Overview

Dataset statistics

Number of variables8
Number of observations2391
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory168.1 KiB
Average record size in memory72.0 B

Variable types

TimeSeries6
Numeric1
Categorical1

Timeseries statistics

Number of series6
Time series length2391
Starting point1818
Ending point2019
Period0.08410041841
2024-04-16T11:43:45.460303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:45.545914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Alerts

Indicator is highly imbalanced (98.2%)Imbalance
Year is non stationaryNon stationary
Month is non stationaryNon stationary
Date In Fraction Of Year is non stationaryNon stationary
Number of Sunspots is non stationaryNon stationary
Standard Deviation is non stationaryNon stationary
Observations is non stationaryNon stationary
Month is seasonalSeasonal
Number of Sunspots is seasonalSeasonal
Standard Deviation is seasonalSeasonal
Observations is seasonalSeasonal
Date In Fraction Of Year is uniformly distributedUniform
Date In Fraction Of Year has unique valuesUnique

Reproduction

Analysis started2024-04-16 18:43:40.586498
Analysis finished2024-04-16 18:43:45.418483
Duration4.83 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Year
Numeric time series

NON STATIONARY 

Distinct202
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1919.2401
Minimum1818
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:45.640545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1818
5-th percentile1829
Q11869
median1920
Q31969
95-th percentile2009
Maximum2019
Range201
Interquartile range (IQR)100

Descriptive statistics

Standard deviation57.976594
Coefficient of variation (CV)0.030208099
Kurtosis-1.1986537
Mean1919.2401
Median Absolute Deviation (MAD)50
Skewness-0.0089611227
Sum4588903
Variance3361.2854
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.2066879967
2024-04-16T11:43:45.714772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-16T11:43:45.997366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:46.044804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1818 12
 
0.5%
1956 12
 
0.5%
1946 12
 
0.5%
1947 12
 
0.5%
1948 12
 
0.5%
1949 12
 
0.5%
1950 12
 
0.5%
1951 12
 
0.5%
1952 12
 
0.5%
1953 12
 
0.5%
Other values (192) 2271
95.0%
ValueCountFrequency (%)
1818 12
0.5%
1819 12
0.5%
1820 12
0.5%
1821 12
0.5%
1822 8
0.3%
1823 3
 
0.1%
1824 7
0.3%
1825 12
0.5%
1826 12
0.5%
1827 12
0.5%
ValueCountFrequency (%)
2019 10
0.4%
2018 12
0.5%
2017 12
0.5%
2016 12
0.5%
2015 12
0.5%
2014 12
0.5%
2013 12
0.5%
2012 12
0.5%
2011 12
0.5%
2010 12
0.5%
2024-04-16T11:43:45.827929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Month
Numeric time series

NON STATIONARY  SEASONAL 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5018821
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:46.111995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39.5
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4504103
Coefficient of variation (CV)0.5306787
Kurtosis-1.2152568
Mean6.5018821
Median Absolute Deviation (MAD)3
Skewness-0.0011981224
Sum15546
Variance11.905331
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.677103056 × 10-25
2024-04-16T11:43:46.171834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
2024-04-16T11:43:46.442496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:46.495816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
10 201
8.4%
7 201
8.4%
3 200
8.4%
1 199
8.3%
12 199
8.3%
8 199
8.3%
9 199
8.3%
5 199
8.3%
4 199
8.3%
6 199
8.3%
Other values (2) 396
16.6%
ValueCountFrequency (%)
1 199
8.3%
2 198
8.3%
3 200
8.4%
4 199
8.3%
5 199
8.3%
6 199
8.3%
7 201
8.4%
8 199
8.3%
9 199
8.3%
10 201
8.4%
ValueCountFrequency (%)
12 199
8.3%
11 198
8.3%
10 201
8.4%
9 199
8.3%
8 199
8.3%
7 201
8.4%
6 199
8.3%
5 199
8.3%
4 199
8.3%
3 200
8.4%
2024-04-16T11:43:46.275162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Day
Real number (ℝ)

Distinct714
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65426
Minimum1
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:46.578771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q115.357143
median15.965517
Q316
95-th percentile19.307895
Maximum30.5
Range29.5
Interquartile range (IQR)0.64285714

Descriptive statistics

Standard deviation2.5234648
Coefficient of variation (CV)0.16119987
Kurtosis9.4541427
Mean15.65426
Median Absolute Deviation (MAD)0.46551724
Skewness-0.54072889
Sum37429.336
Variance6.3678744
MonotonicityNot monotonic
2024-04-16T11:43:46.652689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 717
30.0%
15.5 421
 
17.6%
14.5 89
 
3.7%
15 45
 
1.9%
13 11
 
0.5%
14 10
 
0.4%
17 10
 
0.4%
19 8
 
0.3%
12 8
 
0.3%
13.5 8
 
0.3%
Other values (704) 1064
44.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.5 3
0.1%
2 3
0.1%
2.5 3
0.1%
3 1
 
< 0.1%
4 2
0.1%
4.2 1
 
< 0.1%
4.428571429 1
 
< 0.1%
4.5 2
0.1%
4.666666667 1
 
< 0.1%
ValueCountFrequency (%)
30.5 1
 
< 0.1%
30 1
 
< 0.1%
28.4 1
 
< 0.1%
28 1
 
< 0.1%
27.33333333 1
 
< 0.1%
26.66666667 2
0.1%
26.55555556 1
 
< 0.1%
26.5 1
 
< 0.1%
26 4
0.2%
25 2
0.1%

Date In Fraction Of Year
Numeric time series

NON STATIONARY  UNIFORM  UNIQUE 

Distinct2391
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1919.7381
Minimum1818.0524
Maximum2019.7505
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:46.722520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1818.0524
5-th percentile1829.4954
Q11869.5809
median1920.1243
Q31969.9157
95-th percentile2009.8392
Maximum2019.7505
Range201.69813
Interquartile range (IQR)100.33485

Descriptive statistics

Standard deviation57.976457
Coefficient of variation (CV)0.03020019
Kurtosis-1.1986259
Mean1919.7381
Median Absolute Deviation (MAD)50.166829
Skewness-0.0090305529
Sum4590093.9
Variance3361.2695
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2744674957
2024-04-16T11:43:46.796189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-16T11:43:47.081201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:47.128941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1818.052375 1
 
< 0.1%
1952.4563 1
 
< 0.1%
1953.979667 1
 
< 0.1%
1953.8478 1
 
< 0.1%
1953.784909 1
 
< 0.1%
1953.708138 1
 
< 0.1%
1953.528882 1
 
< 0.1%
1953.21685 1
 
< 0.1%
1953.375522 1
 
< 0.1%
1953.287136 1
 
< 0.1%
Other values (2381) 2381
99.6%
ValueCountFrequency (%)
1818.052375 1
< 0.1%
1818.11125 1
< 0.1%
1818.213083 1
< 0.1%
1818.290762 1
< 0.1%
1818.37264 1
< 0.1%
1818.4548 1
< 0.1%
1818.533905 1
< 0.1%
1818.61945 1
< 0.1%
1818.702278 1
< 0.1%
1818.787273 1
< 0.1%
ValueCountFrequency (%)
2019.7505 1
< 0.1%
2019.67 1
< 0.1%
2019.596 1
< 0.1%
2019.5205 1
< 0.1%
2019.48375 1
< 0.1%
2019.360625 1
< 0.1%
2019.27605 1
< 0.1%
2019.202176 1
< 0.1%
2019.13 1
< 0.1%
2019.047933 1
< 0.1%
2024-04-16T11:43:46.910716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Number of Sunspots
Numeric time series

NON STATIONARY  SEASONAL 

Distinct2209
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.690519
Minimum3
Maximum359.3871
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:47.206886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15.333333
Q134.552381
median73.153846
Q3127.66129
95-th percentile219.49023
Maximum359.3871
Range356.3871
Interquartile range (IQR)93.108909

Descriptive statistics

Standard deviation64.92448
Coefficient of variation (CV)0.73203406
Kurtosis0.40232844
Mean88.690519
Median Absolute Deviation (MAD)43.153846
Skewness0.97595242
Sum212059.03
Variance4215.1881
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.086481976 × 10-16
2024-04-16T11:43:47.283520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-16T11:43:47.830573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:47.880705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
13 9
 
0.4%
12 6
 
0.3%
30 5
 
0.2%
17 5
 
0.2%
15 5
 
0.2%
34 5
 
0.2%
25 4
 
0.2%
11 4
 
0.2%
14 4
 
0.2%
42 3
 
0.1%
Other values (2199) 2341
97.9%
ValueCountFrequency (%)
3 1
< 0.1%
4.25 1
< 0.1%
5 2
0.1%
5.666666667 1
< 0.1%
6.333333333 1
< 0.1%
6.5 1
< 0.1%
6.6 1
< 0.1%
6.666666667 1
< 0.1%
6.75 1
< 0.1%
7 2
0.1%
ValueCountFrequency (%)
359.3870968 1
< 0.1%
343.75 1
< 0.1%
339 1
< 0.1%
334 1
< 0.1%
313.4 1
< 0.1%
307.7096774 1
< 0.1%
300.6315789 1
< 0.1%
298.6333333 1
< 0.1%
298.2903226 1
< 0.1%
293.5806452 1
< 0.1%
2024-04-16T11:43:47.663539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Standard Deviation
Numeric time series

NON STATIONARY  SEASONAL 

Distinct2197
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0192443
Minimum0.2
Maximum23.475
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:47.950560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2.9877778
Q14.9965517
median7.7
Q310.288495
95-th percentile15.089839
Maximum23.475
Range23.275
Interquartile range (IQR)5.2919429

Descriptive statistics

Standard deviation3.7803855
Coefficient of variation (CV)0.47141419
Kurtosis0.28757322
Mean8.0192443
Median Absolute Deviation (MAD)2.6516129
Skewness0.6740666
Sum19174.013
Variance14.291315
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.068834806 × 10-9
2024-04-16T11:43:48.020514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-16T11:43:48.297238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:48.343415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 9
 
0.4%
2.5 6
 
0.3%
3.3 6
 
0.3%
3.15 4
 
0.2%
5.929032258 4
 
0.2%
3.5 4
 
0.2%
8.838709677 4
 
0.2%
7.1 3
 
0.1%
11.70967742 3
 
0.1%
3.466666667 3
 
0.1%
Other values (2187) 2345
98.1%
ValueCountFrequency (%)
0.2 1
< 0.1%
0.55 1
< 0.1%
0.625 1
< 0.1%
0.75 1
< 0.1%
0.815 1
< 0.1%
0.94375 1
< 0.1%
0.9642857143 1
< 0.1%
1.009090909 1
< 0.1%
1.04 1
< 0.1%
1.05 1
< 0.1%
ValueCountFrequency (%)
23.475 1
< 0.1%
22.33 1
< 0.1%
21.87368421 1
< 0.1%
21.46923077 1
< 0.1%
21.31290323 1
< 0.1%
20.75555556 1
< 0.1%
20.58666667 1
< 0.1%
20.58571429 1
< 0.1%
20.58333333 1
< 0.1%
20.45 1
< 0.1%
2024-04-16T11:43:48.129607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Observations
Numeric time series

NON STATIONARY  SEASONAL 

Distinct405
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3818929
Minimum1
Maximum37.588235
Zeros0
Zeros (%)0.0%
Memory size37.4 KiB
2024-04-16T11:43:48.413976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile20.68871
Maximum37.588235
Range36.588235
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2467942
Coefficient of variation (CV)1.6538045
Kurtosis2.4640247
Mean4.3818929
Median Absolute Deviation (MAD)0
Skewness1.9312556
Sum10477.106
Variance52.516027
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8439608383
2024-04-16T11:43:48.485589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-16T11:43:48.766337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-16T11:43:48.813474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1926
80.6%
25 4
 
0.2%
20 4
 
0.2%
18.33333333 3
 
0.1%
15.90322581 3
 
0.1%
20.32258065 3
 
0.1%
18.25806452 3
 
0.1%
20.25806452 3
 
0.1%
19.7 3
 
0.1%
22 3
 
0.1%
Other values (395) 436
 
18.2%
ValueCountFrequency (%)
1 1926
80.6%
5.6 1
 
< 0.1%
7.566666667 1
 
< 0.1%
7.838709677 1
 
< 0.1%
7.870967742 1
 
< 0.1%
8.133333333 1
 
< 0.1%
8.178571429 1
 
< 0.1%
8.266666667 1
 
< 0.1%
8.35483871 1
 
< 0.1%
8.451612903 1
 
< 0.1%
ValueCountFrequency (%)
37.58823529 1
< 0.1%
36.69230769 1
< 0.1%
35.76190476 1
< 0.1%
34.23333333 1
< 0.1%
33.875 1
< 0.1%
33 1
< 0.1%
32.43333333 1
< 0.1%
32.32258065 1
< 0.1%
32.1 1
< 0.1%
31.95833333 1
< 0.1%
2024-04-16T11:43:48.597188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ACF and PACF

Indicator
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.4 KiB
1.0
2387 
0.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7173
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2387
99.8%
0.0 4
 
0.2%

Length

2024-04-16T11:43:48.878117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T11:43:48.932467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2387
99.8%
0.0 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2395
33.4%
. 2391
33.3%
1 2387
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2395
33.4%
. 2391
33.3%
1 2387
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2395
33.4%
. 2391
33.3%
1 2387
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2395
33.4%
. 2391
33.3%
1 2387
33.3%

Interactions

2024-04-16T11:43:44.549631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.150959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.712861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.478632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.000198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.542300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.053602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.624046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.268703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.785270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.551376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.084657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.619347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.123754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.691608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.341731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.865816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.637400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.163547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.689399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.189232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.761343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.415491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.945103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.706851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.238226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.762699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.260895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.836040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.493978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.018841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.781666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.317160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.840634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.332727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:45.145343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.569840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.088298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.859845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.394904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.913130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.418805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:45.209061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:41.639524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.409635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:42.925116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.465506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:43.980727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-04-16T11:43:44.481695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2024-04-16T11:43:45.300658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T11:43:45.380419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearMonthDayDate In Fraction Of YearNumber of SunspotsStandard DeviationObservationsIndicator
1818.01818.01.019.6250001818.05237558.1250009.3000001.01.0
1818.01818.012.015.9000001818.95740055.9000009.1000001.01.0
1818.01818.011.015.5555561818.87411124.4444446.1444441.01.0
1818.01818.010.014.8636361818.78727352.7727278.5727271.01.0
1818.01818.08.014.6000001818.61945052.5500008.8700001.01.0
1818.01818.07.014.3809521818.53390546.7619058.4714291.01.0
1818.01818.09.013.8333331818.70227843.3888898.0722221.01.0
1818.01818.05.016.5200001818.37264088.48000011.5320001.01.0
1818.01818.04.016.6190481818.29076257.5238109.4523811.01.0
1818.01818.03.019.2500001818.21308349.4166678.7583331.01.0
YearMonthDayDate In Fraction Of YearNumber of SunspotsStandard DeviationObservationsIndicator
2019.02019.09.02.0000002019.67000011.0000000.20000010.0000000.0
2019.02019.01.018.0000002019.04793315.9333331.22000024.3333331.0
2019.02019.02.017.0000002019.13000011.5000000.75000015.0000001.0
2019.02019.03.015.2941182019.20217617.1764711.27058828.1176471.0
2019.02019.04.011.2500002019.27605013.6000000.81500033.0000001.0
2019.02019.05.012.1250002019.36062519.1875000.94375033.8750001.0
2019.02019.06.026.0000002019.4837508.7500001.37500025.0000001.0
2019.02019.07.09.5000002019.5205006.7500000.62500018.7500000.0
2019.02019.08.06.0000002019.59600011.5000001.05000025.0000000.0
2019.02019.010.01.5000002019.7505006.5000000.55000019.0000000.0