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)`

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
0.469112 -1.509059
-1.135632 -0.173215
0.119209 -0.861849
-2.104569  1.071804
0.721555 -1.039575
0.271860  0.567020
0.276232 -0.673690
0.113648  0.524988
0.404705 -1.715002
-1.039268 -1.157892

In [3]: df.loc[:, ["a", "c"]]
Out[3]: 
          a         c
0.469112 -1.509059
-1.135632 -0.173215
0.119209 -0.861849
-2.104569  1.071804
0.721555 -1.039575
0.271860  0.567020
0.276232 -0.673690
0.113648  0.524988
0.404705 -1.715002
-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]: 
 -0.091430
 -2.483890
 -0.252728
 -0.626444
 -0.261740
  2.149503
 -0.332214
  0.799331
 -2.377245
  2.104677
dtype: float64

In [24]: df["a"] + df["b"]  # same as the previous expression
Out[24]: 
 -0.091430
 -2.483890
 -0.252728
 -0.626444
 -0.261740
  2.149503
 -0.332214
  0.799331
 -2.377245
  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 Into 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(future_stack=True)  # 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.

Comparison with R / R libraries#

Quick reference#

Querying, filtering, sampling#

Sorting#

Transforming#

Grouping and summarizing#

Base R#

Slicing with R’s c#

aggregate#

match / %in%#

tapply#

subset#

with#

plyr#

ddply#

reshape / reshape2#

meltarray#

meltlist#

meltdf#

cast#

factor#

Slicing with R’s `c`#

`aggregate`#

`match` / `%in%`#

`tapply`#

`subset`#

`with`#

`factor`#