Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations45726
Missing cells29682
Missing cells (%)4.3%
Duplicate rows10
Duplicate rows (%)< 0.1%
Total size in memory24.7 MiB
Average record size in memory566.8 B

Variable types

Text3
Numeric5
Categorical4
DateTime1
Boolean1
Unsupported1

Alerts

source has constant value "NASA"Constant
Dataset has 10 (< 0.1%) duplicate rowsDuplicates
reclat is highly overall correlated with reclat_city and 1 other fieldsHigh correlation
reclat_city is highly overall correlated with reclat and 1 other fieldsHigh correlation
reclong is highly overall correlated with reclat and 1 other fieldsHigh correlation
nametype is highly imbalanced (98.2%)Imbalance
fall is highly imbalanced (83.4%)Imbalance
reclat has 7315 (16.0%) missing valuesMissing
reclong has 7315 (16.0%) missing valuesMissing
GeoLocation has 7315 (16.0%) missing valuesMissing
reclat_city has 7315 (16.0%) missing valuesMissing
mass (g) is highly skewed (γ1 = 76.91847245)Skewed
unhashable is an unsupported type, check if it needs cleaning or further analysisUnsupported
reclat has 6438 (14.1%) zerosZeros
reclong has 6214 (13.6%) zerosZeros

Reproduction

Analysis started2025-09-23 16:05:24.261395
Analysis finished2025-09-23 16:05:27.903078
Duration3.64 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

name
Text

Distinct45716
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2025-09-23T16:05:28.112435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length25
Mean length17.782487
Min length2

Characters and Unicode

Total characters813122
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

Unique45706 ?
Unique (%)> 99.9%

Sample

1st rowAachen
2nd rowAarhus
3rd rowAbee
4th rowAcapulco
5th rowAchiras
ValueCountFrequency (%)
yamato7269
 
5.7%
range6575
 
5.2%
africa4502
 
3.6%
northwest4499
 
3.5%
hills3995
 
3.2%
queen3445
 
2.7%
alexandra3444
 
2.7%
mountains3004
 
2.4%
al2663
 
2.1%
grove2496
 
2.0%
Other values (37726)84860
66.9%
2025-09-23T16:05:28.430957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81032
 
10.0%
a72715
 
8.9%
e48167
 
5.9%
n38392
 
4.7%
034943
 
4.3%
r33097
 
4.1%
i32658
 
4.0%
l31873
 
3.9%
t30898
 
3.8%
o30428
 
3.7%
Other values (86)378919
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)813122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81032
 
10.0%
a72715
 
8.9%
e48167
 
5.9%
n38392
 
4.7%
034943
 
4.3%
r33097
 
4.1%
i32658
 
4.0%
l31873
 
3.9%
t30898
 
3.8%
o30428
 
3.7%
Other values (86)378919
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)813122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81032
 
10.0%
a72715
 
8.9%
e48167
 
5.9%
n38392
 
4.7%
034943
 
4.3%
r33097
 
4.1%
i32658
 
4.0%
l31873
 
3.9%
t30898
 
3.8%
o30428
 
3.7%
Other values (86)378919
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)813122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81032
 
10.0%
a72715
 
8.9%
e48167
 
5.9%
n38392
 
4.7%
034943
 
4.3%
r33097
 
4.1%
i32658
 
4.0%
l31873
 
3.9%
t30898
 
3.8%
o30428
 
3.7%
Other values (86)378919
46.6%

id
Real number (ℝ)

Distinct45716
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26883.906
Minimum1
Maximum57458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.4 KiB
2025-09-23T16:05:28.514760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2388.75
Q112681.25
median24256.5
Q340653.5
95-th percentile54890.75
Maximum57458
Range57457
Interquartile range (IQR)27972.25

Descriptive statistics

Standard deviation16863.446
Coefficient of variation (CV)0.62726917
Kurtosis-1.1601308
Mean26883.906
Median Absolute Deviation (MAD)13264
Skewness0.26653007
Sum1.2292935 × 109
Variance2.843758 × 108
MonotonicityNot monotonic
2025-09-23T16:05:28.609740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12
 
< 0.1%
22
 
< 0.1%
62
 
< 0.1%
102
 
< 0.1%
3702
 
< 0.1%
3792
 
< 0.1%
3902
 
< 0.1%
3922
 
< 0.1%
3982
 
< 0.1%
4172
 
< 0.1%
Other values (45706)45706
> 99.9%
ValueCountFrequency (%)
12
< 0.1%
22
< 0.1%
41
< 0.1%
51
< 0.1%
62
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
102
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
574581
< 0.1%
574571
< 0.1%
574561
< 0.1%
574551
< 0.1%
574541
< 0.1%
574531
< 0.1%
574361
< 0.1%
574351
< 0.1%
574341
< 0.1%
574331
< 0.1%

nametype
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Valid
45651 
Relict
 
75

Length

Max length6
Median length5
Mean length5.0016402
Min length5

Characters and Unicode

Total characters228705
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 (%)
Valid45651
99.8%
Relict75
 
0.2%

Length

2025-09-23T16:05:28.692800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:05:28.736330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
valid45651
99.8%
relict75
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l45726
20.0%
i45726
20.0%
V45651
20.0%
a45651
20.0%
d45651
20.0%
R75
 
< 0.1%
e75
 
< 0.1%
c75
 
< 0.1%
t75
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)228705
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l45726
20.0%
i45726
20.0%
V45651
20.0%
a45651
20.0%
d45651
20.0%
R75
 
< 0.1%
e75
 
< 0.1%
c75
 
< 0.1%
t75
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)228705
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l45726
20.0%
i45726
20.0%
V45651
20.0%
a45651
20.0%
d45651
20.0%
R75
 
< 0.1%
e75
 
< 0.1%
c75
 
< 0.1%
t75
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)228705
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l45726
20.0%
i45726
20.0%
V45651
20.0%
a45651
20.0%
d45651
20.0%
R75
 
< 0.1%
e75
 
< 0.1%
c75
 
< 0.1%
t75
 
< 0.1%
Distinct466
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-09-23T16:05:28.919317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length2
Mean length3.0525303
Min length1

Characters and Unicode

Total characters139580
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 (%)
l68341
17.6%
h57165
15.1%
l54818
10.2%
h64530
9.6%
h44223
 
8.9%
ll52766
 
5.8%
ll62046
 
4.3%
l41256
 
2.7%
iron1070
 
2.3%
h4/5428
 
0.9%
Other values (434)10712
22.6%
2025-09-23T16:05:29.206879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L28467
20.4%
H18396
13.2%
516419
11.8%
616132
11.6%
46930
 
5.0%
e3972
 
2.8%
i3834
 
2.7%
r3648
 
2.6%
t3327
 
2.4%
33278
 
2.3%
Other values (52)35177
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)139580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L28467
20.4%
H18396
13.2%
516419
11.8%
616132
11.6%
46930
 
5.0%
e3972
 
2.8%
i3834
 
2.7%
r3648
 
2.6%
t3327
 
2.4%
33278
 
2.3%
Other values (52)35177
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L28467
20.4%
H18396
13.2%
516419
11.8%
616132
11.6%
46930
 
5.0%
e3972
 
2.8%
i3834
 
2.7%
r3648
 
2.6%
t3327
 
2.4%
33278
 
2.3%
Other values (52)35177
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L28467
20.4%
H18396
13.2%
516419
11.8%
616132
11.6%
46930
 
5.0%
e3972
 
2.8%
i3834
 
2.7%
r3648
 
2.6%
t3327
 
2.4%
33278
 
2.3%
Other values (52)35177
25.2%

mass (g)
Real number (ℝ)

Skewed 

Distinct12576
Distinct (%)27.6%
Missing131
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13278.426
Minimum0
Maximum60000000
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size357.4 KiB
2025-09-23T16:05:29.291007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q17.2
median32.61
Q3202.9
95-th percentile4000
Maximum60000000
Range60000000
Interquartile range (IQR)195.7

Descriptive statistics

Standard deviation574926.01
Coefficient of variation (CV)43.297752
Kurtosis6798.3984
Mean13278.426
Median Absolute Deviation (MAD)30.51
Skewness76.918472
Sum6.0542985 × 108
Variance3.3053992 × 1011
MonotonicityNot monotonic
2025-09-23T16:05:29.380901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3171
 
0.4%
1.2140
 
0.3%
1.4138
 
0.3%
2.1130
 
0.3%
2.4126
 
0.3%
1.6120
 
0.3%
0.5119
 
0.3%
1.1116
 
0.3%
3.8114
 
0.2%
1.5111
 
0.2%
Other values (12566)44310
96.9%
(Missing)131
 
0.3%
ValueCountFrequency (%)
019
< 0.1%
0.012
 
< 0.1%
0.0131
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.061
 
< 0.1%
0.073
 
< 0.1%
0.082
 
< 0.1%
ValueCountFrequency (%)
600000001
< 0.1%
582000001
< 0.1%
500000001
< 0.1%
300000001
< 0.1%
280000001
< 0.1%
260000001
< 0.1%
243000001
< 0.1%
240000001
< 0.1%
230000001
< 0.1%
220000001
< 0.1%

fall
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Found
44609 
Fell
 
1117

Length

Max length5
Median length5
Mean length4.9755719
Min length4

Characters and Unicode

Total characters227513
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 (%)
Found44609
97.6%
Fell1117
 
2.4%

Length

2025-09-23T16:05:29.458536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:05:29.500791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
found44609
97.6%
fell1117
 
2.4%

Most occurring characters

ValueCountFrequency (%)
F45726
20.1%
o44609
19.6%
u44609
19.6%
n44609
19.6%
d44609
19.6%
l2234
 
1.0%
e1117
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)227513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F45726
20.1%
o44609
19.6%
u44609
19.6%
n44609
19.6%
d44609
19.6%
l2234
 
1.0%
e1117
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)227513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F45726
20.1%
o44609
19.6%
u44609
19.6%
n44609
19.6%
d44609
19.6%
l2234
 
1.0%
e1117
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)227513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F45726
20.1%
o44609
19.6%
u44609
19.6%
n44609
19.6%
d44609
19.6%
l2234
 
1.0%
e1117
 
0.5%

year
Date

Distinct265
Distinct (%)0.6%
Missing291
Missing (%)0.6%
Memory size357.4 KiB
Minimum1970-01-01 00:00:00
Maximum1970-01-01 00:00:00.000002
Invalid dates0
Invalid dates (%)0.0%
2025-09-23T16:05:29.562433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:29.652113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reclat
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct12738
Distinct (%)33.2%
Missing7315
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean-39.107095
Minimum-87.36667
Maximum81.16667
Zeros6438
Zeros (%)14.1%
Negative23416
Negative (%)51.2%
Memory size357.4 KiB
2025-09-23T16:05:29.736881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-87.36667
5-th percentile-84.35476
Q1-76.71377
median-71.5
Q30
95-th percentile34.494325
Maximum81.16667
Range168.53334
Interquartile range (IQR)76.71377

Descriptive statistics

Standard deviation46.386011
Coefficient of variation (CV)-1.1861278
Kurtosis-1.4768651
Mean-39.107095
Median Absolute Deviation (MAD)12.76459
Skewness0.49131573
Sum-1502142.6
Variance2151.662
MonotonicityNot monotonic
2025-09-23T16:05:29.824855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06438
 
14.1%
-71.54761
 
10.4%
-843040
 
6.6%
-721506
 
3.3%
-79.683331130
 
2.5%
-76.71667680
 
1.5%
-76.18333539
 
1.2%
-84.21667263
 
0.6%
-86.36667226
 
0.5%
-86.71667217
 
0.5%
Other values (12728)19611
42.9%
(Missing)7315
 
16.0%
ValueCountFrequency (%)
-87.366674
 
< 0.1%
-87.033333
 
< 0.1%
-86.933333
 
< 0.1%
-86.71667217
0.5%
-86.5666717
 
< 0.1%
-86.544881
 
< 0.1%
-86.53791
 
< 0.1%
-86.537341
 
< 0.1%
-86.537251
 
< 0.1%
-86.530351
 
< 0.1%
ValueCountFrequency (%)
81.166671
< 0.1%
76.533331
< 0.1%
76.133331
< 0.1%
72.883331
< 0.1%
72.683331
< 0.1%
70.733331
< 0.1%
701
< 0.1%
69.11
< 0.1%
681
< 0.1%
67.883331
< 0.1%

reclong
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14640
Distinct (%)38.1%
Missing7315
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean61.052594
Minimum-165.43333
Maximum354.47333
Zeros6214
Zeros (%)13.6%
Negative4057
Negative (%)8.9%
Memory size357.4 KiB
2025-09-23T16:05:29.914677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.43333
5-th percentile-90.427
Q10
median35.66667
Q3157.16667
95-th percentile168
Maximum354.47333
Range519.90666
Interquartile range (IQR)157.16667

Descriptive statistics

Standard deviation80.655258
Coefficient of variation (CV)1.3210783
Kurtosis-0.73139356
Mean61.052594
Median Absolute Deviation (MAD)39.53972
Skewness-0.17438133
Sum2345091.2
Variance6505.2706
MonotonicityNot monotonic
2025-09-23T16:05:30.005999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06214
 
13.6%
35.666674985
 
10.9%
1683040
 
6.6%
261506
 
3.3%
159.75657
 
1.4%
159.66667637
 
1.4%
157.16667542
 
1.2%
155.75473
 
1.0%
160.5263
 
0.6%
-70228
 
0.5%
Other values (14630)19866
43.4%
(Missing)7315
 
16.0%
ValueCountFrequency (%)
-165.433339
< 0.1%
-165.1166717
< 0.1%
-163.166671
 
< 0.1%
-162.551
 
< 0.1%
-157.866671
 
< 0.1%
-157.783331
 
< 0.1%
-149.54
 
< 0.1%
-148.552
 
< 0.1%
-1483
 
< 0.1%
-146.266671
 
< 0.1%
ValueCountFrequency (%)
354.473331
 
< 0.1%
178.21
 
< 0.1%
178.083331
 
< 0.1%
175.730281
 
< 0.1%
175.133331
 
< 0.1%
175185
0.4%
174.500431
 
< 0.1%
174.41
 
< 0.1%
172.71
 
< 0.1%
172.61
 
< 0.1%

GeoLocation
Text

Missing 

Distinct17100
Distinct (%)44.5%
Missing7315
Missing (%)16.0%
Memory size2.9 MiB
2025-09-23T16:05:30.228784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.304809
Min length10

Characters and Unicode

Total characters664695
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

Unique16363 ?
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 (%)
0.012652
 
16.5%
35.666674991
 
6.5%
71.54761
 
6.2%
84.03041
 
4.0%
168.03040
 
4.0%
26.01512
 
2.0%
72.01506
 
2.0%
79.683331130
 
1.5%
76.71667680
 
0.9%
159.75657
 
0.9%
Other values (26608)42852
55.8%
2025-09-23T16:05:30.647611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.76822
11.6%
667560
 
10.2%
752499
 
7.9%
049033
 
7.4%
344771
 
6.7%
144476
 
6.7%
542757
 
6.4%
(38411
 
5.8%
38411
 
5.8%
)38411
 
5.8%
Other values (6)171544
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)664695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.76822
11.6%
667560
 
10.2%
752499
 
7.9%
049033
 
7.4%
344771
 
6.7%
144476
 
6.7%
542757
 
6.4%
(38411
 
5.8%
38411
 
5.8%
)38411
 
5.8%
Other values (6)171544
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)664695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.76822
11.6%
667560
 
10.2%
752499
 
7.9%
049033
 
7.4%
344771
 
6.7%
144476
 
6.7%
542757
 
6.4%
(38411
 
5.8%
38411
 
5.8%
)38411
 
5.8%
Other values (6)171544
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)664695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.76822
11.6%
667560
 
10.2%
752499
 
7.9%
049033
 
7.4%
344771
 
6.7%
144476
 
6.7%
542757
 
6.4%
(38411
 
5.8%
38411
 
5.8%
)38411
 
5.8%
Other values (6)171544
25.8%

source
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
NASA
45726 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters182904
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 rowNASA
2nd rowNASA
3rd rowNASA
4th rowNASA
5th rowNASA

Common Values

ValueCountFrequency (%)
NASA45726
100.0%

Length

2025-09-23T16:05:30.723126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:05:30.760603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
nasa45726
100.0%

Most occurring characters

ValueCountFrequency (%)
A91452
50.0%
N45726
25.0%
S45726
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)182904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A91452
50.0%
N45726
25.0%
S45726
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)182904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A91452
50.0%
N45726
25.0%
S45726
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)182904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A91452
50.0%
N45726
25.0%
S45726
25.0%

boolean
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
True
22934 
False
22792 
ValueCountFrequency (%)
True22934
50.2%
False22792
49.8%
2025-09-23T16:05:30.787704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

mixed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
A
22889 
1
22837 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45726
Distinct characters2
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 row1
2nd rowA
3rd row1
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A22889
50.1%
122837
49.9%

Length

2025-09-23T16:05:30.838404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:05:30.878458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a22889
50.1%
122837
49.9%

Most occurring characters

ValueCountFrequency (%)
A22889
50.1%
122837
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)45726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A22889
50.1%
122837
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A22889
50.1%
122837
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A22889
50.1%
122837
49.9%

unhashable
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.1 MiB

reclat_city
Real number (ℝ)

High correlation  Missing 

Distinct38401
Distinct (%)> 99.9%
Missing7315
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean-39.153542
Minimum-104.31717
Maximum77.749011
Zeros0
Zeros (%)0.0%
Negative26603
Negative (%)58.2%
Memory size357.4 KiB
2025-09-23T16:05:30.941185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-104.31717
5-th percentile-87.871058
Q1-78.407752
median-68.975293
Q34.7886449
95-th percentile35.42981
Maximum77.749011
Range182.06618
Interquartile range (IQR)83.196397

Descriptive statistics

Standard deviation46.685687
Coefficient of variation (CV)-1.1923745
Kurtosis-1.446385
Mean-39.153542
Median Absolute Deviation (MAD)17.255843
Skewness0.48160358
Sum-1503926.7
Variance2179.5534
MonotonicityNot monotonic
2025-09-23T16:05:31.032500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.518060082
 
< 0.1%
23.165965892
 
< 0.1%
-23.288646662
 
< 0.1%
36.51658962
 
< 0.1%
43.279571562
 
< 0.1%
49.607269212
 
< 0.1%
-32.58102192
 
< 0.1%
-29.651528212
 
< 0.1%
52.706635472
 
< 0.1%
52.011044342
 
< 0.1%
Other values (38391)38391
84.0%
(Missing)7315
 
16.0%
ValueCountFrequency (%)
-104.31716651
< 0.1%
-102.43123751
< 0.1%
-102.08682531
< 0.1%
-101.55563731
< 0.1%
-101.32692841
< 0.1%
-101.20843411
< 0.1%
-101.01469351
< 0.1%
-100.91912641
< 0.1%
-100.78569471
< 0.1%
-100.57511171
< 0.1%
ValueCountFrequency (%)
77.749010831
< 0.1%
72.806220231
< 0.1%
72.757304231
< 0.1%
72.426079731
< 0.1%
72.258095951
< 0.1%
71.789382971
< 0.1%
71.425431691
< 0.1%
70.897552121
< 0.1%
70.533731831
< 0.1%
70.485239321
< 0.1%

Interactions

2025-09-23T16:05:27.028771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:25.670323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.018305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.365524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.704328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:27.100349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:25.742819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.088719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.435060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.768982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:27.170601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:25.811519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.156898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.504616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.838411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:27.240561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:25.881253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.224580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.570503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.903021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:27.307099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:25.945721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.293178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.634936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:05:26.962852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-23T16:05:31.101561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
booleanfallidmass (g)mixednametypereclatreclat_cityreclong
boolean1.0000.0000.0000.0000.0000.0000.0000.0150.007
fall0.0001.0000.1260.0120.0000.0000.4500.4240.195
id0.0000.1261.000-0.1420.0090.1300.2610.219-0.316
mass (g)0.0000.012-0.1421.0000.0030.0000.4090.424-0.281
mixed0.0000.0000.0090.0031.0000.0000.0130.0000.000
nametype0.0000.0000.1300.0000.0001.0000.3490.3790.044
reclat0.0000.4500.2610.4090.0130.3491.0000.943-0.650
reclat_city0.0150.4240.2190.4240.0000.3790.9431.000-0.618
reclong0.0070.195-0.316-0.2810.0000.044-0.650-0.6181.000

Missing values

2025-09-23T16:05:27.430206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-23T16:05:27.664825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-23T16:05:27.824449image/svg+xmlMatplotlib v3.10.0, 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)fallyearreclatreclongGeoLocationsourcebooleanmixedunhashablereclat_city
0Aachen1ValidL521.0Fell1970-01-01 00:00:00.00000188050.775006.08333(50.775, 6.08333)NASATrue1[1]50.518060
1Aarhus2ValidH6720.0Fell1970-01-01 00:00:00.00000195156.1833310.23333(56.18333, 10.23333)NASAFalseA[1]52.011044
2Abee6ValidEH4107000.0Fell1970-01-01 00:00:00.00000195254.21667-113.00000(54.21667, -113.0)NASAFalse1[1]52.706635
3Acapulco10ValidAcapulcoite1914.0Fell1970-01-01 00:00:00.00000197616.88333-99.90000(16.88333, -99.9)NASAFalseA[1]23.165966
4Achiras370ValidL6780.0Fell1970-01-01 00:00:00.000001902-33.16667-64.95000(-33.16667, -64.95)NASAFalseA[1]-23.288647
5Adhi Kot379ValidEH44239.0Fell1970-01-01 00:00:00.00000191932.1000071.80000(32.1, 71.8)NASATrue1[1]36.516590
6Adzhi-Bogdo (stone)390ValidLL3-6910.0Fell1970-01-01 00:00:00.00000194944.8333395.16667(44.83333, 95.16667)NASATrue1[1]43.279572
7Agen392ValidH530000.0Fell1970-01-01 00:00:00.00000181444.216670.61667(44.21667, 0.61667)NASAFalseA[1]49.607269
8Aguada398ValidL61620.0Fell1970-01-01 00:00:00.000001930-31.60000-65.23333(-31.6, -65.23333)NASAFalse1[1]-32.581022
9Aguila Blanca417ValidL1440.0Fell1970-01-01 00:00:00.000001920-30.86667-64.55000(-30.86667, -64.55)NASAFalseA[1]-29.651528
nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationsourcebooleanmixedunhashablereclat_city
45716Aachen1ValidL521.0Fell1970-01-01 00:00:00.00000188050.775006.08333(50.775, 6.08333)NASATrue1[1]50.518060
45717Aarhus2ValidH6720.0Fell1970-01-01 00:00:00.00000195156.1833310.23333(56.18333, 10.23333)NASAFalseA[1]52.011044
45718Abee6ValidEH4107000.0Fell1970-01-01 00:00:00.00000195254.21667-113.00000(54.21667, -113.0)NASAFalse1[1]52.706635
45719Acapulco10ValidAcapulcoite1914.0Fell1970-01-01 00:00:00.00000197616.88333-99.90000(16.88333, -99.9)NASAFalseA[1]23.165966
45720Achiras370ValidL6780.0Fell1970-01-01 00:00:00.000001902-33.16667-64.95000(-33.16667, -64.95)NASAFalseA[1]-23.288647
45721Adhi Kot379ValidEH44239.0Fell1970-01-01 00:00:00.00000191932.1000071.80000(32.1, 71.8)NASATrue1[1]36.516590
45722Adzhi-Bogdo (stone)390ValidLL3-6910.0Fell1970-01-01 00:00:00.00000194944.8333395.16667(44.83333, 95.16667)NASATrue1[1]43.279572
45723Agen392ValidH530000.0Fell1970-01-01 00:00:00.00000181444.216670.61667(44.21667, 0.61667)NASAFalseA[1]49.607269
45724Aguada398ValidL61620.0Fell1970-01-01 00:00:00.000001930-31.60000-65.23333(-31.6, -65.23333)NASAFalse1[1]-32.581022
45725Aguila Blanca417ValidL1440.0Fell1970-01-01 00:00:00.000001920-30.86667-64.55000(-30.86667, -64.55)NASAFalseA[1]-29.651528

Duplicate rows

Most frequently occurring

nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationsourcebooleanmixedreclat_city# duplicates
0Aachen1ValidL521.0Fell1970-01-01 00:00:00.00000188050.775006.08333(50.775, 6.08333)NASATrue150.5180602
1Aarhus2ValidH6720.0Fell1970-01-01 00:00:00.00000195156.1833310.23333(56.18333, 10.23333)NASAFalseA52.0110442
2Abee6ValidEH4107000.0Fell1970-01-01 00:00:00.00000195254.21667-113.00000(54.21667, -113.0)NASAFalse152.7066352
3Acapulco10ValidAcapulcoite1914.0Fell1970-01-01 00:00:00.00000197616.88333-99.90000(16.88333, -99.9)NASAFalseA23.1659662
4Achiras370ValidL6780.0Fell1970-01-01 00:00:00.000001902-33.16667-64.95000(-33.16667, -64.95)NASAFalseA-23.2886472
5Adhi Kot379ValidEH44239.0Fell1970-01-01 00:00:00.00000191932.1000071.80000(32.1, 71.8)NASATrue136.5165902
6Adzhi-Bogdo (stone)390ValidLL3-6910.0Fell1970-01-01 00:00:00.00000194944.8333395.16667(44.83333, 95.16667)NASATrue143.2795722
7Agen392ValidH530000.0Fell1970-01-01 00:00:00.00000181444.216670.61667(44.21667, 0.61667)NASAFalseA49.6072692
8Aguada398ValidL61620.0Fell1970-01-01 00:00:00.000001930-31.60000-65.23333(-31.6, -65.23333)NASAFalse1-32.5810222
9Aguila Blanca417ValidL1440.0Fell1970-01-01 00:00:00.000001920-30.86667-64.55000(-30.86667, -64.55)NASAFalseA-29.6515282