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Merge pull request #5761 from chappers/r-compare
DOC: Flesh out the R comparison section of docs (GH3980)
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‎doc/source/comparison_with_r.rst

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.. ipython:: python
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:suppress:
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from pandas import *
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import pandas as pd
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import numpy as np
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options.display.max_rows=15
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Comparison with R / R libraries
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function. In R you might want to get the rows of a ``data.frame`` where one
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column's values are less than another column's values:
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.. code-block:: r
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.. code-block:: r
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df <- data.frame(a=rnorm(10), b=rnorm(10))
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subset(df, a <= b)
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df[df$a <= df$b,] # note the comma
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df <- data.frame(a=rnorm(10), b=rnorm(10))
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subset(df, a <= b)
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df[df$a <= df$b,] # note the comma
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In ``pandas``, there are a few ways to perform subsetting. You can use
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:meth:`~pandas.DataFrame.query` or pass an expression as if it were an
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index/slice as well as standard boolean indexing:
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.. ipython:: python
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.. ipython:: python
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from pandas import DataFrame
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from numpy.random import randn
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from pandas import DataFrame
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from numpy import random
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df = DataFrame({'a': randn(10), 'b': randn(10)})
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df.query('a <= b')
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df[df.a <= df.b]
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df.loc[df.a <= df.b]
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df = DataFrame({'a': random.randn(10), 'b': random.randn(10)})
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df.query('a <= b')
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df[df.a <= df.b]
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df.loc[df.a <= df.b]
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For more details and examples see :ref:`the query documentation
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<indexing.query>`.
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An expression using a data.frame called ``df`` in R with the columns ``a`` and
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``b`` would be evaluated using ``with`` like so:
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.. code-block:: r
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.. code-block:: r
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df <- data.frame(a=rnorm(10), b=rnorm(10))
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with(df, a + b)
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df$a + df$b # same as the previous expression
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df <- data.frame(a=rnorm(10), b=rnorm(10))
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with(df, a + b)
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df$a + df$b # same as the previous expression
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In ``pandas`` the equivalent expression, using the
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:meth:`~pandas.DataFrame.eval` method, would be:
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.. ipython:: python
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.. ipython:: python
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df = DataFrame({'a': randn(10), 'b': randn(10)})
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df.eval('a + b')
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df.a + df.b # same as the previous expression
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df = DataFrame({'a': random.randn(10), 'b': random.randn(10)})
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df.eval('a + b')
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df.a + df.b # same as the previous expression
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In certain cases :meth:`~pandas.DataFrame.eval` will be much faster than
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evaluation in pure Python. For more details and examples see :ref:`the eval
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plyr
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----
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``plyr`` is an R library for the split-apply-combine strategy for data
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analysis. The functions revolve around three data structures in R, ``a``
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for ``arrays``, ``l`` for ``lists``, and ``d`` for ``data.frame``. The
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table below shows how these data structures could be mapped in Python.
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+------------+-------------------------------+
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| R | Python |
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+============+===============================+
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| array | list |
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+------------+-------------------------------+
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| lists | dictionary or list of objects |
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+------------+-------------------------------+
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| data.frame | dataframe |
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+------------+-------------------------------+
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|ddply|_
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~~~~~~~~
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An expression using a data.frame called ``df`` in R where you want to
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summarize ``x`` by ``month``:
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.. code-block:: r
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require(plyr)
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df <- data.frame(
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x = runif(120, 1, 168),
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y = runif(120, 7, 334),
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z = runif(120, 1.7, 20.7),
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month = rep(c(5,6,7,8),30),
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week = sample(1:4, 120, TRUE)
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)
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ddply(df, .(month, week), summarize,
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mean = round(mean(x), 2),
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sd = round(sd(x), 2))
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In ``pandas`` the equivalent expression, using the
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:meth:`~pandas.DataFrame.groupby` method, would be:
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.. ipython:: python
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df = DataFrame({
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'x': random.uniform(1., 168., 120),
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'y': random.uniform(7., 334., 120),
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'z': random.uniform(1.7, 20.7, 120),
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'month': [5,6,7,8]*30,
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'week': random.randint(1,4, 120)
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})
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grouped = df.groupby(['month','week'])
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print grouped['x'].agg([np.mean, np.std])
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For more details and examples see :ref:`the groupby documentation
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<groupby.aggregate>`.
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reshape / reshape2
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------------------
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|meltarray|_
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~~~~~~~~~~~~~
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An expression using a 3 dimensional array called ``a`` in R where you want to
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melt it into a data.frame:
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.. code-block:: r
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a <- array(c(1:23, NA), c(2,3,4))
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data.frame(melt(a))
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In Python, since ``a`` is a list, you can simply use list comprehension.
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.. ipython:: python
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a = np.array(range(1,24)+[np.NAN]).reshape(2,3,4)
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DataFrame([tuple(list(x)+[val]) for x, val in np.ndenumerate(a)])
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|meltlist|_
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~~~~~~~~~~~~
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An expression using a list called ``a`` in R where you want to melt it
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into a data.frame:
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.. code-block:: r
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a <- as.list(c(1:4, NA))
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data.frame(melt(a))
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In Python, this list would be a list of tuples, so
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:meth:`~pandas.DataFrame` method would convert it to a dataframe as required.
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.. ipython:: python
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a = list(enumerate(range(1,5)+[np.NAN]))
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DataFrame(a)
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For more details and examples see :ref:`the Into to Data Structures
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documentation <basics.dataframe.from_items>`.
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|meltdf|_
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~~~~~~~~~~~~~~~~
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An expression using a data.frame called ``cheese`` in R where you want to
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reshape the data.frame:
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.. code-block:: r
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cheese <- data.frame(
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first = c('John', 'Mary'),
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last = c('Doe', 'Bo'),
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height = c(5.5, 6.0),
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weight = c(130, 150)
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)
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melt(cheese, id=c("first", "last"))
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In Python, the :meth:`~pandas.melt` method is the R equivalent:
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.. ipython:: python
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cheese = DataFrame({'first' : ['John', 'Mary'],
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'last' : ['Doe', 'Bo'],
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'height' : [5.5, 6.0],
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'weight' : [130, 150]})
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pd.melt(cheese, id_vars=['first', 'last'])
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cheese.set_index(['first', 'last']).stack() # alternative way
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For more details and examples see :ref:`the reshaping documentation
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<reshaping.melt>`.
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|cast|_
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~~~~~~~
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An expression using a data.frame called ``df`` in R to cast into a higher
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dimensional array:
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.. code-block:: r
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df <- data.frame(
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x = runif(12, 1, 168),
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y = runif(12, 7, 334),
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z = runif(12, 1.7, 20.7),
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month = rep(c(5,6,7),4),
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week = rep(c(1,2), 6)
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)
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mdf <- melt(df, id=c("month", "week"))
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acast(mdf, week ~ month ~ variable, mean)
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In Python the best way is to make use of :meth:`~pandas.pivot_table`:
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.. ipython:: python
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df = DataFrame({
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'x': random.uniform(1., 168., 12),
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'y': random.uniform(7., 334., 12),
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'z': random.uniform(1.7, 20.7, 12),
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'month': [5,6,7]*4,
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'week': [1,2]*6
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})
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mdf = pd.melt(df, id_vars=['month', 'week'])
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pd.pivot_table(mdf, values='value', rows=['variable','week'],
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cols=['month'], aggfunc=np.mean)
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For more details and examples see :ref:`the reshaping documentation
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<reshaping.pivot>`.
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.. |with| replace:: ``with``
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.. _with: http://finzi.psych.upenn.edu/R/library/base/html/with.html
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.. |subset| replace:: ``subset``
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.. _subset: http://finzi.psych.upenn.edu/R/library/base/html/subset.html
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.. |ddply| replace:: ``ddply``
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.. _ddply: http://www.inside-r.org/packages/cran/plyr/docs/ddply
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.. |meltarray| replace:: ``melt.array``
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.. _meltarray: http://www.inside-r.org/packages/cran/reshape2/docs/melt.array
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.. |meltlist| replace:: ``melt.list``
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.. meltlist: http://www.inside-r.org/packages/cran/reshape2/docs/melt.list
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.. |meltdf| replace:: ``melt.data.frame``
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.. meltdf: http://www.inside-r.org/packages/cran/reshape2/docs/melt.data.frame
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.. |cast| replace:: ``cast``
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.. cast: http://www.inside-r.org/packages/cran/reshape2/docs/cast
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