Dataset statistics
Number of variables | 4 |
---|---|
Number of observations | 2026 |
Missing cells | 71 |
Missing cells (%) | 0.9% |
Duplicate rows | 0 |
Duplicate rows (%) | 0.0% |
Total size in memory | 267.2 KiB |
Average record size in memory | 135.1 B |
Variable types
Categorical | 1 |
---|---|
Numeric | 2 |
DateTime | 1 |
Alerts
DATE has a high cardinality: 2026 distinct values | High cardinality |
SP500 has 70 (3.5%) missing values | Missing |
DATE is uniformly distributed | Uniform |
DATE has unique values | Unique |
Date has unique values | Unique |
% 1-Day Return has 71 (3.5%) zeros | Zeros |
Reproduction
Analysis started | 2022-03-13 23:55:08.555107 |
---|---|
Analysis finished | 2022-03-13 23:55:09.433427 |
Duration | 0.88 seconds |
Software version | pandas-profiling v3.1.0 |
Download configuration | config.json |
Distinct | 2026 |
---|---|
Distinct (%) | 100.0% |
Missing | 0 |
Missing (%) | 0.0% |
Memory size | 132.7 KiB |
2014-06-06 | 1 |
---|---|
2019-08-02 | 1 |
2019-08-21 | 1 |
2019-08-20 | 1 |
2019-08-19 | 1 |
Other values (2021) |
Length
Max length | 10 |
---|---|
Median length | 10 |
Mean length | 10 |
Min length | 10 |
Characters and Unicode
Total characters | 0 |
---|---|
Distinct characters | 0 |
Distinct categories | 0 ? |
Distinct scripts | 0 ? |
Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
Unique | 2026 ? |
---|---|
Unique (%) | 100.0% |
Sample
1st row | 2014-06-06 |
---|---|
2nd row | 2014-06-09 |
3rd row | 2014-06-10 |
4th row | 2014-06-11 |
5th row | 2014-06-12 |
Common Values
Value | Count | Frequency (%) |
2014-06-06 | 1 | < 0.1% |
2019-08-02 | 1 | < 0.1% |
2019-08-21 | 1 | < 0.1% |
2019-08-20 | 1 | < 0.1% |
2019-08-19 | 1 | < 0.1% |
2019-08-16 | 1 | < 0.1% |
2019-08-15 | 1 | < 0.1% |
2019-08-14 | 1 | < 0.1% |
2019-08-13 | 1 | < 0.1% |
2019-08-12 | 1 | < 0.1% |
Other values (2016) | 2016 |
Length
Histogram of lengths of the category
Value | Count | Frequency (%) |
2014-06-06 | 1 | < 0.1% |
2014-07-02 | 1 | < 0.1% |
2014-06-13 | 1 | < 0.1% |
2014-06-16 | 1 | < 0.1% |
2014-06-17 | 1 | < 0.1% |
2014-06-18 | 1 | < 0.1% |
2014-06-19 | 1 | < 0.1% |
2014-06-20 | 1 | < 0.1% |
2014-06-23 | 1 | < 0.1% |
2014-06-24 | 1 | < 0.1% |
Other values (2016) | 2016 |
Most occurring characters
Value | Count | Frequency (%) |
No values found. |
Most occurring categories
Value | Count | Frequency (%) |
No values found. |
Most frequent character per category
Most occurring scripts
Value | Count | Frequency (%) |
No values found. |
Most frequent character per script
Most occurring blocks
Value | Count | Frequency (%) |
No values found. |
Most frequent character per block
Distinct | 1945 |
---|---|
Distinct (%) | 99.4% |
Missing | 70 |
Missing (%) | 3.5% |
Infinite | 0 |
Infinite (%) | 0.0% |
Mean | 2801.088016 |
Minimum | 1829.08 |
---|---|
Maximum | 4796.56 |
Zeros | 0 |
Zeros (%) | 0.0% |
Negative | 0 |
Negative (%) | 0.0% |
Memory size | 16.0 KiB |
Quantile statistics
Minimum | 1829.08 |
---|---|
5-th percentile | 1961.0275 |
Q1 | 2108.905 |
median | 2670.025 |
Q3 | 3130.0375 |
95-th percentile | 4456.255 |
Maximum | 4796.56 |
Range | 2967.48 |
Interquartile range (IQR) | 1021.1325 |
Descriptive statistics
Standard deviation | 776.9689512 |
---|---|
Coefficient of variation (CV) | 0.2773811271 |
Kurtosis | -0.05808348742 |
Mean | 2801.088016 |
Median Absolute Deviation (MAD) | 548.6 |
Skewness | 0.9570039718 |
Sum | 5478928.16 |
Variance | 603680.7511 |
Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
Value | Count | Frequency (%) |
2066.66 | 2 | 0.1% |
2439.07 | 2 | 0.1% |
2783.02 | 2 | 0.1% |
2268.9 | 2 | 0.1% |
2926.46 | 2 | 0.1% |
2373.47 | 2 | 0.1% |
2095.84 | 2 | 0.1% |
2102.31 | 2 | 0.1% |
2080.15 | 2 | 0.1% |
2723.06 | 2 | 0.1% |
Other values (1935) | 1936 | |
(Missing) | 70 | 3.5% |
Value | Count | Frequency (%) |
1829.08 | 1 | |
1851.86 | 1 | |
1852.21 | 1 | |
1853.44 | 1 | |
1859.33 | 1 | |
1862.49 | 1 | |
1862.76 | 1 | |
1864.78 | 1 | |
1867.61 | 1 | |
1868.99 | 1 |
Value | Count | Frequency (%) |
4796.56 | 1 | |
4793.54 | 1 | |
4793.06 | 1 | |
4791.19 | 1 | |
4786.35 | 1 | |
4778.73 | 1 | |
4766.18 | 1 | |
4726.35 | 1 | |
4725.79 | 1 | |
4713.07 | 1 |
Distinct | 1955 |
---|---|
Distinct (%) | 96.5% |
Missing | 1 |
Missing (%) | < 0.1% |
Infinite | 0 |
Infinite (%) | 0.0% |
Mean | 0.04397695168 |
Minimum | -11.98405028 |
---|---|
Maximum | 9.38276571 |
Zeros | 71 |
Zeros (%) | 3.5% |
Negative | 892 |
Negative (%) | 44.0% |
Memory size | 16.0 KiB |
Quantile statistics
Minimum | -11.98405028 |
---|---|
5-th percentile | -1.630657564 |
Q1 | -0.309931843 |
median | 0.03426822371 |
Q3 | 0.5022110835 |
95-th percentile | 1.468397098 |
Maximum | 9.38276571 |
Range | 21.36681599 |
Interquartile range (IQR) | 0.8121429265 |
Descriptive statistics
Standard deviation | 1.094093047 |
---|---|
Coefficient of variation (CV) | 24.8787832 |
Kurtosis | 19.61983268 |
Mean | 0.04397695168 |
Median Absolute Deviation (MAD) | 0.4114931522 |
Skewness | -0.6434118056 |
Sum | 89.05332716 |
Variance | 1.197039595 |
Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
Value | Count | Frequency (%) |
0 | 71 | 3.5% |
1.301700683 | 1 | < 0.1% |
-0.0506081527 | 1 | < 0.1% |
0.8246825558 | 1 | < 0.1% |
-0.7914764079 | 1 | < 0.1% |
1.210587535 | 1 | < 0.1% |
1.442618345 | 1 | < 0.1% |
0.2464268112 | 1 | < 0.1% |
-2.929276361 | 1 | < 0.1% |
1.476202861 | 1 | < 0.1% |
Other values (1945) | 1945 |
Value | Count | Frequency (%) |
-11.98405028 | 1 | |
-9.511268047 | 1 | |
-7.596968076 | 1 | |
-5.894412157 | 1 | |
-5.183082331 | 1 | |
-4.886841092 | 1 | |
-4.416327867 | 1 | |
-4.414239783 | 1 | |
-4.335952253 | 1 | |
-4.097924428 | 1 |
Value | Count | Frequency (%) |
9.38276571 | 1 | |
9.287119453 | 1 | |
7.033130412 | 1 | |
6.241416084 | 1 | |
5.995482224 | 1 | |
4.959380715 | 1 | |
4.939633578 | 1 | |
4.603922524 | 1 | |
4.220259242 | 1 | |
3.90338454 | 1 |
A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.
First rows
DATE | SP500 | % 1-Day Return | Date | |
---|---|---|---|---|
0 | 2014-06-06 | 1949.44 | NaN | 2014-06-06 |
1 | 2014-06-09 | 1951.27 | 0.093873 | 2014-06-09 |
2 | 2014-06-10 | 1950.79 | -0.024599 | 2014-06-10 |
3 | 2014-06-11 | 1943.89 | -0.353703 | 2014-06-11 |
4 | 2014-06-12 | 1930.11 | -0.708888 | 2014-06-12 |
5 | 2014-06-13 | 1936.16 | 0.313454 | 2014-06-13 |
6 | 2014-06-16 | 1937.78 | 0.083671 | 2014-06-16 |
7 | 2014-06-17 | 1941.99 | 0.217259 | 2014-06-17 |
8 | 2014-06-18 | 1956.98 | 0.771889 | 2014-06-18 |
9 | 2014-06-19 | 1959.48 | 0.127748 | 2014-06-19 |
Last rows
DATE | SP500 | % 1-Day Return | Date | |
---|---|---|---|---|
2016 | 2022-02-28 | 4373.94 | -0.244261 | 2022-02-28 |
2017 | 2022-03-01 | 4306.26 | -1.547346 | 2022-03-01 |
2018 | 2022-03-02 | 4386.54 | 1.864263 | 2022-03-02 |
2019 | 2022-03-03 | 4363.49 | -0.525471 | 2022-03-03 |
2020 | 2022-03-04 | 4328.87 | -0.793402 | 2022-03-04 |
2021 | 2022-03-07 | 4201.09 | -2.951810 | 2022-03-07 |
2022 | 2022-03-08 | 4170.70 | -0.723384 | 2022-03-08 |
2023 | 2022-03-09 | 4277.88 | 2.569832 | 2022-03-09 |
2024 | 2022-03-10 | 4259.52 | -0.429185 | 2022-03-10 |
2025 | 2022-03-11 | 4204.31 | -1.296155 | 2022-03-11 |