Compare data

Compare data#

In this example, we compare data from different manufacturers for common ball-end string types:

  • plain steel

  • phosphor bronze

  • nickel-wound

import pandas as pd

from stringcalc.tension import load_data

First we load all of the available data with the load_data() function.

data = load_data()
data
id uw group gauge group_id
0 DA:PL007 0.000011 D'Addario - Acoustic or Electric Guitar - Plai... 0.007 DA:PL
1 DA:PL008 0.000014 D'Addario - Acoustic or Electric Guitar - Plai... 0.008 DA:PL
2 DA:PL0085 0.000016 D'Addario - Acoustic or Electric Guitar - Plai... 0.0085 DA:PL
3 DA:PL009 0.000018 D'Addario - Acoustic or Electric Guitar - Plai... 0.009 DA:PL
4 DA:PL0095 0.00002 D'Addario - Acoustic or Electric Guitar - Plai... 0.0095 DA:PL
... ... ... ... ... ...
1920 STP:XTPL014 0.000043 D'Addario (STP) - Tin Coated NY Steel 0.014 STP:XTPL
1921 STP:XTPL016 0.000057 D'Addario (STP) - Tin Coated NY Steel 0.016 STP:XTPL
1922 STP:XTPL017 0.000064 D'Addario (STP) - Tin Coated NY Steel 0.017 STP:XTPL
1923 STP:XTPL018 0.000072 D'Addario (STP) - Tin Coated NY Steel 0.018 STP:XTPL
1924 STP:XTPL022 0.000107 D'Addario (STP) - Tin Coated NY Steel 0.022 STP:XTPL

1925 rows × 5 columns

Plain steel#

Plain steel ball-end strings.

group_ids = ["DA:PL", "STP:PL", "GHS:PL", "SJ:PL"]
(
    data.query("group_id in @group_ids")
    .pivot_table(
        index="gauge",
        columns="group_id",
        values=["uw"],
        observed=True,
    )
    .mul(1000)
    .style
    .set_caption("Plain steel")
    .background_gradient(axis=1, cmap="RdBu_r")
)
Plain steel
  uw
group_id DA:PL GHS:PL SJ:PL STP:PL
gauge        
0.007000 0.010850 0.011943
0.008000 0.014180 0.015609 0.014185
0.008500 0.016010 0.017623 0.016013
0.009000 0.017940 0.018100 0.019747 0.017953
0.009500 0.019990 0.021590 0.022004 0.020003
0.010000 0.022150 0.022700 0.024382 0.022164
0.010500 0.024420 0.025000 0.026880 0.024436
0.011000 0.026800 0.029500 0.029500 0.026818
0.011500 0.029300 0.030800 0.032252 0.029312
0.012000 0.031900 0.034000 0.035114 0.031916
0.012500 0.035400 0.038163
0.013000 0.037440 0.038600 0.041212 0.037457
0.013500 0.040370 0.044437 0.040394
0.014000 0.043420 0.047700 0.047794 0.043441
0.015000 0.049840 0.052800 0.054861 0.049869
0.016000 0.056710 0.059000 0.062423 0.056740
0.017000 0.064020 0.068100 0.070470 0.064054
0.018000 0.071770 0.072700 0.079001 0.071811
0.019000 0.079970 0.077200 0.088027 0.080012
0.020000 0.088610 0.084100 0.097537 0.088656
0.021000 0.093200
0.022000 0.107220 0.115900 0.118020
0.023000 0.136500
0.024000 0.127600 0.143200 0.140460
0.026000 0.149750 0.156800
0.028000 0.170600

Note

We multiplied uw, the unit weight (mass per unit length) [lbm/in] by 1000 to facilitate easier comparison.

Note

Within a given row, a red cell background indicates higher values and blue lower. For example, Stringjoy plain steel strings are generally heavier than the others (red), and D’Addario original and STP are always quite close (same color). The lack of a number in a cell indicates that we don’t have data for that gauge.

Phosphor bronze#

Phosphor bronze ball-end strings.

group_ids = ["DA:PB", "STP:PB", "GHS:B", "SJ:PB"]
(
    data.query("group_id in @group_ids")
    .pivot_table(
        index="gauge",
        columns="group_id",
        values=["uw"],
        observed=True,
    )
    .mul(1000)
    .style
    .set_caption("Phosphor bronze")
    .background_gradient(axis=1, cmap="RdBu_r")
)
Phosphor bronze
  uw
group_id DA:PB GHS:B SJ:PB STP:PB
gauge        
0.018000 0.073940
0.020000 0.081060 0.085060 0.089350 0.079816
0.021000 0.089440 0.088655
0.022000 0.098760 0.099710 0.104530 0.097982
0.023000 0.108010 0.107800
0.024000 0.116820 0.124700 0.120500 0.116120
0.025000 0.126860 0.130390 0.126780
0.026000 0.136400 0.155600 0.135800
0.027000 0.148340 0.147070
0.028000 0.164400 0.169960 0.159010
0.029000 0.173810 0.171070
0.030000 0.186600 0.206200 0.191220 0.183860
0.032000 0.210180 0.213900 0.219480 0.206070
0.034000 0.238870 0.243100 0.236440
0.035000 0.253650 0.246920
0.036000 0.268300 0.261100 0.262130
0.038000 0.293100 0.309330 0.286220
0.039000 0.311250 0.302490
0.040000 0.314600 0.340600
0.042000 0.367220 0.359000 0.374510 0.354960
0.044000 0.398700 0.397130
0.045000 0.417510 0.402030
0.046000 0.386800 0.437130
0.047000 0.452890 0.435100
0.048000 0.471600 0.476600
0.049000 0.491510 0.472330
0.050000 0.500500 0.543540
0.052000 0.552230 0.510500 0.576400 0.529540
0.053000 0.569620 0.545320
0.054000 0.611600 0.627280 0.567420
0.056000 0.634770 0.628300 0.676620 0.606660
0.058000 0.648000
0.059000 0.705350 0.772240 0.678020
0.060000 0.730390 0.715400
0.062000 0.776820
0.064000 0.827800
0.066000 0.877180
0.070000 0.968330 1.010200

Nickel-wound#

Nickel-wound ball-end strings.

  • DA:NW and STP:NW: D’Addario nickel-plated steel

  • GHS:N: GHS Nickel Rockers (pure nickel, semi-flattened)

  • GHS:DY: GHS Boomers (nickel-plated steel)

  • SJ:N: Stringjoy nickel (“Signatures”; nickel-plated steel?)

  • SJ:PN: Stringjoy pure nickel (“Broadways”)

group_ids = ["DA:NW", "STP:NW", "GHS:N", "GHS:DY", "SJ:N", "SJ:PN"]
(
    data.query("group_id in @group_ids")
    .pivot_table(
        index="gauge",
        columns="group_id",
        values=["uw"],
        observed=True,
    )
    .mul(1000)
    .style
    .set_caption("Nickel-wound")
    .background_gradient(axis=1, cmap="RdBu_r")
)
Nickel-wound
  uw
group_id DA:NW GHS:DY GHS:N SJ:N SJ:PN STP:NW
gauge            
0.016000 0.078200
0.017000 0.055240
0.018000 0.062150 0.068900 0.074240 0.080930 0.061926
0.019000 0.069470
0.020000 0.074950 0.089300 0.093260 0.087300 0.074003
0.021000 0.082930 0.082453
0.022000 0.091840 0.099100 0.102400 0.096050 0.095950 0.091392
0.024000 0.108570 0.101800 0.120200 0.114850 0.114050 0.108270
0.025000 0.118500
0.026000 0.126710 0.124000 0.152800 0.137740 0.137040 0.126580
0.028000 0.146660 0.128300 0.169500 0.159790 0.159090 0.145400
0.030000 0.172360 0.163400 0.182900 0.181040 0.180340 0.170340
0.032000 0.193470 0.189600 0.218600 0.208180 0.207520 0.190210
0.034000 0.215900 0.233900 0.248500 0.231920 0.231220 0.211290
0.036000 0.239640 0.236500 0.278600 0.248880 0.248180 0.233590
0.037000 0.248190
0.038000 0.264710 0.265500 0.312800 0.281100 0.280400 0.257100
0.039000 0.279320 0.272330
0.040000 0.307600 0.323400 0.308380 0.307580 0.288060
0.042000 0.322790 0.312200 0.370100 0.346250 0.345450 0.314050
0.044000 0.351820 0.321500 0.417400 0.372250 0.371350 0.341260
0.046000 0.382160 0.380600 0.440400 0.401080 0.400180 0.369690
0.048000 0.413820 0.405200 0.499900 0.437380 0.436480 0.399320
0.049000 0.430140 0.414590
0.050000 0.470900 0.533200 0.478770 0.477870 0.430170
0.052000 0.481090 0.478800 0.564000 0.507030 0.506130 0.462230
0.054000 0.538380 0.492000 0.602500 0.557340 0.516170
0.056000 0.575980 0.659000 0.676200 0.603130 0.551290
0.058000 0.673600 0.759000 0.640310
0.059000 0.641910 0.618170
0.060000 0.665420 0.879100 0.752700 0.687620 0.641430
0.062000 0.706970 0.747200 0.680320
0.064000 0.749840 0.798890 0.720420
0.065000 0.749010
0.066000 0.798890
0.068000 0.846140 0.846140 0.817240
0.070000 0.893040 0.886700 0.922560
0.072000 0.941240 0.962390
0.074000 0.988690 1.024200 0.949480
0.080000 1.150110 1.211000 1.160000
0.085000 1.337000
0.090000 1.502800
0.095000 1.776600
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