Overview

Dataset statistics

Number of variables14
Number of observations5000
Missing cells4210
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory547.0 KiB
Average record size in memory112.0 B

Variable types

Numeric5
Categorical9

Alerts

revenue-units has constant value "million dollars" Constant
sales-units has constant value "million kilowatthours" Constant
price-units has constant value "cents per kilowatthour" Constant
customers-units has constant value "number of customers" Constant
period has a high cardinality: 74 distinct values High cardinality
stateid has a high cardinality: 62 distinct values High cardinality
stateDescription has a high cardinality: 62 distinct values High cardinality
Unnamed: 0 is highly correlated with period and 2 other fieldsHigh correlation
revenue is highly correlated with stateid and 3 other fieldsHigh correlation
sales is highly correlated with stateid and 3 other fieldsHigh correlation
price is highly correlated with stateid and 3 other fieldsHigh correlation
customers is highly correlated with stateid and 3 other fieldsHigh correlation
period is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
stateid is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
stateDescription is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
sectorid is highly correlated with sectorName and 1 other fieldsHigh correlation
sectorName is highly correlated with sectorid and 1 other fieldsHigh correlation
revenue-units is highly correlated with price-units and 7 other fieldsHigh correlation
sales-units is highly correlated with price-units and 7 other fieldsHigh correlation
price-units is highly correlated with customers-units and 7 other fieldsHigh correlation
customers-units is highly correlated with price-units and 7 other fieldsHigh correlation
revenue has 827 (16.5%) missing values Missing
sales has 827 (16.5%) missing values Missing
price has 827 (16.5%) missing values Missing
customers has 1729 (34.6%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
revenue has 315 (6.3%) zeros Zeros
sales has 313 (6.3%) zeros Zeros
price has 315 (6.3%) zeros Zeros
customers has 246 (4.9%) zeros Zeros

Reproduction

Analysis started2022-11-17 22:35:38.921490
Analysis finished2022-11-17 22:35:46.122538
Duration7.2 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.5
Minimum0
Maximum4999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-11-17T14:35:46.265152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile249.95
Q11249.75
median2499.5
Q33749.25
95-th percentile4749.05
Maximum4999
Range4999
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1443.520003
Coefficient of variation (CV)0.577523506
Kurtosis-1.2
Mean2499.5
Median Absolute Deviation (MAD)1250
Skewness0
Sum12497500
Variance2083750
MonotonicityStrictly increasing
2022-11-17T14:35:46.431859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
33301
 
< 0.1%
33371
 
< 0.1%
33361
 
< 0.1%
33351
 
< 0.1%
33341
 
< 0.1%
33331
 
< 0.1%
33321
 
< 0.1%
33311
 
< 0.1%
33291
 
< 0.1%
Other values (4990)4990
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
49991
< 0.1%
49981
< 0.1%
49971
< 0.1%
49961
< 0.1%
49951
< 0.1%
49941
< 0.1%
49931
< 0.1%
49921
< 0.1%
49911
< 0.1%
49901
< 0.1%

period
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct74
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2015-01
 
203
2003-06
 
182
2012-12
 
176
2014-09
 
158
2014-06
 
157
Other values (69)
4124 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters35000
Distinct characters11
Distinct categories2 ?
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 row2015-03
2nd row2015-03
3rd row2015-03
4th row2015-03
5th row2015-03

Common Values

ValueCountFrequency (%)
2015-01203
 
4.1%
2003-06182
 
3.6%
2012-12176
 
3.5%
2014-09158
 
3.2%
2014-06157
 
3.1%
2014-08152
 
3.0%
2017-11143
 
2.9%
2005-06141
 
2.8%
2014-07140
 
2.8%
2012-10139
 
2.8%
Other values (64)3409
68.2%

Length

2022-11-17T14:35:46.576960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-01203
 
4.1%
2003-06182
 
3.6%
2012-12176
 
3.5%
2014-09158
 
3.2%
2014-06157
 
3.1%
2014-08152
 
3.0%
2017-11143
 
2.9%
2005-06141
 
2.8%
2014-07140
 
2.8%
2012-10139
 
2.8%
Other values (64)3409
68.2%

Most occurring characters

ValueCountFrequency (%)
010864
31.0%
27171
20.5%
15436
15.5%
-5000
14.3%
41208
 
3.5%
71086
 
3.1%
61026
 
2.9%
5943
 
2.7%
3912
 
2.6%
9727
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
85.7%
Dash Punctuation5000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010864
36.2%
27171
23.9%
15436
18.1%
41208
 
4.0%
71086
 
3.6%
61026
 
3.4%
5943
 
3.1%
3912
 
3.0%
9727
 
2.4%
8627
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010864
31.0%
27171
20.5%
15436
15.5%
-5000
14.3%
41208
 
3.5%
71086
 
3.1%
61026
 
2.9%
5943
 
2.7%
3912
 
2.6%
9727
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII35000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010864
31.0%
27171
20.5%
15436
15.5%
-5000
14.3%
41208
 
3.5%
71086
 
3.1%
61026
 
2.9%
5943
 
2.7%
3912
 
2.6%
9727
 
2.1%

stateid
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct62
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
US
 
127
PA
 
126
ESC
 
120
PACN
 
120
OR
 
118
Other values (57)
4389 

Length

Max length4
Median length2
Mean length2.2648
Min length2

Characters and Unicode

Total characters11324
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowME
2nd rowME
3rd rowMD
4th rowMD
5th rowMD

Common Values

ValueCountFrequency (%)
US127
 
2.5%
PA126
 
2.5%
ESC120
 
2.4%
PACN120
 
2.4%
OR118
 
2.4%
PACC117
 
2.3%
WSC116
 
2.3%
MTN108
 
2.2%
SC106
 
2.1%
MAT105
 
2.1%
Other values (52)3837
76.7%

Length

2022-11-17T14:35:46.706823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us127
 
2.5%
pa126
 
2.5%
esc120
 
2.4%
pacn120
 
2.4%
or118
 
2.4%
pacc117
 
2.3%
wsc116
 
2.3%
mtn108
 
2.2%
sc106
 
2.1%
mat105
 
2.1%
Other values (52)3837
76.7%

Most occurring characters

ValueCountFrequency (%)
N1423
12.6%
A1347
11.9%
C1177
 
10.4%
S776
 
6.9%
T765
 
6.8%
M764
 
6.7%
W664
 
5.9%
I529
 
4.7%
E483
 
4.3%
O432
 
3.8%
Other values (14)2964
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N1423
12.6%
A1347
11.9%
C1177
 
10.4%
S776
 
6.9%
T765
 
6.8%
M764
 
6.7%
W664
 
5.9%
I529
 
4.7%
E483
 
4.3%
O432
 
3.8%
Other values (14)2964
26.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1423
12.6%
A1347
11.9%
C1177
 
10.4%
S776
 
6.9%
T765
 
6.8%
M764
 
6.7%
W664
 
5.9%
I529
 
4.7%
E483
 
4.3%
O432
 
3.8%
Other values (14)2964
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1423
12.6%
A1347
11.9%
C1177
 
10.4%
S776
 
6.9%
T765
 
6.8%
M764
 
6.7%
W664
 
5.9%
I529
 
4.7%
E483
 
4.3%
O432
 
3.8%
Other values (14)2964
26.2%

stateDescription
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct62
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
U.S. Total
 
127
Pennsylvania
 
126
East South Central
 
120
Pacific Noncontiguous
 
120
Oregon
 
118
Other values (57)
4389 

Length

Max length21
Median length14
Mean length10.3702
Min length4

Characters and Unicode

Total characters51851
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
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 rowMaine
2nd rowMaine
3rd rowMaryland
4th rowMaryland
5th rowMaryland

Common Values

ValueCountFrequency (%)
U.S. Total127
 
2.5%
Pennsylvania126
 
2.5%
East South Central120
 
2.4%
Pacific Noncontiguous120
 
2.4%
Oregon118
 
2.4%
Pacific Contiguous117
 
2.3%
West South Central116
 
2.3%
Mountain108
 
2.2%
South Carolina106
 
2.1%
Middle Atlantic105
 
2.1%
Other values (52)3837
76.7%

Length

2022-11-17T14:35:46.883998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south540
 
7.1%
new461
 
6.1%
central433
 
5.7%
north389
 
5.1%
west288
 
3.8%
pacific237
 
3.1%
east223
 
2.9%
atlantic208
 
2.8%
dakota197
 
2.6%
carolina196
 
2.6%
Other values (56)4391
58.1%

Most occurring characters

ValueCountFrequency (%)
a5688
 
11.0%
i4092
 
7.9%
n4092
 
7.9%
o4021
 
7.8%
t3669
 
7.1%
e3012
 
5.8%
s2868
 
5.5%
2563
 
4.9%
r2304
 
4.4%
l2107
 
4.1%
Other values (38)17435
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41392
79.8%
Uppercase Letter7642
 
14.7%
Space Separator2563
 
4.9%
Other Punctuation254
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5688
13.7%
i4092
9.9%
n4092
9.9%
o4021
9.7%
t3669
8.9%
e3012
 
7.3%
s2868
 
6.9%
r2304
 
5.6%
l2107
 
5.1%
h1635
 
4.0%
Other values (14)7904
19.1%
Uppercase Letter
ValueCountFrequency (%)
N1123
14.7%
C940
12.3%
M764
10.0%
S667
 
8.7%
W563
 
7.4%
A545
 
7.1%
P363
 
4.8%
I327
 
4.3%
E324
 
4.2%
O320
 
4.2%
Other values (12)1706
22.3%
Space Separator
ValueCountFrequency (%)
2563
100.0%
Other Punctuation
ValueCountFrequency (%)
.254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49034
94.6%
Common2817
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a5688
 
11.6%
i4092
 
8.3%
n4092
 
8.3%
o4021
 
8.2%
t3669
 
7.5%
e3012
 
6.1%
s2868
 
5.8%
r2304
 
4.7%
l2107
 
4.3%
h1635
 
3.3%
Other values (36)15546
31.7%
Common
ValueCountFrequency (%)
2563
91.0%
.254
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII51851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a5688
 
11.0%
i4092
 
7.9%
n4092
 
7.9%
o4021
 
7.8%
t3669
 
7.1%
e3012
 
5.8%
s2868
 
5.5%
2563
 
4.9%
r2304
 
4.4%
l2107
 
4.1%
Other values (38)17435
33.6%

sectorid
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
RES
844 
IND
840 
COM
831 
TRA
829 
ALL
829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRES
2nd rowTRA
3rd rowALL
4th rowCOM
5th rowIND

Common Values

ValueCountFrequency (%)
RES844
16.9%
IND840
16.8%
COM831
16.6%
TRA829
16.6%
ALL829
16.6%
OTH827
16.5%

Length

2022-11-17T14:35:47.030463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:47.203288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
res844
16.9%
ind840
16.8%
com831
16.6%
tra829
16.6%
all829
16.6%
oth827
16.5%

Most occurring characters

ValueCountFrequency (%)
R1673
11.2%
O1658
11.1%
A1658
11.1%
L1658
11.1%
T1656
11.0%
E844
 
5.6%
S844
 
5.6%
I840
 
5.6%
N840
 
5.6%
D840
 
5.6%
Other values (3)2489
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R1673
11.2%
O1658
11.1%
A1658
11.1%
L1658
11.1%
T1656
11.0%
E844
 
5.6%
S844
 
5.6%
I840
 
5.6%
N840
 
5.6%
D840
 
5.6%
Other values (3)2489
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin15000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1673
11.2%
O1658
11.1%
A1658
11.1%
L1658
11.1%
T1656
11.0%
E844
 
5.6%
S844
 
5.6%
I840
 
5.6%
N840
 
5.6%
D840
 
5.6%
Other values (3)2489
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1673
11.2%
O1658
11.1%
A1658
11.1%
L1658
11.1%
T1656
11.0%
E844
 
5.6%
S844
 
5.6%
I840
 
5.6%
N840
 
5.6%
D840
 
5.6%
Other values (3)2489
16.6%

sectorName
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
residential
844 
industrial
840 
commercial
831 
transportation
829 
all sectors
829 

Length

Max length14
Median length11
Mean length10.1708
Min length5

Characters and Unicode

Total characters50854
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
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 rowresidential
2nd rowtransportation
3rd rowall sectors
4th rowcommercial
5th rowindustrial

Common Values

ValueCountFrequency (%)
residential844
16.9%
industrial840
16.8%
commercial831
16.6%
transportation829
16.6%
all sectors829
16.6%
other827
16.5%

Length

2022-11-17T14:35:47.392120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:47.550420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
residential844
14.5%
industrial840
14.4%
commercial831
14.3%
transportation829
14.2%
all829
14.2%
sectors829
14.2%
other827
14.2%

Most occurring characters

ValueCountFrequency (%)
r5829
11.5%
t5827
11.5%
i5028
9.9%
a5002
9.8%
e4175
8.2%
l4173
8.2%
s4171
8.2%
o4145
8.2%
n3342
6.6%
c2491
 
4.9%
Other values (6)6671
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50025
98.4%
Space Separator829
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r5829
11.7%
t5827
11.6%
i5028
10.1%
a5002
10.0%
e4175
8.3%
l4173
8.3%
s4171
8.3%
o4145
8.3%
n3342
6.7%
c2491
5.0%
Other values (5)5842
11.7%
Space Separator
ValueCountFrequency (%)
829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50025
98.4%
Common829
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r5829
11.7%
t5827
11.6%
i5028
10.1%
a5002
10.0%
e4175
8.3%
l4173
8.3%
s4171
8.3%
o4145
8.3%
n3342
6.7%
c2491
5.0%
Other values (5)5842
11.7%
Common
ValueCountFrequency (%)
829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r5829
11.5%
t5827
11.5%
i5028
9.9%
a5002
9.8%
e4175
8.2%
l4173
8.2%
s4171
8.2%
o4145
8.2%
n3342
6.6%
c2491
 
4.9%
Other values (6)6671
13.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct3850
Distinct (%)92.3%
Missing827
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean746.1430851
Minimum0
Maximum38766.98241
Zeros315
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-11-17T14:35:47.720557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.67822
median142.26718
Q3535.42954
95-th percentile2778.333848
Maximum38766.98241
Range38766.98241
Interquartile range (IQR)502.75132

Descriptive statistics

Standard deviation2582.375159
Coefficient of variation (CV)3.460965076
Kurtosis95.57419009
Mean746.1430851
Median Absolute Deviation (MAD)141.73977
Skewness8.904324789
Sum3113655.094
Variance6668661.464
MonotonicityNot monotonic
2022-11-17T14:35:47.890090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0315
 
6.3%
8 × 10-52
 
< 0.1%
0.0132
 
< 0.1%
0.0832
 
< 0.1%
0.0192
 
< 0.1%
0.019012
 
< 0.1%
0.018162
 
< 0.1%
0.016012
 
< 0.1%
0.0652
 
< 0.1%
0.00292
 
< 0.1%
Other values (3840)3840
76.8%
(Missing)827
 
16.5%
ValueCountFrequency (%)
0315
6.3%
8 × 10-52
 
< 0.1%
0.0021
 
< 0.1%
0.00241
 
< 0.1%
0.00292
 
< 0.1%
0.003031
 
< 0.1%
0.003821
 
< 0.1%
0.0041
 
< 0.1%
0.004121
 
< 0.1%
0.004191
 
< 0.1%
ValueCountFrequency (%)
38766.982411
< 0.1%
37628.742611
< 0.1%
33551.487171
< 0.1%
33322.392731
< 0.1%
32633.935171
< 0.1%
32216.078861
< 0.1%
31829.188041
< 0.1%
31739.198121
< 0.1%
31419.311551
< 0.1%
31336.06941
< 0.1%

sales
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct3853
Distinct (%)92.3%
Missing827
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean7702.317917
Minimum0
Maximum364784.7166
Zeros313
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-11-17T14:35:48.116478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300.43164
median1642.96588
Q35842.55026
95-th percentile27556.89944
Maximum364784.7166
Range364784.7166
Interquartile range (IQR)5542.11862

Descriptive statistics

Standard deviation26225.59496
Coefficient of variation (CV)3.404896454
Kurtosis91.84977065
Mean7702.317917
Median Absolute Deviation (MAD)1629.61563
Skewness8.745755775
Sum32141772.67
Variance687781831.2
MonotonicityNot monotonic
2022-11-17T14:35:48.329105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0313
 
6.3%
0.165282
 
< 0.1%
0.022
 
< 0.1%
0.0022
 
< 0.1%
0.139022
 
< 0.1%
0.592
 
< 0.1%
0.135882
 
< 0.1%
0.1492
 
< 0.1%
0.1322
 
< 0.1%
83389.714611
 
< 0.1%
Other values (3843)3843
76.9%
(Missing)827
 
16.5%
ValueCountFrequency (%)
0313
6.3%
0.0022
 
< 0.1%
0.0031
 
< 0.1%
0.0041
 
< 0.1%
0.0131
 
< 0.1%
0.0151
 
< 0.1%
0.0171
 
< 0.1%
0.022
 
< 0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
ValueCountFrequency (%)
364784.71661
< 0.1%
351523.98461
< 0.1%
337524.5651
< 0.1%
329666.32031
< 0.1%
327742.33631
< 0.1%
319380.92271
< 0.1%
318177.1791
< 0.1%
318089.70771
< 0.1%
312215.7821
< 0.1%
309619.69281
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1309
Distinct (%)31.4%
Missing827
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean9.39730889
Minimum0
Maximum38.27
Zeros315
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-11-17T14:35:48.509393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.95
median9.12
Q311.36
95-th percentile17.348
Maximum38.27
Range38.27
Interquartile range (IQR)4.41

Descriptive statistics

Standard deviation4.77127204
Coefficient of variation (CV)0.5077274884
Kurtosis3.509388008
Mean9.39730889
Median Absolute Deviation (MAD)2.2
Skewness0.8826996607
Sum39214.97
Variance22.76503688
MonotonicityNot monotonic
2022-11-17T14:35:48.687777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0315
 
6.3%
10.4815
 
0.3%
8.3912
 
0.2%
8.3512
 
0.2%
6.811
 
0.2%
7.4211
 
0.2%
10.1310
 
0.2%
8.7710
 
0.2%
8.0110
 
0.2%
8.2810
 
0.2%
Other values (1299)3757
75.1%
(Missing)827
 
16.5%
ValueCountFrequency (%)
0315
6.3%
2.081
 
< 0.1%
3.051
 
< 0.1%
3.461
 
< 0.1%
3.521
 
< 0.1%
3.591
 
< 0.1%
3.711
 
< 0.1%
3.741
 
< 0.1%
3.761
 
< 0.1%
3.791
 
< 0.1%
ValueCountFrequency (%)
38.271
< 0.1%
35.131
< 0.1%
34.841
< 0.1%
34.351
< 0.1%
34.331
< 0.1%
33.061
< 0.1%
32.971
< 0.1%
32.491
< 0.1%
31.991
< 0.1%
31.581
< 0.1%

customers
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2638
Distinct (%)80.6%
Missing1729
Missing (%)34.6%
Infinite0
Infinite (%)0.0%
Mean3429778.594
Minimum0
Maximum153249906
Zeros246
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-11-17T14:35:48.870279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15240.5
median326641
Q32213152
95-th percentile15731672
Maximum153249906
Range153249906
Interquartile range (IQR)2207911.5

Descriptive statistics

Standard deviation13881200.21
Coefficient of variation (CV)4.047258394
Kurtosis83.77401268
Mean3429778.594
Median Absolute Deviation (MAD)326640
Skewness8.804336196
Sum1.121880578 × 1010
Variance1.926877191 × 1014
MonotonicityNot monotonic
2022-11-17T14:35:49.042443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0246
 
4.9%
1143
 
2.9%
257
 
1.1%
637
 
0.7%
335
 
0.7%
527
 
0.5%
414
 
0.3%
810
 
0.2%
136
 
0.1%
76
 
0.1%
Other values (2628)2690
53.8%
(Missing)1729
34.6%
ValueCountFrequency (%)
0246
4.9%
1143
2.9%
257
 
1.1%
335
 
0.7%
414
 
0.3%
527
 
0.5%
637
 
0.7%
76
 
0.1%
810
 
0.2%
96
 
0.1%
ValueCountFrequency (%)
1532499061
< 0.1%
1525782371
< 0.1%
1523553671
< 0.1%
1510952111
< 0.1%
1504545201
< 0.1%
1486569461
< 0.1%
1484883511
< 0.1%
1479160251
< 0.1%
1478594321
< 0.1%
1460859181
< 0.1%

revenue-units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
million dollars
5000 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters75000
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
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 rowmillion dollars
2nd rowmillion dollars
3rd rowmillion dollars
4th rowmillion dollars
5th rowmillion dollars

Common Values

ValueCountFrequency (%)
million dollars5000
100.0%

Length

2022-11-17T14:35:49.193366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:49.307363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
million5000
50.0%
dollars5000
50.0%

Most occurring characters

ValueCountFrequency (%)
l20000
26.7%
i10000
13.3%
o10000
13.3%
m5000
 
6.7%
n5000
 
6.7%
5000
 
6.7%
d5000
 
6.7%
a5000
 
6.7%
r5000
 
6.7%
s5000
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70000
93.3%
Space Separator5000
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l20000
28.6%
i10000
14.3%
o10000
14.3%
m5000
 
7.1%
n5000
 
7.1%
d5000
 
7.1%
a5000
 
7.1%
r5000
 
7.1%
s5000
 
7.1%
Space Separator
ValueCountFrequency (%)
5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin70000
93.3%
Common5000
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l20000
28.6%
i10000
14.3%
o10000
14.3%
m5000
 
7.1%
n5000
 
7.1%
d5000
 
7.1%
a5000
 
7.1%
r5000
 
7.1%
s5000
 
7.1%
Common
ValueCountFrequency (%)
5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII75000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l20000
26.7%
i10000
13.3%
o10000
13.3%
m5000
 
6.7%
n5000
 
6.7%
5000
 
6.7%
d5000
 
6.7%
a5000
 
6.7%
r5000
 
6.7%
s5000
 
6.7%

sales-units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
million kilowatthours
5000 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters105000
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
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 rowmillion kilowatthours
2nd rowmillion kilowatthours
3rd rowmillion kilowatthours
4th rowmillion kilowatthours
5th rowmillion kilowatthours

Common Values

ValueCountFrequency (%)
million kilowatthours5000
100.0%

Length

2022-11-17T14:35:49.401465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:49.514488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
million5000
50.0%
kilowatthours5000
50.0%

Most occurring characters

ValueCountFrequency (%)
i15000
14.3%
l15000
14.3%
o15000
14.3%
t10000
9.5%
m5000
 
4.8%
n5000
 
4.8%
5000
 
4.8%
k5000
 
4.8%
w5000
 
4.8%
a5000
 
4.8%
Other values (4)20000
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100000
95.2%
Space Separator5000
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i15000
15.0%
l15000
15.0%
o15000
15.0%
t10000
10.0%
m5000
 
5.0%
n5000
 
5.0%
k5000
 
5.0%
w5000
 
5.0%
a5000
 
5.0%
h5000
 
5.0%
Other values (3)15000
15.0%
Space Separator
ValueCountFrequency (%)
5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100000
95.2%
Common5000
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i15000
15.0%
l15000
15.0%
o15000
15.0%
t10000
10.0%
m5000
 
5.0%
n5000
 
5.0%
k5000
 
5.0%
w5000
 
5.0%
a5000
 
5.0%
h5000
 
5.0%
Other values (3)15000
15.0%
Common
ValueCountFrequency (%)
5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII105000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i15000
14.3%
l15000
14.3%
o15000
14.3%
t10000
9.5%
m5000
 
4.8%
n5000
 
4.8%
5000
 
4.8%
k5000
 
4.8%
w5000
 
4.8%
a5000
 
4.8%
Other values (4)20000
19.0%

price-units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
cents per kilowatthour
5000 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters110000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
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 rowcents per kilowatthour
2nd rowcents per kilowatthour
3rd rowcents per kilowatthour
4th rowcents per kilowatthour
5th rowcents per kilowatthour

Common Values

ValueCountFrequency (%)
cents per kilowatthour5000
100.0%

Length

2022-11-17T14:35:49.611134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:49.725598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cents5000
33.3%
per5000
33.3%
kilowatthour5000
33.3%

Most occurring characters

ValueCountFrequency (%)
t15000
13.6%
e10000
 
9.1%
10000
 
9.1%
r10000
 
9.1%
o10000
 
9.1%
c5000
 
4.5%
n5000
 
4.5%
s5000
 
4.5%
p5000
 
4.5%
k5000
 
4.5%
Other values (6)30000
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100000
90.9%
Space Separator10000
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t15000
15.0%
e10000
 
10.0%
r10000
 
10.0%
o10000
 
10.0%
c5000
 
5.0%
n5000
 
5.0%
s5000
 
5.0%
p5000
 
5.0%
k5000
 
5.0%
i5000
 
5.0%
Other values (5)25000
25.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100000
90.9%
Common10000
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t15000
15.0%
e10000
 
10.0%
r10000
 
10.0%
o10000
 
10.0%
c5000
 
5.0%
n5000
 
5.0%
s5000
 
5.0%
p5000
 
5.0%
k5000
 
5.0%
i5000
 
5.0%
Other values (5)25000
25.0%
Common
ValueCountFrequency (%)
10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t15000
13.6%
e10000
 
9.1%
10000
 
9.1%
r10000
 
9.1%
o10000
 
9.1%
c5000
 
4.5%
n5000
 
4.5%
s5000
 
4.5%
p5000
 
4.5%
k5000
 
4.5%
Other values (6)30000
27.3%

customers-units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
number of customers
5000 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters95000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
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 rownumber of customers
2nd rownumber of customers
3rd rownumber of customers
4th rownumber of customers
5th rownumber of customers

Common Values

ValueCountFrequency (%)
number of customers5000
100.0%

Length

2022-11-17T14:35:49.822221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-17T14:35:49.936927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
number5000
33.3%
of5000
33.3%
customers5000
33.3%

Most occurring characters

ValueCountFrequency (%)
u10000
10.5%
m10000
10.5%
e10000
10.5%
r10000
10.5%
10000
10.5%
o10000
10.5%
s10000
10.5%
n5000
5.3%
b5000
5.3%
f5000
5.3%
Other values (2)10000
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter85000
89.5%
Space Separator10000
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u10000
11.8%
m10000
11.8%
e10000
11.8%
r10000
11.8%
o10000
11.8%
s10000
11.8%
n5000
5.9%
b5000
5.9%
f5000
5.9%
c5000
5.9%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin85000
89.5%
Common10000
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u10000
11.8%
m10000
11.8%
e10000
11.8%
r10000
11.8%
o10000
11.8%
s10000
11.8%
n5000
5.9%
b5000
5.9%
f5000
5.9%
c5000
5.9%
Common
ValueCountFrequency (%)
10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII95000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u10000
10.5%
m10000
10.5%
e10000
10.5%
r10000
10.5%
10000
10.5%
o10000
10.5%
s10000
10.5%
n5000
5.3%
b5000
5.3%
f5000
5.3%
Other values (2)10000
10.5%

Interactions

2022-11-17T14:35:44.502269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:41.335887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:42.171423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.000113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.755723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.645935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:41.471502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:42.354622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.148063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.899829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.798952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:41.616981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:42.527629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.299973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.051406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.949852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:41.763629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:42.681268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.445672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.199168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:45.102824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:41.970929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:42.836987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:43.598215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-17T14:35:44.349025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-17T14:35:50.037445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-17T14:35:50.237256image/svg+xmlMatplotlib v3.5.1, 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.
2022-11-17T14:35:50.383966image/svg+xmlMatplotlib v3.5.1, 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.
2022-11-17T14:35:50.533301image/svg+xmlMatplotlib v3.5.1, 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.
2022-11-17T14:35:50.690026image/svg+xmlMatplotlib v3.5.1, 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.
2022-11-17T14:35:50.862512image/svg+xmlMatplotlib v3.5.1, 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.

Missing values

2022-11-17T14:35:45.358873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-17T14:35:45.681020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-17T14:35:45.882233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-17T14:35:45.997662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0periodstateidstateDescriptionsectoridsectorNamerevenuesalespricecustomersrevenue-unitssales-unitsprice-unitscustomers-units
002015-03MEMaineRESresidential63.11465426.1477214.81702745.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
112015-03MEMaineTRAtransportation0.000000.000000.000.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
222015-03MDMarylandALLall sectors647.882835312.9963612.192509299.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
332015-03MDMarylandCOMcommercial283.375232435.1270211.64249451.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
442015-03MDMarylandINDindustrial27.91210288.589749.678805.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
552015-03MDMarylandOTHotherNaNNaNNaNNaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
662015-03MDMarylandRESresidential333.307092550.0758513.072251038.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
772015-03MDMarylandTRAtransportation3.2884139.203758.395.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
882015-03MAMassachusettsALLall sectors865.464434636.7083318.673211524.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers
992015-03MAMassachusettsCOMcommercial374.741542162.8588317.33405156.0million dollarsmillion kilowatthourscents per kilowatthournumber of customers

Last rows

Unnamed: 0periodstateidstateDescriptionsectoridsectorNamerevenuesalespricecustomersrevenue-unitssales-unitsprice-unitscustomers-units
499049902003-07CACaliforniaALLall sectors3228.5731023328.7040713.84NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499149912003-07CACaliforniaCOMcommercial1585.0148810437.3157615.19NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499249922003-07CACaliforniaINDindustrial535.481954640.6586011.54NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499349932003-07CACaliforniaOTHotherNaNNaNNaNNaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499449942003-07CACaliforniaRESresidential1103.571138175.1833113.50NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499549952003-07CACaliforniaTRAtransportation4.5051475.546415.96NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499649962003-07COColoradoALLall sectors321.224244621.384386.95NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499749972003-07COColoradoCOMcommercial131.977091955.604226.75NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499849982003-07COColoradoINDindustrial54.407261048.588955.19NaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers
499949992003-07COColoradoOTHotherNaNNaNNaNNaNmillion dollarsmillion kilowatthourscents per kilowatthournumber of customers