This notebook compares pandas and dplyr. The comparison is just on syntax (verbage), not performance. Whether you're an R user looking to switch to pandas (or the other way around), I hope this guide will help ease the transition.

We'll work through the introductory dplyr vignette to analyze some flight data.

I'm working on a better layout to show the two packages side by side. But for now I'm just putting the dplyr code in a comment above each python call.

In [1]:
# Some prep work to get the data from R and into pandas
%matplotlib inline
%load_ext rmagic

import pandas as pd
import seaborn as sns

pd.set_option("display.max_rows", 5)
/Users/tom.augspurger/Envs/py3/lib/python3.4/site-packages/IPython/extensions/rmagic.py:693: UserWarning: The rmagic extension in IPython is deprecated in favour of rpy2.ipython. If available, that will be loaded instead.
http://rpy.sourceforge.net/
  warnings.warn("The rmagic extension in IPython is deprecated in favour of "
In [2]:
%%R
install.packages("nycflights13", repos='http://cran.us.r-project.org')
trying URL 'http://cran.us.r-project.org/src/contrib/nycflights13_0.1.tar.gz'
Content type 'application/x-gzip' length 3585227 bytes (3.4 MB)
opened URL
==================================================
downloaded 3.4 MB


The downloaded source packages are in
	‘/private/var/folders/n5/t6my_m7n17sgt4m7j9vmnrlm0000gp/T/Rtmpn3AEi1/downloaded_packages’
In [3]:
%%R
library(nycflights13)
write.csv(flights, "flights.csv")

Data: nycflights13

In [4]:
flights = pd.read_csv("flights.csv", index_col=0)
In [5]:
# dim(flights)   <--- The R code
flights.shape  # <--- The python code
Out[5]:
(336776, 16)
In [6]:
# head(flights)
flights.head()
Out[6]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
3 2013 1 1 542 2 923 33 AA N619AA 1141 JFK MIA 160 1089 5 42
4 2013 1 1 544 -1 1004 -18 B6 N804JB 725 JFK BQN 183 1576 5 44
5 2013 1 1 554 -6 812 -25 DL N668DN 461 LGA ATL 116 762 5 54

Single table verbs

dplyr has a small set of nicely defined verbs. I've listed their closest pandas verbs.

dplyr pandas
filter() (and slice()) query() (and loc[], iloc[])
arrange() sort()
select() (and rename()) __getitem__ (and rename())
distinct() drop_duplicates()
mutate() (and transmute()) assign
summarise() None
sample_n() and sample_frac() None

Some of the "missing" verbs in pandas are because there are other, different ways of achieving the same goal. For example summarise is spread across mean, std, etc. Others, like sample_n, just haven't been implemented yet.

Filter rows with filter(), query()

In [7]:
# filter(flights, month == 1, day == 1)
flights.query("month == 1 & day == 1")
Out[7]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
841 2013 1 1 NaN NaN NaN NaN AA N3EVAA 1925 LGA MIA NaN 1096 NaN NaN
842 2013 1 1 NaN NaN NaN NaN B6 N618JB 125 JFK FLL NaN 1069 NaN NaN

842 rows × 16 columns

The more verbose version:

In [8]:
# flights[flights$month == 1 & flights$day == 1, ]
flights[(flights.month == 1) & (flights.day == 1)]
Out[8]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
841 2013 1 1 NaN NaN NaN NaN AA N3EVAA 1925 LGA MIA NaN 1096 NaN NaN
842 2013 1 1 NaN NaN NaN NaN B6 N618JB 125 JFK FLL NaN 1069 NaN NaN

842 rows × 16 columns

In [9]:
# slice(flights, 1:10)
flights.iloc[:9]
Out[9]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8 2013 1 1 557 -3 709 -14 EV N829AS 5708 LGA IAD 53 229 5 57
9 2013 1 1 557 -3 838 -8 B6 N593JB 79 JFK MCO 140 944 5 57

9 rows × 16 columns

Arrange rows with arrange(), sort()

In [10]:
# arrange(flights, year, month, day) 
flights.sort(['year', 'month', 'day'])
Out[10]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
111295 2013 12 31 NaN NaN NaN NaN UA NaN 219 EWR ORD NaN 719 NaN NaN
111296 2013 12 31 NaN NaN NaN NaN UA NaN 443 JFK LAX NaN 2475 NaN NaN

336776 rows × 16 columns

In [11]:
# arrange(flights, desc(arr_delay))
flights.sort('arr_delay', ascending=False)
Out[11]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
7073 2013 1 9 641 1301 1242 1272 HA N384HA 51 JFK HNL 640 4983 6 41
235779 2013 6 15 1432 1137 1607 1127 MQ N504MQ 3535 JFK CMH 74 483 14 32
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
336775 2013 9 30 NaN NaN NaN NaN MQ N511MQ 3572 LGA CLE NaN 419 NaN NaN
336776 2013 9 30 NaN NaN NaN NaN MQ N839MQ 3531 LGA RDU NaN 431 NaN NaN

336776 rows × 16 columns

Select columns with select(), []

In [12]:
# select(flights, year, month, day) 
flights[['year', 'month', 'day']]
Out[12]:
year month day
1 2013 1 1
2 2013 1 1
... ... ... ...
336775 2013 9 30
336776 2013 9 30

336776 rows × 3 columns

In [13]:
# select(flights, year:day) 

# No real equivalent here. Although I think this is OK.
# Typically I'll have the columns I want stored in a list
# somewhere, which can be passed right into __getitem__ ([]).
In [14]:
# select(flights, -(year:day)) 

# Again, simliar story. I would just use
# flights.drop(cols_to_drop, axis=1)
# or fligths[flights.columns.difference(pd.Index(cols_to_drop))]
# point to dplyr!
In [15]:
# select(flights, tail_num = tailnum)
flights.rename(columns={'tailnum': 'tail_num'})['tail_num']
Out[15]:
1         N14228
2         N24211
           ...  
336775    N511MQ
336776    N839MQ
Name: tail_num, dtype: object

But like Hadley mentions, not that useful since it only returns the one column. dplyr and pandas compare well here.

In [16]:
# rename(flights, tail_num = tailnum)
flights.rename(columns={'tailnum': 'tail_num'})
Out[16]:
year month day dep_time dep_delay arr_time arr_delay carrier tail_num flight origin dest air_time distance hour minute
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
336775 2013 9 30 NaN NaN NaN NaN MQ N511MQ 3572 LGA CLE NaN 419 NaN NaN
336776 2013 9 30 NaN NaN NaN NaN MQ N839MQ 3531 LGA RDU NaN 431 NaN NaN

336776 rows × 16 columns

Pandas is more verbose, but the the argument to columns can be any mapping. So it's often used with a function to perform a common task, say df.rename(columns=lambda x: x.replace('-', '_')) to replace any dashes with underscores. Also, rename (the pandas version) can be applied to the Index.

Extract distinct (unique) rows

In [17]:
# distinct(select(flights, tailnum))
flights.tailnum.unique()
Out[17]:
array(['N14228', 'N24211', 'N619AA', ..., 'N776SK', 'N785SK', 'N557AS'], dtype=object)

FYI this returns a numpy array instead of a Series.

In [18]:
# distinct(select(flights, origin, dest))
flights[['origin', 'dest']].drop_duplicates()
Out[18]:
origin dest
1 EWR IAH
2 LGA IAH
... ... ...
255456 EWR ANC
275946 EWR LGA

224 rows × 2 columns

OK, so dplyr wins there from a consistency point of view. unique is only defined on Series, not DataFrames. The original intention for drop_duplicates is to check for records that were accidentally included twice. This feels a bit hacky using it to select the distinct combinations, but it works!

Add new columns with mutate()

We at pandas shamelessly stole this for v0.16.0.

In [19]:
# mutate(flights,
#   gain = arr_delay - dep_delay,
#   speed = distance / air_time * 60)

flights.assign(gain=flights.arr_delay - flights.dep_delay,
               speed=flights.distance / flights.air_time * 60)
Out[19]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute gain speed
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17 9 370.044053
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33 16 374.273128
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
336775 2013 9 30 NaN NaN NaN NaN MQ N511MQ 3572 LGA CLE NaN 419 NaN NaN NaN NaN
336776 2013 9 30 NaN NaN NaN NaN MQ N839MQ 3531 LGA RDU NaN 431 NaN NaN NaN NaN

336776 rows × 18 columns

In [20]:
# mutate(flights,
#   gain = arr_delay - dep_delay,
#   gain_per_hour = gain / (air_time / 60)
# )

(flights.assign(gain=flights.arr_delay - flights.dep_delay)
        .assign(gain_per_hour = lambda df: df.gain / (df.air_time / 60)))
Out[20]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute gain gain_per_hour
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 1400 5 17 9 2.378855
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 1416 5 33 16 4.229075
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
336775 2013 9 30 NaN NaN NaN NaN MQ N511MQ 3572 LGA CLE NaN 419 NaN NaN NaN NaN
336776 2013 9 30 NaN NaN NaN NaN MQ N839MQ 3531 LGA RDU NaN 431 NaN NaN NaN NaN

336776 rows × 18 columns

The first example is pretty much identical (aside from the names, mutate vs. assign).

The second example just comes down to language differences. In R, it's possible to implement a function like mutate where you can refer to gain in the line calcuating gain_per_hour, even though gain hasn't actually been calcuated yet.

In Python, you can have arbitrary keyword arguments to functions (which we needed for .assign), but the order of the argumnets is arbitrary. So you can't have something like df.assign(x=df.a / df.b, y=x **2), because you don't know whether x or y will come first (you'd also get an error saying x is undefined.

To work around that with pandas, you'll need to split up the assigns, and pass in a callable to the second assign. The callable looks at itself to find a column named gain. Since the line above returns a DataFrame with the gain column added, the pipeline goes through just fine.

In [21]:
# transmute(flights,
#   gain = arr_delay - dep_delay,
#   gain_per_hour = gain / (air_time / 60)
# )
(flights.assign(gain=flights.arr_delay - flights.dep_delay)
        .assign(gain_per_hour = lambda df: df.gain / (df.air_time / 60))
        [['gain', 'gain_per_hour']])
Out[21]:
gain gain_per_hour
1 9 2.378855
2 16 4.229075
... ... ...
336775 NaN NaN
336776 NaN NaN

336776 rows × 2 columns

Summarise values with summarise()

In [22]:
flights.dep_delay.mean()
Out[22]:
12.639070257304708

Randomly sample rows with sample_n() and sample_frac()

There's an open PR on Github to make this nicer (closer to dplyr). For now you can drop down to numpy.

In [23]:
# sample_n(flights, 10)
flights.loc[np.random.choice(flights.index, 10)]
Out[23]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
37234 2013 10 11 1931 156 2025 125 WN N424WN 2988 LGA MDW 96 725 19 31
84860 2013 12 2 1708 -6 1841 6 EV N15983 5815 EWR RIC 49 277 17 8
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
283800 2013 8 5 815 -5 1039 13 EV N14198 4380 EWR MSP 167 1008 8 15
226411 2013 6 5 1551 -4 1720 -29 B6 N231JB 915 JFK ORD 120 740 15 51

10 rows × 16 columns

In [24]:
# sample_frac(flights, 0.01)
flights.iloc[np.random.randint(0, len(flights),
                               .1 * len(flights))]
Out[24]:
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time distance hour minute
50321 2013 10 25 1932 2 2057 -27 9E N909XJ 3331 JFK RDU 65 427 19 32
132082 2013 2 24 1557 -3 1923 -7 DL N377NW 161 JFK MIA 156 1089 15 57
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
277795 2013 7 29 2113 18 2220 -10 WN N905WN 579 LGA MDW 111 725 21 13
274004 2013 7 25 2023 -5 2229 -18 B6 N524JB 135 JFK PHX 279 2153 20 23

33677 rows × 16 columns

Grouped operations

In [25]:
# planes <- group_by(flights, tailnum)
# delay <- summarise(planes,
#   count = n(),
#   dist = mean(distance, na.rm = TRUE),
#   delay = mean(arr_delay, na.rm = TRUE))
# delay <- filter(delay, count > 20, dist < 2000)

planes = flights.groupby("tailnum")
delay = (planes.agg({"year": "count",
                     "distance": "mean",
                     "arr_delay": "mean"})
               .rename(columns={"distance": "dist",
                                "arr_delay": "delay",
                                "year": "count"})
               .query("count > 20 & dist < 2000"))
delay
Out[25]:
count delay dist
tailnum
N0EGMQ 371 9.982955 676.188679
N10156 153 12.717241 757.947712
... ... ... ...
N999DN 61 14.311475 895.459016
N9EAMQ 248 9.235294 674.665323

2961 rows × 3 columns

For me, dplyr's n() looked is a bit starge at first, but it's already growing on me.

I think pandas is more difficult for this particular example. There isn't as natural a way to mix column-agnostic aggregations (like count) with column-specific aggregations like the other two. You end up writing could like .agg{'year': 'count'} which reads, "I want the count of year", even though you don't care about year specifically. You could just as easily have said .agg('distance': 'count'). Additionally assigning names can't be done as cleanly in pandas; you have to just follow it up with a rename like before.

We may as well reproduce the graph. It looks like ggplots geom_smooth is some kind of lowess smoother. We can either us seaborn:

In [26]:
fig, ax = plt.subplots(figsize=(12, 6))

sns.regplot("dist", "delay", data=delay, lowess=True, ax=ax,
            scatter_kws={'color': 'k', 'alpha': .5, 's': delay['count'] / 10}, ci=90,
            line_kws={'linewidth': 3});

Or using statsmodels directly for more control over the lowess, with an extremely lazy "confidence interval".

In [28]:
import statsmodels.api as sm
In [29]:
smooth = sm.nonparametric.lowess(delay.delay, delay.dist, frac=1/8)
ax = delay.plot(kind='scatter', x='dist', y = 'delay', figsize=(12, 6),
                color='k', alpha=.5, s=delay['count'] / 10)
ax.plot(smooth[:, 0], smooth[:, 1], linewidth=3);
std = smooth[:, 1].std()
ax.fill_between(smooth[:, 0], smooth[:, 1] - std, smooth[:, 1] + std, alpha=.25);
In [30]:
# destinations <- group_by(flights, dest)
# summarise(destinations,
#   planes = n_distinct(tailnum),
#   flights = n()
# )

destinations = flights.groupby('dest')
destinations.agg({
    'tailnum': lambda x: len(x.unique()),
    'year': 'count'
    }).rename(columns={'tailnum': 'planes',
                       'year': 'flights'})
Out[30]:
flights planes
dest
ABQ 254 108
ACK 265 58
... ... ...
TYS 631 273
XNA 1036 176

105 rows × 2 columns

There's a little know feature to groupby.agg: it accepts a dict of dicts mapping columns to {name: aggfunc} pairs. Here's the result:

In [31]:
destinations = flights.groupby('dest')
r = destinations.agg({'tailnum': {'planes': lambda x: len(x.unique())},
                      'year': {'flights': 'count'}})
r
Out[31]:
year tailnum
flights planes
dest
ABQ 254 108
ACK 265 58
... ... ...
TYS 631 273
XNA 1036 176

105 rows × 2 columns

The result is a MultiIndex in the columns which can be a bit awkard to work with (you can drop a level with r.columns.droplevel()). Also the syntax going into the .agg may not be the clearest.

Similar to how dplyr provides optimized C++ versions of most of the summarise functions, pandas uses cython optimized versions for most of the agg methods.

In [32]:
# daily <- group_by(flights, year, month, day)
# (per_day   <- summarise(daily, flights = n()))

daily = flights.groupby(['year', 'month', 'day'])
per_day = daily['distance'].count()
per_day
Out[32]:
year  month  day
2013  1      1      842
             2      943
                   ... 
      12     30     968
             31     776
Name: distance, dtype: int64
In [33]:
# (per_month <- summarise(per_day, flights = sum(flights)))
per_month = per_day.groupby(level=['year', 'month']).sum()
per_month
Out[33]:
year  month
2013  1        27004
      2        24951
               ...  
      11       27268
      12       28135
Name: distance, dtype: int64
In [34]:
# (per_year  <- summarise(per_month, flights = sum(flights)))
per_year = per_month.sum()
per_year
Out[34]:
336776

I'm not sure how dplyr is handling the other columns, like year, in the last example. With pandas, it's clear that we're grouping by them since they're included in the groupby. For the last example, we didn't group by anything, so they aren't included in the result.

Chaining

Any follower of Hadley's twitter account will know how much R users love the %>% (pipe) operator. And for good reason!

In [35]:
# flights %>%
#   group_by(year, month, day) %>%
#   select(arr_delay, dep_delay) %>%
#   summarise(
#     arr = mean(arr_delay, na.rm = TRUE),
#     dep = mean(dep_delay, na.rm = TRUE)
#   ) %>%
#   filter(arr > 30 | dep > 30)
(
flights.groupby(['year', 'month', 'day'])
    [['arr_delay', 'dep_delay']]
    .mean()
    .query('arr_delay > 30 | dep_delay > 30')
)
Out[35]:
arr_delay dep_delay
year month day
2013 1 16 34.247362 24.612865
31 32.602854 28.658363
1 ... ... ...
12 17 55.871856 40.705602
23 32.226042 32.254149

49 rows × 2 columns

Other Data Sources

Pandas has tons IO tools to help you get data in and out, including SQL databases via SQLAlchemy.

Summary

I think pandas held up pretty well, considering this was a vignette written for dplyr. I found the degree of similarity more interesting than the differences. The most difficult task was renaming of columns within an operation; they had to be followed up with a call to rename after the operation, which isn't that burdensome honestly.

More and more it looks like we're moving towards future where being a language or package partisan just doesn't make sense. Not when you can load up a Jupyter (formerly IPython) notebook to call up a library written in R, and hand those results off to python or Julia or whatever for followup, before going back to R to make a cool shiny web app.

There will always be a place for your "utility belt" package like dplyr or pandas, but it wouldn't hurt to be familiar with both.

If you want to contribute to pandas, we're always looking for help at https://github.com/pydata/pandas/. You can get ahold of me directly on twitter.