Package: panda-helper
Panda-Helper: Quickly and easily inspect dataPanda-Helper is a simple data-profiling utility for Pandas DataFrames and SeriesAssess data quality and usefulness with minimal effortQuickly perform initial data exploration,so you can move on to more in-depth analysisDataFrame profiles:Report shapeDetect duplicated rowsDisplay series names and data typesCalculate distribution statistics on null values per row providing a view on data completenessSample DataFrame profileVehicles passing through toll stationsDataFrame-Level Info
-------------------------  ------------
DF Shape                   (1586280, 6)
Duplicated Rows             2184

Column Name                 Data Type
--------------------------  -----------
Plaza ID                    int64
Date                        object
Hour                        int64
Direction                   object
# Vehicles - ETC (E-ZPass)  int64
# Vehicles - Cash/VToll     int64

Summary of Nulls Per Row
--------------------------  -----------
count                       1.58628e+06
min                         0
1%                          0
5%                          0
25%                         0
50%                         0
75%                         0
95%                         0
99%                         0
max                         0
median                      0
mean                        0
median absolute deviation   0
standard deviation          0
skew                        0Series profiles report the:Series data typeCount of non-null values in the seriesNumber of unique valuesCount of null valuesCounts and frequency of the most and least common valuesDistribution statistics for numeric dataSample profile of categorical dataDirection vehicles are travelingDirection Info
----------------  -------
Data Type         object
Count             1586280
Unique Values     2
Null Values       0

Value      Count  % of total
-------  -------  ------------
I         814100  51.32%
O         772180  48.68%Sample profile of numeric dataHourly vehicle counts at tolling points# Vehicles - ETC (E-ZPass) Info
---------------------------------  -------
Data Type                          int64
Count                              1586280
Unique Values                      8987
Null Values                        0

  Value    Count  % of total
-------  -------  ------------
      0     3137  0.20%
     43     1762  0.11%
     44     1743  0.11%
     40     1712  0.11%
     42     1699  0.11%
     41     1682  0.11%
     39     1676  0.11%
     37     1673  0.11%
     48     1659  0.10%
     46     1654  0.10%
     38     1646  0.10%
     45     1641  0.10%
     36     1636  0.10%
     52     1574  0.10%
     47     1572  0.10%
     50     1571  0.10%
     51     1555  0.10%
     53     1547  0.10%
     55     1543  0.10%
     34     1534  0.10%
   8269        1  0.00%
   8438        1  0.00%
   8876        1  0.00%
   8261        1  0.00%
   8694        1  0.00%

Statistic                            Value
-------------------------  ---------------
count                          1.58628e+06
min                            0
1%                            25
5%                            68
25%                          407
50%                         1054
75%                         2071
95%                         3583
99%                         6308
max                        16854
median                      1054
mean                        1373.16
median absolute deviation    751
standard deviation          1253.1
skew                           1.69154Installing Panda-Helperpip install panda-helperUsing Panda-HelperProfiling a DataFrameCreate the DataFrameProfile and then display it or save the profile.importpandasaspdimportpandahelper.reportsasphdata={"user_id":[1,2,3,4,4],"transaction":["purchase","return","purchase","exchange","exchange"],"amount":[100.00,None,1400.00,85.12,85.12],"survey":[None,None,None,"online","online"],}df=pd.DataFrame(data)df_profile=ph.DataFrameProfile(df)df_profileDataFrame-Level Info
-------------------------  ------
DF Shape                   (5, 4)
Obviously Duplicated Rows  1

Column Name    Data Type
-------------  -----------
user_id        int64
transaction    object
amount         float64
survey         object

Summary of Nulls Per Row
--------------------------  --------
count                       5
min                         0
1%                          0
5%                          0
25%                         0
50%                         1
75%                         1
95%                         1.8
99%                         1.96
max                         2
median                      1
mean                        0.8
median absolute deviation   1
standard deviation          0.83666
skew                        0.512241df_profile.save_report("df_profile.txt")Profiling a SeriesCreate the SeriesProfile and then display it or save it. That's it!series_profile=ph.SeriesProfile(df["amount"])series_profileamount Info
-------------  -------
Data Type      float64
Count          4
Unique Values  3
Null Values    1

  Value    Count  % of total
-------  -------  ------------
  85.12        2  50.00%
 100           1  25.00%
1400           1  25.00%

Statistic                       Value
-------------------------  ----------
count                         4
min                          85.12
1%                           85.12
5%                           85.12
25%                          85.12
50%                          92.56
75%                         425
95%                        1205
99%                        1361
max                        1400
median                       92.56
mean                        417.56
median absolute deviation     7.44
standard deviation          654.998
skew                          1.99931series_profile.save_report("amount_profile.txt")Sample data obtained from:https://data.ny.gov/Transportation/Hourly-Traffic-on-Metropolitan-Transportation-Auth/qzve-kjga/datahttps://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95