Overview

Dataset statistics

Number of variables22
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory169.0 B

Variable types

Numeric6
Categorical15
Boolean1

Alerts

pubmed has constant value "26472758" Constant
cas has constant value "hSpCas9" Constant
screentype has constant value "negative selection" Constant
cellline has constant value "Jiyoye" Constant
condition has constant value "viability" Constant
scoredist has constant value "[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]" Constant
name has a high cardinality: 18459 distinct values High cardinality
chr has a high cardinality: 70 distinct values High cardinality
ensg has a high cardinality: 2083 distinct values High cardinality
symbol has a high cardinality: 1956 distinct values High cardinality
sequence has a high cardinality: 18349 distinct values High cardinality
genetargets has a high cardinality: 2083 distinct values High cardinality
rc_initial has a high cardinality: 643 distinct values High cardinality
rc_final has a high cardinality: 548 distinct values High cardinality
log2fc is highly correlated with effectHigh correlation
start is highly correlated with endHigh correlation
end is highly correlated with startHigh correlation
effect is highly correlated with log2fcHigh correlation
log2fc is highly correlated with effectHigh correlation
start is highly correlated with endHigh correlation
end is highly correlated with startHigh correlation
score is highly correlated with hitHigh correlation
hit is highly correlated with scoreHigh correlation
effect is highly correlated with log2fcHigh correlation
log2fc is highly correlated with effectHigh correlation
start is highly correlated with endHigh correlation
end is highly correlated with startHigh correlation
effect is highly correlated with log2fcHigh correlation
strand is highly correlated with pubmed and 5 other fieldsHigh correlation
pubmed is highly correlated with strand and 7 other fieldsHigh correlation
cas is highly correlated with strand and 7 other fieldsHigh correlation
condition is highly correlated with strand and 7 other fieldsHigh correlation
scoredist is highly correlated with strand and 7 other fieldsHigh correlation
cellline is highly correlated with strand and 7 other fieldsHigh correlation
screentype is highly correlated with strand and 7 other fieldsHigh correlation
hit is highly correlated with pubmed and 5 other fieldsHigh correlation
chr is highly correlated with pubmed and 5 other fieldsHigh correlation
Unnamed: 0 is highly correlated with chrHigh correlation
log2fc is highly correlated with effectHigh correlation
chr is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
start is highly correlated with chr and 1 other fieldsHigh correlation
end is highly correlated with chr and 1 other fieldsHigh correlation
score is highly correlated with chr and 1 other fieldsHigh correlation
hit is highly correlated with scoreHigh correlation
effect is highly correlated with log2fcHigh correlation
Unnamed: 0 is uniformly distributed Uniform
name is uniformly distributed Uniform
ensg is uniformly distributed Uniform
sequence is uniformly distributed Uniform
genetargets is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
effect has 2043 (10.2%) zeros Zeros

Reproduction

Analysis started2022-06-10 03:11:10.219661
Analysis finished2022-06-10 03:11:18.322254
Duration8.1 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9999.5
Minimum0
Maximum19999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-06-10T03:11:18.394193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999.95
Q14999.75
median9999.5
Q314999.25
95-th percentile18999.05
Maximum19999
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.5773935724
Kurtosis-1.2
Mean9999.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum199990000
Variance33335000
MonotonicityStrictly increasing
2022-06-10T03:11:18.548943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
133301
 
< 0.1%
133371
 
< 0.1%
133361
 
< 0.1%
133351
 
< 0.1%
133341
 
< 0.1%
133331
 
< 0.1%
133321
 
< 0.1%
133311
 
< 0.1%
133291
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
199991
< 0.1%
199981
< 0.1%
199971
< 0.1%
199961
< 0.1%
199951
< 0.1%
199941
< 0.1%
199931
< 0.1%
199921
< 0.1%
199911
< 0.1%
199901
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct18459
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
sgATP6V1G2_1
 
7
sgABCF1_3
 
7
sgABCF1_10
 
7
sgABCF1_1
 
7
sgAGPAT1_5
 
7
Other values (18454)
19965 

Length

Max length20
Median length18
Mean length9.86395
Min length6

Characters and Unicode

Total characters197279
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17663 ?
Unique (%)88.3%

Sample

1st rowsgA1CF_1
2nd rowsgA1CF_10
3rd rowsgA1CF_2
4th rowsgA1CF_3
5th rowsgA1CF_4

Common Values

ValueCountFrequency (%)
sgATP6V1G2_17
 
< 0.1%
sgABCF1_37
 
< 0.1%
sgABCF1_107
 
< 0.1%
sgABCF1_17
 
< 0.1%
sgAGPAT1_57
 
< 0.1%
sgAGPAT1_67
 
< 0.1%
sgAGPAT1_77
 
< 0.1%
sgAGPAT1_87
 
< 0.1%
sgATAT1_57
 
< 0.1%
sgATAT1_47
 
< 0.1%
Other values (18449)19930
99.7%

Length

2022-06-10T03:11:18.712016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sgatp6v1g2_17
 
< 0.1%
sgapom_97
 
< 0.1%
sgapom_107
 
< 0.1%
sgapom_27
 
< 0.1%
sgabcf1_77
 
< 0.1%
sgabcf1_87
 
< 0.1%
sgabcf1_97
 
< 0.1%
sgagpat1_47
 
< 0.1%
sgagpat1_37
 
< 0.1%
sgagpat1_27
 
< 0.1%
Other values (18449)19930
99.7%

Most occurring characters

ValueCountFrequency (%)
s20000
 
10.1%
g20000
 
10.1%
_20000
 
10.1%
A18827
 
9.5%
114821
 
7.5%
27580
 
3.8%
C7494
 
3.8%
B6879
 
3.5%
P5027
 
2.5%
35006
 
2.5%
Other values (33)71645
36.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter75697
38.4%
Decimal Number53068
26.9%
Lowercase Letter48463
24.6%
Connector Punctuation20000
 
10.1%
Dash Punctuation51
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A18827
24.9%
C7494
 
9.9%
B6879
 
9.1%
P5027
 
6.6%
R4653
 
6.1%
T4191
 
5.5%
D3918
 
5.2%
L3466
 
4.6%
G2712
 
3.6%
N2587
 
3.4%
Other values (16)15943
21.1%
Decimal Number
ValueCountFrequency (%)
114821
27.9%
27580
14.3%
35006
 
9.4%
44290
 
8.1%
53972
 
7.5%
63920
 
7.4%
73617
 
6.8%
03443
 
6.5%
93268
 
6.2%
83151
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
s20000
41.3%
g20000
41.3%
f2821
 
5.8%
r2821
 
5.8%
o2821
 
5.8%
Connector Punctuation
ValueCountFrequency (%)
_20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
-51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124160
62.9%
Common73119
37.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s20000
16.1%
g20000
16.1%
A18827
15.2%
C7494
 
6.0%
B6879
 
5.5%
P5027
 
4.0%
R4653
 
3.7%
T4191
 
3.4%
D3918
 
3.2%
L3466
 
2.8%
Other values (21)29705
23.9%
Common
ValueCountFrequency (%)
_20000
27.4%
114821
20.3%
27580
 
10.4%
35006
 
6.8%
44290
 
5.9%
53972
 
5.4%
63920
 
5.4%
73617
 
4.9%
03443
 
4.7%
93268
 
4.5%
Other values (2)3202
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII197279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s20000
 
10.1%
g20000
 
10.1%
_20000
 
10.1%
A18827
 
9.5%
114821
 
7.5%
27580
 
3.8%
C7494
 
3.8%
B6879
 
3.5%
P5027
 
2.5%
35006
 
2.5%
Other values (33)71645
36.3%

log2fc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12525
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1441097578
Minimum-7.452876964
Maximum7.277540101
Zeros1
Zeros (%)< 0.1%
Negative8863
Negative (%)44.3%
Memory size156.4 KiB
2022-06-10T03:11:18.853641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-7.452876964
5-th percentile-1.93677902
Q1-0.4997152141
median0.1273343809
Q30.7226772072
95-th percentile2.261357163
Maximum7.277540101
Range14.73041706
Interquartile range (IQR)1.222392421

Descriptive statistics

Standard deviation1.431208752
Coefficient of variation (CV)9.931379903
Kurtosis4.752142078
Mean0.1441097578
Median Absolute Deviation (MAD)0.6109299297
Skewness0.3620742912
Sum2882.195155
Variance2.048358492
MonotonicityNot monotonic
2022-06-10T03:11:18.997310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4071753815114
 
0.6%
0.407175381563
 
0.3%
2.72910347635
 
0.2%
0.992137882233
 
0.2%
1.99213788232
 
0.2%
0.407175381532
 
0.2%
3.86660730
 
0.1%
2.40717538229
 
0.1%
-1.17778711928
 
0.1%
3.72910347626
 
0.1%
Other values (12515)19578
97.9%
ValueCountFrequency (%)
-7.4528769641
 
< 0.1%
-7.3872404851
 
< 0.1%
-7.3001837511
 
< 0.1%
-7.2581605361
 
< 0.1%
-6.9635120251
 
< 0.1%
-6.8828434651
 
< 0.1%
-6.8502124611
 
< 0.1%
-6.8119931391
 
< 0.1%
-6.7924969631
 
< 0.1%
-6.6151924323
< 0.1%
ValueCountFrequency (%)
7.2775401011
 
< 0.1%
7.2400653961
 
< 0.1%
7.1753597062
< 0.1%
7.1620628841
 
< 0.1%
7.0365320022
< 0.1%
7.0218852261
 
< 0.1%
6.977030992
< 0.1%
6.9149700221
 
< 0.1%
6.8990284783
< 0.1%
6.8829088121
 
< 0.1%

chr
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
2168 
2
1300 
11
1299 
17
 
1179
12
 
1151
Other values (65)
12903 

Length

Max length24
Median length23
Mean length2.7003
Min length1

Characters and Unicode

Total characters54006
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
12168
 
10.8%
21300
 
6.5%
111299
 
6.5%
171179
 
5.9%
121151
 
5.8%
101132
 
5.7%
191063
 
5.3%
3898
 
4.5%
7840
 
4.2%
16790
 
4.0%
Other values (60)8180
40.9%

Length

2022-06-10T03:11:19.149945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12168
 
10.8%
21300
 
6.5%
111299
 
6.5%
171179
 
5.9%
121151
 
5.8%
101132
 
5.7%
191063
 
5.3%
3898
 
4.5%
7840
 
4.2%
16790
 
4.0%
Other values (60)8180
40.9%

Most occurring characters

ValueCountFrequency (%)
113569
25.1%
C4568
 
8.5%
24484
 
8.3%
_4392
 
8.1%
H4345
 
8.0%
R2404
 
4.5%
72276
 
4.2%
62172
 
4.0%
91783
 
3.3%
01753
 
3.2%
Other values (19)12260
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31021
57.4%
Uppercase Letter18593
34.4%
Connector Punctuation4392
 
8.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C4568
24.6%
H4345
23.4%
R2404
12.9%
S1401
 
7.5%
T1324
 
7.1%
G1217
 
6.5%
M918
 
4.9%
X892
 
4.8%
B319
 
1.7%
O234
 
1.3%
Other values (8)971
 
5.2%
Decimal Number
ValueCountFrequency (%)
113569
43.7%
24484
 
14.5%
72276
 
7.3%
62172
 
7.0%
91783
 
5.7%
01753
 
5.7%
41524
 
4.9%
51452
 
4.7%
31250
 
4.0%
8758
 
2.4%
Connector Punctuation
ValueCountFrequency (%)
_4392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35413
65.6%
Latin18593
34.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
C4568
24.6%
H4345
23.4%
R2404
12.9%
S1401
 
7.5%
T1324
 
7.1%
G1217
 
6.5%
M918
 
4.9%
X892
 
4.8%
B319
 
1.7%
O234
 
1.3%
Other values (8)971
 
5.2%
Common
ValueCountFrequency (%)
113569
38.3%
24484
 
12.7%
_4392
 
12.4%
72276
 
6.4%
62172
 
6.1%
91783
 
5.0%
01753
 
5.0%
41524
 
4.3%
51452
 
4.1%
31250
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII54006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113569
25.1%
C4568
 
8.5%
24484
 
8.3%
_4392
 
8.1%
H4345
 
8.0%
R2404
 
4.5%
72276
 
4.2%
62172
 
4.0%
91783
 
3.3%
01753
 
3.2%
Other values (19)12260
22.7%

start
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19498
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72761333.22
Minimum205615
Maximum247112172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-06-10T03:11:19.288010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum205615
5-th percentile4115365.4
Q131622858.25
median57631653
Q3105548725.5
95-th percentile185397235.7
Maximum247112172
Range246906557
Interquartile range (IQR)73925867.25

Descriptive statistics

Standard deviation55829408.96
Coefficient of variation (CV)0.7672950246
Kurtosis0.3853041092
Mean72761333.22
Median Absolute Deviation (MAD)35423902.5
Skewness0.951766337
Sum1.455226664 × 1012
Variance3.116922905 × 1015
MonotonicityNot monotonic
2022-06-10T03:11:19.444170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
661276954
 
< 0.1%
643736074
 
< 0.1%
643732174
 
< 0.1%
402269374
 
< 0.1%
661492874
 
< 0.1%
678845494
 
< 0.1%
402485274
 
< 0.1%
643948014
 
< 0.1%
661488974
 
< 0.1%
402273274
 
< 0.1%
Other values (19488)19960
99.8%
ValueCountFrequency (%)
2056151
< 0.1%
2056261
< 0.1%
2056371
< 0.1%
2056581
< 0.1%
2059601
< 0.1%
2059781
< 0.1%
2059901
< 0.1%
2060141
< 0.1%
2060311
< 0.1%
2073101
< 0.1%
ValueCountFrequency (%)
2471121721
< 0.1%
2471121351
< 0.1%
2471120921
< 0.1%
2471119981
< 0.1%
2471119751
< 0.1%
2471118361
< 0.1%
2471117131
< 0.1%
2471116811
< 0.1%
2471116201
< 0.1%
2471115231
< 0.1%

end
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19498
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72761356.22
Minimum205638
Maximum247112195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-06-10T03:11:19.601872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum205638
5-th percentile4115388.4
Q131622881.25
median57631676
Q3105548748.5
95-th percentile185397258.7
Maximum247112195
Range246906557
Interquartile range (IQR)73925867.25

Descriptive statistics

Standard deviation55829408.96
Coefficient of variation (CV)0.767294782
Kurtosis0.3853041092
Mean72761356.22
Median Absolute Deviation (MAD)35423902.5
Skewness0.951766337
Sum1.455227124 × 1012
Variance3.116922905 × 1015
MonotonicityNot monotonic
2022-06-10T03:11:19.761548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
661277184
 
< 0.1%
643736304
 
< 0.1%
643732404
 
< 0.1%
402269604
 
< 0.1%
661493104
 
< 0.1%
678845724
 
< 0.1%
402485504
 
< 0.1%
643948244
 
< 0.1%
661489204
 
< 0.1%
402273504
 
< 0.1%
Other values (19488)19960
99.8%
ValueCountFrequency (%)
2056381
< 0.1%
2056491
< 0.1%
2056601
< 0.1%
2056811
< 0.1%
2059831
< 0.1%
2060011
< 0.1%
2060131
< 0.1%
2060371
< 0.1%
2060541
< 0.1%
2073331
< 0.1%
ValueCountFrequency (%)
2471121951
< 0.1%
2471121581
< 0.1%
2471121151
< 0.1%
2471120211
< 0.1%
2471119981
< 0.1%
2471118591
< 0.1%
2471117361
< 0.1%
2471117041
< 0.1%
2471116431
< 0.1%
2471115461
< 0.1%

ensg
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2083
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
ENSG00000172014
 
36
ENSG00000276203
 
34
ENSG00000185894
 
30
ENSG00000174876
 
30
ENSG00000237763
 
30
Other values (2078)
19840 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters300000
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.2%

Sample

1st rowENSG00000148584
2nd rowENSG00000148584
3rd rowENSG00000148584
4th rowENSG00000148584
5th rowENSG00000148584

Common Values

ValueCountFrequency (%)
ENSG0000017201436
 
0.2%
ENSG0000027620334
 
0.2%
ENSG0000018589430
 
0.1%
ENSG0000017487630
 
0.1%
ENSG0000023776330
 
0.1%
ENSG0000018773330
 
0.1%
ENSG0000018379530
 
0.1%
ENSG0000018375330
 
0.1%
ENSG0000018314829
 
0.1%
ENSG0000018713425
 
0.1%
Other values (2073)19696
98.5%

Length

2022-06-10T03:11:19.902491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ensg0000017201436
 
0.2%
ensg0000027620334
 
0.2%
ensg0000018589430
 
0.1%
ensg0000023776330
 
0.1%
ensg0000018773330
 
0.1%
ensg0000018379530
 
0.1%
ensg0000018375330
 
0.1%
ensg0000017487630
 
0.1%
ensg0000018314829
 
0.1%
ensg0000018713425
 
0.1%
Other values (2073)19696
98.5%

Most occurring characters

ValueCountFrequency (%)
0112027
37.3%
124286
 
8.1%
E20000
 
6.7%
N20000
 
6.7%
S20000
 
6.7%
G20000
 
6.7%
212286
 
4.1%
611286
 
3.8%
711211
 
3.7%
310807
 
3.6%
Other values (4)38097
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number220000
73.3%
Uppercase Letter80000
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112027
50.9%
124286
 
11.0%
212286
 
5.6%
611286
 
5.1%
711211
 
5.1%
310807
 
4.9%
810196
 
4.6%
410049
 
4.6%
59399
 
4.3%
98453
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
E20000
25.0%
N20000
25.0%
S20000
25.0%
G20000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common220000
73.3%
Latin80000
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0112027
50.9%
124286
 
11.0%
212286
 
5.6%
611286
 
5.1%
711211
 
5.1%
310807
 
4.9%
810196
 
4.6%
410049
 
4.6%
59399
 
4.3%
98453
 
3.8%
Latin
ValueCountFrequency (%)
E20000
25.0%
N20000
25.0%
S20000
25.0%
G20000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112027
37.3%
124286
 
8.1%
E20000
 
6.7%
N20000
 
6.7%
S20000
 
6.7%
G20000
 
6.7%
212286
 
4.1%
611286
 
3.8%
711211
 
3.7%
310807
 
3.6%
Other values (4)38097
 
12.7%

symbol
Categorical

HIGH CARDINALITY

Distinct1956
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
APOM
 
70
ATP6V1G2
 
70
BAG6
 
70
ATAT1
 
70
ABCF1
 
70
Other values (1951)
19650 

Length

Max length15
Median length13
Mean length5.76775
Min length2

Characters and Unicode

Total characters115355
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.2%

Sample

1st rowA1CF
2nd rowA1CF
3rd rowA1CF
4th rowA1CF
5th rowA1CF

Common Values

ValueCountFrequency (%)
APOM70
 
0.4%
ATP6V1G270
 
0.4%
BAG670
 
0.4%
ATAT170
 
0.4%
ABCF170
 
0.4%
AGER68
 
0.3%
AGPAT163
 
0.3%
BRD263
 
0.3%
AIF160
 
0.3%
ARL17B57
 
0.3%
Other values (1946)19339
96.7%

Length

2022-06-10T03:11:20.025058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apom70
 
0.4%
atat170
 
0.4%
abcf170
 
0.4%
atp6v1g270
 
0.4%
bag670
 
0.4%
ager68
 
0.3%
agpat163
 
0.3%
brd263
 
0.3%
aif160
 
0.3%
arl17b57
 
0.3%
Other values (1946)19339
96.7%

Most occurring characters

ValueCountFrequency (%)
A18588
16.1%
110743
 
9.3%
C7459
 
6.5%
B6795
 
5.9%
25593
 
4.8%
P5158
 
4.5%
R4662
 
4.0%
T4253
 
3.7%
D3932
 
3.4%
L3524
 
3.1%
Other values (30)44648
38.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter75977
65.9%
Decimal Number31032
26.9%
Lowercase Letter8247
 
7.1%
Dash Punctuation99
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A18588
24.5%
C7459
9.8%
B6795
 
8.9%
P5158
 
6.8%
R4662
 
6.1%
T4253
 
5.6%
D3932
 
5.2%
L3524
 
4.6%
G2765
 
3.6%
N2611
 
3.4%
Other values (16)16230
21.4%
Decimal Number
ValueCountFrequency (%)
110743
34.6%
25593
18.0%
33084
 
9.9%
42259
 
7.3%
51976
 
6.4%
61873
 
6.0%
71654
 
5.3%
01412
 
4.6%
91331
 
4.3%
81107
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
o2749
33.3%
f2749
33.3%
r2749
33.3%
Dash Punctuation
ValueCountFrequency (%)
-99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84224
73.0%
Common31131
 
27.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A18588
22.1%
C7459
 
8.9%
B6795
 
8.1%
P5158
 
6.1%
R4662
 
5.5%
T4253
 
5.0%
D3932
 
4.7%
L3524
 
4.2%
G2765
 
3.3%
o2749
 
3.3%
Other values (19)24339
28.9%
Common
ValueCountFrequency (%)
110743
34.5%
25593
18.0%
33084
 
9.9%
42259
 
7.3%
51976
 
6.3%
61873
 
6.0%
71654
 
5.3%
01412
 
4.5%
91331
 
4.3%
81107
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII115355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A18588
16.1%
110743
 
9.3%
C7459
 
6.5%
B6795
 
5.9%
25593
 
4.8%
P5158
 
4.5%
R4662
 
4.0%
T4253
 
3.7%
D3932
 
3.4%
L3524
 
3.1%
Other values (30)44648
38.7%

sequence
Categorical

HIGH CARDINALITY
UNIFORM

Distinct18349
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
GGAGAGTGACCACTTGCACATGG
 
16
ATAGTCAATGGACAAGGAGAAGG
 
16
GTGCTTCAATATGTGCACCATGG
 
16
TCATGCGAAGATCCAGGTAAAGG
 
16
TGAGGTGCTCTCACTATACACGG
 
16
Other values (18344)
19920 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters460000
Distinct characters4
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

Unique17645 ?
Unique (%)88.2%

Sample

1st rowGCAGCATCCCAACCAGGTGGAGG
2nd rowGCGGGAGTGAGAGGACTGGGCGG
3rd rowATGACTCTCATACTCCACGAAGG
4th rowGAGTCATCGAGCAGCTGCCATGG
5th rowAGTCACCCTAGCAAAACCAGTGG

Common Values

ValueCountFrequency (%)
GGAGAGTGACCACTTGCACATGG16
 
0.1%
ATAGTCAATGGACAAGGAGAAGG16
 
0.1%
GTGCTTCAATATGTGCACCATGG16
 
0.1%
TCATGCGAAGATCCAGGTAAAGG16
 
0.1%
TGAGGTGCTCTCACTATACACGG16
 
0.1%
GAGCGATATTTAGCTCCCAAGGG12
 
0.1%
AGCCAGGACACGTGCAGGACAGG9
 
< 0.1%
TTCTAGGTAATTGATCTGGGTGG9
 
< 0.1%
TGTGGTAATGCTGTGAGTGCAGG9
 
< 0.1%
GTTGGGCCACCAAATGATAATGG9
 
< 0.1%
Other values (18339)19872
99.4%

Length

2022-06-10T03:11:20.147888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ggagagtgaccacttgcacatgg16
 
0.1%
gtgcttcaatatgtgcaccatgg16
 
0.1%
tcatgcgaagatccaggtaaagg16
 
0.1%
tgaggtgctctcactatacacgg16
 
0.1%
atagtcaatggacaaggagaagg16
 
0.1%
gagcgatatttagctcccaaggg12
 
0.1%
tacctgtgaaaatctggggcagg9
 
< 0.1%
gactagatgcaacaatgttgggg9
 
< 0.1%
catacctttggactgaagcaagg9
 
< 0.1%
tggtcttctcgatcttgcactgg9
 
< 0.1%
Other values (18339)19872
99.4%

Most occurring characters

ValueCountFrequency (%)
G172114
37.4%
A112466
24.4%
C102550
22.3%
T72870
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter460000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G172114
37.4%
A112466
24.4%
C102550
22.3%
T72870
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin460000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G172114
37.4%
A112466
24.4%
C102550
22.3%
T72870
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII460000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G172114
37.4%
A112466
24.4%
C102550
22.3%
T72870
15.8%

strand
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
+
10079 
-
9921 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories2 ?
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 row+
2nd row-
3rd row+
4th row-
5th row-

Common Values

ValueCountFrequency (%)
+10079
50.4%
-9921
49.6%

Length

2022-06-10T03:11:20.265898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:20.386492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
20000
100.0%

Most occurring characters

ValueCountFrequency (%)
+10079
50.4%
-9921
49.6%

Most occurring categories

ValueCountFrequency (%)
Math Symbol10079
50.4%
Dash Punctuation9921
49.6%

Most frequent character per category

Math Symbol
ValueCountFrequency (%)
+10079
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+10079
50.4%
-9921
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+10079
50.4%
-9921
49.6%

pubmed
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
26472758
20000 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters160000
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 row26472758
2nd row26472758
3rd row26472758
4th row26472758
5th row26472758

Common Values

ValueCountFrequency (%)
2647275820000
100.0%

Length

2022-06-10T03:11:20.488936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:20.602898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2647275820000
100.0%

Most occurring characters

ValueCountFrequency (%)
240000
25.0%
740000
25.0%
620000
12.5%
420000
12.5%
520000
12.5%
820000
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
240000
25.0%
740000
25.0%
620000
12.5%
420000
12.5%
520000
12.5%
820000
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common160000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
240000
25.0%
740000
25.0%
620000
12.5%
420000
12.5%
520000
12.5%
820000
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII160000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240000
25.0%
740000
25.0%
620000
12.5%
420000
12.5%
520000
12.5%
820000
12.5%

cas
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
hSpCas9
20000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140000
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
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 rowhSpCas9
2nd rowhSpCas9
3rd rowhSpCas9
4th rowhSpCas9
5th rowhSpCas9

Common Values

ValueCountFrequency (%)
hSpCas920000
100.0%

Length

2022-06-10T03:11:20.698714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:20.812156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hspcas920000
100.0%

Most occurring characters

ValueCountFrequency (%)
h20000
14.3%
S20000
14.3%
p20000
14.3%
C20000
14.3%
a20000
14.3%
s20000
14.3%
920000
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter80000
57.1%
Uppercase Letter40000
28.6%
Decimal Number20000
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h20000
25.0%
p20000
25.0%
a20000
25.0%
s20000
25.0%
Uppercase Letter
ValueCountFrequency (%)
S20000
50.0%
C20000
50.0%
Decimal Number
ValueCountFrequency (%)
920000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin120000
85.7%
Common20000
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
h20000
16.7%
S20000
16.7%
p20000
16.7%
C20000
16.7%
a20000
16.7%
s20000
16.7%
Common
ValueCountFrequency (%)
920000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII140000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h20000
14.3%
S20000
14.3%
p20000
14.3%
C20000
14.3%
a20000
14.3%
s20000
14.3%
920000
14.3%

screentype
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
negative selection
20000 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters360000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
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 rownegative selection
2nd rownegative selection
3rd rownegative selection
4th rownegative selection
5th rownegative selection

Common Values

ValueCountFrequency (%)
negative selection20000
100.0%

Length

2022-06-10T03:11:20.908055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:21.217864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
negative20000
50.0%
selection20000
50.0%

Most occurring characters

ValueCountFrequency (%)
e80000
22.2%
n40000
11.1%
t40000
11.1%
i40000
11.1%
g20000
 
5.6%
a20000
 
5.6%
v20000
 
5.6%
20000
 
5.6%
s20000
 
5.6%
l20000
 
5.6%
Other values (2)40000
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter340000
94.4%
Space Separator20000
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e80000
23.5%
n40000
11.8%
t40000
11.8%
i40000
11.8%
g20000
 
5.9%
a20000
 
5.9%
v20000
 
5.9%
s20000
 
5.9%
l20000
 
5.9%
c20000
 
5.9%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin340000
94.4%
Common20000
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e80000
23.5%
n40000
11.8%
t40000
11.8%
i40000
11.8%
g20000
 
5.9%
a20000
 
5.9%
v20000
 
5.9%
s20000
 
5.9%
l20000
 
5.9%
c20000
 
5.9%
Common
ValueCountFrequency (%)
20000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e80000
22.2%
n40000
11.1%
t40000
11.1%
i40000
11.1%
g20000
 
5.6%
a20000
 
5.6%
v20000
 
5.6%
20000
 
5.6%
s20000
 
5.6%
l20000
 
5.6%
Other values (2)40000
11.1%

cellline
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Jiyoye
20000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters120000
Distinct characters5
Distinct categories2 ?
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 rowJiyoye
2nd rowJiyoye
3rd rowJiyoye
4th rowJiyoye
5th rowJiyoye

Common Values

ValueCountFrequency (%)
Jiyoye20000
100.0%

Length

2022-06-10T03:11:21.320128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:21.434672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jiyoye20000
100.0%

Most occurring characters

ValueCountFrequency (%)
y40000
33.3%
J20000
16.7%
i20000
16.7%
o20000
16.7%
e20000
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100000
83.3%
Uppercase Letter20000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y40000
40.0%
i20000
20.0%
o20000
20.0%
e20000
20.0%
Uppercase Letter
ValueCountFrequency (%)
J20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin120000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y40000
33.3%
J20000
16.7%
i20000
16.7%
o20000
16.7%
e20000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y40000
33.3%
J20000
16.7%
i20000
16.7%
o20000
16.7%
e20000
16.7%

score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1378
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6260735334
Minimum1.248735335 × 10-5
Maximum0.9999813664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-06-10T03:11:21.548581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.248735335 × 10-5
5-th percentile0.0117820868
Q10.4176767223
median0.7141961313
Q30.8857393361
95-th percentile0.9832672947
Maximum0.9999813664
Range0.999968879
Interquartile range (IQR)0.4680626138

Descriptive statistics

Standard deviation0.3051406301
Coefficient of variation (CV)0.4873878447
Kurtosis-0.7156432969
Mean0.6260735334
Median Absolute Deviation (MAD)0.2061126918
Skewness-0.6982329418
Sum12521.47067
Variance0.09311080414
MonotonicityNot monotonic
2022-06-10T03:11:21.701743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.897696761126
 
0.6%
0.5215339035100
 
0.5%
0.972695133492
 
0.5%
0.0330950409690
 
0.4%
0.990971919581
 
0.4%
0.426284991681
 
0.4%
0.876679276880
 
0.4%
0.922833384380
 
0.4%
0.222194971979
 
0.4%
0.991270795578
 
0.4%
Other values (1368)19113
95.6%
ValueCountFrequency (%)
1.248735335 × 10-510
0.1%
1.656722788 × 10-519
0.1%
2.533789228 × 10-510
0.1%
2.752990342 × 10-510
0.1%
2.763798739 × 10-510
0.1%
2.959303515 × 10-510
0.1%
3.073828034 × 10-510
0.1%
3.605121453 × 10-510
0.1%
3.853987714 × 10-51
 
< 0.1%
4.495479604 × 10-510
0.1%
ValueCountFrequency (%)
0.999981366465
0.3%
0.999811176510
 
0.1%
0.99970122866
 
< 0.1%
0.99891428312
 
< 0.1%
0.998786864770
0.4%
0.998626146210
 
0.1%
0.998441201650
0.2%
0.998253569610
 
0.1%
0.99817576620
 
0.1%
0.998070292810
 
0.1%

hit
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
False
18395 
True
 
1605
ValueCountFrequency (%)
False18395
92.0%
True1605
 
8.0%
2022-06-10T03:11:21.844456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

condition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
viability
20000 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters180000
Distinct characters7
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 rowviability
2nd rowviability
3rd rowviability
4th rowviability
5th rowviability

Common Values

ValueCountFrequency (%)
viability20000
100.0%

Length

2022-06-10T03:11:21.946486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:22.061943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
viability20000
100.0%

Most occurring characters

ValueCountFrequency (%)
i60000
33.3%
v20000
 
11.1%
a20000
 
11.1%
b20000
 
11.1%
l20000
 
11.1%
t20000
 
11.1%
y20000
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter180000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i60000
33.3%
v20000
 
11.1%
a20000
 
11.1%
b20000
 
11.1%
l20000
 
11.1%
t20000
 
11.1%
y20000
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin180000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i60000
33.3%
v20000
 
11.1%
a20000
 
11.1%
b20000
 
11.1%
l20000
 
11.1%
t20000
 
11.1%
y20000
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII180000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i60000
33.3%
v20000
 
11.1%
a20000
 
11.1%
b20000
 
11.1%
l20000
 
11.1%
t20000
 
11.1%
y20000
 
11.1%

genetargets
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2083
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
ANKRD20A4::ENSG00000172014
 
36
ANKRD20A3::ENSG00000276203
 
34
BPY2C::ENSG00000185894
 
30
AMY1B::ENSG00000174876
 
30
AMY1A::ENSG00000237763
 
30
Other values (2078)
19840 

Length

Max length32
Median length30
Mean length22.76775
Min length19

Characters and Unicode

Total characters455355
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.2%

Sample

1st rowA1CF::ENSG00000148584
2nd rowA1CF::ENSG00000148584
3rd rowA1CF::ENSG00000148584
4th rowA1CF::ENSG00000148584
5th rowA1CF::ENSG00000148584

Common Values

ValueCountFrequency (%)
ANKRD20A4::ENSG0000017201436
 
0.2%
ANKRD20A3::ENSG0000027620334
 
0.2%
BPY2C::ENSG0000018589430
 
0.1%
AMY1B::ENSG0000017487630
 
0.1%
AMY1A::ENSG0000023776330
 
0.1%
AMY1C::ENSG0000018773330
 
0.1%
BPY2B::ENSG0000018379530
 
0.1%
BPY2::ENSG0000018375330
 
0.1%
ANKRD20A2::ENSG0000018314829
 
0.1%
AKR1C1::ENSG0000018713425
 
0.1%
Other values (2073)19696
98.5%

Length

2022-06-10T03:11:22.167482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ankrd20a4::ensg0000017201436
 
0.2%
ankrd20a3::ensg0000027620334
 
0.2%
bpy2c::ensg0000018589430
 
0.1%
amy1a::ensg0000023776330
 
0.1%
amy1c::ensg0000018773330
 
0.1%
bpy2b::ensg0000018379530
 
0.1%
bpy2::ensg0000018375330
 
0.1%
amy1b::ensg0000017487630
 
0.1%
ankrd20a2::ensg0000018314829
 
0.1%
akr1c1::ensg0000018713425
 
0.1%
Other values (2073)19696
98.5%

Most occurring characters

ValueCountFrequency (%)
0113439
24.9%
:40000
 
8.8%
135029
 
7.7%
G22765
 
5.0%
N22611
 
5.0%
S22243
 
4.9%
E21290
 
4.7%
A18588
 
4.1%
217879
 
3.9%
313891
 
3.1%
Other values (31)127620
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251032
55.1%
Uppercase Letter155977
34.3%
Other Punctuation40000
 
8.8%
Lowercase Letter8247
 
1.8%
Dash Punctuation99
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G22765
14.6%
N22611
14.5%
S22243
14.3%
E21290
13.6%
A18588
11.9%
C7459
 
4.8%
B6795
 
4.4%
P5158
 
3.3%
R4662
 
3.0%
T4253
 
2.7%
Other values (16)20153
12.9%
Decimal Number
ValueCountFrequency (%)
0113439
45.2%
135029
 
14.0%
217879
 
7.1%
313891
 
5.5%
613159
 
5.2%
712865
 
5.1%
412308
 
4.9%
511375
 
4.5%
811303
 
4.5%
99784
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
o2749
33.3%
r2749
33.3%
f2749
33.3%
Other Punctuation
ValueCountFrequency (%)
:40000
100.0%
Dash Punctuation
ValueCountFrequency (%)
-99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common291131
63.9%
Latin164224
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
G22765
13.9%
N22611
13.8%
S22243
13.5%
E21290
13.0%
A18588
11.3%
C7459
 
4.5%
B6795
 
4.1%
P5158
 
3.1%
R4662
 
2.8%
T4253
 
2.6%
Other values (19)28400
17.3%
Common
ValueCountFrequency (%)
0113439
39.0%
:40000
 
13.7%
135029
 
12.0%
217879
 
6.1%
313891
 
4.8%
613159
 
4.5%
712865
 
4.4%
412308
 
4.2%
511375
 
3.9%
811303
 
3.9%
Other values (2)9883
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII455355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113439
24.9%
:40000
 
8.8%
135029
 
7.7%
G22765
 
5.0%
N22611
 
5.0%
S22243
 
4.9%
E21290
 
4.7%
A18588
 
4.1%
217879
 
3.9%
313891
 
3.1%
Other values (31)127620
28.0%

scoredist
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]
20000 

Length

Max length584
Median length584
Mean length584
Min length584

Characters and Unicode

Total characters11680000
Distinct characters15
Distinct categories5 ?
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 row[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]
2nd row[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]
3rd row[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]
4th row[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]
5th row[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]

Common Values

ValueCountFrequency (%)
[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]20000
100.0%

Length

2022-06-10T03:11:22.291481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T03:11:22.415183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.0720000
100.0%

Most occurring characters

ValueCountFrequency (%)
,1980000
17.0%
.1920000
16.4%
01800000
15.4%
[1020000
8.7%
]1020000
8.7%
1840000
7.2%
4540000
 
4.6%
5440000
 
3.8%
2440000
 
3.8%
3380000
 
3.3%
Other values (5)1300000
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5700000
48.8%
Other Punctuation3900000
33.4%
Open Punctuation1020000
 
8.7%
Close Punctuation1020000
 
8.7%
Dash Punctuation40000
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01800000
31.6%
1840000
14.7%
4540000
 
9.5%
5440000
 
7.7%
2440000
 
7.7%
3380000
 
6.7%
6340000
 
6.0%
8340000
 
6.0%
9300000
 
5.3%
7280000
 
4.9%
Other Punctuation
ValueCountFrequency (%)
,1980000
50.8%
.1920000
49.2%
Open Punctuation
ValueCountFrequency (%)
[1020000
100.0%
Close Punctuation
ValueCountFrequency (%)
]1020000
100.0%
Dash Punctuation
ValueCountFrequency (%)
-40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11680000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,1980000
17.0%
.1920000
16.4%
01800000
15.4%
[1020000
8.7%
]1020000
8.7%
1840000
7.2%
4540000
 
4.6%
5440000
 
3.8%
2440000
 
3.8%
3380000
 
3.3%
Other values (5)1300000
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11680000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,1980000
17.0%
.1920000
16.4%
01800000
15.4%
[1020000
8.7%
]1020000
8.7%
1840000
7.2%
4540000
 
4.6%
5440000
 
3.8%
2440000
 
3.8%
3380000
 
3.3%
Other values (5)1300000
11.1%

effect
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62725
Minimum-9
Maximum9
Zeros2043
Zeros (%)10.2%
Negative7930
Negative (%)39.6%
Memory size156.4 KiB
2022-06-10T03:11:22.556549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-8
Q1-4
median1
Q35
95-th percentile9
Maximum9
Range18
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.200486486
Coefficient of variation (CV)8.290931026
Kurtosis-1.059658099
Mean0.62725
Median Absolute Deviation (MAD)4
Skewness-0.1165224787
Sum12545
Variance27.04505969
MonotonicityNot monotonic
2022-06-10T03:11:22.665589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
02043
 
10.2%
71142
 
5.7%
51135
 
5.7%
91135
 
5.7%
41117
 
5.6%
31114
 
5.6%
81108
 
5.5%
21102
 
5.5%
11100
 
5.5%
61074
 
5.4%
Other values (9)7930
39.6%
ValueCountFrequency (%)
-9665
 
3.3%
-8721
 
3.6%
-7893
4.5%
-6973
4.9%
-5902
4.5%
-4925
4.6%
-3962
4.8%
-2934
4.7%
-1955
4.8%
02043
10.2%
ValueCountFrequency (%)
91135
5.7%
81108
5.5%
71142
5.7%
61074
5.4%
51135
5.7%
41117
5.6%
31114
5.6%
21102
5.5%
11100
5.5%
02043
10.2%

rc_initial
Categorical

HIGH CARDINALITY

Distinct643
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
[0]
 
745
[77]
 
126
[65]
 
111
[61]
 
110
[120]
 
104
Other values (638)
18804 

Length

Max length6
Median length5
Mean length4.5367
Min length3

Characters and Unicode

Total characters90734
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)0.5%

Sample

1st row[260]
2nd row[17]
3rd row[75]
4th row[47]
5th row[58]

Common Values

ValueCountFrequency (%)
[0]745
 
3.7%
[77]126
 
0.6%
[65]111
 
0.6%
[61]110
 
0.5%
[120]104
 
0.5%
[74]103
 
0.5%
[102]102
 
0.5%
[38]101
 
0.5%
[69]100
 
0.5%
[97]100
 
0.5%
Other values (633)18298
91.5%

Length

2022-06-10T03:11:22.793167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0745
 
3.7%
77126
 
0.6%
65111
 
0.6%
61110
 
0.5%
120104
 
0.5%
74103
 
0.5%
102102
 
0.5%
38101
 
0.5%
69100
 
0.5%
97100
 
0.5%
Other values (633)18298
91.5%

Most occurring characters

ValueCountFrequency (%)
[20000
22.0%
]20000
22.0%
110739
11.8%
27330
 
8.1%
35127
 
5.7%
04306
 
4.7%
44290
 
4.7%
54131
 
4.6%
73871
 
4.3%
63825
 
4.2%
Other values (2)7115
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50734
55.9%
Open Punctuation20000
 
22.0%
Close Punctuation20000
 
22.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110739
21.2%
27330
14.4%
35127
10.1%
04306
8.5%
44290
 
8.5%
54131
 
8.1%
73871
 
7.6%
63825
 
7.5%
83595
 
7.1%
93520
 
6.9%
Open Punctuation
ValueCountFrequency (%)
[20000
100.0%
Close Punctuation
ValueCountFrequency (%)
]20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common90734
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
[20000
22.0%
]20000
22.0%
110739
11.8%
27330
 
8.1%
35127
 
5.7%
04306
 
4.7%
44290
 
4.7%
54131
 
4.6%
73871
 
4.3%
63825
 
4.2%
Other values (2)7115
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII90734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[20000
22.0%
]20000
22.0%
110739
11.8%
27330
 
8.1%
35127
 
5.7%
04306
 
4.7%
44290
 
4.7%
54131
 
4.6%
73871
 
4.3%
63825
 
4.2%
Other values (2)7115
 
7.8%

rc_final
Categorical

HIGH CARDINALITY

Distinct548
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
[0]
 
253
[75]
 
141
[35]
 
136
[25]
 
132
[36]
 
130
Other values (543)
19208 

Length

Max length6
Median length5
Mean length4.42495
Min length3

Characters and Unicode

Total characters88499
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)0.4%

Sample

1st row[244]
2nd row[59]
3rd row[153]
4th row[105]
5th row[57]

Common Values

ValueCountFrequency (%)
[0]253
 
1.3%
[75]141
 
0.7%
[35]136
 
0.7%
[25]132
 
0.7%
[36]130
 
0.7%
[27]128
 
0.6%
[47]128
 
0.6%
[76]127
 
0.6%
[80]127
 
0.6%
[50]126
 
0.6%
Other values (538)18572
92.9%

Length

2022-06-10T03:11:22.910473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0253
 
1.3%
75141
 
0.7%
35136
 
0.7%
25132
 
0.7%
36130
 
0.7%
47128
 
0.6%
27128
 
0.6%
76127
 
0.6%
80127
 
0.6%
50126
 
0.6%
Other values (538)18572
92.9%

Most occurring characters

ValueCountFrequency (%)
[20000
22.6%
]20000
22.6%
110644
12.0%
26345
 
7.2%
34887
 
5.5%
44249
 
4.8%
54166
 
4.7%
63863
 
4.4%
73828
 
4.3%
03588
 
4.1%
Other values (2)6929
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48499
54.8%
Open Punctuation20000
22.6%
Close Punctuation20000
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110644
21.9%
26345
13.1%
34887
10.1%
44249
 
8.8%
54166
 
8.6%
63863
 
8.0%
73828
 
7.9%
03588
 
7.4%
93476
 
7.2%
83453
 
7.1%
Open Punctuation
ValueCountFrequency (%)
[20000
100.0%
Close Punctuation
ValueCountFrequency (%)
]20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common88499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
[20000
22.6%
]20000
22.6%
110644
12.0%
26345
 
7.2%
34887
 
5.5%
44249
 
4.8%
54166
 
4.7%
63863
 
4.4%
73828
 
4.3%
03588
 
4.1%
Other values (2)6929
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII88499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[20000
22.6%
]20000
22.6%
110644
12.0%
26345
 
7.2%
34887
 
5.5%
44249
 
4.8%
54166
 
4.7%
63863
 
4.4%
73828
 
4.3%
03588
 
4.1%
Other values (2)6929
 
7.8%

Interactions

2022-06-10T03:11:16.563783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:12.497308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.312135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.266616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.037708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.805599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.697920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:12.641057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.457051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.400437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.172092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.939512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.835458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:12.782005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.592055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.537741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.305194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.070763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.962282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:12.913364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.720546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.662910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.430266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.194348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:17.089341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.045081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.009082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.786153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.553477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.317520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:17.214056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:13.176493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.135792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:14.909198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:15.679899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T03:11:16.438437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-10T03:11:23.016914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-10T03:11:23.181237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-10T03:11:23.345091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-10T03:11:23.509081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-10T03:11:23.665973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-10T03:11:17.667138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-10T03:11:18.141127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0namelog2fcchrstartendensgsymbolsequencestrandpubmedcasscreentypecelllinescorehitconditiongenetargetsscoredisteffectrc_initialrc_final
00sgA1CF_10.315907105084407350844096ENSG00000148584A1CFGCAGCATCCCAACCAGGTGGAGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]2[260][244]
11sgA1CF_102.144141105081401150814034ENSG00000148584A1CFGCGGGAGTGAGAGGACTGGGCGG-26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]9[17][59]
22sgA1CF_21.426034105083611150836134ENSG00000148584A1CFATGACTCTCATACTCCACGAAGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]8[75][153]
33sgA1CF_31.550133105083609550836118ENSG00000148584A1CFGAGTCATCGAGCAGCTGCCATGG-26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]8[47][105]
44sgA1CF_40.382513105081623450816257ENSG00000148584A1CFAGTCACCCTAGCAAAACCAGTGG-26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]3[58][57]
55sgA1CF_50.993477105081611950816142ENSG00000148584A1CFGATCCCACCACAACCTACCTTGG-26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]6[358][538]
66sgA1CF_60.407175105081599850816021ENSG00000148584A1CFGGTGTTACCTCTAACAGAAGGGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]3[0][0]
77sgA1CF_72.992138105081002050810043ENSG00000148584A1CFGCTTTGGAGGTGTGAAAGGGTGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]9[1][11]
88sgA1CF_80.431221105081386150813884ENSG00000148584A1CFGGAGCGAGTTTAATTCCTTGGGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]3[118][120]
99sgA1CF_9-1.101838105081614250816165ENSG00000148584A1CFATAAACTTGGCCCAAAGAGTAGG+26472758hSpCas9negative selectionJiyoye0.386247FalseviabilityA1CF::ENSG00000148584[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]-6[36][12]

Last rows

Unnamed: 0namelog2fcchrstartendensgsymbolsequencestrandpubmedcasscreentypecelllinescorehitconditiongenetargetsscoredisteffectrc_initialrc_final
1999019990sgC2orf70_20.65653522657595826575981ENSG00000173557C2orf70GTCCTGGAAGTACTTGAGGGTGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]5[105][125]
1999119991sgC2orf70_3-1.93324922657603926576062ENSG00000173557C2orf70GGGGTTGGTGGAGAAGATGGTGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]-8[156][30]
1999219992sgC2orf70_41.46857622657761926577642ENSG00000173557C2orf70GGCAGGCACTCACAGGACGGTGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]8[68][143]
1999319993sgC2orf70_52.75632522657591926575942ENSG00000173557C2orf70GCCAAAGGAGAAGGCCACAGTGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]9[20][106]
1999419994sgC2orf70_60.91578722656262126562644ENSG00000173557C2orf70TCAGTAGGGTGCCCGCGCTGCGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]6[96][137]
1999519995sgC2orf70_7-1.49639522656266826562691ENSG00000173557C2orf70TTACCCGGGCATGAGTCCAGGGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]-7[216][57]
1999619996sgC2orf70_80.39274822657614226576165ENSG00000173557C2orf70TCGTAAGCTCTGTGGAGCGATGG-26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]3[200][198]
1999719997sgC2orf70_90.04962322656261026562633ENSG00000173557C2orf70ACCATGGCCTCCCGCAGCGCGGG+26472758hSpCas9negative selectionJiyoye0.842464FalseviabilityC2orf70::ENSG00000173557[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]0[327][255]
1999819998sgC2orf71_10.53757222907382729073850ENSG00000179270C2orf71GTACCCAAGATACTTCCAAATGG-26472758hSpCas9negative selectionJiyoye0.980295FalseviabilityC2orf71::ENSG00000179270[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]4[221][242]
1999919999sgC2orf71_10-0.50127122907218229072205ENSG00000179270C2orf71GAAATGCAATCCCCATCCTGAGG-26472758hSpCas9negative selectionJiyoye0.980295FalseviabilityC2orf71::ENSG00000179270[[-0.04,0.34],[-0.03,0.65],[0,1.41],[0,1.45],[0,1.48],[0.01,1.52],[0.02,1.4],[0.02,1.36],[0.03,1.15],[0.04,0.98],[0.06,0.55],[0.08,0.42],[0.12,0.38],[0.14,0.4],[0.14,0.4],[0.15,0.41],[0.16,0.42],[0.16,0.42],[0.18,0.43],[0.22,0.42],[0.26,0.42],[0.28,0.46],[0.31,0.52],[0.31,0.53],[0.34,0.52],[0.39,0.59],[0.39,0.59],[0.39,0.59],[0.39,0.6],[0.47,0.62],[0.48,0.63],[0.5,0.68],[0.59,0.85],[0.67,1.02],[0.69,1.07],[0.72,1.12],[0.75,1.25],[0.76,1.26],[0.78,1.37],[0.78,1.41],[0.79,1.45],[0.8,1.49],[0.82,1.63],[0.84,1.71],[0.85,1.73],[0.88,1.74],[0.99,1.6],[1,1.13],[1.05,0.09],[1.05,0.07]]-4[579][308]