&e09 ^UddlmZddlmZddlmZmZmZmZmZm Z m Z m Z ddl Z ddlmZddlmZddlmZmZmZmZmZmZddlmZdd lmZdd lmZerdd l m!Z!dd l"m#Z#m$Z$m%Z%dd l&m'Z'ddl(m)Z)e dddde j*deee+eefdf Z,de-d<GddZ.d d!dZ/dS)") annotations)Iterable) TYPE_CHECKINGAnyDictrListOptionalUnioncastN) TypeAlias) type_util)INDEX_IDENTIFIERColumnConfigMappingInputapply_data_specific_configsmarshall_column_configprocess_config_mappingupdate_column_config)marshall_styler)Arrow)gather_metrics)ndarray) DataFrameIndexSeries)Styler)DeltaGeneratorrrrrrr DataceZdZed ddddddddZeddddZeddd dZed!dZdS)" ArrowMixin dataframeNF)use_container_width hide_index column_order column_configdatarwidth int | Noneheightr!boolr" bool | Noner#Iterable[str] | Noner$ColumnConfigMappingInput | Nonereturn'DeltaGenerator'ct|}t} || _|r|| _|r|| _|r || jdd<tjj| _t|tj rtj || _ntj|} tj|rF|j} t't)| } t+| || tj|d} t/|| | dtj| | _|t3|t4d|it7| ||jd| S)uDisplay a dataframe as an interactive table. This command works with dataframes from Pandas, PyArrow, Snowpark, and PySpark. It can also display several other types that can be converted to dataframes, e.g. numpy arrays, lists, sets and dictionaries. Parameters ---------- data : pandas.DataFrame, pandas.Series, pandas.Styler, pandas.Index, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None The data to display. If 'data' is a pandas.Styler, it will be used to style its underlying DataFrame. Streamlit supports custom cell values and colors. It does not support some of the more exotic pandas styling features, like bar charts, hovering, and captions. width : int or None Desired width of the dataframe expressed in pixels. If None, the width will be automatically calculated based on the column content. height : int or None Desired height of the dataframe expressed in pixels. If None, a default height is used. use_container_width : bool If True, set the dataframe width to the width of the parent container. This takes precedence over the width argument. hide_index : bool or None Whether to hide the index column(s). If None (default), the visibility of index columns is automatically determined based on the data. column_order : Iterable of str or None Specifies the display order of columns. This also affects which columns are visible. For example, ``column_order=("col2", "col1")`` will display 'col2' first, followed by 'col1', and will hide all other non-index columns. If None (default), the order is inherited from the original data structure. column_config : dict or None Configures how columns are displayed, e.g. their title, visibility, type, or format. This needs to be a dictionary where each key is a column name and the value is one of: * ``None`` to hide the column. * A string to set the display label of the column. * One of the column types defined under ``st.column_config``, e.g. ``st.column_config.NumberColumn("Dollar values”, format=”$ %d")`` to show a column as dollar amounts. See more info on the available column types and config options `here `_. To configure the index column(s), use ``_index`` as the column name. Examples -------- >>> 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: 410px 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: 410px Or you can customize the 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 NF) ensure_copy)check_arrow_compatibilityhiddenarrow_data_frame)r ArrowProtor!r&r(r# EditingMode READ_ONLY editing_mode isinstancepaTabler pyarrow_table_to_bytesr%determine_data_formatis_pandas_stylerdg_get_delta_path_strstrhashrconvert_anything_to_dfrdata_frame_to_bytesrrr_enqueue)selfr%r&r(r!r"r#r$column_config_mappingproto data_format delta_path default_uuiddata_dfs 8lib/python3.11/site-packages/streamlit/elements/arrow.pyr zArrowMixin.dataframe8sJ!7} E E $7!  EK  "!EL  1$0E qqq !'3= dBH % % @"9$??EJJ $9$??K)$// ;"W88:: "4 #3#344 t\::: 6tOOOG '%*/     #6w??EJ  ! %'7(J9O    u&;<<<w 2E:::tablecrtj|stj|tjrtj|d}|j}tt|}t}t||||j d|S)a,Display a static table. This differs from ``st.dataframe`` in that the table in this case is static: its entire contents are laid out directly on the page. Parameters ---------- data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None The table data. Example ------- >>> 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 d)max_unevaluated_rows arrow_table) r is_snowpark_data_objectis_type_PYSPARK_DF_TYPE_STRrBr>r?r@rAr4marshallrD)rEr%rIrJrGs rLrNzArrowMixin.tables<  ,T 2 2 Ti6G )07 7  T3DsSSSD W0022 4 ++,,  l+++w u555rMadd_rows'Data'Optional['DeltaGenerator']c (|jj|fi|S)aConcatenate 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 )r>_arrow_add_rows)rEr%kwargss rLrWzArrowMixin.add_rowss"h'tw&t66v666rMc"td|S)zGet our DeltaGenerator.r)r )rEs rLr>z ArrowMixin.dgSs$d+++rM)NNN)r%rr&r'r(r'r!r)r"r*r#r+r$r,r-r.N)r%rr-r.)r%rXr-rY)r-r.) __name__ __module__ __qualname__rr rNrWpropertyr>rMrLrr7s^K   ! u; %*"&-19=u;u;u;u;u;! u;n^G*6*6*6*6*6X^J37373737 37j,,,X,,,rMrrGr4r%rJ Optional[str]r-NonecRtj|r0t|ts Jdt |||t|t jrtj||_dStj |}tj ||_dS)a[Marshall pandas.DataFrame into an Arrow proto. Parameters ---------- proto : proto.Arrow Output. The protobuf for Streamlit Arrow proto. data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.DataFrame, Iterable, dict, or None Something that is or can be converted to a dataframe. default_uuid : Optional[str] If pandas.Styler UUID is not provided, this value will be used. This attribute is optional and only used for pandas.Styler, other elements (e.g. charts) can ignore it. z.Default UUID must be a string for Styler data.N) r r=r8r@rr9r:r;r%rBrC)rGr%rJdfs rLrVrVYs"!$''3 #   < < ; < < < t\222$!!75d;;  -d 3 32266 rMr^)rGr4r%rrJrdr-re)0 __future__rcollections.abcrtypingrrrrr r r pyarrowr9typing_extensionsr streamlitr *streamlit.elements.lib.column_config_utilsrrrrrr*streamlit.elements.lib.pandas_styler_utilsrstreamlit.proto.Arrow_pb2rr4streamlit.runtime.metrics_utilrnumpyrpandasrrrpandas.io.formats.stylerstreamlit.delta_generatorrr:r@r__annotations__rrVrcrMrLrws #""""""$$$$$$RRRRRRRRRRRRRRRRRRRR''''''GFFFFF9999999999999//////////......888888   H  d3i      _,_,_,_,_,_,_,_,D 7777777rM