Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations45716
Missing cells22498
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory88.0 B

Variable types

Text3
Numeric6
Categorical2

Alerts

nametype is highly imbalanced (98.2%) Imbalance
fall is highly imbalanced (83.6%) Imbalance
reclat has 7315 (16.0%) missing values Missing
reclong has 7315 (16.0%) missing values Missing
GeoLocation has 7315 (16.0%) missing values Missing
mass (g) is highly skewed (γ1 = 76.91011732) Skewed
name has unique values Unique
id has unique values Unique
reclat has 6438 (14.1%) zeros Zeros
reclong has 6214 (13.6%) zeros Zeros

Reproduction

Analysis started2026-04-11 07:38:40.401681
Analysis finished2026-04-11 07:38:42.898115
Duration2.5 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

name
Text

Unique 

Distinct45716
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:43.229330image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length28
Median length25
Mean length17.784605
Min length2

Characters and Unicode

Total characters813041
Distinct characters96
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

Unique45716 ?
Unique (%)100.0%

Sample

1st rowAachen
2nd rowAarhus
3rd rowAbee
4th rowAcapulco
5th rowAchiras
ValueCountFrequency (%)
yamato 7269
 
5.7%
range 6575
 
5.2%
africa 4502
 
3.6%
northwest 4499
 
3.5%
hills 3995
 
3.2%
queen 3445
 
2.7%
alexandra 3444
 
2.7%
mountains 3004
 
2.4%
al 2663
 
2.1%
grove 2496
 
2.0%
Other values (37726) 84847
66.9%
2026-04-11T09:38:43.556822image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81029
 
10.0%
a 72706
 
8.9%
e 48162
 
5.9%
n 38388
 
4.7%
0 34943
 
4.3%
r 33095
 
4.1%
i 32654
 
4.0%
l 31870
 
3.9%
t 30896
 
3.8%
o 30423
 
3.7%
Other values (86) 378875
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 813041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81029
 
10.0%
a 72706
 
8.9%
e 48162
 
5.9%
n 38388
 
4.7%
0 34943
 
4.3%
r 33095
 
4.1%
i 32654
 
4.0%
l 31870
 
3.9%
t 30896
 
3.8%
o 30423
 
3.7%
Other values (86) 378875
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 813041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81029
 
10.0%
a 72706
 
8.9%
e 48162
 
5.9%
n 38388
 
4.7%
0 34943
 
4.3%
r 33095
 
4.1%
i 32654
 
4.0%
l 31870
 
3.9%
t 30896
 
3.8%
o 30423
 
3.7%
Other values (86) 378875
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 813041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81029
 
10.0%
a 72706
 
8.9%
e 48162
 
5.9%
n 38388
 
4.7%
0 34943
 
4.3%
r 33095
 
4.1%
i 32654
 
4.0%
l 31870
 
3.9%
t 30896
 
3.8%
o 30423
 
3.7%
Other values (86) 378875
46.6%

id
Real number (ℝ)

Unique 

Distinct45716
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26889.735
Minimum1
Maximum57458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:43.652792image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2434.75
Q112688.75
median24261.5
Q340656.75
95-th percentile54891.25
Maximum57458
Range57457
Interquartile range (IQR)27968

Descriptive statistics

Standard deviation16860.683
Coefficient of variation (CV)0.62703046
Kurtosis-1.1602565
Mean26889.735
Median Absolute Deviation (MAD)13264
Skewness0.26654597
Sum1.2292911 × 109
Variance2.8428263 × 108
MonotonicityNot monotonic
2026-04-11T09:38:43.786293image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
30414 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
370 1
 
< 0.1%
379 1
 
< 0.1%
390 1
 
< 0.1%
392 1
 
< 0.1%
398 1
 
< 0.1%
Other values (45706) 45706
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
57458 1
< 0.1%
57457 1
< 0.1%
57456 1
< 0.1%
57455 1
< 0.1%
57454 1
< 0.1%
57453 1
< 0.1%
57436 1
< 0.1%
57435 1
< 0.1%
57434 1
< 0.1%
57433 1
< 0.1%

nametype
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
Valid
45641 
Relict
 
75

Length

Max length6
Median length5
Mean length5.0016406
Min length5

Characters and Unicode

Total characters228655
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Valid 45641
99.8%
Relict 75
 
0.2%

Length

2026-04-11T09:38:43.883346image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-11T09:38:43.933733image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
valid 45641
99.8%
relict 75
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 45716
20.0%
i 45716
20.0%
V 45641
20.0%
a 45641
20.0%
d 45641
20.0%
R 75
 
< 0.1%
e 75
 
< 0.1%
c 75
 
< 0.1%
t 75
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 45716
20.0%
i 45716
20.0%
V 45641
20.0%
a 45641
20.0%
d 45641
20.0%
R 75
 
< 0.1%
e 75
 
< 0.1%
c 75
 
< 0.1%
t 75
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 45716
20.0%
i 45716
20.0%
V 45641
20.0%
a 45641
20.0%
d 45641
20.0%
R 75
 
< 0.1%
e 75
 
< 0.1%
c 75
 
< 0.1%
t 75
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 45716
20.0%
i 45716
20.0%
V 45641
20.0%
a 45641
20.0%
d 45641
20.0%
R 75
 
< 0.1%
e 75
 
< 0.1%
c 75
 
< 0.1%
t 75
 
< 0.1%

recclass
Text

Distinct466
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:44.101957image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length26
Median length2
Mean length3.0524762
Min length1

Characters and Unicode

Total characters139547
Distinct characters62
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

Unique145 ?
Unique (%)0.3%

Sample

1st rowL5
2nd rowH6
3rd rowEH4
4th rowAcapulcoite
5th rowL6
ValueCountFrequency (%)
l6 8339
17.6%
h5 7164
15.1%
l5 4817
10.2%
h6 4529
9.6%
h4 4223
 
8.9%
ll5 2766
 
5.8%
ll6 2046
 
4.3%
l4 1256
 
2.7%
iron 1070
 
2.3%
h4/5 428
 
0.9%
Other values (434) 10707
22.6%
2026-04-11T09:38:44.394983image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 28461
20.4%
H 18392
13.2%
5 16417
11.8%
6 16128
11.6%
4 6928
 
5.0%
e 3971
 
2.8%
i 3833
 
2.7%
r 3648
 
2.6%
t 3326
 
2.4%
3 3277
 
2.3%
Other values (52) 35166
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 28461
20.4%
H 18392
13.2%
5 16417
11.8%
6 16128
11.6%
4 6928
 
5.0%
e 3971
 
2.8%
i 3833
 
2.7%
r 3648
 
2.6%
t 3326
 
2.4%
3 3277
 
2.3%
Other values (52) 35166
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 28461
20.4%
H 18392
13.2%
5 16417
11.8%
6 16128
11.6%
4 6928
 
5.0%
e 3971
 
2.8%
i 3833
 
2.7%
r 3648
 
2.6%
t 3326
 
2.4%
3 3277
 
2.3%
Other values (52) 35166
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 28461
20.4%
H 18392
13.2%
5 16417
11.8%
6 16128
11.6%
4 6928
 
5.0%
e 3971
 
2.8%
i 3833
 
2.7%
r 3648
 
2.6%
t 3326
 
2.4%
3 3277
 
2.3%
Other values (52) 35166
25.2%

mass (g)
Real number (ℝ)

Skewed 

Distinct12576
Distinct (%)27.6%
Missing131
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13278.079
Minimum0
Maximum60000000
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:44.492727image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q17.2
median32.6
Q3202.6
95-th percentile4000
Maximum60000000
Range60000000
Interquartile range (IQR)195.4

Descriptive statistics

Standard deviation574988.88
Coefficient of variation (CV)43.303621
Kurtosis6796.9162
Mean13278.079
Median Absolute Deviation (MAD)30.5
Skewness76.910117
Sum6.0528121 × 108
Variance3.3061221 × 1011
MonotonicityNot monotonic
2026-04-11T09:38:44.623142image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
1.3 171
 
0.4%
1.2 140
 
0.3%
1.4 138
 
0.3%
2.1 130
 
0.3%
2.4 126
 
0.3%
1.6 120
 
0.3%
0.5 119
 
0.3%
1.1 116
 
0.3%
3.8 114
 
0.2%
1.5 111
 
0.2%
Other values (12566) 44300
96.9%
(Missing) 131
 
0.3%
ValueCountFrequency (%)
0 19
< 0.1%
0.01 2
 
< 0.1%
0.013 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 3
 
< 0.1%
0.08 2
 
< 0.1%
ValueCountFrequency (%)
60000000 1
< 0.1%
58200000 1
< 0.1%
50000000 1
< 0.1%
30000000 1
< 0.1%
28000000 1
< 0.1%
26000000 1
< 0.1%
24300000 1
< 0.1%
24000000 1
< 0.1%
23000000 1
< 0.1%
22000000 1
< 0.1%

log_mass
Real number (ℝ)

Distinct12576
Distinct (%)27.6%
Missing131
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3.8938042
Minimum0
Maximum17.909855
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:44.742797image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.74193734
Q12.1041342
median3.5145261
Q35.3161573
95-th percentile8.2942996
Maximum17.909855
Range17.909855
Interquartile range (IQR)3.2120231

Descriptive statistics

Standard deviation2.363192
Coefficient of variation (CV)0.60691085
Kurtosis1.0383649
Mean3.8938042
Median Absolute Deviation (MAD)1.5680447
Skewness0.90722379
Sum177499.07
Variance5.5846767
MonotonicityNot monotonic
2026-04-11T09:38:44.895287image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
0.8329091229 171
 
0.4%
0.7884573604 140
 
0.3%
0.8754687374 138
 
0.3%
1.131402111 130
 
0.3%
1.223775432 126
 
0.3%
0.955511445 120
 
0.3%
0.4054651081 119
 
0.3%
0.7419373447 116
 
0.3%
1.568615918 114
 
0.2%
0.9162907319 111
 
0.2%
Other values (12566) 44300
96.9%
(Missing) 131
 
0.3%
ValueCountFrequency (%)
0 19
< 0.1%
0.009950330853 2
 
< 0.1%
0.01291622527 1
 
< 0.1%
0.0198026273 1
 
< 0.1%
0.02955880224 1
 
< 0.1%
0.03922071315 1
 
< 0.1%
0.04879016417 1
 
< 0.1%
0.05826890812 1
 
< 0.1%
0.06765864847 3
 
< 0.1%
0.07696104114 2
 
< 0.1%
ValueCountFrequency (%)
17.90985514 1
< 0.1%
17.87939593 1
< 0.1%
17.72753358 1
< 0.1%
17.21670797 1
< 0.1%
17.1477151 1
< 0.1%
17.07360713 1
< 0.1%
17.00598695 1
< 0.1%
16.99356443 1
< 0.1%
16.95100482 1
< 0.1%
16.90655306 1
< 0.1%

fall
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
Found
44609 
Fell
 
1107

Length

Max length5
Median length5
Mean length4.9757853
Min length4

Characters and Unicode

Total characters227473
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 rowFell
2nd rowFell
3rd rowFell
4th rowFell
5th rowFell

Common Values

ValueCountFrequency (%)
Found 44609
97.6%
Fell 1107
 
2.4%

Length

2026-04-11T09:38:44.998721image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-11T09:38:45.045519image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
found 44609
97.6%
fell 1107
 
2.4%

Most occurring characters

ValueCountFrequency (%)
F 45716
20.1%
o 44609
19.6%
u 44609
19.6%
n 44609
19.6%
d 44609
19.6%
l 2214
 
1.0%
e 1107
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 45716
20.1%
o 44609
19.6%
u 44609
19.6%
n 44609
19.6%
d 44609
19.6%
l 2214
 
1.0%
e 1107
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 45716
20.1%
o 44609
19.6%
u 44609
19.6%
n 44609
19.6%
d 44609
19.6%
l 2214
 
1.0%
e 1107
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 45716
20.1%
o 44609
19.6%
u 44609
19.6%
n 44609
19.6%
d 44609
19.6%
l 2214
 
1.0%
e 1107
 
0.5%

year
Real number (ℝ)

Distinct265
Distinct (%)0.6%
Missing291
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1991.8288
Minimum860
Maximum2101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.3 KiB
2026-04-11T09:38:45.127687image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum860
5-th percentile1969
Q11987
median1998
Q32003
95-th percentile2009
Maximum2101
Range1241
Interquartile range (IQR)16

Descriptive statistics

Standard deviation25.052766
Coefficient of variation (CV)0.012577771
Kurtosis215.99969
Mean1991.8288
Median Absolute Deviation (MAD)8
Skewness-8.8248153
Sum90478824
Variance627.64109
MonotonicityNot monotonic
2026-04-11T09:38:45.254511image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
2003 3323
 
7.3%
1979 3046
 
6.7%
1998 2697
 
5.9%
2006 2456
 
5.4%
1988 2296
 
5.0%
2002 2078
 
4.5%
2004 1940
 
4.2%
2000 1792
 
3.9%
1997 1696
 
3.7%
1999 1691
 
3.7%
Other values (255) 22410
49.0%
ValueCountFrequency (%)
860 1
< 0.1%
920 1
< 0.1%
1399 1
< 0.1%
1490 1
< 0.1%
1491 1
< 0.1%
1495 1
< 0.1%
1519 1
< 0.1%
1575 1
< 0.1%
1583 1
< 0.1%
1600 1
< 0.1%
ValueCountFrequency (%)
2101 1
 
< 0.1%
2013 11
 
< 0.1%
2012 234
 
0.5%
2011 713
 
1.6%
2010 1005
2.2%
2009 1497
3.3%
2008 957
 
2.1%
2007 1189
2.6%
2006 2456
5.4%
2005 875
 
1.9%

reclat
Real number (ℝ)

Missing  Zeros 

Distinct12738
Distinct (%)33.2%
Missing7315
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean-39.12258
Minimum-87.36667
Maximum81.16667
Zeros6438
Zeros (%)14.1%
Negative23413
Negative (%)51.2%
Memory size357.3 KiB
2026-04-11T09:38:45.426821image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum-87.36667
5-th percentile-84.35516
Q1-76.71424
median-71.5
Q30
95-th percentile34.49058
Maximum81.16667
Range168.53334
Interquartile range (IQR)76.71424

Descriptive statistics

Standard deviation46.378511
Coefficient of variation (CV)-1.1854666
Kurtosis-1.4768361
Mean-39.12258
Median Absolute Deviation (MAD)12.76421
Skewness0.49161039
Sum-1502346.2
Variance2150.9663
MonotonicityNot monotonic
2026-04-11T09:38:45.646508image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
0 6438
 
14.1%
-71.5 4761
 
10.4%
-84 3040
 
6.6%
-72 1506
 
3.3%
-79.68333 1130
 
2.5%
-76.71667 680
 
1.5%
-76.18333 539
 
1.2%
-84.21667 263
 
0.6%
-86.36667 226
 
0.5%
-86.71667 217
 
0.5%
Other values (12728) 19601
42.9%
(Missing) 7315
 
16.0%
ValueCountFrequency (%)
-87.36667 4
 
< 0.1%
-87.03333 3
 
< 0.1%
-86.93333 3
 
< 0.1%
-86.71667 217
0.5%
-86.56667 17
 
< 0.1%
-86.54488 1
 
< 0.1%
-86.5379 1
 
< 0.1%
-86.53734 1
 
< 0.1%
-86.53725 1
 
< 0.1%
-86.53035 1
 
< 0.1%
ValueCountFrequency (%)
81.16667 1
< 0.1%
76.53333 1
< 0.1%
76.13333 1
< 0.1%
72.88333 1
< 0.1%
72.68333 1
< 0.1%
70.73333 1
< 0.1%
70 1
< 0.1%
69.1 1
< 0.1%
68 1
< 0.1%
67.88333 1
< 0.1%

reclong
Real number (ℝ)

Missing  Zeros 

Distinct14640
Distinct (%)38.1%
Missing7315
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean61.074319
Minimum-165.43333
Maximum354.47333
Zeros6214
Zeros (%)13.6%
Negative4052
Negative (%)8.9%
Memory size357.3 KiB
2026-04-11T09:38:45.773395image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum-165.43333
5-th percentile-90.36556
Q10
median35.66667
Q3157.16667
95-th percentile168
Maximum354.47333
Range519.90666
Interquartile range (IQR)157.16667

Descriptive statistics

Standard deviation80.647298
Coefficient of variation (CV)1.3204781
Kurtosis-0.7311811
Mean61.074319
Median Absolute Deviation (MAD)39.53972
Skewness-0.17449637
Sum2345314.9
Variance6503.9867
MonotonicityNot monotonic
2026-04-11T09:38:46.060998image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
0 6214
 
13.6%
35.66667 4985
 
10.9%
168 3040
 
6.6%
26 1506
 
3.3%
159.75 657
 
1.4%
159.66667 637
 
1.4%
157.16667 542
 
1.2%
155.75 473
 
1.0%
160.5 263
 
0.6%
-70 228
 
0.5%
Other values (14630) 19856
43.4%
(Missing) 7315
 
16.0%
ValueCountFrequency (%)
-165.43333 9
< 0.1%
-165.11667 17
< 0.1%
-163.16667 1
 
< 0.1%
-162.55 1
 
< 0.1%
-157.86667 1
 
< 0.1%
-157.78333 1
 
< 0.1%
-149.5 4
 
< 0.1%
-148.55 2
 
< 0.1%
-148 3
 
< 0.1%
-146.26667 1
 
< 0.1%
ValueCountFrequency (%)
354.47333 1
 
< 0.1%
178.2 1
 
< 0.1%
178.08333 1
 
< 0.1%
175.73028 1
 
< 0.1%
175.13333 1
 
< 0.1%
175 185
0.4%
174.50043 1
 
< 0.1%
174.4 1
 
< 0.1%
172.7 1
 
< 0.1%
172.6 1
 
< 0.1%

GeoLocation
Text

Missing 

Distinct17100
Distinct (%)44.5%
Missing7315
Missing (%)16.0%
Memory size357.3 KiB
2026-04-11T09:38:46.281931image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.304654
Min length10

Characters and Unicode

Total characters664516
Distinct characters16
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

Unique16373 ?
Unique (%)42.6%

Sample

1st row(50.775, 6.08333)
2nd row(56.18333, 10.23333)
3rd row(54.21667, -113.0)
4th row(16.88333, -99.9)
5th row(-33.16667, -64.95)
ValueCountFrequency (%)
0.0 12652
 
16.5%
35.66667 4991
 
6.5%
71.5 4761
 
6.2%
84.0 3041
 
4.0%
168.0 3040
 
4.0%
26.0 1512
 
2.0%
72.0 1506
 
2.0%
79.68333 1130
 
1.5%
76.71667 680
 
0.9%
159.75 657
 
0.9%
Other values (26608) 42832
55.8%
2026-04-11T09:38:46.574062image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 76802
11.6%
6 67537
 
10.2%
7 52490
 
7.9%
0 49027
 
7.4%
3 44744
 
6.7%
1 44463
 
6.7%
5 42748
 
6.4%
( 38401
 
5.8%
38401
 
5.8%
) 38401
 
5.8%
Other values (6) 171502
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 664516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 76802
11.6%
6 67537
 
10.2%
7 52490
 
7.9%
0 49027
 
7.4%
3 44744
 
6.7%
1 44463
 
6.7%
5 42748
 
6.4%
( 38401
 
5.8%
38401
 
5.8%
) 38401
 
5.8%
Other values (6) 171502
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 664516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 76802
11.6%
6 67537
 
10.2%
7 52490
 
7.9%
0 49027
 
7.4%
3 44744
 
6.7%
1 44463
 
6.7%
5 42748
 
6.4%
( 38401
 
5.8%
38401
 
5.8%
) 38401
 
5.8%
Other values (6) 171502
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 664516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 76802
11.6%
6 67537
 
10.2%
7 52490
 
7.9%
0 49027
 
7.4%
3 44744
 
6.7%
1 44463
 
6.7%
5 42748
 
6.4%
( 38401
 
5.8%
38401
 
5.8%
) 38401
 
5.8%
Other values (6) 171502
25.8%

Missing values

2026-04-11T09:38:42.522140image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
A simple visualization of nullity by column.
2026-04-11T09:38:42.644719image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-04-11T09:38:42.799633image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameidnametyperecclassmass (g)log_massfallyearreclatreclongGeoLocation
0Aachen1ValidL521.03.091042Fell1880.050.775006.08333(50.775, 6.08333)
1Aarhus2ValidH6720.06.580639Fell1951.056.1833310.23333(56.18333, 10.23333)
2Abee6ValidEH4107000.011.580593Fell1952.054.21667-113.00000(54.21667, -113.0)
3Acapulco10ValidAcapulcoite1914.07.557473Fell1976.016.88333-99.90000(16.88333, -99.9)
4Achiras370ValidL6780.06.660575Fell1902.0-33.16667-64.95000(-33.16667, -64.95)
5Adhi Kot379ValidEH44239.08.352319Fell1919.032.1000071.80000(32.1, 71.8)
6Adzhi-Bogdo (stone)390ValidLL3-6910.06.814543Fell1949.044.8333395.16667(44.83333, 95.16667)
7Agen392ValidH530000.010.308986Fell1814.044.216670.61667(44.21667, 0.61667)
8Aguada398ValidL61620.07.390799Fell1930.0-31.60000-65.23333(-31.6, -65.23333)
9Aguila Blanca417ValidL1440.07.273093Fell1920.0-30.86667-64.55000(-30.86667, -64.55)
nameidnametyperecclassmass (g)log_massfallyearreclatreclongGeoLocation
45706Zerkaly31354ValidH516000.09.680406Found1956.052.1333381.96667(52.13333, 81.96667)
45707Zhaoping54609ValidIron, IAB complex2000000.014.508658Found1983.024.23333111.18333(24.23333, 111.18333)
45708Zhigansk30405ValidIron, IIIAB900000.013.710151Found1966.068.00000128.30000(68.0, 128.3)
45709Zhongxiang30406ValidIron100000.011.512935Found1981.031.20000112.50000(31.2, 112.5)
45710Zillah 00131355ValidL61475.07.297091Found1990.029.0370017.01850(29.037, 17.0185)
45711Zillah 00231356ValidEucrite172.05.153292Found1990.029.0370017.01850(29.037, 17.0185)
45712Zinder30409ValidPallasite, ungrouped46.03.850148Found1999.013.783338.96667(13.78333, 8.96667)
45713Zlin30410ValidH43.31.458615Found1939.049.2500017.66667(49.25, 17.66667)
45714Zubkovsky31357ValidL62167.07.681560Found2003.049.7891741.50460(49.78917, 41.5046)
45715Zulu Queen30414ValidL3.7200.05.303305Found1976.033.98333-115.68333(33.98333, -115.68333)