>>> import seaserpent as ss
>>> ss.get_auth_token(username='USER',
...                   password='PASSWORD',
...                   server='https://cloud.seatable.io')
{'token': 'somelongassstring1234567@£$^@£$^£'}

>>> import seaserpent as ss
>>> # Initialize the table
>>> # (if there are multiple tables with this name you need to provide the base too)
>>> table = ss.Table(table='MyTable')
>>> table
SeaTable <"MyTable", 10 rows, 2 columns>
>>> # Inspect the first couple rows
>>> table.head()
    column1     labels
0         1          A
1         2          B
2         3          C

>>> # Fetching a column returns a promise
>>> c = table['column1']  # this works too: c = table.column1
>>> c
Column <column="column1", table="LH_bodies", datatype=number>
>>> # To get the values
>>> c.values
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> # Filters are automatically translated into SQL query
>>> table.loc[table.column1 >= 7]
    column1     labels
0         7          H
1         8          I
2         9          J
>>> table.loc[table.labels.isin(['D', 'E']) ]
    column1     labels
0         4          D
1         5          E
>>> # Download the whole table as pandas DataFrame
>>> df = table.to_frame()

>>> # First we need to re-initialize the table with write access
>>> table = ss.Table(table='MyTable', read_only=False)
>>> table.add_column(col_name='checked', col_type=bool)
>>> # The column will be empty
>>> table.head()
    column1     labels   checked
0         1          A      None
1         2          B      None
2         3          C      None

>>> # Overwrite the whole column
>>> table['checked'] = False
>>> table.head()
    column1     labels   checked
0         1          A     False
1         2          B     False
2         3          C     False
>>> # Alternatively pass a list of values
>>> table['checked'] = [False, True, False]
>>> table.head()
    column1     labels   checked
0         1          A     False
1         2          B      True
2         3          C     False
>>> # Write to a subset of the column
>>> table.loc[:2, 'checked'] = False
>>> table.loc[table.labels == 'C', 'checked'] = True
>>> table.head()
    column1     labels   checked
0         1          A     False
1         2          B     False
2         3          C      True
>>> # To write only changed values to the table
>>> # (faster & better for logs)
>>> values = table.checked.values
>>> values[0:2] = True  # Change only two values
>>> table.checked.update(values)

>>> table['checked'].delete()
>>> table.head()
    column1     labels
0         1          A
1         2          B
2         3          C
>>> # Alternatively you can also clear an entire column
>>> table.checked.clear()
>>> table.head()
    column1     labels   checked
0         1          A      None
1         2          B      None
2         3          C      None

>>> table = ss.Table.new(table_name='MyNewTable', base='MyBase')

>>> table = ss.Table.from_frame(df, table_name='MyNewTable', base='MyBase')

>>> table.link(other_table='OtherTable',    # name of the other table (must be same base)
...            link_on='Column1',           # column in this table to link on
...            link_on_other='ColumnA',     # column in other table to link on
...            link_col='OtherTableLinks')  # name of column to store links in

>>> table.add_linked_column(col_name='LinkedData',      # name of new column
...                         link_col='OtherTableLinks', # column with link(s) to other table
...                         link_on='some_value',       # which column in other table to link to
...                         formula='lookup')           # how to aggregate data (lookup, mean, max, etc)