Overview

Brought to you by YData

Dataset statistics

Number of variables6
Number of observations1248
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.6 KiB
Average record size in memory210.6 B

Variable types

Numeric3
Text1
DateTime2

Alerts

latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation

Reproduction

Analysis started2025-08-18 08:00:23.929058
Analysis finished2025-08-18 08:00:26.490335
Duration2.56 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

latitude
Real number (ℝ)

High correlation 

Distinct297
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.242348
Minimum23.71
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2025-08-18T13:45:26.621491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.71
5-th percentile27.2835
Q127.73
median27.94
Q328.65
95-th percentile29.81
Maximum30.5
Range6.79
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation0.83851772
Coefficient of variation (CV)0.029690085
Kurtosis1.2368092
Mean28.242348
Median Absolute Deviation (MAD)0.29
Skewness0.44398603
Sum35246.45
Variance0.70311197
MonotonicityNot monotonic
2025-08-18T13:45:26.805539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.8 26
 
2.1%
27.79 24
 
1.9%
27.69 20
 
1.6%
27.92 19
 
1.5%
27.66 19
 
1.5%
27.7 18
 
1.4%
27.84 18
 
1.4%
27.67 18
 
1.4%
27.75 17
 
1.4%
27.87 17
 
1.4%
Other values (287) 1052
84.3%
ValueCountFrequency (%)
23.71 1
0.1%
23.83 1
0.1%
24.17 1
0.1%
25.63 1
0.1%
26.12 1
0.1%
26.63 1
0.1%
26.68 1
0.1%
26.74 1
0.1%
26.75 1
0.1%
26.76 1
0.1%
ValueCountFrequency (%)
30.5 1
0.1%
30.37 1
0.1%
30.31 1
0.1%
30.3 1
0.1%
30.25 1
0.1%
30.24 1
0.1%
30.22 1
0.1%
30.2 1
0.1%
30.16 1
0.1%
30.15 1
0.1%

longitude
Real number (ℝ)

High correlation 

Distinct508
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.65617
Minimum69.85
Maximum89.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2025-08-18T13:45:26.971676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum69.85
5-th percentile81.05
Q182.53
median85.345
Q386.1
95-th percentile87.8
Maximum89.88
Range20.03
Interquartile range (IQR)3.57

Descriptive statistics

Standard deviation2.1429375
Coefficient of variation (CV)0.025313424
Kurtosis0.79404533
Mean84.65617
Median Absolute Deviation (MAD)0.905
Skewness-0.74731489
Sum105650.9
Variance4.5921813
MonotonicityNot monotonic
2025-08-18T13:45:27.155206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.09 12
 
1.0%
86.2 12
 
1.0%
86.07 11
 
0.9%
85.29 10
 
0.8%
85.16 10
 
0.8%
86.17 10
 
0.8%
85.87 10
 
0.8%
85.83 9
 
0.7%
86.16 9
 
0.7%
86 9
 
0.7%
Other values (498) 1146
91.8%
ValueCountFrequency (%)
69.85 1
0.1%
79.26 1
0.1%
80.1 1
0.1%
80.37 2
0.2%
80.39 1
0.1%
80.45 1
0.1%
80.47 1
0.1%
80.48 1
0.1%
80.51 1
0.1%
80.53 1
0.1%
ValueCountFrequency (%)
89.88 1
0.1%
89.09 1
0.1%
88.51 1
0.1%
88.38 1
0.1%
88.36 1
0.1%
88.32 1
0.1%
88.21 1
0.1%
88.19 1
0.1%
88.18 1
0.1%
88.16 1
0.1%

magnitude
Real number (ℝ)

Distinct31
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4435897
Minimum4
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2025-08-18T13:45:27.343947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14.1
median4.3
Q34.6
95-th percentile5.5
Maximum7.6
Range3.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.49950153
Coefficient of variation (CV)0.11240946
Kurtosis4.694201
Mean4.4435897
Median Absolute Deviation (MAD)0.2
Skewness1.888297
Sum5545.6
Variance0.24950178
MonotonicityNot monotonic
2025-08-18T13:45:27.462373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 239
19.2%
4.1 189
15.1%
4.2 171
13.7%
4.3 107
8.6%
4.5 98
7.9%
4.4 88
 
7.1%
4.6 63
 
5.0%
5 50
 
4.0%
4.7 44
 
3.5%
4.8 37
 
3.0%
Other values (21) 162
13.0%
ValueCountFrequency (%)
4 239
19.2%
4.1 189
15.1%
4.2 171
13.7%
4.3 107
8.6%
4.4 88
 
7.1%
4.5 98
7.9%
4.6 63
 
5.0%
4.7 44
 
3.5%
4.8 37
 
3.0%
4.9 23
 
1.8%
ValueCountFrequency (%)
7.6 1
0.1%
7 2
0.2%
6.9 2
0.2%
6.8 1
0.1%
6.6 2
0.2%
6.5 1
0.1%
6.4 1
0.1%
6.3 2
0.2%
6.2 1
0.1%
6.1 1
0.1%

epicenter
Text

Distinct105
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
2025-08-18T13:45:27.704712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length25
Mean length8.2764423
Min length4

Characters and Unicode

Total characters10329
Distinct characters44
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

Unique40 ?
Unique (%)3.2%

Sample

1st rowJajarkot
2nd rowJajarkot
3rd rowDarchula
4th rowHumla
5th rowTingri China
ValueCountFrequency (%)
dolakha 203
 
15.9%
sindhupalchowk 116
 
9.1%
bajhang 74
 
5.8%
bajura 58
 
4.5%
sindhupalchok 58
 
4.5%
dhading 56
 
4.4%
taplejung 52
 
4.1%
gorkha 51
 
4.0%
rasuwa 48
 
3.8%
nuwakot 42
 
3.3%
Other values (95) 521
40.7%
2025-08-18T13:45:28.106309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1818
17.6%
h 1022
 
9.9%
u 702
 
6.8%
k 648
 
6.3%
l 633
 
6.1%
o 586
 
5.7%
n 557
 
5.4%
i 404
 
3.9%
D 327
 
3.2%
p 318
 
3.1%
Other values (34) 3314
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9001
87.1%
Uppercase Letter 1297
 
12.6%
Space Separator 31
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1818
20.2%
h 1022
11.4%
u 702
 
7.8%
k 648
 
7.2%
l 633
 
7.0%
o 586
 
6.5%
n 557
 
6.2%
i 404
 
4.5%
p 318
 
3.5%
g 315
 
3.5%
Other values (13) 1998
22.2%
Uppercase Letter
ValueCountFrequency (%)
D 327
25.2%
S 238
18.4%
B 178
13.7%
R 90
 
6.9%
T 85
 
6.6%
K 67
 
5.2%
G 51
 
3.9%
N 43
 
3.3%
L 36
 
2.8%
M 35
 
2.7%
Other values (10) 147
11.3%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10298
99.7%
Common 31
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1818
17.7%
h 1022
 
9.9%
u 702
 
6.8%
k 648
 
6.3%
l 633
 
6.1%
o 586
 
5.7%
n 557
 
5.4%
i 404
 
3.9%
D 327
 
3.2%
p 318
 
3.1%
Other values (33) 3283
31.9%
Common
ValueCountFrequency (%)
31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1818
17.6%
h 1022
 
9.9%
u 702
 
6.8%
k 648
 
6.3%
l 633
 
6.1%
o 586
 
5.7%
n 557
 
5.4%
i 404
 
3.9%
D 327
 
3.2%
p 318
 
3.1%
Other values (34) 3314
32.1%

ad_date
Date

Distinct834
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
Minimum1994-03-08 00:00:00
Maximum2025-04-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-18T13:45:28.267773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:28.467377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

datetime
Date

Distinct1239
Distinct (%)99.4%
Missing1
Missing (%)0.1%
Memory size19.5 KiB
Minimum1994-03-08 02:05:00
Maximum2025-04-04 14:25:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-18T13:45:28.606346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:28.784594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-08-18T13:45:25.724667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:24.823673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:25.278827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:25.877400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:24.988388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:25.421743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:26.006543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:25.138097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T13:45:25.588337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-18T13:45:28.916665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
latitudelongitudemagnitude
latitude1.000-0.8710.070
longitude-0.8711.000-0.018
magnitude0.070-0.0181.000

Missing values

2025-08-18T13:45:26.283311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-18T13:45:26.399195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

latitudelongitudemagnitudeepicenterad_datedatetime
028.9682.125.5Jajarkot4/4/20254/4/2025 14:25
128.9582.125.2Jajarkot4/4/20254/4/2025 14:22
230.0280.844.0Darchula4/3/20254/3/2025 11:19
329.6981.824.5Humla3/26/20253/26/2025 13:59
428.7086.745.5Tingri China3/26/20253/26/2025 12:42
529.1981.444.3Achham3/18/20253/18/2025 0:48
628.2884.134.3Kaski3/10/20253/10/2025 22:26
728.4287.635.9Dinggye China3/8/20253/8/2025 8:50
828.4683.224.1Baglung3/8/20253/8/2025 0:35
928.5383.334.0Myagdi3/7/20253/7/2025 21:29
latitudelongitudemagnitudeepicenterad_datedatetime
123929.7780.704.4Darchula11/7/199411/7/1994 8:40
124028.8281.974.5Jajarkot10/24/199410/24/1994 0:00
124128.8282.224.5Jajarkot10/22/199410/22/1994 7:50
124226.8186.094.1Dhanusha9/27/19949/27/1994 13:57
124326.9286.624.0Udayapur8/16/19948/16/1994 19:59
124427.8286.195.1Dolakha6/25/19946/25/1994 8:32
124529.8082.414.5Mugu5/25/19945/25/1994 13:25
124629.8181.774.0Bajura5/25/19945/25/1994 6:15
124728.5283.034.4Baglung5/3/19945/3/1994 18:45
124829.3381.834.0Kalikot3/8/19943/8/1994 2:05