Overview

Dataset statistics

Number of variables81
Number of observations1460
Missing cells7829
Missing cells (%)6.6%
Total size in memory924.0 KiB
Average record size in memory648.1 B

Variable types

Numeric38
Text43

Alerts

LotFrontage has 259 (17.7%) missing valuesMissing
Alley has 1369 (93.8%) missing valuesMissing
MasVnrType has 872 (59.7%) missing valuesMissing
BsmtQual has 37 (2.5%) missing valuesMissing
BsmtCond has 37 (2.5%) missing valuesMissing
BsmtExposure has 38 (2.6%) missing valuesMissing
BsmtFinType1 has 37 (2.5%) missing valuesMissing
BsmtFinType2 has 38 (2.6%) missing valuesMissing
FireplaceQu has 690 (47.3%) missing valuesMissing
GarageType has 81 (5.5%) missing valuesMissing
GarageYrBlt has 81 (5.5%) missing valuesMissing
GarageFinish has 81 (5.5%) missing valuesMissing
GarageQual has 81 (5.5%) missing valuesMissing
GarageCond has 81 (5.5%) missing valuesMissing
PoolQC has 1453 (99.5%) missing valuesMissing
Fence has 1179 (80.8%) missing valuesMissing
MiscFeature has 1406 (96.3%) missing valuesMissing
MiscVal is highly skewed (γ1 = 24.47679419)Skewed
Id has unique valuesUnique
MasVnrArea has 861 (59.0%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
BsmtFullBath has 856 (58.6%) zerosZeros
BsmtHalfBath has 1378 (94.4%) zerosZeros
HalfBath has 913 (62.5%) zerosZeros
Fireplaces has 690 (47.3%) zerosZeros
GarageCars has 81 (5.5%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros
MiscVal has 1408 (96.4%) zerosZeros

Reproduction

Analysis started2026-03-12 11:27:33.843934
Analysis finished2026-03-12 11:27:34.048898
Duration0.2 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Unique 

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.116796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2026-03-12T12:27:34.250948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726027
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.314646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.30057099
Coefficient of variation (CV)0.7434553226
Kurtosis1.580187965
Mean56.89726027
Median Absolute Deviation (MAD)30
Skewness1.407656747
Sum83070
Variance1789.338306
MonotonicityNot monotonic
2026-03-12T12:27:34.379837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20536
36.7%
60299
20.5%
50144
 
9.9%
12087
 
6.0%
3069
 
4.7%
16063
 
4.3%
7060
 
4.1%
8058
 
4.0%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.2%
ValueCountFrequency (%)
20536
36.7%
3069
 
4.7%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
4.3%
12087
6.0%
9052
3.6%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.425899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.034246575
Min length2

Characters and Unicode

Total characters2970
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 rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
rl1151
78.3%
rm218
 
14.8%
fv65
 
4.4%
rh16
 
1.1%
c10
 
0.7%
all10
 
0.7%
2026-03-12T12:27:34.535715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R1385
46.6%
L1151
38.8%
M218
 
7.3%
F65
 
2.2%
V65
 
2.2%
l20
 
0.7%
H16
 
0.5%
C10
 
0.3%
10
 
0.3%
(10
 
0.3%
Other values (2)20
 
0.7%

LotFrontage
Real number (ℝ)

Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.04995837
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.599157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.28475177
Coefficient of variation (CV)0.3466776047
Kurtosis17.45286726
Mean70.04995837
Median Absolute Deviation (MAD)11
Skewness2.163569142
Sum84130
Variance589.7491687
MonotonicityNot monotonic
2026-03-12T12:27:34.661232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60143
 
9.8%
7070
 
4.8%
8069
 
4.7%
5057
 
3.9%
7553
 
3.6%
6544
 
3.0%
8540
 
2.7%
7825
 
1.7%
9023
 
1.6%
2123
 
1.6%
Other values (100)654
44.8%
(Missing)259
 
17.7%
ValueCountFrequency (%)
2123
1.6%
2419
1.3%
306
 
0.4%
325
 
0.3%
331
 
0.1%
ValueCountFrequency (%)
3132
0.1%
1821
0.1%
1742
0.1%
1681
0.1%
1601
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.724927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotonicityNot monotonic
2026-03-12T12:27:34.788067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
900014
 
1.0%
840014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
91008
 
0.5%
81258
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
0.1%
14771
0.1%
14911
0.1%
15261
0.1%
15332
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%

Street
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:34.832720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
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 rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave
ValueCountFrequency (%)
pave1454
99.6%
grvl6
 
0.4%
2026-03-12T12:27:34.945946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v1460
25.0%
P1454
24.9%
a1454
24.9%
e1454
24.9%
G6
 
0.1%
r6
 
0.1%
l6
 
0.1%

Alley
Text

Missing 

Distinct2
Distinct (%)2.2%
Missing1369
Missing (%)93.8%
Memory size11.5 KiB
2026-03-12T12:27:35.000844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters364
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 rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave
ValueCountFrequency (%)
grvl50
54.9%
pave41
45.1%
2026-03-12T12:27:35.112556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v91
25.0%
G50
13.7%
r50
13.7%
l50
13.7%
P41
11.3%
a41
11.3%
e41
11.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.162133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
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 rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1
ValueCountFrequency (%)
reg925
63.4%
ir1484
33.2%
ir241
 
2.8%
ir310
 
0.7%
2026-03-12T12:27:35.269249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R1460
33.3%
e925
21.1%
g925
21.1%
I535
 
12.2%
1484
 
11.1%
241
 
0.9%
310
 
0.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.317055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters10
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 rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl
ValueCountFrequency (%)
lvl1311
89.8%
bnk63
 
4.3%
hls50
 
3.4%
low36
 
2.5%
2026-03-12T12:27:35.420670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L1397
31.9%
v1311
29.9%
l1311
29.9%
B63
 
1.4%
n63
 
1.4%
k63
 
1.4%
H50
 
1.1%
S50
 
1.1%
o36
 
0.8%
w36
 
0.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.478317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8760
Distinct characters11
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub
ValueCountFrequency (%)
allpub1459
99.9%
nosewa1
 
0.1%
2026-03-12T12:27:35.589577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l2918
33.3%
A1459
16.7%
P1459
16.7%
u1459
16.7%
b1459
16.7%
N1
 
< 0.1%
o1
 
< 0.1%
S1
 
< 0.1%
e1
 
< 0.1%
W1
 
< 0.1%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.645931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.959589041
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
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 rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2
ValueCountFrequency (%)
inside1052
72.1%
corner263
 
18.0%
culdsac94
 
6.4%
fr247
 
3.2%
fr34
 
0.3%
2026-03-12T12:27:35.768084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1315
15.1%
n1315
15.1%
I1052
12.1%
s1052
12.1%
i1052
12.1%
d1052
12.1%
r526
 
6.0%
C357
 
4.1%
o263
 
3.0%
S94
 
1.1%
Other values (9)623
7.2%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.814702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl
ValueCountFrequency (%)
gtl1382
94.7%
mod65
 
4.5%
sev13
 
0.9%
2026-03-12T12:27:35.918335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G1382
31.6%
t1382
31.6%
l1382
31.6%
M65
 
1.5%
o65
 
1.5%
d65
 
1.5%
S13
 
0.3%
e13
 
0.3%
v13
 
0.3%
Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:35.999389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.494520548
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
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 rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge
ValueCountFrequency (%)
names225
15.4%
collgcr150
 
10.3%
oldtown113
 
7.7%
edwards100
 
6.8%
somerst86
 
5.9%
gilbert79
 
5.4%
nridght77
 
5.3%
sawyer74
 
5.1%
nwames73
 
5.0%
sawyerw59
 
4.0%
Other values (15)424
29.0%
2026-03-12T12:27:36.145290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r931
 
9.8%
e905
 
9.5%
l622
 
6.6%
d506
 
5.3%
s486
 
5.1%
o483
 
5.1%
m439
 
4.6%
N425
 
4.5%
w414
 
4.4%
C407
 
4.3%
Other values (28)3864
40.8%
Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:36.205690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.121232877
Min length4

Characters and Unicode

Total characters6017
Distinct characters14
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 rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm1260
86.3%
feedr81
 
5.5%
artery48
 
3.3%
rran26
 
1.8%
posn19
 
1.3%
rrae11
 
0.8%
posa8
 
0.5%
rrnn5
 
0.3%
rrne2
 
0.1%
2026-03-12T12:27:36.321817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1437
23.9%
o1287
21.4%
N1286
21.4%
m1260
20.9%
e223
 
3.7%
A93
 
1.5%
R88
 
1.5%
F81
 
1.3%
d81
 
1.3%
t48
 
0.8%
Other values (4)133
 
2.2%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:36.376963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.006849315
Min length4

Characters and Unicode

Total characters5850
Distinct characters14
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

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm1445
99.0%
feedr6
 
0.4%
artery2
 
0.1%
rrnn2
 
0.1%
posn2
 
0.1%
posa1
 
0.1%
rran1
 
0.1%
rrae1
 
0.1%
2026-03-12T12:27:36.495718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1455
24.9%
N1449
24.8%
o1448
24.8%
m1445
24.7%
e15
 
0.3%
R8
 
0.1%
F6
 
0.1%
d6
 
0.1%
A5
 
0.1%
n3
 
0.1%
Other values (4)10
 
0.2%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:36.548824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.299315068
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
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 row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam
ValueCountFrequency (%)
1fam1220
83.6%
twnhse114
 
7.8%
duplex52
 
3.6%
twnhs43
 
2.9%
2fmcon31
 
2.1%
2026-03-12T12:27:36.672518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m1251
19.9%
11220
19.4%
a1220
19.4%
F1220
19.4%
n188
 
3.0%
T157
 
2.5%
w157
 
2.5%
h157
 
2.5%
s157
 
2.5%
E114
 
1.8%
Other values (10)436
 
6.9%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:36.734510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.910958904
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
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 row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story
ValueCountFrequency (%)
1story726
49.7%
2story445
30.5%
1.5fin154
 
10.5%
slvl65
 
4.5%
sfoyer37
 
2.5%
1.5unf14
 
1.0%
2.5unf11
 
0.8%
2.5fin8
 
0.5%
2026-03-12T12:27:36.859863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1273
14.8%
o1208
14.0%
r1208
14.0%
y1208
14.0%
t1171
13.6%
1894
10.4%
2464
 
5.4%
F199
 
2.3%
5187
 
2.2%
.187
 
2.2%
Other values (8)631
7.3%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:36.916466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotonicityNot monotonic
2026-03-12T12:27:36.963811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.006832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotonicityNot monotonic
2026-03-12T12:27:37.056535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.113952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotonicityNot monotonic
2026-03-12T12:27:37.178078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
0.3%
18821
 
0.1%
18852
0.1%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.241548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotonicityNot monotonic
2026-03-12T12:27:37.305437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.361647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.62260274
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
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 rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable
ValueCountFrequency (%)
gable1141
78.2%
hip286
 
19.6%
flat13
 
0.9%
gambrel11
 
0.8%
mansard7
 
0.5%
shed2
 
0.1%
2026-03-12T12:27:37.479884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1179
17.5%
l1165
17.3%
e1154
17.1%
G1152
17.1%
b1152
17.1%
H286
 
4.2%
i286
 
4.2%
p286
 
4.2%
r18
 
0.3%
t13
 
0.2%
Other values (8)58
 
0.9%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.544120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.996575342
Min length4

Characters and Unicode

Total characters10215
Distinct characters25
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

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg
ValueCountFrequency (%)
compshg1434
98.2%
tar&grv11
 
0.8%
wdshngl6
 
0.4%
wdshake5
 
0.3%
metal1
 
0.1%
membran1
 
0.1%
roll1
 
0.1%
clytile1
 
0.1%
2026-03-12T12:27:37.666495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1445
14.1%
h1445
14.1%
g1440
14.1%
C1435
14.0%
m1435
14.0%
o1435
14.0%
p1434
14.0%
r23
 
0.2%
a18
 
0.2%
T12
 
0.1%
Other values (15)93
 
0.9%
Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.736071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.979452055
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
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

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd515
30.9%
hdboard222
13.3%
metalsd220
13.2%
wd206
 
12.4%
sdng206
 
12.4%
plywood108
 
6.5%
cemntbd61
 
3.7%
brkface50
 
3.0%
wdshing26
 
1.6%
stucco25
 
1.5%
Other values (6)27
 
1.6%
2026-03-12T12:27:37.876654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1786
17.5%
S1016
 
10.0%
l844
 
8.3%
n831
 
8.2%
y623
 
6.1%
i541
 
5.3%
V515
 
5.1%
a492
 
4.8%
o468
 
4.6%
B336
 
3.3%
Other values (22)2738
26.9%
Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:37.950847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.973287671
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
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

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd504
29.6%
wd235
13.8%
metalsd214
12.6%
hdboard207
12.2%
sdng197
 
11.6%
plywood142
 
8.3%
cmentbd60
 
3.5%
shng38
 
2.2%
stucco26
 
1.5%
brkface25
 
1.5%
Other values (8)54
 
3.2%
2026-03-12T12:27:38.083376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1766
17.3%
S1017
 
10.0%
l861
 
8.5%
n834
 
8.2%
y646
 
6.3%
o523
 
5.1%
V504
 
5.0%
i504
 
5.0%
a446
 
4.4%
t316
 
3.1%
Other values (23)2764
27.1%

MasVnrType
Text

Missing 

Distinct3
Distinct (%)0.5%
Missing872
Missing (%)59.7%
Memory size11.5 KiB
2026-03-12T12:27:38.144519image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.539115646
Min length5

Characters and Unicode

Total characters3845
Distinct characters13
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 rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowStone
5th rowStone
ValueCountFrequency (%)
brkface445
75.7%
stone128
 
21.8%
brkcmn15
 
2.6%
2026-03-12T12:27:38.270868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e573
14.9%
B460
12.0%
r460
12.0%
k460
12.0%
F445
11.6%
a445
11.6%
c445
11.6%
n143
 
3.7%
S128
 
3.3%
t128
 
3.3%
Other values (3)158
 
4.1%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.6852617
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:38.569790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.0662066
Coefficient of variation (CV)1.746306115
Kurtosis10.08241732
Mean103.6852617
Median Absolute Deviation (MAD)0
Skewness2.66908421
Sum150551
Variance32784.97117
MonotonicityNot monotonic
2026-03-12T12:27:38.634221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0861
59.0%
728
 
0.5%
1088
 
0.5%
1808
 
0.5%
1207
 
0.5%
167
 
0.5%
3406
 
0.4%
1066
 
0.4%
806
 
0.4%
2006
 
0.4%
Other values (317)529
36.2%
(Missing)8
 
0.5%
ValueCountFrequency (%)
0861
59.0%
12
 
0.1%
111
 
0.1%
141
 
0.1%
167
 
0.5%
ValueCountFrequency (%)
16001
0.1%
13781
0.1%
11701
0.1%
11291
0.1%
11151
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:38.689803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
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 rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta906
62.1%
gd488
33.4%
ex52
 
3.6%
fa14
 
1.0%
2026-03-12T12:27:38.808584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T906
31.0%
A906
31.0%
G488
16.7%
d488
16.7%
E52
 
1.8%
x52
 
1.8%
F14
 
0.5%
a14
 
0.5%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:38.859036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta1282
87.8%
gd146
 
10.0%
fa28
 
1.9%
ex3
 
0.2%
po1
 
0.1%
2026-03-12T12:27:38.972637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1282
43.9%
A1282
43.9%
G146
 
5.0%
d146
 
5.0%
F28
 
1.0%
a28
 
1.0%
E3
 
0.1%
x3
 
0.1%
P1
 
< 0.1%
o1
 
< 0.1%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:39.033364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.515753425
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
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 rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc
ValueCountFrequency (%)
pconc647
44.3%
cblock634
43.4%
brktil146
 
10.0%
slab24
 
1.6%
stone6
 
0.4%
wood3
 
0.2%
2026-03-12T12:27:39.153885image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1293
16.1%
C1281
15.9%
c1281
15.9%
l804
10.0%
B780
9.7%
k780
9.7%
n653
8.1%
P647
8.0%
i146
 
1.8%
T146
 
1.8%
Other values (8)242
 
3.0%

BsmtQual
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2026-03-12T12:27:39.216401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
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 rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta649
45.6%
gd618
43.4%
ex121
 
8.5%
fa35
 
2.5%
2026-03-12T12:27:39.342698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T649
22.8%
A649
22.8%
G618
21.7%
d618
21.7%
E121
 
4.3%
x121
 
4.3%
F35
 
1.2%
a35
 
1.2%

BsmtCond
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2026-03-12T12:27:39.392782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
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 rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA
ValueCountFrequency (%)
ta1311
92.1%
gd65
 
4.6%
fa45
 
3.2%
po2
 
0.1%
2026-03-12T12:27:39.502165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1311
46.1%
A1311
46.1%
G65
 
2.3%
d65
 
2.3%
F45
 
1.6%
a45
 
1.6%
P2
 
0.1%
o2
 
0.1%

BsmtExposure
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
2026-03-12T12:27:39.546080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
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 rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv
ValueCountFrequency (%)
no953
67.0%
av221
 
15.5%
gd134
 
9.4%
mn114
 
8.0%
2026-03-12T12:27:39.653877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N953
33.5%
o953
33.5%
A221
 
7.8%
v221
 
7.8%
G134
 
4.7%
d134
 
4.7%
M114
 
4.0%
n114
 
4.0%

BsmtFinType1
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2026-03-12T12:27:39.716448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4269
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 rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ
ValueCountFrequency (%)
unf430
30.2%
glq418
29.4%
alq220
15.5%
blq148
 
10.4%
rec133
 
9.3%
lwq74
 
5.2%
2026-03-12T12:27:39.835834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L860
20.1%
Q860
20.1%
U430
10.1%
n430
10.1%
f430
10.1%
G418
9.8%
A220
 
5.2%
B148
 
3.5%
R133
 
3.1%
e133
 
3.1%
Other values (2)207
 
4.8%

BsmtFinSF1
Real number (ℝ)

Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.639726
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:39.899457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.0980908
Coefficient of variation (CV)1.028082167
Kurtosis11.11823629
Mean443.639726
Median Absolute Deviation (MAD)383.5
Skewness1.685503072
Sum647714
Variance208025.4685
MonotonicityNot monotonic
2026-03-12T12:27:39.959632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
32.0%
2412
 
0.8%
169
 
0.6%
6865
 
0.3%
6625
 
0.3%
205
 
0.3%
9365
 
0.3%
6165
 
0.3%
5604
 
0.3%
5534
 
0.3%
Other values (627)939
64.3%
ValueCountFrequency (%)
0467
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%

BsmtFinType2
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
2026-03-12T12:27:40.004029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4266
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 rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf
ValueCountFrequency (%)
unf1256
88.3%
rec54
 
3.8%
lwq46
 
3.2%
blq33
 
2.3%
alq19
 
1.3%
glq14
 
1.0%
2026-03-12T12:27:40.110441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U1256
29.4%
n1256
29.4%
f1256
29.4%
L112
 
2.6%
Q112
 
2.6%
R54
 
1.3%
e54
 
1.3%
c54
 
1.3%
w46
 
1.1%
B33
 
0.8%
Other values (2)33
 
0.8%

BsmtFinSF2
Real number (ℝ)

Zeros 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.54931507
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.175273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.3192728
Coefficient of variation (CV)3.465556315
Kurtosis20.11333755
Mean46.54931507
Median Absolute Deviation (MAD)0
Skewness4.255261109
Sum67962
Variance26023.90778
MonotonicityNot monotonic
2026-03-12T12:27:40.237793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01293
88.6%
1805
 
0.3%
3743
 
0.2%
5512
 
0.1%
1472
 
0.1%
2942
 
0.1%
3912
 
0.1%
5392
 
0.1%
962
 
0.1%
4802
 
0.1%
Other values (134)145
 
9.9%
ValueCountFrequency (%)
01293
88.6%
281
 
0.1%
321
 
0.1%
351
 
0.1%
401
 
0.1%
ValueCountFrequency (%)
14741
0.1%
11271
0.1%
11201
0.1%
10851
0.1%
10801
0.1%

BsmtUnfSF
Real number (ℝ)

Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.240411
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.300194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.8669553
Coefficient of variation (CV)0.7789765094
Kurtosis0.4749939878
Mean567.240411
Median Absolute Deviation (MAD)288
Skewness0.9202684528
Sum828171
Variance195246.4062
MonotonicityNot monotonic
2026-03-12T12:27:40.362401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3848
 
0.5%
6007
 
0.5%
3007
 
0.5%
5727
 
0.5%
2706
 
0.4%
6256
 
0.4%
6726
 
0.4%
4406
 
0.4%
Other values (770)1280
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%

TotalBsmtSF
Real number (ℝ)

Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.421637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotonicityNot monotonic
2026-03-12T12:27:40.484709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
78011
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%

Heating
Text

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.535517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.000684932
Min length4

Characters and Unicode

Total characters5841
Distinct characters13
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA
ValueCountFrequency (%)
gasa1428
97.8%
gasw18
 
1.2%
grav7
 
0.5%
wall4
 
0.3%
othw2
 
0.1%
floor1
 
0.1%
2026-03-12T12:27:40.646216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1457
24.9%
G1453
24.9%
s1446
24.8%
A1428
24.4%
W24
 
0.4%
l9
 
0.2%
r8
 
0.1%
v7
 
0.1%
O2
 
< 0.1%
t2
 
< 0.1%
Other values (3)5
 
0.1%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.703342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx
ValueCountFrequency (%)
ex741
50.8%
ta428
29.3%
gd241
 
16.5%
fa49
 
3.4%
po1
 
0.1%
2026-03-12T12:27:40.818793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E741
25.4%
x741
25.4%
T428
14.7%
A428
14.7%
G241
 
8.3%
d241
 
8.3%
F49
 
1.7%
a49
 
1.7%
P1
 
< 0.1%
o1
 
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:40.858910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
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 rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y1365
93.5%
n95
 
6.5%
2026-03-12T12:27:40.960955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y1365
93.5%
N95
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y1365
93.5%
N95
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y1365
93.5%
N95
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y1365
93.5%
N95
 
6.5%
Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
2026-03-12T12:27:41.016171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.998629198
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr
ValueCountFrequency (%)
sbrkr1334
91.4%
fusea94
 
6.4%
fusef27
 
1.9%
fusep3
 
0.2%
mix1
 
0.1%
2026-03-12T12:27:41.134809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2668
36.6%
S1334
18.3%
B1334
18.3%
k1334
18.3%
F151
 
2.1%
u124
 
1.7%
s124
 
1.7%
e124
 
1.7%
A94
 
1.3%
P3
 
< 0.1%
Other values (3)3
 
< 0.1%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.200527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotonicityNot monotonic
2026-03-12T12:27:41.260608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
4837
 
0.5%
9607
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%

2ndFlrSF
Real number (ℝ)

Zeros 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9924658
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.320139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.5284359
Coefficient of variation (CV)1.258034335
Kurtosis-0.5534635576
Mean346.9924658
Median Absolute Deviation (MAD)0
Skewness0.8130298163
Sum506609
Variance190557.0753
MonotonicityNot monotonic
2026-03-12T12:27:41.379677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0829
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.5%
6728
 
0.5%
6007
 
0.5%
7207
 
0.5%
8966
 
0.4%
8625
 
0.3%
7805
 
0.3%
Other values (407)566
38.8%
ValueCountFrequency (%)
0829
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%

LowQualFinSF
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.844520548
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.431288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.62308143
Coefficient of variation (CV)8.319430317
Kurtosis83.23481667
Mean5.844520548
Median Absolute Deviation (MAD)0
Skewness9.011341288
Sum8533
Variance2364.204048
MonotonicityNot monotonic
2026-03-12T12:27:41.484541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01434
98.2%
803
 
0.2%
3602
 
0.1%
2051
 
0.1%
4791
 
0.1%
3971
 
0.1%
5141
 
0.1%
1201
 
0.1%
4811
 
0.1%
2321
 
0.1%
Other values (14)14
 
1.0%
ValueCountFrequency (%)
01434
98.2%
531
 
0.1%
803
 
0.2%
1201
 
0.1%
1441
 
0.1%
ValueCountFrequency (%)
5721
0.1%
5281
0.1%
5151
0.1%
5141
0.1%
5131
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.544061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotonicityNot monotonic
2026-03-12T12:27:41.608036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
145610
 
0.7%
84810
 
0.7%
12009
 
0.6%
9129
 
0.6%
8168
 
0.5%
10928
 
0.5%
17287
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
0.1%
4381
0.1%
4801
0.1%
5201
0.1%
6051
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%

BsmtFullBath
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4253424658
Minimum0
Maximum3
Zeros856
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.656568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5189106061
Coefficient of variation (CV)1.219983067
Kurtosis-0.8390982655
Mean0.4253424658
Median Absolute Deviation (MAD)0
Skewness0.5960666097
Sum621
Variance0.2692682171
MonotonicityNot monotonic
2026-03-12T12:27:41.700698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%
ValueCountFrequency (%)
31
 
0.1%
215
 
1.0%
1588
40.3%
0856
58.6%

BsmtHalfBath
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05753424658
Minimum0
Maximum2
Zeros1378
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.746176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2387526463
Coefficient of variation (CV)4.149748376
Kurtosis16.39664195
Mean0.05753424658
Median Absolute Deviation (MAD)0
Skewness4.103402698
Sum84
Variance0.05700282611
MonotonicityNot monotonic
2026-03-12T12:27:41.796582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%
ValueCountFrequency (%)
22
 
0.1%
180
 
5.5%
01378
94.4%

FullBath
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.565068493
Minimum0
Maximum3
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.845407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5509158013
Coefficient of variation (CV)0.3520074704
Kurtosis-0.8570428213
Mean1.565068493
Median Absolute Deviation (MAD)0
Skewness0.0365615584
Sum2285
Variance0.3035082201
MonotonicityNot monotonic
2026-03-12T12:27:41.888897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%
ValueCountFrequency (%)
09
 
0.6%
1650
44.5%
2768
52.6%
333
 
2.3%
ValueCountFrequency (%)
333
 
2.3%
2768
52.6%
1650
44.5%
09
 
0.6%

HalfBath
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3828767123
Minimum0
Maximum2
Zeros913
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:41.933433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5028853811
Coefficient of variation (CV)1.313439457
Kurtosis-1.076927284
Mean0.3828767123
Median Absolute Deviation (MAD)0
Skewness0.6758974482
Sum559
Variance0.2528937065
MonotonicityNot monotonic
2026-03-12T12:27:41.982085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%
ValueCountFrequency (%)
212
 
0.8%
1535
36.6%
0913
62.5%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.030723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotonicityNot monotonic
2026-03-12T12:27:42.082057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%

KitchenAbvGr
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.046575342
Minimum0
Maximum3
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.128223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2203381984
Coefficient of variation (CV)0.2105325718
Kurtosis21.53240384
Mean1.046575342
Median Absolute Deviation (MAD)0
Skewness4.488396777
Sum1528
Variance0.04854892167
MonotonicityNot monotonic
2026-03-12T12:27:42.174494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
11392
95.3%
265
 
4.5%
32
 
0.1%
ValueCountFrequency (%)
32
 
0.1%
265
 
4.5%
11392
95.3%
01
 
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.224634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
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 rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd
ValueCountFrequency (%)
ta735
50.3%
gd586
40.1%
ex100
 
6.8%
fa39
 
2.7%
2026-03-12T12:27:42.345315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T735
25.2%
A735
25.2%
G586
20.1%
d586
20.1%
E100
 
3.4%
x100
 
3.4%
F39
 
1.3%
a39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517808219
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.399646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625393291
Coefficient of variation (CV)0.2493772808
Kurtosis0.8807615657
Mean6.517808219
Median Absolute Deviation (MAD)1
Skewness0.6763408364
Sum9516
Variance2.641903349
MonotonicityNot monotonic
2026-03-12T12:27:42.449577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6402
27.5%
7329
22.5%
5275
18.8%
8187
12.8%
497
 
6.6%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
497
 
6.6%
5275
18.8%
6402
27.5%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
3.2%
975
5.1%
Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.495880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.057534247
Min length3

Characters and Unicode

Total characters4464
Distinct characters15
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

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp
ValueCountFrequency (%)
typ1360
93.2%
min234
 
2.3%
min131
 
2.1%
mod15
 
1.0%
maj114
 
1.0%
maj25
 
0.3%
sev1
 
0.1%
2026-03-12T12:27:42.606929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1360
30.5%
y1360
30.5%
p1360
30.5%
M99
 
2.2%
i65
 
1.5%
n65
 
1.5%
145
 
1.0%
239
 
0.9%
a19
 
0.4%
j19
 
0.4%
Other values (5)33
 
0.7%

Fireplaces
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6130136986
Minimum0
Maximum3
Zeros690
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:42.659408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6446663863
Coefficient of variation (CV)1.051634552
Kurtosis-0.2172372075
Mean0.6130136986
Median Absolute Deviation (MAD)1
Skewness0.6495651831
Sum895
Variance0.4155947496
MonotonicityNot monotonic
2026-03-12T12:27:42.707625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%
ValueCountFrequency (%)
35
 
0.3%
2115
 
7.9%
1650
44.5%
0690
47.3%

FireplaceQu
Text

Missing 

Distinct5
Distinct (%)0.6%
Missing690
Missing (%)47.3%
Memory size11.5 KiB
2026-03-12T12:27:42.901887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1540
Distinct characters10
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 rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
gd380
49.4%
ta313
40.6%
fa33
 
4.3%
ex24
 
3.1%
po20
 
2.6%
2026-03-12T12:27:43.018570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G380
24.7%
d380
24.7%
T313
20.3%
A313
20.3%
F33
 
2.1%
a33
 
2.1%
E24
 
1.6%
x24
 
1.6%
P20
 
1.3%
o20
 
1.3%

GarageType
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2026-03-12T12:27:43.076876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.084118927
Min length6

Characters and Unicode

Total characters8390
Distinct characters24
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 rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd
ValueCountFrequency (%)
attchd870
63.1%
detchd387
28.1%
builtin88
 
6.4%
basment19
 
1.4%
carport9
 
0.7%
2types6
 
0.4%
2026-03-12T12:27:43.191727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t2243
26.7%
c1257
15.0%
h1257
15.0%
d1257
15.0%
A870
 
10.4%
e412
 
4.9%
D387
 
4.6%
n107
 
1.3%
B107
 
1.3%
u88
 
1.0%
Other values (14)405
 
4.8%

GarageYrBlt
Real number (ℝ)

Missing 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.506164
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:43.258574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.68972477
Coefficient of variation (CV)0.01247897288
Kurtosis-0.418340998
Mean1978.506164
Median Absolute Deviation (MAD)21
Skewness-0.6494146239
Sum2728360
Variance609.5825091
MonotonicityNot monotonic
2026-03-12T12:27:43.324008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200565
 
4.5%
200659
 
4.0%
200453
 
3.6%
200350
 
3.4%
200749
 
3.4%
197735
 
2.4%
199831
 
2.1%
199930
 
2.1%
197629
 
2.0%
200829
 
2.0%
Other values (87)949
65.0%
(Missing)81
 
5.5%
ValueCountFrequency (%)
19001
 
0.1%
19061
 
0.1%
19081
 
0.1%
19103
0.2%
19142
0.1%
ValueCountFrequency (%)
20103
 
0.2%
200921
 
1.4%
200829
2.0%
200749
3.4%
200659
4.0%

GarageFinish
Text

Missing 

Distinct3
Distinct (%)0.2%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2026-03-12T12:27:43.376554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4137
Distinct characters6
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 rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn
ValueCountFrequency (%)
unf605
43.9%
rfn422
30.6%
fin352
25.5%
2026-03-12T12:27:43.486524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1379
33.3%
F774
18.7%
U605
14.6%
f605
14.6%
R422
 
10.2%
i352
 
8.5%

GarageCars
Real number (ℝ)

Zeros 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.767123288
Minimum0
Maximum4
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:43.541009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7473150101
Coefficient of variation (CV)0.4228991918
Kurtosis0.220997764
Mean1.767123288
Median Absolute Deviation (MAD)0
Skewness-0.3425489297
Sum2580
Variance0.5584797243
MonotonicityNot monotonic
2026-03-12T12:27:43.589212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%
ValueCountFrequency (%)
081
 
5.5%
1369
25.3%
2824
56.4%
3181
 
12.4%
45
 
0.3%
ValueCountFrequency (%)
45
 
0.3%
3181
 
12.4%
2824
56.4%
1369
25.3%
081
 
5.5%

GarageArea
Real number (ℝ)

Zeros 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.980137
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:43.645688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.8048415
Coefficient of variation (CV)0.452037675
Kurtosis0.9170672023
Mean472.980137
Median Absolute Deviation (MAD)120
Skewness0.1799809067
Sum690551
Variance45712.51023
MonotonicityNot monotonic
2026-03-12T12:27:43.708531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.5%
44049
 
3.4%
57647
 
3.2%
24038
 
2.6%
48434
 
2.3%
52833
 
2.3%
28827
 
1.8%
40025
 
1.7%
26424
 
1.6%
48024
 
1.6%
Other values (431)1078
73.8%
ValueCountFrequency (%)
081
5.5%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%

GarageQual
Text

Missing 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2026-03-12T12:27:43.754307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
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 rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta1311
95.1%
fa48
 
3.5%
gd14
 
1.0%
ex3
 
0.2%
po3
 
0.2%
2026-03-12T12:27:43.863708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1311
47.5%
A1311
47.5%
F48
 
1.7%
a48
 
1.7%
G14
 
0.5%
d14
 
0.5%
E3
 
0.1%
x3
 
0.1%
P3
 
0.1%
o3
 
0.1%

GarageCond
Text

Missing 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2026-03-12T12:27:43.910741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
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 rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta1326
96.2%
fa35
 
2.5%
gd9
 
0.7%
po7
 
0.5%
ex2
 
0.1%
2026-03-12T12:27:44.020450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1326
48.1%
A1326
48.1%
F35
 
1.3%
a35
 
1.3%
G9
 
0.3%
d9
 
0.3%
P7
 
0.3%
o7
 
0.3%
E2
 
0.1%
x2
 
0.1%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.061888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
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 rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y1340
91.8%
n90
 
6.2%
p30
 
2.1%
2026-03-12T12:27:44.164544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

WoodDeckSF
Real number (ℝ)

Zeros 

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.24452055
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.230118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.3387944
Coefficient of variation (CV)1.329931901
Kurtosis2.992950925
Mean94.24452055
Median Absolute Deviation (MAD)0
Skewness1.541375757
Sum137597
Variance15709.81337
MonotonicityNot monotonic
2026-03-12T12:27:44.291841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
52.1%
19238
 
2.6%
10036
 
2.5%
14433
 
2.3%
12031
 
2.1%
16828
 
1.9%
14015
 
1.0%
22414
 
1.0%
20810
 
0.7%
24010
 
0.7%
Other values (264)484
33.2%
ValueCountFrequency (%)
0761
52.1%
122
 
0.1%
242
 
0.1%
262
 
0.1%
282
 
0.1%
ValueCountFrequency (%)
8571
0.1%
7361
0.1%
7281
0.1%
6701
0.1%
6681
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.66027397
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.349129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.25602768
Coefficient of variation (CV)1.419966538
Kurtosis8.490335806
Mean46.66027397
Median Absolute Deviation (MAD)25
Skewness2.36434174
Sum68124
Variance4389.861203
MonotonicityNot monotonic
2026-03-12T12:27:44.410475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0656
44.9%
3629
 
2.0%
4822
 
1.5%
2021
 
1.4%
4019
 
1.3%
4519
 
1.3%
2416
 
1.1%
3016
 
1.1%
6015
 
1.0%
3914
 
1.0%
Other values (192)633
43.4%
ValueCountFrequency (%)
0656
44.9%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%

EnclosedPorch
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95410959
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.471371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.1191486
Coefficient of variation (CV)2.783950237
Kurtosis10.43076594
Mean21.95410959
Median Absolute Deviation (MAD)0
Skewness3.089871904
Sum32053
Variance3735.550326
MonotonicityNot monotonic
2026-03-12T12:27:44.530884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01252
85.8%
11215
 
1.0%
966
 
0.4%
1925
 
0.3%
1445
 
0.3%
1205
 
0.3%
2165
 
0.3%
1564
 
0.3%
1164
 
0.3%
2524
 
0.3%
Other values (110)155
 
10.6%
ValueCountFrequency (%)
01252
85.8%
191
 
0.1%
201
 
0.1%
241
 
0.1%
301
 
0.1%
ValueCountFrequency (%)
5521
0.1%
3861
0.1%
3301
0.1%
3181
0.1%
3011
0.1%

3SsnPorch
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589041
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.585088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.31733056
Coefficient of variation (CV)8.598493896
Kurtosis123.6623794
Mean3.409589041
Median Absolute Deviation (MAD)0
Skewness10.30434203
Sum4978
Variance859.505871
MonotonicityNot monotonic
2026-03-12T12:27:44.639217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01436
98.4%
1683
 
0.2%
1442
 
0.1%
1802
 
0.1%
2162
 
0.1%
2901
 
0.1%
1531
 
0.1%
961
 
0.1%
231
 
0.1%
1621
 
0.1%
Other values (10)10
 
0.7%
ValueCountFrequency (%)
01436
98.4%
231
 
0.1%
961
 
0.1%
1301
 
0.1%
1401
 
0.1%
ValueCountFrequency (%)
5081
0.1%
4071
0.1%
3201
0.1%
3041
0.1%
2901
0.1%

ScreenPorch
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0609589
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.698931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.75741528
Coefficient of variation (CV)3.70211589
Kurtosis18.43906784
Mean15.0609589
Median Absolute Deviation (MAD)0
Skewness4.122213743
Sum21989
Variance3108.889359
MonotonicityNot monotonic
2026-03-12T12:27:44.765419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01344
92.1%
1926
 
0.4%
1205
 
0.3%
2245
 
0.3%
1894
 
0.3%
1804
 
0.3%
1473
 
0.2%
903
 
0.2%
1603
 
0.2%
1443
 
0.2%
Other values (66)80
 
5.5%
ValueCountFrequency (%)
01344
92.1%
401
 
0.1%
531
 
0.1%
601
 
0.1%
631
 
0.1%
ValueCountFrequency (%)
4801
0.1%
4401
0.1%
4101
0.1%
3961
0.1%
3851
0.1%

PoolArea
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75890411
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:44.815836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.17730694
Coefficient of variation (CV)14.56277759
Kurtosis223.2684989
Mean2.75890411
Median Absolute Deviation (MAD)0
Skewness14.82837364
Sum4028
Variance1614.215993
MonotonicityNot monotonic
2026-03-12T12:27:44.866204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01453
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01453
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
ValueCountFrequency (%)
7381
0.1%
6481
0.1%
5761
0.1%
5551
0.1%
5191
0.1%

PoolQC
Text

Missing 

Distinct3
Distinct (%)42.9%
Missing1453
Missing (%)99.5%
Memory size11.5 KiB
2026-03-12T12:27:44.913057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14
Distinct characters6
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 rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd
ValueCountFrequency (%)
gd3
42.9%
ex2
28.6%
fa2
28.6%
2026-03-12T12:27:45.026998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G3
21.4%
d3
21.4%
E2
14.3%
x2
14.3%
F2
14.3%
a2
14.3%

Fence
Text

Missing 

Distinct4
Distinct (%)1.4%
Missing1179
Missing (%)80.8%
Memory size11.5 KiB
2026-03-12T12:27:45.083217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.768683274
Min length4

Characters and Unicode

Total characters1340
Distinct characters10
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 rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv
ValueCountFrequency (%)
mnprv157
55.9%
gdprv59
 
21.0%
gdwo54
 
19.2%
mnww11
 
3.9%
2026-03-12T12:27:45.190734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P216
16.1%
r216
16.1%
v216
16.1%
M168
12.5%
n168
12.5%
G113
8.4%
d113
8.4%
W65
 
4.9%
o54
 
4.0%
w11
 
0.8%

MiscFeature
Text

Missing 

Distinct4
Distinct (%)7.4%
Missing1406
Missing (%)96.3%
Memory size11.5 KiB
2026-03-12T12:27:45.243192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters216
Distinct characters13
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

Unique1 ?
Unique (%)1.9%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed
ValueCountFrequency (%)
shed49
90.7%
gar22
 
3.7%
othr2
 
3.7%
tenc1
 
1.9%
2026-03-12T12:27:45.355884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h51
23.6%
e50
23.1%
S49
22.7%
d49
22.7%
r4
 
1.9%
G2
 
0.9%
a2
 
0.9%
22
 
0.9%
O2
 
0.9%
t2
 
0.9%
Other values (3)3
 
1.4%

MiscVal
Real number (ℝ)

Skewed  Zeros 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.4890411
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:45.414400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.1230245
Coefficient of variation (CV)11.408001
Kurtosis701.0033423
Mean43.4890411
Median Absolute Deviation (MAD)0
Skewness24.47679419
Sum63494
Variance246138.0554
MonotonicityNot monotonic
2026-03-12T12:27:45.468961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01408
96.4%
40011
 
0.8%
5008
 
0.5%
7005
 
0.3%
4504
 
0.3%
6004
 
0.3%
20004
 
0.3%
12002
 
0.1%
4802
 
0.1%
155001
 
0.1%
Other values (11)11
 
0.8%
ValueCountFrequency (%)
01408
96.4%
541
 
0.1%
3501
 
0.1%
40011
 
0.8%
4504
 
0.3%
ValueCountFrequency (%)
155001
 
0.1%
83001
 
0.1%
35001
 
0.1%
25001
 
0.1%
20004
0.3%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.321917808
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:45.523225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.703626208
Coefficient of variation (CV)0.4276591836
Kurtosis-0.4041093415
Mean6.321917808
Median Absolute Deviation (MAD)2
Skewness0.2120529851
Sum9230
Variance7.309594675
MonotonicityNot monotonic
2026-03-12T12:27:45.571215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.3%
7234
16.0%
5204
14.0%
4141
9.7%
8122
8.4%
3106
7.3%
1089
 
6.1%
1179
 
5.4%
963
 
4.3%
1259
 
4.0%
Other values (2)110
7.5%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3106
7.3%
4141
9.7%
5204
14.0%
ValueCountFrequency (%)
1259
4.0%
1179
5.4%
1089
6.1%
963
4.3%
8122
8.4%

YrSold
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.815753
Minimum2006
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:45.618514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12007
median2008
Q32009
95-th percentile2010
Maximum2010
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.328095121
Coefficient of variation (CV)0.0006614626458
Kurtosis-1.190600571
Mean2007.815753
Median Absolute Deviation (MAD)1
Skewness0.09626851387
Sum2931411
Variance1.763836649
MonotonicityNot monotonic
2026-03-12T12:27:45.671011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%
ValueCountFrequency (%)
2006314
21.5%
2007329
22.5%
2008304
20.8%
2009338
23.2%
2010175
12.0%
ValueCountFrequency (%)
2010175
12.0%
2009338
23.2%
2008304
20.8%
2007329
22.5%
2006314
21.5%
Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:45.716041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.158219178
Min length2

Characters and Unicode

Total characters3151
Distinct characters13
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 rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD
ValueCountFrequency (%)
wd1267
86.8%
new122
 
8.4%
cod43
 
2.9%
conld9
 
0.6%
conli5
 
0.3%
conlw5
 
0.3%
cwd4
 
0.3%
oth3
 
0.2%
con2
 
0.1%
2026-03-12T12:27:45.824789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D1323
42.0%
W1271
40.3%
w127
 
4.0%
N122
 
3.9%
e122
 
3.9%
C68
 
2.2%
O46
 
1.5%
o21
 
0.7%
n21
 
0.7%
L19
 
0.6%
Other values (3)11
 
0.3%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:45.884606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.157534247
Min length6

Characters and Unicode

Total characters8990
Distinct characters18
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 rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal
ValueCountFrequency (%)
normal1198
82.1%
partial125
 
8.6%
abnorml101
 
6.9%
family20
 
1.4%
alloca12
 
0.8%
adjland4
 
0.3%
2026-03-12T12:27:45.999629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1484
16.5%
l1468
16.3%
r1424
15.8%
m1319
14.7%
o1311
14.6%
N1198
13.3%
i145
 
1.6%
P125
 
1.4%
t125
 
1.4%
A117
 
1.3%
Other values (8)274
 
3.0%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2026-03-12T12:27:46.065262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2026-03-12T12:27:46.131869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%