Overview

Dataset statistics

Number of variables13
Number of observations3388
Missing cells13582
Missing cells (%)30.8%
Total size in memory1.2 MiB
Average record size in memory370.8 B

Variable types

Categorical4
Numeric9

Alerts

country_cd has constant value "ESP" Constant
patient_id has a high cardinality: 3388 distinct values High cardinality
time_dx_to_surgery_nm has 540 (15.9%) missing values Missing
time_dx_to_radiotherapy_nm has 2937 (86.7%) missing values Missing
time_dx_to_chemotherapy_nm has 3338 (98.5%) missing values Missing
time_dx_to_immunotherapy_nm has 3384 (99.9%) missing values Missing
time_dx_to_hormonotherapy_nm has 3353 (99.0%) missing values Missing
patient_id has unique values Unique
time_dx_to_surgery_nm has 270 (8.0%) zeros Zeros
time_dx_to_radiotherapy_nm has 104 (3.1%) zeros Zeros

Reproduction

Analysis started2022-06-13 15:11:23.187780
Analysis finished2022-06-13 15:11:23.449383
Duration0.26 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

patient_id
Categorical

HIGH CARDINALITY
UNIQUE

Distinct3388
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size254.9 KiB
BFhVwQ6BVgDpJkCO9RDX
 
1
usU7NMcH4CvoSHQRQS/G
 
1
NIW2B4sQpjpKJULqSFuE
 
1
2x/47TVlHy0O25Sezb4p
 
1
e4DLcvb7h9Mg4y5PXdw2
 
1
Other values (3383)
3383 

Characters and Unicode

Total characters67760
Distinct characters64
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

Unique3388 ?
Unique (%)100.0%

Sample

1st rowBFhVwQ6BVgDpJkCO9RDX
2nd rowfN83/0/azfY4tGTQCddC
3rd rowVJ2R3H6FpqEmNS2Hnfte
4th rowflSDjMT7JS1yTfH6/Zs3
5th rowwJ0gZ42gWXGrljQdQjHe

Common Values

ValueCountFrequency (%)
BFhVwQ6BVgDpJkCO9RDX1
 
< 0.1%
usU7NMcH4CvoSHQRQS/G1
 
< 0.1%
NIW2B4sQpjpKJULqSFuE1
 
< 0.1%
2x/47TVlHy0O25Sezb4p1
 
< 0.1%
e4DLcvb7h9Mg4y5PXdw21
 
< 0.1%
pnHVKAFsi8nfopz2q3+H1
 
< 0.1%
uoLuanwc8hjnzTqYzl/y1
 
< 0.1%
G3mt7Ckj9j7dqXdCmtjx1
 
< 0.1%
VcNSbc9Lov23E9UoUKlT1
 
< 0.1%
dmwurfnIpVfbnuQvB+mi1
 
< 0.1%
Other values (3378)3378
99.7%
ValueCountFrequency (%)
bfhvwq6bvgdpjkco9rdx1
 
< 0.1%
dc7sxyjsv+qikm9vdxyu1
 
< 0.1%
qrduyk68mkvlzumzdtuv1
 
< 0.1%
cubg2yshrkxdgstiarsc1
 
< 0.1%
vj2r3h6fpqemns2hnfte1
 
< 0.1%
flsdjmt7js1ytfh6/zs31
 
< 0.1%
wj0gz42gwxgrljqdqjhe1
 
< 0.1%
ktf60fbyo4ba13qagwv51
 
< 0.1%
vq3ytad39p0h/dyzwd741
 
< 0.1%
ngbtp9tuzdhjp12wbk2s1
 
< 0.1%
Other values (3378)3378
99.7%

Most occurring characters

ValueCountFrequency (%)
q1155
 
1.7%
D1127
 
1.7%
l1110
 
1.6%
x1108
 
1.6%
o1102
 
1.6%
e1101
 
1.6%
41100
 
1.6%
01099
 
1.6%
f1098
 
1.6%
a1096
 
1.6%
Other values (54)56664
83.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter27882
41.1%
Uppercase Letter27263
40.2%
Decimal Number10538
 
15.6%
Other Punctuation1048
 
1.5%
Math Symbol1029
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
q1155
 
4.1%
l1110
 
4.0%
x1108
 
4.0%
o1102
 
4.0%
e1101
 
3.9%
f1098
 
3.9%
a1096
 
3.9%
s1090
 
3.9%
i1088
 
3.9%
b1088
 
3.9%
Other values (16)16846
60.4%
Uppercase Letter
ValueCountFrequency (%)
D1127
 
4.1%
F1094
 
4.0%
W1091
 
4.0%
R1089
 
4.0%
T1083
 
4.0%
C1078
 
4.0%
Z1078
 
4.0%
N1071
 
3.9%
J1064
 
3.9%
Y1061
 
3.9%
Other values (16)16427
60.3%
Decimal Number
ValueCountFrequency (%)
41100
10.4%
01099
10.4%
31073
10.2%
81058
10.0%
21057
10.0%
91051
10.0%
71037
9.8%
61032
9.8%
11017
9.7%
51014
9.6%
Other Punctuation
ValueCountFrequency (%)
/1048
100.0%
Math Symbol
ValueCountFrequency (%)
+1029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55145
81.4%
Common12615
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
q1155
 
2.1%
D1127
 
2.0%
l1110
 
2.0%
x1108
 
2.0%
o1102
 
2.0%
e1101
 
2.0%
f1098
 
2.0%
a1096
 
2.0%
F1094
 
2.0%
W1091
 
2.0%
Other values (42)44063
79.9%
Common
ValueCountFrequency (%)
41100
8.7%
01099
8.7%
31073
8.5%
81058
8.4%
21057
8.4%
91051
8.3%
/1048
8.3%
71037
8.2%
61032
8.2%
+1029
8.2%
Other values (2)2031
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII67760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
q1155
 
1.7%
D1127
 
1.7%
l1110
 
1.6%
x1108
 
1.6%
o1102
 
1.6%
e1101
 
1.6%
41100
 
1.6%
01099
 
1.6%
f1098
 
1.6%
a1096
 
1.6%
Other values (54)56664
83.6%

age_nm
Real number (ℝ≥0)

Distinct58
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.94155844
Minimum25
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:23.527568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile39.35
Q150
median60
Q368
95-th percentile77
Maximum82
Range57
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.8165091
Coefficient of variation (CV)0.2004783962
Kurtosis-0.7371841217
Mean58.94155844
Median Absolute Deviation (MAD)9
Skewness-0.2047017639
Sum199694
Variance139.6298874
MonotonicityNot monotonic
2022-06-13T17:11:23.660552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70112
 
3.3%
61112
 
3.3%
69105
 
3.1%
66103
 
3.0%
64103
 
3.0%
59101
 
3.0%
68100
 
3.0%
50100
 
3.0%
6398
 
2.9%
5797
 
2.9%
Other values (48)2357
69.6%
ValueCountFrequency (%)
252
 
0.1%
261
 
< 0.1%
273
 
0.1%
281
 
< 0.1%
296
 
0.2%
306
 
0.2%
3112
0.4%
327
0.2%
3316
0.5%
3410
0.3%
ValueCountFrequency (%)
821
 
< 0.1%
811
 
< 0.1%
8051
1.5%
7954
1.6%
7844
1.3%
7772
2.1%
7668
2.0%
7574
2.2%
7463
1.9%
7360
1.8%

socecon_lvl_cd
Real number (ℝ≥0)

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.248819362
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:23.751718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5722768736
Coefficient of variation (CV)0.2544788093
Kurtosis-0.2525204358
Mean2.248819362
Median Absolute Deviation (MAD)0
Skewness0.04540182247
Sum7619
Variance0.32750082
MonotonicityNot monotonic
2022-06-13T17:11:23.821642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
22102
62.0%
31048
30.9%
1227
 
6.7%
411
 
0.3%
ValueCountFrequency (%)
1227
 
6.7%
22102
62.0%
31048
30.9%
411
 
0.3%
ValueCountFrequency (%)
411
 
0.3%
31048
30.9%
22102
62.0%
1227
 
6.7%

country_cd
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
ESP
3388 

Characters and Unicode

Total characters10164
Distinct characters3
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 rowESP
2nd rowESP
3rd rowESP
4th rowESP
5th rowESP

Common Values

ValueCountFrequency (%)
ESP3388
100.0%

Category Frequency Plot

2022-06-13T17:11:23.908256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
esp3388
100.0%

Most occurring characters

ValueCountFrequency (%)
E3388
33.3%
S3388
33.3%
P3388
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10164
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E3388
33.3%
S3388
33.3%
P3388
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin10164
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E3388
33.3%
S3388
33.3%
P3388
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E3388
33.3%
S3388
33.3%
P3388
33.3%
Distinct40
Distinct (%)1.2%
Missing30
Missing (%)0.9%
Memory size197.8 KiB
ESP
3064 
ROM
 
68
COL
 
28
MAR
 
27
ECU
 
17
Other values (35)
 
154

Characters and Unicode

Total characters10074
Distinct characters24
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

Unique11 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
ESP3064
90.4%
ROM68
 
2.0%
COL28
 
0.8%
MAR27
 
0.8%
ECU17
 
0.5%
NIC14
 
0.4%
ARG13
 
0.4%
CUB10
 
0.3%
VEN9
 
0.3%
BGR9
 
0.3%
Other values (30)99
 
2.9%
(Missing)30
 
0.9%
ValueCountFrequency (%)
esp3064
91.2%
rom68
 
2.0%
col28
 
0.8%
mar27
 
0.8%
ecu17
 
0.5%
nic14
 
0.4%
arg13
 
0.4%
cub10
 
0.3%
ven9
 
0.3%
bgr9
 
0.3%
Other values (30)99
 
2.9%

Most occurring characters

ValueCountFrequency (%)
E3109
30.9%
P3077
30.5%
S3066
30.4%
R161
 
1.6%
M109
 
1.1%
O105
 
1.0%
C83
 
0.8%
A64
 
0.6%
U48
 
0.5%
N41
 
0.4%
Other values (14)211
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10074
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E3109
30.9%
P3077
30.5%
S3066
30.4%
R161
 
1.6%
M109
 
1.1%
O105
 
1.0%
C83
 
0.8%
A64
 
0.6%
U48
 
0.5%
N41
 
0.4%
Other values (14)211
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin10074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E3109
30.9%
P3077
30.5%
S3066
30.4%
R161
 
1.6%
M109
 
1.1%
O105
 
1.0%
C83
 
0.8%
A64
 
0.6%
U48
 
0.5%
N41
 
0.4%
Other values (14)211
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E3109
30.9%
P3077
30.5%
S3066
30.4%
R161
 
1.6%
M109
 
1.1%
O105
 
1.0%
C83
 
0.8%
A64
 
0.6%
U48
 
0.5%
N41
 
0.4%
Other values (14)211
 
2.1%

hospital_id
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size337.5 KiB
HOSPITAL UNIVERSITARIO MIGUEL SERVET
1344 
HOSPITAL CLINICO UNIVERSITARIO LOZANO BLESA
560 
HOSPITAL NUESTRA SEÑORA DE GRACIA
490 
HOSPITAL DE BARBASTRO
301 
HOSPITAL GENERAL SAN JORGE
298 
Other values (5)
395 

Characters and Unicode

Total characters112146
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 rowHOSPITAL NUESTRA SEÑORA DE GRACIA
2nd rowHOSPITAL NUESTRA SEÑORA DE GRACIA
3rd rowHOSPITAL GENERAL SAN JORGE
4th rowHOSPITAL UNIVERSITARIO MIGUEL SERVET
5th rowHOSPITAL CLINICO UNIVERSITARIO LOZANO BLESA

Common Values

ValueCountFrequency (%)
HOSPITAL UNIVERSITARIO MIGUEL SERVET1344
39.7%
HOSPITAL CLINICO UNIVERSITARIO LOZANO BLESA560
16.5%
HOSPITAL NUESTRA SEÑORA DE GRACIA490
 
14.5%
HOSPITAL DE BARBASTRO301
 
8.9%
HOSPITAL GENERAL SAN JORGE298
 
8.8%
HOSPITAL OBISPO POLANCO169
 
5.0%
HOSPITAL DE ALCAÑIZ139
 
4.1%
HOSPITAL ERNEST LLUCH MARTÍN85
 
2.5%
HOSPITAL DE JACA SALUD1
 
< 0.1%
HOSPITAL ROYO VILLANOVA1
 
< 0.1%

Category Frequency Plot

2022-06-13T17:11:24.053916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hospital3388
24.2%
universitario1904
13.6%
miguel1344
 
9.6%
servet1344
 
9.6%
de931
 
6.7%
clinico560
 
4.0%
lozano560
 
4.0%
blesa560
 
4.0%
nuestra490
 
3.5%
señora490
 
3.5%
Other values (15)2421
17.3%

Most occurring characters

ValueCountFrequency (%)
I12363
11.0%
11094
9.9%
A10107
9.0%
E9471
 
8.4%
S9030
 
8.1%
O8740
 
7.8%
R7991
 
7.1%
T7597
 
6.8%
L7191
 
6.4%
N4450
 
4.0%
Other values (14)24112
21.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter101052
90.1%
Space Separator11094
 
9.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I12363
12.2%
A10107
10.0%
E9471
9.4%
S9030
8.9%
O8740
8.6%
R7991
 
7.9%
T7597
 
7.5%
L7191
 
7.1%
N4450
 
4.4%
U3824
 
3.8%
Other values (13)20288
20.1%
Space Separator
ValueCountFrequency (%)
11094
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101052
90.1%
Common11094
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I12363
12.2%
A10107
10.0%
E9471
9.4%
S9030
8.9%
O8740
8.6%
R7991
 
7.9%
T7597
 
7.5%
L7191
 
7.1%
N4450
 
4.4%
U3824
 
3.8%
Other values (13)20288
20.1%
Common
ValueCountFrequency (%)
11094
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII111432
99.4%
None714
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I12363
11.1%
11094
10.0%
A10107
9.1%
E9471
8.5%
S9030
 
8.1%
O8740
 
7.8%
R7991
 
7.2%
T7597
 
6.8%
L7191
 
6.5%
N4450
 
4.0%
Other values (12)23398
21.0%
None
ValueCountFrequency (%)
Ñ629
88.1%
Í85
 
11.9%

ttm_type_cd
Real number (ℝ≥0)

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.207497048
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:24.157370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5701795218
Coefficient of variation (CV)0.4721995159
Kurtosis19.89699629
Mean1.207497048
Median Absolute Deviation (MAD)0
Skewness3.957616005
Sum4091
Variance0.325104687
MonotonicityNot monotonic
2022-06-13T17:11:24.229070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
12848
84.1%
2451
 
13.3%
350
 
1.5%
535
 
1.0%
44
 
0.1%
ValueCountFrequency (%)
12848
84.1%
2451
 
13.3%
350
 
1.5%
44
 
0.1%
535
 
1.0%
ValueCountFrequency (%)
535
 
1.0%
44
 
0.1%
350
 
1.5%
2451
 
13.3%
12848
84.1%

time_dx_to_surgery_nm
Real number (ℝ≥0)

MISSING
ZEROS

Distinct159
Distinct (%)5.6%
Missing540
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean23.09375
Minimum0
Maximum180
Zeros270
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:24.328405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q330
95-th percentile84
Maximum180
Range180
Interquartile range (IQR)28

Descriptive statistics

Standard deviation31.62017719
Coefficient of variation (CV)1.369209296
Kurtosis8.07118545
Mean23.09375
Median Absolute Deviation (MAD)12
Skewness2.642656997
Sum65771
Variance999.8356055
MonotonicityNot monotonic
2022-06-13T17:11:24.440089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1412
 
12.2%
0270
 
8.0%
8105
 
3.1%
690
 
2.7%
782
 
2.4%
275
 
2.2%
575
 
2.2%
1569
 
2.0%
1367
 
2.0%
960
 
1.8%
Other values (149)1543
45.5%
(Missing)540
 
15.9%
ValueCountFrequency (%)
0270
8.0%
1412
12.2%
275
 
2.2%
337
 
1.1%
458
 
1.7%
575
 
2.2%
690
 
2.7%
782
 
2.4%
8105
 
3.1%
960
 
1.8%
ValueCountFrequency (%)
1802
0.1%
1793
0.1%
1784
0.1%
1772
0.1%
1763
0.1%
1733
0.1%
1721
 
< 0.1%
1711
 
< 0.1%
1701
 
< 0.1%
1692
0.1%

time_dx_to_radiotherapy_nm
Real number (ℝ≥0)

MISSING
ZEROS

Distinct76
Distinct (%)16.9%
Missing2937
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean22.27050998
Minimum0
Maximum174
Zeros104
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:24.558490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q334
95-th percentile64
Maximum174
Range174
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.93016904
Coefficient of variation (CV)1.119425153
Kurtosis9.248623077
Mean22.27050998
Median Absolute Deviation (MAD)16
Skewness2.405311333
Sum10044
Variance621.5133284
MonotonicityNot monotonic
2022-06-13T17:11:24.674252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
3.1%
2317
 
0.5%
3615
 
0.4%
1414
 
0.4%
2814
 
0.4%
1313
 
0.4%
3511
 
0.3%
811
 
0.3%
1210
 
0.3%
3710
 
0.3%
Other values (66)232
 
6.8%
(Missing)2937
86.7%
ValueCountFrequency (%)
0104
3.1%
17
 
0.2%
24
 
0.1%
34
 
0.1%
47
 
0.2%
57
 
0.2%
65
 
0.1%
77
 
0.2%
811
 
0.3%
96
 
0.2%
ValueCountFrequency (%)
1741
< 0.1%
1671
< 0.1%
1542
0.1%
1331
< 0.1%
1111
< 0.1%
1041
< 0.1%
1031
< 0.1%
912
0.1%
841
< 0.1%
821
< 0.1%

time_dx_to_chemotherapy_nm
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)46.0%
Missing3338
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean16.2
Minimum0
Maximum160
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:24.777654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q317.25
95-th percentile92
Maximum160
Range160
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation31.83631606
Coefficient of variation (CV)1.965204695
Kurtosis10.06471328
Mean16.2
Median Absolute Deviation (MAD)4.5
Skewness3.101016981
Sum810
Variance1013.55102
MonotonicityNot monotonic
2022-06-13T17:11:24.865091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
016
 
0.5%
17
 
0.2%
153
 
0.1%
62
 
0.1%
202
 
0.1%
52
 
0.1%
72
 
0.1%
271
 
< 0.1%
1601
 
< 0.1%
181
 
< 0.1%
Other values (13)13
 
0.4%
(Missing)3338
98.5%
ValueCountFrequency (%)
016
0.5%
17
0.2%
31
 
< 0.1%
41
 
< 0.1%
52
 
0.1%
62
 
0.1%
72
 
0.1%
81
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
1601
< 0.1%
1141
< 0.1%
1011
< 0.1%
811
< 0.1%
371
< 0.1%
361
< 0.1%
281
< 0.1%
271
< 0.1%
221
< 0.1%
202
0.1%

time_dx_to_immunotherapy_nm
Real number (ℝ≥0)

MISSING

Distinct3
Distinct (%)75.0%
Missing3384
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean2.75
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:24.952940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q10.75
median1
Q33
95-th percentile7.8
Maximum9
Range9
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation4.193248542
Coefficient of variation (CV)1.524817652
Kurtosis3.769996182
Mean2.75
Median Absolute Deviation (MAD)0.5
Skewness1.922521714
Sum11
Variance17.58333333
MonotonicityNot monotonic
2022-06-13T17:11:25.018733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
12
 
0.1%
91
 
< 0.1%
01
 
< 0.1%
(Missing)3384
99.9%
ValueCountFrequency (%)
01
< 0.1%
12
0.1%
91
< 0.1%
ValueCountFrequency (%)
91
< 0.1%
12
0.1%
01
< 0.1%

time_dx_to_hormonotherapy_nm
Real number (ℝ≥0)

MISSING

Distinct27
Distinct (%)77.1%
Missing3353
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean42.02857143
Minimum0
Maximum139
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:25.097285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.5
median26
Q354.5
95-th percentile132
Maximum139
Range139
Interquartile range (IQR)44

Descriptive statistics

Standard deviation46.50394399
Coefficient of variation (CV)1.106484051
Kurtosis-0.2453549422
Mean42.02857143
Median Absolute Deviation (MAD)22
Skewness1.10536215
Sum1471
Variance2162.616807
MonotonicityNot monotonic
2022-06-13T17:11:25.186412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
05
 
0.1%
123
 
0.1%
1182
 
0.1%
1322
 
0.1%
1251
 
< 0.1%
331
 
< 0.1%
381
 
< 0.1%
21
 
< 0.1%
471
 
< 0.1%
281
 
< 0.1%
Other values (17)17
 
0.5%
(Missing)3353
99.0%
ValueCountFrequency (%)
05
0.1%
11
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
123
0.1%
131
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
1391
< 0.1%
1322
0.1%
1251
< 0.1%
1221
< 0.1%
1182
0.1%
711
< 0.1%
561
< 0.1%
531
< 0.1%
471
< 0.1%
421
< 0.1%

period
Real number (ℝ≥0)

Distinct60
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.43329398
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2022-06-13T17:11:25.294794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median31
Q347
95-th percentile58
Maximum60
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.41358456
Coefficient of variation (CV)0.5539853561
Kurtosis-1.233362411
Mean31.43329398
Median Absolute Deviation (MAD)15
Skewness-0.0394052124
Sum106496
Variance303.2329272
MonotonicityNot monotonic
2022-06-13T17:11:25.404744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5478
 
2.3%
1775
 
2.2%
3971
 
2.1%
2671
 
2.1%
5170
 
2.1%
4869
 
2.0%
5669
 
2.0%
5766
 
1.9%
5265
 
1.9%
1865
 
1.9%
Other values (50)2689
79.4%
ValueCountFrequency (%)
141
1.2%
248
1.4%
361
1.8%
449
1.4%
558
1.7%
657
1.7%
739
1.2%
862
1.8%
949
1.4%
1047
1.4%
ValueCountFrequency (%)
6056
1.7%
5961
1.8%
5865
1.9%
5766
1.9%
5669
2.0%
5560
1.8%
5478
2.3%
5351
1.5%
5265
1.9%
5170
2.1%