Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations4063
Missing cells23512
Missing cells (%)23.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory954.3 KiB
Average record size in memory240.5 B

Variable types

Numeric14
Categorical11

Alerts

awareness_of_electricity_consumption_of_renters has constant value "1.0" Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
LECO_csc_area is highly overall correlated with type_of_electricity_meterHigh correlation
charging_method_of_renters_for_electricity is highly overall correlated with floor_which_house_located and 2 other fieldsHigh correlation
floor_which_house_located is highly overall correlated with charging_method_of_renters_for_electricityHigh correlation
highest_level_of_education_of_the_chief_wage_earner is highly overall correlated with socio_economic_classHigh correlation
is_there_business_carried_out_in_the_household is highly overall correlated with type_of_businessHigh correlation
no_of_storeys is highly overall correlated with charging_method_of_renters_for_electricity and 2 other fieldsHigh correlation
occupy_renters_boarders is highly overall correlated with own_the_house_or_living_on_rentHigh correlation
own_the_house_or_living_on_rent is highly overall correlated with charging_method_of_renters_for_electricity and 1 other fieldsHigh correlation
socio_economic_class is highly overall correlated with highest_level_of_education_of_the_chief_wage_earnerHigh correlation
type_of_business is highly overall correlated with is_there_business_carried_out_in_the_household and 1 other fieldsHigh correlation
type_of_electricity_meter is highly overall correlated with LECO_csc_areaHigh correlation
type_of_house is highly overall correlated with no_of_storeysHigh correlation
own_the_house_or_living_on_rent is highly imbalanced (68.5%) Imbalance
occupy_renters_boarders is highly imbalanced (86.2%) Imbalance
is_there_business_carried_out_in_the_household is highly imbalanced (73.2%) Imbalance
any_constructions_or_renovations_in_the_household is highly imbalanced (71.3%) Imbalance
occupy_renters_boarders has 536 (13.2%) missing values Missing
awareness_of_electricity_consumption_of_renters has 3959 (97.4%) missing values Missing
no_of_storeys has 3814 (93.9%) missing values Missing
charging_method_of_renters_for_electricity has 3959 (97.4%) missing values Missing
charged_method_for_rent_for_electricity has 3527 (86.8%) missing values Missing
type_of_business has 3877 (95.4%) missing values Missing
whom_or_how_the_house_was_designed has 1280 (31.5%) missing values Missing
availability_of_certificate_of_compliance has 1280 (31.5%) missing values Missing
main_material_used_for_roof_of_the_house has 1280 (31.5%) missing values Missing

Reproduction

Analysis started2024-11-18 08:35:57.414065
Analysis finished2024-11-18 08:36:16.291615
Duration18.88 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

no_of_electricity_meters
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0762983
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:16.328453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31628519
Coefficient of variation (CV)0.29386387
Kurtosis51.158272
Mean1.0762983
Median Absolute Deviation (MAD)0
Skewness5.6452849
Sum4373
Variance0.10003632
MonotonicityNot monotonic
2024-11-18T14:06:16.411032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3799
93.5%
2 225
 
5.5%
3 36
 
0.9%
5 1
 
< 0.1%
7 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
1 3799
93.5%
2 225
 
5.5%
3 36
 
0.9%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 36
 
0.9%
2 225
 
5.5%
1 3799
93.5%

LECO_csc_area
Categorical

High correlation 

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
MORATUWA NORTH
533 
MORATUWA SOUTH
370 
PANADURA
357 
GALLE
 
216
KESELWATTA
 
206
Other values (18)
2381 

Length

Max length14
Median length11
Mean length9.6475511
Min length5

Characters and Unicode

Total characters39198
Distinct characters22
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 rowGALLE
2nd rowGALLE
3rd rowGALLE
4th rowBORALASGAMUWA
5th rowKOLONNAWA

Common Values

ValueCountFrequency (%)
MORATUWA NORTH 533
 
13.1%
MORATUWA SOUTH 370
 
9.1%
PANADURA 357
 
8.8%
GALLE 216
 
5.3%
KESELWATTA 206
 
5.1%
MAHARAGAMA 202
 
5.0%
PAYAGALA 196
 
4.8%
KALUTARA 189
 
4.7%
HIKKADUWA 163
 
4.0%
ALUTHGAMA 158
 
3.9%
Other values (13) 1473
36.3%

Length

2024-11-18T14:06:16.509933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moratuwa 903
18.2%
north 533
 
10.7%
south 370
 
7.5%
panadura 357
 
7.2%
galle 216
 
4.3%
keselwatta 206
 
4.1%
maharagama 202
 
4.1%
payagala 196
 
3.9%
kalutara 189
 
3.8%
hikkaduwa 163
 
3.3%
Other values (14) 1631
32.8%

Most occurring characters

ValueCountFrequency (%)
A 10055
25.7%
T 3417
 
8.7%
O 2914
 
7.4%
U 2568
 
6.6%
R 2454
 
6.3%
M 2125
 
5.4%
L 1849
 
4.7%
W 1788
 
4.6%
N 1693
 
4.3%
H 1565
 
4.0%
Other values (12) 8770
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10055
25.7%
T 3417
 
8.7%
O 2914
 
7.4%
U 2568
 
6.6%
R 2454
 
6.3%
M 2125
 
5.4%
L 1849
 
4.7%
W 1788
 
4.6%
N 1693
 
4.3%
H 1565
 
4.0%
Other values (12) 8770
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10055
25.7%
T 3417
 
8.7%
O 2914
 
7.4%
U 2568
 
6.6%
R 2454
 
6.3%
M 2125
 
5.4%
L 1849
 
4.7%
W 1788
 
4.6%
N 1693
 
4.3%
H 1565
 
4.0%
Other values (12) 8770
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10055
25.7%
T 3417
 
8.7%
O 2914
 
7.4%
U 2568
 
6.6%
R 2454
 
6.3%
M 2125
 
5.4%
L 1849
 
4.7%
W 1788
 
4.6%
N 1693
 
4.3%
H 1565
 
4.0%
Other values (12) 8770
22.4%

own_the_house_or_living_on_rent
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
1
3527 
2
482 
4
 
50
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
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

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

Length

2024-11-18T14:06:16.598940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:16.682973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3527
86.8%
2 482
 
11.9%
4 50
 
1.2%
3 4
 
0.1%

occupy_renters_boarders
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing536
Missing (%)13.2%
Memory size192.5 KiB
3.0
3423 
1.0
 
72
2.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10581
Distinct characters5
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 row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 3423
84.2%
1.0 72
 
1.8%
2.0 32
 
0.8%
(Missing) 536
 
13.2%

Length

2024-11-18T14:06:16.777313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:16.859218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3423
97.1%
1.0 72
 
2.0%
2.0 32
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 3527
33.3%
0 3527
33.3%
3 3423
32.4%
1 72
 
0.7%
2 32
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3527
33.3%
0 3527
33.3%
3 3423
32.4%
1 72
 
0.7%
2 32
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3527
33.3%
0 3527
33.3%
3 3423
32.4%
1 72
 
0.7%
2 32
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3527
33.3%
0 3527
33.3%
3 3423
32.4%
1 72
 
0.7%
2 32
 
0.3%

awareness_of_electricity_consumption_of_renters
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.0%
Missing3959
Missing (%)97.4%
Memory size192.5 KiB
1.0
104 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters312
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 104
 
2.6%
(Missing) 3959
97.4%

Length

2024-11-18T14:06:16.944851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:17.015802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 104
100.0%

Most occurring characters

ValueCountFrequency (%)
1 104
33.3%
. 104
33.3%
0 104
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 104
33.3%
. 104
33.3%
0 104
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 104
33.3%
. 104
33.3%
0 104
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 104
33.3%
. 104
33.3%
0 104
33.3%

built_year_of_the_house
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6022643
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:17.076392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7987946
Coefficient of variation (CV)0.49935108
Kurtosis-0.86253795
Mean3.6022643
Median Absolute Deviation (MAD)1
Skewness0.11934928
Sum14636
Variance3.235662
MonotonicityNot monotonic
2024-11-18T14:06:17.155805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 918
22.6%
5 758
18.7%
1 740
18.2%
3 615
15.1%
2 482
11.9%
7 325
 
8.0%
6 225
 
5.5%
ValueCountFrequency (%)
1 740
18.2%
2 482
11.9%
3 615
15.1%
4 918
22.6%
5 758
18.7%
6 225
 
5.5%
7 325
 
8.0%
ValueCountFrequency (%)
7 325
 
8.0%
6 225
 
5.5%
5 758
18.7%
4 918
22.6%
3 615
15.1%
2 482
11.9%
1 740
18.2%

type_of_house
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5439331
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:17.239618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99307151
Coefficient of variation (CV)0.64320892
Kurtosis19.557807
Mean1.5439331
Median Absolute Deviation (MAD)0
Skewness3.7390786
Sum6273
Variance0.98619103
MonotonicityNot monotonic
2024-11-18T14:06:17.590488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2482
61.1%
2 1332
32.8%
3 113
 
2.8%
5 80
 
2.0%
6 13
 
0.3%
9 11
 
0.3%
8 11
 
0.3%
4 10
 
0.2%
7 9
 
0.2%
10 2
 
< 0.1%
ValueCountFrequency (%)
1 2482
61.1%
2 1332
32.8%
3 113
 
2.8%
4 10
 
0.2%
5 80
 
2.0%
6 13
 
0.3%
7 9
 
0.2%
8 11
 
0.3%
9 11
 
0.3%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 11
 
0.3%
8 11
 
0.3%
7 9
 
0.2%
6 13
 
0.3%
5 80
 
2.0%
4 10
 
0.2%
3 113
 
2.8%
2 1332
32.8%
1 2482
61.1%

floor_which_house_located
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.91361063
Minimum-1
Maximum11
Zeros17
Zeros (%)0.4%
Negative3970
Negative (%)97.7%
Memory size192.5 KiB
2024-11-18T14:06:17.671645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum11
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70600528
Coefficient of variation (CV)-0.77276386
Kurtosis129.58793
Mean-0.91361063
Median Absolute Deviation (MAD)0
Skewness10.656777
Sum-3712
Variance0.49844346
MonotonicityNot monotonic
2024-11-18T14:06:17.758099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-1 3970
97.7%
1 24
 
0.6%
2 18
 
0.4%
0 17
 
0.4%
3 7
 
0.2%
4 7
 
0.2%
5 4
 
0.1%
8 4
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
Other values (3) 6
 
0.1%
ValueCountFrequency (%)
-1 3970
97.7%
0 17
 
0.4%
1 24
 
0.6%
2 18
 
0.4%
3 7
 
0.2%
4 7
 
0.2%
5 4
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 4
 
0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 3
 
0.1%
8 4
 
0.1%
7 3
 
0.1%
6 3
 
0.1%
5 4
 
0.1%
4 7
 
0.2%
3 7
 
0.2%
2 18
0.4%

no_of_storeys
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)2.4%
Missing3814
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean1.746988
Minimum0
Maximum5
Zeros35
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:17.838583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1378039
Coefficient of variation (CV)0.65129465
Kurtosis-1.2583883
Mean1.746988
Median Absolute Deviation (MAD)1
Skewness0.013050986
Sum435
Variance1.2945977
MonotonicityNot monotonic
2024-11-18T14:06:17.921803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 93
 
2.3%
1 91
 
2.2%
0 35
 
0.9%
2 28
 
0.7%
4 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 3814
93.9%
ValueCountFrequency (%)
0 35
 
0.9%
1 91
2.2%
2 28
 
0.7%
3 93
2.3%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 1
 
< 0.1%
3 93
2.3%
2 28
 
0.7%
1 91
2.2%
0 35
 
0.9%

floor_area
Real number (ℝ)

Distinct387
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1987.4175
Minimum100
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:18.024593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1607.555
median1000
Q32000
95-th percentile3000
Maximum99999
Range99899
Interquartile range (IQR)1392.445

Descriptive statistics

Standard deviation7923.4353
Coefficient of variation (CV)3.9867996
Kurtosis147.08142
Mean1987.4175
Median Absolute Deviation (MAD)500
Skewness12.119169
Sum8074877.3
Variance62780827
MonotonicityNot monotonic
2024-11-18T14:06:18.142511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 277
 
6.8%
1000 264
 
6.5%
1200 256
 
6.3%
800 203
 
5.0%
600 202
 
5.0%
2400 180
 
4.4%
1500 175
 
4.3%
2000 171
 
4.2%
500 140
 
3.4%
400 111
 
2.7%
Other values (377) 2084
51.3%
ValueCountFrequency (%)
100 12
0.3%
108 1
 
< 0.1%
120 2
 
< 0.1%
125 1
 
< 0.1%
136.5 1
 
< 0.1%
140 2
 
< 0.1%
143 1
 
< 0.1%
144 1
 
< 0.1%
150 22
0.5%
160 1
 
< 0.1%
ValueCountFrequency (%)
99999 26
0.6%
9000 2
 
< 0.1%
6000 1
 
< 0.1%
5000 1
 
< 0.1%
4700 1
 
< 0.1%
4600 7
 
0.2%
4400 8
 
0.2%
4200 15
0.4%
4000 23
0.6%
3960 2
 
< 0.1%

no_of_household_members
Real number (ℝ)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0044302
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:18.237543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6872622
Coefficient of variation (CV)0.42134889
Kurtosis1.2549374
Mean4.0044302
Median Absolute Deviation (MAD)1
Skewness0.68467186
Sum16270
Variance2.8468538
MonotonicityNot monotonic
2024-11-18T14:06:18.326296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 1014
25.0%
3 818
20.1%
5 772
19.0%
2 602
14.8%
6 407
10.0%
1 186
 
4.6%
7 144
 
3.5%
8 65
 
1.6%
9 29
 
0.7%
10 15
 
0.4%
Other values (3) 11
 
0.3%
ValueCountFrequency (%)
1 186
 
4.6%
2 602
14.8%
3 818
20.1%
4 1014
25.0%
5 772
19.0%
6 407
10.0%
7 144
 
3.5%
8 65
 
1.6%
9 29
 
0.7%
10 15
 
0.4%
ValueCountFrequency (%)
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 15
 
0.4%
9 29
 
0.7%
8 65
 
1.6%
7 144
 
3.5%
6 407
10.0%
5 772
19.0%
4 1014
25.0%

charging_method_of_renters_for_electricity
Categorical

High correlation  Missing 

Distinct5
Distinct (%)4.8%
Missing3959
Missing (%)97.4%
Memory size192.5 KiB
1.0
34 
5.0
24 
2.0
21 
3.0
19 
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters312
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 row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 34
 
0.8%
5.0 24
 
0.6%
2.0 21
 
0.5%
3.0 19
 
0.5%
4.0 6
 
0.1%
(Missing) 3959
97.4%

Length

2024-11-18T14:06:18.417882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:18.502652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 34
32.7%
5.0 24
23.1%
2.0 21
20.2%
3.0 19
18.3%
4.0 6
 
5.8%

Most occurring characters

ValueCountFrequency (%)
. 104
33.3%
0 104
33.3%
1 34
 
10.9%
5 24
 
7.7%
2 21
 
6.7%
3 19
 
6.1%
4 6
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 104
33.3%
0 104
33.3%
1 34
 
10.9%
5 24
 
7.7%
2 21
 
6.7%
3 19
 
6.1%
4 6
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 104
33.3%
0 104
33.3%
1 34
 
10.9%
5 24
 
7.7%
2 21
 
6.7%
3 19
 
6.1%
4 6
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 104
33.3%
0 104
33.3%
1 34
 
10.9%
5 24
 
7.7%
2 21
 
6.7%
3 19
 
6.1%
4 6
 
1.9%

charged_method_for_rent_for_electricity
Real number (ℝ)

Missing 

Distinct6
Distinct (%)1.1%
Missing3527
Missing (%)86.8%
Infinite0
Infinite (%)0.0%
Mean1.2294776
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:18.586035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2.25
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94401633
Coefficient of variation (CV)0.76781905
Kurtosis18.995525
Mean1.2294776
Median Absolute Deviation (MAD)0
Skewness4.4520099
Sum659
Variance0.89116683
MonotonicityNot monotonic
2024-11-18T14:06:18.670498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 496
 
12.2%
6 17
 
0.4%
2 13
 
0.3%
3 6
 
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
(Missing) 3527
86.8%
ValueCountFrequency (%)
1 496
12.2%
2 13
 
0.3%
3 6
 
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
6 17
 
0.4%
ValueCountFrequency (%)
6 17
 
0.4%
5 1
 
< 0.1%
4 3
 
0.1%
3 6
 
0.1%
2 13
 
0.3%
1 496
12.2%

is_there_business_carried_out_in_the_household
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
0
3877 
1
 
186

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
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 row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

Length

2024-11-18T14:06:18.761038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:18.841468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3877
95.4%
1 186
 
4.6%

type_of_business
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.6%
Missing3877
Missing (%)95.4%
Memory size192.5 KiB
3.0
121 
1.0
63 
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters558
Distinct characters5
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 row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 121
 
3.0%
1.0 63
 
1.6%
2.0 2
 
< 0.1%
(Missing) 3877
95.4%

Length

2024-11-18T14:06:18.926753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:19.011089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 121
65.1%
1.0 63
33.9%
2.0 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 186
33.3%
0 186
33.3%
3 121
21.7%
1 63
 
11.3%
2 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 186
33.3%
0 186
33.3%
3 121
21.7%
1 63
 
11.3%
2 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 186
33.3%
0 186
33.3%
3 121
21.7%
1 63
 
11.3%
2 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 186
33.3%
0 186
33.3%
3 121
21.7%
1 63
 
11.3%
2 2
 
0.4%

whom_or_how_the_house_was_designed
Real number (ℝ)

Missing 

Distinct6
Distinct (%)0.2%
Missing1280
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean2.9238232
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:19.088426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.1515088
Coefficient of variation (CV)0.73585461
Kurtosis-1.5456763
Mean2.9238232
Median Absolute Deviation (MAD)1
Skewness0.44931759
Sum8137
Variance4.6289901
MonotonicityNot monotonic
2024-11-18T14:06:19.174305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1389
34.2%
6 738
18.2%
4 359
 
8.8%
3 125
 
3.1%
2 117
 
2.9%
5 55
 
1.4%
(Missing) 1280
31.5%
ValueCountFrequency (%)
1 1389
34.2%
2 117
 
2.9%
3 125
 
3.1%
4 359
 
8.8%
5 55
 
1.4%
6 738
18.2%
ValueCountFrequency (%)
6 738
18.2%
5 55
 
1.4%
4 359
 
8.8%
3 125
 
3.1%
2 117
 
2.9%
1 1389
34.2%
Distinct3
Distinct (%)0.1%
Missing1280
Missing (%)31.5%
Memory size192.5 KiB
1.0
1268 
2.0
851 
0.0
664 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8349
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

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1268
31.2%
2.0 851
20.9%
0.0 664
16.3%
(Missing) 1280
31.5%

Length

2024-11-18T14:06:19.268565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:19.346479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1268
45.6%
2.0 851
30.6%
0.0 664
23.9%

Most occurring characters

ValueCountFrequency (%)
0 3447
41.3%
. 2783
33.3%
1 1268
 
15.2%
2 851
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3447
41.3%
. 2783
33.3%
1 1268
 
15.2%
2 851
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3447
41.3%
. 2783
33.3%
1 1268
 
15.2%
2 851
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3447
41.3%
. 2783
33.3%
1 1268
 
15.2%
2 851
 
10.2%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4767413
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:19.426430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.634354
Coefficient of variation (CV)1.0636371
Kurtosis5.4584519
Mean2.4767413
Median Absolute Deviation (MAD)1
Skewness2.5815581
Sum10063
Variance6.9398208
MonotonicityNot monotonic
2024-11-18T14:06:19.512636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 1837
45.2%
1 1621
39.9%
11 299
 
7.4%
4 191
 
4.7%
7 49
 
1.2%
5 29
 
0.7%
3 19
 
0.5%
10 9
 
0.2%
9 8
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 1621
39.9%
2 1837
45.2%
3 19
 
0.5%
4 191
 
4.7%
5 29
 
0.7%
7 49
 
1.2%
8 1
 
< 0.1%
9 8
 
0.2%
10 9
 
0.2%
11 299
 
7.4%
ValueCountFrequency (%)
11 299
 
7.4%
10 9
 
0.2%
9 8
 
0.2%
8 1
 
< 0.1%
7 49
 
1.2%
5 29
 
0.7%
4 191
 
4.7%
3 19
 
0.5%
2 1837
45.2%
1 1621
39.9%

main_material_used_for_roof_of_the_house
Real number (ℝ)

Missing 

Distinct8
Distinct (%)0.3%
Missing1280
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean2.093065
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:19.594604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82006123
Coefficient of variation (CV)0.39179921
Kurtosis18.909896
Mean2.093065
Median Absolute Deviation (MAD)0
Skewness2.695379
Sum5825
Variance0.67250042
MonotonicityNot monotonic
2024-11-18T14:06:19.683183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 1665
41.0%
3 577
 
14.2%
1 494
 
12.2%
5 19
 
0.5%
4 14
 
0.3%
9 8
 
0.2%
8 5
 
0.1%
7 1
 
< 0.1%
(Missing) 1280
31.5%
ValueCountFrequency (%)
1 494
 
12.2%
2 1665
41.0%
3 577
 
14.2%
4 14
 
0.3%
5 19
 
0.5%
7 1
 
< 0.1%
8 5
 
0.1%
9 8
 
0.2%
ValueCountFrequency (%)
9 8
 
0.2%
8 5
 
0.1%
7 1
 
< 0.1%
5 19
 
0.5%
4 14
 
0.3%
3 577
 
14.2%
2 1665
41.0%
1 494
 
12.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
0
3859 
1
 
204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
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 row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

Length

2024-11-18T14:06:19.781273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:19.859291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3859
95.0%
1 204
 
5.0%

highest_level_of_education_of_the_chief_wage_earner
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4176717
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:19.928632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3204272
Coefficient of variation (CV)0.29889665
Kurtosis-0.11769917
Mean4.4176717
Median Absolute Deviation (MAD)1
Skewness0.58894582
Sum17949
Variance1.7435281
MonotonicityNot monotonic
2024-11-18T14:06:20.010903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 1879
46.2%
3 672
 
16.5%
5 563
 
13.9%
7 528
 
13.0%
6 272
 
6.7%
2 125
 
3.1%
1 24
 
0.6%
ValueCountFrequency (%)
1 24
 
0.6%
2 125
 
3.1%
3 672
 
16.5%
4 1879
46.2%
5 563
 
13.9%
6 272
 
6.7%
7 528
 
13.0%
ValueCountFrequency (%)
7 528
 
13.0%
6 272
 
6.7%
5 563
 
13.9%
4 1879
46.2%
3 672
 
16.5%
2 125
 
3.1%
1 24
 
0.6%
Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8562638
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:20.098426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1687349
Coefficient of variation (CV)0.65250468
Kurtosis-0.65681245
Mean4.8562638
Median Absolute Deviation (MAD)2
Skewness0.59021035
Sum19731
Variance10.040881
MonotonicityNot monotonic
2024-11-18T14:06:20.188920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 1239
30.5%
8 483
 
11.9%
7 452
 
11.1%
3 398
 
9.8%
1 334
 
8.2%
10 262
 
6.4%
5 251
 
6.2%
6 237
 
5.8%
11 160
 
3.9%
4 153
 
3.8%
Other values (9) 94
 
2.3%
ValueCountFrequency (%)
1 334
 
8.2%
2 1239
30.5%
3 398
 
9.8%
4 153
 
3.8%
5 251
 
6.2%
6 237
 
5.8%
7 452
 
11.1%
8 483
 
11.9%
9 53
 
1.3%
10 262
 
6.4%
ValueCountFrequency (%)
19 2
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 16
 
0.4%
12 16
 
0.4%
11 160
3.9%
10 262
6.4%

socio_economic_class
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
3
1485 
2
868 
1
786 
4
669 
5
255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters5
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 row3
2nd row4
3rd row1
4th row1
5th row4

Common Values

ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%

Length

2024-11-18T14:06:20.287245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:20.375148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%

Most occurring characters

ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1485
36.5%
2 868
21.4%
1 786
19.3%
4 669
16.5%
5 255
 
6.3%
Distinct86
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33295637
Minimum5000
Maximum1 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:06:20.488380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile20000
Q140000
median60000
Q3100000
95-th percentile200000
Maximum1 × 109
Range9.99995 × 108
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation1.7923726 × 108
Coefficient of variation (CV)5.3832055
Kurtosis25.163093
Mean33295637
Median Absolute Deviation (MAD)20000
Skewness5.2106339
Sum1.3528017 × 1011
Variance3.2125994 × 1016
MonotonicityNot monotonic
2024-11-18T14:06:20.606879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 535
13.2%
100000 498
12.3%
60000 417
 
10.3%
40000 294
 
7.2%
30000 249
 
6.1%
80000 223
 
5.5%
70000 218
 
5.4%
150000 191
 
4.7%
75000 158
 
3.9%
999999999 135
 
3.3%
Other values (76) 1145
28.2%
ValueCountFrequency (%)
5000 6
 
0.1%
6000 2
 
< 0.1%
7000 1
 
< 0.1%
7500 2
 
< 0.1%
8000 3
 
0.1%
10000 36
0.9%
11000 1
 
< 0.1%
12000 10
 
0.2%
13000 1
 
< 0.1%
15000 64
1.6%
ValueCountFrequency (%)
999999999 135
3.3%
275000 2
 
< 0.1%
270000 1
 
< 0.1%
250000 31
 
0.8%
230000 3
 
0.1%
225000 2
 
< 0.1%
220000 1
 
< 0.1%
215000 1
 
< 0.1%
200000 113
2.8%
180000 6
 
0.1%

type_of_electricity_meter
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
1
2186 
2
1877 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
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 row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Length

2024-11-18T14:06:20.707756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:06:20.788062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Most occurring characters

ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2186
53.8%
2 1877
46.2%

Interactions

2024-11-18T14:06:14.469048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.539229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.762617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.236233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.396473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.609164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.722266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.173079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.293161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.393965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.516383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.659727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.855897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.289931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.552462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.633732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.143952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.317628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.503815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.685055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.820302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.255292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.378729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.482805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.597729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.753573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.215882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.376079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.638289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.721386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.228242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.400879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.590662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.765300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.930049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.339382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.459209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.565061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.677748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.843825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.300322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.463736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.717297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.801042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.306197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.473196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.673728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.842965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.003953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.414337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.539341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.639204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.754890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.924421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.378345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.542333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.798970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.887732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.386781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.549912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.758007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.920592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.085403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.494340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.613879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.718135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.834593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.009530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.460153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.625738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.871672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:58.962876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.460486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.624503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.830031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.990925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.157825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.565450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.691263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.790331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.905842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.089706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.531784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.699464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.953451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.044601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.543120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.703046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.915958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.072032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.237223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.645404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.768766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.870833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.994731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.174095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.614684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.783424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.034189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.134513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.634546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.795022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.999853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.145780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.314976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.721154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.844115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.948313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.090955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.258096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.695832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.865545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.112978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.227995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.717610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.882785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.077045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.232995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.392597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.798502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.921219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.023714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.171225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.336169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.775531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.947299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.196345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.314112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.798700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.963695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.164851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.311980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.472372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.876442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.994354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.101108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.250958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.420310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.856423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.029870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.276320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.392795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.888729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.045198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.244858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.385563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.550611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:06.954173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.070322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.179101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.325754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.501705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:12.937404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.113383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.367939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.492170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:00.977806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.138879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.342215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.474228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.637915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.041343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.151763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.266908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.412781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.593000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.027074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.207186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.453603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.585726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.064640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.223927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.430391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.559527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.722526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.126360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.233387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.349922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.495324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.679909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.114985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.296324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:15.541516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:05:59.676014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:01.150857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:02.307942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:03.519511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:04.637453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:05.805532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:07.210040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:08.313876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:09.435068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:10.577918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:11.769256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:13.202829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:14.381634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-18T14:06:20.869215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
LECO_csc_areaany_constructions_or_renovations_in_the_householdavailability_of_certificate_of_compliancebuilt_year_of_the_housecharged_method_for_rent_for_electricitycharging_method_of_renters_for_electricityfloor_areafloor_which_house_locatedhighest_level_of_education_of_the_chief_wage_earneris_there_business_carried_out_in_the_householdmain_material_used_for_roof_of_the_housemain_material_used_for_walls_of_the_houseno_of_electricity_metersno_of_household_membersno_of_storeysoccupation_of_education_of_the_chief_wage_earneroccupy_renters_boardersown_the_house_or_living_on_rentsocio_economic_classtotal_monthly_expenditure_of_last_monthtype_of_businesstype_of_electricity_metertype_of_housewhom_or_how_the_house_was_designed
LECO_csc_area1.0000.0620.2400.0790.0760.1710.0410.0770.1770.0280.1580.1100.0570.0580.1930.1150.0750.1080.1770.1010.1720.5020.1240.181
any_constructions_or_renovations_in_the_household0.0621.0000.0640.1500.0000.1500.0000.0000.0000.0410.0710.0460.0000.0000.0000.0340.0090.0780.0000.0130.0000.0000.0470.101
availability_of_certificate_of_compliance0.2400.0641.0000.2850.1820.0000.0430.0350.2130.0050.1170.2480.0780.0220.2380.1710.0410.2780.2170.0330.0000.1250.2280.435
built_year_of_the_house0.0790.1500.2851.000-0.0940.0200.0200.0730.0270.0390.2970.2010.0190.0520.0880.0180.0120.4420.0230.0850.0930.0750.2570.005
charged_method_for_rent_for_electricity0.0760.0000.182-0.0941.0000.000-0.003-0.040-0.0430.000-0.030-0.1310.064-0.103-0.060-0.0760.0000.2670.030-0.0780.0000.0470.035-0.127
charging_method_of_renters_for_electricity0.1710.1500.0000.0200.0001.0000.0001.0000.1560.0750.0000.0420.0000.1630.5770.0000.3611.0000.1030.0000.0000.0000.2000.000
floor_area0.0410.0000.0430.020-0.0030.0001.000-0.0990.3460.000-0.016-0.1790.0870.0220.3740.2440.0000.0000.0070.2690.0000.0230.232-0.319
floor_which_house_located0.0770.0000.0350.073-0.0401.000-0.0991.000-0.0370.0000.1800.146-0.033-0.012-0.349-0.0490.0000.0300.048-0.0260.0000.0200.2970.095
highest_level_of_education_of_the_chief_wage_earner0.1770.0000.2130.027-0.0430.1560.346-0.0371.0000.0420.069-0.1750.074-0.0500.3450.3020.0000.0360.6500.3340.1590.1760.262-0.233
is_there_business_carried_out_in_the_household0.0280.0410.0050.0390.0000.0750.0000.0000.0421.0000.0000.0000.0000.0000.0000.1400.0000.0000.0520.0001.0000.0080.0000.000
main_material_used_for_roof_of_the_house0.1580.0710.1170.297-0.0300.000-0.0160.1800.0690.0001.0000.0550.1310.0350.0480.0650.1060.0000.0570.0490.0000.1600.339-0.094
main_material_used_for_walls_of_the_house0.1100.0460.2480.201-0.1310.042-0.1790.146-0.1750.0000.0551.000-0.0450.008-0.493-0.1080.0000.2700.066-0.1220.0000.042-0.0760.327
no_of_electricity_meters0.0570.0000.0780.0190.0640.0000.087-0.0330.0740.0000.131-0.0451.000-0.0350.1000.1060.0000.0000.0480.0540.0510.1010.248-0.070
no_of_household_members0.0580.0000.0220.052-0.1030.1630.022-0.012-0.0500.0000.0350.008-0.0351.0000.018-0.0070.0980.0210.0630.2420.0000.0000.015-0.002
no_of_storeys0.1930.0000.2380.088-0.0600.5770.374-0.3490.3450.0000.048-0.4930.1000.0181.0000.3860.0000.1140.1570.3521.0000.138-0.671-0.339
occupation_of_education_of_the_chief_wage_earner0.1150.0340.1710.018-0.0760.0000.244-0.0490.3020.1400.065-0.1080.106-0.0070.3861.0000.0000.0000.4440.2480.1050.1410.172-0.123
occupy_renters_boarders0.0750.0090.0410.0120.0000.3610.0000.0000.0000.0000.1060.0000.0000.0980.0000.0001.0001.0000.0000.0220.0000.0000.0000.000
own_the_house_or_living_on_rent0.1080.0780.2780.4420.2671.0000.0000.0300.0360.0000.0000.2700.0000.0210.1140.0001.0001.0000.0390.0000.0000.0330.0630.261
socio_economic_class0.1770.0000.2170.0230.0300.1030.0070.0480.6500.0520.0570.0660.0480.0630.1570.4440.0000.0391.0000.0390.1280.1920.1840.168
total_monthly_expenditure_of_last_month0.1010.0130.0330.085-0.0780.0000.269-0.0260.3340.0000.049-0.1220.0540.2420.3520.2480.0220.0000.0391.0000.0000.0000.198-0.167
type_of_business0.1720.0000.0000.0930.0000.0000.0000.0000.1591.0000.0000.0000.0510.0001.0000.1050.0000.0000.1280.0001.0000.0000.0000.000
type_of_electricity_meter0.5020.0000.1250.0750.0470.0000.0230.0200.1760.0080.1600.0420.1010.0000.1380.1410.0000.0330.1920.0000.0001.0000.2200.101
type_of_house0.1240.0470.2280.2570.0350.2000.2320.2970.2620.0000.339-0.0760.2480.015-0.6710.1720.0000.0630.1840.1980.0000.2201.000-0.201
whom_or_how_the_house_was_designed0.1810.1010.4350.005-0.1270.000-0.3190.095-0.2330.000-0.0940.327-0.070-0.002-0.339-0.1230.0000.2610.168-0.1670.0000.101-0.2011.000

Missing values

2024-11-18T14:06:15.681484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T14:06:15.971056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-18T14:06:16.185013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

no_of_electricity_metersLECO_csc_areaown_the_house_or_living_on_rentoccupy_renters_boardersawareness_of_electricity_consumption_of_rentersbuilt_year_of_the_housetype_of_housefloor_which_house_locatedno_of_storeysfloor_areano_of_household_memberscharging_method_of_renters_for_electricitycharged_method_for_rent_for_electricityis_there_business_carried_out_in_the_householdtype_of_businesswhom_or_how_the_house_was_designedavailability_of_certificate_of_compliancemain_material_used_for_walls_of_the_housemain_material_used_for_roof_of_the_houseany_constructions_or_renovations_in_the_householdhighest_level_of_education_of_the_chief_wage_earneroccupation_of_education_of_the_chief_wage_earnersocio_economic_classtotal_monthly_expenditure_of_last_monthtype_of_electricity_meter
household_ID
ID00011GALLE13.0NaN42-1NaN1500.04NaNNaN0NaN1.00.012.00423350002
ID00021GALLE13.0NaN11-1NaN440.03NaNNaN0NaN5.01.012.00514400002
ID00031GALLE13.0NaN21-1NaN2500.04NaNNaN0NaN1.00.011.007612500001
ID00041BORALASGAMUWA13.0NaN52-1NaN2600.04NaNNaN0NaN1.00.023.0061111000001
ID00051KOLONNAWA13.0NaN55101.0480.02NaNNaN0NaN1.01.0113.00324600001
ID00061KOLONNAWA13.0NaN5512.0440.06NaNNaN0NaN5.01.023.003151000001
ID00071KOLONNAWA13.0NaN5521.0480.04NaNNaN0NaN5.01.023.00483600001
ID00082GALLE13.0NaN52-1NaN1400.05NaNNaN0NaN1.01.022.006321500001
ID00091GALLE13.0NaN41-1NaN350.02NaNNaN0NaN1.00.012.00423150002
ID00101GALLE13.0NaN31-1NaN1000.07NaNNaN0NaN1.00.011.00315500002
no_of_electricity_metersLECO_csc_areaown_the_house_or_living_on_rentoccupy_renters_boardersawareness_of_electricity_consumption_of_rentersbuilt_year_of_the_housetype_of_housefloor_which_house_locatedno_of_storeysfloor_areano_of_household_memberscharging_method_of_renters_for_electricitycharged_method_for_rent_for_electricityis_there_business_carried_out_in_the_householdtype_of_businesswhom_or_how_the_house_was_designedavailability_of_certificate_of_compliancemain_material_used_for_walls_of_the_housemain_material_used_for_roof_of_the_houseany_constructions_or_renovations_in_the_householdhighest_level_of_education_of_the_chief_wage_earneroccupation_of_education_of_the_chief_wage_earnersocio_economic_classtotal_monthly_expenditure_of_last_monthtype_of_electricity_meter
household_ID
ID40541NEGOMBO2NaNNaN21-1NaN1250.06NaN1.00NaNNaNNaN1NaN0423700001
ID40551NEGOMBO13.0NaN42-1NaN1500.03NaNNaN0NaNNaNNaN1NaN0462600001
ID40561GALLE13.0NaN41-1NaN2400.03NaNNaN0NaNNaNNaN2NaN03159999999992
ID40571GALLE13.0NaN11-1NaN2400.02NaNNaN0NaNNaNNaN4NaN0423250002
ID40581GALLE13.0NaN31-1NaN3000.06NaNNaN0NaNNaNNaN2NaN052380002
ID40591NUGEGODA13.0NaN62-1NaN400.04NaNNaN0NaNNaNNaN1NaN07321500001
ID40601HIKKADUWA13.0NaN11-1NaN3000.01NaNNaN0NaNNaNNaN5NaN0492500002
ID40611WATTALA13.0NaN41-1NaN680.02NaNNaN0NaNNaNNaN2NaN0414200002
ID40621ALUTHGAMA13.0NaN21-1NaN700.02NaNNaN0NaNNaNNaN4NaN04102300002
ID40631ALUTHGAMA13.0NaN52-1NaN2400.05NaNNaN0NaNNaNNaN2NaN03241000002

Duplicate rows

Most frequently occurring

no_of_electricity_metersLECO_csc_areaown_the_house_or_living_on_rentoccupy_renters_boardersawareness_of_electricity_consumption_of_rentersbuilt_year_of_the_housetype_of_housefloor_which_house_locatedno_of_storeysfloor_areano_of_household_memberscharging_method_of_renters_for_electricitycharged_method_for_rent_for_electricityis_there_business_carried_out_in_the_householdtype_of_businesswhom_or_how_the_house_was_designedavailability_of_certificate_of_compliancemain_material_used_for_walls_of_the_housemain_material_used_for_roof_of_the_houseany_constructions_or_renovations_in_the_householdhighest_level_of_education_of_the_chief_wage_earneroccupation_of_education_of_the_chief_wage_earnersocio_economic_classtotal_monthly_expenditure_of_last_monthtype_of_electricity_meter# duplicates
01NEGOMBO13.0NaN11-1NaN600.02NaNNaN0NaNNaNNaN2NaN03345000012