#!/usr/bin/env python
# coding: utf-8

# This notebook compares [pandas](http://pandas.pydata.org)
# and [dplyr](http://cran.r-project.org/web/packages/dplyr/index.html).
# 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](http://cran.r-project.org/web/packages/dplyr/vignettes/introduction.html) 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
get_ipython().run_line_magic('matplotlib', 'inline')
get_ipython().run_line_magic('load_ext', 'rmagic')

import pandas as pd
import seaborn as sns

pd.set_option("display.max_rows", 5)


# In[2]:


get_ipython().run_cell_magic('R', '', 'install.packages("nycflights13", repos=\'http://cran.us.r-project.org\')\n')


# In[3]:


get_ipython().run_cell_magic('R', '', 'library(nycflights13)\nwrite.csv(flights, "flights.csv")\n')


# # 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


# In[6]:


# head(flights)
flights.head()


# # Single table verbs

# ``dplyr`` has a small set of nicely defined verbs. I've listed their closest pandas verbs.
# 
# 
# <table>
#   <tr>
#     <td><b>dplyr</b></td>
#     <td><b>pandas</b></td>
#   </tr>
#   <tr>
#     <td>filter() (and slice())</td>
#     <td>query() (and loc[], iloc[])</td>
#   </tr>
#   <tr>
#     <td>arrange()</td>
#     <td>sort()</td>
#   </tr>
#   <tr>
#   <td>select() (and rename())</td>
#     <td>__getitem__ (and rename())</td>
#   </tr>
#   <tr>
#   <td>distinct()</td>
#     <td>drop_duplicates()</td>
#   </tr>
#   <tr>
#     <td>mutate() (and transmute())</td>
#     <td>assign</td>
#   </tr>
#   <tr>
#     <td>summarise()</td>
#     <td>None</td>
#   </tr>
#   <tr>
#     <td>sample_n() and sample_frac()</td>
#     <td>None</td>
#   </tr>
# </table>
# 
# 
# 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")


# The more verbose version:

# In[8]:


# flights[flights$month == 1 & flights$day == 1, ]
flights[(flights.month == 1) & (flights.day == 1)]


# In[9]:


# slice(flights, 1:10)
flights.iloc[:9]


# # Arrange rows with arrange(), sort()

# In[10]:


# arrange(flights, year, month, day) 
flights.sort(['year', 'month', 'day'])


# In[11]:


# arrange(flights, desc(arr_delay))
flights.sort('arr_delay', ascending=False)


# # Select columns with select(), []

# In[12]:


# select(flights, year, month, day) 
flights[['year', 'month', 'day']]


# 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']


# 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'})


# 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()


# FYI this returns a numpy array instead of a Series.

# In[18]:


# distinct(select(flights, origin, dest))
flights[['origin', 'dest']].drop_duplicates()


# 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](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enhancements-assign).

# 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)


# 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)))


# 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']])


# # Summarise values with summarise()

# In[22]:


flights.dep_delay.mean()


# # Randomly sample rows with sample_n() and sample_frac()

# There's an open PR on [Github](https://github.com/pydata/pandas/pull/9666) 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)]


# In[24]:


# sample_frac(flights, 0.01)
flights.iloc[np.random.randint(0, len(flights),
                               .1 * len(flights))]


# # 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


# 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](http://stanford.edu/~mwaskom/software/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'})


# 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


# 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](http://cython.org) 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


# In[33]:


# (per_month <- summarise(per_day, flights = sum(flights)))
per_month = per_day.groupby(level=['year', 'month']).sum()
per_month


# In[34]:


# (per_year  <- summarise(per_month, flights = sum(flights)))
per_year = per_month.sum()
per_year


# 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](https://twitter.com/hadleywickham/) 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')
)


# # Other Data Sources

# Pandas has tons [IO tools](http://pandas.pydata.org/pandas-docs/version/0.15.0/io.html) to help you get data in and out, including SQL databases via [SQLAlchemy](http://www.sqlalchemy.org).

# # 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](http://jupyter.org) (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](http://shiny.rstudio.com) 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](https://twitter.com/tomaugspurger).