Overview

Brought to you by anjha

Dataset statistics

Number of variables10
Number of observations4692
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory249.1 B

Variable types

DateTime1
Categorical2
Numeric6
Text1

Alerts

New deaths has constant value "0" Constant
Cured/Discharged/Migrated is highly overall correlated with New cases and 2 other fieldsHigh correlation
Latitude is highly overall correlated with Name of State / UTHigh correlation
Longitude is highly overall correlated with Name of State / UTHigh correlation
Name of State / UT is highly overall correlated with Latitude and 1 other fieldsHigh correlation
New cases is highly overall correlated with Cured/Discharged/Migrated and 2 other fieldsHigh correlation
New recovered is highly overall correlated with Cured/Discharged/Migrated and 2 other fieldsHigh correlation
Total Confirmed cases is highly overall correlated with Cured/Discharged/Migrated and 2 other fieldsHigh correlation
Cured/Discharged/Migrated has 611 (13.0%) zeros Zeros
New cases has 1156 (24.6%) zeros Zeros
New recovered has 1747 (37.2%) zeros Zeros

Reproduction

Analysis started2025-03-31 06:30:26.535565
Analysis finished2025-03-31 06:30:29.765975
Duration3.23 seconds
Download configurationconfig.json

Variables

Date
Date

Distinct186
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size36.8 KiB
Minimum2020-01-30 00:00:00
Maximum2020-08-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-31T12:00:29.834890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.924936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Name of State / UT
Categorical

High correlation 

Distinct40
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size308.9 KiB
Kerala
 
186
Delhi
 
154
Rajasthan
 
152
Uttar Pradesh
 
152
Haryana
 
152
Other values (35)
3896 

Length

Max length40
Median length27
Mean length10.382566
Min length3

Characters and Unicode

Total characters48715
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
Kerala 186
 
4.0%
Delhi 154
 
3.3%
Rajasthan 152
 
3.2%
Uttar Pradesh 152
 
3.2%
Haryana 152
 
3.2%
Tamil Nadu 149
 
3.2%
Punjab 147
 
3.1%
Karnataka 147
 
3.1%
Maharashtra 147
 
3.1%
Andhra Pradesh 144
 
3.1%
Other values (30) 3162
67.4%

Length

2025-03-31T12:00:30.007615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 688
 
9.8%
and 544
 
7.7%
kerala 186
 
2.6%
delhi 154
 
2.2%
rajasthan 152
 
2.2%
uttar 152
 
2.2%
haryana 152
 
2.2%
ladakh 149
 
2.1%
tamil 149
 
2.1%
nadu 149
 
2.1%
Other values (40) 4573
64.9%

Most occurring characters

ValueCountFrequency (%)
a 10380
21.3%
r 4008
 
8.2%
h 3717
 
7.6%
n 2982
 
6.1%
d 2907
 
6.0%
2356
 
4.8%
s 2047
 
4.2%
i 2020
 
4.1%
e 1906
 
3.9%
t 1598
 
3.3%
Other values (34) 14794
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10380
21.3%
r 4008
 
8.2%
h 3717
 
7.6%
n 2982
 
6.1%
d 2907
 
6.0%
2356
 
4.8%
s 2047
 
4.2%
i 2020
 
4.1%
e 1906
 
3.9%
t 1598
 
3.3%
Other values (34) 14794
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10380
21.3%
r 4008
 
8.2%
h 3717
 
7.6%
n 2982
 
6.1%
d 2907
 
6.0%
2356
 
4.8%
s 2047
 
4.2%
i 2020
 
4.1%
e 1906
 
3.9%
t 1598
 
3.3%
Other values (34) 14794
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10380
21.3%
r 4008
 
8.2%
h 3717
 
7.6%
n 2982
 
6.1%
d 2907
 
6.0%
2356
 
4.8%
s 2047
 
4.2%
i 2020
 
4.1%
e 1906
 
3.9%
t 1598
 
3.3%
Other values (34) 14794
30.4%

Latitude
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.185327
Minimum0
Maximum34.2996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2025-03-31T12:00:30.087165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.1271
Q118.1124
median23.9408
Q328.218
95-th percentile33.7782
Maximum34.2996
Range34.2996
Interquartile range (IQR)10.1056

Descriptive statistics

Standard deviation6.6359133
Coefficient of variation (CV)0.28621177
Kurtosis-0.76738611
Mean23.185327
Median Absolute Deviation (MAD)4.2772
Skewness-0.37079423
Sum108785.55
Variance44.035345
MonotonicityNot monotonic
2025-03-31T12:00:30.166660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10.8505 186
 
4.0%
28.7041 154
 
3.3%
18.1124 153
 
3.3%
29.0588 152
 
3.2%
27.0238 152
 
3.2%
26.8467 152
 
3.2%
11.1271 149
 
3.2%
34.2996 149
 
3.2%
19.7515 147
 
3.1%
31.1471 147
 
3.1%
Other values (26) 3151
67.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
10.8505 186
4.0%
11.1271 149
3.2%
11.7401 130
2.8%
11.9416 138
2.9%
15.2993 130
2.8%
15.3173 147
3.1%
15.9129 144
3.1%
18.1124 153
3.3%
19.7515 147
3.1%
ValueCountFrequency (%)
34.2996 149
3.2%
33.7782 147
3.1%
31.1471 147
3.1%
31.1048 135
2.9%
30.7333 137
2.9%
30.0668 141
3.0%
29.0588 152
3.2%
28.7041 154
3.3%
28.218 122
2.6%
27.533 71
1.5%

Longitude
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.451837
Minimum0
Maximum94.7278
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2025-03-31T12:00:30.244315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.124
Q176.2711
median79.0193
Q385.3131
95-th percentile93.9063
Maximum94.7278
Range94.7278
Interquartile range (IQR)9.042

Descriptive statistics

Standard deviation6.9594747
Coefficient of variation (CV)0.085442821
Kurtosis3.0335977
Mean81.451837
Median Absolute Deviation (MAD)3.3054
Skewness0.33726064
Sum382172.02
Variance48.434288
MonotonicityNot monotonic
2025-03-31T12:00:30.317524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
79.0193 294
 
6.3%
75.7139 294
 
6.3%
78.6569 284
 
6.1%
92.9376 255
 
5.4%
76.2711 186
 
4.0%
77.1025 154
 
3.3%
76.0856 152
 
3.2%
74.2179 152
 
3.2%
80.9462 152
 
3.2%
78.2932 149
 
3.2%
Other values (22) 2620
55.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
71.1924 136
2.9%
73.0169 89
 
1.9%
74.124 130
2.8%
74.2179 152
3.2%
75.3412 147
3.1%
75.7139 294
6.3%
76.0856 152
3.2%
76.2711 186
4.0%
76.5762 147
3.1%
ValueCountFrequency (%)
94.7278 122
2.6%
94.5624 69
 
1.5%
93.9063 132
2.8%
92.9376 255
5.4%
92.6586 130
2.8%
91.9882 118
2.5%
91.3662 111
2.4%
88.5122 71
 
1.5%
87.855 128
2.7%
85.3131 134
2.9%

Total Confirmed cases
Real number (ℝ)

High correlation 

Distinct2570
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11393.925
Minimum1
Maximum468265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2025-03-31T12:00:30.393913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q139
median619
Q35233
95-th percentile56790.55
Maximum468265
Range468264
Interquartile range (IQR)5194

Descriptive statistics

Standard deviation37208.601
Coefficient of variation (CV)3.2656525
Kurtosis54.352622
Mean11393.925
Median Absolute Deviation (MAD)617
Skewness6.5549647
Sum53460297
Variance1.38448 × 109
MonotonicityNot monotonic
2025-03-31T12:00:30.556148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 274
 
5.8%
2 107
 
2.3%
7 89
 
1.9%
3 88
 
1.9%
33 65
 
1.4%
13 42
 
0.9%
14 37
 
0.8%
4 33
 
0.7%
18 30
 
0.6%
9 25
 
0.5%
Other values (2560) 3902
83.2%
ValueCountFrequency (%)
1 274
5.8%
2 107
 
2.3%
3 88
 
1.9%
4 33
 
0.7%
5 23
 
0.5%
6 24
 
0.5%
7 89
 
1.9%
8 18
 
0.4%
9 25
 
0.5%
10 19
 
0.4%
ValueCountFrequency (%)
468265 1
< 0.1%
457956 1
< 0.1%
450196 1
< 0.1%
441228 1
< 0.1%
431719 1
< 0.1%
422118 1
< 0.1%
411798 1
< 0.1%
400651 1
< 0.1%
391440 1
< 0.1%
383723 1
< 0.1%

Death
Text

Distinct839
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size269.2 KiB
2025-03-31T12:00:30.715035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.723572
Min length1

Characters and Unicode

Total characters8087
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique551 ?
Unique (%)11.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1461
31.1%
1 429
 
9.1%
3 170
 
3.6%
2 157
 
3.3%
4 117
 
2.5%
5 97
 
2.1%
7 71
 
1.5%
11 67
 
1.4%
6 62
 
1.3%
8 54
 
1.2%
Other values (828) 2007
42.8%
2025-03-31T12:00:30.966262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1839
22.7%
1 1487
18.4%
2 892
11.0%
3 816
10.1%
4 647
 
8.0%
5 589
 
7.3%
6 485
 
6.0%
7 462
 
5.7%
8 458
 
5.7%
9 411
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1839
22.7%
1 1487
18.4%
2 892
11.0%
3 816
10.1%
4 647
 
8.0%
5 589
 
7.3%
6 485
 
6.0%
7 462
 
5.7%
8 458
 
5.7%
9 411
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1839
22.7%
1 1487
18.4%
2 892
11.0%
3 816
10.1%
4 647
 
8.0%
5 589
 
7.3%
6 485
 
6.0%
7 462
 
5.7%
8 458
 
5.7%
9 411
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1839
22.7%
1 1487
18.4%
2 892
11.0%
3 816
10.1%
4 647
 
8.0%
5 589
 
7.3%
6 485
 
6.0%
7 462
 
5.7%
8 458
 
5.7%
9 411
 
5.1%

Cured/Discharged/Migrated
Real number (ℝ)

High correlation  Zeros 

Distinct2143
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6908.1306
Minimum0
Maximum305521
Zeros611
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2025-03-31T12:00:31.061171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median197.5
Q32736
95-th percentile33712.05
Maximum305521
Range305521
Interquartile range (IQR)2727

Descriptive statistics

Standard deviation23390.671
Coefficient of variation (CV)3.3859625
Kurtosis53.22666
Mean6908.1306
Median Absolute Deviation (MAD)197.5
Skewness6.5329054
Sum32412949
Variance5.471235 × 108
MonotonicityNot monotonic
2025-03-31T12:00:31.135721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
13.0%
1 211
 
4.5%
2 94
 
2.0%
3 78
 
1.7%
33 53
 
1.1%
7 48
 
1.0%
10 39
 
0.8%
5 37
 
0.8%
11 36
 
0.8%
6 33
 
0.7%
Other values (2133) 3452
73.6%
ValueCountFrequency (%)
0 611
13.0%
1 211
 
4.5%
2 94
 
2.0%
3 78
 
1.7%
4 31
 
0.7%
5 37
 
0.8%
6 33
 
0.7%
7 48
 
1.0%
8 14
 
0.3%
9 32
 
0.7%
ValueCountFrequency (%)
305521 1
< 0.1%
299356 1
< 0.1%
287030 1
< 0.1%
276809 1
< 0.1%
266883 1
< 0.1%
256158 1
< 0.1%
248615 1
< 0.1%
239755 1
< 0.1%
232277 1
< 0.1%
221944 1
< 0.1%

New cases
Real number (ℝ)

High correlation  Zeros 

Distinct1058
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.64301
Minimum0
Maximum18366
Zeros1156
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2025-03-31T12:00:31.215190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median26
Q3210.25
95-th percentile2150
Maximum18366
Range18366
Interquartile range (IQR)209.25

Descriptive statistics

Standard deviation1259.7489
Coefficient of variation (CV)3.0091245
Kurtosis41.861841
Mean418.64301
Median Absolute Deviation (MAD)26
Skewness5.6983426
Sum1964273
Variance1586967.4
MonotonicityNot monotonic
2025-03-31T12:00:31.294742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1156
24.6%
1 193
 
4.1%
2 119
 
2.5%
4 82
 
1.7%
3 81
 
1.7%
5 73
 
1.6%
6 56
 
1.2%
7 50
 
1.1%
8 47
 
1.0%
9 43
 
0.9%
Other values (1048) 2792
59.5%
ValueCountFrequency (%)
0 1156
24.6%
1 193
 
4.1%
2 119
 
2.5%
3 81
 
1.7%
4 82
 
1.7%
5 73
 
1.6%
6 56
 
1.2%
7 50
 
1.1%
8 47
 
1.0%
9 43
 
0.9%
ValueCountFrequency (%)
18366 1
< 0.1%
16949 1
< 0.1%
13438 1
< 0.1%
11923 1
< 0.1%
11147 1
< 0.1%
10576 1
< 0.1%
10415 1
< 0.1%
10376 1
< 0.1%
10320 1
< 0.1%
10309 1
< 0.1%

New deaths
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.9 KiB
0
4692 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4692
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 4692
100.0%

Length

2025-03-31T12:00:31.359165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-31T12:00:31.438124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4692
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
100.0%

New recovered
Real number (ℝ)

High correlation  Zeros 

Distinct901
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.06905
Minimum-1
Maximum13401
Zeros1747
Zeros (%)37.2%
Negative3
Negative (%)0.1%
Memory size36.8 KiB
2025-03-31T12:00:31.502175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median8
Q3119
95-th percentile1315
Maximum13401
Range13402
Interquartile range (IQR)119

Descriptive statistics

Standard deviation947.92581
Coefficient of variation (CV)3.3487441
Kurtosis53.054766
Mean283.06905
Median Absolute Deviation (MAD)8
Skewness6.468866
Sum1328160
Variance898563.34
MonotonicityNot monotonic
2025-03-31T12:00:31.613635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1747
37.2%
1 161
 
3.4%
2 105
 
2.2%
3 87
 
1.9%
4 81
 
1.7%
5 57
 
1.2%
7 56
 
1.2%
8 45
 
1.0%
10 44
 
0.9%
6 37
 
0.8%
Other values (891) 2272
48.4%
ValueCountFrequency (%)
-1 3
 
0.1%
0 1747
37.2%
1 161
 
3.4%
2 105
 
2.2%
3 87
 
1.9%
4 81
 
1.7%
5 57
 
1.2%
6 37
 
0.8%
7 56
 
1.2%
8 45
 
1.0%
ValueCountFrequency (%)
13401 1
< 0.1%
12750 1
< 0.1%
12326 1
< 0.1%
10725 1
< 0.1%
10333 1
< 0.1%
10221 1
< 0.1%
9926 1
< 0.1%
8860 1
< 0.1%
8729 1
< 0.1%
8706 1
< 0.1%

Interactions

2025-03-31T12:00:29.016649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:26.838459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.240397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.592088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.085675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.567218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.111933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:26.914457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.296577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.659182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.159409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.632546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.180252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:26.977453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.358840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.726624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.254456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.709569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.226087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.033443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.415429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.809266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.326245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.792526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.306715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.097594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.473583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.884727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.403147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.869799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:29.373101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.166901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.531673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:27.954438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.473219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-31T12:00:28.940658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-03-31T12:00:31.676356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Cured/Discharged/MigratedLatitudeLongitudeName of State / UTNew casesNew recoveredTotal Confirmed cases
Cured/Discharged/Migrated1.000-0.046-0.2000.1930.8670.8920.973
Latitude-0.0461.000-0.0000.997-0.087-0.082-0.056
Longitude-0.200-0.0001.0000.996-0.222-0.200-0.237
Name of State / UT0.1930.9970.9961.0000.2030.1650.214
New cases0.867-0.087-0.2220.2031.0000.8800.912
New recovered0.892-0.082-0.2000.1650.8801.0000.889
Total Confirmed cases0.973-0.056-0.2370.2140.9120.8891.000

Missing values

2025-03-31T12:00:29.493811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-31T12:00:29.669576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateName of State / UTLatitudeLongitudeTotal Confirmed casesDeathCured/Discharged/MigratedNew casesNew deathsNew recovered
02020-01-30Kerala10.850576.27111.000.0000
12020-01-31Kerala10.850576.27111.000.0000
22020-02-01Kerala10.850576.27112.000.0100
32020-02-02Kerala10.850576.27113.000.0100
42020-02-03Kerala10.850576.27113.000.0000
52020-02-04Kerala10.850576.27113.000.0000
62020-02-05Kerala10.850576.27113.000.0000
72020-02-06Kerala10.850576.27113.000.0000
82020-02-07Kerala10.850576.27113.000.0000
92020-02-08Kerala10.850576.27113.000.0000
DateName of State / UTLatitudeLongitudeTotal Confirmed casesDeathCured/Discharged/MigratedNew casesNew deathsNew recovered
46822020-08-06Puducherry11.941679.80834433.0652668.02860131
46832020-08-06Punjab31.147175.341219856.049112943.08410452
46842020-08-06Rajasthan27.023874.217947272.074533849.059301017
46852020-08-06Sikkim27.533088.5122800.01303.01704
46862020-08-06Tamil Nadu11.127178.6569273460.04461214815.0517506031
46872020-08-06Telangana18.112479.019373050.058952103.0209201289
46882020-08-06Tripura23.940891.98825725.0313793.097068
46892020-08-06Uttar Pradesh26.846780.9462104388.0185760558.0407803287
46902020-08-06Uttarakhand30.066879.01938254.0985233.02460386
46912020-08-06West Bengal22.986887.855083800.0184658962.0281602078