Overview

Dataset statistics

Number of variables70
Number of observations897110
Missing cells0
Missing cells (%)0.0%
Duplicate rows1999
Duplicate rows (%)0.2%
Total size in memory479.1 MiB
Average record size in memory560.0 B

Variable types

Numeric6
Categorical64

Warnings

Dataset has 1999 (0.2%) duplicate rowsDuplicates
ApprovalFY is highly correlated with UrbanRural_0 and 1 other fieldsHigh correlation
Term is highly correlated with GrAppv and 1 other fieldsHigh correlation
GrAppv is highly correlated with Term and 1 other fieldsHigh correlation
SBA_Appv is highly correlated with Term and 1 other fieldsHigh correlation
NewExist_1 is highly correlated with NewExist_2High correlation
NewExist_2 is highly correlated with NewExist_1High correlation
UrbanRural_0 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
UrbanRural_1 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
RevLineCr_3 is highly correlated with RevLineCr_4High correlation
RevLineCr_4 is highly correlated with RevLineCr_3High correlation
FranchiseCode_6 is highly correlated with FranchiseCode_10High correlation
FranchiseCode_7 is highly correlated with FranchiseCode_9High correlation
FranchiseCode_9 is highly correlated with FranchiseCode_7High correlation
FranchiseCode_10 is highly correlated with FranchiseCode_6High correlation
FranchiseCode_11 is highly correlated with FranchiseCode_12High correlation
FranchiseCode_12 is highly correlated with FranchiseCode_11High correlation
LowDoc_3 is highly correlated with LowDoc_4High correlation
LowDoc_4 is highly correlated with LowDoc_3High correlation
ApprovalFY is highly correlated with UrbanRural_0 and 1 other fieldsHigh correlation
Term is highly correlated with GrAppv and 1 other fieldsHigh correlation
GrAppv is highly correlated with Term and 1 other fieldsHigh correlation
SBA_Appv is highly correlated with Term and 1 other fieldsHigh correlation
NewExist_1 is highly correlated with NewExist_2High correlation
NewExist_2 is highly correlated with NewExist_1High correlation
UrbanRural_0 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
UrbanRural_1 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
RevLineCr_3 is highly correlated with RevLineCr_4High correlation
RevLineCr_4 is highly correlated with RevLineCr_3High correlation
FranchiseCode_6 is highly correlated with FranchiseCode_10High correlation
FranchiseCode_7 is highly correlated with FranchiseCode_9High correlation
FranchiseCode_9 is highly correlated with FranchiseCode_7High correlation
FranchiseCode_10 is highly correlated with FranchiseCode_6High correlation
FranchiseCode_11 is highly correlated with FranchiseCode_12High correlation
FranchiseCode_12 is highly correlated with FranchiseCode_11High correlation
LowDoc_3 is highly correlated with LowDoc_4High correlation
LowDoc_4 is highly correlated with LowDoc_3High correlation
ApprovalFY is highly correlated with UrbanRural_0 and 1 other fieldsHigh correlation
GrAppv is highly correlated with SBA_AppvHigh correlation
SBA_Appv is highly correlated with GrAppvHigh correlation
NewExist_1 is highly correlated with NewExist_2High correlation
NewExist_2 is highly correlated with NewExist_1High correlation
UrbanRural_0 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
UrbanRural_1 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
RevLineCr_3 is highly correlated with RevLineCr_4High correlation
RevLineCr_4 is highly correlated with RevLineCr_3High correlation
FranchiseCode_6 is highly correlated with FranchiseCode_10High correlation
FranchiseCode_7 is highly correlated with FranchiseCode_9High correlation
FranchiseCode_9 is highly correlated with FranchiseCode_7High correlation
FranchiseCode_10 is highly correlated with FranchiseCode_6High correlation
FranchiseCode_11 is highly correlated with FranchiseCode_12High correlation
FranchiseCode_12 is highly correlated with FranchiseCode_11High correlation
LowDoc_3 is highly correlated with LowDoc_4High correlation
LowDoc_4 is highly correlated with LowDoc_3High correlation
FranchiseCode_6 is highly correlated with FranchiseCode_5 and 5 other fieldsHigh correlation
RevLineCr_3 is highly correlated with UrbanRural_0 and 3 other fieldsHigh correlation
UrbanRural_2 is highly correlated with UrbanRural_1High correlation
Term is highly correlated with MIS_logicalHigh correlation
FranchiseCode_5 is highly correlated with FranchiseCode_6 and 5 other fieldsHigh correlation
UrbanRural_0 is highly correlated with RevLineCr_3 and 2 other fieldsHigh correlation
SBA_Appv is highly correlated with GrAppvHigh correlation
NewExist_1 is highly correlated with NewExist_2High correlation
NAICS_6 is highly correlated with NAICS_7High correlation
GrAppv is highly correlated with SBA_AppvHigh correlation
NAICS_7 is highly correlated with NAICS_6High correlation
NewExist_2 is highly correlated with NewExist_1High correlation
MIS_logical is highly correlated with TermHigh correlation
RevLineCr_4 is highly correlated with RevLineCr_3 and 1 other fieldsHigh correlation
LowDoc_3 is highly correlated with LowDoc_4High correlation
LowDoc_4 is highly correlated with LowDoc_3High correlation
FranchiseCode_4 is highly correlated with FranchiseCode_6 and 4 other fieldsHigh correlation
FranchiseCode_7 is highly correlated with FranchiseCode_6 and 4 other fieldsHigh correlation
FranchiseCode_8 is highly correlated with FranchiseCode_6 and 5 other fieldsHigh correlation
FranchiseCode_10 is highly correlated with FranchiseCode_6 and 5 other fieldsHigh correlation
ApprovalFY is highly correlated with RevLineCr_3 and 5 other fieldsHigh correlation
FranchiseCode_12 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
FranchiseCode_11 is highly correlated with ApprovalFY and 1 other fieldsHigh correlation
FranchiseCode_9 is highly correlated with FranchiseCode_6 and 5 other fieldsHigh correlation
UrbanRural_1 is highly correlated with RevLineCr_3 and 3 other fieldsHigh correlation
LowDoc_3 is highly correlated with LowDoc_4High correlation
LowDoc_4 is highly correlated with LowDoc_3High correlation
FranchiseCode_6 is highly correlated with FranchiseCode_10High correlation
FranchiseCode_7 is highly correlated with FranchiseCode_9High correlation
RevLineCr_3 is highly correlated with RevLineCr_4High correlation
FranchiseCode_10 is highly correlated with FranchiseCode_6High correlation
UrbanRural_0 is highly correlated with UrbanRural_1High correlation
FranchiseCode_12 is highly correlated with FranchiseCode_11High correlation
NewExist_1 is highly correlated with NewExist_2High correlation
FranchiseCode_9 is highly correlated with FranchiseCode_7High correlation
NewExist_2 is highly correlated with NewExist_1High correlation
FranchiseCode_11 is highly correlated with FranchiseCode_12High correlation
RevLineCr_4 is highly correlated with RevLineCr_3High correlation
UrbanRural_1 is highly correlated with UrbanRural_0High correlation
NoEmp is highly skewed (γ1 = 80.43184587) Skewed
CreateJob is highly skewed (γ1 = 36.94951907) Skewed
CreateJob has 627597 (70.0%) zeros Zeros

Reproduction

Analysis started2021-07-09 07:28:21.387644
Analysis finished2021-07-09 08:07:31.540426
Duration39 minutes and 10.15 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ApprovalFY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.1388191
Minimum3
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:31.789130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29
Q135
median40
Q344
95-th percentile47
Maximum52
Range49
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.913433721
Coefficient of variation (CV)0.1510887108
Kurtosis-0.09380845167
Mean39.1388191
Median Absolute Deviation (MAD)4
Skewness-0.582931566
Sum35111826
Variance34.96869837
MonotonicityNot monotonic
2021-07-09T04:07:32.114820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4376956
 
8.6%
4475754
 
8.4%
4571646
 
8.0%
4268195
 
7.6%
4158000
 
6.5%
3345688
 
5.1%
4044307
 
4.9%
3440021
 
4.5%
4639457
 
4.4%
3537718
 
4.2%
Other values (39)339368
37.8%
ValueCountFrequency (%)
31
 
< 0.1%
51
 
< 0.1%
63
 
< 0.1%
78
 
< 0.1%
818
 
< 0.1%
925
 
< 0.1%
1049
< 0.1%
1142
< 0.1%
1229
< 0.1%
1365
< 0.1%
ValueCountFrequency (%)
52268
 
< 0.1%
512451
 
0.3%
505986
 
0.7%
4912561
 
1.4%
4816824
 
1.9%
4719100
 
2.1%
4639457
4.4%
4571646
8.0%
4475754
8.4%
4376956
8.6%

Term
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct412
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.8450469
Minimum0
Maximum569
Zeros806
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:32.442038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q160
median84
Q3120
95-th percentile300
Maximum569
Range569
Interquartile range (IQR)60

Descriptive statistics

Standard deviation78.89679885
Coefficient of variation (CV)0.7117755919
Kurtosis0.1797071233
Mean110.8450469
Median Absolute Deviation (MAD)33
Skewness1.118982766
Sum99440200
Variance6224.704869
MonotonicityNot monotonic
2021-07-09T04:07:32.743887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84228918
25.5%
6089869
 
10.0%
24085951
 
9.6%
12077619
 
8.7%
30044686
 
5.0%
18028134
 
3.1%
3619712
 
2.2%
1216981
 
1.9%
4815596
 
1.7%
729416
 
1.0%
Other values (402)280228
31.2%
ValueCountFrequency (%)
0806
 
0.1%
11604
0.2%
21808
0.2%
32107
0.2%
42166
0.2%
51861
0.2%
63050
0.3%
71758
0.2%
81689
0.2%
91873
0.2%
ValueCountFrequency (%)
5691
< 0.1%
5271
< 0.1%
5111
< 0.1%
5051
< 0.1%
4811
< 0.1%
4801
< 0.1%
4611
< 0.1%
4491
< 0.1%
4451
< 0.1%
4431
< 0.1%

NoEmp
Real number (ℝ≥0)

SKEWED

Distinct598
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.40978587
Minimum0
Maximum9999
Zeros6617
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:33.088374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile40
Maximum9999
Range9999
Interquartile range (IQR)8

Descriptive statistics

Standard deviation73.79273173
Coefficient of variation (CV)6.467494884
Kurtosis8019.927484
Mean11.40978587
Median Absolute Deviation (MAD)3
Skewness80.43184587
Sum10235833
Variance5445.367257
MonotonicityNot monotonic
2021-07-09T04:07:33.418591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153474
17.1%
2137944
15.4%
390496
10.1%
473511
 
8.2%
560196
 
6.7%
645688
 
5.1%
1031491
 
3.5%
731436
 
3.5%
831325
 
3.5%
1220796
 
2.3%
Other values (588)220753
24.6%
ValueCountFrequency (%)
06617
 
0.7%
1153474
17.1%
2137944
15.4%
390496
10.1%
473511
8.2%
560196
 
6.7%
645688
 
5.1%
731436
 
3.5%
831325
 
3.5%
918112
 
2.0%
ValueCountFrequency (%)
99994
< 0.1%
99921
 
< 0.1%
99451
 
< 0.1%
90901
 
< 0.1%
90002
 
< 0.1%
85001
 
< 0.1%
80411
 
< 0.1%
80181
 
< 0.1%
80007
< 0.1%
79991
 
< 0.1%

CreateJob
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct245
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.443683606
Minimum0
Maximum8800
Zeros627597
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:33.763975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum8800
Range8800
Interquartile range (IQR)1

Descriptive statistics

Standard deviation236.9576612
Coefficient of variation (CV)28.06330415
Kurtosis1366.796706
Mean8.443683606
Median Absolute Deviation (MAD)0
Skewness36.94951907
Sum7574913
Variance56148.93321
MonotonicityNot monotonic
2021-07-09T04:07:34.080111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0627597
70.0%
163005
 
7.0%
257749
 
6.4%
328774
 
3.2%
420492
 
2.3%
518673
 
2.1%
1011593
 
1.3%
611004
 
1.2%
87373
 
0.8%
76374
 
0.7%
Other values (235)44476
 
5.0%
ValueCountFrequency (%)
0627597
70.0%
163005
 
7.0%
257749
 
6.4%
328774
 
3.2%
420492
 
2.3%
518673
 
2.1%
611004
 
1.2%
76374
 
0.7%
87373
 
0.8%
93330
 
0.4%
ValueCountFrequency (%)
8800648
0.1%
56211
 
< 0.1%
51991
 
< 0.1%
50851
 
< 0.1%
35001
 
< 0.1%
31001
 
< 0.1%
30004
 
< 0.1%
25151
 
< 0.1%
21401
 
< 0.1%
20201
 
< 0.1%

GrAppv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22087
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192779.9608
Minimum1000
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:34.413010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile10000
Q135000
median90000
Q3225000
95-th percentile750000
Maximum5000000
Range4999000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation281241.0579
Coefficient of variation (CV)1.458870812
Kurtosis17.72497823
Mean192779.9608
Median Absolute Deviation (MAD)65000
Skewness3.353750202
Sum1.729448306 × 1011
Variance7.909653267 × 1010
MonotonicityNot monotonic
2021-07-09T04:07:34.774201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000069346
 
7.7%
2500051195
 
5.7%
10000050950
 
5.7%
1000038263
 
4.3%
15000027615
 
3.1%
2000023393
 
2.6%
3500023173
 
2.6%
3000020986
 
2.3%
500018891
 
2.1%
1500018438
 
2.1%
Other values (22077)554860
61.8%
ValueCountFrequency (%)
100055
< 0.1%
12001
 
< 0.1%
15009
 
< 0.1%
16001
 
< 0.1%
17001
 
< 0.1%
200075
< 0.1%
21001
 
< 0.1%
23001
 
< 0.1%
24001
 
< 0.1%
250073
< 0.1%
ValueCountFrequency (%)
500000016
< 0.1%
49500001
 
< 0.1%
49085001
 
< 0.1%
49000001
 
< 0.1%
48720001
 
< 0.1%
48300001
 
< 0.1%
47889001
 
< 0.1%
47500001
 
< 0.1%
47250001
 
< 0.1%
47000001
 
< 0.1%

SBA_Appv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38268
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149518.6946
Minimum500
Maximum3750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-07-09T04:07:35.138730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile5000
Q121250
median62050
Q3175000
95-th percentile627000
Maximum3750000
Range3749500
Interquartile range (IQR)153750

Descriptive statistics

Standard deviation226175.3253
Coefficient of variation (CV)1.512689271
Kurtosis19.2113793
Mean149518.6946
Median Absolute Deviation (MAD)49550
Skewness3.401006149
Sum1.341347162 × 1011
Variance5.115527776 × 1010
MonotonicityNot monotonic
2021-07-09T04:07:35.492839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500049544
 
5.5%
1250040092
 
4.5%
500031035
 
3.5%
5000025038
 
2.8%
1000016990
 
1.9%
1750016136
 
1.8%
1500014475
 
1.6%
750012755
 
1.4%
12750011942
 
1.3%
8000010956
 
1.2%
Other values (38258)668147
74.5%
ValueCountFrequency (%)
50055
< 0.1%
6001
 
< 0.1%
7509
 
< 0.1%
8001
 
< 0.1%
8501
 
< 0.1%
100075
< 0.1%
10501
 
< 0.1%
11501
 
< 0.1%
12001
 
< 0.1%
125073
< 0.1%
ValueCountFrequency (%)
375000014
< 0.1%
37480001
 
< 0.1%
37260001
 
< 0.1%
37125002
 
< 0.1%
37000001
 
< 0.1%
36813751
 
< 0.1%
36790001
 
< 0.1%
36750001
 
< 0.1%
36612001
 
< 0.1%
36585001
 
< 0.1%

NewExist_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
896082 
1
 
1028

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

Length

2021-07-09T04:07:36.085779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:36.261939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0896082
99.9%
11028
 
0.1%

NewExist_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
643396 
0
253714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

Length

2021-07-09T04:07:36.781261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:36.950847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

Most occurring characters

ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1643396
71.7%
0253714
 
28.3%

NewExist_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
644556 
1
252554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

Length

2021-07-09T04:07:37.458740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:37.632958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0644556
71.8%
1252554
 
28.2%

NewExist_U
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
896978 
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

Length

2021-07-09T04:07:38.078086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:38.249023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0896978
> 99.9%
1132
 
< 0.1%

UrbanRural_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
574284 
1
322826 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

Length

2021-07-09T04:07:38.710932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:38.885553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

Most occurring characters

ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0574284
64.0%
1322826
36.0%

UrbanRural_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
469232 
0
427878 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

Length

2021-07-09T04:07:39.331415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:39.500472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

Most occurring characters

ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1469232
52.3%
0427878
47.7%

UrbanRural_2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
792058 
1
105052 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Length

2021-07-09T04:07:39.996708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:40.161088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0792058
88.3%
1105052
 
11.7%

Month_Apr
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
817086 
1
 
80024

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Length

2021-07-09T04:07:40.641896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:40.806353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0817086
91.1%
180024
 
8.9%

Month_Aug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
818501 
1
 
78609

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Length

2021-07-09T04:07:41.305825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:41.486019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0818501
91.2%
178609
 
8.8%

Month_Dec
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
827344 
1
 
69766

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Length

2021-07-09T04:07:41.969673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:42.136197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0827344
92.2%
169766
 
7.8%

Month_Feb
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
830936 
1
 
66174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Length

2021-07-09T04:07:42.617088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:42.781521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0830936
92.6%
166174
 
7.4%

Month_Jan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
830196 
1
 
66914

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Length

2021-07-09T04:07:43.253129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:43.421144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0830196
92.5%
166914
 
7.5%

Month_Jul
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
820779 
1
 
76331

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Length

2021-07-09T04:07:43.903267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:44.060564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0820779
91.5%
176331
 
8.5%

Month_Jun
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
818963 
1
 
78147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Length

2021-07-09T04:07:44.529069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:44.690580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0818963
91.3%
178147
 
8.7%

Month_Mar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
813690 
1
83420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Length

2021-07-09T04:07:45.160361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:45.325108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0813690
90.7%
183420
 
9.3%

Month_May
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
820061 
1
 
77049

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Length

2021-07-09T04:07:45.691855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:45.833456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0820061
91.4%
177049
 
8.6%

Month_Nov
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
828861 
1
 
68249

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Length

2021-07-09T04:07:46.261392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:46.452510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0828861
92.4%
168249
 
7.6%

Month_Oct
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
827491 
1
 
69619

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Length

2021-07-09T04:07:46.959899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:47.126115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0827491
92.2%
169619
 
7.8%

Month_Sep
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
814302 
1
82808 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

Length

2021-07-09T04:07:47.595818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:47.761708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0814302
90.8%
182808
 
9.2%

MIS_logical
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
739552 
0
157558 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

Length

2021-07-09T04:07:48.212311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:48.398968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

Most occurring characters

ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1739552
82.4%
0157558
 
17.6%

RevLineCr_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
897084 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

Length

2021-07-09T04:07:48.839390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:49.005523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0897084
> 99.9%
126
 
< 0.1%

RevLineCr_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
877313 
1
 
19797

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

Length

2021-07-09T04:07:49.519938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:49.762882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0877313
97.8%
119797
 
2.2%

RevLineCr_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
458110 
0
439000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

Length

2021-07-09T04:07:50.266982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:50.441922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

Most occurring characters

ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1458110
51.1%
0439000
48.9%

RevLineCr_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
624410 
0
272700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

Length

2021-07-09T04:07:50.941704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:51.139189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

Most occurring characters

ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1624410
69.6%
0272700
30.4%

FranchiseCode_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
895571 
1
 
1539

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

Length

2021-07-09T04:07:51.580501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:51.749705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0895571
99.8%
11539
 
0.2%

FranchiseCode_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
892494 
1
 
4616

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

Length

2021-07-09T04:07:52.211318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:52.393265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0892494
99.5%
14616
 
0.5%

FranchiseCode_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
888127 
1
 
8983

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

Length

2021-07-09T04:07:52.849297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:53.027732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0888127
99.0%
18983
 
1.0%

FranchiseCode_4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
883041 
1
 
14069

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

Length

2021-07-09T04:07:53.482115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:53.658171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0883041
98.4%
114069
 
1.6%

FranchiseCode_5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
880560 
1
 
16550

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

Length

2021-07-09T04:07:54.057527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:54.229840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0880560
98.2%
116550
 
1.8%

FranchiseCode_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
873540 
1
 
23570

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

Length

2021-07-09T04:07:54.687239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:54.870274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0873540
97.4%
123570
 
2.6%

FranchiseCode_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
874343 
1
 
22767

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

Length

2021-07-09T04:07:55.304418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:55.477656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0874343
97.5%
122767
 
2.5%

FranchiseCode_8
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
869651 
1
 
27459

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

Length

2021-07-09T04:07:55.930315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:56.108824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0869651
96.9%
127459
 
3.1%

FranchiseCode_9
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
874260 
1
 
22850

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

Length

2021-07-09T04:07:57.624734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:57.832905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0874260
97.5%
122850
 
2.5%

FranchiseCode_10
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
872091 
1
 
25019

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

Length

2021-07-09T04:07:58.284121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:58.464028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0872091
97.2%
125019
 
2.8%

FranchiseCode_11
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
664627 
1
232483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

Length

2021-07-09T04:07:58.914832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:59.092578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0664627
74.1%
1232483
 
25.9%

FranchiseCode_12
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
664349 
0
232761 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

Length

2021-07-09T04:07:59.529819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:07:59.706946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

Most occurring characters

ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1664349
74.1%
0232761
 
25.9%

LowDoc_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
895125 
1
 
1985

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

Length

2021-07-09T04:08:00.178025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:00.355738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0895125
99.8%
11985
 
0.2%

LowDoc_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
893854 
1
 
3256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

Length

2021-07-09T04:08:00.820978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:00.997286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0893854
99.6%
13256
 
0.4%

LowDoc_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
782375 
0
114735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

Length

2021-07-09T04:08:01.492826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:01.644752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

Most occurring characters

ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1782375
87.2%
0114735
 
12.8%

LowDoc_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
782039 
1
115071 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

Length

2021-07-09T04:08:01.976606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:02.117244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0782039
87.2%
1115071
 
12.8%

NAICS_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
888515 
1
 
8595

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

Length

2021-07-09T04:08:02.518385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:02.743965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0888515
99.0%
18595
 
1.0%

NAICS_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
828402 
1
 
68708

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

Length

2021-07-09T04:08:03.251659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:03.432549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0828402
92.3%
168708
 
7.7%

NAICS_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
730890 
1
166220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

Length

2021-07-09T04:08:03.908197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:04.084293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0730890
81.5%
1166220
 
18.5%

NAICS_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
650892 
1
246218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

Length

2021-07-09T04:08:04.600065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:04.774043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

Most occurring characters

ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0650892
72.6%
1246218
 
27.4%

NAICS_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
627353 
1
269757 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

Length

2021-07-09T04:08:05.266862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:05.440886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

Most occurring characters

ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0627353
69.9%
1269757
30.1%

NAICS_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
574571 
1
322539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

Length

2021-07-09T04:08:05.895887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:06.067161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

Most occurring characters

ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0574571
64.0%
1322539
36.0%

NAICS_7
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
544817 
1
352293 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

Length

2021-07-09T04:08:06.585198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:06.765646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

Most occurring characters

ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0544817
60.7%
1352293
39.3%

NAICS_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
546555 
1
350555 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

Length

2021-07-09T04:08:07.299879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:07.471266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

Most occurring characters

ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0546555
60.9%
1350555
39.1%

NAICS_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
564675 
0
332435 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

Length

2021-07-09T04:08:07.907377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:08.095848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

Most occurring characters

ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1564675
62.9%
0332435
37.1%

NAICS_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
545137 
1
351973 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

Length

2021-07-09T04:08:08.563689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:08.736859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

Most occurring characters

ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0545137
60.8%
1351973
39.2%

NAICS_11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
591219 
1
305891 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Length

2021-07-09T04:08:09.206865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:09.381039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Most occurring characters

ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0591219
65.9%
1305891
34.1%

Bank_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
890016 
1
 
7094

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Length

2021-07-09T04:08:09.801420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:09.962718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0890016
99.2%
17094
 
0.8%

Bank_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
858741 
1
 
38369

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Length

2021-07-09T04:08:10.375756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:10.544739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0858741
95.7%
138369
 
4.3%

Bank_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
826565 
1
 
70545

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Length

2021-07-09T04:08:11.009170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:11.171341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0826565
92.1%
170545
 
7.9%

Bank_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
770848 
1
126262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Length

2021-07-09T04:08:11.634801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:11.797315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0770848
85.9%
1126262
 
14.1%

Bank_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
746892 
1
150218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Length

2021-07-09T04:08:12.239537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:12.402868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0746892
83.3%
1150218
 
16.7%

Bank_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
680441 
1
216669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Length

2021-07-09T04:08:12.890292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:13.067698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Most occurring characters

ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0680441
75.8%
1216669
 
24.2%

Bank_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
637200 
1
259910 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Length

2021-07-09T04:08:13.539367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:13.728130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Most occurring characters

ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0637200
71.0%
1259910
29.0%

Bank_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
499523 
1
397587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Length

2021-07-09T04:08:14.778018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:14.947882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Most occurring characters

ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0499523
55.7%
1397587
44.3%

Bank_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
603100 
1
294010 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Length

2021-07-09T04:08:15.416506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:15.590598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Most occurring characters

ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0603100
67.2%
1294010
32.8%

Bank_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
461643 
1
435467 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Length

2021-07-09T04:08:16.035109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:16.200446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Most occurring characters

ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0461643
51.5%
1435467
48.5%

Bank_11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
531199 
0
365911 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Length

2021-07-09T04:08:16.623319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:16.788681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Most occurring characters

ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1531199
59.2%
0365911
40.8%

Bank_12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
1
479000 
0
418110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Length

2021-07-09T04:08:17.222064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:17.335759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Most occurring characters

ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1479000
53.4%
0418110
46.6%

Bank_13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
0
459368 
1
437742 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897110
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Length

2021-07-09T04:08:17.709937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-09T04:08:17.864404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Most occurring characters

ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number897110
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common897110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII897110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0459368
51.2%
1437742
48.8%

Interactions

2021-07-09T04:06:22.321472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:23.293801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:24.180465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:24.975049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:25.737170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:26.460953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:27.311167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:28.225364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:29.074252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:29.869180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:30.699288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:31.588234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:32.530690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:33.518426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:34.300220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:35.080793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:35.911513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:36.755277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:37.642848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:38.512764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:39.364112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:40.189876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:41.036014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:41.764181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:42.544723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:43.437694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:44.170179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:44.955180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:45.703022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:46.574509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:47.599216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:48.550236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:49.424861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:50.271779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:51.228478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-09T04:06:52.198512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-07-09T04:08:18.279922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-09T04:08:23.328238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-09T04:08:28.636269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-09T04:08:33.707294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-09T04:08:38.671637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-09T04:06:53.490543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-09T04:07:13.861898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ApprovalFYTermNoEmpCreateJobGrAppvSBA_AppvNewExist_0NewExist_1NewExist_2NewExist_UUrbanRural_0UrbanRural_1UrbanRural_2Month_AprMonth_AugMonth_DecMonth_FebMonth_JanMonth_JulMonth_JunMonth_MarMonth_MayMonth_NovMonth_OctMonth_SepMIS_logicalRevLineCr_1RevLineCr_2RevLineCr_3RevLineCr_4FranchiseCode_1FranchiseCode_2FranchiseCode_3FranchiseCode_4FranchiseCode_5FranchiseCode_6FranchiseCode_7FranchiseCode_8FranchiseCode_9FranchiseCode_10FranchiseCode_11FranchiseCode_12LowDoc_1LowDoc_2LowDoc_3LowDoc_4NAICS_1NAICS_2NAICS_3NAICS_4NAICS_5NAICS_6NAICS_7NAICS_8NAICS_9NAICS_10NAICS_11Bank_1Bank_2Bank_3Bank_4Bank_5Bank_6Bank_7Bank_8Bank_9Bank_10Bank_11Bank_12Bank_13
035844060000480000010100000100000000100010000000000010001000000000010000000000001
135602040000320000010100000100000000100010000000000010001000000000100000000000010
235180702870002152500100100000100000000100010000000000010010000000000110000000000011
335602035000280000100100000100000000100010000000000010001000000001000000000000100
4352401472290002290000100100000100000000100010000000000010010000000001000000000000101
5351201905170003877500100100000100000000100010000000000010010000000001010000000000110
618454506000004999980010100000000100000000010000000000100010000000001000000000000111
735841045000360000010100000100000000100010000000000010001000000001100000000001000
835297203050002287500010100000100000000100010000000000010010000000001110000000001001
935843070000560000010100000100000000100010000000000010001000000001000000000001010

Last rows

ApprovalFYTermNoEmpCreateJobGrAppvSBA_AppvNewExist_0NewExist_1NewExist_2NewExist_UUrbanRural_0UrbanRural_1UrbanRural_2Month_AprMonth_AugMonth_DecMonth_FebMonth_JanMonth_JulMonth_JunMonth_MarMonth_MayMonth_NovMonth_OctMonth_SepMIS_logicalRevLineCr_1RevLineCr_2RevLineCr_3RevLineCr_4FranchiseCode_1FranchiseCode_2FranchiseCode_3FranchiseCode_4FranchiseCode_5FranchiseCode_6FranchiseCode_7FranchiseCode_8FranchiseCode_9FranchiseCode_10FranchiseCode_11FranchiseCode_12LowDoc_1LowDoc_2LowDoc_3LowDoc_4NAICS_1NAICS_2NAICS_3NAICS_4NAICS_5NAICS_6NAICS_7NAICS_8NAICS_9NAICS_10NAICS_11Bank_1Bank_2Bank_3Bank_4Bank_5Bank_6Bank_7Bank_8Bank_9Bank_10Bank_11Bank_12Bank_13
8971003560101000050000100100000100000000100100000000000010010000000001000000000100011
8971013518020128000960000100100000100000000100100000000000010010000000011000000111101110
897102356020050000250000100100000100000000100100000000000010010011010011110000000001100
89710335364002000001500000100100000100000000100010000000000010010010111100110000000100110
89710435845079000632000010100000100000000100010000000000010001000000001000000010011001
89710535606070000560000100100000100000000100100000000000010010000000000010000000100011
89710635606085000425000100100000100000000100110000000000010010000100101110000000100011
897107351082603000002250000100100000100000000100010000000000010010001101110100000000111101
89710835606075000600000100100000100000000000010000000000010001000000001000000011110001
89710935481030000240000010100000100000000100010000000000010010000000001000000101010101

Duplicate rows

Most frequently occurring

ApprovalFYTermNoEmpCreateJobGrAppvSBA_AppvNewExist_0NewExist_1NewExist_2NewExist_UUrbanRural_0UrbanRural_1UrbanRural_2Month_AprMonth_AugMonth_DecMonth_FebMonth_JanMonth_JulMonth_JunMonth_MarMonth_MayMonth_NovMonth_OctMonth_SepMIS_logicalRevLineCr_1RevLineCr_2RevLineCr_3RevLineCr_4FranchiseCode_1FranchiseCode_2FranchiseCode_3FranchiseCode_4FranchiseCode_5FranchiseCode_6FranchiseCode_7FranchiseCode_8FranchiseCode_9FranchiseCode_10FranchiseCode_11FranchiseCode_12LowDoc_1LowDoc_2LowDoc_3LowDoc_4NAICS_1NAICS_2NAICS_3NAICS_4NAICS_5NAICS_6NAICS_7NAICS_8NAICS_9NAICS_10NAICS_11Bank_1Bank_2Bank_3Bank_4Bank_5Bank_6Bank_7Bank_8Bank_9Bank_10Bank_11Bank_12Bank_13# duplicates
285346050250001250001001000000001000001001000000000000100100000000010000000000001119
155544841010000500000100101000000000001001100000000001000100001100110000000000011008
37435601015000750001001000000000000011001100000000000100100000000010000000000110007
462366010399253194000101000000000010001000100000000000100010000000010000001100111117
171545128204000200001000010000100000001001100000000000100100001100101000010011011107
2273452120800006000001001000100000000001000100000000000100100000011101000001101000006
265346030250001250001001001000000000001001000000000000100100000000010000000000001116
277346040250001250001001001000000000001001000000000000100100000000010000000000001116
74141247104000200001000010000000000011001100000000000100100001100101000010011011106
1241431282824000200000100010000000000011000100000000001000100001100101000010011011106