Posted: April 20, 2022

Data Manipulation and Visualization Techniques in Julia – Part II

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Learn how to create new dataframes with Part I.

Basic mathematical operations

As discussed in my previous post, basic arithmetic operations can be performed on individual columns.

# Subtraction
df_2.a - df_2.b

subtraction.jl hosted with ❤ by GitHub

10-element Vector{Float64}:

-0.5474996670806442
 0.5174063588946236
-0.564150142575268
 0.12873854328766576
 0.2741519215981265
 0.20241852864291987
 0.09324017568958975
-0.41716724316286524
 0.2693306887583933
-0.5967498723478988

You’ll have to use the “.” operator for element-wise division.

df_2.["a"] ./ df_2["b"]

elementwise operation.jl hosted with ❤ by GitHub

10-element Vector{Float64}:

0.06754620232737023
3.013387340201863
0.4169119702423886
1.2293455286486041
1.4462537614868343
8.482279426917298
1.1103752688515762
0.21238611891693882
3.1244976300403002
0.38733760512833965

Basic operations

Rearranging columns

r” is a regex search string. Here, any column with a string “work” will be selected and moved to the first place. You can write the full column name as well.

## Method to rearrange columns in a dataframe
select!(df_1, r"work", :)

rearranging columns.jl hosted with ❤ by GitHub

work experiencenameteam
Int64StringString
15VivekEPAT
8VirajMarketing
7RohanSales
10IshanQuantra

Adding a new column in a dataframe

Here we add another column, “c”, to the dataframe df_2.

df_2.c = rand(10)
df_2

adding new column.jl hosted with ❤ by GitHub

abc
Float64Float64Float64
0.8450110.7203060.962749
0.6476650.04090360.10846
0.4272670.2213690.197592
0.4136420.3748320.967406
0.4779940.1184610.0233091
0.08490060.1576790.936764
0.04774050.8453320.296003
0.5189090.1593050.514714
0.934990.2595790.620951
0.600340.1159110.0224133

Dataframe-to-matrix conversion

Matrix(df_2)

dataframe to matrix.jl hosted with ❤ by GitHub

10×3 Matrix{Float64}:

0.0396604  0.58716    0.741712
0.774389   0.256983   0.429361
0.403371   0.967521   0.989583
0.690069   0.56133    0.50599
0.888493   0.614341   0.152574
0.229472   0.0270531  0.932589
0.937996   0.844756   0.0745573
0.112492   0.52966    0.712178
0.396105   0.126774   0.397762
0.377277   0.974027   0.685073

Visit QuantInsti to read the full article: https://blog.quantinsti.com/data-manipulation-visualization-using-julia/.

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