Overview

Dataset statistics

Number of variables9
Number of observations3939
Missing cells31
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory277.1 KiB
Average record size in memory72.0 B

Variable types

Numeric8
DateTime1

Alerts

df_index is highly correlated with Adj Close and 5 other fieldsHigh correlation
Adj Close is highly correlated with df_index and 5 other fieldsHigh correlation
Close is highly correlated with df_index and 5 other fieldsHigh correlation
High is highly correlated with df_index and 5 other fieldsHigh correlation
Low is highly correlated with df_index and 5 other fieldsHigh correlation
Open is highly correlated with df_index and 5 other fieldsHigh correlation
Volume is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with Adj Close and 5 other fieldsHigh correlation
Adj Close is highly correlated with df_index and 4 other fieldsHigh correlation
Close is highly correlated with df_index and 5 other fieldsHigh correlation
High is highly correlated with df_index and 5 other fieldsHigh correlation
Low is highly correlated with df_index and 5 other fieldsHigh correlation
Open is highly correlated with df_index and 5 other fieldsHigh correlation
Volume is highly correlated with df_index and 4 other fieldsHigh correlation
df_index is highly correlated with Adj Close and 5 other fieldsHigh correlation
Adj Close is highly correlated with df_index and 5 other fieldsHigh correlation
Close is highly correlated with df_index and 5 other fieldsHigh correlation
High is highly correlated with df_index and 5 other fieldsHigh correlation
Low is highly correlated with df_index and 5 other fieldsHigh correlation
Open is highly correlated with df_index and 5 other fieldsHigh correlation
Volume is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with Adj Close and 5 other fieldsHigh correlation
Adj Close is highly correlated with df_index and 5 other fieldsHigh correlation
Close is highly correlated with df_index and 5 other fieldsHigh correlation
High is highly correlated with df_index and 5 other fieldsHigh correlation
Low is highly correlated with df_index and 5 other fieldsHigh correlation
Open is highly correlated with df_index and 5 other fieldsHigh correlation
Volume is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique

Reproduction

Analysis started2022-03-13 23:51:12.338352
Analysis finished2022-03-13 23:51:18.572567
Duration6.23 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3939
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13183
Minimum11214
Maximum15152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:18.628843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11214
5-th percentile11410.9
Q112198.5
median13183
Q314167.5
95-th percentile14955.1
Maximum15152
Range3938
Interquartile range (IQR)1969

Descriptive statistics

Standard deviation1137.235684
Coefficient of variation (CV)0.08626531773
Kurtosis-1.2
Mean13183
Median Absolute Deviation (MAD)985
Skewness0
Sum51927837
Variance1293305
MonotonicityStrictly increasing
2022-03-13T19:51:18.705767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112141
 
< 0.1%
138301
 
< 0.1%
138321
 
< 0.1%
138331
 
< 0.1%
138341
 
< 0.1%
138351
 
< 0.1%
138361
 
< 0.1%
138371
 
< 0.1%
138381
 
< 0.1%
138391
 
< 0.1%
Other values (3929)3929
99.7%
ValueCountFrequency (%)
112141
< 0.1%
112151
< 0.1%
112161
< 0.1%
112171
< 0.1%
112181
< 0.1%
112191
< 0.1%
112201
< 0.1%
112211
< 0.1%
112221
< 0.1%
112231
< 0.1%
ValueCountFrequency (%)
151521
< 0.1%
151511
< 0.1%
151501
< 0.1%
151491
< 0.1%
151481
< 0.1%
151471
< 0.1%
151461
< 0.1%
151451
< 0.1%
151441
< 0.1%
151431
< 0.1%

Date
Date

UNIQUE

Distinct3939
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
Minimum2006-07-03 00:00:00
Maximum2022-02-15 00:00:00
2022-03-13T19:51:18.788299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:18.863720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Adj Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3858
Distinct (%)98.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean33.80212348
Minimum1.556117773
Maximum182.0099945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:18.938445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.556117773
5-th percentile2.759551799
Q17.689222932
median20.43898392
Q341.31961346
95-th percentile132.9122452
Maximum182.0099945
Range180.4538767
Interquartile range (IQR)33.63039052

Descriptive statistics

Standard deviation39.21592141
Coefficient of variation (CV)1.160161474
Kurtosis2.985167341
Mean33.80212348
Median Absolute Deviation (MAD)14.72293115
Skewness1.923305281
Sum132977.5538
Variance1537.888492
MonotonicityNot monotonic
2022-03-13T19:51:19.013852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8050718313
 
0.1%
29.664848332
 
0.1%
2.9175286292
 
0.1%
10.38148882
 
0.1%
126.84999852
 
0.1%
4.6560683252
 
0.1%
41.720161442
 
0.1%
5.6968574522
 
0.1%
3.0624837882
 
0.1%
3.2965004442
 
0.1%
Other values (3848)3913
99.3%
(Missing)5
 
0.1%
ValueCountFrequency (%)
1.5561177731
< 0.1%
1.6046402451
< 0.1%
1.6083260771
< 0.1%
1.6246031521
< 0.1%
1.626445771
< 0.1%
1.661455871
< 0.1%
1.6890956161
< 0.1%
1.7013795381
< 0.1%
1.7090570931
< 0.1%
1.7127428051
< 0.1%
ValueCountFrequency (%)
182.00999451
< 0.1%
180.33000181
< 0.1%
179.69999691
< 0.1%
179.44999691
< 0.1%
179.38000491
< 0.1%
179.30000311
< 0.1%
179.28999331
< 0.1%
178.19999691
< 0.1%
177.57000731
< 0.1%
176.27999882
0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3782
Distinct (%)96.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.19240694
Minimum1.80964303
Maximum182.0099945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:19.091609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.80964303
5-th percentile3.209142792
Q18.941964388
median23.01500034
Q342.80375004
95-th percentile133.1100006
Maximum182.0099945
Range180.2003515
Interquartile range (IQR)33.86178565

Descriptive statistics

Standard deviation38.89158536
Coefficient of variation (CV)1.10511297
Kurtosis2.924992715
Mean35.19240694
Median Absolute Deviation (MAD)15.97160721
Skewness1.894809255
Sum138446.9289
Variance1512.555412
MonotonicityNot monotonic
2022-03-13T19:51:19.164261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.334999083
 
0.1%
26.704999923
 
0.1%
43.1253
 
0.1%
31.649999623
 
0.1%
4.4250001913
 
0.1%
25.18753
 
0.1%
24.905000693
 
0.1%
28.497499472
 
0.1%
28.100000382
 
0.1%
91.027496342
 
0.1%
Other values (3772)3907
99.2%
(Missing)5
 
0.1%
ValueCountFrequency (%)
1.809643031
< 0.1%
1.8660709861
< 0.1%
1.8703570371
< 0.1%
1.8892860411
< 0.1%
1.8914289471
< 0.1%
1.9321429731
< 0.1%
1.964285971
< 0.1%
1.9785710571
< 0.1%
1.9874999521
< 0.1%
1.9917860031
< 0.1%
ValueCountFrequency (%)
182.00999451
< 0.1%
180.33000181
< 0.1%
179.69999691
< 0.1%
179.44999691
< 0.1%
179.38000491
< 0.1%
179.30000311
< 0.1%
179.28999331
< 0.1%
178.19999691
< 0.1%
177.57000731
< 0.1%
176.27999882
0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3731
Distinct (%)94.8%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.55010744
Minimum1.888929009
Maximum182.9400024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:19.241525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.888929009
5-th percentile3.264161038
Q19.066696882
median23.14428616
Q343.22812462
95-th percentile134.6470009
Maximum182.9400024
Range181.0510734
Interquartile range (IQR)34.16142774

Descriptive statistics

Standard deviation39.31340668
Coefficient of variation (CV)1.105859012
Kurtosis2.926426275
Mean35.55010744
Median Absolute Deviation (MAD)16.03803492
Skewness1.896425119
Sum139854.1227
Variance1545.543945
MonotonicityNot monotonic
2022-03-13T19:51:19.314685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2142860894
 
0.1%
7.5714287763
 
0.1%
23.924999243
 
0.1%
24.222499853
 
0.1%
6.4285712243
 
0.1%
6.7214288713
 
0.1%
29.360000613
 
0.1%
24.469999313
 
0.1%
2.9285709863
 
0.1%
12.85714343
 
0.1%
Other values (3721)3903
99.1%
(Missing)5
 
0.1%
ValueCountFrequency (%)
1.8889290091
< 0.1%
1.8967859751
< 0.1%
1.9232139591
< 0.1%
1.9328570371
< 0.1%
1.9671430591
< 0.1%
1.9728569981
< 0.1%
1.9996429681
< 0.1%
2.0174999241
< 0.1%
2.0196430681
< 0.1%
2.0499999521
< 0.1%
ValueCountFrequency (%)
182.94000241
< 0.1%
182.88000491
< 0.1%
182.13000491
< 0.1%
181.33000181
< 0.1%
181.13999941
< 0.1%
180.63000491
< 0.1%
180.57000731
< 0.1%
180.41999821
< 0.1%
180.16999821
< 0.1%
179.63000491
< 0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3755
Distinct (%)95.4%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean34.80573631
Minimum1.791429043
Maximum179.1199951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:19.391428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.791429043
5-th percentile3.153071129
Q18.832767963
median22.78232193
Q342.45625114
95-th percentile131.6339981
Maximum179.1199951
Range177.3285661
Interquartile range (IQR)33.62348318

Descriptive statistics

Standard deviation38.43283598
Coefficient of variation (CV)1.104209824
Kurtosis2.926871897
Mean34.80573631
Median Absolute Deviation (MAD)15.82160616
Skewness1.89367752
Sum136925.7667
Variance1477.082881
MonotonicityNot monotonic
2022-03-13T19:51:19.466951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.81253
 
0.1%
5.8928570753
 
0.1%
23.524999623
 
0.1%
24.577499393
 
0.1%
27.257499692
 
0.1%
3.1214289672
 
0.1%
35.724998472
 
0.1%
27.200000762
 
0.1%
6.5303568842
 
0.1%
23.260000232
 
0.1%
Other values (3745)3910
99.3%
(Missing)5
 
0.1%
ValueCountFrequency (%)
1.7914290431
< 0.1%
1.8360710141
< 0.1%
1.8446429971
< 0.1%
1.8517860171
< 0.1%
1.8700000051
< 0.1%
1.8899999861
< 0.1%
1.9464290141
< 0.1%
1.947499991
< 0.1%
1.9524999861
< 0.1%
1.9860709911
< 0.1%
ValueCountFrequency (%)
179.11999511
< 0.1%
178.52999881
< 0.1%
178.13999941
< 0.1%
178.08999631
< 0.1%
177.71000671
< 0.1%
177.25999451
< 0.1%
177.07000731
< 0.1%
175.52999881
< 0.1%
175.27000431
< 0.1%
174.89999391
< 0.1%

Open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3774
Distinct (%)95.9%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.1771034
Minimum1.847499967
Maximum182.6300049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:19.547454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.847499967
5-th percentile3.209517968
Q18.95357132
median23.0587492
Q342.83874989
95-th percentile133.4275047
Maximum182.6300049
Range180.7825049
Interquartile range (IQR)33.88517857

Descriptive statistics

Standard deviation38.86557329
Coefficient of variation (CV)1.104854281
Kurtosis2.92634114
Mean35.1771034
Median Absolute Deviation (MAD)15.95750141
Skewness1.895410245
Sum138386.7248
Variance1510.532787
MonotonicityNot monotonic
2022-03-13T19:51:19.625068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.754
 
0.1%
18.53571323
 
0.1%
27.53
 
0.1%
39.3753
 
0.1%
15.858928683
 
0.1%
29.010000233
 
0.1%
4.9639291763
 
0.1%
127.81999973
 
0.1%
243
 
0.1%
29.379999162
 
0.1%
Other values (3764)3904
99.1%
(Missing)5
 
0.1%
ValueCountFrequency (%)
1.8474999671
< 0.1%
1.8582140211
< 0.1%
1.8751
< 0.1%
1.8914289471
< 0.1%
1.8985710141
< 0.1%
1.9682140351
< 0.1%
1.9703569411
< 0.1%
1.9814289811
< 0.1%
1.9892859461
< 0.1%
2.0389289861
< 0.1%
ValueCountFrequency (%)
182.63000491
< 0.1%
181.11999511
< 0.1%
180.16000371
< 0.1%
179.61000061
< 0.1%
179.47000121
< 0.1%
179.33000181
< 0.1%
179.27999881
< 0.1%
178.08999631
< 0.1%
177.83000181
< 0.1%
177.08999631
< 0.1%

Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3923
Distinct (%)99.7%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean405159272.4
Minimum41000000
Maximum3372969600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2022-03-13T19:51:19.702570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum41000000
5-th percentile74793940
Q1125360900
median271800000
Q3565728100
95-th percentile1158813460
Maximum3372969600
Range3331969600
Interquartile range (IQR)440367200

Descriptive statistics

Standard deviation381807519.2
Coefficient of variation (CV)0.9423640164
Kurtosis5.593281967
Mean405159272.4
Median Absolute Deviation (MAD)169109000
Skewness1.968112002
Sum1.593896578 × 1012
Variance1.457769817 × 1017
MonotonicityNot monotonic
2022-03-13T19:51:19.779202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7684572002
 
0.1%
2007600002
 
0.1%
1503472002
 
0.1%
1466400002
 
0.1%
909567002
 
0.1%
1186556002
 
0.1%
4207868002
 
0.1%
6841968002
 
0.1%
1461188002
 
0.1%
3916388002
 
0.1%
Other values (3913)3914
99.4%
(Missing)5
 
0.1%
ValueCountFrequency (%)
410000001
< 0.1%
454480001
< 0.1%
459036001
< 0.1%
463977001
< 0.1%
466176001
< 0.1%
466913001
< 0.1%
484788001
< 0.1%
484935001
< 0.1%
485972001
< 0.1%
486064001
< 0.1%
ValueCountFrequency (%)
33729696001
< 0.1%
33492984001
< 0.1%
29528800001
< 0.1%
26220572001
< 0.1%
24347540001
< 0.1%
23646056001
< 0.1%
23432780001
< 0.1%
23282224001
< 0.1%
22943984001
< 0.1%
22488088001
< 0.1%

Volume Chg
Real number (ℝ)

Distinct3934
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.302410089
Minimum-71.22664985
Maximum509.0525839
Zeros5
Zeros (%)0.1%
Negative2057
Negative (%)52.2%
Memory size30.9 KiB
2022-03-13T19:51:19.859320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-71.22664985
5-th percentile-38.22593762
Q1-18.32462815
median-1.875260507
Q320.61966701
95-th percentile69.29515318
Maximum509.0525839
Range580.2792337
Interquartile range (IQR)38.94429517

Descriptive statistics

Standard deviation37.3724172
Coefficient of variation (CV)7.04819442
Kurtosis15.56564922
Mean5.302410089
Median Absolute Deviation (MAD)18.80018823
Skewness2.445409609
Sum20880.89093
Variance1396.697568
MonotonicityNot monotonic
2022-03-13T19:51:19.931013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.1%
14.518524171
 
< 0.1%
2.8839751011
 
< 0.1%
2.9396142661
 
< 0.1%
-11.273347321
 
< 0.1%
5.6263118371
 
< 0.1%
-58.157468721
 
< 0.1%
136.96616391
 
< 0.1%
4.9084356841
 
< 0.1%
26.75717171
 
< 0.1%
Other values (3924)3924
99.6%
ValueCountFrequency (%)
-71.226649851
< 0.1%
-70.524481931
< 0.1%
-70.050000651
< 0.1%
-69.897396971
< 0.1%
-66.907925441
< 0.1%
-66.576369261
< 0.1%
-66.192166661
< 0.1%
-65.413413071
< 0.1%
-64.640180081
< 0.1%
-64.282660211
< 0.1%
ValueCountFrequency (%)
509.05258391
< 0.1%
320.18188781
< 0.1%
309.53368121
< 0.1%
294.49744931
< 0.1%
262.83033461
< 0.1%
261.71003581
< 0.1%
237.76355661
< 0.1%
227.13406481
< 0.1%
210.22398161
< 0.1%
209.07292771
< 0.1%

Interactions

2022-03-13T19:51:17.609500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:12.660323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:13.266421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:13.852541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:15.234785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-13T19:51:15.880026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-03-13T19:51:17.537974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-03-13T19:51:19.998563image/svg+xmlMatplotlib v3.3.4, 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.
2022-03-13T19:51:20.089466image/svg+xmlMatplotlib v3.3.4, 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.
2022-03-13T19:51:20.179451image/svg+xmlMatplotlib v3.3.4, 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.
2022-03-13T19:51:20.262659image/svg+xmlMatplotlib v3.3.4, 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

2022-03-13T19:51:18.231248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-13T19:51:18.352311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-13T19:51:18.449558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-13T19:51:18.527757image/svg+xmlMatplotlib v3.3.4, 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

df_indexDateAdj CloseCloseHighLowOpenVolumeVolume Chg
0112142006-07-031.7796922.0696432.0778572.0478572.0542861.947708e+08NaN
1112152006-07-051.7505172.0357142.0571432.0200002.0410715.182408e+08166.077256
2112162006-07-061.7127431.9917862.0500001.9860712.0389296.332088e+0822.184282
3112172006-07-071.7013801.9785712.0196431.9525001.9814297.993608e+0826.239686
4112182006-07-101.6890961.9642862.0175001.9464291.9892865.293456e+08-33.778889
5112192006-07-111.7090571.9875001.9996431.9475001.9682148.250228e+0855.857119
6112202006-07-121.6264461.8914291.9728571.8900001.9703579.273292e+0812.400433
7112212006-07-131.6046401.8660711.9328571.8360711.8582141.249906e+0934.785576
8112222006-07-141.5561181.8096431.8889291.7914291.8750009.930368e+08-20.551081
9112232006-07-171.6083261.8703571.8967861.8446431.8475001.024542e+093.172652

Last rows

df_indexDateAdj CloseCloseHighLowOpenVolumeVolume Chg
3929151432022-02-02175.839996175.839996175.880005173.330002174.75000084914300.0-1.507414
3930151442022-02-03172.899994172.899994176.240005172.119995174.47999689418100.05.303936
3931151452022-02-04172.389999172.389999174.100006170.679993171.67999382391400.0-7.858252
3932151462022-02-07171.660004171.660004173.949997170.949997172.86000177251200.0-6.238758
3933151472022-02-08174.830002174.830002175.350006171.429993171.72999674829200.0-3.135226
3934151482022-02-09176.279999176.279999176.649994174.899994176.05000371285000.0-4.736386
3935151492022-02-10172.119995172.119995175.479996171.550003174.13999990865900.027.468472
3936151502022-02-11168.639999168.639999173.080002168.039993172.33000298566000.08.474136
3937151512022-02-14168.880005168.880005169.580002166.559998167.36999586185500.0-12.560619
3938151522022-02-15172.789993172.789993172.949997170.250000170.97000162527400.0-27.450209