diff options
| -rwxr-xr-x | examples/table_display.py | 29 |
1 files changed, 12 insertions, 17 deletions
diff --git a/examples/table_display.py b/examples/table_display.py index 7df178db..e461dd47 100755 --- a/examples/table_display.py +++ b/examples/table_display.py @@ -52,14 +52,13 @@ def two_dec(num: float) -> str: # ############ Table data formatted as an iterable of iterable fields ############ - -EXAMPLE_ITERABLE_DATA = [['Shanghai', 'Shanghai', 'China', 'Asia', 24183300, 6340.5], - ['Beijing', 'Hebei', 'China', 'Asia', 20794000, 1749.57], - ['Karachi', 'Sindh', 'Pakistan', 'Asia', 14910352, 615.58], - ['Shenzen', 'Guangdong', 'China', 'Asia', 13723000, 1493.32], - ['Guangzho', 'Guangdong', 'China', 'Asia', 13081000, 1347.81], - ['Mumbai', 'Maharashtra', 'India', 'Asia', 12442373, 465.78], - ['Istanbul', 'Istanbul', 'Turkey', 'Eurasia', 12661000, 620.29], +EXAMPLE_ITERABLE_DATA = [['Shanghai (上海)', 'Shanghai', 'China', 'Asia', 24183300, 6340.5], + ['Beijing (北京市)', 'Hebei', 'China', 'Asia', 20794000, 1749.57], + ['Karachi (کراچی)', 'Sindh', 'Pakistan', 'Asia', 14910352, 615.58], + ['Shenzen (深圳市)', 'Guangdong', 'China', 'Asia', 13723000, 1493.32], + ['Guangzho (广州市)', 'Guangdong', 'China', 'Asia', 13081000, 1347.81], + ['Mumbai (बॉम्बे हिंदी)', 'Maharashtra', 'India', 'Asia', 12442373, 465.78], + ['Istanbul (İstanbuld)', 'Istanbul', 'Turkey', 'Eurasia', 12661000, 620.29], ] # Calculate population density @@ -68,7 +67,7 @@ for row in EXAMPLE_ITERABLE_DATA: # # Column headers plus optional formatting info for each column -columns = [tf.Column('City', header_halign=tf.ColumnAlignment.AlignCenter), +columns = [tf.Column('City', width=11, header_halign=tf.ColumnAlignment.AlignCenter), tf.Column('Province', header_halign=tf.ColumnAlignment.AlignCenter), 'Country', # NOTE: If you don't need any special effects, you can just pass a string tf.Column('Continent', cell_halign=tf.ColumnAlignment.AlignCenter), @@ -109,14 +108,10 @@ def pop_density(data: CityInfo): return '' -EXAMPLE_OBJECT_DATA = [CityInfo('Shanghai', 'Shanghai', 'China', 'Asia', 24183300, 6340.5), - CityInfo('Beijing', 'Hebei', 'China', 'Asia', 20794000, 1749.57), - CityInfo('Karachi', 'Sindh', 'Pakistan', 'Asia', 14910352, 615.58), - CityInfo('Shenzen', 'Guangdong', 'China', 'Asia', 13723000, 1493.32), - CityInfo('Guangzho', 'Guangdong', 'China', 'Asia', 13081000, 1347.81), - CityInfo('Mumbai', 'Maharashtra', 'India', 'Asia', 12442373, 465.78), - CityInfo('Istanbul', 'Istanbul', 'Turkey', 'Eurasia', 12661000, 620.29), - ] +# Convert the Iterable of Iterables data to an Iterable of non-iterable objects for demonstration purposes +EXAMPLE_OBJECT_DATA = [] +for city in EXAMPLE_ITERABLE_DATA: + EXAMPLE_OBJECT_DATA.append(CityInfo(city[0], city[1], city[2], city[3], city[4], city[5])) # If table entries are python objects, all columns must be defined with the object attribute to query for each field # - attributes can be fields or functions. If a function is provided, the formatter will automatically call |