def on_update_store_original_update_columns_original_configuration_data(
original_df_json,
):
"""Tab:Columns, Section: Original Configuration"""
original_df = from_session(original_df_json)
columns_data = get_dt_colunas_data(original_df)
return get_table_dfcolumns(columns_data, id="origin", df=original_df)
def on_change_modified_df_update_columns_result_configuration(modified_df_json, data):
"""Show the modified columns in the Result configuration"""
if not data:
raise PreventUpdate
modified_df = from_session(modified_df_json)
return get_table_dfcolumns(data, id="new", df=modified_df)
def on_update_content_on_state_update_modified_df(original_df_json, data):
"""Change the data of the Store for modified df"""
if not data:
raise PreventUpdate
original_df = from_session(original_df_json)
modified_df = modify_original_df(original_df, data)
return to_session(modified_df)
def on_add_filter_update_table_data(filter_data, modified_df_json):
df = pd.DataFrame()
if modified_df_json:
df = from_session(modified_df_json)
if not df.empty and filter_data:
for filter_row in filter_data:
print(
f"FILTRANDO: {filter_row['field']}->{filter_row['comp']}->{filter_row['value']}"
)
typed_value = value_as_type(df, filter_row["field"], filter_row["value"])
if filter_row["comp"] == "eq":
df = df[df[filter_row["field"]] == typed_value]
if filter_row["comp"] == "ne":
df = df[df[filter_row["field"]] != typed_value]
if filter_row["comp"] == "gt":
df = df[df[filter_row["field"]] > typed_value]
if filter_row["comp"] == "ge":
df = df[df[filter_row["field"]] >= typed_value]
if filter_row["comp"] == "lt":
df = df[df[filter_row["field"]] < typed_value]
if filter_row["comp"] == "le":
df = df[df[filter_row["field"]] <= typed_value]
if filter_row["comp"] == "isnull":
df = df[df[filter_row["field"]].isna()]
if filter_row["comp"] == "notnull":
df = df[df[filter_row["field"]].notna()]
if filter_row["comp"] == "between":
if ";" in typed_value:
start, end = typed_value.split(";")
start = value_as_type(df, filter_row["field"], start)
end = value_as_type(df, filter_row["field"], end)
df = df[df[filter_row["field"]].between(start, end)]
if filter_row["comp"] == "contains":
try:
df = df[df[filter_row["field"]].str.contains(typed_value)]
except AttributeError:
print("Error using contains in numeric column")
return df.to_dict("records") if df is not None else []
def on_change_modified_df_state_update_information_columns(df_json, info_column):
"""Show information when column dropdown, in Information tab is changed."""
info_children = []
if df_json and info_column != "-":
df = from_session(df_json)
dados = df[info_column]
if is_numeric_dtype(dados):
info_children = get_numeric_information_gui(dados, info_column)
elif is_string_dtype(dados):
info_children = get_string_information_gui(dados, info_column)
# TODO: Mostrar mais tipos
# elif is_bool_dtype(dados):
# info_children = get_bool_information_gui(dados, info_column)
# elif is_datetime64_any_dtype(dados):
# info_children = get_datetime_information_gui(dados, info_column)
return info_children
def on_update_modified_df_update_update_filterdata_gui(original_df_json):
"""Tab:Filter Data, Section: Fields and Data"""
modified_df = from_session(original_df_json)
return get_tab_filtering_components(modified_df)
def on_update_modified_df_update_informations_fields(original_df_json):
"""Tab:Information, Section: Combobox of column"""
modified_df = from_session(original_df_json)
return get_information_components(modified_df)
def on_update_store_original_show_sample_data(original_df_json):
"""Tab:Colunas, Section: Deal with the dataframe columns"""
original_df = from_session(original_df_json)
return get_dt_colunas_data(original_df)
def on_load_sample_file_update_store_original(original_df_json):
"""Tab:Data, Section: Table with first rows"""
original_df = from_session(original_df_json)
return get_sample_df_data_children(original_df)
