diff --git "a/Finance-RAG/artifacts/report.html" "b/Finance-RAG/artifacts/report.html" new file mode 100644--- /dev/null +++ "b/Finance-RAG/artifacts/report.html" @@ -0,0 +1,30808 @@ +Pandas Profiling Report

Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations659
Missing cells3246
Missing cells (%)27.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory92.8 KiB
Average record size in memory144.2 B

Variable types

Numeric6
Text3
Unsupported3
Categorical6

Alerts

Period/Year has constant value "July 2024" Constant
Balance Carryforward has constant value "0" Constant
Account Number is highly overall correlated with GRP and 1 other fieldsHigh correlation
Credit 1- 4 is highly overall correlated with Single EntryHigh correlation
Debit 1- 4 is highly overall correlated with NetHigh correlation
Dept is highly overall correlated with Internal Grouping and 1 other fieldsHigh correlation
GRP is highly overall correlated with Account NumberHigh correlation
Internal Grouping is highly overall correlated with Dept and 1 other fieldsHigh correlation
Net is highly overall correlated with Debit 1- 4High correlation
Profit Center.1 is highly overall correlated with Dept and 1 other fieldsHigh correlation
Single Entry is highly overall correlated with Account Number and 1 other fieldsHigh correlation
Adjustment has 633 (96.1%) missing values Missing
Single Entry has 647 (98.2%) missing values Missing
CM has 659 (100.0%) missing values Missing
Remarks-1 has 648 (98.3%) missing values Missing
Remarks has 659 (100.0%) missing values Missing
Profit Center is an unsupported type, check if it needs cleaning or further analysis Unsupported
CM is an unsupported type, check if it needs cleaning or further analysis Unsupported
Remarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
Debit 1- 4 has 245 (37.2%) zeros Zeros
Credit 1- 4 has 423 (64.2%) zeros Zeros
Net has 217 (32.9%) zeros Zeros

Reproduction

Analysis started2024-10-19 11:27:40.690951
Analysis finished2024-10-19 11:27:48.840215
Duration8.15 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Account Number
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40973963
Minimum31100018
Maximum42700001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-10-19T16:57:48.991174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31100018
5-th percentile31290002
Q141400024
median41600605
Q341800226
95-th percentile42500003
Maximum42700001
Range11599983
Interquartile range (IQR)400202

Descriptive statistics

Standard deviation2681832.4
Coefficient of variation (CV)0.065452112
Kurtosis9.3407321
Mean40973963
Median Absolute Deviation (MAD)200563
Skewness-3.3173318
Sum2.7001841 × 1010
Variance7.1922249 × 1012
MonotonicityNot monotonic
2024-10-19T16:57:49.252250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41800241 27
 
4.1%
41600634 24
 
3.6%
42500001 21
 
3.2%
41600001 19
 
2.9%
41400042 18
 
2.7%
41400041 18
 
2.7%
41400001 18
 
2.7%
41400004 18
 
2.7%
41400043 17
 
2.6%
41200002 14
 
2.1%
Other values (89) 465
70.6%
ValueCountFrequency (%)
31100018 1
 
0.2%
31100042 1
 
0.2%
31100050 6
0.9%
31100053 1
 
0.2%
31100055 14
2.1%
31100056 1
 
0.2%
31100057 2
 
0.3%
31200005 3
 
0.5%
31200006 2
 
0.3%
31200011 2
 
0.3%
ValueCountFrequency (%)
42700001 4
 
0.6%
42500101 1
 
0.2%
42500021 3
 
0.5%
42500020 5
0.8%
42500019 3
 
0.5%
42500012 2
 
0.3%
42500007 11
1.7%
42500006 1
 
0.2%
42500004 2
 
0.3%
42500003 2
 
0.3%

Account Number.1
Text

Distinct99
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-10-19T16:57:49.950450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length37
Median length27
Mean length18.886191
Min length5

Characters and Unicode

Total characters12446
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)3.5%

Sample

1st rowChips Manufature
2nd rowCommercial Sales
3rd rowOther Value added
4th rowFreight income
5th rowInterest Income
ValueCountFrequency (%)
156
 
8.9%
charges 150
 
8.6%
rent 47
 
2.7%
allowance 45
 
2.6%
epf 36
 
2.1%
employer 35
 
2.0%
contribution 35
 
2.0%
expenses 33
 
1.9%
on 32
 
1.8%
cogs 30
 
1.7%
Other values (146) 1146
65.7%
2024-10-19T16:57:50.905663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1124
 
9.0%
1096
 
8.8%
a 981
 
7.9%
n 969
 
7.8%
r 760
 
6.1%
o 736
 
5.9%
i 688
 
5.5%
s 680
 
5.5%
t 538
 
4.3%
l 431
 
3.5%
Other values (39) 4443
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9410
75.6%
Uppercase Letter 1720
 
13.8%
Space Separator 1096
 
8.8%
Dash Punctuation 141
 
1.1%
Other Punctuation 79
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1124
11.9%
a 981
10.4%
n 969
10.3%
r 760
 
8.1%
o 736
 
7.8%
i 688
 
7.3%
s 680
 
7.2%
t 538
 
5.7%
l 431
 
4.6%
g 384
 
4.1%
Other values (13) 2119
22.5%
Uppercase Letter
ValueCountFrequency (%)
C 307
17.8%
E 170
9.9%
S 154
 
9.0%
T 115
 
6.7%
R 111
 
6.5%
P 108
 
6.3%
A 102
 
5.9%
O 99
 
5.8%
F 83
 
4.8%
M 75
 
4.4%
Other values (11) 396
23.0%
Other Punctuation
ValueCountFrequency (%)
& 40
50.6%
/ 20
25.3%
. 19
24.1%
Space Separator
ValueCountFrequency (%)
1096
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11130
89.4%
Common 1316
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1124
 
10.1%
a 981
 
8.8%
n 969
 
8.7%
r 760
 
6.8%
o 736
 
6.6%
i 688
 
6.2%
s 680
 
6.1%
t 538
 
4.8%
l 431
 
3.9%
g 384
 
3.5%
Other values (34) 3839
34.5%
Common
ValueCountFrequency (%)
1096
83.3%
- 141
 
10.7%
& 40
 
3.0%
/ 20
 
1.5%
. 19
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1124
 
9.0%
1096
 
8.8%
a 981
 
7.9%
n 969
 
7.8%
r 760
 
6.1%
o 736
 
5.9%
i 688
 
5.5%
s 680
 
5.5%
t 538
 
4.3%
l 431
 
3.5%
Other values (39) 4443
35.7%

Profit Center
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.3 KiB

Profit Center.1
Categorical

High correlation 

Distinct28
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Maharashtra-Eng
67 
CGP - Maharashtra
64 
SV Agri - Corporate
49 
Value added services
46 
CGP - Tamil Nadu
44 
Other values (23)
389 

Length

Max length20
Median length18
Mean length15.581184
Min length6

Characters and Unicode

Total characters10268
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowCGP - Maharashtra
2nd rowCGP - Maharashtra
3rd rowCGP - Maharashtra
4th rowCGP - Maharashtra
5th rowCGP - Maharashtra

Common Values

ValueCountFrequency (%)
Maharashtra-Eng 67
 
10.2%
CGP - Maharashtra 64
 
9.7%
SV Agri - Corporate 49
 
7.4%
Value added services 46
 
7.0%
CGP - Tamil Nadu 44
 
6.7%
Exports 35
 
5.3%
Haryana_SV Agri 34
 
5.2%
Chakan Shop 31
 
4.7%
Moshi Pune_SV Agri 28
 
4.2%
Narayangaon Shop 25
 
3.8%
Other values (18) 236
35.8%

Length

2024-10-19T16:57:51.218878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
199
 
11.9%
agri 187
 
11.2%
cgp 108
 
6.5%
sv 105
 
6.3%
maharashtra 83
 
5.0%
shop 81
 
4.9%
maharashtra-eng 67
 
4.0%
corporate 49
 
2.9%
pune_sv 48
 
2.9%
moshi 48
 
2.9%
Other values (32) 691
41.5%

Most occurring characters

ValueCountFrequency (%)
a 1393
 
13.6%
1007
 
9.8%
r 930
 
9.1%
h 593
 
5.8%
e 436
 
4.2%
i 398
 
3.9%
S 386
 
3.8%
g 368
 
3.6%
s 365
 
3.6%
- 313
 
3.0%
Other values (32) 4079
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6692
65.2%
Uppercase Letter 2087
 
20.3%
Space Separator 1007
 
9.8%
Dash Punctuation 313
 
3.0%
Connector Punctuation 149
 
1.5%
Decimal Number 20
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1393
20.8%
r 930
13.9%
h 593
8.9%
e 436
 
6.5%
i 398
 
5.9%
g 368
 
5.5%
s 365
 
5.5%
o 310
 
4.6%
n 286
 
4.3%
t 268
 
4.0%
Other values (13) 1345
20.1%
Uppercase Letter
ValueCountFrequency (%)
S 386
18.5%
V 280
13.4%
A 242
11.6%
P 228
10.9%
M 225
10.8%
C 211
10.1%
G 108
 
5.2%
E 102
 
4.9%
T 92
 
4.4%
N 69
 
3.3%
Other values (5) 144
 
6.9%
Space Separator
ValueCountFrequency (%)
1007
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 313
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 149
100.0%
Decimal Number
ValueCountFrequency (%)
2 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8779
85.5%
Common 1489
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1393
15.9%
r 930
 
10.6%
h 593
 
6.8%
e 436
 
5.0%
i 398
 
4.5%
S 386
 
4.4%
g 368
 
4.2%
s 365
 
4.2%
o 310
 
3.5%
n 286
 
3.3%
Other values (28) 3314
37.7%
Common
ValueCountFrequency (%)
1007
67.6%
- 313
 
21.0%
_ 149
 
10.0%
2 20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1393
 
13.6%
1007
 
9.8%
r 930
 
9.1%
h 593
 
5.8%
e 436
 
4.2%
i 398
 
3.9%
S 386
 
3.8%
g 368
 
3.6%
s 365
 
3.6%
- 313
 
3.0%
Other values (32) 4079
39.7%

Period/Year
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
July 2024
659 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters5931
Distinct characters8
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 rowJuly 2024
2nd rowJuly 2024
3rd rowJuly 2024
4th rowJuly 2024
5th rowJuly 2024

Common Values

ValueCountFrequency (%)
July 2024 659
100.0%

Length

2024-10-19T16:57:51.452539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T16:57:51.636715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
july 659
50.0%
2024 659
50.0%

Most occurring characters

ValueCountFrequency (%)
2 1318
22.2%
J 659
11.1%
u 659
11.1%
l 659
11.1%
y 659
11.1%
659
11.1%
0 659
11.1%
4 659
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2636
44.4%
Lowercase Letter 1977
33.3%
Uppercase Letter 659
 
11.1%
Space Separator 659
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1318
50.0%
0 659
25.0%
4 659
25.0%
Lowercase Letter
ValueCountFrequency (%)
u 659
33.3%
l 659
33.3%
y 659
33.3%
Uppercase Letter
ValueCountFrequency (%)
J 659
100.0%
Space Separator
ValueCountFrequency (%)
659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3295
55.6%
Latin 2636
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1318
40.0%
659
20.0%
0 659
20.0%
4 659
20.0%
Latin
ValueCountFrequency (%)
J 659
25.0%
u 659
25.0%
l 659
25.0%
y 659
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1318
22.2%
J 659
11.1%
u 659
11.1%
l 659
11.1%
y 659
11.1%
659
11.1%
0 659
11.1%
4 659
11.1%

Balance Carryforward
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters659
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 659
100.0%

Length

2024-10-19T16:57:52.170430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T16:57:52.345230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 659
100.0%

Most occurring characters

ValueCountFrequency (%)
0 659
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 659
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 659
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 659
100.0%

Debit 1- 4
Real number (ℝ)

High correlation  Zeros 

Distinct389
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333086.97
Minimum0
Maximum57368393
Zeros245
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-10-19T16:57:52.520413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2340
Q336750
95-th percentile1060655.2
Maximum57368393
Range57368393
Interquartile range (IQR)36750

Descriptive statistics

Standard deviation2590294.1
Coefficient of variation (CV)7.7766298
Kurtosis368.49742
Mean333086.97
Median Absolute Deviation (MAD)2340
Skewness17.79096
Sum2.1950432 × 108
Variance6.7096236 × 1012
MonotonicityNot monotonic
2024-10-19T16:57:52.785443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
37.2%
4800 4
 
0.6%
10000 4
 
0.6%
1000 3
 
0.5%
5400 3
 
0.5%
27000 3
 
0.5%
3000 3
 
0.5%
4000 3
 
0.5%
16200 2
 
0.3%
12500 2
 
0.3%
Other values (379) 387
58.7%
ValueCountFrequency (%)
0 245
37.2%
0.22 1
 
0.2%
0.7 1
 
0.2%
0.76 1
 
0.2%
0.93 1
 
0.2%
0.96 1
 
0.2%
1.24 1
 
0.2%
1.25 1
 
0.2%
1.37 1
 
0.2%
2.18 1
 
0.2%
ValueCountFrequency (%)
57368393.36 1
0.2%
22016487.24 1
0.2%
17379673.22 1
0.2%
10880655 1
0.2%
7422012 1
0.2%
5866870 1
0.2%
5040589.4 1
0.2%
4572739 1
0.2%
4325196.28 1
0.2%
3740074.02 1
0.2%

Credit 1- 4
Real number (ℝ)

High correlation  Zeros 

Distinct221
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324709.09
Minimum0
Maximum67134089
Zeros423
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-10-19T16:57:53.058068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34100
95-th percentile823923.93
Maximum67134089
Range67134089
Interquartile range (IQR)4100

Descriptive statistics

Standard deviation3010932.3
Coefficient of variation (CV)9.272707
Kurtosis379.61166
Mean324709.09
Median Absolute Deviation (MAD)0
Skewness18.144871
Sum2.1398329 × 108
Variance9.0657131 × 1012
MonotonicityNot monotonic
2024-10-19T16:57:53.316871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 423
64.2%
3600 4
 
0.6%
30000 3
 
0.5%
3200 3
 
0.5%
168 3
 
0.5%
10000 2
 
0.3%
5376 2
 
0.3%
12500 2
 
0.3%
18000 2
 
0.3%
10800 2
 
0.3%
Other values (211) 213
32.3%
ValueCountFrequency (%)
0 423
64.2%
0.04 1
 
0.2%
0.08 1
 
0.2%
0.18 1
 
0.2%
0.3 1
 
0.2%
0.5 1
 
0.2%
0.64 1
 
0.2%
0.65 1
 
0.2%
0.74 1
 
0.2%
1.12 1
 
0.2%
ValueCountFrequency (%)
67134088.81 1
0.2%
26344583.52 1
0.2%
15955693 1
0.2%
14896211.52 1
0.2%
9831506.86 1
0.2%
9429763.12 1
0.2%
7439980 1
0.2%
4142869.7 1
0.2%
4060703 1
0.2%
3842142 1
0.2%

Adjustment
Real number (ℝ)

Missing 

Distinct26
Distinct (%)100.0%
Missing633
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-4644525
Maximum4644525
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)2.0%
Memory size5.3 KiB
2024-10-19T16:57:53.571699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4644525
5-th percentile-1630127.1
Q1-882128.75
median0
Q3882128.75
95-th percentile1630127.1
Maximum4644525
Range9289050
Interquartile range (IQR)1764257.5

Descriptive statistics

Standard deviation1588368.5
Coefficient of variation (CV)nan
Kurtosis4.3242513
Mean0
Median Absolute Deviation (MAD)1009505
Skewness-0.00012205931
Sum0
Variance2.5229143 × 1012
MonotonicityNot monotonic
2024-10-19T16:57:53.778754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-1200000 1
 
0.2%
330671 1
 
0.2%
172493 1
 
0.2%
-368220 1
 
0.2%
-9120 1
 
0.2%
-33844 1
 
0.2%
37549 1
 
0.2%
-1256519.34 1
 
0.2%
1034366 1
 
0.2%
-1080000 1
 
0.2%
Other values (16) 16
 
2.4%
(Missing) 633
96.1%
ValueCountFrequency (%)
-4644525 1
0.2%
-1754663 1
0.2%
-1256519.34 1
0.2%
-1200000 1
0.2%
-1080000 1
0.2%
-1034366 1
0.2%
-1009505 1
0.2%
-500000 1
0.2%
-461959 1
0.2%
-368220 1
0.2%
ValueCountFrequency (%)
4644525 1
0.2%
1754663 1
0.2%
1256519.34 1
0.2%
1200000 1
0.2%
1080000 1
0.2%
1034366 1
0.2%
1009505 1
0.2%
500000 1
0.2%
461959 1
0.2%
330671 1
0.2%

Single Entry
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)75.0%
Missing647
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean-573039.53
Minimum-1990665
Maximum-55610.36
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)1.8%
Memory size5.3 KiB
2024-10-19T16:57:53.987513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1990665
5-th percentile-1390799.2
Q1-550000
median-500000
Q3-298799.75
95-th percentile-135024.66
Maximum-55610.36
Range1935054.6
Interquartile range (IQR)251200.25

Descriptive statistics

Standard deviation500721.76
Coefficient of variation (CV)-0.87379969
Kurtosis6.3999095
Mean-573039.53
Median Absolute Deviation (MAD)186250
Skewness-2.2869081
Sum-6876474.4
Variance2.5072228 × 1011
MonotonicityNot monotonic
2024-10-19T16:57:54.193602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
-500000 4
 
0.6%
-1990665 1
 
0.2%
-900000 1
 
0.2%
-212699 1
 
0.2%
-327500 1
 
0.2%
-200000 1
 
0.2%
-55610.36 1
 
0.2%
-490000 1
 
0.2%
-700000 1
 
0.2%
(Missing) 647
98.2%
ValueCountFrequency (%)
-1990665 1
 
0.2%
-900000 1
 
0.2%
-700000 1
 
0.2%
-500000 4
0.6%
-490000 1
 
0.2%
-327500 1
 
0.2%
-212699 1
 
0.2%
-200000 1
 
0.2%
-55610.36 1
 
0.2%
ValueCountFrequency (%)
-55610.36 1
 
0.2%
-200000 1
 
0.2%
-212699 1
 
0.2%
-327500 1
 
0.2%
-490000 1
 
0.2%
-500000 4
0.6%
-700000 1
 
0.2%
-900000 1
 
0.2%
-1990665 1
 
0.2%

Net
Real number (ℝ)

High correlation  Zeros 

Distinct414
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2056.8288
Minimum-56253434
Maximum48546392
Zeros217
Zeros (%)32.9%
Negative76
Negative (%)11.5%
Memory size5.3 KiB
2024-10-19T16:57:54.454933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-56253434
5-th percentile-74942.975
Q10
median510
Q316855.55
95-th percentile485439
Maximum48546392
Range1.0479983 × 108
Interquartile range (IQR)16855.55

Descriptive statistics

Standard deviation3474539.9
Coefficient of variation (CV)-1689.2703
Kurtosis172.66532
Mean-2056.8288
Median Absolute Deviation (MAD)2010
Skewness-2.284961
Sum-1355450.2
Variance1.2072427 × 1013
MonotonicityNot monotonic
2024-10-19T16:57:54.716465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 217
32.9%
12500 4
 
0.6%
1000 3
 
0.5%
1600 3
 
0.5%
10000 3
 
0.5%
4000 3
 
0.5%
3000 3
 
0.5%
1800 3
 
0.5%
23000 2
 
0.3%
7000 2
 
0.3%
Other values (404) 416
63.1%
ValueCountFrequency (%)
-56253433.81 1
0.2%
-25144583.52 1
0.2%
-15610720 1
0.2%
-14896211.52 1
0.2%
-9429763.12 1
0.2%
-6020910 1
0.2%
-5013057.72 1
0.2%
-4205809.2 1
0.2%
-2082538.81 1
0.2%
-1995171 1
0.2%
ValueCountFrequency (%)
48546391.5 1
0.2%
26661012.24 1
0.2%
17879673.22 1
0.2%
4394044 1
0.2%
3740074.02 1
0.2%
3389308.6 1
0.2%
3290830.28 1
0.2%
3163313.23 1
0.2%
2600000.35 1
0.2%
1896032.6 1
0.2%

Concanate
Text

Distinct651
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-10-19T16:57:55.179833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters7908
Distinct characters13
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

Unique643 ?
Unique (%)97.6%

Sample

1st row311000501200
2nd row311000551200
3rd row311000571200
4th row312000111200
5th row313000011200
ValueCountFrequency (%)
418002411211 2
 
0.3%
418002321200 2
 
0.3%
416006141211 2
 
0.3%
418002411200 2
 
0.3%
415016101211 2
 
0.3%
415016011211 2
 
0.3%
418002061201 2
 
0.3%
416006341216 2
 
0.3%
312000111200 1
 
0.2%
411000111200 1
 
0.2%
Other values (641) 641
97.3%
2024-10-19T16:57:55.815246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2448
31.0%
1 1921
24.3%
2 1215
15.4%
4 1038
13.1%
6 393
 
5.0%
3 249
 
3.1%
5 186
 
2.4%
8 163
 
2.1%
9 83
 
1.0%
7 74
 
0.9%
Other values (3) 138
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7770
98.3%
Uppercase Letter 138
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2448
31.5%
1 1921
24.7%
2 1215
15.6%
4 1038
13.4%
6 393
 
5.1%
3 249
 
3.2%
5 186
 
2.4%
8 163
 
2.1%
9 83
 
1.1%
7 74
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
S 46
33.3%
V 46
33.3%
T 46
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7770
98.3%
Latin 138
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2448
31.5%
1 1921
24.7%
2 1215
15.6%
4 1038
13.4%
6 393
 
5.1%
3 249
 
3.2%
5 186
 
2.4%
8 163
 
2.1%
9 83
 
1.1%
7 74
 
1.0%
Latin
ValueCountFrequency (%)
S 46
33.3%
V 46
33.3%
T 46
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2448
31.0%
1 1921
24.3%
2 1215
15.4%
4 1038
13.1%
6 393
 
5.0%
3 249
 
3.1%
5 186
 
2.4%
8 163
 
2.1%
9 83
 
1.0%
7 74
 
0.9%
Other values (3) 138
 
1.7%

GRP
Categorical

High correlation 

Distinct46
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
DC: Operations salaries and wages
97 
Cost Of Goods Sold (Cogs)
72 
Handling
56 
IDE: Miscellaneous Expenses
44 
IDE: Travelling and Conveyance
36 
Other values (41)
354 

Length

Max length42
Median length33
Mean length23.578149
Min length8

Characters and Unicode

Total characters15538
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.4%

Sample

1st rowNet Revenue
2nd rowNet Revenue
3rd rowNet Revenue
4th rowNet Revenue
5th rowNet Revenue

Common Values

ValueCountFrequency (%)
DC: Operations salaries and wages 97
 
14.7%
Cost Of Goods Sold (Cogs) 72
 
10.9%
Handling 56
 
8.5%
IDE: Miscellaneous Expenses 44
 
6.7%
IDE: Travelling and Conveyance 36
 
5.5%
Net Revenue 35
 
5.3%
Outbound Logistics 21
 
3.2%
Consumables 20
 
3.0%
DC: OPERATIONS EMPLOYEE BENEFITS 18
 
2.7%
Value Additions 17
 
2.6%
Other values (36) 243
36.9%

Length

2024-10-19T16:57:56.100027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 261
 
11.3%
dc 170
 
7.4%
ide 170
 
7.4%
operations 131
 
5.7%
wages 122
 
5.3%
salaries 122
 
5.3%
expenses 74
 
3.2%
goods 72
 
3.1%
sold 72
 
3.1%
cogs 72
 
3.1%
Other values (64) 1034
45.0%

Most occurring characters

ValueCountFrequency (%)
1641
 
10.6%
e 1322
 
8.5%
s 1236
 
8.0%
a 1189
 
7.7%
n 1144
 
7.4%
o 917
 
5.9%
i 715
 
4.6%
d 566
 
3.6%
t 562
 
3.6%
l 544
 
3.5%
Other values (38) 5702
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10586
68.1%
Uppercase Letter 2764
 
17.8%
Space Separator 1641
 
10.6%
Other Punctuation 403
 
2.6%
Close Punctuation 72
 
0.5%
Open Punctuation 72
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1322
12.5%
s 1236
11.7%
a 1189
11.2%
n 1144
10.8%
o 917
8.7%
i 715
 
6.8%
d 566
 
5.3%
t 562
 
5.3%
l 544
 
5.1%
r 503
 
4.8%
Other values (13) 1888
17.8%
Uppercase Letter
ValueCountFrequency (%)
C 498
18.0%
D 361
13.1%
E 353
12.8%
O 284
10.3%
I 225
8.1%
S 168
 
6.1%
R 112
 
4.1%
M 103
 
3.7%
T 91
 
3.3%
P 85
 
3.1%
Other values (10) 484
17.5%
Other Punctuation
ValueCountFrequency (%)
: 392
97.3%
, 11
 
2.7%
Space Separator
ValueCountFrequency (%)
1641
100.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13350
85.9%
Common 2188
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1322
 
9.9%
s 1236
 
9.3%
a 1189
 
8.9%
n 1144
 
8.6%
o 917
 
6.9%
i 715
 
5.4%
d 566
 
4.2%
t 562
 
4.2%
l 544
 
4.1%
r 503
 
3.8%
Other values (33) 4652
34.8%
Common
ValueCountFrequency (%)
1641
75.0%
: 392
 
17.9%
) 72
 
3.3%
( 72
 
3.3%
, 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1641
 
10.6%
e 1322
 
8.5%
s 1236
 
8.0%
a 1189
 
7.7%
n 1144
 
7.4%
o 917
 
5.9%
i 715
 
4.6%
d 566
 
3.6%
t 562
 
3.6%
l 544
 
3.5%
Other values (38) 5702
36.7%

CM
Unsupported

Missing  Rejected  Unsupported 

Missing659
Missing (%)100.0%
Memory size5.3 KiB

Dept
Categorical

High correlation 

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Shops
168 
Tuber
104 
CGP
92 
Engg
67 
VAS
58 
Other values (4)
170 

Length

Max length10
Median length9
Mean length5.2989378
Min length3

Characters and Unicode

Total characters3492
Distinct characters26
Distinct categories3 ?
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 rowCGP
2nd rowCGP
3rd rowCGP
4th rowCGP
5th rowCGP

Common Values

ValueCountFrequency (%)
Shops 168
25.5%
Tuber 104
15.8%
CGP 92
14.0%
Engg 67
 
10.2%
VAS 58
 
8.8%
Corporate 49
 
7.4%
Carisma 44
 
6.7%
Cold Store 42
 
6.4%
Exports 35
 
5.3%

Length

2024-10-19T16:57:56.347543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T16:57:56.577974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
shops 168
24.0%
tuber 104
14.8%
cgp 92
13.1%
engg 67
 
9.6%
vas 58
 
8.3%
corporate 49
 
7.0%
carisma 44
 
6.3%
cold 42
 
6.0%
store 42
 
6.0%
exports 35
 
5.0%

Most occurring characters

ValueCountFrequency (%)
o 385
 
11.0%
r 323
 
9.2%
S 268
 
7.7%
p 252
 
7.2%
s 247
 
7.1%
C 227
 
6.5%
e 195
 
5.6%
h 168
 
4.8%
a 137
 
3.9%
g 134
 
3.8%
Other values (16) 1156
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2449
70.1%
Uppercase Letter 1001
28.7%
Space Separator 42
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 385
15.7%
r 323
13.2%
p 252
10.3%
s 247
10.1%
e 195
8.0%
h 168
6.9%
a 137
 
5.6%
g 134
 
5.5%
t 126
 
5.1%
u 104
 
4.2%
Other values (7) 378
15.4%
Uppercase Letter
ValueCountFrequency (%)
S 268
26.8%
C 227
22.7%
T 104
 
10.4%
E 102
 
10.2%
P 92
 
9.2%
G 92
 
9.2%
V 58
 
5.8%
A 58
 
5.8%
Space Separator
ValueCountFrequency (%)
42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3450
98.8%
Common 42
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 385
 
11.2%
r 323
 
9.4%
S 268
 
7.8%
p 252
 
7.3%
s 247
 
7.2%
C 227
 
6.6%
e 195
 
5.7%
h 168
 
4.9%
a 137
 
4.0%
g 134
 
3.9%
Other values (15) 1114
32.3%
Common
ValueCountFrequency (%)
42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 385
 
11.0%
r 323
 
9.2%
S 268
 
7.7%
p 252
 
7.2%
s 247
 
7.1%
C 227
 
6.5%
e 195
 
5.6%
h 168
 
4.8%
a 137
 
3.9%
g 134
 
3.8%
Other values (16) 1156
33.1%

Internal Grouping
Categorical

High correlation 

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Shops
168 
Tuber
104 
CGP
92 
Engg
67 
VAS
58 
Other values (4)
170 

Length

Max length10
Median length9
Mean length5.2989378
Min length3

Characters and Unicode

Total characters3492
Distinct characters26
Distinct categories3 ?
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 rowCGP
2nd rowCGP
3rd rowCGP
4th rowCGP
5th rowCGP

Common Values

ValueCountFrequency (%)
Shops 168
25.5%
Tuber 104
15.8%
CGP 92
14.0%
Engg 67
 
10.2%
VAS 58
 
8.8%
Corporate 49
 
7.4%
Carisma 44
 
6.7%
Cold Store 42
 
6.4%
Exports 35
 
5.3%

Length

2024-10-19T16:57:56.840765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T16:57:57.086434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
shops 168
24.0%
tuber 104
14.8%
cgp 92
13.1%
engg 67
 
9.6%
vas 58
 
8.3%
corporate 49
 
7.0%
carisma 44
 
6.3%
cold 42
 
6.0%
store 42
 
6.0%
exports 35
 
5.0%

Most occurring characters

ValueCountFrequency (%)
o 385
 
11.0%
r 323
 
9.2%
S 268
 
7.7%
p 252
 
7.2%
s 247
 
7.1%
C 227
 
6.5%
e 195
 
5.6%
h 168
 
4.8%
a 137
 
3.9%
g 134
 
3.8%
Other values (16) 1156
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2449
70.1%
Uppercase Letter 1001
28.7%
Space Separator 42
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 385
15.7%
r 323
13.2%
p 252
10.3%
s 247
10.1%
e 195
8.0%
h 168
6.9%
a 137
 
5.6%
g 134
 
5.5%
t 126
 
5.1%
u 104
 
4.2%
Other values (7) 378
15.4%
Uppercase Letter
ValueCountFrequency (%)
S 268
26.8%
C 227
22.7%
T 104
 
10.4%
E 102
 
10.2%
P 92
 
9.2%
G 92
 
9.2%
V 58
 
5.8%
A 58
 
5.8%
Space Separator
ValueCountFrequency (%)
42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3450
98.8%
Common 42
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 385
 
11.2%
r 323
 
9.4%
S 268
 
7.8%
p 252
 
7.3%
s 247
 
7.2%
C 227
 
6.6%
e 195
 
5.7%
h 168
 
4.9%
a 137
 
4.0%
g 134
 
3.9%
Other values (15) 1114
32.3%
Common
ValueCountFrequency (%)
42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 385
 
11.0%
r 323
 
9.2%
S 268
 
7.7%
p 252
 
7.2%
s 247
 
7.1%
C 227
 
6.5%
e 195
 
5.6%
h 168
 
4.8%
a 137
 
3.9%
g 134
 
3.8%
Other values (16) 1156
33.1%

Remarks-1
Text

Missing 

Distinct7
Distinct (%)63.6%
Missing648
Missing (%)98.3%
Memory size5.3 KiB
2024-10-19T16:57:57.498227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length49
Mean length38.818182
Min length13

Characters and Unicode

Total characters427
Distinct characters36
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

Unique6 ?
Unique (%)54.5%

Sample

1st rowIndie Capital Entry to be passed in Books
2nd rowProvision Reversed entry to be passed in books
3rd rowProvision Reversed entry to be passed in books
4th rowConsumables trf to R&D
5th rowProvision Reversed entry to be passed in books
ValueCountFrequency (%)
to 10
13.2%
in 9
11.8%
be 8
10.5%
passed 8
10.5%
books 8
10.5%
entry 7
9.2%
provision 6
7.9%
reversed 5
 
6.6%
starch 1
 
1.3%
export 1
 
1.3%
Other values (13) 13
17.1%
2024-10-19T16:57:58.138404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
15.2%
e 48
11.2%
o 45
10.5%
s 37
 
8.7%
n 28
 
6.6%
t 26
 
6.1%
i 23
 
5.4%
r 23
 
5.4%
d 17
 
4.0%
b 15
 
3.5%
Other values (26) 100
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 328
76.8%
Space Separator 65
 
15.2%
Uppercase Letter 32
 
7.5%
Other Punctuation 2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48
14.6%
o 45
13.7%
s 37
11.3%
n 28
8.5%
t 26
7.9%
i 23
7.0%
r 23
7.0%
d 17
 
5.2%
b 15
 
4.6%
v 12
 
3.7%
Other values (11) 54
16.5%
Uppercase Letter
ValueCountFrequency (%)
P 7
21.9%
R 6
18.8%
I 4
12.5%
B 3
9.4%
C 3
9.4%
E 2
 
6.2%
D 2
 
6.2%
G 1
 
3.1%
S 1
 
3.1%
A 1
 
3.1%
Other values (2) 2
 
6.2%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360
84.3%
Common 67
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48
13.3%
o 45
12.5%
s 37
10.3%
n 28
 
7.8%
t 26
 
7.2%
i 23
 
6.4%
r 23
 
6.4%
d 17
 
4.7%
b 15
 
4.2%
v 12
 
3.3%
Other values (23) 86
23.9%
Common
ValueCountFrequency (%)
65
97.0%
& 1
 
1.5%
. 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
15.2%
e 48
11.2%
o 45
10.5%
s 37
 
8.7%
n 28
 
6.6%
t 26
 
6.1%
i 23
 
5.4%
r 23
 
5.4%
d 17
 
4.0%
b 15
 
3.5%
Other values (26) 100
23.4%

Remarks
Unsupported

Missing  Rejected  Unsupported 

Missing659
Missing (%)100.0%
Memory size5.3 KiB

Interactions

2024-10-19T16:57:46.838804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:41.610070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:42.673067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.759107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:44.854176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.774893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:47.022143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:41.810904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:42.857870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.953579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.005305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.956991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:47.188740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:41.993390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.041197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:44.132142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.173030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:46.132140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:47.386571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:42.184291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.225474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:44.319875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.332977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:46.329420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:47.528349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:42.317880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.384276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:44.472812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.465114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:46.488529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:47.703665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:42.499899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:43.583068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:44.660424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:45.608237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T16:57:46.671613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-19T16:57:58.316923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Account NumberAdjustmentCredit 1- 4Debit 1- 4DeptGRPInternal GroupingNetProfit Center.1Single Entry
Account Number1.000-0.154-0.430-0.1490.0000.9060.000-0.0050.0390.689
Adjustment-0.1541.0000.286-0.3020.0000.2280.0000.1400.000NaN
Credit 1- 4-0.4300.2861.0000.4010.0530.0000.0530.0320.000-0.568
Debit 1- 4-0.149-0.3020.4011.0000.0710.0000.0710.7670.000-0.128
Dept0.0000.0000.0530.0711.0000.3131.0000.0630.9810.000
GRP0.9060.2280.0000.0000.3131.0000.3130.0000.0000.000
Internal Grouping0.0000.0000.0530.0711.0000.3131.0000.0630.9810.000
Net-0.0050.1400.0320.7670.0630.0000.0631.0000.0000.287
Profit Center.10.0390.0000.0000.0000.9810.0000.9810.0001.0000.000
Single Entry0.689NaN-0.568-0.1280.0000.0000.0000.2870.0001.000

Missing values

2024-10-19T16:57:47.982743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-19T16:57:48.436054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-19T16:57:48.737423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Account NumberAccount Number.1Profit CenterProfit Center.1Period/YearBalance CarryforwardDebit 1- 4Credit 1- 4AdjustmentSingle EntryNetConcanateGRPCMDeptInternal GroupingRemarks-1Remarks
031100050Chips Manufature1200CGP - MaharashtraJuly 2024010880655.0067134088.81NaNNaN-56253433.81311000501200Net RevenueNaNCGPCGPNaNNaN
131100055Commercial Sales1200CGP - MaharashtraJuly 20240704793.002161451.441256519.34NaN-200139.10311000551200Net RevenueNaNCGPCGPNaNNaN
231100057Other Value added1200CGP - MaharashtraJuly 202400.009120.009120.00NaN0.00311000571200Net RevenueNaNCGPCGPNaNNaN
331200011Freight income1200CGP - MaharashtraJuly 202400.00680442.00NaNNaN-680442.00312000111200Net RevenueNaNCGPCGPNaNNaN
431300001Interest Income1200CGP - MaharashtraJuly 202400.000.00NaNNaN0.00313000011200Net RevenueNaNCGPCGPNaNNaN
531300012Miscellaneous Income1200CGP - MaharashtraJuly 202400.00150.00NaNNaN-150.00313000121200Other indirect incomeNaNCGPCGPNaNNaN
641100011Consumption - Scrap Materials1200CGP - MaharashtraJuly 202401527854.710.00NaNNaN1527854.71411000111200Dump and WastagesNaNCGPCGPNaNNaN
741200001COGS - Finished Goods1200CGP - MaharashtraJuly 202401240269.14166698.00-1754663.00NaN-681091.86412000011200Cost Of Goods Sold (Cogs)NaNCGPCGPNaNNaN
841200002COGS - Trading Goods1200CGP - MaharashtraJuly 2024057368393.369831506.861009505.00NaN48546391.50412000021200Cost Of Goods Sold (Cogs)NaNCGPCGPNaNNaN
941200005COGS - Semi-Finished1200CGP - MaharashtraJuly 202401181206.00389790.00NaNNaN791416.00412000051200Cost Of Goods Sold (Cogs)NaNCGPCGPNaNNaN
Account NumberAccount Number.1Profit CenterProfit Center.1Period/YearBalance CarryforwardDebit 1- 4Credit 1- 4AdjustmentSingle EntryNetConcanateGRPCMDeptInternal GroupingRemarks-1Remarks
64941600410Grading ChargesSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0041600410SVT8Value AdditionsNaNTuberTuberNaNNaN
65041600606Transport InwardSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0041600606SVT8Inbound LogisticsNaNTuberTuberNaNNaN
65141600614Discount on PurchasesSVT8Tuber - HaryanaJuly 2024013300.000.0NaNNaN13300.0041600614SVT8Cost Of Goods Sold (Cogs)NaNTuberTuberNaNNaN
65241600634Labour ChargesSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0041600634SVT8HandlingNaNTuberTuberNaNNaN
65341600638Weighing ChargeSVT8Tuber - HaryanaJuly 20240150.000.0NaNNaN150.0041600638SVT8HandlingNaNTuberTuberNaNNaN
65441600640Detention ChargesSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0041600640SVT8IDE: Miscellaneous ExpensesNaNTuberTuberNaNNaN
65541800241Round OffSVT8Tuber - HaryanaJuly 2024033.120.0NaNNaN33.1241800241SVT8IDE: Miscellaneous ExpensesNaNTuberTuberNaNNaN
65642500001Transportation - OutwardSVT8Tuber - HaryanaJuly 2024029724.000.0NaNNaN29724.0042500001SVT8Outbound LogisticsNaNTuberTuberNaNNaN
65742500007Discounts on SalesSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0042500007SVT8Cost Of Goods Sold (Cogs)NaNTuberTuberNaNNaN
65842500019ClaimsSVT8Tuber - HaryanaJuly 202400.000.0NaNNaN0.0042500019SVT8IDE: Miscellaneous ExpensesNaNTuberTuberNaNNaN
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