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¶
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- 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 exampledf[cols[1:3]]
ordf.drop(cols[1:3])
, but doing this by column name is a bit messy.
Sorting¶
R |
pandas |
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Transforming¶
R |
pandas |
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Grouping and summarizing¶
R |
pandas |
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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 d e f ... 24 25 26 27 28 29
0 -1.344312 0.844885 1.075770 -0.109050 1.643563 -1.469388 ... -1.170299 -0.226169 0.410835 0.813850 0.132003 -0.827317
1 -0.076467 -1.187678 1.130127 -1.436737 -1.413681 1.607920 ... 0.959726 -1.110336 -0.619976 0.149748 -0.732339 0.687738
2 0.176444 0.403310 -0.154951 0.301624 -2.179861 -1.369849 ... 0.084844 0.432390 1.519970 -0.493662 0.600178 0.274230
3 0.132885 -0.023688 2.410179 1.450520 0.206053 -0.251905 ... -2.484478 -0.281461 0.030711 0.109121 1.126203 -0.977349
4 1.474071 -0.064034 -1.282782 0.781836 -1.071357 0.441153 ... -1.197071 -1.066969 -0.303421 -0.858447 0.306996 -0.028665
.. ... ... ... ... ... ... ... ... ... ... ... ... ...
25 1.492125 -0.068190 0.681456 1.221829 -0.434352 1.204815 ... 1.944517 0.042344 -0.307904 0.428572 0.880609 0.487645
26 0.725238 0.624607 -0.141185 -0.143948 -0.328162 2.095086 ... -0.846188 1.190624 0.778507 1.008500 1.424017 0.717110
27 1.262419 1.950057 0.301038 -0.933858 0.814946 0.181439 ... -1.341814 0.334281 -0.162227 1.007824 2.826008 1.458383
28 -1.585746 -0.899734 0.921494 -0.211762 -0.059182 0.058308 ... 0.403620 -0.026602 -0.240481 0.577223 -1.088417 0.326687
29 -0.986248 0.169729 -1.158091 1.019673 0.646039 0.917399 ... -1.209247 -0.671466 0.332872 -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=np.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([np.mean, np.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() # 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=np.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.