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¶

`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 [1]: from pandas import DataFrame

In [2]: 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 [3]: g = df.groupby(['by1','by2'])

In [4]: g[['v1','v2']].mean()
Out[4]: 
           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

[8 rows x 2 columns]

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

In [6]: s.isin([2, 4])
Out[6]: 
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 [7]: s = pd.Series(np.arange(5),dtype=np.float32)

In [8]: Series(pd.match(s,[2,4],np.nan))
Out[8]: 
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 [9]: import random

In [10]: import string

In [11]: 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 [12]: baseball.pivot_table(values='batting avg', cols='team', aggfunc=np.max)
Out[12]: 
team
team 1    0.321235
team 2    0.399140
team 3    0.386815
team 4    0.387197
team 5    0.392086
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 [13]: df = DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

In [14]: df.query('a <= b')
Out[14]: 
          a         b
2 -0.838260  0.980077
6 -0.017685  0.027505
7 -0.182877  0.703105
9 -1.717420 -0.986426

[4 rows x 2 columns]

In [15]: df[df.a <= df.b]
Out[15]: 
          a         b
2 -0.838260  0.980077
6 -0.017685  0.027505
7 -0.182877  0.703105
9 -1.717420 -0.986426

[4 rows x 2 columns]

In [16]: df.loc[df.a <= df.b]
Out[16]: 
          a         b
2 -0.838260  0.980077
6 -0.017685  0.027505
7 -0.182877  0.703105
9 -1.717420 -0.986426

[4 rows x 2 columns]

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

In [18]: df.eval('a + b')
Out[18]: 
 -0.163194
  0.985872
  2.864538
  0.782622
  0.962818
  1.974849
  0.258445
 -2.288045
 -0.800437
  2.667426
dtype: float64

In [19]: df.a + df.b  # same as the previous expression
Out[19]: 
 -0.163194
  0.985872
  2.864538
  0.782622
  0.962818
  1.974849
  0.258445
 -2.288045
 -0.800437
  2.667426
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 [20]: 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 [21]: grouped = df.groupby(['month','week'])

In [22]: print grouped['x'].agg([np.mean, np.std])
                  mean        std
month week                       
5     1      74.750543  37.602035
      2      91.420601  56.817107
      3      80.270102  55.994654
6     1      81.840060  50.966643
      2      97.434542  59.919288
      3      79.867371  47.377914
7     1      83.997435  39.391772
      2      86.244632  41.066830
      3     108.811608  45.048738
8     1      81.647843  50.264539
      2      94.056653  47.677568
      3      76.004631  47.048914

[12 rows x 2 columns]

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

In [24]: DataFrame([tuple(list(x)+[val]) for x, val in np.ndenumerate(a)])
Out[24]: 
1  2   3
 0  0  0   1
 0  0  1   2
 0  0  2   3
 0  0  3   4
 0  1  0   5
 0  1  1   6
 0  1  2   7
 0  1  3   8
 0  2  0   9
 0  2  1  10
0  2  2  11
0  2  3  12
1  0  0  13
1  0  1  14
1  0  2  15
   .. .. .. ...

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

In [26]: DataFrame(a)
Out[26]: 
   0   1
0  0   1
1  1   2
2  2   3
3  3   4
4  4 NaN

[5 rows x 2 columns]

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

In [28]: pd.melt(cheese, id_vars=['first', 'last'])
Out[28]: 
  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

[4 rows x 4 columns]

In [29]: cheese.set_index(['first', 'last']).stack() # alternative way
Out[29]: 
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 [30]: 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 [31]: mdf = pd.melt(df, id_vars=['month', 'week'])

In [32]: pd.pivot_table(mdf, values='value', rows=['variable','week'],
   ....:                  cols=['month'], aggfunc=np.mean)
   ....: 
Out[32]: 
month                   5           6           7
variable week                                    
x        1      89.863679   78.824388   50.832050
         2     132.209447   36.715123   75.566345
y        1     216.526257  110.507591   11.484571
         2     153.506838  239.965235  160.223954
z        1      15.536152    8.826941    7.015962
         2      14.646656   17.064267   11.806954

[6 rows x 3 columns]

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 [33]: 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 [34]: df.pivot_table(values='Amount', rows='Animal', cols='FeedType', aggfunc='sum')
Out[34]: 
FeedType   A   B
Animal          
Animal1   10   5
Animal2    2  13
Animal3    6 NaN

[3 rows x 2 columns]

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

In [35]: df.groupby(['Animal','FeedType'])['Amount'].sum()
Out[35]: 
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.

Table Of Contents

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Comparison with R / R libraries¶

Base R¶

`aggregate`¶

`match` / `%in%`¶

`tapply`¶

`subset`¶

`with`¶

zoo¶

xts¶

plyr¶

`ddply`¶

reshape / reshape2¶

`melt.array`¶

`melt.list`¶

`melt.data.frame`¶

`cast`¶

Navigation

Table Of Contents

Search

Comparison with R / R libraries¶

Base R¶

zoo¶

xts¶

plyr¶

reshape / reshape2¶

Navigation