02. basicinfo

Introduction to DataFrames¶

Bogumił Kamiński, May 23, 2018

Reference¶

• https://github.com/JuliaComputing/JuliaBoxTutorials/tree/master/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames

Series¶

• https://deepstat.tistory.com/69 (01. constructors)(in English)
• https://deepstat.tistory.com/70 (01. constructors)(한글)
• https://deepstat.tistory.com/71 (02. basicinfo)(in English)
• https://deepstat.tistory.com/72 (02. basicinfo)(한글)

In [1]:

using DataFrames # load package

Getting basic information about a data frame¶

Let's start by creating a DataFrame object, x, so that we can learn how to get information on that data frame.

In [2]:

x = DataFrame(A = [1, 2], B = [1.0, missing], C = ["a", "b"])

Out[2]:

	A	B	C
	Int64	Float64⍰	String
1	1	1.0	a
2	2	missing	b

The standard size function works to get dimensions of the DataFrame,

In [3]:

size(x), size(x, 1), size(x, 2)

Out[3]:

((2, 3), 2, 3)

as well as nrow and ncol from R; length gives number of columns.

In [4]:

nrow(x), ncol(x), length(x)

Out[4]:

(2, 3, 3)

describe gives basic summary statistics of data in your DataFrame.

In [5]:

describe(x)

Out[5]:

	variable	mean	min	median	max	nunique	nmissing	eltype
	Symbol	Union…	Any	Union…	Any	Union…	Union…	DataType
1	A	1.5	1	1.5	2			Int64
2	B	1.0	1.0	1.0	1.0		1	Float64
3	C		a		b	2		String

Use showcols to get informaton about columns stored in a DataFrame.

In [6]:

showcols(x)

┌ Warning: `showcols(df::AbstractDataFrame, all::Bool=false, values::Bool=true)` is deprecated, use `describe(df, stats=[:eltype, :nmissing, :first, :last])` instead.
│   caller = showcols(::DataFrame) at deprecated.jl:54
└ @ DataFrames ./deprecated.jl:54

Out[6]:

	variable	eltype	nmissing	first	last
	Symbol	DataType	Union…	Any	Any
1	A	Int64		1	2
2	B	Float64	1	1.0	missing
3	C	String		a	b

names will return the names of all columns,

In [7]:

names(x)

Out[7]:

3-element Array{Symbol,1}:
 :A
 :B
 :C

and eltypes returns their types.

In [8]:

eltypes(x)

Out[8]:

3-element Array{Type,1}:
 Int64                  
 Union{Missing, Float64}
 String                 

Here we create some large DataFrame

In [9]:

y = DataFrame(rand(1:10, 1000, 10));

and then we can use head to peek into its top rows

In [10]:

head(y)

Out[10]:

	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64
1	7	1	2	1	4	3	5	1	5	10
2	10	2	2	5	9	6	2	10	9	3
3	8	10	8	10	4	7	4	4	10	3
4	6	3	4	8	1	3	2	8	7	10
5	4	10	2	3	10	3	4	3	4	3
6	7	5	6	10	6	7	9	5	7	8

and tail to see its bottom rows.

In [11]:

tail(y, 3)

Out[11]:

	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64	Int64
1	4	1	8	7	1	1	3	9	8	7
2	10	10	3	2	10	5	9	1	10	9
3	6	6	2	7	8	7	3	3	7	2

Most elementary get and set operations¶

Given the DataFrame, x, here are three ways to grab one of its columns as a Vector:

In [12]:

x[1], x[:A], x[:, 1]

┌ Warning: indexing with colon as row will create a copy in the future use df[col_inds] to get the columns without copying
│   caller = top-level scope at In[12]:1
└ @ Core In[12]:1

Out[12]:

([1, 2], [1, 2], [1, 2])

To grab one row as a DataFrame, we can index as follows.

In [13]:

x[1, :]

Out[13]:

	A	B	C
	Int64	Float64⍰	String
1	1	1.0	a

We can grab a single cell or element with the same syntax to grab an element of an array.

In [14]:

x[1, 1]

Out[14]:

1

Assignment can be done in ranges to a scalar,

In [15]:

x[1:2, 1:2] = 1
x

Out[15]:

	A	B	C
	Int64	Float64⍰	String
1	1	1.0	a
2	1	1.0	b

to a vector of length equal to the number of assigned rows,

In [16]:

x[1:2, 1:2] = [1,2]
x

Out[16]:

	A	B	C
	Int64	Float64⍰	String
1	1	1.0	a
2	2	2.0	b

or to another data frame of matching size.

In [17]:

x[1:2, 1:2] = DataFrame([5 6; 7 8])
x

Out[17]:

	A	B	C
	Int64	Float64⍰	String
1	5	6.0	a
2	7	8.0	b