# R vs Python: head to head data analysis

The epic battle between R vs Python goes on. Here we are comparing both of them in terms of generic tasks of data scientist’s like reading CSV, finding data summary, PCA, model building, plotting, and many more.

**By Vik Paruchuri, **Dataquest.io.

There have been dozens of articles written comparing Python and R from a subjective standpoint. We’ll add our own views at some point, but this article aims to look at the languages more objectively. We’ll analyze a dataset side by side in Python and R, and show what code is needed in both languages to achieve the same result. This will let us understand the strengths and weaknesses of each language without the conjecture. At Dataquest, we teach both languages, and think both have a place in a data science toolkit.

We’ll be analyzing a dataset of NBA players and their performance in the 2013-2014 season. You can download the file here. For each step in the analysis, we’ll show the Python and R code, along with some explanation and discussion of the different approaches. Without further ado, let’s get this head to head matchup started!

### Read in a csv file

**R**

`nba <- read.csv("nba_2013.csv")`

**Python**

`import pandas`

nba = pandas.read_csv("nba_2013.csv")

The above code will load the csv file `nba_2013.csv`

, which contains data on NBA players from the 2013-2014 season, into the variable `nba`

in both languages. The only real difference is that in Python, we need to import the pandas library to get access to Dataframes. Dataframes are available in both R and Python, and are two-dimensional arrays (matrices) where each column can be of a different datatype. At the end of this step, the csv file has been loaded by both languages into a dataframe.

### Find the number of players

**R**

`dim(nba)`

[1] 481 31

**Python**

`nba.shape`

(481, 31)

This prints out the number of players and the number of columns in each. We have `481`

rows, or players, and `31`

columns containing data on the players.

### Look at the first row of the data

**R**

`head(nba, 1)`

` player pos age bref_team_id`

1 Quincy Acy SF 23 TOT

[output truncated]

**Python**

`nba.head(1)`

` player pos age bref_team_id`

0 Quincy Acy SF 23 TOT

[output truncated]

`This is pretty much identical. Both print out the first row of the data, and the syntax is very similar. Python is more object-oriented here, and `

`head`

is a method on the dataframe object, and R has a separate `head`

function. This is a common theme you’ll see as you start to do analysis with these languages, where Python is more object-oriented, and R is more functional.

### Find the average of each statistic

Let’s find the average value for each statistic. The columns, as you can see, have names like `fg`

(field goals made), and `ast`

(assists). These are the season statistics for the player. If you want a fuller explanation of all the stats, look here.

**R**

`sapply(nba, mean, na.rm=TRUE)`

player NA pos NA age 26.5093555093555 bref_team_id NA [output truncated]

**Python**

`nba.mean()`

age 26.509356 g 53.253638 gs 25.571726 [output truncated]

There are some major differences in approach here. In both, we’re applying a function across the dataframe columns. In python, the mean method on dataframes will find the mean of each column by default.

In R, taking the mean of string values will just result in `NA`

– not available. However, we do need to ignore `NA`

values when we take the mean (requiring us to pass `na.rm=TRUE`

into the `mean`

function). If we don’t, we end up with `NA`

for the mean of columns like `x3p.`

. This column is three point percentage. Some players didn’t take three point shots, so their percentage is missing. If we try the `mean`

function in R, we get `NA`

as a response, unless we specify `na.rm=TRUE`

, which ignores `NA`

values when taking the mean. The `.mean()`

method in Python already ignores these values by default.

### Make pairwise scatterplots

One common way to explore a dataset is to see how different columns correlate to others. We’ll compare the `ast`

, `fg`

, and `trb`

columns.

**R**

`library(GGally)`

ggpairs(nba[,c("ast", "fg", "trb")])

**Python**

`import seaborn as sns`

import matplotlib.pyplot as plt

sns.pairplot(nba[["ast", "fg", "trb"]])

plt.show()

We get very similar plots in the end, but this shows how the R data science ecosystem has many smaller packages (GGally is a helper package for ggplot2, the most-used R plotting package), and many more visualization packages in general. In Python, matplotlib is the primary plotting package, and seaborn is a widely used layer over matplotlib. With visualization in Python, there is usually one main way to do something, whereas in R, there are many packages supporting different methods of doing things (there are at least a half dozen packages to make pair plots, for instance).