gƍ:UddlmZddlZddlmZddlmZmZmZm Z m Z m Z m Z ddl mZddlmZddlmZmZmZmZmZmZddlmZdd lmZdd lmZdd lmZdd l m!Z!m"Z"m#Z#dd l$m%Z%ddl&m'Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.m/Z/ddl0m1Z1m2Z2er&ddl3m4Z4m5Z5ddl6mZ7ddl8m9Z9ddl:m;Z;ddlm?Z?e dZ@deAd<hdZBdeAd<Gdd e d!"ZCGd#d$e d!"ZDeGd%d&ZE d-d'ZFGd(d)ZG d.d*ZH d/ d0d+ZId/d1d,ZJy)2) annotationsN) dataclass) TYPE_CHECKINGAnyFinalLiteral TypedDictcastoverload) TypeAlias)dataframe_util)INDEX_IDENTIFIERColumnConfigMappingInputapply_data_specific_configsmarshall_column_configprocess_config_mappingupdate_column_config)AttributeDictionary)current_form_id)marshall_styler)check_widget_policies)Keycompute_and_register_element_idto_key)StreamlitAPIException)Arrow) ForwardMsg)gather_metrics)enqueue_messageget_script_run_ctx)WidgetCallbackregister_widget)HashableIterable)typing) DataFrame)Data)DeltaGenerator)AddRowsMetadata) single-row multi-row single-column multi-columnr SelectionMode>r+r*r-r,zFinal[set[SelectionMode]]_SELECTION_MODESc&eZdZUdZded<ded<y)DataframeSelectionStatea The schema for the dataframe selection state. The selection state is stored in a dictionary-like object that supports both key and attribute notation. Selection states cannot be programmatically changed or set through Session State. .. warning:: If a user sorts a dataframe, row selections will be reset. If your users need to sort and filter the dataframe to make selections, direct them to use the search function in the dataframe toolbar instead. Attributes ---------- rows : list[int] The selected rows, identified by their integer position. The integer positions match the original dataframe, even if the user sorts the dataframe in their browser. For a ``pandas.DataFrame``, you can retrieve data from its interger position using methods like ``.iloc[]`` or ``.iat[]``. columns : list[str] The selected columns, identified by their names. Example ------- The following example has multi-row and multi-column selections enabled. Try selecting some rows. To select multiple columns, hold ``Ctrl`` while selecting columns. Hold ``Shift`` to select a range of columns. >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> if "df" not in st.session_state: >>> st.session_state.df = pd.DataFrame( ... np.random.randn(12, 5), columns=["a", "b", "c", "d", "e"] ... ) >>> >>> event = st.dataframe( ... st.session_state.df, ... key="data", ... on_select="rerun", ... selection_mode=["multi-row", "multi-column"], ... ) >>> >>> event.selection .. output:: https://doc-dataframe-events-selection-state.streamlit.app height: 600px z list[int]rowsz list[str]columnsN__name__ __module__ __qualname____doc____annotations__M/var/www/openai/venv/lib/python3.12/site-packages/streamlit/elements/arrow.pyr1r1Ms3j O r;r1F)totalceZdZUdZded<y)DataframeStateaX The schema for the dataframe event state. The event state is stored in a dictionary-like object that supports both key and attribute notation. Event states cannot be programmatically changed or set through Session State. Only selection events are supported at this time. Attributes ---------- selection : dict The state of the ``on_select`` event. This attribute returns a dictionary-like object that supports both key and attribute notation. The attributes are described by the ``DataframeSelectionState`` dictionary schema. r1 selectionNr4r:r;r<r?r?s('&r;r?c"eZdZdZdddZddZy)DataframeSelectionSerdez[DataframeSelectionSerde is used to serialize and deserialize the dataframe selection state.cdggdi}||ntj|}d|vr|}ttt |S)Nr@)r2r3)jsonloadsr r?r)selfui_value widget_idempty_selection_stateselection_states r< deserializez#DataframeSelectionSerde.deserializesU 1 &.%5 !4::h;O  o -3ON$7$HIIr;c8tj|tS)N)default)rDdumpsstr)rF editing_states r< serializez!DataframeSelectionSerde.serializeszz-55r;N))rG str | NonerHrOreturnr?)rPr?rTrO)r5r6r7r8rKrQr:r;r<rBrBseJ 6r;rBct|tr|h}n t|}|jtst d|dt|j ddhr t d|j ddhr t dg}|D]}|dk(r*|jtjj2|dk(r*|jtjja|dk(r*|jtjj|dk(s|jtjjt|S) z2Parse and check the user provided selection modes.zInvalid selection mode: z. Valid options are: r*r+zJOnly one of `single-row` or `multi-row` can be selected as selection mode.r,r-zPOnly one of `single-column` or `multi-column` can be selected as selection mode.) isinstancerOsetissubsetr/r issupersetappend ArrowProtor. SINGLE_ROW MULTI_ROW SINGLE_COLUMN MULTI_COLUMN)selection_modeselection_mode_setparsed_selection_modess r<parse_selection_modercsJ.#&,-!0  & &'7 8#&~&67""2!3 5  $$lK%@A# X  $$o~%FG# ^   , \ ) " ) )**B*B*M*M N { * " ) )**B*B*L*L M  . " ) )**B*B*P*P Q ~ - " ) )**B*B*O*O P- % &&r;c jeZdZe ddddddddd ddZe dddddddd ddZed  ddddddddd dd Zed ddd Zed dddZeddZ y) ArrowMixinNFignorer+)use_container_width hide_index column_order column_configkey on_selectr`cyNr: rFdatawidthheightrgrhrirjrkrlr`s r< dataframezArrowMixin.dataframer;)rgrhrirjrkr`cyrnr:ros r<rszArrowMixin.dataframertr;rsc ddl} | dvrt| std| dt|}| dk7} | r8t| } t |j || rt t| nddd| t|}t}||_ |r||_ |r||_ |r||jddtjj|_t#|| j$rt'j(||_nt'j,|}t'j.|r;|j j1}t3t5|}t7|||t'j8|d }t;||t'j<||_|t?|t@d |itC||| r|jDjGtI| tK|j |_&tO}tQd ||jL|j*||||j|jR| | |_*tW}tY|jTt| r| nd|jZ|j\|d }|j j_d|t t`|jbS|j j_d|S)u,Display a dataframe as an interactive table. This command works with a wide variety of collection-like and dataframe-like object types. Parameters ---------- data : dataframe-like, collection-like, or None The data to display. Dataframe-like objects include dataframe and series objects from popular libraries like Dask, Modin, Numpy, pandas, Polars, PyArrow, Snowpark, Xarray, and more. You can use database cursors and clients that comply with the `Python Database API Specification v2.0 (PEP 249) `_. Additionally, you can use anything that supports the `Python dataframe interchange protocol `_. For example, you can use the following: - ``pandas.DataFrame``, ``pandas.Series``, ``pandas.Index``, ``pandas.Styler``, and ``pandas.Array`` - ``polars.DataFrame``, ``polars.LazyFrame``, and ``polars.Series`` - ``snowflake.snowpark.dataframe.DataFrame``, ``snowflake.snowpark.table.Table`` If a data type is not recognized, Streamlit will convert the object to a ``pandas.DataFrame`` or ``pyarrow.Table`` using a ``.to_pandas()`` or ``.to_arrow()`` method, respectively, if available. If ``data`` is a ``pandas.Styler``, it will be used to style its underlying ``pandas.DataFrame``. Streamlit supports custom cell values and colors. It does not support some of the more exotic styling options, like bar charts, hovering, and captions. For these styling options, use column configuration instead. Text and number formatting from ``column_config`` always takes precedence over text and number formatting from ``pandas.Styler``. Collection-like objects include all Python-native ``Collection`` types, such as ``dict``, ``list``, and ``set``. If ``data`` is ``None``, Streamlit renders an empty table. width : int or None Desired width of the dataframe expressed in pixels. If ``width`` is ``None`` (default), Streamlit sets the dataframe width to fit its contents up to the width of the parent container. If ``width`` is greater than the width of the parent container, Streamlit sets the dataframe width to match the width of the parent container. height : int or None Desired height of the dataframe expressed in pixels. If ``height`` is ``None`` (default), Streamlit sets the height to show at most ten rows. Vertical scrolling within the dataframe element is enabled when the height does not accomodate all rows. use_container_width : bool Whether to override ``width`` with the width of the parent container. If ``use_container_width`` is ``False`` (default), Streamlit sets the dataframe's width according to ``width``. If ``use_container_width`` is ``True``, Streamlit sets the width of the dataframe to match the width of the parent container. hide_index : bool or None Whether to hide the index column(s). If ``hide_index`` is ``None`` (default), the visibility of index columns is automatically determined based on the data. column_order : Iterable of str or None The ordered list of columns to display. If ``column_order`` is ``None`` (default), Streamlit displays all columns in the order inherited from the underlying data structure. If ``column_order`` is a list, the indicated columns will display in the order they appear within the list. Columns may be omitted or repeated within the list. For example, ``column_order=("col2", "col1")`` will display ``"col2"`` first, followed by ``"col1"``, and will hide all other non-index columns. column_config : dict or None Configuration to customize how columns display. If ``column_config`` is ``None`` (default), columns are styled based on the underlying data type of each column. Column configuration can modify column names, visibility, type, width, or format, among other things. ``column_config`` must be a dictionary where each key is a column name and the associated value is one of the following: - ``None``: Streamlit hides the column. - A string: Streamlit changes the display label of the column to the given string. - A column type within ``st.column_config``: Streamlit applies the defined configuration to the column. For example, use ``st.column_config.NumberColumn("Dollar values”, format=”$ %d")`` to change the displayed name of the column to "Dollar values" and add a "$" prefix in each cell. For more info on the available column types and config options, see `Column configuration `_. To configure the index column(s), use ``_index`` as the column name. key : str An optional string to use for giving this element a stable identity. If ``key`` is ``None`` (default), this element's identity will be determined based on the values of the other parameters. Additionally, if selections are activated and ``key`` is provided, Streamlit will register the key in Session State to store the selection state. The selection state is read-only. on_select : "ignore" or "rerun" or callable How the dataframe should respond to user selection events. This controls whether or not the dataframe behaves like an input widget. ``on_select`` can be one of the following: - ``"ignore"`` (default): Streamlit will not react to any selection events in the dataframe. The dataframe will not behave like an input widget. - ``"rerun"``: Streamlit will rerun the app when the user selects rows or columns in the dataframe. In this case, ``st.dataframe`` will return the selection data as a dictionary. - A ``callable``: Streamlit will rerun the app and execute the ``callable`` as a callback function before the rest of the app. In this case, ``st.dataframe`` will return the selection data as a dictionary. selection_mode : "single-row", "multi-row", "single-column", "multi-column", or Iterable of these The types of selections Streamlit should allow when selections are enabled with ``on_select``. This can be one of the following: - "multi-row" (default): Multiple rows can be selected at a time. - "single-row": Only one row can be selected at a time. - "multi-column": Multiple columns can be selected at a time. - "single-column": Only one column can be selected at a time. - An ``Iterable`` of the above options: The table will allow selection based on the modes specified. When column selections are enabled, column sorting is disabled. Returns ------- element or dict If ``on_select`` is ``"ignore"`` (default), this command returns an internal placeholder for the dataframe element that can be used with the ``.add_rows()`` method. Otherwise, this command returns a dictionary-like object that supports both key and attribute notation. The attributes are described by the ``DataframeState`` dictionary schema. Examples -------- **Example 1: Display a dataframe** >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> df = pd.DataFrame(np.random.randn(50, 20), columns=("col %d" % i for i in range(20))) >>> >>> st.dataframe(df) # Same as st.write(df) .. output:: https://doc-dataframe.streamlit.app/ height: 500px **Example 2: Use Pandas Styler** You can also pass a Pandas Styler object to change the style of the rendered DataFrame: >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> df = pd.DataFrame(np.random.randn(10, 20), columns=("col %d" % i for i in range(20))) >>> >>> st.dataframe(df.style.highlight_max(axis=0)) .. output:: https://doc-dataframe1.streamlit.app/ height: 500px **Example 3: Use column configuration** You can customize a dataframe via ``column_config``, ``hide_index``, or ``column_order``. >>> import random >>> import pandas as pd >>> import streamlit as st >>> >>> df = pd.DataFrame( >>> { >>> "name": ["Roadmap", "Extras", "Issues"], >>> "url": ["https://roadmap.streamlit.app", "https://extras.streamlit.app", "https://issues.streamlit.app"], >>> "stars": [random.randint(0, 1000) for _ in range(3)], >>> "views_history": [[random.randint(0, 5000) for _ in range(30)] for _ in range(3)], >>> } >>> ) >>> st.dataframe( >>> df, >>> column_config={ >>> "name": "App name", >>> "stars": st.column_config.NumberColumn( >>> "Github Stars", >>> help="Number of stars on GitHub", >>> format="%d ⭐", >>> ), >>> "url": st.column_config.LinkColumn("App URL"), >>> "views_history": st.column_config.LineChartColumn( >>> "Views (past 30 days)", y_min=0, y_max=5000 >>> ), >>> }, >>> hide_index=True, >>> ) .. output:: https://doc-dataframe-config.streamlit.app/ height: 350px **Example 4: Customize your index** You can use column configuration to format your index. >>> import streamlit as st >>> import pandas as pd >>> from datetime import date >>> >>> df = pd.DataFrame( >>> { >>> "Date": [date(2024, 1, 1), date(2024, 2, 1), date(2024, 3, 1)], >>> "Total": [13429, 23564, 23452], >>> } >>> ) >>> df.set_index("Date", inplace=True) >>> >>> config = { >>> "_index": st.column_config.DateColumn("Month", format="MMM YYYY"), >>> "Total": st.column_config.NumberColumn("Total ($)"), >>> } >>> >>> st.dataframe(df, column_config=config) .. output:: https://doc-dataframe-config-index.streamlit.app/ height: 225px rN)rfrerunzYou have passed zH to `on_select`. But only 'ignore', 'rerun', or a callable is supported.rfF) on_change default_valuewrites_allowedenable_check_callback_rules) ensure_copyhiddenrs) user_keyform_idrprqrrrgrirjr`is_selection_activated string_value)on_change_handler deserializer serializerctx value_typearrow_data_frame)2pyarrowcallablerrrdgr r!rr[rgrqrrri EditingMode READ_ONLY editing_moderVTabler "convert_arrow_table_to_arrow_bytesrpdetermine_data_formatis_pandas_styler_get_delta_path_strrOhashrconvert_anything_to_pandas_dfr convert_pandas_df_to_arrow_bytesrrrr`extendrcrrr rr3idrBr"rKrQ_enqueuer?value)rFrprqrrrgrhrirjrkrlr`par is_callbackcolumn_config_mappingproto data_format delta_path default_uuiddata_dfrserde widget_states r<rszArrowMixin.dataframes`  / /8K'"9+-uv Sk!*h!6 !"9-K !=H$~y9d"$,7  !7} E $7! EK !EL $0E  q !'33== dBHH %'JJ4PEJ )>>tDK..t4"WW88: "4 #34 t\:%BB%G ((={ K'HHQEJ  ! %'7(J9O  u&;< !  ' '(<^(L M+DGG4EM$&C6 ZZ$7"//#mm-'= EH,-E*/7 /B)".. ??) L GG  / 7 (:(:; ;77##$6> >r;tablectj|rtj|d}|jj }t t |}t}t||||jjd|S)aDisplay a static table. While ``st.dataframe`` is geared towards large datasets and interactive data exploration, ``st.table`` is useful for displaying small, styled tables without sorting or scrolling. For example, ``st.table`` may be the preferred way to display a confusion matrix or leaderboard. Additionally, ``st.table`` supports Markdown. Parameters ---------- data : Anything supported by st.dataframe The table data. All cells including the index and column headers can optionally contain GitHub-flavored Markdown. Syntax information can be found at: https://github.github.com/gfm. See the ``body`` parameter of |st.markdown|_ for additional, supported Markdown directives. .. |st.markdown| replace:: ``st.markdown`` .. _st.markdown: https://docs.streamlit.io/develop/api-reference/text/st.markdown Examples -------- **Example 1: Display a simple dataframe as a static table** >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> df = pd.DataFrame( ... np.random.randn(10, 5), columns=("col %d" % i for i in range(5)) ... ) >>> >>> st.table(df) .. output:: https://doc-table.streamlit.app/ height: 480px **Example 2: Display a table of Markdown strings** >>> import streamlit as st >>> import pandas as pd >>> >>> df = pd.DataFrame( ... { ... "Command": ["**st.table**", "*st.dataframe*"], ... "Type": ["`static`", "`interactive`"], ... "Docs": [ ... "[:rainbow[docs]](https://docs.streamlit.io/develop/api-reference/data/st.dataframe)", ... "[:book:](https://docs.streamlit.io/develop/api-reference/data/st.table)", ... ], ... } ... ) >>> st.table(df) .. output:: https://doc-table-markdown.streamlit.app/ height: 200px d)max_unevaluated_rows arrow_table) r is_unevaluated_data_objectrrrrOrr[marshallr)rFrprrrs r<rzArrowMixin.tablexsuJ  4 4T :!??3DWW002 4 +,  l+ww u55r;add_rowsc 0t|j|fi|S)aLConcatenate a dataframe to the bottom of the current one. Parameters ---------- data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, Iterable, dict, or None Table to concat. Optional. **kwargs : pandas.DataFrame, numpy.ndarray, Iterable, dict, or None The named dataset to concat. Optional. You can only pass in 1 dataset (including the one in the data parameter). Example ------- >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> df1 = pd.DataFrame( ... np.random.randn(50, 20), columns=("col %d" % i for i in range(20)) ... ) >>> >>> my_table = st.table(df1) >>> >>> df2 = pd.DataFrame( ... np.random.randn(50, 20), columns=("col %d" % i for i in range(20)) ... ) >>> >>> my_table.add_rows(df2) >>> # Now the table shown in the Streamlit app contains the data for >>> # df1 followed by the data for df2. You can do the same thing with plots. For example, if you want to add more data to a line chart: >>> # Assuming df1 and df2 from the example above still exist... >>> my_chart = st.line_chart(df1) >>> my_chart.add_rows(df2) >>> # Now the chart shown in the Streamlit app contains the data for >>> # df1 followed by the data for df2. And for plots whose datasets are named, you can pass the data with a keyword argument where the key is the name: >>> my_chart = st.vega_lite_chart( ... { ... "mark": "line", ... "encoding": {"x": "a", "y": "b"}, ... "datasets": { ... "some_fancy_name": df1, # <-- named dataset ... }, ... "data": {"name": "some_fancy_name"}, ... } ... ) >>> my_chart.add_rows(some_fancy_name=df2) # <-- name used as keyword )_arrow_add_rowsr)rFrpkwargss r<rzArrowMixin.add_rowssttww777r;ctd|S)zGet our DeltaGenerator.r()r )rFs r<rz ArrowMixin.dgs$d++r;)NNN)rpr'rq int | Nonerrrrgboolrh bool | NoneriIterable[str] | NonerjColumnConfigMappingInput | Nonerk Key | NonerlzLiteral['ignore']r`'SelectionMode | Iterable[SelectionMode]rTr()rpr'rqrrrrrgrrhrrirrjrrkrrlz!Literal['rerun'] | WidgetCallbackr`rrTr?)rpr'rqrrrrrgrrhrrirrjrrkrrlz+Literal['ignore', 'rerun'] | WidgetCallbackr`rrTzDeltaGenerator | DataframeStatern)rpr'rTr()rpr'rTDeltaGenerator | None)rTr() r5r6r7r rsrrrpropertyrr:r;r<reresS  !  %*"&-19='/BM     "   + 7  % @     !  %*"&-19=BM     "   + 7  5 @    K  ! u? %*"&-19=AIBMu?u?u? u? " u? u?+u?7u?u??u?@u? )u?!u?n GQ6Q6fJ98 98v,,r;rec|s0tj|r|dfStj|dfSddlm}|||S)Nr)prep_chart_data_for_add_rows)r rr+streamlit.elements.lib.built_in_chart_utilsr)rpadd_rows_metadatars r<_prep_data_for_add_rowsrsK   * *4 0: ;;DA4GGY '.? @@r;c |j |j|S|jjs td|t |dk(rd}n-t |dk(r|j \}}n tdd|jj vrs|jj drZ|jj dj7t||jj dj}||fi|yt||jj d\}|jj d<t}|jj|jjddtt|j!}t#|j$j&j(|||r6||j$j&_d|j$j&_t/||S) a2Concatenate a dataframe to the bottom of the current one. Parameters ---------- data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, dict, or None Table to concat. Optional. **kwargs : pandas.DataFrame, numpy.ndarray, Iterable, dict, or None The named dataset to concat. Optional. You can only pass in 1 dataset (including the one in the data parameter). Example ------- >>> import streamlit as st >>> import pandas as pd >>> import numpy as np >>> >>> df1 = pd.DataFrame( ... np.random.randn(50, 20), columns=("col %d" % i for i in range(20)) ... ) >>> my_table = st.table(df1) >>> >>> df2 = pd.DataFrame( ... np.random.randn(50, 20), columns=("col %d" % i for i in range(20)) ... ) >>> my_table.add_rows(df2) >>> # Now the table shown in the Streamlit app contains the data for >>> # df1 followed by the data for df2. You can do the same thing with plots. For example, if you want to add more data to a line chart: >>> # Assuming df1 and df2 from the example above still exist... >>> my_chart = st.line_chart(df1) >>> my_chart.add_rows(df2) >>> # Now the chart shown in the Streamlit app contains the data for >>> # df1 followed by the data for df2. And for plots whose datasets are named, you can pass the data with a keyword argument where the key is the name: >>> my_chart = st.vega_lite_chart( ... { ... "mark": "line", ... "encoding": {"x": "a", "y": "b"}, ... "datasets": { ... "some_fancy_name": df1, # <-- named dataset ... }, ... "data": {"name": "some_fancy_name"}, ... } ... ) >>> my_chart.add_rows(some_fancy_name=df2) # <-- name used as keyword Nz&Only existing elements can `add_rows`.rrRzLWrong number of arguments to add_rows().Command requires exactly one datasetrT)_root_container_cursor is_lockedrlenpopitemprops last_indexgetattr chart_commandrrrmetadatarOrrrdeltaarrow_add_rowsrpnamehas_namer)rrprr st_methodnew_datamsgrs r<rr$sz !RZZ%7 ::  #$LMM CK1, V ^^% d$ 3   rzz/// JJ  0 1 JJ  0 1 < < DB 0 01D E S ST $!&!6M  ,-73Hbjj23 ,C!#!6!6CLLAtB22456L SYY % % * *HlC (,   %,0   )C Ir;ctj|r$t|tsJdt |||tj ||_y)aXMarshall pandas.DataFrame into an Arrow proto. Parameters ---------- proto : proto.Arrow Output. The protobuf for Streamlit Arrow proto. data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.DataFrame, Iterable, dict, or None Something that is or can be converted to a dataframe. default_uuid : str | None If pandas.Styler UUID is not provided, this value will be used. This attribute is optional and only used for pandas.Styler, other elements (e.g. charts) can ignore it. z.Default UUID must be a string for Styler data.N)r rrVrOrconvert_anything_to_arrow_bytesrp)rrprs r<rrsN$&&t,,, < , t\2??EEJr;)r`rrTz'set[ArrowProto.SelectionMode.ValueType])rpr'rzAddRowsMetadata | NonerTz#tuple[Data, AddRowsMetadata | None]rn)rr(rpr'rzIDataFrame | npt.NDArray[Any] | Iterable[Any] | dict[Hashable, Any] | NonerTr)rr[rpr'rrSrTNone)K __future__rrD dataclassesrr%rrrrr r r typing_extensionsr streamlitr *streamlit.elements.lib.column_config_utilsrrrrrr"streamlit.elements.lib.event_utilsr!streamlit.elements.lib.form_utilsr*streamlit.elements.lib.pandas_styler_utilsrstreamlit.elements.lib.policiesrstreamlit.elements.lib.utilsrrrstreamlit.errorsrstreamlit.proto.Arrow_pb2rr[streamlit.proto.ForwardMsg_pb2rstreamlit.runtime.metrics_utilr7streamlit.runtime.scriptrunner_utils.script_run_contextrr streamlit.runtime.stater!r"collections.abcr#r$numpynptpandasr&streamlit.dataframe_utilr'streamlit.delta_generatorr(rr)r.r9r/r1r?rBrcrerrrr:r;r<rsO# !($C=FAUU2959D2# -8K#> y/+7iu7t'Ye'0 66 6.%';%',%'Pl,l,^A A-A)A0mm m R m  m`Fr;