Le samedi 12 novembre 2016 à 05:53 -0800, [email protected] a écrit :
> Hello,
> I'm starting with Julia and wanted to use DataFrames in homeworks to
> present the data, but I've stumbled upon a problem.
> When running the below code you'll see three lists of Float64 values.
>
> Problem is when I'm showing the DataFrame I'm loosing lots of
> information, because of the display precision.
> Is there a way to change it? In Pandas I would do
> Pandas.set_option('display.precision', 20).
I'm not sure I understand what result you want, but, I don't think
there's a way to set the precision at the moment. The best solution
would be to have a mechanism in Julia Base, which would apply equally
to arrays and data frames.
Regards
> xlist = [(1/8)^i for i=1:20]
>
> fx = map(x -> sqrt(x^2 + 1) -1, xlist)
> gx = map(x -> x^2/(sqrt(x^2 + 1)+1), xlist)
>
> println("|x | f(x) | g(x)
> |")
> for i=1:20
> # @printf("|%1.60f | %1.60f | %1.60f |\n", xlist[i], fx[i], gx[i])
> println("$(xlist[i]) \t\t| $(fx[i]) \t\t| $(gx[i])")
> end
>
> using DataFrames
> df = DataFrame(x = xlist, f_x = fx, g_x = gx)
>
> # rename!(df, ["b", "c"], ["f(x)", "g(x)"])
> showall(df)
> println("\n")
>
>
>
> |x | f(x) | g(x) |
> 0.125 | 0.0077822185373186414 |
> 0.0077822185373187065
> 0.015625 | 0.00012206286282867573 |
> 0.00012206286282875901
> 0.001953125 | 1.9073468138230965e-6 |
> 1.907346813826566e-6
> 0.000244140625 | 2.9802321943606103e-8
> | 2.9802321943606116e-8
> 3.0517578125e-5 | 4.656612873077393e-10
> | 4.6566128719931904e-10
> 3.814697265625e-6 | 7.275957614183426e-12
> | 7.275957614156956e-12
> 4.76837158203125e-7 | 1.1368683772161603e-13
> | 1.1368683772160957e-13
> 5.960464477539063e-8 | 1.7763568394002505e-15
> | 1.7763568394002489e-15
> 7.450580596923828e-9 | 0.0 |
> 2.7755575615628914e-17
> 9.313225746154785e-10 | 0.0 |
> 4.336808689942018e-19
> 1.1641532182693481e-10 | 0.0 |
> 6.776263578034403e-21
> 1.4551915228366852e-11 | 0.0 |
> 1.0587911840678754e-22
> 1.8189894035458565e-12 | 0.0 |
> 1.6543612251060553e-24
> 2.2737367544323206e-13 | 0.0 |
> 2.5849394142282115e-26
> 2.842170943040401e-14 | 0.0 |
> 4.0389678347315804e-28
> 3.552713678800501e-15 | 0.0 |
> 6.310887241768095e-30
> 4.440892098500626e-16 | 0.0 |
> 9.860761315262648e-32
> 5.551115123125783e-17 | 0.0 |
> 1.5407439555097887e-33
> 6.938893903907228e-18 | 0.0 |
> 2.407412430484045e-35
> 8.673617379884035e-19 | 0.0 |
> 3.76158192263132e-37
> 20×3 DataFrames.DataFrame
> │ Row │ x │ f_x │ g_x │
> ├─────┼─────────────┼─────────────┼─────────────┤
> │ 1 │ 0.125 │ 0.00778222 │ 0.00778222 │
> │ 2 │ 0.015625 │ 0.000122063 │ 0.000122063 │
> │ 3 │ 0.00195313 │ 1.90735e-6 │ 1.90735e-6 │
> │ 4 │ 0.000244141 │ 2.98023e-8 │ 2.98023e-8 │
> │ 5 │ 3.05176e-5 │ 4.65661e-10 │ 4.65661e-10 │
> │ 6 │ 3.8147e-6 │ 7.27596e-12 │ 7.27596e-12 │
> │ 7 │ 4.76837e-7 │ 1.13687e-13 │ 1.13687e-13 │
> │ 8 │ 5.96046e-8 │ 1.77636e-15 │ 1.77636e-15 │
> │ 9 │ 7.45058e-9 │ 0.0 │ 2.77556e-17 │
> │ 10 │ 9.31323e-10 │ 0.0 │ 4.33681e-19 │
> │ 11 │ 1.16415e-10 │ 0.0 │ 6.77626e-21 │
> │ 12 │ 1.45519e-11 │ 0.0 │ 1.05879e-22 │
> │ 13 │ 1.81899e-12 │ 0.0 │ 1.65436e-24 │
> │ 14 │ 2.27374e-13 │ 0.0 │ 2.58494e-26 │
> │ 15 │ 2.84217e-14 │ 0.0 │ 4.03897e-28 │
> │ 16 │ 3.55271e-15 │ 0.0 │ 6.31089e-30 │
> │ 17 │ 4.44089e-16 │ 0.0 │ 9.86076e-32 │
> │ 18 │ 5.55112e-17 │ 0.0 │ 1.54074e-33 │
> │ 19 │ 6.93889e-18 │ 0.0 │ 2.40741e-35 │
> │ 20 │ 8.67362e-19 │ 0.0 │ 3.76158e-37 │
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