Overview

Dataset statistics

Number of variables17
Number of observations50000
Missing cells1811
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.2 MiB
Average record size in memory1.1 KiB

Variable types

Text5
Categorical10
Numeric2

Alerts

Provider_Name has constant value "PhageScope"Constant
Provider_Release has constant value "rolling"Constant
Provider_Snapshot_Date has constant value "2026-07-05"Constant
Provider_Schema_Profile has constant value "phagescope_v2"Constant
Input_File is highly overall correlated with Input_Retrieved_At and 2 other fieldsHigh correlation
Input_Retrieved_At is highly overall correlated with Input_File and 2 other fieldsHigh correlation
Input_Source_Key is highly overall correlated with Input_File and 2 other fieldsHigh correlation
Source_DB is highly overall correlated with Input_File and 2 other fieldsHigh correlation
Lifestyle is highly imbalanced (50.3%)Imbalance
Taxonomy has 1811 (3.6%) missing valuesMissing

Reproduction

Analysis started2026-07-15 06:23:24.756373
Analysis finished2026-07-15 06:23:31.727437
Duration6.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct49973
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2026-07-15T06:23:31.960962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length116
Median length111
Mean length39.55794
Min length5

Characters and Unicode

Total characters1977897
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49946 ?
Unique (%)99.9%

Sample

1st rowOPD_20042
2nd rowShi_2020_14.k81_99141
3rd rowStation78_MES_COMBINED_FINAL_NODE_5336_length_11981_cov_5.262200
4th rowMa_2019_SRR413722_NODE_2357_length_12320_cov_50.786547
5th rowZhang_2015_057.ctg_256
ValueCountFrequency (%)
imgvr_uvig_3300045988_053345|3300045988|ga0495776_1520442
 
< 0.1%
imgvr_uvig_3300045988_070462|3300045988|ga0495776_0541682
 
< 0.1%
imgvr_uvig_3300045988_039797|3300045988|ga0495776_0675842
 
< 0.1%
imgvr_uvig_2574180270_000001|2574180270|2574228708|1001324-10400502
 
< 0.1%
imgvr_uvig_3300029432_000359|3300029432|ga0243805_100272|31829-757412
 
< 0.1%
imgvr_uvig_3300045988_048425|3300045988|ga0495776_0130772
 
< 0.1%
imgvr_uvig_2590828746_000001|2590828746|25908690482
 
< 0.1%
imgvr_uvig_2579778988_000001|2579778988|2579839126|710391-7491172
 
< 0.1%
imgvr_uvig_2703718751_000001|2703718751|2703733036|12584-767772
 
< 0.1%
imgvr_uvig_3300043078_000037|3300043078|ga0484756_0000802
 
< 0.1%
Other values (49963)49980
> 99.9%
2026-07-15T06:23:32.535173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0242224
 
12.2%
_180572
 
9.1%
3128586
 
6.5%
1117162
 
5.9%
294954
 
4.8%
486989
 
4.4%
883748
 
4.2%
578676
 
4.0%
672182
 
3.6%
770197
 
3.5%
Other values (59)822607
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1977897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0242224
 
12.2%
_180572
 
9.1%
3128586
 
6.5%
1117162
 
5.9%
294954
 
4.8%
486989
 
4.4%
883748
 
4.2%
578676
 
4.0%
672182
 
3.6%
770197
 
3.5%
Other values (59)822607
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1977897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0242224
 
12.2%
_180572
 
9.1%
3128586
 
6.5%
1117162
 
5.9%
294954
 
4.8%
486989
 
4.4%
883748
 
4.2%
578676
 
4.0%
672182
 
3.6%
770197
 
3.5%
Other values (59)822607
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1977897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0242224
 
12.2%
_180572
 
9.1%
3128586
 
6.5%
1117162
 
5.9%
294954
 
4.8%
486989
 
4.4%
883748
 
4.2%
578676
 
4.0%
672182
 
3.6%
770197
 
3.5%
Other values (59)822607
41.6%

Source_DB
Categorical

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
MetaVR
8301 
GOV2
7361 
MGV
6905 
IMGVR
6577 
GPD
5290 
Other values (21)
15566 

Length

Max length9
Median length8
Mean length4.43738
Min length3

Characters and Unicode

Total characters221869
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPD
2nd rowOVD
3rd rowGOV2
4th rowGVD
5th rowOVD

Common Values

ValueCountFrequency (%)
MetaVR8301
16.6%
GOV27361
14.7%
MGV6905
13.8%
IMGVR6577
13.2%
GPD5290
10.6%
TemPhD2385
 
4.8%
ELGV2042
 
4.1%
URPC1693
 
3.4%
TYMEFLIES1689
 
3.4%
CHVD1662
 
3.3%
Other values (16)6095
12.2%

Length

2026-07-15T06:23:32.718380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metavr8301
16.6%
gov27361
14.7%
mgv6905
13.8%
imgvr6577
13.2%
gpd5290
10.6%
temphd2385
 
4.8%
elgv2042
 
4.1%
urpc1693
 
3.4%
tymeflies1689
 
3.4%
chvd1662
 
3.3%
Other values (16)6095
12.2%

Most occurring characters

ValueCountFrequency (%)
V37121
16.7%
G31052
14.0%
M23605
10.6%
R16734
 
7.5%
D13072
 
5.9%
e11244
 
5.1%
P10931
 
4.9%
O9277
 
4.2%
I8643
 
3.9%
a8533
 
3.8%
Other values (23)51657
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)221869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V37121
16.7%
G31052
14.0%
M23605
10.6%
R16734
 
7.5%
D13072
 
5.9%
e11244
 
5.1%
P10931
 
4.9%
O9277
 
4.2%
I8643
 
3.9%
a8533
 
3.8%
Other values (23)51657
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)221869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V37121
16.7%
G31052
14.0%
M23605
10.6%
R16734
 
7.5%
D13072
 
5.9%
e11244
 
5.1%
P10931
 
4.9%
O9277
 
4.2%
I8643
 
3.9%
a8533
 
3.8%
Other values (23)51657
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)221869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V37121
16.7%
G31052
14.0%
M23605
10.6%
R16734
 
7.5%
D13072
 
5.9%
e11244
 
5.1%
P10931
 
4.9%
O9277
 
4.2%
I8643
 
3.9%
a8533
 
3.8%
Other values (23)51657
23.3%

Length
Real number (ℝ)

Distinct35035
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39752.452
Minimum243
Maximum683254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2026-07-15T06:23:32.922730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum243
5-th percentile6434.9
Q117264
median35556
Q347354.5
95-th percentile96816.25
Maximum683254
Range683011
Interquartile range (IQR)30090.5

Descriptive statistics

Standard deviation34623.299
Coefficient of variation (CV)0.87097265
Kurtosis31.524922
Mean39752.452
Median Absolute Deviation (MAD)15601.5
Skewness3.9914874
Sum1.9876226 × 109
Variance1.1987728 × 109
MonotonicityNot monotonic
2026-07-15T06:23:33.174254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
971923
 
< 0.1%
5142121
 
< 0.1%
10927220
 
< 0.1%
8158919
 
< 0.1%
1961117
 
< 0.1%
4427317
 
< 0.1%
3960316
 
< 0.1%
1149816
 
< 0.1%
538616
 
< 0.1%
4404415
 
< 0.1%
Other values (35025)49820
99.6%
ValueCountFrequency (%)
2431
< 0.1%
10611
< 0.1%
13161
< 0.1%
15111
< 0.1%
16241
< 0.1%
18211
< 0.1%
20341
< 0.1%
20811
< 0.1%
21261
< 0.1%
21271
< 0.1%
ValueCountFrequency (%)
6832541
< 0.1%
6599501
< 0.1%
6364961
< 0.1%
5430351
< 0.1%
5416641
< 0.1%
5366071
< 0.1%
5327291
< 0.1%
5235721
< 0.1%
5125711
< 0.1%
4992731
< 0.1%

GC_content
Real number (ℝ)

Distinct49112
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.785999
Minimum20.612366
Maximum74.620549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2026-07-15T06:23:33.430241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.612366
5-th percentile31.076725
Q137.250062
median43.813794
Q351.593812
95-th percentile62.05973
Maximum74.620549
Range54.008183
Interquartile range (IQR)14.343751

Descriptive statistics

Standard deviation9.5393826
Coefficient of variation (CV)0.21299921
Kurtosis-0.54792772
Mean44.785999
Median Absolute Deviation (MAD)7.1023806
Skewness0.37502601
Sum2239299.9
Variance90.99982
MonotonicityNot monotonic
2026-07-15T06:23:33.665705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.7085035313
 
< 0.1%
37.5675642512
 
< 0.1%
50.3546259511
 
< 0.1%
45.3071235111
 
< 0.1%
29.8829150510
 
< 0.1%
53.326768179
 
< 0.1%
44.778269378
 
< 0.1%
47.825200147
 
< 0.1%
32.722039637
 
< 0.1%
45.306208367
 
< 0.1%
Other values (49102)49905
99.8%
ValueCountFrequency (%)
20.612366231
< 0.1%
20.639972621
< 0.1%
20.847156111
< 0.1%
21.116504851
< 0.1%
21.311709511
< 0.1%
21.65032681
< 0.1%
22.14647021
< 0.1%
22.150882831
< 0.1%
22.163620751
< 0.1%
22.676013411
< 0.1%
ValueCountFrequency (%)
74.620549131
< 0.1%
73.935965031
< 0.1%
73.41269191
< 0.1%
73.182016811
< 0.1%
73.107257481
< 0.1%
72.845840331
< 0.1%
72.735805981
< 0.1%
72.691972711
< 0.1%
72.657367251
< 0.1%
72.635442971
< 0.1%

Taxonomy
Text

Missing 

Distinct185
Distinct (%)0.4%
Missing1811
Missing (%)3.6%
Memory size3.6 MiB
2026-07-15T06:23:33.839600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length42
Mean length12.890535
Min length1

Characters and Unicode

Total characters621182
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.1%

Sample

1st rowCaudoviricetes
2nd rowCaudoviricetes
3rd rowNCLDV
4th rowCaudoviricetes
5th rowCaudovirales
ValueCountFrequency (%)
caudoviricetes22809
47.3%
caudovirales21474
44.6%
microviridae1284
 
2.7%
siphoviridae835
 
1.7%
myoviridae356
 
0.7%
inoviridae233
 
0.5%
podoviridae165
 
0.3%
crassvirales134
 
0.3%
128
 
0.3%
ncldv85
 
0.2%
Other values (175)686
 
1.4%
2026-07-15T06:23:34.139786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i76631
12.3%
e71005
11.4%
a69969
11.3%
r49702
8.0%
v48035
7.7%
d47886
7.7%
o47573
7.7%
s45152
7.3%
C44573
7.2%
u44516
7.2%
Other values (44)76140
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)621182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i76631
12.3%
e71005
11.4%
a69969
11.3%
r49702
8.0%
v48035
7.7%
d47886
7.7%
o47573
7.7%
s45152
7.3%
C44573
7.2%
u44516
7.2%
Other values (44)76140
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)621182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i76631
12.3%
e71005
11.4%
a69969
11.3%
r49702
8.0%
v48035
7.7%
d47886
7.7%
o47573
7.7%
s45152
7.3%
C44573
7.2%
u44516
7.2%
Other values (44)76140
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)621182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i76631
12.3%
e71005
11.4%
a69969
11.3%
r49702
8.0%
v48035
7.7%
d47886
7.7%
o47573
7.7%
s45152
7.3%
C44573
7.2%
u44516
7.2%
Other values (44)76140
12.3%

Completeness
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
High-quality
19687 
Low-quality
12428 
Medium-quality
10744 
Complete
6307 
Not-determined
 
834

Length

Max length14
Median length12
Mean length11.71
Min length8

Characters and Unicode

Total characters585500
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium-quality
2nd rowMedium-quality
3rd rowLow-quality
4th rowNot-determined
5th rowMedium-quality

Common Values

ValueCountFrequency (%)
High-quality19687
39.4%
Low-quality12428
24.9%
Medium-quality10744
21.5%
Complete6307
 
12.6%
Not-determined834
 
1.7%

Length

2026-07-15T06:23:34.291780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:34.436001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high-quality19687
39.4%
low-quality12428
24.9%
medium-quality10744
21.5%
complete6307
 
12.6%
not-determined834
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i74124
12.7%
u53603
 
9.2%
t50834
 
8.7%
l49166
 
8.4%
-43693
 
7.5%
q42859
 
7.3%
a42859
 
7.3%
y42859
 
7.3%
e25860
 
4.4%
H19687
 
3.4%
Other values (13)139956
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)585500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i74124
12.7%
u53603
 
9.2%
t50834
 
8.7%
l49166
 
8.4%
-43693
 
7.5%
q42859
 
7.3%
a42859
 
7.3%
y42859
 
7.3%
e25860
 
4.4%
H19687
 
3.4%
Other values (13)139956
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)585500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i74124
12.7%
u53603
 
9.2%
t50834
 
8.7%
l49166
 
8.4%
-43693
 
7.5%
q42859
 
7.3%
a42859
 
7.3%
y42859
 
7.3%
e25860
 
4.4%
H19687
 
3.4%
Other values (13)139956
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)585500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i74124
12.7%
u53603
 
9.2%
t50834
 
8.7%
l49166
 
8.4%
-43693
 
7.5%
q42859
 
7.3%
a42859
 
7.3%
y42859
 
7.3%
e25860
 
4.4%
H19687
 
3.4%
Other values (13)139956
23.9%

Host
Text

Distinct2306
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2026-07-15T06:23:34.682407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length324
Median length270
Mean length19.02174
Min length1

Characters and Unicode

Total characters951087
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1279 ?
Unique (%)2.6%

Sample

1st rowHaemophilus influenzae
2nd rowStreptococcus pyogenes
3rd rowLawsonia intracellularis
4th rowBacteroides vulgatus
5th rowStreptococcus mitis
ValueCountFrequency (%)
salmonella5840
 
6.5%
enterica5798
 
6.5%
bacteroides2784
 
3.1%
bacillus2439
 
2.7%
cereus2196
 
2.5%
escherichia1848
 
2.1%
faecalibacterium1839
 
2.1%
coli1786
 
2.0%
intracellularis1632
 
1.8%
lawsonia1632
 
1.8%
Other values (2329)61575
68.9%
2026-07-15T06:23:35.171580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a99777
 
10.5%
e94916
 
10.0%
i92237
 
9.7%
c71522
 
7.5%
l65663
 
6.9%
o61598
 
6.5%
r59746
 
6.3%
s58007
 
6.1%
t49704
 
5.2%
n41953
 
4.4%
Other values (59)255964
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)951087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a99777
 
10.5%
e94916
 
10.0%
i92237
 
9.7%
c71522
 
7.5%
l65663
 
6.9%
o61598
 
6.5%
r59746
 
6.3%
s58007
 
6.1%
t49704
 
5.2%
n41953
 
4.4%
Other values (59)255964
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)951087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a99777
 
10.5%
e94916
 
10.0%
i92237
 
9.7%
c71522
 
7.5%
l65663
 
6.9%
o61598
 
6.5%
r59746
 
6.3%
s58007
 
6.1%
t49704
 
5.2%
n41953
 
4.4%
Other values (59)255964
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)951087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a99777
 
10.5%
e94916
 
10.0%
i92237
 
9.7%
c71522
 
7.5%
l65663
 
6.9%
o61598
 
6.5%
r59746
 
6.3%
s58007
 
6.1%
t49704
 
5.2%
n41953
 
4.4%
Other values (59)255964
26.9%

Lifestyle
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
virulent
31171 
temperate
18596 
-
 
214
filtered
 
19

Length

Max length9
Median length8
Mean length8.34196
Min length1

Characters and Unicode

Total characters417098
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtemperate
2nd rowtemperate
3rd rowvirulent
4th rowvirulent
5th rowtemperate

Common Values

ValueCountFrequency (%)
virulent31171
62.3%
temperate18596
37.2%
-214
 
0.4%
filtered19
 
< 0.1%

Length

2026-07-15T06:23:35.509489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:35.641275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
virulent31171
62.3%
temperate18596
37.2%
214
 
0.4%
filtered19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e86997
20.9%
t68382
16.4%
r49786
11.9%
i31190
 
7.5%
l31190
 
7.5%
v31171
 
7.5%
u31171
 
7.5%
n31171
 
7.5%
m18596
 
4.5%
p18596
 
4.5%
Other values (4)18848
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)417098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e86997
20.9%
t68382
16.4%
r49786
11.9%
i31190
 
7.5%
l31190
 
7.5%
v31171
 
7.5%
u31171
 
7.5%
n31171
 
7.5%
m18596
 
4.5%
p18596
 
4.5%
Other values (4)18848
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)417098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e86997
20.9%
t68382
16.4%
r49786
11.9%
i31190
 
7.5%
l31190
 
7.5%
v31171
 
7.5%
u31171
 
7.5%
n31171
 
7.5%
m18596
 
4.5%
p18596
 
4.5%
Other values (4)18848
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)417098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e86997
20.9%
t68382
16.4%
r49786
11.9%
i31190
 
7.5%
l31190
 
7.5%
v31171
 
7.5%
u31171
 
7.5%
n31171
 
7.5%
m18596
 
4.5%
p18596
 
4.5%
Other values (4)18848
 
4.5%

Cluster
Text

Distinct46116
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2026-07-15T06:23:35.894927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.73896
Min length9

Characters and Unicode

Total characters686948
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43669 ?
Unique (%)87.3%

Sample

1st rowCluster_420212
2nd rowCluster_169165
3rd rowcluster_546576
4th rowcluster_117228
5th rowCluster_299099
ValueCountFrequency (%)
cluster_24210746
 
0.1%
cluster_6819326
 
0.1%
cluster_13498326
 
0.1%
cluster_617824
 
< 0.1%
cluster_19672824
 
< 0.1%
cluster_45547121
 
< 0.1%
cluster_32593719
 
< 0.1%
cluster_49926819
 
< 0.1%
cluster_23779415
 
< 0.1%
cluster_16319615
 
< 0.1%
Other values (45225)49765
99.5%
2026-07-15T06:23:36.315085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u50000
 
7.3%
s50000
 
7.3%
t50000
 
7.3%
e50000
 
7.3%
r50000
 
7.3%
_50000
 
7.3%
l50000
 
7.3%
136976
 
5.4%
236012
 
5.2%
c32287
 
4.7%
Other values (9)231673
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)686948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u50000
 
7.3%
s50000
 
7.3%
t50000
 
7.3%
e50000
 
7.3%
r50000
 
7.3%
_50000
 
7.3%
l50000
 
7.3%
136976
 
5.4%
236012
 
5.2%
c32287
 
4.7%
Other values (9)231673
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)686948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u50000
 
7.3%
s50000
 
7.3%
t50000
 
7.3%
e50000
 
7.3%
r50000
 
7.3%
_50000
 
7.3%
l50000
 
7.3%
136976
 
5.4%
236012
 
5.2%
c32287
 
4.7%
Other values (9)231673
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)686948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u50000
 
7.3%
s50000
 
7.3%
t50000
 
7.3%
e50000
 
7.3%
r50000
 
7.3%
_50000
 
7.3%
l50000
 
7.3%
136976
 
5.4%
236012
 
5.2%
c32287
 
4.7%
Other values (9)231673
33.7%
Distinct48027
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2026-07-15T06:23:36.550988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.7946
Min length12

Characters and Unicode

Total characters839730
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46684 ?
Unique (%)93.4%

Sample

1st rowSubcluster_574429
2nd rowSubcluster_230509
3rd rowsubcluster_658213
4th rowsubcluster_147788
5th rowSubcluster_407686
ValueCountFrequency (%)
subcluster_32949228
 
0.1%
subcluster_16975426
 
0.1%
subcluster_840523
 
< 0.1%
subcluster_68325419
 
< 0.1%
subcluster_44509518
 
< 0.1%
subcluster_67519614
 
< 0.1%
subcluster_17201612
 
< 0.1%
subcluster_19049611
 
< 0.1%
subcluster_54219511
 
< 0.1%
subcluster_63944011
 
< 0.1%
Other values (47308)49827
99.7%
2026-07-15T06:23:36.934494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u100000
 
11.9%
s82287
 
9.8%
_50000
 
6.0%
b50000
 
6.0%
c50000
 
6.0%
l50000
 
6.0%
t50000
 
6.0%
e50000
 
6.0%
r50000
 
6.0%
134721
 
4.1%
Other values (10)272722
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)839730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u100000
 
11.9%
s82287
 
9.8%
_50000
 
6.0%
b50000
 
6.0%
c50000
 
6.0%
l50000
 
6.0%
t50000
 
6.0%
e50000
 
6.0%
r50000
 
6.0%
134721
 
4.1%
Other values (10)272722
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)839730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u100000
 
11.9%
s82287
 
9.8%
_50000
 
6.0%
b50000
 
6.0%
c50000
 
6.0%
l50000
 
6.0%
t50000
 
6.0%
e50000
 
6.0%
r50000
 
6.0%
134721
 
4.1%
Other values (10)272722
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)839730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u100000
 
11.9%
s82287
 
9.8%
_50000
 
6.0%
b50000
 
6.0%
c50000
 
6.0%
l50000
 
6.0%
t50000
 
6.0%
e50000
 
6.0%
r50000
 
6.0%
134721
 
4.1%
Other values (10)272722
32.5%

Provider_Name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
PhageScope
50000 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhageScope
2nd rowPhageScope
3rd rowPhageScope
4th rowPhageScope
5th rowPhageScope

Common Values

ValueCountFrequency (%)
PhageScope50000
100.0%

Length

2026-07-15T06:23:37.097092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:37.208688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phagescope50000
100.0%

Most occurring characters

ValueCountFrequency (%)
e100000
20.0%
P50000
10.0%
h50000
10.0%
a50000
10.0%
g50000
10.0%
S50000
10.0%
c50000
10.0%
o50000
10.0%
p50000
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e100000
20.0%
P50000
10.0%
h50000
10.0%
a50000
10.0%
g50000
10.0%
S50000
10.0%
c50000
10.0%
o50000
10.0%
p50000
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e100000
20.0%
P50000
10.0%
h50000
10.0%
a50000
10.0%
g50000
10.0%
S50000
10.0%
c50000
10.0%
o50000
10.0%
p50000
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e100000
20.0%
P50000
10.0%
h50000
10.0%
a50000
10.0%
g50000
10.0%
S50000
10.0%
c50000
10.0%
o50000
10.0%
p50000
10.0%

Provider_Release
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
rolling
50000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters350000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrolling
2nd rowrolling
3rd rowrolling
4th rowrolling
5th rowrolling

Common Values

ValueCountFrequency (%)
rolling50000
100.0%

Length

2026-07-15T06:23:37.335446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:37.431355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rolling50000
100.0%

Most occurring characters

ValueCountFrequency (%)
l100000
28.6%
r50000
14.3%
o50000
14.3%
i50000
14.3%
n50000
14.3%
g50000
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)350000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l100000
28.6%
r50000
14.3%
o50000
14.3%
i50000
14.3%
n50000
14.3%
g50000
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)350000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l100000
28.6%
r50000
14.3%
o50000
14.3%
i50000
14.3%
n50000
14.3%
g50000
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)350000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l100000
28.6%
r50000
14.3%
o50000
14.3%
i50000
14.3%
n50000
14.3%
g50000
14.3%

Provider_Snapshot_Date
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
2026-07-05
50000 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2026-07-05
2nd row2026-07-05
3rd row2026-07-05
4th row2026-07-05
5th row2026-07-05

Common Values

ValueCountFrequency (%)
2026-07-0550000
100.0%

Length

2026-07-15T06:23:37.564767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:37.672144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2026-07-0550000
100.0%

Most occurring characters

ValueCountFrequency (%)
0150000
30.0%
2100000
20.0%
-100000
20.0%
650000
 
10.0%
750000
 
10.0%
550000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0150000
30.0%
2100000
20.0%
-100000
20.0%
650000
 
10.0%
750000
 
10.0%
550000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0150000
30.0%
2100000
20.0%
-100000
20.0%
650000
 
10.0%
750000
 
10.0%
550000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0150000
30.0%
2100000
20.0%
-100000
20.0%
650000
 
10.0%
750000
 
10.0%
550000
 
10.0%

Provider_Schema_Profile
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
phagescope_v2
50000 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters650000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowphagescope_v2
2nd rowphagescope_v2
3rd rowphagescope_v2
4th rowphagescope_v2
5th rowphagescope_v2

Common Values

ValueCountFrequency (%)
phagescope_v250000
100.0%

Length

2026-07-15T06:23:37.789451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-07-15T06:23:37.884272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phagescope_v250000
100.0%

Most occurring characters

ValueCountFrequency (%)
p100000
15.4%
e100000
15.4%
h50000
7.7%
a50000
7.7%
g50000
7.7%
s50000
7.7%
c50000
7.7%
o50000
7.7%
_50000
7.7%
v50000
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)650000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p100000
15.4%
e100000
15.4%
h50000
7.7%
a50000
7.7%
g50000
7.7%
s50000
7.7%
c50000
7.7%
o50000
7.7%
_50000
7.7%
v50000
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)650000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p100000
15.4%
e100000
15.4%
h50000
7.7%
a50000
7.7%
g50000
7.7%
s50000
7.7%
c50000
7.7%
o50000
7.7%
_50000
7.7%
v50000
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)650000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p100000
15.4%
e100000
15.4%
h50000
7.7%
a50000
7.7%
g50000
7.7%
s50000
7.7%
c50000
7.7%
o50000
7.7%
_50000
7.7%
v50000
7.7%

Input_Source_Key
Categorical

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
MetaVR_Phage_Metadata_URL
8301 
GOV2_Phage_Metadata_URL
7361 
MGV_Phage_Metadata_URL
6905 
IMGVR_Phage_Metadata_URL
6577 
GPD_Phage_Metadata_URL
5290 
Other values (21)
15566 

Length

Max length28
Median length27
Mean length23.43738
Min length22

Characters and Unicode

Total characters1171869
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPD_Phage_Metadata_URL
2nd rowOVD_Phage_Metadata_URL
3rd rowGOV2_Phage_Metadata_URL
4th rowGVD_Phage_Metadata_URL
5th rowOVD_Phage_Metadata_URL

Common Values

ValueCountFrequency (%)
MetaVR_Phage_Metadata_URL8301
16.6%
GOV2_Phage_Metadata_URL7361
14.7%
MGV_Phage_Metadata_URL6905
13.8%
IMGVR_Phage_Metadata_URL6577
13.2%
GPD_Phage_Metadata_URL5290
10.6%
TemPhD_Phage_Metadata_URL2385
 
4.8%
ELGV_Phage_Metadata_URL2042
 
4.1%
URPC_Phage_Metadata_URL1693
 
3.4%
TYMEFLIES_Phage_Metadata_URL1689
 
3.4%
CHVD_Phage_Metadata_URL1662
 
3.3%
Other values (16)6095
12.2%

Length

2026-07-15T06:23:38.018640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metavr_phage_metadata_url8301
16.6%
gov2_phage_metadata_url7361
14.7%
mgv_phage_metadata_url6905
13.8%
imgvr_phage_metadata_url6577
13.2%
gpd_phage_metadata_url5290
10.6%
temphd_phage_metadata_url2385
 
4.8%
elgv_phage_metadata_url2042
 
4.1%
urpc_phage_metadata_url1693
 
3.4%
tymeflies_phage_metadata_url1689
 
3.4%
chvd_phage_metadata_url1662
 
3.3%
Other values (16)6095
12.2%

Most occurring characters

ValueCountFrequency (%)
a208533
17.8%
_150000
12.8%
e111244
9.5%
t108301
9.2%
M73605
 
6.3%
R66734
 
5.7%
P60931
 
5.2%
L53735
 
4.6%
h52536
 
4.5%
U52499
 
4.5%
Other values (25)233751
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1171869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a208533
17.8%
_150000
12.8%
e111244
9.5%
t108301
9.2%
M73605
 
6.3%
R66734
 
5.7%
P60931
 
5.2%
L53735
 
4.6%
h52536
 
4.5%
U52499
 
4.5%
Other values (25)233751
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1171869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a208533
17.8%
_150000
12.8%
e111244
9.5%
t108301
9.2%
M73605
 
6.3%
R66734
 
5.7%
P60931
 
5.2%
L53735
 
4.6%
h52536
 
4.5%
U52499
 
4.5%
Other values (25)233751
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1171869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a208533
17.8%
_150000
12.8%
e111244
9.5%
t108301
9.2%
M73605
 
6.3%
R66734
 
5.7%
P60931
 
5.2%
L53735
 
4.6%
h52536
 
4.5%
U52499
 
4.5%
Other values (25)233751
19.9%

Input_File
Categorical

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
MetaVR_Phage_Metadata_URL.tsv
8301 
GOV2_Phage_Metadata_URL.tsv
7361 
MGV_Phage_Metadata_URL.tsv
6905 
IMGVR_Phage_Metadata_URL.tsv
6577 
GPD_Phage_Metadata_URL.tsv
5290 
Other values (21)
15566 

Length

Max length32
Median length31
Mean length27.43738
Min length26

Characters and Unicode

Total characters1371869
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPD_Phage_Metadata_URL.tsv
2nd rowOVD_Phage_Metadata_URL.tsv
3rd rowGOV2_Phage_Metadata_URL.tsv
4th rowGVD_Phage_Metadata_URL.tsv
5th rowOVD_Phage_Metadata_URL.tsv

Common Values

ValueCountFrequency (%)
MetaVR_Phage_Metadata_URL.tsv8301
16.6%
GOV2_Phage_Metadata_URL.tsv7361
14.7%
MGV_Phage_Metadata_URL.tsv6905
13.8%
IMGVR_Phage_Metadata_URL.tsv6577
13.2%
GPD_Phage_Metadata_URL.tsv5290
10.6%
TemPhD_Phage_Metadata_URL.tsv2385
 
4.8%
ELGV_Phage_Metadata_URL.tsv2042
 
4.1%
URPC_Phage_Metadata_URL.tsv1693
 
3.4%
TYMEFLIES_Phage_Metadata_URL.tsv1689
 
3.4%
CHVD_Phage_Metadata_URL.tsv1662
 
3.3%
Other values (16)6095
12.2%

Length

2026-07-15T06:23:38.209083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metavr_phage_metadata_url.tsv8301
16.6%
gov2_phage_metadata_url.tsv7361
14.7%
mgv_phage_metadata_url.tsv6905
13.8%
imgvr_phage_metadata_url.tsv6577
13.2%
gpd_phage_metadata_url.tsv5290
10.6%
temphd_phage_metadata_url.tsv2385
 
4.8%
elgv_phage_metadata_url.tsv2042
 
4.1%
urpc_phage_metadata_url.tsv1693
 
3.4%
tymeflies_phage_metadata_url.tsv1689
 
3.4%
chvd_phage_metadata_url.tsv1662
 
3.3%
Other values (16)6095
12.2%

Most occurring characters

ValueCountFrequency (%)
a208533
15.2%
t158301
11.5%
_150000
 
10.9%
e111244
 
8.1%
M73605
 
5.4%
R66734
 
4.9%
P60931
 
4.4%
L53735
 
3.9%
h52536
 
3.8%
U52499
 
3.8%
Other values (27)383751
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1371869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a208533
15.2%
t158301
11.5%
_150000
 
10.9%
e111244
 
8.1%
M73605
 
5.4%
R66734
 
4.9%
P60931
 
4.4%
L53735
 
3.9%
h52536
 
3.8%
U52499
 
3.8%
Other values (27)383751
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1371869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a208533
15.2%
t158301
11.5%
_150000
 
10.9%
e111244
 
8.1%
M73605
 
5.4%
R66734
 
4.9%
P60931
 
4.4%
L53735
 
3.9%
h52536
 
3.8%
U52499
 
3.8%
Other values (27)383751
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1371869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a208533
15.2%
t158301
11.5%
_150000
 
10.9%
e111244
 
8.1%
M73605
 
5.4%
R66734
 
4.9%
P60931
 
4.4%
L53735
 
3.9%
h52536
 
3.8%
U52499
 
3.8%
Other values (27)383751
28.0%

Input_Retrieved_At
Categorical

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2026-07-15T06:22:28Z
8301 
2026-07-15T06:09:07Z
7361 
2026-07-15T06:09:30Z
6905 
2026-07-15T06:07:22Z
6577 
2026-07-15T06:12:46Z
5290 
Other values (21)
15566 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1000000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2026-07-15T06:13:08Z
2nd row2026-07-15T06:11:21Z
3rd row2026-07-15T06:09:07Z
4th row2026-07-15T06:20:33Z
5th row2026-07-15T06:11:21Z

Common Values

ValueCountFrequency (%)
2026-07-15T06:22:28Z8301
16.6%
2026-07-15T06:09:07Z7361
14.7%
2026-07-15T06:09:30Z6905
13.8%
2026-07-15T06:07:22Z6577
13.2%
2026-07-15T06:12:46Z5290
10.6%
2026-07-15T06:07:01Z2385
 
4.8%
2026-07-15T06:11:02Z2042
 
4.1%
2026-07-15T06:16:10Z1693
 
3.4%
2026-07-15T06:14:55Z1689
 
3.4%
2026-07-15T06:06:30Z1662
 
3.3%
Other values (16)6095
12.2%

Length

2026-07-15T06:23:38.396391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2026-07-15t06:22:28z8301
16.6%
2026-07-15t06:09:07z7361
14.7%
2026-07-15t06:09:30z6905
13.8%
2026-07-15t06:07:22z6577
13.2%
2026-07-15t06:12:46z5290
10.6%
2026-07-15t06:07:01z2385
 
4.8%
2026-07-15t06:11:02z2042
 
4.1%
2026-07-15t06:16:10z1693
 
3.4%
2026-07-15t06:14:55z1689
 
3.4%
2026-07-15t06:06:30z1662
 
3.3%
Other values (16)6095
12.2%

Most occurring characters

ValueCountFrequency (%)
0200446
20.0%
2150100
15.0%
6109022
10.9%
-100000
10.0%
:100000
10.0%
175681
 
7.6%
766635
 
6.7%
553974
 
5.4%
T50000
 
5.0%
Z50000
 
5.0%
Other values (4)44142
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0200446
20.0%
2150100
15.0%
6109022
10.9%
-100000
10.0%
:100000
10.0%
175681
 
7.6%
766635
 
6.7%
553974
 
5.4%
T50000
 
5.0%
Z50000
 
5.0%
Other values (4)44142
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0200446
20.0%
2150100
15.0%
6109022
10.9%
-100000
10.0%
:100000
10.0%
175681
 
7.6%
766635
 
6.7%
553974
 
5.4%
T50000
 
5.0%
Z50000
 
5.0%
Other values (4)44142
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0200446
20.0%
2150100
15.0%
6109022
10.9%
-100000
10.0%
:100000
10.0%
175681
 
7.6%
766635
 
6.7%
553974
 
5.4%
T50000
 
5.0%
Z50000
 
5.0%
Other values (4)44142
 
4.4%

Interactions

2026-07-15T06:23:30.493986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-07-15T06:23:30.121388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-07-15T06:23:30.673666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-07-15T06:23:30.313803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-07-15T06:23:38.534913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CompletenessGC_contentInput_FileInput_Retrieved_AtInput_Source_KeyLengthLifestyleSource_DB
Completeness1.0000.1150.4980.4980.4980.1180.1830.498
GC_content0.1151.0000.1480.1480.1480.0700.1690.148
Input_File0.4980.1481.0001.0001.0000.2360.2901.000
Input_Retrieved_At0.4980.1481.0001.0001.0000.2360.2901.000
Input_Source_Key0.4980.1481.0001.0001.0000.2360.2901.000
Length0.1180.0700.2360.2360.2361.0000.0610.236
Lifestyle0.1830.1690.2900.2900.2900.0611.0000.290
Source_DB0.4980.1481.0001.0001.0000.2360.2901.000

Missing values

2026-07-15T06:23:31.005540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-07-15T06:23:31.374298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Phage_IDSource_DBLengthGC_contentTaxonomyCompletenessHostLifestyleClusterSubclusterProvider_NameProvider_ReleaseProvider_Snapshot_DateProvider_Schema_ProfileInput_Source_KeyInput_FileInput_Retrieved_At
1051748OPD_20042OPD21174.038.698404CaudoviricetesMedium-qualityHaemophilus influenzaetemperateCluster_420212Subcluster_574429PhageScoperolling2026-07-05phagescope_v2OPD_Phage_Metadata_URLOPD_Phage_Metadata_URL.tsv2026-07-15T06:13:08Z
1025623Shi_2020_14.k81_99141OVD18793.047.730538CaudoviricetesMedium-qualityStreptococcus pyogenestemperateCluster_169165Subcluster_230509PhageScoperolling2026-07-05phagescope_v2OVD_Phage_Metadata_URLOVD_Phage_Metadata_URL.tsv2026-07-15T06:11:21Z
756049Station78_MES_COMBINED_FINAL_NODE_5336_length_11981_cov_5.262200GOV211981.034.295969NaNLow-qualityLawsonia intracellularisvirulentcluster_546576subcluster_658213PhageScoperolling2026-07-05phagescope_v2GOV2_Phage_Metadata_URLGOV2_Phage_Metadata_URL.tsv2026-07-15T06:09:07Z
27047Ma_2019_SRR413722_NODE_2357_length_12320_cov_50.786547GVD12320.040.259740NCLDVNot-determinedBacteroides vulgatusvirulentcluster_117228subcluster_147788PhageScoperolling2026-07-05phagescope_v2GVD_Phage_Metadata_URLGVD_Phage_Metadata_URL.tsv2026-07-15T06:20:33Z
1033699Zhang_2015_057.ctg_256OVD21394.038.085445CaudoviricetesMedium-qualityStreptococcus mitistemperateCluster_299099Subcluster_407686PhageScoperolling2026-07-05phagescope_v2OVD_Phage_Metadata_URLOVD_Phage_Metadata_URL.tsv2026-07-15T06:11:21Z
344605MGV-GENOME-0357274MGV76256.040.166020CaudoviralesHigh-qualityLawsonia intracellularisvirulentcluster_147711subcluster_186294PhageScoperolling2026-07-05phagescope_v2MGV_Phage_Metadata_URLMGV_Phage_Metadata_URL.tsv2026-07-15T06:09:30Z
176125uvig_575842GPD15995.039.406064CaudoviralesLow-qualityRuminococcus bicirculansvirulentcluster_234930subcluster_295559PhageScoperolling2026-07-05phagescope_v2GPD_Phage_Metadata_URLGPD_Phage_Metadata_URL.tsv2026-07-15T06:12:46Z
439557TemPhD_cluster_8126TemPhD16932.045.824474CaudoviralesLow-qualitySalmonella entericatemperatecluster_233119subcluster_293424PhageScoperolling2026-07-05phagescope_v2TemPhD_Phage_Metadata_URLTemPhD_Phage_Metadata_URL.tsv2026-07-15T06:07:01Z
423258TemPhD_cluster_53599TemPhD14881.050.534238CaudoviralesLow-qualityEscherichia colitemperatecluster_253743subcluster_318715PhageScoperolling2026-07-05phagescope_v2TemPhD_Phage_Metadata_URLTemPhD_Phage_Metadata_URL.tsv2026-07-15T06:07:01Z
1276171IMGVR_UViG_3300083410_006530|3300083410|Ga0678260_006530MetaVR38637.028.845407CaudoviricetesHigh-qualitySalmonella entericavirulentCluster_192134Subcluster_261525PhageScoperolling2026-07-05phagescope_v2MetaVR_Phage_Metadata_URLMetaVR_Phage_Metadata_URL.tsv2026-07-15T06:22:28Z
Phage_IDSource_DBLengthGC_contentTaxonomyCompletenessHostLifestyleClusterSubclusterProvider_NameProvider_ReleaseProvider_Snapshot_DateProvider_Schema_ProfileInput_Source_KeyInput_FileInput_Retrieved_At
1200667IMGVR_UViG_3300041085_000347|3300041085|Ga0452209_001768MetaVR49391.044.240854CaudoviricetesCompleteSalmonella entericavirulentCluster_402664Subcluster_550400PhageScoperolling2026-07-05phagescope_v2MetaVR_Phage_Metadata_URLMetaVR_Phage_Metadata_URL.tsv2026-07-15T06:22:28Z
1229387IMGVR_UViG_3300045988_086009|3300045988|Ga0495776_109779MetaVR41351.044.971101CaudoviricetesHigh-qualityLachnospira eligenstemperateCluster_410242Subcluster_560890PhageScoperolling2026-07-05phagescope_v2MetaVR_Phage_Metadata_URLMetaVR_Phage_Metadata_URL.tsv2026-07-15T06:22:28Z
270860MGV-GENOME-0260329MGV38506.051.563393CaudoviralesMedium-qualityBacteroidesvirulentcluster_203687subcluster_256398PhageScoperolling2026-07-05phagescope_v2MGV_Phage_Metadata_URLMGV_Phage_Metadata_URL.tsv2026-07-15T06:09:30Z
11131043300043438__vRhyme_29TYMEFLIES75511.030.305518CaudoviricetesMedium-qualityParabacteroides gordoniivirulentCluster_27637Subcluster_37998PhageScoperolling2026-07-05phagescope_v2TYMEFLIES_Phage_Metadata_URLTYMEFLIES_Phage_Metadata_URL.tsv2026-07-15T06:14:55Z
805846Station196_SUR_ALL_assembly_NODE_4361_length_12537_cov_8.620253GOV212537.035.447077NaNMedium-qualityClostridioides difficilevirulentcluster_301188subcluster_363539PhageScoperolling2026-07-05phagescope_v2GOV2_Phage_Metadata_URLGOV2_Phage_Metadata_URL.tsv2026-07-15T06:09:07Z
628002IMGVR_UViG_2657245449_000005|2657245449|2657301437|896501-933899IMGVR37399.052.589641CaudoviricetesHigh-qualityKlebsiella pneumoniaetemperatecluster_136540subcluster_164738PhageScoperolling2026-07-05phagescope_v2IMGVR_Phage_Metadata_URLIMGVR_Phage_Metadata_URL.tsv2026-07-15T06:07:22Z
81641uvig_146438GPD51832.043.895663CaudoviralesMedium-qualityBlautia A massiliensis;NAtemperatecluster_80931subcluster_101674PhageScoperolling2026-07-05phagescope_v2GPD_Phage_Metadata_URLGPD_Phage_Metadata_URL.tsv2026-07-15T06:12:46Z
309079MGV-GENOME-0113179MGV16661.057.511554CaudoviralesMedium-qualitySphingomonas koreensisvirulentcluster_94380subcluster_118841PhageScoperolling2026-07-05phagescope_v2MGV_Phage_Metadata_URLMGV_Phage_Metadata_URL.tsv2026-07-15T06:09:30Z
321568MGV-GENOME-0301414MGV47037.034.736484CaudoviralesHigh-qualityStreptococcus mitistemperatecluster_177184subcluster_223641PhageScoperolling2026-07-05phagescope_v2MGV_Phage_Metadata_URLMGV_Phage_Metadata_URL.tsv2026-07-15T06:09:30Z
687492Station124_MXL_ALL_assembly_NODE_111_length_57056_cov_12.337608GOV257056.037.640213CaudoviralesHigh-qualityLawsonia intracellularisvirulentcluster_271651subcluster_327949PhageScoperolling2026-07-05phagescope_v2GOV2_Phage_Metadata_URLGOV2_Phage_Metadata_URL.tsv2026-07-15T06:09:07Z