import pandas as pd train_transaction_raw = pd.read_csv('data/ieee-fraud-detection.zip Folder/train_transaction.csv') import TEF train_transaction = TEF.auto_set_dtypes(train_transaction_raw, set_object=[0]) TEF.dfmeta(train_transaction) TEF.plot_1var(train_transaction) TEF.plot_1var_by_cat_y(train_transaction, 'isFraud') TEF.fit(train_transaction, 'isFraud', verbose=2)
Disclaimer and Caveat
Every ML practitioner knows it is a risky behavior to fit a model without understanding the data. The purpose of this article is to introduce the universal usage of TEF only instead of detailed exploration. Within these code, we can only have a rough understanding about the dataset.
In the following section I will walk through these codes for this ieee fraud detection dataset. A more detailed exploration, feature engineering, and model selection may be published in the future.
Load data
import pandas as pd train_transaction_raw = pd.read_csv('data/ieee-fraud-detection.zip Folder/train_transaction.csv')
Set data types
import TEF train_transaction = TEF.auto_set_dtypes(train_transaction_raw)
before dtypes: float64(376), int64(4), object(14)
after dtypes: bool(1), category(12), float64(376), int64(3), object(2)
possible identifier cols: 0 TransactionID
consider using set_object=[0]
possible category cols: 55 V1 (2 levls), 56 V2 (9 levls), 58 V4 (7 levls), 59 V5 (7 levls), 62 V8 (9 levls), 63 V9 (9 levls), 64 V10 (5 levls), 65 V11 (6 levls), 66 V12 (4 levls), 67 V13 (7 levls), 68 V14 (2 levls), 69 V15 (8 levls), 73 V19 (8 levls), 75 V21 (6 levls), 76 V22 (9 levls), 79 V25 (7 levls), 81 V27 (4 levls), 82 V28 (4 levls), 83 V29 (6 levls), 84 V30 (8 levls), 85 V31 (8 levls), 87 V33 (7 levls), 89 V35 (4 levls), 90 V36 (6 levls), 95 V41 (2 levls), 96 V42 (9 levls), 97 V43 (9 levls), 100 V46 (7 levls), 101 V47 (9 levls), 102 V48 (6 levls), 103 V49 (6 levls), 104 V50 (6 levls), 105 V51 (7 levls), 106 V52 (9 levls), 107 V53 (6 levls), 108 V54 (7 levls), 111 V57 (7 levls), 115 V61 (7 levls), 117 V63 (8 levls), 118 V64 (8 levls), 119 V65 (2 levls), 120 V66 (8 levls), 121 V67 (9 levls), 122 V68 (3 levls), 123 V69 (6 levls), 124 V70 (7 levls), 125 V71 (7 levls), 127 V73 (8 levls), 128 V74 (9 levls), 129 V75 (5 levls), 130 V76 (7 levls), 133 V79 (8 levls), 136 V82 (8 levls), 137 V83 (8 levls), 138 V84 (8 levls), 139 V85 (8 levls), 142 V88 (2 levls), 143 V89 (3 levls), 144 V90 (6 levls), 145 V91 (7 levls), 146 V92 (8 levls), 147 V93 (8 levls), 148 V94 (3 levls), 161 V107 (2 levls), 162 V108 (8 levls), 163 V109 (8 levls), 164 V110 (8 levls), 168 V114 (7 levls), 169 V115 (7 levls), 170 V116 (7 levls), 171 V117 (4 levls), 172 V118 (4 levls), 173 V119 (4 levls), 174 V120 (4 levls), 175 V121 (4 levls), 176 V122 (4 levls), 195 V141 (6 levls), 227 V173 (8 levls), 228 V174 (9 levls), 248 V194 (8 levls), 294 V240 (6 levls), 295 V241 (5 levls), 314 V260 (9 levls), 340 V286 (9 levls), 359 V305 (2 levls)
consider using set_category=[55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 73, 75, 76, 79, 81, 82, 83, 84, 85, 87, 89, 90, 95, 96, 97, 100, 101, 102, 103, 104, 105, 106, 107, 108, 111, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 133, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 161, 162, 163, 164, 168, 169, 170, 171, 172, 173, 174, 175, 176, 195, 227, 228, 248, 294, 295, 314, 340, 359]
It detects the first column, TransactionID is potentially an identifier column, because it has unique values for every row and contains string “ID” in columns name. Add argument set_object=[0]
if you accept this suggestion.
We ignore those suggestion for categorical variables because we don’t know what do them actually mean, you can add verbose=0 to suppress the printouts.
train_transaction = TEF.auto_set_dtypes(train_transaction_raw, set_object=[0], verbose=0)
Exploration
For the purpose of exploration, we will use TEF.dfmeta
, TEF.plot_1var
, and TEF.plot_1var_by_cat_y
. However, notice a part of the generated result from the TEF.dfmeta
and TEF.plot_1var
here are duplicated works compared to what Kaggle has already provided on the data section. The detailed view and column view there has things similar to dtype, NaNs, mean, std, and quantiles like here.
Nevertheless, TEF is build for universal purpose. We can use these functions not only for Kaggle datasets.
TEF.dfmeta(train_transaction)
col name | idx | dtype | NaNs | unique counts | unique levs | summary | possible NaNs | possible dup lev | row 110762 | row 238050 | row 546101 |
---|---|---|---|---|---|---|---|---|---|---|---|
TransactionID | 0 | object | 0 0% |
590540 100% |
other 100% | 3097762 | 3225050 | 3533101 | |||
isFraud | 1 | bool | 0 0% |
2 0% |
False True |
False 97% True 3% |
False | False | False | ||
TransactionDT | 2 | int64 | 0 0% |
573349 97% |
[86400.0, 3027057.75, 7306527.5, 11246620.0, 15811131.0] mean: 7372311.31 std: 4617223.65 cv: 0.63 skew: 0.13* log skew: -1.29 |
2162036 | 5614247 | 14414373 | |||
TransactionAmt | 3 | float64 | 0 0% |
20902 4% |
[0.251, 43.321000000000005, 68.769, 125.0, 31937.391] mean: 135.03 std: 239.16 cv: 1.77 skew: 14.37* log skew: 0.41 |
100 | 108.5 | 300 | |||
ProductCD | 4 | category | 0 0% |
5 0% |
W C R H S |
W 74% C 12% R 6% H 6% S 2% |
R | W | R | ||
card1 | 5 | int64 | 0 0% |
13553 2% |
[1000.0, 6019.0, 9678.0, 14184.0, 18396.0] mean: 9898.73 std: 4901.17 cv: 0.5 skew: -0.04* log skew: -1.01 |
9803 | 4702 | 12118 | |||
card2 | 6 | float64 | 8933 2% |
501 0% |
[100.0, 214.0, 361.0, 512.0, 600.0] mean: 362.56 std: 157.79 cv: 0.44 skew: -0.2* |
583 | 111 | 399 | |||
card3 | 7 | float64 | 1565 0% |
115 0% |
[100.0, 150.0, 150.0, 150.0, 231.0] mean: 153.19 std: 11.34 cv: 0.07 skew: 2.02* log skew: 1.40 |
150 | 150 | 150 | |||
card4 | 8 | category | 1577 0% |
5 0% |
visa mastercard american express discover nan |
visa 65% mastercard 32% american express 1% discover 1% nan 0% |
visa | mastercard | american express | ||
card5 | 9 | float64 | 4259 1% |
120 0% |
[100.0, 166.0, 226.0, 226.0, 237.0] mean: 199.28 std: 41.24 cv: 0.21 skew: -1.22* log skew: -1.43 |
226 | 224 | 150 | |||
card6 | 10 | category | 1571 0% |
5 0% |
debit credit nan debit or credit charge card |
debit 74% credit 25% nan 0% debit or credit 0% charge card 0% |
(credit, debit or credit); (debit, debit or credit) | credit | debit | credit | |
addr1 | 11 | float64 | 65706 11% |
333 0% |
[100.0, 204.0, 299.0, 330.0, 540.0] mean: 290.73 std: 101.74 cv: 0.35 skew: 0.37* log skew: -0.40 |
327 | 476 | 325 | |||
addr2 | 12 | float64 | 65706 11% |
75 0% |
[10.0, 87.0, 87.0, 87.0, 102.0] mean: 86.80 std: 2.69 cv: 0.03 skew: -14.5* log skew: -21.46 |
87 | 87 | 87 | |||
dist1 | 13 | float64 | 352271 60% |
2652 0% |
[0.0, 3.0, 8.0, 24.0, 10286.0] mean: 118.50 std: 371.87 cv: 3.14 skew: 5.11* log skew: 0.95 |
nan | nan | nan | |||
dist2 | 14 | float64 | 552913 94% |
1752 0% |
[0.0, 7.0, 37.0, 206.0, 11623.0] mean: 231.86 std: 529.05 cv: 2.28 skew: 5.97* |
nan | nan | nan | |||
P_emaildomain | 15 | object | 94456 16% |
60 0% |
other 100% | (gmail.com, mail.com); (gmail.com, gmail); (yahoo.com, yahoo.com.mx); (mail.com, hotmail.com); (mail.com, rocketmail.com); (mail.com, embarqmail.com); (mail.com, ymail.com); (mail.com, protonmail.com); (hotmail.com, hotmail.co.uk); (live.com.mx, live.com) | gmail.com | gmail.com | anonymous.com | ||
R_emaildomain | 16 | object | 453249 77% |
61 0% |
other 100% | (gmail.com, mail.com); (gmail.com, gmail); (hotmail.com, mail.com); (hotmail.com, hotmail.co.uk); (live.com.mx, live.com); (yahoo.com, yahoo.com.mx); (ymail.com, mail.com); (mail.com, embarqmail.com); (mail.com, rocketmail.com); (mail.com, protonmail.com) | gmail.com | nan | gmail.com | ||
C1 | 17 | float64 | 0 0% |
1657 0% |
[0.0, 1.0, 1.0, 3.0, 4685.0] mean: 14.09 std: 133.57 cv: 9.48 skew: 23.96* log skew: 2.53 |
1 | 1 | 1 | |||
C2 | 18 | float64 | 0 0% |
1216 0% |
[0.0, 1.0, 1.0, 3.0, 5691.0] mean: 15.27 std: 154.67 cv: 10.13 skew: 23.68* log skew: 2.50 |
1 | 2 | 1 | |||
C3 | 19 | float64 | 0 0% |
27 0% |
[0.0, 0.0, 0.0, 0.0, 26.0] mean: 0.01 std: 0.15 cv: 26.67 skew: 88.95* log skew: 4.30 |
0 | 0 | 0 | |||
C4 | 20 | float64 | 0 0% |
1260 0% |
[0.0, 0.0, 0.0, 0.0, 2253.0] mean: 4.09 std: 68.85 cv: 16.82 skew: 22.08* log skew: 5.00 |
1 | 0 | 1 | |||
C5 | 21 | float64 | 0 0% |
319 0% |
[0.0, 0.0, 0.0, 1.0, 349.0] mean: 5.57 std: 25.79 cv: 4.63 skew: 5.79* log skew: 1.87 |
0 | 0 | 0 | |||
C6 | 22 | float64 | 0 0% |
1328 0% |
[0.0, 1.0, 1.0, 2.0, 2253.0] mean: 9.07 std: 71.51 cv: 7.88 skew: 19.77* log skew: 2.79 |
1 | 2 | 1 | |||
C7 | 23 | float64 | 0 0% |
1103 0% |
[0.0, 0.0, 0.0, 0.0, 2255.0] mean: 2.85 std: 61.73 cv: 21.67 skew: 27.19* log skew: 4.20 |
0 | 0 | 0 | |||
C8 | 24 | float64 | 0 0% |
1253 0% |
[0.0, 0.0, 0.0, 0.0, 3331.0] mean: 5.14 std: 95.38 cv: 18.54 skew: 26.08* log skew: 3.99 |
1 | 0 | 1 | |||
C9 | 25 | float64 | 0 0% |
205 0% |
[0.0, 0.0, 1.0, 2.0, 210.0] mean: 4.48 std: 16.67 cv: 3.72 skew: 5.69* log skew: 2.30 |
0 | 1 | 0 | |||
C10 | 26 | float64 | 0 0% |
1231 0% |
[0.0, 0.0, 0.0, 0.0, 3257.0] mean: 5.24 std: 95.58 cv: 18.24 skew: 25.22* log skew: 3.85 |
1 | 0 | 1 | |||
C11 | 27 | float64 | 0 0% |
1476 0% |
[0.0, 1.0, 1.0, 2.0, 3188.0] mean: 10.24 std: 94.34 cv: 9.21 skew: 22.36* log skew: 3.02 |
1 | 1 | 1 | |||
C12 | 28 | float64 | 0 0% |
1199 0% |
[0.0, 0.0, 0.0, 0.0, 3188.0] mean: 4.08 std: 86.67 cv: 21.26 skew: 27.42* log skew: 4.57 |
0 | 0 | 0 | |||
C13 | 29 | float64 | 0 0% |
1597 0% |
[0.0, 1.0, 3.0, 12.0, 2918.0] mean: 32.54 std: 129.36 cv: 3.98 skew: 8.99* log skew: 1.08 |
1 | 2 | 1 | |||
C14 | 30 | float64 | 0 0% |
1108 0% |
[0.0, 1.0, 1.0, 2.0, 1429.0] mean: 8.30 std: 49.54 cv: 5.97 skew: 16.53* log skew: 2.57 |
1 | 1 | 1 | |||
D1 | 31 | float64 | 1269 0% |
642 0% |
[0.0, 0.0, 3.0, 122.0, 640.0] mean: 94.35 std: 157.66 cv: 1.67 skew: 1.81* log skew: -0.88 |
0 | 5 | 0 | |||
D2 | 32 | float64 | 280797 48% |
642 0% |
[0.0, 26.0, 97.0, 276.0, 640.0] mean: 169.56 std: 177.32 cv: 1.05 skew: 1.02* |
nan | 5 | nan | |||
D3 | 33 | float64 | 262878 45% |
650 0% |
[0.0, 1.0, 8.0, 27.0, 819.0] mean: 28.34 std: 62.38 cv: 2.2 skew: 4.54* log skew: 0.07 |
nan | 6 | nan | |||
D4 | 34 | float64 | 168922 29% |
809 0% |
[-122.0, 0.0, 26.0, 253.0, 869.0] mean: 140.00 std: 191.10 cv: 1.36 skew: 1.17* |
nan | 0 | nan | |||
D5 | 35 | float64 | 309841 52% |
689 0% |
[0.0, 1.0, 10.0, 32.0, 819.0] mean: 42.34 std: 89.00 cv: 2.1 skew: 3.39* |
nan | nan | nan | |||
D6 | 36 | float64 | 517353 88% |
830 0% |
[-83.0, 0.0, 0.0, 40.0, 873.0] mean: 69.81 std: 143.67 cv: 2.06 skew: 2.26* |
nan | nan | nan | |||
D7 | 37 | float64 | 551623 93% |
598 0% |
[0.0, 0.0, 0.0, 17.0, 843.0] mean: 41.64 std: 99.74 cv: 2.4 skew: 2.95* |
nan | nan | nan | |||
D8 | 38 | float64 | 515614 87% |
12354 2% |
[0.0, 0.9583330154418944, 37.875, 187.9583282470703, 1707.7916259765625] mean: 146.06 std: 231.66 cv: 1.59 skew: 2.24* |
nan | nan | 0.791666 | |||
D9 | 39 | float64 | 515614 87% |
25 0% |
[0.0, 0.20833300054073334, 0.6666659712791443, 0.8333330154418945, 0.9583330154418944] mean: 0.56 std: 0.32 cv: 0.56 skew: -0.59* |
nan | nan | 0.791666 | |||
D10 | 40 | float64 | 76022 13% |
819 0% |
[0.0, 0.0, 15.0, 197.0, 876.0] mean: 123.98 std: 182.62 cv: 1.47 skew: 1.39* log skew: -1.01 |
nan | 0 | nan | |||
D11 | 41 | float64 | 279287 47% |
677 0% |
[-53.0, 0.0, 43.0, 274.0, 670.0] mean: 146.62 std: 186.04 cv: 1.27 skew: 1.05* |
nan | nan | nan | |||
D12 | 42 | float64 | 525823 89% |
636 0% |
[-83.0, 0.0, 0.0, 13.0, 648.0] mean: 54.04 std: 124.27 cv: 2.3 skew: 2.46* |
nan | nan | nan | |||
D13 | 43 | float64 | 528588 90% |
578 0% |
[0.0, 0.0, 0.0, 0.0, 847.0] mean: 17.90 std: 67.61 cv: 3.78 skew: 5.41* |
nan | nan | nan | |||
D14 | 44 | float64 | 528353 89% |
803 0% |
[-193.0, 0.0, 0.0, 2.0, 878.0] mean: 57.72 std: 136.31 cv: 2.36 skew: 2.58* |
nan | nan | nan | |||
D15 | 45 | float64 | 89113 15% |
860 0% |
[-83.0, 0.0, 52.0, 314.0, 879.0] mean: 163.74 std: 202.73 cv: 1.24 skew: 0.96* |
nan | 6 | nan | |||
M1 | 46 | category | 271100 46% |
3 0% |
T nan F |
T 54% nan 46% F 0% |
nan | nan | nan | ||
M2 | 47 | category | 271100 46% |
3 0% |
T nan F |
T 48% nan 46% F 6% |
nan | nan | nan | ||
M3 | 48 | category | 271100 46% |
3 0% |
nan T F |
nan 46% T 43% F 11% |
nan | nan | nan | ||
M4 | 49 | category | 281444 48% |
4 0% |
nan M0 M2 M1 |
nan 48% M0 33% M2 10% M1 9% |
nan | M0 | nan | ||
M5 | 50 | category | 350482 59% |
3 0% |
nan F T |
nan 59% F 22% T 18% |
nan | F | nan | ||
M6 | 51 | category | 169360 29% |
3 0% |
F T nan |
F 39% T 33% nan 29% |
nan | F | nan | ||
M7 | 52 | category | 346265 59% |
3 0% |
nan F T |
nan 59% F 36% T 6% |
nan | nan | nan | ||
M8 | 53 | category | 346252 59% |
3 0% |
nan F T |
nan 59% F 26% T 15% |
nan | nan | nan | ||
M9 | 54 | category | 346252 59% |
3 0% |
nan T F |
nan 59% T 35% F 7% |
nan | nan | nan | ||
V1 | 55 | float64 | 279287 47% |
3 0% |
1.0 nan 0.0 |
1.0 53% nan 47% 0.0 0% |
nan | nan | nan | ||
V2 | 56 | float64 | 279287 47% |
10 0% |
1.0 nan 2.0 3.0 4.0 5.0 6.0 0.0 7.0 8.0 |
[0.0, 1.0, 1.0, 1.0, 8.0] mean: 1.05 std: 0.24 cv: 0.23 skew: 6.76* log skew: 5.20 |
nan | nan | nan | ||
V3 | 57 | float64 | 279287 47% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.08 std: 0.32 cv: 0.3 skew: 5.38* log skew: 3.94 |
nan | nan | nan | |||
V4 | 58 | float64 | 279287 47% |
8 0% |
nan 1.0 0.0 2.0 3.0 4.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 0.85 std: 0.44 cv: 0.52 skew: -0.43* log skew: 5.76 |
nan | nan | nan | ||
V5 | 59 | float64 | 279287 47% |
8 0% |
nan 1.0 0.0 2.0 3.0 4.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 0.88 std: 0.48 cv: 0.54 skew: 0.23* log skew: 4.33 |
nan | nan | nan | ||
V6 | 60 | float64 | 279287 47% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.05 std: 0.24 cv: 0.23 skew: 6.71* log skew: 5.12 |
nan | nan | nan | |||
V7 | 61 | float64 | 279287 47% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.07 std: 0.30 cv: 0.28 skew: 5.5* log skew: 4.01 |
nan | nan | nan | |||
V8 | 62 | float64 | 279287 47% |
10 0% |
1.0 nan 2.0 3.0 4.0 0.0 6.0 5.0 7.0 8.0 |
[0.0, 1.0, 1.0, 1.0, 8.0] mean: 1.03 std: 0.19 cv: 0.18 skew: 8.59* log skew: 6.63 |
nan | nan | nan | ||
V9 | 63 | float64 | 279287 47% |
10 0% |
1.0 nan 2.0 3.0 4.0 0.0 5.0 7.0 6.0 8.0 |
[0.0, 1.0, 1.0, 1.0, 8.0] mean: 1.04 std: 0.23 cv: 0.22 skew: 6.93* log skew: 5.33 |
nan | nan | nan | ||
V10 | 64 | float64 | 279287 47% |
6 0% |
nan 0.0 1.0 2.0 3.0 4.0 |
nan 47% 0.0 29% 1.0 23% 2.0 1% 3.0 0% 4.0 0% |
nan | nan | nan | ||
V11 | 65 | float64 | 279287 47% |
7 0% |
nan 0.0 1.0 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.48 std: 0.55 cv: 1.15 skew: 0.74* |
nan | nan | nan | ||
V12 | 66 | float64 | 76073 13% |
5 0% |
1.0 0.0 nan 2.0 3.0 |
1.0 48% 0.0 39% nan 13% 2.0 1% 3.0 0% |
nan | 0 | nan | ||
V13 | 67 | float64 | 76073 13% |
8 0% |
1.0 0.0 nan 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 1.0, 1.0, 6.0] mean: 0.60 std: 0.53 cv: 0.89 skew: 0.08* log skew: 5.35 |
nan | 0 | nan | ||
V14 | 68 | float64 | 76073 13% |
3 0% |
1.0 nan 0.0 |
1.0 87% nan 13% 0.0 0% |
nan | 1 | nan | ||
V15 | 69 | float64 | 76073 13% |
9 0% |
0.0 nan 1.0 2.0 3.0 7.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.12 std: 0.33 cv: 2.72 skew: 2.59* |
nan | 0 | nan | ||
V16 | 70 | float64 | 76073 13% |
16 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.12 std: 0.34 cv: 2.78 skew: 3.83* |
nan | 0 | nan | |||
V17 | 71 | float64 | 76073 13% |
17 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.13 std: 0.36 cv: 2.72 skew: 3.79* |
nan | 0 | nan | |||
V18 | 72 | float64 | 76073 13% |
17 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.14 std: 0.37 cv: 2.75 skew: 4.01* |
nan | 0 | nan | |||
V19 | 73 | float64 | 76073 13% |
9 0% |
1.0 0.0 nan 2.0 3.0 4.0 7.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 0.82 std: 0.43 cv: 0.52 skew: -0.88* log skew: 7.94 |
nan | 1 | nan | ||
V20 | 74 | float64 | 76073 13% |
16 0% |
[0.0, 1.0, 1.0, 1.0, 15.0] mean: 0.85 std: 0.46 cv: 0.54 skew: 0.25* log skew: 5.29 |
nan | 1 | nan | |||
V21 | 75 | float64 | 76073 13% |
7 0% |
0.0 nan 1.0 2.0 3.0 5.0 4.0 |
[0.0, 0.0, 0.0, 0.0, 5.0] mean: 0.13 std: 0.34 cv: 2.61 skew: 2.31* |
nan | 0 | nan | ||
V22 | 76 | float64 | 76073 13% |
10 0% |
0.0 nan 1.0 2.0 3.0 7.0 4.0 5.0 6.0 8.0 |
[0.0, 0.0, 0.0, 0.0, 8.0] mean: 0.13 std: 0.36 cv: 2.72 skew: 3.63* |
nan | 0 | nan | ||
V23 | 77 | float64 | 76073 13% |
15 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 1.03 std: 0.25 cv: 0.24 skew: 12.12* log skew: 6.67 |
nan | 1 | nan | |||
V24 | 78 | float64 | 76073 13% |
15 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 1.06 std: 0.31 cv: 0.29 skew: 9.09* log skew: 4.93 |
nan | 1 | nan | |||
V25 | 79 | float64 | 76073 13% |
8 0% |
1.0 nan 0.0 2.0 3.0 4.0 7.0 5.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 0.98 std: 0.19 cv: 0.19 skew: -2.65* log skew: 14.02 |
nan | 1 | nan | ||
V26 | 80 | float64 | 76073 13% |
14 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 0.99 std: 0.21 cv: 0.21 skew: 2.69* log skew: 9.77 |
nan | 1 | nan | |||
V27 | 81 | float64 | 76073 13% |
5 0% |
0.0 nan 1.0 2.0 4.0 |
0.0 87% nan 13% 1.0 0% 2.0 0% 4.0 0% |
nan | 0 | nan | ||
V28 | 82 | float64 | 76073 13% |
5 0% |
0.0 nan 1.0 2.0 4.0 |
0.0 87% nan 13% 1.0 0% 2.0 0% 4.0 0% |
nan | 0 | nan | ||
V29 | 83 | float64 | 76073 13% |
7 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.39 std: 0.51 cv: 1.32 skew: 0.77* |
nan | 0 | nan | ||
V30 | 84 | float64 | 76073 13% |
9 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 9.0 6.0 |
[0.0, 0.0, 0.0, 1.0, 9.0] mean: 0.41 std: 0.55 cv: 1.36 skew: 1.28* |
nan | 0 | nan | ||
V31 | 85 | float64 | 76073 13% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 7.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.14 std: 0.36 cv: 2.53 skew: 2.44* |
nan | 0 | nan | ||
V32 | 86 | float64 | 76073 13% |
16 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.14 std: 0.37 cv: 2.59 skew: 3.53* |
nan | 0 | nan | |||
V33 | 87 | float64 | 76073 13% |
8 0% |
0.0 nan 1.0 2.0 3.0 4.0 7.0 5.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.13 std: 0.34 cv: 2.61 skew: 2.34* |
nan | 0 | nan | ||
V34 | 88 | float64 | 76073 13% |
14 0% |
[0.0, 0.0, 0.0, 0.0, 13.0] mean: 0.14 std: 0.36 cv: 2.57 skew: 2.95* |
nan | 0 | nan | |||
V35 | 89 | float64 | 168969 29% |
5 0% |
1.0 0.0 nan 2.0 3.0 |
1.0 37% 0.0 33% nan 29% 2.0 1% 3.0 0% |
nan | 0 | nan | ||
V36 | 90 | float64 | 168969 29% |
7 0% |
1.0 0.0 nan 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 1.0, 1.0, 5.0] mean: 0.58 std: 0.54 cv: 0.93 skew: 0.18* |
nan | 0 | nan | ||
V37 | 91 | float64 | 168969 29% |
56 0% |
[0.0, 1.0, 1.0, 1.0, 54.0] mean: 1.11 std: 0.69 cv: 0.62 skew: 22.63* |
nan | 1 | nan | |||
V38 | 92 | float64 | 168969 29% |
56 0% |
[0.0, 1.0, 1.0, 1.0, 54.0] mean: 1.16 std: 0.86 cv: 0.74 skew: 18.36* |
nan | 1 | nan | |||
V39 | 93 | float64 | 168969 29% |
17 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.17 std: 0.45 cv: 2.72 skew: 5.95* |
nan | 0 | nan | |||
V40 | 94 | float64 | 168969 29% |
19 0% |
[0.0, 0.0, 0.0, 0.0, 24.0] mean: 0.18 std: 0.51 cv: 2.86 skew: 6.65* |
nan | 0 | nan | |||
V41 | 95 | float64 | 168969 29% |
3 0% |
1.0 nan 0.0 |
1.0 71% nan 29% 0.0 0% |
nan | 1 | nan | ||
V42 | 96 | float64 | 168969 29% |
10 0% |
0.0 nan 1.0 2.0 3.0 4.0 7.0 6.0 5.0 8.0 |
[0.0, 0.0, 0.0, 0.0, 8.0] mean: 0.16 std: 0.38 cv: 2.45 skew: 2.44* |
nan | 0 | nan | ||
V43 | 97 | float64 | 168969 29% |
10 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 |
[0.0, 0.0, 0.0, 0.0, 8.0] mean: 0.17 std: 0.43 cv: 2.57 skew: 3.09* |
nan | 0 | nan | ||
V44 | 98 | float64 | 168969 29% |
50 0% |
[0.0, 1.0, 1.0, 1.0, 48.0] mean: 1.08 std: 0.64 cv: 0.59 skew: 29.04* |
nan | 1 | nan | |||
V45 | 99 | float64 | 168969 29% |
50 0% |
[0.0, 1.0, 1.0, 1.0, 48.0] mean: 1.12 std: 0.73 cv: 0.65 skew: 23.22* |
nan | 1 | nan | |||
V46 | 100 | float64 | 168969 29% |
8 0% |
1.0 nan 2.0 3.0 0.0 4.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 1.02 std: 0.17 cv: 0.16 skew: 7.76* |
nan | 1 | nan | ||
V47 | 101 | float64 | 168969 29% |
10 0% |
1.0 nan 2.0 3.0 0.0 4.0 6.0 5.0 12.0 7.0 |
[0.0, 1.0, 1.0, 1.0, 12.0] mean: 1.04 std: 0.23 cv: 0.22 skew: 10.04* |
nan | 1 | nan | ||
V48 | 102 | float64 | 168969 29% |
7 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.38 std: 0.51 cv: 1.33 skew: 0.77* |
nan | 0 | nan | ||
V49 | 103 | float64 | 168969 29% |
7 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.40 std: 0.54 cv: 1.36 skew: 1.13* |
nan | 0 | nan | ||
V50 | 104 | float64 | 168969 29% |
7 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 0.0, 5.0] mean: 0.16 std: 0.37 cv: 2.27 skew: 1.91* |
nan | 0 | nan | ||
V51 | 105 | float64 | 168969 29% |
8 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 6.0] mean: 0.17 std: 0.40 cv: 2.37 skew: 2.41* |
nan | 0 | nan | ||
V52 | 106 | float64 | 168969 29% |
10 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 12.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 12.0] mean: 0.18 std: 0.44 cv: 2.4 skew: 3.3* |
nan | 0 | nan | ||
V53 | 107 | float64 | 77096 13% |
7 0% |
1.0 0.0 nan 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 1.0, 1.0, 5.0] mean: 0.58 std: 0.51 cv: 0.89 skew: -0.09* log skew: 8.48 |
nan | 1 | nan | ||
V54 | 108 | float64 | 77096 13% |
8 0% |
1.0 0.0 nan 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 1.0, 1.0, 6.0] mean: 0.62 std: 0.53 cv: 0.86 skew: 0.09* log skew: 5.24 |
nan | 1 | nan | ||
V55 | 109 | float64 | 77096 13% |
19 0% |
[0.0, 1.0, 1.0, 1.0, 17.0] mean: 1.07 std: 0.39 cv: 0.37 skew: 10.42* log skew: 5.53 |
nan | 1 | nan | |||
V56 | 110 | float64 | 77096 13% |
53 0% |
[0.0, 1.0, 1.0, 1.0, 51.0] mean: 1.12 std: 0.66 cv: 0.59 skew: 23.56* log skew: 4.42 |
nan | 2 | nan | |||
V57 | 111 | float64 | 77096 13% |
8 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 6.0] mean: 0.13 std: 0.35 cv: 2.72 skew: 2.68* |
nan | 0 | nan | ||
V58 | 112 | float64 | 77096 13% |
12 0% |
[0.0, 0.0, 0.0, 0.0, 10.0] mean: 0.13 std: 0.37 cv: 2.82 skew: 3.79* |
nan | 0 | nan | |||
V59 | 113 | float64 | 77096 13% |
18 0% |
[0.0, 0.0, 0.0, 0.0, 16.0] mean: 0.13 std: 0.38 cv: 2.82 skew: 4.38* |
nan | 0 | nan | |||
V60 | 114 | float64 | 77096 13% |
18 0% |
[0.0, 0.0, 0.0, 0.0, 16.0] mean: 0.14 std: 0.42 cv: 2.93 skew: 4.99* |
nan | 0 | nan | |||
V61 | 115 | float64 | 77096 13% |
8 0% |
1.0 0.0 nan 2.0 3.0 5.0 4.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 0.83 std: 0.44 cv: 0.53 skew: -0.59* log skew: 6.51 |
nan | 1 | nan | ||
V62 | 116 | float64 | 77096 13% |
12 0% |
[0.0, 1.0, 1.0, 1.0, 10.0] mean: 0.87 std: 0.48 cv: 0.56 skew: 0.61* log skew: 4.62 |
nan | 2 | nan | |||
V63 | 117 | float64 | 77096 13% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.13 std: 0.36 cv: 2.72 skew: 2.83* |
nan | 0 | nan | ||
V64 | 118 | float64 | 77096 13% |
9 0% |
0.0 nan 1.0 2.0 3.0 6.0 4.0 5.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.14 std: 0.41 cv: 2.86 skew: 3.85* |
nan | 0 | nan | ||
V65 | 119 | float64 | 77096 13% |
3 0% |
1.0 nan 0.0 |
1.0 87% nan 13% 0.0 0% |
nan | 1 | nan | ||
V66 | 120 | float64 | 77096 13% |
9 0% |
1.0 nan 0.0 2.0 3.0 4.0 5.0 6.0 7.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 0.98 std: 0.22 cv: 0.22 skew: -0.81* log skew: 9.24 |
nan | 1 | nan | ||
V67 | 121 | float64 | 77096 13% |
10 0% |
1.0 nan 0.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 |
[0.0, 1.0, 1.0, 1.0, 8.0] mean: 1.00 std: 0.25 cv: 0.25 skew: 1.33* log skew: 6.53 |
nan | 1 | nan | ||
V68 | 122 | float64 | 77096 13% |
4 0% |
0.0 nan 1.0 2.0 |
0.0 87% nan 13% 1.0 0% 2.0 0% |
nan | 0 | nan | ||
V69 | 123 | float64 | 77096 13% |
7 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.39 std: 0.51 cv: 1.32 skew: 0.79* |
nan | 0 | nan | ||
V70 | 124 | float64 | 77096 13% |
8 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 1.0, 6.0] mean: 0.41 std: 0.55 cv: 1.36 skew: 1.23* |
nan | 0 | nan | ||
V71 | 125 | float64 | 77096 13% |
8 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 6.0] mean: 0.14 std: 0.36 cv: 2.59 skew: 2.56* |
nan | 0 | nan | ||
V72 | 126 | float64 | 77096 13% |
12 0% |
[0.0, 0.0, 0.0, 0.0, 10.0] mean: 0.15 std: 0.39 cv: 2.68 skew: 3.61* |
nan | 0 | nan | |||
V73 | 127 | float64 | 77096 13% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.14 std: 0.37 cv: 2.62 skew: 2.65* |
nan | 0 | nan | ||
V74 | 128 | float64 | 77096 13% |
10 0% |
0.0 nan 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 |
[0.0, 0.0, 0.0, 0.0, 8.0] mean: 0.15 std: 0.39 cv: 2.58 skew: 2.81* |
nan | 0 | nan | ||
V75 | 129 | float64 | 89164 15% |
6 0% |
1.0 0.0 nan 2.0 3.0 4.0 |
1.0 45% 0.0 39% nan 15% 2.0 1% 3.0 0% 4.0 0% |
nan | 1 | nan | ||
V76 | 130 | float64 | 89164 15% |
8 0% |
1.0 0.0 nan 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 1.0, 1.0, 6.0] mean: 0.59 std: 0.54 cv: 0.92 skew: 0.18* log skew: 5.23 |
nan | 1 | nan | ||
V77 | 131 | float64 | 89164 15% |
32 0% |
[0.0, 1.0, 1.0, 1.0, 30.0] mean: 1.09 std: 0.53 cv: 0.49 skew: 13.23* log skew: 5.54 |
nan | 1 | nan | |||
V78 | 132 | float64 | 89164 15% |
33 0% |
[0.0, 1.0, 1.0, 1.0, 31.0] mean: 1.14 std: 0.78 cv: 0.68 skew: 15.27* log skew: 4.45 |
nan | 2 | nan | |||
V79 | 133 | float64 | 89164 15% |
9 0% |
0.0 nan 1.0 2.0 3.0 7.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.14 std: 0.38 cv: 2.78 skew: 3.89* |
nan | 0 | nan | ||
V80 | 134 | float64 | 89164 15% |
21 0% |
[0.0, 0.0, 0.0, 0.0, 19.0] mean: 0.14 std: 0.41 cv: 2.85 skew: 5.69* |
nan | 0 | nan | |||
V81 | 135 | float64 | 89164 15% |
21 0% |
[0.0, 0.0, 0.0, 0.0, 19.0] mean: 0.15 std: 0.45 cv: 2.96 skew: 5.94* |
nan | 0 | nan | |||
V82 | 136 | float64 | 89164 15% |
9 0% |
1.0 nan 0.0 2.0 3.0 4.0 6.0 5.0 7.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 0.84 std: 0.42 cv: 0.5 skew: -0.71* log skew: 6.57 |
nan | 1 | nan | ||
V83 | 137 | float64 | 89164 15% |
9 0% |
1.0 nan 0.0 2.0 3.0 4.0 7.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 0.88 std: 0.47 cv: 0.53 skew: 0.56* log skew: 4.66 |
nan | 2 | nan | ||
V84 | 138 | float64 | 89164 15% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 7.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.14 std: 0.36 cv: 2.64 skew: 2.71* |
nan | 0 | nan | ||
V85 | 139 | float64 | 89164 15% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 6.0 5.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.15 std: 0.42 cv: 2.8 skew: 3.74* |
nan | 0 | nan | ||
V86 | 140 | float64 | 89164 15% |
32 0% |
[0.0, 1.0, 1.0, 1.0, 30.0] mean: 1.06 std: 0.42 cv: 0.39 skew: 16.93* log skew: 5.74 |
nan | 1 | nan | |||
V87 | 141 | float64 | 89164 15% |
32 0% |
[0.0, 1.0, 1.0, 1.0, 30.0] mean: 1.10 std: 0.51 cv: 0.47 skew: 14.8* log skew: 4.47 |
nan | 1 | nan | |||
V88 | 142 | float64 | 89164 15% |
3 0% |
1.0 nan 0.0 |
1.0 85% nan 15% 0.0 0% |
nan | 1 | nan | ||
V89 | 143 | float64 | 89164 15% |
4 0% |
0.0 nan 1.0 2.0 |
0.0 85% nan 15% 1.0 0% 2.0 0% |
nan | 0 | nan | ||
V90 | 144 | float64 | 89164 15% |
7 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 |
[0.0, 0.0, 0.0, 1.0, 5.0] mean: 0.40 std: 0.52 cv: 1.28 skew: 0.74* |
nan | 0 | nan | ||
V91 | 145 | float64 | 89164 15% |
8 0% |
0.0 1.0 nan 2.0 3.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 1.0, 6.0] mean: 0.42 std: 0.56 cv: 1.34 skew: 1.25* |
nan | 0 | nan | ||
V92 | 146 | float64 | 89164 15% |
9 0% |
0.0 nan 1.0 2.0 3.0 4.0 6.0 5.0 7.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.15 std: 0.38 cv: 2.5 skew: 2.53* |
nan | 0 | nan | ||
V93 | 147 | float64 | 89164 15% |
9 0% |
0.0 nan 1.0 2.0 3.0 7.0 4.0 5.0 6.0 |
[0.0, 0.0, 0.0, 0.0, 7.0] mean: 0.15 std: 0.40 cv: 2.6 skew: 3.59* |
nan | 0 | nan | ||
V94 | 148 | float64 | 89164 15% |
4 0% |
0.0 nan 1.0 2.0 |
0.0 73% nan 15% 1.0 12% 2.0 0% |
nan | 0 | nan | ||
V95 | 149 | float64 | 314 0% |
882 0% |
[0.0, 0.0, 0.0, 0.0, 880.0] mean: 1.04 std: 21.03 cv: 20.26 skew: 30.24* log skew: 4.18 |
0 | 0 | 1 | |||
V96 | 150 | float64 | 314 0% |
1411 0% |
[0.0, 0.0, 0.0, 1.0, 1410.0] mean: 3.01 std: 40.24 cv: 13.39 skew: 26.03* log skew: 2.05 |
0 | 0 | 1 | |||
V97 | 151 | float64 | 314 0% |
977 0% |
[0.0, 0.0, 0.0, 0.0, 976.0] mean: 1.72 std: 27.70 cv: 16.11 skew: 25.32* log skew: 3.14 |
0 | 0 | 1 | |||
V98 | 152 | float64 | 314 0% |
14 0% |
[0.0, 0.0, 0.0, 0.0, 12.0] mean: 0.06 std: 0.28 cv: 4.6 skew: 6.39* log skew: 2.89 |
0 | 0 | 0 | |||
V99 | 153 | float64 | 314 0% |
90 0% |
[0.0, 0.0, 0.0, 1.0, 88.0] mean: 0.89 std: 2.72 cv: 3.04 skew: 11.06* log skew: 1.14 |
0 | 0 | 0 | |||
V100 | 154 | float64 | 314 0% |
30 0% |
[0.0, 0.0, 0.0, 0.0, 28.0] mean: 0.27 std: 0.95 cv: 3.46 skew: 8.74* log skew: 1.79 |
0 | 0 | 0 | |||
V101 | 155 | float64 | 314 0% |
871 0% |
[0.0, 0.0, 0.0, 0.0, 869.0] mean: 0.89 std: 20.58 cv: 23.15 skew: 30.63* log skew: 3.44 |
0 | 0 | 0 | |||
V102 | 156 | float64 | 314 0% |
1286 0% |
[0.0, 0.0, 0.0, 0.0, 1285.0] mean: 1.83 std: 35.93 cv: 19.66 skew: 26.99* log skew: 2.49 |
0 | 0 | 0 | |||
V103 | 157 | float64 | 314 0% |
929 0% |
[0.0, 0.0, 0.0, 0.0, 928.0] mean: 1.28 std: 25.69 cv: 20.08 skew: 26.03* log skew: 2.91 |
0 | 0 | 0 | |||
V104 | 158 | float64 | 314 0% |
17 0% |
[0.0, 0.0, 0.0, 0.0, 15.0] mean: 0.09 std: 0.65 cv: 7.59 skew: 14.88* log skew: 2.23 |
0 | 0 | 1 | |||
V105 | 159 | float64 | 314 0% |
101 0% |
[0.0, 0.0, 0.0, 0.0, 99.0] mean: 0.28 std: 3.37 cv: 12.0 skew: 20.89* log skew: 2.72 |
0 | 0 | 1 | |||
V106 | 160 | float64 | 314 0% |
57 0% |
[0.0, 0.0, 0.0, 0.0, 55.0] mean: 0.16 std: 1.83 cv: 11.09 skew: 19.49* log skew: 2.70 |
0 | 0 | 1 | |||
V107 | 161 | float64 | 314 0% |
3 0% |
1.0 nan 0.0 |
1.0 100% nan 0% 0.0 0% |
1 | 1 | 1 | ||
V108 | 162 | float64 | 314 0% |
9 0% |
1.0 2.0 nan 0.0 3.0 4.0 5.0 7.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 1.00 std: 0.08 cv: 0.08 skew: 17.82* log skew: 15.68 |
1 | 1 | 1 | ||
V109 | 163 | float64 | 314 0% |
9 0% |
1.0 2.0 nan 3.0 0.0 4.0 5.0 7.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 1.01 std: 0.13 cv: 0.13 skew: 9.73* log skew: 8.47 |
1 | 1 | 1 | ||
V110 | 164 | float64 | 314 0% |
9 0% |
1.0 2.0 nan 0.0 3.0 4.0 5.0 7.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 7.0] mean: 1.01 std: 0.10 cv: 0.1 skew: 13.75* log skew: 11.92 |
1 | 1 | 1 | ||
V111 | 165 | float64 | 314 0% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.00 std: 0.07 cv: 0.07 skew: 31.71* log skew: 22.78 |
1 | 1 | 1 | |||
V112 | 166 | float64 | 314 0% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.01 std: 0.08 cv: 0.08 skew: 23.15* log skew: 15.40 |
1 | 1 | 1 | |||
V113 | 167 | float64 | 314 0% |
11 0% |
[0.0, 1.0, 1.0, 1.0, 9.0] mean: 1.00 std: 0.07 cv: 0.07 skew: 28.97* log skew: 19.83 |
1 | 1 | 1 | |||
V114 | 168 | float64 | 314 0% |
8 0% |
1.0 2.0 nan 3.0 0.0 4.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 1.01 std: 0.11 cv: 0.11 skew: 13.06* log skew: 11.17 |
1 | 1 | 1 | ||
V115 | 169 | float64 | 314 0% |
8 0% |
1.0 2.0 3.0 nan 4.0 0.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 1.03 std: 0.19 cv: 0.18 skew: 6.59* log skew: 5.73 |
1 | 1 | 1 | ||
V116 | 170 | float64 | 314 0% |
8 0% |
1.0 2.0 3.0 nan 0.0 4.0 5.0 6.0 |
[0.0, 1.0, 1.0, 1.0, 6.0] mean: 1.02 std: 0.14 cv: 0.13 skew: 9.79* log skew: 8.43 |
1 | 1 | 1 | ||
V117 | 171 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V118 | 172 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V119 | 173 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V120 | 174 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V121 | 175 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V122 | 176 | float64 | 314 0% |
5 0% |
1.0 2.0 nan 0.0 3.0 |
1.0 100% 2.0 0% nan 0% 0.0 0% 3.0 0% |
1 | 1 | 1 | ||
V123 | 177 | float64 | 314 0% |
15 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 1.03 std: 0.23 cv: 0.22 skew: 12.88* log skew: 7.06 |
1 | 1 | 1 | |||
V124 | 178 | float64 | 314 0% |
15 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 1.09 std: 0.37 cv: 0.34 skew: 6.09* log skew: 3.83 |
1 | 1 | 1 | |||
V125 | 179 | float64 | 314 0% |
15 0% |
[0.0, 1.0, 1.0, 1.0, 13.0] mean: 1.05 std: 0.28 cv: 0.27 skew: 9.0* log skew: 5.37 |
1 | 1 | 1 | |||
V126 | 180 | float64 | 314 0% |
10300 2% |
[0.0, 0.0, 0.0, 0.0, 160000.0] mean: 129.98 std: 2346.95 cv: 18.06 skew: 29.58* log skew: 1.27 |
0 | 0 | 300 | |||
V127 | 181 | float64 | 314 0% |
24415 4% |
[0.0, 0.0, 0.0, 107.9499969482422, 160000.0] mean: 336.61 std: 4238.67 cv: 12.59 skew: 25.68* log skew: 0.93 |
0 | 0 | 300 | |||
V128 | 182 | float64 | 314 0% |
14508 2% |
[0.0, 0.0, 0.0, 0.0, 160000.0] mean: 204.09 std: 3010.26 cv: 14.75 skew: 25.05* log skew: 1.16 |
0 | 0 | 300 | |||
V129 | 183 | float64 | 314 0% |
1969 0% |
[0.0, 0.0, 0.0, 0.0, 55125.0] mean: 8.77 std: 113.83 cv: 12.98 skew: 240.27* log skew: 0.52 |
0 | 0 | 0 | |||
V130 | 184 | float64 | 314 0% |
12333 2% |
[0.0, 0.0, 0.0, 59.0, 55125.0] mean: 92.17 std: 315.96 cv: 3.43 skew: 26.3* log skew: 0.40 |
0 | 0 | 0 | |||
V131 | 185 | float64 | 314 0% |
4445 1% |
[0.0, 0.0, 0.0, 0.0, 55125.0] mean: 31.13 std: 161.16 cv: 5.18 skew: 89.6* log skew: 0.46 |
0 | 0 | 0 | |||
V132 | 186 | float64 | 314 0% |
6561 1% |
[0.0, 0.0, 0.0, 0.0, 93736.0] mean: 103.51 std: 2266.11 cv: 21.89 skew: 30.23* log skew: 1.43 |
0 | 0 | 0 | |||
V133 | 187 | float64 | 314 0% |
9950 2% |
[0.0, 0.0, 0.0, 0.0, 133915.0] mean: 204.89 std: 3796.32 cv: 18.53 skew: 26.86* log skew: 1.15 |
0 | 0 | 0 | |||
V134 | 188 | float64 | 314 0% |
8179 1% |
[0.0, 0.0, 0.0, 0.0, 98476.0] mean: 145.97 std: 2772.99 cv: 19.0 skew: 25.99* log skew: 1.28 |
0 | 0 | 0 | |||
V135 | 189 | float64 | 314 0% |
3725 1% |
[0.0, 0.0, 0.0, 0.0, 90750.0] mean: 17.25 std: 293.85 cv: 17.03 skew: 144.88* log skew: 0.37 |
0 | 0 | 300 | |||
V136 | 190 | float64 | 314 0% |
4853 1% |
[0.0, 0.0, 0.0, 0.0, 90750.0] mean: 38.82 std: 451.81 cv: 11.64 skew: 49.02* log skew: 0.76 |
0 | 0 | 300 | |||
V137 | 191 | float64 | 314 0% |
4253 1% |
[0.0, 0.0, 0.0, 0.0, 90750.0] mean: 26.37 std: 348.33 cv: 13.21 skew: 90.58* log skew: 0.61 |
0 | 0 | 300 | |||
V138 | 192 | float64 | 508595 86% |
24 0% |
[0.0, 0.0, 0.0, 0.0, 22.0] mean: 0.04 std: 0.43 cv: 11.76 skew: 22.58* |
0 | nan | 0 | |||
V139 | 193 | float64 | 508595 86% |
35 0% |
[0.0, 0.0, 1.0, 1.0, 33.0] mean: 1.07 std: 1.33 cv: 1.24 skew: 5.91* |
1 | nan | 2 | |||
V140 | 194 | float64 | 508595 86% |
35 0% |
[0.0, 0.0, 1.0, 1.0, 33.0] mean: 1.13 std: 1.47 cv: 1.3 skew: 5.58* |
1 | nan | 2 | |||
V141 | 195 | float64 | 508595 86% |
7 0% |
nan 0.0 1.0 2.0 3.0 5.0 4.0 |
[0.0, 0.0, 0.0, 0.0, 5.0] mean: 0.04 std: 0.22 cv: 5.71 skew: 6.75* |
0 | nan | 0 | ||
V142 | 196 | float64 | 508595 86% |
11 0% |
[0.0, 0.0, 0.0, 0.0, 9.0] mean: 0.05 std: 0.31 cv: 6.46 skew: 9.99* |
0 | nan | 0 | |||
V143 | 197 | float64 | 508589 86% |
871 0% |
[0.0, 0.0, 0.0, 0.0, 869.0] mean: 8.40 std: 55.27 cv: 6.58 skew: 11.0* |
0 | nan | 0 | |||
V144 | 198 | float64 | 508589 86% |
64 0% |
[0.0, 0.0, 0.0, 0.0, 62.0] mean: 3.71 std: 10.49 cv: 2.83 skew: 3.11* |
0 | nan | 1 | |||
V145 | 199 | float64 | 508589 86% |
261 0% |
[0.0, 0.0, 0.0, 1.0, 297.0] mean: 22.11 std: 64.37 cv: 2.91 skew: 3.02* |
0 | nan | 1 | |||
V146 | 200 | float64 | 508595 86% |
26 0% |
[0.0, 0.0, 0.0, 0.0, 24.0] mean: 0.16 std: 0.68 cv: 4.37 skew: 9.97* |
0 | nan | 1 | |||
V147 | 201 | float64 | 508595 86% |
28 0% |
[0.0, 0.0, 0.0, 0.0, 26.0] mean: 0.17 std: 0.75 cv: 4.45 skew: 9.94* |
0 | nan | 1 | |||
V148 | 202 | float64 | 508595 86% |
22 0% |
[0.0, 0.0, 1.0, 1.0, 20.0] mean: 0.77 std: 0.58 cv: 0.76 skew: 6.01* |
1 | nan | 1 | |||
V149 | 203 | float64 | 508595 86% |
22 0% |
[0.0, 0.0, 1.0, 1.0, 20.0] mean: 0.78 std: 0.63 cv: 0.81 skew: 6.55* |
1 | nan | 1 | |||
V150 | 204 | float64 | 508589 86% |
1997 0% |
[1.0, 1.0, 1.0, 1.0, 3389.0] mean: 277.60 std: 829.58 cv: 2.99 skew: 2.85* |
1 | nan | 1 | |||
V151 | 205 | float64 | 508589 86% |
57 0% |
[1.0, 1.0, 1.0, 1.0, 57.0] mean: 6.46 std: 15.23 cv: 2.36 skew: 2.6* |
1 | nan | 1 | |||
V152 | 206 | float64 | 508589 86% |
40 0% |
[1.0, 1.0, 1.0, 1.0, 69.0] mean: 9.43 std: 21.55 cv: 2.29 skew: 2.2* |
1 | nan | 1 | |||
V153 | 207 | float64 | 508595 86% |
20 0% |
[0.0, 0.0, 1.0, 1.0, 18.0] mean: 0.75 std: 0.53 cv: 0.71 skew: 4.7* |
1 | nan | 1 | |||
V154 | 208 | float64 | 508595 86% |
20 0% |
[0.0, 0.0, 1.0, 1.0, 18.0] mean: 0.76 std: 0.55 cv: 0.72 skew: 4.76* |
1 | nan | 1 | |||
V155 | 209 | float64 | 508595 86% |
26 0% |
[0.0, 0.0, 1.0, 1.0, 24.0] mean: 0.77 std: 0.60 cv: 0.79 skew: 7.94* |
1 | nan | 1 | |||
V156 | 210 | float64 | 508595 86% |
26 0% |
[0.0, 0.0, 1.0, 1.0, 24.0] mean: 0.78 std: 0.65 cv: 0.83 skew: 8.06* |
1 | nan | 1 | |||
V157 | 211 | float64 | 508595 86% |
26 0% |
[0.0, 0.0, 1.0, 1.0, 24.0] mean: 0.82 std: 0.68 cv: 0.83 skew: 6.13* |
1 | nan | 1 | |||
V158 | 212 | float64 | 508595 86% |
26 0% |
[0.0, 0.0, 1.0, 1.0, 24.0] mean: 0.83 std: 0.73 cv: 0.88 skew: 6.23* |
1 | nan | 1 | |||
V159 | 213 | float64 | 508589 86% |
6664 1% |
[0.0, 0.0, 0.0, 0.0, 55125.0] mean: 2719.30 std: 8355.45 cv: 3.07 skew: 3.05* |
0 | nan | 0 | |||
V160 | 214 | float64 | 508589 86% |
9622 2% |
[0.0, 0.0, 0.0, 0.0, 641511.4375] mean: 47453.18 std: 142076.07 cv: 2.99 skew: 3.0* |
0 | nan | 0 | |||
V161 | 215 | float64 | 508595 86% |
80 0% |
[0.0, 0.0, 0.0, 0.0, 3300.0] mean: 4.84 std: 58.93 cv: 12.17 skew: 32.79* |
0 | nan | 0 | |||
V162 | 216 | float64 | 508595 86% |
186 0% |
[0.0, 0.0, 0.0, 0.0, 3300.0] mean: 6.59 std: 69.20 cv: 10.49 skew: 25.2* |
0 | nan | 0 | |||
V163 | 217 | float64 | 508595 86% |
107 0% |
[0.0, 0.0, 0.0, 0.0, 3300.0] mean: 5.51 std: 63.08 cv: 11.46 skew: 30.29* |
0 | nan | 0 | |||
V164 | 218 | float64 | 508589 86% |
1979 0% |
[0.0, 0.0, 0.0, 0.0, 93736.0] mean: 877.89 std: 6049.17 cv: 6.89 skew: 11.03* |
0 | nan | 0 | |||
V165 | 219 | float64 | 508589 86% |
2548 0% |
[0.0, 0.0, 0.0, 0.0, 98476.0] mean: 2239.91 std: 8223.26 cv: 3.67 skew: 6.81* |
0 | nan | 0 | |||
V166 | 220 | float64 | 508589 86% |
988 0% |
[0.0, 0.0, 0.0, 0.0, 104060.0] mean: 359.47 std: 1244.46 cv: 3.46 skew: 27.33* |
0 | nan | 300 | |||
V167 | 221 | float64 | 450909 76% |
874 0% |
[0.0, 0.0, 0.0, 0.0, 872.0] mean: 3.93 std: 42.20 cv: 10.74 skew: 14.95* |
0 | nan | 1 | |||
V168 | 222 | float64 | 450909 76% |
966 0% |
[0.0, 0.0, 0.0, 1.0, 964.0] mean: 5.86 std: 54.03 cv: 9.22 skew: 12.49* |
0 | nan | 1 | |||
V169 | 223 | float64 | 450721 76% |
21 0% |
[0.0, 0.0, 0.0, 0.0, 19.0] mean: 0.17 std: 0.90 cv: 5.38 skew: 8.84* |
0 | nan | 0 | |||
V170 | 224 | float64 | 450721 76% |
50 0% |
[0.0, 1.0, 1.0, 1.0, 48.0] mean: 1.44 std: 1.75 cv: 1.22 skew: 8.98* |
1 | nan | 2 | |||
V171 | 225 | float64 | 450721 76% |
63 0% |
[0.0, 1.0, 1.0, 1.0, 61.0] mean: 1.69 std: 2.44 cv: 1.45 skew: 6.72* |
1 | nan | 2 | |||
V172 | 226 | float64 | 450909 76% |
33 0% |