The first session in our statistical learning in Python series will briefly touch on some of the core components of Python's scientific computing stack that we will use extensively later in the course. We will not only introduce two important libraries for data wrangling, numpy and pandas, but also show how to create plots using matplotlib. Please note that this is not a thorough introduction to these libraries; instead, we would like to point out what basic functionality they provide and how they differ from their counterparts in R.
But before we get into the details we will briefly describe how to setup a Python environment and what packages you need to install in order to run the code examples in this notebook.
To run the R examples in this code you also need:
You can find instructions how to install
rpy2 here . If you have an working R environment on your machine the following command should install
$ pip install -U rpy2
To test if
rpy2 was installed correctly run:
$ python -m 'rpy2.tests'
If you run on Anaconda and it complains that it misses
libreadline.so please install the following conda package:
$ conda install python=2.7.5=2
IPython is an interactive computing environment for Python. It is a great tool for interactive data analysis and programming in general. Amongst other things it features a web-based notebook server that supports code, documentation, inline plots, and much more. In fact, all blog posts in this series will be written using IPython notebooks with the advantage that you can simply download it from here and either run it locally or view it on nbviewer.
The goal of this session is to get familiar with the basics of how to work with data in Python. The basic data containers that are used to manipulate data in Python are n-dimensional arrays that act either as vectors, matrices, or tensors.
In contrast to statistical computing environments like R, the fundamental data structures for data analysis in Python are not built into the computing environment but are available via dedicated 3rd party libraries. These libraries are called
Numpy is the lingua-franca in the Python scientific computing ecosystem. It basically provides an n-dimensional array object that holds elements of a specific
numpy.int32). Most packages that we will discuss in this series will directly operate on arrays. Numpy also provides common operations on arrays such as element-wise arithmetic, indexing/slicing, and basic linear algebra (dot product, matrix decompositions, ...).
Below we show some basic working with numpy arrays:
from __future__ import division # always use floating point division import numpy as np # convention, use alias ``np`` # a one dimensional array x = np.array([2, 7, 5]) print 'x:', x # print x # a sequence starting from 4 to 12 with a step size of 3 y = np.arange(4, 12, 3) print 'y:', y # element-wise operations on arrays print 'x + y:', x + y print 'x / y:', x / y print 'x ^ y:', x ** y # python uses ** for exponentiation
x: [2 7 5] y: [ 4 7 10] x + y: [ 6 14 15] x / y: [ 0.5 1. 0.5] x ^ y: [ 16 823543 9765625]
If you need any help on operations such as
np.arange you can access its documentation by either typing
help(np.arange) or -- if you use IPython -- write a
'?' after the command:
You can index and slice an array using square brackets
. To slice an array, numpy uses Python's slicing syntax
x[start:end:step] where step is the step size which is optional. If you omit
end it will use the beginning or end, respectively. Python uses exclusive semantics meaning that the element with position
end is not included in the result.
Indexing can be done either by position or by using a boolean mask:
print x # second element of x print x[1:3] # slice of x that includes second and third elements print print x[-2] # indexing using negative indices - starts from -1 print x[-np.array([1, 2])] # fancy indexing using index array print print x[np.array([False, True, True])] # indexing using boolean mask
7 [7 5] 7 [5 7] [7 5]
For two or more dimensional arrays we just add slicing/indexing arguments, to select the whole dimension you can simply put a colon (
# reshape sequence to 2d array (=matrix) where rows hold contiguous sequences # then transpose so that columns hold contiguous sub sequences z_temp = np.arange(1, 13).reshape((3,4)) print "z_temp" print z_temp print # transpose z = z_temp.T print "z = z_temp.T (transpose of z_temp)" print z print # slicing along two dimensions a = z[2:4, 1:3] print "a = z[2:4, 1:3]" print a print # slicing along 2nd dimension b = z[:, 1:3] print "b = z[:, 1:3]" print b print # first column, returns 1d array c = z[:, 0] print "c = z[:, 0]" print c # one dimensional print # first column but return 2d array (remember: exclusive semantics) cc = z[:, 0:1] print "cc = z[:, 0:1]" print cc # two dimensional; column vector
z_temp [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] z = z_temp.T (transpose of z_temp) [[ 1 5 9] [ 2 6 10] [ 3 7 11] [ 4 8 12]] a = z[2:4, 1:3] [[ 7 11] [ 8 12]] b = z[:, 1:3] [[ 5 9] [ 6 10] [ 7 11] [ 8 12]] c = z[:, 0] [1 2 3 4] cc = z[:, 0:1] [   ]
To get information on the dimensionality and shape of an array you will find the following methods useful:
print z.shape # number of elements along each axis (=dimension) print z.ndim # number of dimensions print z[:, 0].ndim # return first column as 1d array
(4, 3) 2 1
In numpy, slicing will return a new array that is basically a view on the original array, thus, it doesn't require copying any memory. Indexing (in numpy often called fancy indexing), on the other hand, always copies the underlying memory.
R differentiates between vectors and matrices whereas in numpy both are unified by the n-dimensional
There are a number of crucial differences in how indexing and slicing are handled in Python vs. R.
Note that the examples below require the Python package
rpy2 to be installed.
# allows execution of R code in IPython try: %load_ext rmagic except ImportError: print "Please install rpy2 to run the R/Python comparision code examples"
The rmagic extension is already loaded. To reload it, use: %reload_ext rmagic
Python uses 0-based indexing whereas indices in R start from 1:
x = np.arange(5) # seq has excl semantics x
%%R # tells IPython that the following lines will be R code x <- seq(0, 4) # seq has incl semantics print(x)
Python uses exclusive semantics for slicing whereas R uses inclusive semantics:
x[0:2] # doesnt include index 2
%%R x <- seq(0, 4) # seq has incl semantics print(x[1:2]) # includes index 2
 0 1
Negative indices have different semantics: in Python they are used to index from the end on an array whereas in R they are used to drop positions:
x[-2] # second element from the end
%%R x <- seq(0, 4) # seq has incl semantics print(x[-2]) # drop 2nd position, ie 1
 0 2 3 4
If you index on a specific position of a matrix both R and Python will return a vector (ie. array with one less dimension). If you want to retain the dimensionality, R supports a
drop=FALSE argument whereas in Python you have to use slicing instead:
X = np.arange(4).reshape((2, 2)).T # 2d array X[0:1, :] # still 2d array - slice selects one element
%%R X = matrix(seq(0, 3), 2, 2) print(X[1, , drop=FALSE]) # use drop=FALSE
[,1] [,2] [1,] 0 2
pandas provides a key data structure: the
pandas.DataFrame; as can be inferred from the name it behaves very much like an R data frame. Pandas data frames address three deficiencies of arrays:
Data frames are extremely useful for data munging. They provide a large range of operations such as filter, join, and group-by aggregation.
Below we briefly show some of the core functionality of pandas data frames using some sample data from the website of the book "Introduction to Statistical Learning":
import pandas as pd # convention, alias ``pd`` # Load car dataset auto = pd.read_csv("http://www-bcf.usc.edu/~gareth/ISL/Auto.csv") auto.head() # print the first lines
|0||18||8||307||130||3504||12.0||70||1||chevrolet chevelle malibu|
|1||15||8||350||165||3693||11.5||70||1||buick skylark 320|
|3||16||8||304||150||3433||12.0||70||1||amc rebel sst|
One of the first things you should do when you work with a new dataset is to look at some summary statistics such as mean, min, max, the number of missing values and quantiles. For this, pandas provides the convenience method
You can use the dot
. or bracket
 notation to access columns of the dataset. To add new columns you have to use the bracket
mpg = auto.mpg # get mpg column weight = auto['weight'] # get weight column auto['mpg_per_weight'] = mpg / weight print auto[['mpg', 'weight', 'mpg_per_weight']].head()
mpg weight mpg_per_weight 0 18 3504 0.005137 1 15 3693 0.004062 2 18 3436 0.005239 3 16 3433 0.004661 4 17 3449 0.004929
Columns and rows of a data frame are labeled, to access/manipulate the labels either use
Index([u'mpg', u'cylinders', u'displacement', u'horsepower', u'weight', u'acceleration', u'year', u'origin', u'name', u'mpg_per_weight'], dtype=object) Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)
Indexing and slicing work similar as for numpy arrays just that you can also use column and row labels instead of positions:
auto.ix[0:5, ['weight', 'mpg']] # select the first 5 rows and two columns weight and mpg
For more information on
pandas please consult the excellent online documentation or the references at the end of this post.
The major difference between the data frame in R and pandas from a user's point of view is that pandas uses an object-oriented interface (ie methods) whereas R uses a functional interface:
|0||18||8||307||130||3504||12.0||70||1||chevrolet chevelle malibu||0.005137|
|1||15||8||350||165||3693||11.5||70||1||buick skylark 320||0.004062|
# this command pushes the pandas.DataFrame auto to R-land %Rpush auto
%%R auto = data.frame(auto) print(head(auto, 2))
mpg cylinders displacement horsepower weight acceleration year origin 0 18 8 307 130 3504 12.0 70 1 1 15 8 350 165 3693 11.5 70 1 name mpg_per_weight 0 chevrolet chevelle malibu 0.005136986 1 buick skylark 320 0.004061738
Below is a table that shows some of methods that pandas DataFrame provides and the corresponding functions in R:
%pylab inline import pandas as pd import matplotlib.pyplot as plt data = np.random.randn(500) # array of 500 random numbers
Populating the interactive namespace from numpy and matplotlib
To make a histogram you can use the
plt.hist(data) plt.ylabel("Counts") plt.title("The Gaussian Distribution")
<matplotlib.text.Text at 0x5e46d50>
Like R, you can specify various options to change the plotting behavior. For example, to make a histogram of frequency rather than of raw counts you pass the argument
You can also easily make a scatter plot
x = np.random.randn(50) y = np.random.randn(50) plt.plot(x, y, 'bo') # b for blue, o for circles plt.xlabel("x") plt.ylabel("y") plt.title("A scatterplot")
<matplotlib.text.Text at 0x5e5a890>
Matplotlib supports Matlab-style plotting commands, where you can quickly specify color (b for blue, r for red, k for black etc.) and a symbol for the plotting character (
'-' for solid lines,
'--' for dashed lines,
'*' for stars, ...)
s = np.arange(11) plt.plot(s, s ** 2, 'r--')
[<matplotlib.lines.Line2D at 0x687bd50>]
There is also a scatter command that also creates scatterplots
<matplotlib.collections.PathCollection at 0x667a390>
Boxplots are very useful to compare two distributions
plt.boxplot([x, y]) # Pass a list of two arrays to plot them side-by-side plt.title("Two box plots, side-by-side")
<matplotlib.text.Text at 0x6155410>
Pandas provides a convenience interface to matplotlib, you can create plots by using the
# create a scatterplot of weight vs "miles per galone" auto.plot(x='weight', y='mpg', style='bo') plt.title("Scatterplot of weight and mpg") # create a histogram of "miles per galone" plt.figure() auto.hist('mpg') plt.title("Histogram of mpg (miles per galone)")
<matplotlib.text.Text at 0x64e8190>
<matplotlib.figure.Figure at 0x6890c10>
from pandas.tools.plotting import scatter_matrix _ = scatter_matrix(auto[['mpg', 'cylinders', 'displacement']], figsize=(14, 10))
Matplotlib has a rich set of features to manipulate and style statistical graphics. Over the next few weeks we will cover many of them to help you make charts that you find visually appealing, but for now this should be enough to get you up and running in Python.
For a more in-depth discussion of the Python scientific computing ecosystem we strongly recommend the Python Scientific Lecture Notes. The lecture notes contain lots of code examples from applied science such as signal processing, image processing, and machine learning.
Wes Mckinney, the original author of pandas, wrote a great book on using Python for data analysis. It is not only the primary reference to pandas but also features a concise yet profound introduction to Python, numpy and matplotlib.