前情提要：

• 概述
  • Julia是一門牛B、‘無恥’的語言
• 本地環(huán)境教程
  • Julia1.0.0安裝指南(含 Juno IDE)
    • Windows教程
    • Mac教程
  • 目前兼容的機器學(xué)習(xí)程序包
• 在線環(huán)境教程
  • 無痛體驗：幾行代碼識別圖片內(nèi)容
  • 如何進行Julia無痛體驗
  • 深度定制免費無痛環(huán)境
  • 如何科學(xué)的找程序包

前段時間把環(huán)境和各種版本情況理了一遍同蜻，應(yīng)該說該有的學(xué)習(xí)基礎(chǔ)設(shè)施都有了疾呻。

筆者關(guān)注的是機器學(xué)習(xí)方面的顺献，因此會側(cè)重去看這些方面的資料蜀涨。

說到機器學(xué)習(xí)，首先得要有數(shù)據(jù)姐赡，不然學(xué)習(xí)個啥呢。有了數(shù)據(jù)之后柠掂，那么很多語言的第一步就是處理數(shù)據(jù)项滑。Julia也一樣，有專門的數(shù)據(jù)處理程序包涯贞。

今天就說一下DataFrames這個程序包枪狂。用過其他機器學(xué)習(xí)語言的都知道，DataFrames就是數(shù)據(jù)框宋渔，中文直譯州疾。

筆者自己學(xué)習(xí)程序語言的方式是不喜歡去看書的（程序書是用來查的），程序一定是一邊寫一邊看一邊用才會掌握的好和快皇拣，尤其是看牛人寫的程序严蓖。

記得前面有教程說過如何導(dǎo)入Github上的程序庫吧。

來审磁，先記得這個地址：https://github.com/scidom/StatsLearningByExample.jl.git

然后點開Juliabox上Git 這個按鈕

會出來一個對話框：

粘貼剛才那個地址到 Git Clone URL框里谈飒，然后點后面的+號（如果需要修改一下同步到Juliabox的文件夾名字的請按+號前修改）。然后按下[OK]态蒂。

很快Juliabox就會把https://github.com/scidom/StatsLearningByExample.jl.git里的內(nèi)容同步過來杭措。

然后你的Juliabox就會多出來這個文件夾（StatsLearningByExample）：

接下來點進去02-DataFrames目錄：

點開第一個02-01-DataFramesBasics.ipynb,

然后點Cell下面的Run All:

這個命令就會把所有的代碼跑一遍。

這個就是今天的課程钾恢，因為已經(jīng)有代碼且作者已經(jīng)用英文講解了手素，所以接下來會倒序（按照程序代碼來說是倒序）重點總結(jié)一下：

（文末有筆者運行結(jié)果，方便暫時不能操作的同學(xué)看）：

• DataFrames是由DataArrays組成的

  • DataFrames本身的基本信息獲取
  • 可以對DataFrames內(nèi)數(shù)據(jù)進行簡單統(tǒng)計描述
  • 可以獲得DataFrames某行/某列數(shù)據(jù)

• DataArrays可以進行矩陣運算

• DataArrays里有個特殊成員NA（缺失值）

  • 所有數(shù)值和NA進行運算結(jié)果都是NA

  • NA（缺失值）和NaN（Not a Number: 不是數(shù)字）是兩個東西瘩蚪，數(shù)值類型也不一樣

有幾個注意的地方：

這個地方其實應(yīng)該是文檔的注釋部分泉懦，作者應(yīng)該沒注意弄，變成代碼運行報錯了疹瘦。

改成文檔格式即可崩哩，忽略也可以。

還有這里：

第8句寫了不可能通過DataArray([0.1, NA, -2.4])語句直接完成DataArray構(gòu)建言沐，這里應(yīng)該是個錯誤示范（應(yīng)該報錯）邓嘹。

作者希望提示大家用第9句的語法來完成構(gòu)建動作。

實際上险胰，作者寫的是2年前汹押，現(xiàn)在我們看到的是兩種語法都得出了正確結(jié)果。也就是目前兩種寫法都可以起便。

其他的就不多解釋了棚贾，大家要學(xué)習(xí)應(yīng)該能看懂窖维。

以下是筆者運行的結(jié)果，供參考：

Introduction to DataFrames

In [1]:

using DataArrays
using DataFrames

Missing values?

• A missing value is represented by NA in Julia.
• NA is not part of Base, it is provided by the DataArrays package.
• NA poisons other values.

In [2]:

# NA poisons other values
1+NA

Out[2]:

missing

In [3]:

# Check if the evaluation of an expression results in NA
isna(1+NA)

Out[3]:

true

In [4]:

# Note the difference between NaN and NA
(isa(NaN, Float64), isa(NA, Float64))

Out[4]:

(true, false)

DataArrays

• DataArray's are used for representing arrays that contain missing values
• DataArray{T} allows storing T or NA
• In other words, DataArray{T} adds NA's to Array{T}
• PooledDataArray{T} is used for storing data efficiently.
• PooledDataArray{T} compresses DataArray{T}.

Constructing DataArrays

In [5]:

# Call the DataArray() constructor by passing a Vector to it
DataArray([0.1, 0.5, -2.4])

Out[5]:

3-element DataArrays.DataArray{Float64,1}:
  0.1
  0.5
 -2.4

In [6]:

# Construct a DataArray by calling the @data() macro with a Vector input argument
@data([0.1, 0.5, -2.4])

Out[6]:

3-element DataArrays.DataArray{Float64,1}:
  0.1
  0.5
 -2.4

In [7]:

# Convert Vector to DataArray
convert(DataArray, [0.1, 0.5, -2.4])

Out[7]:

3-element DataArrays.DataArray{Float64,1}:
  0.1
  0.5
 -2.4

In [8]:

# It is not possible to call DataArray() with NA in its input argument
DataArray([0.1, NA, -2.4])

Out[8]:

3-element DataArrays.DataArray{Float64,1}:
  0.1     
   missing
 -2.4     

In [9]:

# However, it is possible to pass NA to the @data() macro
@data([0.1, NA, -2.4])

Out[9]:

3-element DataArrays.DataArray{Float64,1}:
  0.1     
   missing
 -2.4     

In [10]:

# The DataArray() constructor can be called with a Matrix input argument
DataArray([0.4 1.2; 3.5 7.2])

Out[10]:

2×2 DataArrays.DataArray{Float64,2}:
 0.4  1.2
 3.5  7.2

In [11]:

# The @data() macro can also be called with a Matrix input argument
@data([0.4 1.2; 3.5 7.2])

Out[11]:

2×2 DataArrays.DataArray{Float64,2}:
 0.4  1.2
 3.5  7.2

In [12]:

# Convert a Matrix to DataArray
convert(DataArray, [0.4 1.2; 3.5 7.2])

Out[12]:

2×2 DataArrays.DataArray{Float64,2}:
 0.4  1.2
 3.5  7.2

Numerical computing with DataArrays

In [13]:

# Numerical computing can be done with data vectors
x = @data([0.1, NA, -2.4])
y = @data([-9.9, 0.5, 6.7])
x+y

Out[13]:

3-element DataArrays.DataArray{Float64,1}:
 -9.8     
   missing
  4.3     

In [14]:

# To remove missing values (NA), call dropna()
x = @data([0.1, NA, -2.4])
dropna(x)

Out[14]:

2-element Array{Float64,1}:
  0.1
 -2.4

In [15]:

# Numerical computing can be done with data matrices and data vectors
A = @data([0.4 1.2 4.4; NA 7.2 3.9; 5.1 1.8 4.5])
y = @data([-9.9, 0.5, 6.7])
A*y

Out[15]:

3-element DataArrays.DataArray{Float64,1}:
  26.12    
    missing
 -19.44    

DataFrames

• DataFrame's are used for representing data tables.
• A DataFrame is a list of DataArray's.
• So every DataArray of a DataFrame represents a column of the corresponding data table.
• DataFrame's accommodate heterogeneous data that might contain missing values.
• Every column (DataArray) of a DataFrame has its own type.

Example 02-01-01: NBA champions

Constructing DataFrames

In [16]:

# Call the DataFrame() constructor with keyword arguments (columns) of type Vector
DataFrame(
  player = ["Larry Bird", "Magic Johnson", "Michael Jordan", "Scottie Pippen"],
  champions = [3, 5, 6, 6]
)

Out[16]:

In [17]:

# Start with an empty DataFrame and populate it
ChampionsFrame = DataFrame()
ChampionsFrame[:player] = ["Larry Bird", "Magic Johnson", "Michael Jordan", "Scottie Pippen"]
ChampionsFrame[:champions] = [3, 5, 6, 6]
ChampionsFrame

Out[17]:

Provide CSV-like tabular data to construct a new DataFrame

In [19]:

# Call the DataFrame() constructor with keyword arguments (columns) of type DataArray
player = @data(["Larry Bird", "Magic Johnson", "Michael Jordan", "Scottie Pippen"])
champions = @data([3, 5, 6, 6])
ChampionsFrame = DataFrame(player=player, champions=champions)

Out[19]:

In [20]:

# Construct a DataFrame by joining two existing DataFrames
height = [2.06, 2.06, 1.98, 2.03]
HeightsFrame = DataFrame(player=player, height=height)
join(ChampionsFrame, HeightsFrame, on = :player)

Out[20]:

Quering basic information about DataFrames

In [21]:

# Get number of rows of a DataFrame
size(ChampionsFrame, 1)

Out[21]:

4

In [22]:

# Get number of columns of a DataFrame
size(ChampionsFrame, 2)

Out[22]:

2

In [23]:

# Get a numeric summary of a DataFrame
describe(ChampionsFrame)

Out[23]:

Indexing DataFrames

In [24]:

# Index DataFrame by column name to get a specific column
ChampionsFrame[:player]

Out[24]:

4-element DataArrays.DataArray{String,1}:
 "Larry Bird"    
 "Magic Johnson" 
 "Michael Jordan"
 "Scottie Pippen"

In [25]:

# Index DataFrame by row numbers to get specific rows
ChampionsFrame[2:3, :]

Out[25]:

KevinZhang
Aug 30, 2018