pandas change to to_dict instead of to_json and fix rich elems

marimo/_output/data/data.py

@@ -5,6 +5,7 @@
 import io
 from typing import Any, Union
 
+from marimo._dependencies.dependencies import DependencyManager
 from marimo._plugins.core.media import is_data_empty
 from marimo._runtime.virtual_file import (
     EMPTY_VIRTUAL_FILE,
@@ -237,5 +238,27 @@ def convert_bigint(obj: Any) -> Any:
         convert_bigint(as_json),
         indent=None,
         separators=(",", ":"),
-        default=str,
+        default=custom_json,
     )
+
+
+def custom_json(obj: Any) -> Any:
+    if hasattr(obj, "_mime_"):
+        mimetype, data = obj._mime_()
+        return {"mimetype": mimetype, "data": data}
+
+    if DependencyManager.pandas.imported():
+        import pandas as pd
+
+        # TODO: Port this logic over to msgspec encoder
+        if isinstance(
+            obj,
+            (
+                pd.TimedeltaIndex,
+                pd.DatetimeIndex,
+                pd.IntervalIndex,
+                pd.PeriodIndex,
+            ),
+        ):
+            return obj.astype(str).tolist()
+    return str(obj)

marimo/_plugins/ui/_impl/tables/pandas_table.py

@@ -84,6 +84,11 @@ def to_csv_str(
             def to_json_str(
                 self, format_mapping: Optional[FormatMapping] = None
             ) -> str:
+                def to_json(result: pd.DataFrame) -> list[dict[str, Any]]:
+                    # Use to_dict instead of to_json
+                    # nans, infs, -infs are properly serialized
+                    return result.to_dict(orient="records")  # type: ignore
+
                 from pandas.api.types import (
                     is_complex_dtype,
                     is_object_dtype,
@@ -123,13 +128,7 @@ def to_json_str(
                         "Error handling complex or timedelta64 dtype",
                         exc_info=e,
                     )
-                    return sanitize_json_bigint(
-                        result.to_json(
-                            orient="records",
-                            date_format="iso",
-                            default_handler=str,
-                        )
-                    )
+                    return sanitize_json_bigint(to_json(result))
 
                 # Flatten row multi-index
                 if isinstance(result.index, pd.MultiIndex) or (
@@ -182,13 +181,7 @@ def to_json_str(
                                 "Indexes with more than one level are not well supported, call reset_index() or use mo.plain(df)"
                             )
 
-                return sanitize_json_bigint(
-                    result.to_json(
-                        orient="records",
-                        date_format="iso",
-                        default_handler=str,
-                    )
-                )
+                return sanitize_json_bigint(to_json(result))
 
             def _infer_dtype(self, column: ColumnName) -> str:
                 # Typically, pandas dtypes returns a generic dtype

marimo/_smoke_tests/tables/big_ints.py

@@ -1,12 +1,14 @@
 import marimo
 
-__generated_with = "0.15.5"
+__generated_with = "0.17.6"
 app = marimo.App(width="medium")
 
 
 @app.cell(hide_code=True)
 def _(mo):
-    mo.md(r"""## Big Ints""")
+    mo.md(r"""
+    ## Big Ints
+    """)
     return
 
 
@@ -34,18 +36,18 @@ def _():
 
 
 @app.cell
-def _(data):
+def _(data, mo):
     import pandas as pd
 
-    pd.DataFrame(data)
+    mo.vstack([mo.plain(pd.DataFrame(data)), pd.DataFrame(data)])
     return
 
 
 @app.cell
-def _(data):
+def _(data, mo):
     import polars as pl
 
-    pl.DataFrame(data)
+    mo.vstack([mo.plain(pl.DataFrame(data)), pl.DataFrame(data)])
     return
 
 

marimo/_smoke_tests/tables/images.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.15.5"
+__generated_with = "0.17.8"
 app = marimo.App(width="medium")
 
 

marimo/_smoke_tests/tables/nans.py

@@ -0,0 +1,42 @@
+import marimo
+
+__generated_with = "0.17.6"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import polars as pl
+    import pandas as pd
+    import numpy as np
+    return mo, np, pd, pl
+
+
+@app.cell
+def _(mo, np, pl):
+    polars_df = pl.DataFrame({"nans": [1.0, np.nan, np.inf, -np.inf, None]})
+    mo.vstack([mo.plain(polars_df), polars_df])
+    return
+
+
+@app.cell
+def _(np, pd, pl):
+    pl.DataFrame(
+        {"nans_not_strict": [1.0, np.nan, np.inf, -np.inf, None, pd.NA, pd.NaT]},
+        strict=False,
+    )
+    return
+
+
+@app.cell
+def _(mo, np, pd):
+    pandas_df = pd.DataFrame(
+        {"nans": [1, None, pd.NaT, np.nan, pd.NA, np.inf, -np.inf]}
+    )
+    mo.vstack([mo.plain(pandas_df), pandas_df])
+    return
+
+
+if __name__ == "__main__":
+    app.run()

tests/_plugins/ui/_impl/tables/snapshots/narwhals.json

@@ -1,56 +1,89 @@
 [
-  {
-    "strings": "a",
-    "bool": true,
-    "int": 1,
-    "float": 1.0,
-    "datetime": "2021-01-01 00:00:00",
-    "date": "2021-01-01",
-    "struct": { "a": 1, "b": 2 },
-    "list": [1, 2],
-    "array": [1],
-    "nulls": null,
-    "category": "cat",
-    "set": "{1, 2}",
-    "imaginary": "(1+2j)",
-    "time": "12:30:00",
-    "duration": "1 day, 0:00:00",
-    "mixed_list": ["1", "two"]
-  },
-  {
-    "strings": "b",
-    "bool": false,
-    "int": 2,
-    "float": 2.0,
-    "datetime": "2021-01-02 00:00:00",
-    "date": "2021-01-02",
-    "struct": { "a": 3, "b": 4 },
-    "list": [3, 4],
-    "array": [2],
-    "nulls": "data",
-    "category": "dog",
-    "set": "{3, 4}",
-    "imaginary": "(3+4j)",
-    "time": "13:45:00",
-    "duration": "2 days, 0:00:00",
-    "mixed_list": ["3.0", "0.0"]
-  },
-  {
-    "strings": "c",
-    "bool": true,
-    "int": 3,
-    "float": 3.0,
-    "datetime": "2021-01-03 00:00:00",
-    "date": "2021-01-03",
-    "struct": { "a": 5, "b": 6 },
-    "list": [5, 6],
-    "array": [3],
-    "nulls": null,
-    "category": "mouse",
-    "set": "{5, 6}",
-    "imaginary": "(5+6j)",
-    "time": "14:15:00",
-    "duration": "3 days, 0:00:00",
-    "mixed_list": [null, "2021-01-01 00:00:00.000000"]
-  }
-]
+    {
+        "strings": "a",
+        "bool": true,
+        "int": 1,
+        "float": 1.0,
+        "datetime": "2021-01-01 00:00:00",
+        "date": "2021-01-01",
+        "struct": {
+            "a": 1,
+            "b": 2
+        },
+        "list": [
+            1,
+            2
+        ],
+        "array": [
+            1
+        ],
+        "nulls": null,
+        "category": "cat",
+        "set": "{1, 2}",
+        "imaginary": "(1+2j)",
+        "time": "12:30:00",
+        "duration": "1 day, 0:00:00",
+        "mixed_list": [
+            "1",
+            "two"
+        ]
+    },
+    {
+        "strings": "b",
+        "bool": false,
+        "int": 2,
+        "float": 2.0,
+        "datetime": "2021-01-02 00:00:00",
+        "date": "2021-01-02",
+        "struct": {
+            "a": 3,
+            "b": 4
+        },
+        "list": [
+            3,
+            4
+        ],
+        "array": [
+            2
+        ],
+        "nulls": "data",
+        "category": "dog",
+        "set": "{3, 4}",
+        "imaginary": "(3+4j)",
+        "time": "13:45:00",
+        "duration": "2 days, 0:00:00",
+        "mixed_list": [
+            "3.0",
+            "0.0"
+        ]
+    },
+    {
+        "strings": "c",
+        "bool": true,
+        "int": 3,
+        "float": 3.0,
+        "datetime": "2021-01-03 00:00:00",
+        "date": "2021-01-03",
+        "struct": {
+            "a": 5,
+            "b": 6
+        },
+        "list": [
+            5,
+            6
+        ],
+        "array": [
+            3
+        ],
+        "nulls": null,
+        "category": "mouse",
+        "set": "{5, 6}",
+        "imaginary": "(5+6j)",
+        "time": "14:15:00",
+        "duration": "3 days, 0:00:00",
+        "mixed_list": [
+            null,
+            "2021-01-01 00:00:00.000000"
+        ]
+    }
+]

tests/_plugins/ui/_impl/tables/snapshots/pandas.csv

@@ -1,4 +1,4 @@
-strings,bool,int,float,datetime,date,struct,list,nulls,category,set,imaginary,time,duration,mixed_list,bytes
-a,True,1,1.0,2021-01-01,2021-01-01,"{'a': 1, 'b': 2}","[1, 2]",,cat,"{1, 2}",(1+2j),12:30:00,1 days,"[1, 'two']",b'\x00\x00\x00\x00\x01\xc0U\xe8\xb1n1\xc0T@D\xf1?Bc\x95\x83'
-b,False,2,2.0,2021-01-02,2021-01-02,"{'a': 3, 'b': 4}","[3, 4]",data,dog,"{3, 4}",(3+4j),13:45:00,2 days,"[3.0, False]",b'world'
-c,True,3,3.0,2021-01-03,2021-01-03,"{'a': 5, 'b': 6}","[5, 6]",,mouse,"{5, 6}",(5+6j),14:15:00,3 days,"[None, datetime.datetime(2021, 1, 1, 0, 0)]",b'bytes'
+strings,bool,int,float,datetime,date,nanosecond,datetime_index,period_range,interval_range,delta,none,infs,struct,list,nulls,category,set,imaginary,time,duration,mixed_list,bytes
+a,True,1,1.0,2021-01-01,2021-01-01,2017-03-22 15:16:45,"DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)",2017Q1,"(2017-01-01 00:00:00, 2017-01-02 00:00:00]","TimedeltaIndex(['1 days 06:05:01.000030'], dtype='timedelta64[ns]', freq=None)",,inf,"{'a': 1, 'b': 2}","[1, 2]",,cat,"{1, 2}",(1+2j),12:30:00,1 days,"[1, 'two']",b'\x00\x00\x00\x00\x01\xc0U\xe8\xb1n1\xc0T@D\xf1?Bc\x95\x83'
+b,False,2,2.0,2021-01-02,2021-01-02,2017-07-14 02:40:00,"DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)",2017Q2,"(2017-01-02 00:00:00, 2017-01-03 00:00:00]","TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500'], dtype='timedelta64[ns]', freq=None)",,inf,"{'a': 3, 'b': 4}","[3, 4]",data,dog,"{3, 4}",(3+4j),13:45:00,2 days,"[3.0, False]",b'world'
+c,True,3,3.0,2021-01-03,2021-01-03,2017-11-06 20:26:40,"DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)",2017Q3,"(2017-01-03 00:00:00, 2017-01-04 00:00:00]","TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None)",,-inf,"{'a': 5, 'b': 6}","[5, 6]",,mouse,"{5, 6}",(5+6j),14:15:00,3 days,"[None, datetime.datetime(2021, 1, 1, 0, 0)]",b'bytes'

tests/_plugins/ui/_impl/tables/snapshots/pandas.field_types.json

@@ -41,6 +41,55 @@
             "object"
         ]
     ],
+    [
+        "nanosecond",
+        [
+            "datetime",
+            "datetime64[ns]"
+        ]
+    ],
+    [
+        "datetime_index",
+        [
+            "string",
+            "object"
+        ]
+    ],
+    [
+        "period_range",
+        [
+            "unknown",
+            "period[Q-DEC]"
+        ]
+    ],
+    [
+        "interval_range",
+        [
+            "string",
+            "interval[datetime64[ns], right]"
+        ]
+    ],
+    [
+        "delta",
+        [
+            "string",
+            "object"
+        ]
+    ],
+    [
+        "none",
+        [
+            "string",
+            "object"
+        ]
+    ],
+    [
+        "infs",
+        [
+            "number",
+            "float64"
+        ]
+    ],
     [
         "struct",
         [