Markdown preview - Pandas dataframes#
itables.to_markdown_table() returns the Markdown table that itables.show()
prints when it can’t display an interactive table at all - see
Markdown preview for why, and when, this is
shown. This page shows it for each of our test Pandas dataframes; see
Pandas dataframes for the same dataframes rendered
as interactive tables.
import itables
dict_of_test_dfs = itables.sample_pandas_dfs.get_dict_of_test_dfs()
empty#
print(itables.to_markdown_table(dict_of_test_dfs["empty"]))
| |
| |
No rows#
print(itables.to_markdown_table(dict_of_test_dfs["no_rows"]))
| a |
| - |
No columns#
print(itables.to_markdown_table(dict_of_test_dfs["no_columns"]))
| |
| - |
| a |
No rows one column#
print(itables.to_markdown_table(dict_of_test_dfs["no_rows_one_column"]))
| | a |
| | - |
No columns one row#
print(itables.to_markdown_table(dict_of_test_dfs["no_columns_one_row"]))
| |
| - |
| a |
bool#
print(itables.to_markdown_table(dict_of_test_dfs["bool"]))
| a | b | c | d |
| ---- | ----- | ----- | ----- |
| True | True | False | False |
| True | False | True | False |
Nullable boolean#
print(itables.to_markdown_table(dict_of_test_dfs["nullable_boolean"]))
| a | b | c | d |
| ---- | ----- | ----- | ----- |
| True | True | False | <NA> |
| True | False | <NA> | False |
| <NA> | False | True | False |
int#
print(itables.to_markdown_table(dict_of_test_dfs["int"]))
| a | b | c | d | e |
| -- | -- | -- | -- | -- |
| -1 | 2 | -3 | 4 | -5 |
| 6 | -7 | 8 | -9 | 10 |
Nullable integer#
print(itables.to_markdown_table(dict_of_test_dfs["nullable_int"]))
| a | b | c |
| ---- | -- | ---- |
| -1 | 2 | -3 |
| 4 | -5 | 6 |
| <NA> | 7 | <NA> |
float#
print(itables.to_markdown_table(dict_of_test_dfs["float"]))
| int | inf | nan | math | unsorted |
| --- | ---- | --- | -------- | ---------- |
| 0.0 | inf | NaN | 3.141593 | 100.000000 |
| 1.0 | -inf | NaN | 2.718282 | NaN |
| 2.0 | inf | NaN | 3.141593 | NaN |
| 3.0 | -inf | NaN | 2.718282 | 20.000000 |
| 4.0 | inf | NaN | 3.141593 | 3.141593 |
| 5.0 | -inf | NaN | 2.718282 | 2.718282 |
| 6.0 | inf | NaN | 3.141593 | 0.000000 |
| 7.0 | -inf | NaN | 2.718282 | -0.000000 |
| 8.0 | inf | NaN | 3.141593 | inf |
| 9.0 | -inf | NaN | 2.718282 | -inf |
float_types#
print(itables.to_markdown_table(dict_of_test_dfs["float_types"]))
| float32 | float64 |
| -------- | -------- |
| 1.000000 | 1.000000 |
| 0.000000 | 0.000000 |
| 3.141593 | 3.141593 |
| NaN | NaN |
| inf | inf |
| -inf | -inf |
str#
print(itables.to_markdown_table(dict_of_test_dfs["str"]))
| text_column | very_long_text_column |
| ----------- | ----------------------------------------------------------------------- |
| some | a very very very very very very very very very very very very long text |
| text | a very very very very very very very very very very very very long text |
time#
print(itables.to_markdown_table(dict_of_test_dfs["time"]))
| datetime | timestamp | timedelta |
| ---------- | -------------------------------- | --------------- |
| 2000-01-01 | NaT | 2 days 00:00:00 |
| 2001-01-01 | 2000-01-01 18:55:33 | 0 days 00:00:50 |
| NaT | 2001-01-01 18:55:55.456654-04:56 | NaT |
date_range#
print(itables.to_markdown_table(dict_of_test_dfs["date_range"]))
| timestamps |
| -------------------------- |
| 2026-07-19 22:58:09.117380 |
| 2026-07-19 22:58:10.117380 |
| 2026-07-19 22:58:11.117380 |
| 2026-07-19 22:58:12.117380 |
| 2026-07-19 22:58:13.117380 |
ordered_categories#
print(itables.to_markdown_table(dict_of_test_dfs["ordered_categories"]))
| categorical_index | int |
| ----------------- | --- |
| first | 0 |
| second | 1 |
| third | 2 |
| fourth | 3 |
ordered_categories_in_multiindex#
print(itables.to_markdown_table(dict_of_test_dfs["ordered_categories_in_multiindex"]))
| categorical_index | integer_index | int |
| ----------------- | ------------- | --- |
| first | 0 | 0 |
| second | 1 | 1 |
| third | 2 | 2 |
| fourth | 3 | 3 |
object#
print(itables.to_markdown_table(dict_of_test_dfs["object"]))
| dict | list |
| ---------------- | ------ |
| {'a': 1} | [a] |
| {'b': 2, 'c': 3} | [1, 2] |
multiindex#
print(itables.to_markdown_table(dict_of_test_dfs["multiindex"]))
| | | 1 | 2 | 1 | 2 |
| - | - | -- | -- | -- | -- |
| C | 3 | 0 | 1 | 2 | 3 |
| C | 4 | 4 | 5 | 6 | 7 |
| D | 3 | 8 | 9 | 10 | 11 |
| D | 4 | 12 | 13 | 14 | 15 |
countries#
print(itables.to_markdown_table(dict_of_test_dfs["countries"]))
| code | region | country | capital | longitude | latitude |
| ---- | -------------------------- | -------------------- | ---------------- | ----------- | ---------- |
| AW | Latin America & Caribbean | Aruba | Oranjestad | -70.016700 | 12.516700 |
| AF | South Asia | Afghanistan | Kabul | 69.176100 | 34.522800 |
| AO | Sub-Saharan Africa | Angola | Luanda | 13.242000 | -8.811550 |
| AL | Europe & Central Asia | Albania | Tirane | 19.817200 | 41.331700 |
| AD | Europe & Central Asia | Andorra | Andorra la Vella | 1.521800 | 42.507500 |
| AE | Middle East & North Africa | United Arab Emirates | Abu Dhabi | 54.370500 | 24.476400 |
| AR | Latin America & Caribbean | Argentina | Buenos Aires | -58.417300 | -34.611800 |
| AM | Europe & Central Asia | Armenia | Yerevan | 44.509000 | 40.159600 |
| AS | East Asia & Pacific | American Samoa | Pago Pago | -170.691000 | -14.284600 |
| AG | Latin America & Caribbean | Antigua and Barbuda | Saint John's | -61.845600 | 17.117500 |
*(198 more rows not shown)*
capital#
print(itables.to_markdown_table(dict_of_test_dfs["capital"]))
| region | country | capital |
| -------------------------- | -------------------- | ---------------- |
| Latin America & Caribbean | Aruba | Oranjestad |
| South Asia | Afghanistan | Kabul |
| Sub-Saharan Africa | Angola | Luanda |
| Europe & Central Asia | Albania | Tirane |
| Europe & Central Asia | Andorra | Andorra la Vella |
| Middle East & North Africa | United Arab Emirates | Abu Dhabi |
| Latin America & Caribbean | Argentina | Buenos Aires |
| Europe & Central Asia | Armenia | Yerevan |
| East Asia & Pacific | American Samoa | Pago Pago |
| Latin America & Caribbean | Antigua and Barbuda | Saint John's |
*(198 more rows not shown)*
complex_index#
print(itables.to_markdown_table(dict_of_test_dfs["complex_index"]))
| region | country | | | | |
| -------------------------- | -------------------- | -- | ---------------- | ----------- | ---------- |
| Latin America & Caribbean | Aruba | AW | Oranjestad | -70.016700 | 12.516700 |
| South Asia | Afghanistan | AF | Kabul | 69.176100 | 34.522800 |
| Sub-Saharan Africa | Angola | AO | Luanda | 13.242000 | -8.811550 |
| Europe & Central Asia | Albania | AL | Tirane | 19.817200 | 41.331700 |
| Europe & Central Asia | Andorra | AD | Andorra la Vella | 1.521800 | 42.507500 |
| Middle East & North Africa | United Arab Emirates | AE | Abu Dhabi | 54.370500 | 24.476400 |
| Latin America & Caribbean | Argentina | AR | Buenos Aires | -58.417300 | -34.611800 |
| Europe & Central Asia | Armenia | AM | Yerevan | 44.509000 | 40.159600 |
| East Asia & Pacific | American Samoa | AS | Pago Pago | -170.691000 | -14.284600 |
| Latin America & Caribbean | Antigua and Barbuda | AG | Saint John's | -61.845600 | 17.117500 |
*(198 more rows not shown)*
int_float_str#
print(itables.to_markdown_table(dict_of_test_dfs["int_float_str"]))
| int | float | str |
| --- | -------- | --- |
| 0 | 5.000000 | a |
| 1 | 4.949495 | b |
| 2 | 4.898990 | c |
| 3 | 4.848485 | d |
| 4 | 4.797980 | e |
| 5 | 4.747475 | f |
| 6 | 4.696970 | g |
| 7 | 4.646465 | h |
| 8 | 4.595960 | i |
| 9 | 4.545455 | j |
*(90 more rows not shown)*
wide#
print(itables.to_markdown_table(dict_of_test_dfs["wide"]))
| | column_0 | column_1 | column_2 | column_3 | column_4 | column_5 | column_6 | column_7 | column_8 | column_9 | column_10 | column_11 | column_12 | column_13 | column_14 | column_15 | column_16 | column_17 | column_18 | column_19 | column_20 | column_21 | column_22 | column_23 | column_24 | column_25 | column_26 | column_27 | column_28 | column_29 | column_30 | column_31 | column_32 | column_33 | column_34 | column_35 | column_36 | column_37 | column_38 | column_39 | column_40 | column_41 | column_42 | column_43 | column_44 | column_55 | column_56 | column_57 | column_58 | column_59 | column_60 | column_61 | column_62 | column_63 | column_64 | column_65 | column_66 | column_67 | column_68 | column_69 | column_70 | column_71 | column_72 | column_73 | column_74 | column_75 | column_76 | column_77 | column_78 | column_79 | column_80 | column_81 | column_82 | column_83 | column_84 | column_85 | column_86 | column_87 | column_88 | column_89 | column_90 | column_91 | column_92 | column_93 | column_94 | column_95 | column_96 | column_97 | column_98 | column_99 |
| ----- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |
| row_0 | 0.000000 | 0.000100 | 0.000200 | 0.000300 | 0.000400 | 0.000500 | 0.000600 | 0.000700 | 0.000800 | 0.000900 | 0.001000 | 0.001100 | 0.001200 | 0.001300 | 0.001400 | 0.001500 | 0.001600 | 0.001700 | 0.001800 | 0.001900 | 0.002000 | 0.002100 | 0.002200 | 0.002300 | 0.002400 | 0.002500 | 0.002600 | 0.002700 | 0.002800 | 0.002900 | 0.003000 | 0.003100 | 0.003200 | 0.003300 | 0.003400 | 0.003500 | 0.003600 | 0.003700 | 0.003800 | 0.003900 | 0.004000 | 0.004100 | 0.004200 | 0.004300 | 0.004400 | 0.005501 | 0.005601 | 0.005701 | 0.005801 | 0.005901 | 0.006001 | 0.006101 | 0.006201 | 0.006301 | 0.006401 | 0.006501 | 0.006601 | 0.006701 | 0.006801 | 0.006901 | 0.007001 | 0.007101 | 0.007201 | 0.007301 | 0.007401 | 0.007501 | 0.007601 | 0.007701 | 0.007801 | 0.007901 | 0.008001 | 0.008101 | 0.008201 | 0.008301 | 0.008401 | 0.008501 | 0.008601 | 0.008701 | 0.008801 | 0.008901 | 0.009001 | 0.009101 | 0.009201 | 0.009301 | 0.009401 | 0.009501 | 0.009601 | 0.009701 | 0.009801 | 0.009901 |
| row_1 | 0.010001 | 0.010101 | 0.010201 | 0.010301 | 0.010401 | 0.010501 | 0.010601 | 0.010701 | 0.010801 | 0.010901 | 0.011001 | 0.011101 | 0.011201 | 0.011301 | 0.011401 | 0.011501 | 0.011601 | 0.011701 | 0.011801 | 0.011901 | 0.012001 | 0.012101 | 0.012201 | 0.012301 | 0.012401 | 0.012501 | 0.012601 | 0.012701 | 0.012801 | 0.012901 | 0.013001 | 0.013101 | 0.013201 | 0.013301 | 0.013401 | 0.013501 | 0.013601 | 0.013701 | 0.013801 | 0.013901 | 0.014001 | 0.014101 | 0.014201 | 0.014301 | 0.014401 | 0.015502 | 0.015602 | 0.015702 | 0.015802 | 0.015902 | 0.016002 | 0.016102 | 0.016202 | 0.016302 | 0.016402 | 0.016502 | 0.016602 | 0.016702 | 0.016802 | 0.016902 | 0.017002 | 0.017102 | 0.017202 | 0.017302 | 0.017402 | 0.017502 | 0.017602 | 0.017702 | 0.017802 | 0.017902 | 0.018002 | 0.018102 | 0.018202 | 0.018302 | 0.018402 | 0.018502 | 0.018602 | 0.018702 | 0.018802 | 0.018902 | 0.019002 | 0.019102 | 0.019202 | 0.019302 | 0.019402 | 0.019502 | 0.019602 | 0.019702 | 0.019802 | 0.019902 |
| row_2 | 0.020002 | 0.020102 | 0.020202 | 0.020302 | 0.020402 | 0.020502 | 0.020602 | 0.020702 | 0.020802 | 0.020902 | 0.021002 | 0.021102 | 0.021202 | 0.021302 | 0.021402 | 0.021502 | 0.021602 | 0.021702 | 0.021802 | 0.021902 | 0.022002 | 0.022102 | 0.022202 | 0.022302 | 0.022402 | 0.022502 | 0.022602 | 0.022702 | 0.022802 | 0.022902 | 0.023002 | 0.023102 | 0.023202 | 0.023302 | 0.023402 | 0.023502 | 0.023602 | 0.023702 | 0.023802 | 0.023902 | 0.024002 | 0.024102 | 0.024202 | 0.024302 | 0.024402 | 0.025503 | 0.025603 | 0.025703 | 0.025803 | 0.025903 | 0.026003 | 0.026103 | 0.026203 | 0.026303 | 0.026403 | 0.026503 | 0.026603 | 0.026703 | 0.026803 | 0.026903 | 0.027003 | 0.027103 | 0.027203 | 0.027303 | 0.027403 | 0.027503 | 0.027603 | 0.027703 | 0.027803 | 0.027903 | 0.028003 | 0.028103 | 0.028203 | 0.028303 | 0.028403 | 0.028503 | 0.028603 | 0.028703 | 0.028803 | 0.028903 | 0.029003 | 0.029103 | 0.029203 | 0.029303 | 0.029403 | 0.029503 | 0.029603 | 0.029703 | 0.029803 | 0.029903 |
| row_3 | 0.030003 | 0.030103 | 0.030203 | 0.030303 | 0.030403 | 0.030503 | 0.030603 | 0.030703 | 0.030803 | 0.030903 | 0.031003 | 0.031103 | 0.031203 | 0.031303 | 0.031403 | 0.031503 | 0.031603 | 0.031703 | 0.031803 | 0.031903 | 0.032003 | 0.032103 | 0.032203 | 0.032303 | 0.032403 | 0.032503 | 0.032603 | 0.032703 | 0.032803 | 0.032903 | 0.033003 | 0.033103 | 0.033203 | 0.033303 | 0.033403 | 0.033503 | 0.033603 | 0.033703 | 0.033803 | 0.033903 | 0.034003 | 0.034103 | 0.034203 | 0.034303 | 0.034403 | 0.035504 | 0.035604 | 0.035704 | 0.035804 | 0.035904 | 0.036004 | 0.036104 | 0.036204 | 0.036304 | 0.036404 | 0.036504 | 0.036604 | 0.036704 | 0.036804 | 0.036904 | 0.037004 | 0.037104 | 0.037204 | 0.037304 | 0.037404 | 0.037504 | 0.037604 | 0.037704 | 0.037804 | 0.037904 | 0.038004 | 0.038104 | 0.038204 | 0.038304 | 0.038404 | 0.038504 | 0.038604 | 0.038704 | 0.038804 | 0.038904 | 0.039004 | 0.039104 | 0.039204 | 0.039304 | 0.039404 | 0.039504 | 0.039604 | 0.039704 | 0.039804 | 0.039904 |
| row_4 | 0.040004 | 0.040104 | 0.040204 | 0.040304 | 0.040404 | 0.040504 | 0.040604 | 0.040704 | 0.040804 | 0.040904 | 0.041004 | 0.041104 | 0.041204 | 0.041304 | 0.041404 | 0.041504 | 0.041604 | 0.041704 | 0.041804 | 0.041904 | 0.042004 | 0.042104 | 0.042204 | 0.042304 | 0.042404 | 0.042504 | 0.042604 | 0.042704 | 0.042804 | 0.042904 | 0.043004 | 0.043104 | 0.043204 | 0.043304 | 0.043404 | 0.043504 | 0.043604 | 0.043704 | 0.043804 | 0.043904 | 0.044004 | 0.044104 | 0.044204 | 0.044304 | 0.044404 | 0.045505 | 0.045605 | 0.045705 | 0.045805 | 0.045905 | 0.046005 | 0.046105 | 0.046205 | 0.046305 | 0.046405 | 0.046505 | 0.046605 | 0.046705 | 0.046805 | 0.046905 | 0.047005 | 0.047105 | 0.047205 | 0.047305 | 0.047405 | 0.047505 | 0.047605 | 0.047705 | 0.047805 | 0.047905 | 0.048005 | 0.048105 | 0.048205 | 0.048305 | 0.048405 | 0.048505 | 0.048605 | 0.048705 | 0.048805 | 0.048905 | 0.049005 | 0.049105 | 0.049205 | 0.049305 | 0.049405 | 0.049505 | 0.049605 | 0.049705 | 0.049805 | 0.049905 |
| row_5 | 0.050005 | 0.050105 | 0.050205 | 0.050305 | 0.050405 | 0.050505 | 0.050605 | 0.050705 | 0.050805 | 0.050905 | 0.051005 | 0.051105 | 0.051205 | 0.051305 | 0.051405 | 0.051505 | 0.051605 | 0.051705 | 0.051805 | 0.051905 | 0.052005 | 0.052105 | 0.052205 | 0.052305 | 0.052405 | 0.052505 | 0.052605 | 0.052705 | 0.052805 | 0.052905 | 0.053005 | 0.053105 | 0.053205 | 0.053305 | 0.053405 | 0.053505 | 0.053605 | 0.053705 | 0.053805 | 0.053905 | 0.054005 | 0.054105 | 0.054205 | 0.054305 | 0.054405 | 0.055506 | 0.055606 | 0.055706 | 0.055806 | 0.055906 | 0.056006 | 0.056106 | 0.056206 | 0.056306 | 0.056406 | 0.056506 | 0.056606 | 0.056706 | 0.056806 | 0.056906 | 0.057006 | 0.057106 | 0.057206 | 0.057306 | 0.057406 | 0.057506 | 0.057606 | 0.057706 | 0.057806 | 0.057906 | 0.058006 | 0.058106 | 0.058206 | 0.058306 | 0.058406 | 0.058506 | 0.058606 | 0.058706 | 0.058806 | 0.058906 | 0.059006 | 0.059106 | 0.059206 | 0.059306 | 0.059406 | 0.059506 | 0.059606 | 0.059706 | 0.059806 | 0.059906 |
| row_6 | 0.060006 | 0.060106 | 0.060206 | 0.060306 | 0.060406 | 0.060506 | 0.060606 | 0.060706 | 0.060806 | 0.060906 | 0.061006 | 0.061106 | 0.061206 | 0.061306 | 0.061406 | 0.061506 | 0.061606 | 0.061706 | 0.061806 | 0.061906 | 0.062006 | 0.062106 | 0.062206 | 0.062306 | 0.062406 | 0.062506 | 0.062606 | 0.062706 | 0.062806 | 0.062906 | 0.063006 | 0.063106 | 0.063206 | 0.063306 | 0.063406 | 0.063506 | 0.063606 | 0.063706 | 0.063806 | 0.063906 | 0.064006 | 0.064106 | 0.064206 | 0.064306 | 0.064406 | 0.065507 | 0.065607 | 0.065707 | 0.065807 | 0.065907 | 0.066007 | 0.066107 | 0.066207 | 0.066307 | 0.066407 | 0.066507 | 0.066607 | 0.066707 | 0.066807 | 0.066907 | 0.067007 | 0.067107 | 0.067207 | 0.067307 | 0.067407 | 0.067507 | 0.067607 | 0.067707 | 0.067807 | 0.067907 | 0.068007 | 0.068107 | 0.068207 | 0.068307 | 0.068407 | 0.068507 | 0.068607 | 0.068707 | 0.068807 | 0.068907 | 0.069007 | 0.069107 | 0.069207 | 0.069307 | 0.069407 | 0.069507 | 0.069607 | 0.069707 | 0.069807 | 0.069907 |
| row_7 | 0.070007 | 0.070107 | 0.070207 | 0.070307 | 0.070407 | 0.070507 | 0.070607 | 0.070707 | 0.070807 | 0.070907 | 0.071007 | 0.071107 | 0.071207 | 0.071307 | 0.071407 | 0.071507 | 0.071607 | 0.071707 | 0.071807 | 0.071907 | 0.072007 | 0.072107 | 0.072207 | 0.072307 | 0.072407 | 0.072507 | 0.072607 | 0.072707 | 0.072807 | 0.072907 | 0.073007 | 0.073107 | 0.073207 | 0.073307 | 0.073407 | 0.073507 | 0.073607 | 0.073707 | 0.073807 | 0.073907 | 0.074007 | 0.074107 | 0.074207 | 0.074307 | 0.074407 | 0.075508 | 0.075608 | 0.075708 | 0.075808 | 0.075908 | 0.076008 | 0.076108 | 0.076208 | 0.076308 | 0.076408 | 0.076508 | 0.076608 | 0.076708 | 0.076808 | 0.076908 | 0.077008 | 0.077108 | 0.077208 | 0.077308 | 0.077408 | 0.077508 | 0.077608 | 0.077708 | 0.077808 | 0.077908 | 0.078008 | 0.078108 | 0.078208 | 0.078308 | 0.078408 | 0.078508 | 0.078608 | 0.078708 | 0.078808 | 0.078908 | 0.079008 | 0.079108 | 0.079208 | 0.079308 | 0.079408 | 0.079508 | 0.079608 | 0.079708 | 0.079808 | 0.079908 |
| row_8 | 0.080008 | 0.080108 | 0.080208 | 0.080308 | 0.080408 | 0.080508 | 0.080608 | 0.080708 | 0.080808 | 0.080908 | 0.081008 | 0.081108 | 0.081208 | 0.081308 | 0.081408 | 0.081508 | 0.081608 | 0.081708 | 0.081808 | 0.081908 | 0.082008 | 0.082108 | 0.082208 | 0.082308 | 0.082408 | 0.082508 | 0.082608 | 0.082708 | 0.082808 | 0.082908 | 0.083008 | 0.083108 | 0.083208 | 0.083308 | 0.083408 | 0.083508 | 0.083608 | 0.083708 | 0.083808 | 0.083908 | 0.084008 | 0.084108 | 0.084208 | 0.084308 | 0.084408 | 0.085509 | 0.085609 | 0.085709 | 0.085809 | 0.085909 | 0.086009 | 0.086109 | 0.086209 | 0.086309 | 0.086409 | 0.086509 | 0.086609 | 0.086709 | 0.086809 | 0.086909 | 0.087009 | 0.087109 | 0.087209 | 0.087309 | 0.087409 | 0.087509 | 0.087609 | 0.087709 | 0.087809 | 0.087909 | 0.088009 | 0.088109 | 0.088209 | 0.088309 | 0.088409 | 0.088509 | 0.088609 | 0.088709 | 0.088809 | 0.088909 | 0.089009 | 0.089109 | 0.089209 | 0.089309 | 0.089409 | 0.089509 | 0.089609 | 0.089709 | 0.089809 | 0.089909 |
| row_9 | 0.090009 | 0.090109 | 0.090209 | 0.090309 | 0.090409 | 0.090509 | 0.090609 | 0.090709 | 0.090809 | 0.090909 | 0.091009 | 0.091109 | 0.091209 | 0.091309 | 0.091409 | 0.091509 | 0.091609 | 0.091709 | 0.091809 | 0.091909 | 0.092009 | 0.092109 | 0.092209 | 0.092309 | 0.092409 | 0.092509 | 0.092609 | 0.092709 | 0.092809 | 0.092909 | 0.093009 | 0.093109 | 0.093209 | 0.093309 | 0.093409 | 0.093509 | 0.093609 | 0.093709 | 0.093809 | 0.093909 | 0.094009 | 0.094109 | 0.094209 | 0.094309 | 0.094409 | 0.095510 | 0.095610 | 0.095710 | 0.095810 | 0.095910 | 0.096010 | 0.096110 | 0.096210 | 0.096310 | 0.096410 | 0.096510 | 0.096610 | 0.096710 | 0.096810 | 0.096910 | 0.097010 | 0.097110 | 0.097210 | 0.097310 | 0.097410 | 0.097510 | 0.097610 | 0.097710 | 0.097810 | 0.097910 | 0.098010 | 0.098110 | 0.098210 | 0.098310 | 0.098410 | 0.098510 | 0.098610 | 0.098710 | 0.098810 | 0.098910 | 0.099010 | 0.099110 | 0.099210 | 0.099310 | 0.099410 | 0.099510 | 0.099610 | 0.099710 | 0.099810 | 0.099910 |
*(90 more rows and 10 more columns not shown)*
long_column_names#
print(itables.to_markdown_table(dict_of_test_dfs["long_column_names"]))
| short name | very very very very very long name | very very very very very very very very very very long name | very very very very very very very very very very very very very very very very very very very very long name | nospaceinveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryverylongname | nospaceinveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryverylongname |
| ---------- | ---------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 0 | 0 | 1 | 2 | 3 | 3 |
| 0 | 0 | 1 | 2 | 3 | 3 |
| 0 | 0 | 1 | 2 | 3 | 3 |
| 0 | 0 | 1 | 2 | 3 | 3 |
| 0 | 0 | 1 | 2 | 3 | 3 |
sorted_index#
print(itables.to_markdown_table(dict_of_test_dfs["sorted_index"]))
| i | x | y |
| - | --- | --- |
| 0 | 0.0 | 0.0 |
| 1 | 1.0 | 0.1 |
| 2 | 2.0 | 0.2 |
reverse_sorted_index#
print(itables.to_markdown_table(dict_of_test_dfs["reverse_sorted_index"]))
| i | x | y |
| - | --- | --- |
| 2 | 0.0 | 0.0 |
| 1 | 1.0 | 0.1 |
| 0 | 2.0 | 0.2 |
sorted_multiindex#
print(itables.to_markdown_table(dict_of_test_dfs["sorted_multiindex"]))
| i | j | x | y |
| - | - | --- | --- |
| 0 | 3 | 0.0 | 0.0 |
| 1 | 4 | 1.0 | 0.1 |
| 2 | 5 | 2.0 | 0.2 |
unsorted_index#
print(itables.to_markdown_table(dict_of_test_dfs["unsorted_index"]))
| i | x | y |
| - | --- | --- |
| 0 | 0.0 | 0.0 |
| 2 | 1.0 | 0.1 |
| 1 | 2.0 | 0.2 |
duplicated_columns#
print(itables.to_markdown_table(dict_of_test_dfs["duplicated_columns"]))
| A | A | A | A |
| - | - | - | - |
| 0 | 2 | 4 | 5 |
| 1 | 3 | 6 | 7 |
named_column_index#
print(itables.to_markdown_table(dict_of_test_dfs["named_column_index"]))
| columns | a |
| ------- | - |
| | 1 |
big_integers#
print(itables.to_markdown_table(dict_of_test_dfs["big_integers"]))
| bigint | expected |
| -------------------- | -------------------- |
| -1234567890123456789 | -1234567890123456789 |
| 1234567890123456789 | 1234567890123456789 |
| 2345678901234567890 | 2345678901234567890 |
| 3456789012345678901 | 3456789012345678901 |