Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations16270
Missing cells45187
Missing cells (%)18.5%
Duplicate rows542
Duplicate rows (%)3.3%
Total size in memory2.5 MiB
Average record size in memory160.5 B

Variable types

Categorical4
Numeric11

Alerts

Dataset has 542 (3.3%) duplicate rowsDuplicates
current_attendance_in_any_education_instituition has 418 (2.6%) missing values Missing
highest_level_of_education has 775 (4.8%) missing values Missing
main_activity_engaged_in has 2133 (13.1%) missing values Missing
main_occupation has 9902 (60.9%) missing values Missing
daily_wage_owner_or_not has 10090 (62.0%) missing values Missing
employment_status_of_the_main_occupation has 9902 (60.9%) missing values Missing
member_went_out_for_work_or_not_during_last_week has 11967 (73.6%) missing values Missing
highest_level_of_education has 216 (1.3%) zeros Zeros
no_of_hours_stayed_at_home_during_last_week has 699 (4.3%) zeros Zeros

Reproduction

Analysis started2024-11-18 08:36:49.218635
Analysis finished2024-11-18 08:37:01.677034
Duration12.46 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

member_ID
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size770.3 KiB
I_1
4063 
I_2
3877 
I_3
3275 
I_4
2457 
I_5
1443 
Other values (8)
1155 

Length

Max length4
Median length3
Mean length3.0027658
Min length3

Characters and Unicode

Total characters48855
Distinct characters12
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 rowI_1
2nd rowI_2
3rd rowI_3
4th rowI_4
5th rowI_1

Common Values

ValueCountFrequency (%)
I_1 4063
25.0%
I_2 3877
23.8%
I_3 3275
20.1%
I_4 2457
15.1%
I_5 1443
 
8.9%
I_6 671
 
4.1%
I_7 264
 
1.6%
I_8 120
 
0.7%
I_9 55
 
0.3%
I_10 26
 
0.2%
Other values (3) 19
 
0.1%

Length

2024-11-18T14:07:02.015243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i_1 4063
25.0%
i_2 3877
23.8%
i_3 3275
20.1%
i_4 2457
15.1%
i_5 1443
 
8.9%
i_6 671
 
4.1%
i_7 264
 
1.6%
i_8 120
 
0.7%
i_9 55
 
0.3%
i_10 26
 
0.2%
Other values (3) 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 16270
33.3%
_ 16270
33.3%
1 4119
 
8.4%
2 3883
 
7.9%
3 3277
 
6.7%
4 2457
 
5.0%
5 1443
 
3.0%
6 671
 
1.4%
7 264
 
0.5%
8 120
 
0.2%
Other values (2) 81
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 16270
33.3%
_ 16270
33.3%
1 4119
 
8.4%
2 3883
 
7.9%
3 3277
 
6.7%
4 2457
 
5.0%
5 1443
 
3.0%
6 671
 
1.4%
7 264
 
0.5%
8 120
 
0.2%
Other values (2) 81
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 16270
33.3%
_ 16270
33.3%
1 4119
 
8.4%
2 3883
 
7.9%
3 3277
 
6.7%
4 2457
 
5.0%
5 1443
 
3.0%
6 671
 
1.4%
7 264
 
0.5%
8 120
 
0.2%
Other values (2) 81
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 16270
33.3%
_ 16270
33.3%
1 4119
 
8.4%
2 3883
 
7.9%
3 3277
 
6.7%
4 2457
 
5.0%
5 1443
 
3.0%
6 671
 
1.4%
7 264
 
0.5%
8 120
 
0.2%
Other values (2) 81
 
0.2%

age
Real number (ℝ)

Distinct97
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.39287
Minimum0
Maximum98
Zeros149
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:02.121654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median38
Q356
95-th percentile75
Maximum98
Range98
Interquartile range (IQR)37

Descriptive statistics

Standard deviation22.075172
Coefficient of variation (CV)0.57498103
Kurtosis-0.99725102
Mean38.39287
Median Absolute Deviation (MAD)18
Skewness0.14195376
Sum624652
Variance487.31322
MonotonicityNot monotonic
2024-11-18T14:07:02.239694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 282
 
1.7%
17 278
 
1.7%
23 276
 
1.7%
15 267
 
1.6%
18 266
 
1.6%
20 263
 
1.6%
16 256
 
1.6%
42 252
 
1.5%
22 251
 
1.5%
45 245
 
1.5%
Other values (87) 13634
83.8%
ValueCountFrequency (%)
0 149
0.9%
1 131
0.8%
2 138
0.8%
3 186
1.1%
4 171
1.1%
5 177
1.1%
6 163
1.0%
7 164
1.0%
8 196
1.2%
9 198
1.2%
ValueCountFrequency (%)
98 1
 
< 0.1%
96 1
 
< 0.1%
95 3
 
< 0.1%
93 8
 
< 0.1%
92 5
 
< 0.1%
91 7
 
< 0.1%
90 17
0.1%
89 20
0.1%
88 16
0.1%
87 14
0.1%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8720344
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:02.338047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile75
Maximum109
Range108
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.1174
Coefficient of variation (CV)2.5555529
Kurtosis11.327254
Mean7.8720344
Median Absolute Deviation (MAD)1
Skewness3.5888571
Sum128078
Variance404.70979
MonotonicityNot monotonic
2024-11-18T14:07:02.432205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 5654
34.8%
1 4012
24.7%
2 3226
19.8%
5 1198
 
7.4%
75 685
 
4.2%
6 666
 
4.1%
4 434
 
2.7%
97 237
 
1.5%
86 101
 
0.6%
109 52
 
0.3%
Other values (2) 5
 
< 0.1%
ValueCountFrequency (%)
1 4012
24.7%
2 3226
19.8%
3 5654
34.8%
4 434
 
2.7%
5 1198
 
7.4%
6 666
 
4.1%
12 3
 
< 0.1%
75 685
 
4.2%
86 101
 
0.6%
88 2
 
< 0.1%
ValueCountFrequency (%)
109 52
 
0.3%
97 237
 
1.5%
88 2
 
< 0.1%
86 101
 
0.6%
75 685
 
4.2%
12 3
 
< 0.1%
6 666
 
4.1%
5 1198
 
7.4%
4 434
 
2.7%
3 5654
34.8%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.3 KiB
1
8386 
0
7884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16270
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 row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

Length

2024-11-18T14:07:02.528921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:07:02.611191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

Most occurring characters

ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8386
51.5%
0 7884
48.5%

ethnicity
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4357714
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:02.686498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0512815
Coefficient of variation (CV)0.73220675
Kurtosis4.8198478
Mean1.4357714
Median Absolute Deviation (MAD)0
Skewness2.326844
Sum23360
Variance1.1051928
MonotonicityNot monotonic
2024-11-18T14:07:02.776961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 13560
83.3%
4 1968
 
12.1%
2 572
 
3.5%
3 82
 
0.5%
6 42
 
0.3%
5 32
 
0.2%
9 14
 
0.1%
ValueCountFrequency (%)
1 13560
83.3%
2 572
 
3.5%
3 82
 
0.5%
4 1968
 
12.1%
5 32
 
0.2%
6 42
 
0.3%
9 14
 
0.1%
ValueCountFrequency (%)
9 14
 
0.1%
6 42
 
0.3%
5 32
 
0.2%
4 1968
 
12.1%
3 82
 
0.5%
2 572
 
3.5%
1 13560
83.3%

religion
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8679164
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:02.866954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.306145
Coefficient of variation (CV)0.69925236
Kurtosis-0.14726716
Mean1.8679164
Median Absolute Deviation (MAD)0
Skewness1.0956388
Sum30391
Variance1.7060147
MonotonicityNot monotonic
2024-11-18T14:07:02.954608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 10807
66.4%
4 2442
 
15.0%
3 2053
 
12.6%
5 547
 
3.4%
2 407
 
2.5%
9 8
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
1 10807
66.4%
2 407
 
2.5%
3 2053
 
12.6%
4 2442
 
15.0%
5 547
 
3.4%
6 6
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
9 8
 
< 0.1%
6 6
 
< 0.1%
5 547
 
3.4%
4 2442
 
15.0%
3 2053
 
12.6%
2 407
 
2.5%
1 10807
66.4%

marital_status
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9127843
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:03.043568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1643284
Coefficient of variation (CV)0.60870866
Kurtosis13.576298
Mean1.9127843
Median Absolute Deviation (MAD)1
Skewness2.9990392
Sum31121
Variance1.3556605
MonotonicityNot monotonic
2024-11-18T14:07:03.135569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 7417
45.6%
1 6330
38.9%
3 1311
 
8.1%
4 893
 
5.5%
9 138
 
0.8%
7 64
 
0.4%
8 52
 
0.3%
5 44
 
0.3%
6 21
 
0.1%
ValueCountFrequency (%)
1 6330
38.9%
2 7417
45.6%
3 1311
 
8.1%
4 893
 
5.5%
5 44
 
0.3%
6 21
 
0.1%
7 64
 
0.4%
8 52
 
0.3%
9 138
 
0.8%
ValueCountFrequency (%)
9 138
 
0.8%
8 52
 
0.3%
7 64
 
0.4%
6 21
 
0.1%
5 44
 
0.3%
4 893
 
5.5%
3 1311
 
8.1%
2 7417
45.6%
1 6330
38.9%
Distinct8
Distinct (%)0.1%
Missing418
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.4575448
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:03.226375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median8
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5556012
Coefficient of variation (CV)0.39575432
Kurtosis-0.64997855
Mean6.4575448
Median Absolute Deviation (MAD)0
Skewness-1.1196853
Sum102365
Variance6.5310976
MonotonicityNot monotonic
2024-11-18T14:07:03.313926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 11435
70.3%
2 2964
 
18.2%
3 560
 
3.4%
1 298
 
1.8%
4 233
 
1.4%
5 191
 
1.2%
6 105
 
0.6%
7 66
 
0.4%
(Missing) 418
 
2.6%
ValueCountFrequency (%)
1 298
 
1.8%
2 2964
 
18.2%
3 560
 
3.4%
4 233
 
1.4%
5 191
 
1.2%
6 105
 
0.6%
7 66
 
0.4%
8 11435
70.3%
ValueCountFrequency (%)
8 11435
70.3%
7 66
 
0.4%
6 105
 
0.6%
5 191
 
1.2%
4 233
 
1.4%
3 560
 
3.4%
2 2964
 
18.2%
1 298
 
1.8%

highest_level_of_education
Real number (ℝ)

Missing  Zeros 

Distinct20
Distinct (%)0.1%
Missing775
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean11.294095
Minimum0
Maximum19
Zeros216
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:03.406969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median12
Q314
95-th percentile16
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6555127
Coefficient of variation (CV)0.32366584
Kurtosis0.90851411
Mean11.294095
Median Absolute Deviation (MAD)2
Skewness-1.0071066
Sum175002
Variance13.362773
MonotonicityNot monotonic
2024-11-18T14:07:03.503219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14 2989
18.4%
12 2697
16.6%
11 2484
15.3%
16 1333
8.2%
13 1219
7.5%
9 772
 
4.7%
10 638
 
3.9%
6 567
 
3.5%
8 509
 
3.1%
7 407
 
2.5%
Other values (10) 1880
11.6%
(Missing) 775
 
4.8%
ValueCountFrequency (%)
0 216
 
1.3%
1 143
 
0.9%
2 161
 
1.0%
3 233
 
1.4%
4 277
 
1.7%
5 327
2.0%
6 567
3.5%
7 407
2.5%
8 509
3.1%
9 772
4.7%
ValueCountFrequency (%)
19 42
 
0.3%
18 84
 
0.5%
17 235
 
1.4%
16 1333
8.2%
15 162
 
1.0%
14 2989
18.4%
13 1219
7.5%
12 2697
16.6%
11 2484
15.3%
10 638
 
3.9%

main_activity_engaged_in
Real number (ℝ)

Missing 

Distinct10
Distinct (%)0.1%
Missing2133
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean4.5640518
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:03.597366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.210986
Coefficient of variation (CV)0.70353846
Kurtosis-1.7877888
Mean4.5640518
Median Absolute Deviation (MAD)3
Skewness-0.030884242
Sum64522
Variance10.310431
MonotonicityNot monotonic
2024-11-18T14:07:03.679029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 5473
33.6%
7 3465
21.3%
8 2513
15.4%
9 859
 
5.3%
2 707
 
4.3%
4 364
 
2.2%
5 266
 
1.6%
3 215
 
1.3%
6 159
 
1.0%
10 116
 
0.7%
(Missing) 2133
 
13.1%
ValueCountFrequency (%)
1 5473
33.6%
2 707
 
4.3%
3 215
 
1.3%
4 364
 
2.2%
5 266
 
1.6%
6 159
 
1.0%
7 3465
21.3%
8 2513
15.4%
9 859
 
5.3%
10 116
 
0.7%
ValueCountFrequency (%)
10 116
 
0.7%
9 859
 
5.3%
8 2513
15.4%
7 3465
21.3%
6 159
 
1.0%
5 266
 
1.6%
4 364
 
2.2%
3 215
 
1.3%
2 707
 
4.3%
1 5473
33.6%

main_occupation
Real number (ℝ)

Missing 

Distinct11
Distinct (%)0.2%
Missing9902
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean5.8406093
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:03.764058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile11
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.312635
Coefficient of variation (CV)1.7656779
Kurtosis72.386715
Mean5.8406093
Median Absolute Deviation (MAD)2
Skewness8.3275904
Sum37193
Variance106.35043
MonotonicityNot monotonic
2024-11-18T14:07:03.852542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 1777
 
10.9%
2 1272
 
7.8%
9 605
 
3.7%
4 527
 
3.2%
3 501
 
3.1%
1 469
 
2.9%
6 333
 
2.0%
11 296
 
1.8%
7 271
 
1.7%
8 245
 
1.5%
(Missing) 9902
60.9%
ValueCountFrequency (%)
1 469
 
2.9%
2 1272
7.8%
3 501
 
3.1%
4 527
 
3.2%
5 1777
10.9%
6 333
 
2.0%
7 271
 
1.7%
8 245
 
1.5%
9 605
 
3.7%
11 296
 
1.8%
ValueCountFrequency (%)
99 72
 
0.4%
11 296
 
1.8%
9 605
 
3.7%
8 245
 
1.5%
7 271
 
1.7%
6 333
 
2.0%
5 1777
10.9%
4 527
 
3.2%
3 501
 
3.1%
2 1272
7.8%

daily_wage_owner_or_not
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing10090
Missing (%)62.0%
Memory size770.3 KiB
2.0
4064 
1.0
2116 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18540
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 4064
25.0%
1.0 2116
 
13.0%
(Missing) 10090
62.0%

Length

2024-11-18T14:07:03.948447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:07:04.026326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 4064
65.8%
1.0 2116
34.2%

Most occurring characters

ValueCountFrequency (%)
. 6180
33.3%
0 6180
33.3%
2 4064
21.9%
1 2116
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6180
33.3%
0 6180
33.3%
2 4064
21.9%
1 2116
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6180
33.3%
0 6180
33.3%
2 4064
21.9%
1 2116
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6180
33.3%
0 6180
33.3%
2 4064
21.9%
1 2116
 
11.4%

employment_status_of_the_main_occupation
Real number (ℝ)

Missing 

Distinct6
Distinct (%)0.1%
Missing9902
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean3.182946
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:04.100510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2221368
Coefficient of variation (CV)0.38396404
Kurtosis-0.079889298
Mean3.182946
Median Absolute Deviation (MAD)0
Skewness0.079925575
Sum20269
Variance1.4936183
MonotonicityNot monotonic
2024-11-18T14:07:04.188310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 3698
 
22.7%
5 1084
 
6.7%
1 859
 
5.3%
4 415
 
2.6%
2 159
 
1.0%
6 153
 
0.9%
(Missing) 9902
60.9%
ValueCountFrequency (%)
1 859
 
5.3%
2 159
 
1.0%
3 3698
22.7%
4 415
 
2.6%
5 1084
 
6.7%
6 153
 
0.9%
ValueCountFrequency (%)
6 153
 
0.9%
5 1084
 
6.7%
4 415
 
2.6%
3 3698
22.7%
2 159
 
1.0%
1 859
 
5.3%
Distinct326
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.84774
Minimum0
Maximum168
Zeros699
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size770.3 KiB
2024-11-18T14:07:04.293403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q196
median140
Q3168
95-th percentile168
Maximum168
Range168
Interquartile range (IQR)72

Descriptive statistics

Standard deviation47.768165
Coefficient of variation (CV)0.38261138
Kurtosis0.29131003
Mean124.84774
Median Absolute Deviation (MAD)28
Skewness-1.0383694
Sum2031272.7
Variance2281.7976
MonotonicityNot monotonic
2024-11-18T14:07:04.412251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168 5380
33.1%
84 761
 
4.7%
0 699
 
4.3%
160 449
 
2.8%
150 439
 
2.7%
120 422
 
2.6%
140 360
 
2.2%
100 333
 
2.0%
108 301
 
1.9%
96 282
 
1.7%
Other values (316) 6844
42.1%
ValueCountFrequency (%)
0 699
4.3%
0.142 1
 
< 0.1%
0.147 1
 
< 0.1%
0.159 1
 
< 0.1%
0.168 1
 
< 0.1%
0.25 1
 
< 0.1%
0.3 1
 
< 0.1%
1 13
 
0.1%
2 13
 
0.1%
2.3 2
 
< 0.1%
ValueCountFrequency (%)
168 5380
33.1%
167.5 1
 
< 0.1%
167.3 1
 
< 0.1%
167.25 1
 
< 0.1%
167 36
 
0.2%
166.5 1
 
< 0.1%
166 87
 
0.5%
165.9 1
 
< 0.1%
165.75 1
 
< 0.1%
165.7 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing11967
Missing (%)73.6%
Memory size770.3 KiB
1.0
2764 
3.0
857 
2.0
682 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12909
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 row1.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 2764
 
17.0%
3.0 857
 
5.3%
2.0 682
 
4.2%
(Missing) 11967
73.6%

Length

2024-11-18T14:07:04.518193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:07:04.602805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2764
64.2%
3.0 857
 
19.9%
2.0 682
 
15.8%

Most occurring characters

ValueCountFrequency (%)
. 4303
33.3%
0 4303
33.3%
1 2764
21.4%
3 857
 
6.6%
2 682
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4303
33.3%
0 4303
33.3%
1 2764
21.4%
3 857
 
6.6%
2 682
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4303
33.3%
0 4303
33.3%
1 2764
21.4%
3 857
 
6.6%
2 682
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4303
33.3%
0 4303
33.3%
1 2764
21.4%
3 857
 
6.6%
2 682
 
5.3%

Interactions

2024-11-18T14:07:00.142318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:49.566193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.807771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.814010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.861309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.868234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.881615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.908823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.212986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.187387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.156579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.230557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:49.646658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.891389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.903116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.948454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.956992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.971280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.996613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.293552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.276719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.249004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.320486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:49.742776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.979046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.992796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.037275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.045077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.062581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.360349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.378831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.359036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.333376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.417489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:49.836049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.074380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.094337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.132745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.141195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.160099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.460150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.471185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.448551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.424546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.513917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:49.928808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.175606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.192674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.227712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.236550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.257004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.557298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.563289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.539277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.515080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.624162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.021014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.274719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.294673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.323433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.332813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.355426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.657346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.655818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.629580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.606735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.723186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.116055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.372453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.392298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.420997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.430752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.452274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.755529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.746616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.720164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.696578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.820930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.205931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.465902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.490308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.515994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.527291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.549313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.850283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.838459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.809314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.787511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.906864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.540689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.547974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.574061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.600296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.610566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.634446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:56.935716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.915772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.902028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.882756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.992540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.625495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.633691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.664615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.684647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.696333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.720475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.022977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.005083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.982727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.966762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:01.084029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:50.714008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:51.722580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:52.766305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:53.773966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:54.784111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:55.812368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:57.115651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:58.098367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:06:59.067342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:07:00.049672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Missing values

2024-11-18T14:07:01.214081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T14:07:01.409663image/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.
2024-11-18T14:07:01.576028image/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

member_IDagerelationship_to_the_head_of_householdgenderethnicityreligionmarital_statuscurrent_attendance_in_any_education_instituitionhighest_level_of_educationmain_activity_engaged_inmain_occupationdaily_wage_owner_or_notemployment_status_of_the_main_occupationno_of_hours_stayed_at_home_during_last_weekmember_went_out_for_work_or_not_during_last_week
household_ID
ID0001I_171101128.014.02.099.02.01.0168.03.0
ID0001I_266211128.014.07.0NaNNaNNaN168.0NaN
ID0001I_332301128.017.01.02.02.01.070.01.0
ID0001I_430411128.017.01.02.02.01.0150.01.0
ID0002I_185101128.07.04.0NaNNaNNaN168.0NaN
ID0002I_266501128.014.02.07.02.03.00.02.0
ID0002I_359311128.014.02.04.02.01.0168.03.0
ID0003I_144101128.016.02.02.02.01.0100.01.0
ID0003I_241211128.017.02.02.02.01.0100.01.0
ID0003I_374511148.016.04.0NaNNaNNaN168.0NaN
member_IDagerelationship_to_the_head_of_householdgenderethnicityreligionmarital_statuscurrent_attendance_in_any_education_instituitionhighest_level_of_educationmain_activity_engaged_inmain_occupationdaily_wage_owner_or_notemployment_status_of_the_main_occupationno_of_hours_stayed_at_home_during_last_weekmember_went_out_for_work_or_not_during_last_week
household_ID
ID4060I_178111118.011.07.0NaNNaNNaN130.0NaN
ID4061I_182111148.012.07.0NaNNaNNaN168.0NaN
ID4061I_253301118.012.01.09.01.03.070.0NaN
ID4062I_173101128.012.01.05.01.05.0168.0NaN
ID4062I_266211128.012.07.0NaNNaNNaN168.0NaN
ID4063I_162111148.011.07.0NaNNaNNaN48.0NaN
ID4063I_249401128.011.01.05.01.03.0120.0NaN
ID4063I_342311128.014.07.0NaNNaNNaN48.0NaN
ID4063I_437301118.011.07.0NaNNaNNaN168.0NaN
ID4063I_536301118.011.01.03.02.03.00.0NaN

Duplicate rows

Most frequently occurring

member_IDagerelationship_to_the_head_of_householdgenderethnicityreligionmarital_statuscurrent_attendance_in_any_education_instituitionhighest_level_of_educationmain_activity_engaged_inmain_occupationdaily_wage_owner_or_notemployment_status_of_the_main_occupationno_of_hours_stayed_at_home_during_last_weekmember_went_out_for_work_or_not_during_last_week# duplicates
103I_242211128.014.07.0NaNNaNNaN168.0NaN9
163I_251211128.014.07.0NaNNaNNaN168.0NaN9
356I_4031111NaNNaNNaNNaNNaNNaN168.0NaN9
130I_246211128.014.07.0NaNNaNNaN168.0NaN8
245I_267211128.012.07.0NaNNaNNaN168.0NaN8
261I_3130111NaNNaNNaNNaNNaNNaN168.0NaN8
366I_4231111NaNNaNNaNNaNNaNNaN168.0NaN8
79I_238211128.014.07.0NaNNaNNaN168.0NaN7
146I_248211128.014.07.0NaNNaNNaN168.0NaN7
167I_252211128.014.07.0NaNNaNNaN168.0NaN7