In [1]:
import pandas as pd
import pygwalker as pyg
In [2]:
df = pd.read_csv("./superstore.csv")
df = df[df["Market"] == "APAC"]
df.head()
Out[2]:
Category | City | Country | Customer.ID | Customer.Name | Discount | Market | 记录数 | Order.Date | Order.ID | ... | Sales | Segment | Ship.Date | Ship.Mode | Shipping.Cost | State | Sub.Category | Year | Market2 | weeknum | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3318 | Office Supplies | Hyderabad | India | NM-184451 | Nathan Mautz | 0.0 | APAC | 1 | 2011-01-26 00:00:00.000 | IN-2011-31496 | ... | 79 | Home Office | 2011-01-28 00:00:00.000 | Second Class | 13.82 | Telangana | Paper | 2011 | APAC | 5 |
3319 | Office Supplies | Kowloon | Hong Kong | DB-132701 | Deborah Brumfield | 0.0 | APAC | 1 | 2011-02-14 00:00:00.000 | IN-2011-22662 | ... | 58 | Home Office | 2011-02-16 00:00:00.000 | Second Class | 6.70 | Hong Kong | Fasteners | 2011 | APAC | 8 |
3329 | Office Supplies | Jieyang | China | PS-190451 | Penelope Sewall | 0.0 | APAC | 1 | 2011-04-16 00:00:00.000 | IN-2011-74490 | ... | 18 | Home Office | 2011-04-17 00:00:00.000 | First Class | 4.20 | Guangdong | Binders | 2011 | APAC | 16 |
3330 | Office Supplies | Jieyang | China | PS-190451 | Penelope Sewall | 0.0 | APAC | 1 | 2011-04-16 00:00:00.000 | IN-2011-74490 | ... | 200 | Home Office | 2011-04-17 00:00:00.000 | First Class | 10.67 | Guangdong | Envelopes | 2011 | APAC | 16 |
3331 | Office Supplies | Jieyang | China | PS-190451 | Penelope Sewall | 0.0 | APAC | 1 | 2011-04-16 00:00:00.000 | IN-2011-74490 | ... | 145 | Home Office | 2011-04-17 00:00:00.000 | First Class | 36.53 | Guangdong | Paper | 2011 | APAC | 16 |
5 rows × 27 columns
In [3]:
pyg.walk(df, spec="./Alice_config.json")