = pd.DataFrame(np.random.randn(n, n), columns=columns)

    In [8]: df.iloc[:, np.r_[:10, 24:30]]
    Out[8]:
               a         b         c  ...        27        28        29
    0  -1.344312  0.844885  1.075770  ...  0.813850  0.132003 -0.827317
    1  -0.076467 -1.187678  1.130127  ...  0.149748 -0.732339  0.687738
    2   0.176444  0.403310 -0.154951  ... -0.493662  0.600178  0.274230
    3   0.132885 -0.023688  2.410179  ...  0.109121  1.126203 -0.977349
    4   1.474071 -0.064034 -1.282782  ... -0.858447  0.306996 -0.028665
    ..       ...       ...       ...  ...       ...       ...       ...
    25  1.492125 -0.068190  0.681456  ...  0.428572  0.880609  0.487645
    26  0.725238  0.624607 -0.141185  ...  1.008500  1.424017  0.717110
    27  1.262419  1.950057  0.301038  ...  1.007824  2.826008  1.458383
    28 -1.585746 -0.899734  0.921494  ...  0.577223 -1.088417  0.326687
    29 -0.986248  0.169729 -1.158091  ... -2.013086 -1.602549  0.333109

    [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]: df = pd.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 [10]: g = df.groupby(["by1", "by2"])

    In [11]: g[["v1", "v2"]].mean()
    Out[11]:
                v1    v2
    by1  by2
    1    95    5.0  55.0
         99    5.0  55.0
    2    95    7.0  77.0
         99    NaN   NaN
    big  damp  3.0  33.0
    blue dry   3.0  33.0
    red  red   4.0  44.0
         wet   1.0  11.0

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

    In [13]: s.isin([2, 4])
    Out[13]:
    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))

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 [14]: import random

    In [15]: import string

    In [16]: baseball = pd.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(0.200, 0.400, 25),
       ....:     }
       ....: )
       ....:

    In [17]: baseball.pivot_table(values="batting avg", columns="team", aggfunc="max")
    Out[17]:
    team           team 1    team 2    team 3    team 4    team 5
    batting avg  0.352134  0.295327  0.397191  0.394457  0.396194

For more details and examples see the reshaping documentation.

### `subset`

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 [18]: df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})

    In [19]: df.query("a <= b")
    Out[19]:
              a         b
    1  0.174950  0.552887
    2 -0.023167  0.148084
    3 -0.495291 -0.300218
    4 -0.860736  0.197378
    5 -1.134146  1.720780
    7 -0.290098  0.083515
    8  0.238636  0.946550

    In [20]: df[df["a"] <= df["b"]]
    Out[20]:
              a         b
    1  0.174950  0.552887
    2 -0.023167  0.148084
    3 -0.495291 -0.300218
    4 -0.860736  0.197378
    5 -1.134146  1.720780
    7 -0.290098  0.083515
    8  0.238636  0.946550

    In [21]: df.loc[df["a"] <= df["b"]]
    Out[21]:
              a         b
    1  0.174950  0.552887
    2 -0.023167  0.148084
    3 -0.495291 -0.300218
    4 -0.860736  0.197378
    5 -1.134146  1.720780
    7 -0.290098  0.083515
    8  0.238636  0.946550

For more details and examples see the query documentation.

### `with`

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 [22]: df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})

    In [23]: df.eval("a + b")
    Out[23]:
    0   -0.091430
    1   -2.483890
    2   -0.252728
    3   -0.626444
    4   -0.261740
    5    2.149503
    6   -0.332214
    7    0.799331
    8   -2.377245
    9    2.104677
    dtype: float64

    In [24]: df["a"] + df["b"]  # same as the previous expression
    Out[24]:
    0   -0.091430
    1   -2.483890
    2   -0.252728
    3   -0.626444
    4   -0.261740
    5    2.149503
    6   -0.332214
    7    0.799331
    8   -2.377245
    9    2.104677
    dtype: float64

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

## 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 [25]: df = pd.DataFrame(
       ....:     {
       ....:         "x": np.random.uniform(1.0, 168.0, 120),
       ....:         "y": np.random.uniform(7.0, 334.0, 120),
       ....:         "z": np.random.uniform(1.7, 20.7, 120),
       ....:         "month": [5, 6, 7, 8] * 30,
       ....:         "week": np.random.randint(1, 4, 120),
       ....:     }
       ....: )
       ....:

    In [26]: grouped = df.groupby(["month", "week"])

    In [27]: grouped["x"].agg(["mean", "std"])
    Out[27]:
                      mean        std
    month week
    5     1      63.653367  40.601965
          2      78.126605  53.342400
          3      92.091886  57.630110
    6     1      81.747070  54.339218
          2      70.971205  54.687287
          3     100.968344  54.010081
    7     1      61.576332  38.844274
          2      61.733510  48.209013
          3      71.688795  37.595638
    8     1      62.741922  34.618153
          2      91.774627  49.790202
          3      73.936856  60.773900

For more details and examples see the groupby documentation.

## reshape / reshape2

### meltarray

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

    In [29]: pd.DataFrame([tuple(list(x) + [val]) for x, val in np.ndenumerate(a)])
    Out[29]:
        0  1  2     3
    0   0  0  0   1.0
    1   0  0  1   2.0
    2   0  0  2   3.0
    3   0  0  3   4.0
    4   0  1  0   5.0
    .. .. .. ..   ...
    19  1  1  3  20.0
    20  1  2  0  21.0
    21  1  2  1  22.0
    22  1  2  2  23.0
    23  1  2  3   NaN

    [24 rows x 4 columns]

### meltlist

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 [30]: a = list(enumerate(list(range(1, 5)) + [np.nan]))

    In [31]: pd.DataFrame(a)
    Out[31]:
       0    1
    0  0  1.0
    1  1  2.0
    2  2  3.0
    3  3  4.0
    4  4  NaN

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

### meltdf

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

    In [33]: pd.melt(cheese, id_vars=["first", "last"])
    Out[33]:
      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 [34]: cheese.set_index(["first", "last"]).stack()  # alternative way
    Out[34]:
    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 [35]: df = pd.DataFrame(
       ....:     {
       ....:         "x": np.random.uniform(1.0, 168.0, 12),
       ....:         "y": np.random.uniform(7.0, 334.0, 12),
       ....:         "z": np.random.uniform(1.7, 20.7, 12),
       ....:         "month": [5, 6, 7] * 4,
       ....:         "week": [1, 2] * 6,
       ....:     }
       ....: )
       ....:

    In [36]: mdf = pd.melt(df, id_vars=["month", "week"])

    In [37]: pd.pivot_table(
       ....:     mdf,
       ....:     values="value",
       ....:     index=["variable", "week"],
       ....:     columns=["month"],
       ....:     aggfunc="mean",
       ....: )
       ....:
    Out[37]:
    month                  5           6           7
    variable week
    x        1     93.888747   98.762034   55.219673
             2     94.391427   38.112932   83.942781
    y        1     94.306912  279.454811  227.840449
             2     87.392662  193.028166  173.899260
    z        1     11.016009   10.079307   16.170549
             2      8.476111   17.638509   19.003494

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 [38]: df = pd.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 [39]: df.pivot_table(values="Amount", index="Animal", columns="FeedType", aggfunc="sum")
    Out[39]:
    FeedType     A     B
    Animal
    Animal1   10.0   5.0
    Animal2    2.0  13.0
    Animal3    6.0   NaN

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

    In [40]: df.groupby(["Animal", "FeedType"])["Amount"].sum()
    Out[40]:
    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.

### `factor`

pandas has a data type for categorical data.

    cut(c(1,2,3,4,5,6), 3)
    factor(c(1,2,3,2,2,3))

In pandas this is accomplished with `pd.cut` and `astype("category")`:

    In [41]: pd.cut(pd.Series([1, 2, 3, 4, 5, 6]), 3)
    Out[41]:
    0    (0.995, 2.667]
    1    (0.995, 2.667]
    2    (2.667, 4.333]
    3    (2.667, 4.333]
    4      (4.333, 6.0]
    5      (4.333, 6.0]
    dtype: category
    Categories (3, interval[float64, right]): [(0.995, 2.667] < (2.667, 4.333] < (4.333, 6.0]]

    In [42]: pd.Series([1, 2, 3, 2, 2, 3]).astype("category")
    Out[42]:
    0    1
    1    2
    2    3
    3    2
    4    2
    5    3
    dtype: category
    Categories (3, int64): [1, 2, 3]

For more details and examples see categorical introduction and the API documentation. There is also a documentation regarding the differences to R’s factor.