polars.dataframe 有时显示点而不是数据
polars.dataframe sometimes displays dots instead of datas
使用polars.dataframe时,有时会显示点而不是实际数据。我不知道我缺少什么。(使用 pandas 时没问题)。你能告诉我应该怎么做吗?
import polars as pl
import pandas as pd
class new:
xyxy = '124'
a = [[[0.45372647047042847, 0.7791867852210999, 0.05796612799167633,
0.08813457936048508, 0.9122178554534912, 0, 'corn'],
[0.5337053537368774, 0.605276882648468, 0.043029140681028366, 0.06894499808549881,
0.8814031481742859, 0, 'corn'],
[0.47244399785995483, 0.5134297609329224, 0.03258286789059639, 0.054770857095718384,
0.8650641441345215, 0, 'corn'],
[0.4817340672016144, 0.42551395297050476, 0.02438574656844139, 0.04052922874689102,
0.8646907806396484, 0, 'corn'],
[0.5215370059013367, 0.4616119861602783, 0.027680961415171623, 0.04423023760318756,
0.8433780670166016, 0, 'corn'],
[0.5168840885162354, 0.4077163636684418, 0.021290680393576622, 0.034322340041399,
0.8073480129241943, 0, 'corn'],
[0.4868599772453308, 0.3901885747909546, 0.01746474765241146, 0.02876533754169941,
0.631712794303894, 0, 'corn'],
[0.5133631825447083, 0.3870452046394348, 0.014495659619569778, 0.02186509035527706,
0.6174931526184082, 0, 'corn'],
[0.5155017375946045, 0.3974197208881378, 0.01627129688858986, 0.03393130749464035,
0.4413506090641022, 0, 'corn']]]
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh
columns
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb,
cb]):
setattr(new, k, [pl.DataFrame(x, columns=c, orient="row") for x in a])
#setattr(new, k, [pd.DataFrame(x, columns=c, orient="row") for x in a])
print (new.xyxy[0])
使用polars.Config.set_tbl_rows
控制显示行数:
pl.Config.set_tbl_rows(1000)
print(new.xyxy[0])
# Output
shape: (9, 7)
┌────────────────┬────────────────┬────────────────┬────────────────┬───────────────┬───────┬──────┐
│ xmin ┆ ymin ┆ xmax ┆ ymax ┆ confidence ┆ class ┆ name │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i64 ┆ str │
╞════════════════╪════════════════╪════════════════╪════════════════╪═══════════════╪═══════╪══════╡
│ 0.453726470470 ┆ 0.779186785221 ┆ 0.057966127991 ┆ 0.088134579360 ┆ 0.91221785545 ┆ 0 ┆ corn │
│ 42847 ┆ 0999 ┆ 67633 ┆ 48508 ┆ 34912 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.533705353736 ┆ 0.605276882648 ┆ 0.043029140681 ┆ 0.068944998085 ┆ 0.88140314817 ┆ 0 ┆ corn │
│ 8774 ┆ 468 ┆ 028366 ┆ 49881 ┆ 42859 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.472443997859 ┆ 0.513429760932 ┆ 0.032582867890 ┆ 0.054770857095 ┆ 0.86506414413 ┆ 0 ┆ corn │
│ 95483 ┆ 9224 ┆ 59639 ┆ 718384 ┆ 45215 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.481734067201 ┆ 0.425513952970 ┆ 0.024385746568 ┆ 0.040529228746 ┆ 0.86469078063 ┆ 0 ┆ corn │
│ 6144 ┆ 50476 ┆ 44139 ┆ 89102 ┆ 96484 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.521537005901 ┆ 0.461611986160 ┆ 0.027680961415 ┆ 0.044230237603 ┆ 0.84337806701 ┆ 0 ┆ corn │
│ 3367 ┆ 2783 ┆ 171623 ┆ 18756 ┆ 66016 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.516884088516 ┆ 0.407716363668 ┆ 0.021290680393 ┆ 0.034322340041 ┆ 0.80734801292 ┆ 0 ┆ corn │
│ 2354 ┆ 4418 ┆ 576622 ┆ 399 ┆ 41943 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.486859977245 ┆ 0.390188574790 ┆ 0.017464747652 ┆ 0.028765337541 ┆ 0.63171279430 ┆ 0 ┆ corn │
│ 3308 ┆ 9546 ┆ 41146 ┆ 69941 ┆ 3894 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.513363182544 ┆ 0.387045204639 ┆ 0.014495659619 ┆ 0.021865090355 ┆ 0.61749315261 ┆ 0 ┆ corn │
│ 7083 ┆ 4348 ┆ 569778 ┆ 27706 ┆ 84082 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.515501737594 ┆ 0.397419720888 ┆ 0.016271296888 ┆ 0.033931307494 ┆ 0.44135060906 ┆ 0 ┆ corn │
│ 6045 ┆ 1378 ┆ 58986 ┆ 64035 ┆ 41022 ┆ ┆ │
└────────────────┴────────────────┴────────────────┴────────────────┴───────────────┴───────┴──────┘
使用polars.dataframe时,有时会显示点而不是实际数据。我不知道我缺少什么。(使用 pandas 时没问题)。你能告诉我应该怎么做吗?
import polars as pl
import pandas as pd
class new:
xyxy = '124'
a = [[[0.45372647047042847, 0.7791867852210999, 0.05796612799167633,
0.08813457936048508, 0.9122178554534912, 0, 'corn'],
[0.5337053537368774, 0.605276882648468, 0.043029140681028366, 0.06894499808549881,
0.8814031481742859, 0, 'corn'],
[0.47244399785995483, 0.5134297609329224, 0.03258286789059639, 0.054770857095718384,
0.8650641441345215, 0, 'corn'],
[0.4817340672016144, 0.42551395297050476, 0.02438574656844139, 0.04052922874689102,
0.8646907806396484, 0, 'corn'],
[0.5215370059013367, 0.4616119861602783, 0.027680961415171623, 0.04423023760318756,
0.8433780670166016, 0, 'corn'],
[0.5168840885162354, 0.4077163636684418, 0.021290680393576622, 0.034322340041399,
0.8073480129241943, 0, 'corn'],
[0.4868599772453308, 0.3901885747909546, 0.01746474765241146, 0.02876533754169941,
0.631712794303894, 0, 'corn'],
[0.5133631825447083, 0.3870452046394348, 0.014495659619569778, 0.02186509035527706,
0.6174931526184082, 0, 'corn'],
[0.5155017375946045, 0.3974197208881378, 0.01627129688858986, 0.03393130749464035,
0.4413506090641022, 0, 'corn']]]
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh
columns
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb,
cb]):
setattr(new, k, [pl.DataFrame(x, columns=c, orient="row") for x in a])
#setattr(new, k, [pd.DataFrame(x, columns=c, orient="row") for x in a])
print (new.xyxy[0])
使用polars.Config.set_tbl_rows
控制显示行数:
pl.Config.set_tbl_rows(1000)
print(new.xyxy[0])
# Output
shape: (9, 7)
┌────────────────┬────────────────┬────────────────┬────────────────┬───────────────┬───────┬──────┐
│ xmin ┆ ymin ┆ xmax ┆ ymax ┆ confidence ┆ class ┆ name │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i64 ┆ str │
╞════════════════╪════════════════╪════════════════╪════════════════╪═══════════════╪═══════╪══════╡
│ 0.453726470470 ┆ 0.779186785221 ┆ 0.057966127991 ┆ 0.088134579360 ┆ 0.91221785545 ┆ 0 ┆ corn │
│ 42847 ┆ 0999 ┆ 67633 ┆ 48508 ┆ 34912 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.533705353736 ┆ 0.605276882648 ┆ 0.043029140681 ┆ 0.068944998085 ┆ 0.88140314817 ┆ 0 ┆ corn │
│ 8774 ┆ 468 ┆ 028366 ┆ 49881 ┆ 42859 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.472443997859 ┆ 0.513429760932 ┆ 0.032582867890 ┆ 0.054770857095 ┆ 0.86506414413 ┆ 0 ┆ corn │
│ 95483 ┆ 9224 ┆ 59639 ┆ 718384 ┆ 45215 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.481734067201 ┆ 0.425513952970 ┆ 0.024385746568 ┆ 0.040529228746 ┆ 0.86469078063 ┆ 0 ┆ corn │
│ 6144 ┆ 50476 ┆ 44139 ┆ 89102 ┆ 96484 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.521537005901 ┆ 0.461611986160 ┆ 0.027680961415 ┆ 0.044230237603 ┆ 0.84337806701 ┆ 0 ┆ corn │
│ 3367 ┆ 2783 ┆ 171623 ┆ 18756 ┆ 66016 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.516884088516 ┆ 0.407716363668 ┆ 0.021290680393 ┆ 0.034322340041 ┆ 0.80734801292 ┆ 0 ┆ corn │
│ 2354 ┆ 4418 ┆ 576622 ┆ 399 ┆ 41943 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.486859977245 ┆ 0.390188574790 ┆ 0.017464747652 ┆ 0.028765337541 ┆ 0.63171279430 ┆ 0 ┆ corn │
│ 3308 ┆ 9546 ┆ 41146 ┆ 69941 ┆ 3894 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.513363182544 ┆ 0.387045204639 ┆ 0.014495659619 ┆ 0.021865090355 ┆ 0.61749315261 ┆ 0 ┆ corn │
│ 7083 ┆ 4348 ┆ 569778 ┆ 27706 ┆ 84082 ┆ ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ 0.515501737594 ┆ 0.397419720888 ┆ 0.016271296888 ┆ 0.033931307494 ┆ 0.44135060906 ┆ 0 ┆ corn │
│ 6045 ┆ 1378 ┆ 58986 ┆ 64035 ┆ 41022 ┆ ┆ │
└────────────────┴────────────────┴────────────────┴────────────────┴───────────────┴───────┴──────┘