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Posted April 20, 2022 at 8:30 pm
Learn how to create new dataframes with Part I.
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
0.38733760512833965r” 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 |
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 |
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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