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.bsubtraction.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
-0.5967498723478988You’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
0.38733760512833965Basic 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 experience | name | team |
|---|---|---|
| Int64 | String | String |
| 15 | Vivek | EPAT |
| 8 | Viraj | Marketing |
| 7 | Rohan | Sales |
| 10 | Ishan | Quantra |
Adding a new column in a dataframe
Here we add another column, “c”, to the dataframe df_2.
df_2.c = rand(10)
df_2adding new column.jl hosted with ❤ by GitHub
| a | b | c |
|---|---|---|
| Float64 | Float64 | Float64 |
| 0.845011 | 0.720306 | 0.962749 |
| 0.647665 | 0.0409036 | 0.10846 |
| 0.427267 | 0.221369 | 0.197592 |
| 0.413642 | 0.374832 | 0.967406 |
| 0.477994 | 0.118461 | 0.0233091 |
| 0.0849006 | 0.157679 | 0.936764 |
| 0.0477405 | 0.845332 | 0.296003 |
| 0.518909 | 0.159305 | 0.514714 |
| 0.93499 | 0.259579 | 0.620951 |
| 0.60034 | 0.115911 | 0.0224133 |
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 0.685073Visit QuantInsti to read the full article: https://blog.quantinsti.com/data-manipulation-visualization-using-julia/.
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