pydatalookr is a rip-off of R's dlookr package see here. Updates will come but for now here are the features:
- Diagnose quality of data.
- Summarize dataframe.
- Show and treat outliers.
Coming
Four main functions of pydatalook:
- Diagnose dataframe
- Diagnose numeric columns
- Diagnose categorical columns
- Diagnose outliers
- Plot outliers
import pydatalook
import pandas as pd
data = pd.read_csv("bank-additional.csv", delimiter=";")pydata.diagnose(data)
# dtype missing_value_cnt missing_value_ratio unique_value_cnt unique_value_ratio
# age int64 0 0.0 67 1.626608
# job object 0 0.0 12 0.291333
# marital object 0 0.0 4 0.097111
# education object 0 0.0 8 0.194222
# default object 0 0.0 3 0.072833
# housing object 0 0.0 3 0.072833
# loan object 0 0.0 3 0.072833
# contact object 0 0.0 2 0.048555
# month object 0 0.0 10 0.242777
# day_of_week object 0 0.0 5 0.121389
# duration int64 0 0.0 828 20.101966
# campaign int64 0 0.0 25 0.606943
# pdays int64 0 0.0 21 0.509832
# previous int64 0 0.0 7 0.169944
# poutcome object 0 0.0 3 0.072833
# emp.var.rate float64 0 0.0 10 0.242777
# cons.price.idx float64 0 0.0 26 0.631221
# cons.conf.idx float64 0 0.0 26 0.631221
# euribor3m float64 0 0.0 234 5.680991
# nr.employed float64 0 0.0 11 0.267055
# y object 0 0.0 2 0.048555pydata.diagnose_numeric(data)
# dtype count mean std min 25% 50% 75% max median zeros_cnt negative_cnt outliers_cnt
# age int64 4119 40.113620 10.313362 18.000 32.000 38.000 47.000 88.000 38.000 0 0 39
# duration int64 4119 256.788055 254.703736 0.000 103.000 181.000 317.000 3643.000 181.000 1 0 291
# campaign int64 4119 2.537266 2.568159 1.000 1.000 2.000 3.000 35.000 2.000 0 0 235
# pdays int64 4119 960.422190 191.922786 0.000 999.000 999.000 999.000 999.000 999.000 2 0 160
# previous int64 4119 0.190337 0.541788 0.000 0.000 0.000 0.000 6.000 0.000 3523 0 596
# emp.var.rate float64 4119 0.084972 1.563114 -3.400 -1.800 1.100 1.400 1.400 1.100 0 1735 0
# cons.price.idx float64 4119 93.579704 0.579349 92.201 93.075 93.749 93.994 94.767 93.749 0 0 0
# cons.conf.idx float64 4119 -40.499102 4.594578 -50.800 -42.700 -41.800 -36.400 -26.900 -41.800 0 4119 43
# euribor3m float64 4119 3.621356 1.733591 0.635 1.334 4.857 4.961 5.045 4.857 0 0 0
# nr.employed float64 4119 5166.481695 73.667904 4963.600 5099.100 5191.000 5228.100 5228.100 5191.000 0 0 0pydata.diagnose_categorical(data)
# variable levels freq count ratio rank
# 0 job admin. 1012 4119 24.569070 1
# 1 job blue-collar 884 4119 21.461520 2
# 2 job entrepreneur 148 4119 3.593105 8
# 3 job housemaid 110 4119 2.670551 10
# 4 job management 324 4119 7.865987 5
# 5 job retired 166 4119 4.030104 6
# 6 job self-employed 159 4119 3.860160 7
# 7 job services 393 4119 9.541151 4
# 8 job student 82 4119 1.990774 11
# 9 job technician 691 4119 16.775916 3
# 10 job unemployed 111 4119 2.694829 9
# 11 job unknown 39 4119 0.946832 12
# 12 marital divorced 446 4119 10.827871 3
# 13 marital married 2509 4119 60.912843 1
# 14 marital single 1153 4119 27.992231 2
# 15 marital unknown 11 4119 0.267055 4
# 16 education basic.4y 429 4119 10.415149 5
# 17 education basic.6y 228 4119 5.535324 6
# 18 education basic.9y 574 4119 13.935421 3
# 19 education high.school 921 4119 22.359796 2
# 20 education illiterate 1 4119 0.024278 8
# 21 education professional.course 535 4119 12.988589 4
# 22 education university.degree 1264 4119 30.687060 1
# 23 education unknown 167 4119 4.054382 7
# 24 default no 3315 4119 80.480699 1
# 25 default unknown 803 4119 19.495023 2
# 26 default yes 1 4119 0.024278 3
# 27 housing no 1839 4119 44.646759 2
# 28 housing unknown 105 4119 2.549162 3
# 29 housing yes 2175 4119 52.804079 1
# 30 loan no 3349 4119 81.306142 1
# 31 loan unknown 105 4119 2.549162 3
# 32 loan yes 665 4119 16.144695 2
# 33 contact cellular 2652 4119 64.384559 1
# 34 contact telephone 1467 4119 35.615441 2
# 35 month apr 215 4119 5.219714 6
# 36 month aug 636 4119 15.440641 3
# 37 month dec 22 4119 0.534110 10
# 38 month jul 711 4119 17.261471 2
# 39 month jun 530 4119 12.867201 4
# 40 month mar 48 4119 1.165331 9
# 41 month may 1378 4119 33.454722 1
# 42 month nov 446 4119 10.827871 5
# 43 month oct 69 4119 1.675164 7
# 44 month sep 64 4119 1.553775 8
# 45 day_of_week fri 768 4119 18.645302 5
# 46 day_of_week mon 855 4119 20.757465 2
# 47 day_of_week thu 860 4119 20.878854 1
# 48 day_of_week tue 841 4119 20.417577 3
# 49 day_of_week wed 795 4119 19.300801 4
# 50 poutcome failure 454 4119 11.022093 2
# 51 poutcome nonexistent 3523 4119 85.530469 1
# 52 poutcome success 142 4119 3.447439 3
# 53 y no 3668 4119 89.050740 1
# 54 y yes 451 4119 10.949260 2pydata.diagnose_outlier(data)
# outliers_cnt outliers_ratio outliers_mean with_outliers_mean without_outliers_mean rate
# age 39 0.946832 76.769231 40.113620 39.763235 1.913795
# duration 291 7.064822 956.749141 256.788055 203.577847 3.725832
# campaign 235 5.705268 10.612766 2.537266 2.048661 4.182756
# pdays 160 3.884438 5.862500 960.422190 999.000000 0.006104
# previous 596 14.469531 1.315436 0.190337 0.000000 6.911074
# emp.var.rate 0 0.000000 NaN 0.084972 0.084972 NaN
# cons.price.idx 0 0.000000 NaN 93.579704 93.579704 NaN
# cons.conf.idx 43 1.043943 -26.900000 -40.499102 -40.642566 0.664212
# euribor3m 0 0.000000 NaN 3.621356 3.621356 NaN
# nr.employed 0 0.000000 NaN 5166.481695 5166.481695 NaNThis wil generate a couple of image files.
pydata.plot_outliers(data)