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.

For transfer of DataFrame objects from pandas to R, one option is to use HDF5 files, see External Compatibility for an example.

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 -1.039575 -0.424972
1  0.567020 -1.087401
2 -0.673690 -1.478427
3  0.524988  0.577046
4 -1.715002 -0.370647
5 -1.157892  0.844885
6  1.075770  1.643563
7 -1.469388 -0.674600
8 -1.776904 -1.294524
9  0.413738 -0.472035

In [3]: df.loc[:, ['a', 'c']]
Out[3]: 
          a         c
0 -1.039575 -0.424972
1  0.567020 -1.087401
2 -0.673690 -1.478427
3  0.524988  0.577046
4 -1.715002 -0.370647
5 -1.157892  0.844885
6  1.075770  1.643563
7 -1.469388 -0.674600
8 -1.776904 -1.294524
9  0.413738 -0.472035

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         d         e         f         g  \
0  -0.013960 -0.362543 -0.006154 -0.923061  0.895717  0.805244 -1.206412   
1   0.545952 -1.219217 -1.226825  0.769804 -1.281247 -0.727707 -0.121306   
2   2.396780  0.014871  3.357427 -0.317441 -1.236269  0.896171 -0.487602   
3  -0.988387  0.094055  1.262731  1.289997  0.082423 -0.055758  0.536580   
4  -1.340896  1.846883 -1.328865  1.682706 -1.717693  0.888782  0.228440   
5   0.464000  0.227371 -0.496922  0.306389 -2.290613 -1.134623 -1.561819   
6  -0.507516 -0.230096  0.394500 -1.934370 -1.652499  1.488753 -0.896484   
..       ...       ...       ...       ...       ...       ...       ...   
23 -0.083272 -0.273955 -0.772369 -1.242807 -0.386336 -0.182486  0.164816   
24  2.071413 -1.364763  1.122066  0.066847  1.751987  0.419071 -1.118283   
25  0.036609  0.359986  1.211905  0.850427  1.554957 -0.888463 -1.508808   
26 -1.179240  0.238923  1.756671 -0.747571  0.543625 -0.159609 -0.051458   
27  0.025645  0.932436 -1.694531 -0.182236 -1.072710  0.466764 -0.072673   
28  0.439086  0.812684 -0.128932 -0.142506 -1.137207  0.462001 -0.159466   
29 -0.909806 -0.312006  0.383630 -0.631606  1.321415 -0.004799 -2.008210   

           7         8         9        24        25        26        27  \
0   2.565646  1.431256  1.340309  0.875906 -2.211372  0.974466 -2.006747   
1  -0.097883  0.695775  0.341734 -1.743161 -0.826591 -0.345352  1.314232   
2  -0.082240 -2.182937  0.380396  1.266143  0.299368 -0.863838  0.408204   
3  -0.489682  0.369374 -0.034571  0.221471 -0.744471  0.758527  1.729689   
4   0.901805  1.171216  0.520260  0.650776 -1.461665 -1.137707 -0.891060   
5  -0.260838  0.281957  1.523962 -0.008434  1.952541 -1.056652  0.533946   
6   0.576897  1.146000  1.487349  2.015523 -1.833722  1.771740 -0.670027   
..       ...       ...       ...       ...       ...       ...       ...   
23  0.065624  0.307665 -1.898358  1.389045 -0.873585 -0.699862  0.812477   
24  1.010694  0.877138 -0.611561 -1.040389 -0.796211  0.241596  0.385922   
25 -0.617855  0.536164  2.175585  1.872601 -2.513465 -0.139184  0.810491   
26  0.937882  0.617547  0.287918 -1.584814  0.307941  1.809049  0.296237   
27 -0.026233 -0.051744  0.001402  0.150664 -3.060395  0.040268  0.066091   
28 -1.788308  0.753604  0.918071  0.922729  0.869610  0.364726 -0.226101   
29 -0.481634 -2.056211 -2.106095  0.039227  0.211283  1.440190 -0.989193   

          28        29  
0  -0.410001 -0.078638  
1   0.690579  0.995761  
2  -1.048089 -0.025747  
3  -0.964980 -0.845696  
4  -0.693921  1.613616  
5  -1.226970  0.040403  
6   0.049307 -0.521493  
..       ...       ...  
23 -0.469503  1.142702  
24 -0.486078  0.433042  
25  0.571599 -0.000676  
26 -0.143550  0.289401  
27 -0.192862  1.979055  
28 -0.657647 -0.952699  
29  0.313335 -0.399709  

[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(.200, .400, 25)
   ....:    })
   ....: 

In [17]: baseball.pivot_table(values='batting avg', columns='team', aggfunc=np.max)
Out[17]: 
team
team 1    0.394457
team 2    0.395730
team 3    0.343015
team 4    0.388863
team 5    0.377379
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 [18]: df = pd.DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

In [19]: df.query('a <= b')
Out[19]: 
          a         b
0 -1.003455 -0.990738
1  0.083515  0.548796
3 -0.524392  0.904400
4 -0.837804  0.746374
8 -0.507219  0.245479

In [20]: df[df.a <= df.b]
Out[20]: 
          a         b
0 -1.003455 -0.990738
1  0.083515  0.548796
3 -0.524392  0.904400
4 -0.837804  0.746374
8 -0.507219  0.245479

In [21]: df.loc[df.a <= df.b]
Out[21]: 
          a         b
0 -1.003455 -0.990738
1  0.083515  0.548796
3 -0.524392  0.904400
4 -0.837804  0.746374
8 -0.507219  0.245479

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

In [23]: df.eval('a + b')
Out[23]: 
0   -0.920205
1   -0.860236
2    1.154370
3    0.188140
4   -1.163718
5    0.001397
6   -0.825694
7   -1.138198
8   -1.708034
9    1.148616
dtype: float64

In [24]: df.a + df.b  # same as the previous expression
Out[24]: 
0   -0.920205
1   -0.860236
2    1.154370
3    0.188140
4   -1.163718
5    0.001397
6   -0.825694
7   -1.138198
8   -1.708034
9    1.148616
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., 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 [26]: grouped = df.groupby(['month','week'])

In [27]: grouped['x'].agg([np.mean, np.std])
Out[27]: 
                  mean        std
month week                       
5     1      71.840596  52.886392
      2      71.904794  55.786805
      3      89.845632  49.892367
6     1      97.730877  52.442172
      2      93.369836  47.178389
      3      96.592088  58.773744
7     1      59.255715  43.442336
      2      69.634012  28.607369
      3      84.510992  59.761096
8     1     104.787666  31.745437
      2      69.717872  53.747188
      3      79.892221  52.950459

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 [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
5   0  1  1   6.0
6   0  1  2   7.0
.. .. .. ..   ...
17  1  1  1  18.0
18  1  1  2  19.0
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]

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 [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.

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

In [37]: pd.pivot_table(mdf, values='value', index=['variable','week'],
   ....:                  columns=['month'], aggfunc=np.mean)
   ....: 
Out[37]: 
month                   5           6           7
variable week                                    
x        1     114.001700  132.227290   65.808204
         2     124.669553  147.495706   82.882820
y        1     225.636630  301.864228   91.706834
         2      57.692665  215.851669  218.004383
z        1      17.793871    7.124644   17.679823
         2      15.068355   13.873974    9.394966

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

New in version 0.15.

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]
5        (4.333, 6]
dtype: category
Categories (3, object): [(0.995, 2.667] < (2.667, 4.333] < (4.333, 6]]

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.

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