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How to use convert_df_to_csv method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

findash.py

Source:findash.py

...112        113    114    115    @st.cache116    def convert_df_to_csv(df):117        return df.to_csv().encode('utf-8')118    st.download_button(label="Download Summary",data=convert_df_to_csv(Summary),file_name='StockSummary.csv',mime='text/csv',)119    120#==============================================================================121# Tab 2122#==============================================================================123124def tab2():125    126    #Dashboard Title127    annotated_text(("Stock Analysis","Chart","#3498DB"))128129    130    # Add table to show stock data131    @st.cache132    def GetStockData(tickers, start_date, end_date):133        return pd.concat([si.get_data(tick, start_date, end_date) for tick in tickers])134    135    col1,col2 = st.columns([2,2])136    137    #To select Period138    select_data =  ['1mo', '3mo','6mo','ytd','1y','2y','5y','max']139    default  = select_data.index('1y')140    select_Period =  col1.selectbox("Select Period", select_data,index = default)141    142    #To Select interval143    select_interval = ['1d','1mo']144    interval= col2.selectbox("Select Interval", select_interval)145    146    #To select Graph147    select_graph = st.radio("Select Graph", ["Line","Candle"])148    149    st.write('<style>div.row-widget.stRadio > div{flex-direction:row;}</style>', unsafe_allow_html=True)150 151    #getting the stock data 152    data = yf.download(ticker, period = select_Period,interval = interval)153    154    data['diff'] = data['Close'] - data['Open']155    data.loc[data['diff']>=0, 'color'] = 'green'156    data.loc[data['diff']<0, 'color'] = 'red'157    158    159    160    if ticker != '-':161        stock_price = GetStockData([ticker], start_date, end_date)162        163    #check box to display the data164    show_data = st.checkbox("Show data")  165        166    if show_data:167            st.write('Stock price data')168            st.dataframe(stock_price)169            170    if select_graph == "Line":     171       if ticker != '-':172           data = yf.download(ticker, period = select_Period,interval = interval)173           174           fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 175               vertical_spacing=0.07, subplot_titles=('Stock Trend', 'Volume'), 176               row_width=[0.2, 0.7])177           fig.add_trace(go.Scatter(x=data.index, y=data['Close'],name="Stock Trend",showlegend=True),row= 1,col = 1)178           fig.add_trace(go.Bar(x=data.index, y=data['Volume'],name="Volume",showlegend=True),row=2,col = 1)179           fig.update(layout_xaxis_rangeslider_visible=False)180           fig.update_layout(title="Stock Summary Line Plot", yaxis_title="Close Price")181           fig.update_layout(width = 1000 , height = 600)182           st.plotly_chart(fig)183184    elif select_graph == "Candle":185        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 186               vertical_spacing=0.03, subplot_titles=('Stock Trend', 'Volume'), 187               row_width=[0.2, 0.7])188189        #candlestick190        fig.add_trace(go.Candlestick(x=data.index, open=data["Open"], high=data["High"],191                        low=data["Low"], close=data["Close"], name="Stock Trend"), 192                        row=1, col=1)193        fig.update_layout(title="Stock Summary Candlestick Plot", yaxis_title="Close Price")194        195        #Volume196        fig.add_trace(go.Bar(x=data.index, y=data['Volume'],name="Volume",showlegend=True), row=2, col=1)197        fig.add_trace(go.Scatter(x=data.index,y=data['Close'].rolling(window=50).mean(),marker_color='orange',name='50 Day MA'))198        #This removes rangeslider 199        fig.update(layout_xaxis_rangeslider_visible=False)200    201        fig.update_layout(202        width=1000,203        height=600,204        autosize=False,205        template="plotly_white")206        st.plotly_chart(fig)207208209210#==============================================================================211# Tab 3 - Statistics212#==============================================================================       213def tab3():214     215#Dashboard Header 216    annotated_text(("Stock","Statistics","#3498DB"))217    218    219# Getting stock data220    def GetStatsEval(ticker):221        return si.get_stats_valuation(ticker)222    def GetStats(ticker):223        return si.get_stats(ticker)224    225    def convert_df_to_csv(df):226        return df.to_csv().encode('utf-8')227    228    229    if ticker != '-':230        statsval = GetStatsEval(ticker)231        statsval = statsval.rename(columns={0:'Valuation Measures',1:'USD'})232        233        #Valuation Measures234        annotated_text(("VALUATION","MEASURES","#3498DB"))235        st.dataframe(statsval,height = 1000)236    #Get Remaining stats237    if ticker != '-':238        stat = GetStats(ticker)239        stat = stat.set_index('Attribute')240        241        #stock Price History242        annotated_text(("STOCK PRICE","HISTORY","#3498DB"))243        Sph = stat.iloc[0:7,]244        st.dataframe(Sph,height = 1000)245        246        #share statistics247        annotated_text(("SHARE","STATISTICS","#3498DB"))248        Shs = stat.iloc[7:19,]249        st.dataframe(Shs,height = 1000)250        251        #Dividend & Splits252        annotated_text(("DIVIDEND","SPLITS","#3498DB"))253        Div = stat.iloc[19:29,]254        st.table(Div)255        256        #Financial Highlights257        annotated_text(("FINANCIAL","HIGHLIGHTS","#3498DB"))258        Finh = stat.iloc[29:31,]259        st.table(Finh)260        261        #Profitability262        annotated_text(("STOCK","PROFITABILITY","#3498DB"))263        Prof = stat.iloc[31:33,]264        st.dataframe(Prof,height = 1000)265        266        #Management Effectiveness267        annotated_text(("Management","Effectiveness","#3498DB"))268        Meff = stat.iloc[33:35,]269        st.dataframe(Meff,height = 1000)270        271        #Income Statement272        IncS = stat.iloc[35:43,]273        annotated_text(("INCOME","STATEMENT","#3498DB"))274        st.dataframe(IncS,height = 1000)275        276        #Balance Sheet277        annotated_text(("BALANCE","SHEET","#3498DB"))278        BalS = stat.iloc[43:49,]279        st.dataframe(BalS,height = 1000)280        281        #Cash Flow282        annotated_text(("CASH","FLOW","#3498DB"))283        Caf = stat.iloc[49:51,]284        st.dataframe(Caf,height = 1000)285        286        287        288# =============================================================================289#         df = stat.style.set_properties(**{'background-color': 'black',290#                            'color': 'lawngreen',291#                            'border-color': 'white'})292# =============================================================================293294    #Download Required Data 295    data_to_download = ["Valuation Measures","stock Price History","share statistics","Dividend & Splits","Financial Highlights",296                        "Profitability","Management Effectiveness","Income Statement","Balance Sheet","Cash Flow"]297    to_download = st.selectbox("Choose Data to Download", data_to_download)    298    299    #Conditions to selecr the300    if to_download == 'Valuation Measures':301           st.download_button(label="Download Stats",data=convert_df_to_csv(statsval),file_name='ValuationMeasures.csv',mime='text/csv',)302    elif to_download == 'stock Price History':303         st.download_button(label="Download Stats",data=convert_df_to_csv(Sph),file_name='stockPriceHistory.csv',mime='text/csv',)304    elif to_download == 'share statistics':305            st.download_button(label="Download Stats",data=convert_df_to_csv(Shs),file_name='shareStatistics.csv',mime='text/csv',)306    elif to_download == 'Dividend & Splits':307         st.download_button(label="Download Stats",data=convert_df_to_csv(Div),file_name='DividendAndSplits.csv',mime='text/csv',)308    elif to_download == 'Financial Highlights':309            st.download_button(label="Download Stats",data=convert_df_to_csv(Finh),file_name='FinancialHighlights.csv',mime='text/csv',) 310    elif to_download == 'Profitability':311         st.download_button(label="Download Stats",data=convert_df_to_csv(Prof),file_name='Profitability.csv',mime='text/csv',)312    elif to_download == 'Management Effectiveness':313            st.download_button(label="Download Stats",data=convert_df_to_csv(Meff),file_name='ManagementEffectiveness.csv',mime='text/csv',)314    elif to_download == 'Income Statement':315         st.download_button(label="Download Stats",data=convert_df_to_csv(IncS),file_name='IncomeStatement.csv',mime='text/csv',)316    elif to_download == 'Balance Sheet':317            st.download_button(label="Download Stats",data=convert_df_to_csv(BalS),file_name='BalanceSheet.csv',mime='text/csv',)318    elif to_download == 'Cash Flow':319            st.download_button(label="Download Stats",data=convert_df_to_csv(Caf),file_name='CashFlow.csv',mime='text/csv',)320        321              322    323                324    325    326327#==============================================================================328# Tab 4 - Financials 329#==============================================================================       330def tab4():331     332     annotated_text(("Stock", "Financials","#33ADFF"))333     col1,col2 = st.columns([2,2])
...

CTCscanner.py

Source:CTCscanner.py

...278    #AgGrid(tx_log_df, key = 'txs', editable = True, fit_columns_on_grid_load = True)279        280# Download as a csv281    @st.cache282    def convert_df_to_csv(df):283        return df.to_csv().encode('utf-8')284    st.download_button(285        label = 'Download as CSV',286        data=convert_df_to_csv(tx_log_df),287        file_name='transactions.csv',288        mime='text/csv',289        )   290        291        292                293# CUSTOMER DATABASE294if selected == 'Customer Database':295# Tile and file names296    st.title('Customer Database')297298# Filter299    customer_database_df['DOB'] = pd.to_datetime(customer_database_df['DOB']).dt.date300    customer_database_df = customer_database_df.sort_values('Date_Approved', ascending=True)301    customer_database_df.set_index('ID', inplace=True)302            303    st.write(customer_database_df.shape)304    st.dataframe(customer_database_df)305    #AgGrid(customer_database_df, key = 'customers', editable = True, fit_columns_on_grid_load = True)306               307# Download as a csv308    @st.cache309    def convert_df_to_csv(df):310        return df.to_csv().encode('utf-8')311    st.download_button(312        label = 'Download as CSV',313        data=convert_df_to_csv(customer_database_df),314        file_name='all_customers',315        mime='text/csv',316        )317        318319        320# AGGREGATE TOTALS321if selected == 'Aggregate Volumes':322# Tile and file names323    st.title('Aggregate Volumes')324        325# Frame326    agg_from_concat = concat_df[['Agg_Volume', 'Percentile', 'ID_x', 'Name_y', 'Company_Name', 'Last_TX', 'Status', 'Risk_Rating',327            'Statements_Needed', 'Statements_Collected',  'Review_Needed', 'Last_Review', 'Notes_y']]328                         329    agg_from_concat.drop_duplicates(subset=['ID_x'], inplace=True)330    agg_from_concat.set_index('ID_x', inplace=True)331        332# Print333    st.write(agg_from_concat.shape)  334    st.dataframe(agg_from_concat)335336# Download as a csv337    @st.cache338    def convert_df_to_csv(df):339        return df.to_csv().encode('utf-8')340    st.download_button(341        label = 'Download as CSV',342        data=convert_df_to_csv(agg_from_concat),343        file_name='agg_volumes.csv',344        mime='text/csv',345        )346   347        348# SHARED WALLET349if selected == 'Shared Wallet Scanner':350# Tile and file names351    st.title('Shared Wallet Scanner')352        353# Print354    st.write(shared_wallets_df.shape)355    st.dataframe(shared_wallets_df)356357# Download as a csv358    @st.cache359    def convert_df_to_csv(df):360        return df.to_csv().encode('utf-8')361    st.download_button(362        label = 'Download as CSV',363        data=convert_df_to_csv(shared_wallets_df),364        file_name='shared_wallets.csv',365        mime='text/csv',366        )367368369370# OFAC MATCHES371if selected == 'OFAC Matches':372# Tile and file names373    st.title('OFAC Matches')374    375# Frame376    ofac_from_concat = concat_df[['Address', 'Address_Match', 'ID_x', 'Name_y', 'Company_Name', 'Last_TX', 'Status', 'Risk_Rating',377                                 'Notes_y']]378    ofac_from_concat.sort_values('Last_TX', ascending=True, inplace=True)379    ofac_from_concat.set_index('Address', inplace=True)380    381# Filter382    ofac_from_concat = ofac_from_concat.loc[(ofac_from_concat['Address_Match'] == True)]383    384    BL_addresses.drop_duplicates()385    address_list = BL_addresses.set_index('Address').to_dict()['Note']386    387# Print388    st.write(ofac_from_concat.shape)389    st.dataframe(ofac_from_concat)390    391# Download as a csv392    @st.cache393    def convert_df_to_csv(df):394        return df.to_csv().encode('utf-8')395    st.download_button(396        label = 'Download as CSV',397        data=convert_df_to_csv(ofac_from_concat),398        file_name='ofac_wallets.csv',399        mime='text/csv',400        )401    402# Show BL403    show_bl = st.checkbox("Show Blacklist")404    if show_bl:405        st.write(address_list)406    407408409# All410if selected == 'All Data':411    412# Tile and file names413    st.title('All')414    concat_df.drop(['indicator_column', 'indicator_column2'], axis=1, inplace=True)415    concat_df = concat_df.sort_values(by='Control', ascending=True)416    concat_df.set_index('Control', inplace=True)417          418# Define filter419    def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:420        """421        Adds a UI on top of a dataframe to let viewers filter columns422423        Args:424        df (pd.DataFrame): Original dataframe425426        Returns:427        pd.DataFrame: Filtered dataframe428        """429        modify = st.checkbox("Add filters", key = "modify")430431        if not modify:432            return df433        df = df.copy()434435        # Try to convert datetimes into a standard format (datetime, no timezone)436        for col in df.columns:437            if is_object_dtype(df[col]):438                try:439                    df[col] = pd.to_datetime(df[col])440                except Exception:441                    pass442443            if is_datetime64_any_dtype(df[col]):444                df[col] = df[col].dt.tz_localize(None)445446        modification_container = st.container()447448        with modification_container:449            to_filter_columns = st.multiselect("Filter On", df.columns, key = "to_filter_columns")450            for column in to_filter_columns:451                left, right = st.columns((1, 20))452                # Treat columns with < 10 unique values as categorical453                if is_categorical_dtype(df[column]) or df[column].nunique() < 10:454                    user_cat_input = right.multiselect(f"Values for {column}",455                                                           df[column].unique(),456                                                           default=list(df[column].unique()),457                                                           )458                    df = df[df[column].isin(user_cat_input)]459                elif is_numeric_dtype(df[column]):460                    _min = float(df[column].min())461                    _max = float(df[column].max())462                    step = (_max - _min) / 100463                    user_num_input = right.slider(f"Values for {column}",464                                                      min_value=_min,465                                                      max_value=_max,466                                                      value=(_min, _max),467                                                      step=step,468                                                      )469                    470                    df = df[df[column].between(*user_num_input)]471                elif is_datetime64_any_dtype(df[column]):472                    user_date_input = right.date_input(f"Values for {column}",473                                                           value=(474                                                               df[column].min(),475                                                               df[column].max(),476                                                               ),477                                                           )478                    if len(user_date_input) == 2:479                        user_date_input = tuple(map(pd.to_datetime, user_date_input))480                        start_date, end_date = user_date_input481                        df = df.loc[df[column].between(start_date, end_date)]482                else:483                    user_text_input = right.text_input(f"Substring or regex in {column}",484                                                           )485                    if user_text_input:486                        df = df[df[column].astype(str).str.contains(user_text_input)]487488489            check2 = st.checkbox("Remove Columns", key = "check2")490491            if not check2:492                return df493            df = df.copy()494495            modification_container2 = st.container()496            columns = list(df.columns.unique())497498            with modification_container2:499                to_remove_columns = st.multiselect("Remove Columns", columns, default = columns, key = "to_remove_columns")500                df = df[to_remove_columns]501502            return df503    504# Print505    df = concat_df506    df1 = filter_dataframe(df)507    st.write(df1.shape)508    st.dataframe(df1)509510# Download as a csv511    @st.cache512    def convert_df_to_csv(df):513        return df.to_csv().encode('utf-8')514    st.download_button(515        label = 'Download as CSV',516        data=convert_df_to_csv(df1),517        file_name='custom_data_filters.csv',518        mime='text/csv',519        )               520            521 522    523# CUSTOMER SEARCH524if selected == 'Customer Search':525    526# Tile and file names
...

app.py

Source:app.py

...148                        prediction_MLGL = pd.DataFrame(data = [prediction_MLGL], columns = colonnes )149                        # concat choosen features and prediction to create dataframe150                        result_mlgl = pd.concat([input_df,prediction_MLGL], axis = 1)151                        # convert result to csv and save it152                        def convert_df_to_csv(result_mlgl):153                            return result_mlgl.to_csv().encode('utf-8')    154                        st.download_button(label="Download data as CSV",data = convert_df_to_csv(result_mlgl),155                                                file_name='prediction_TZ_MLGL.csv',mime='text/csv')156        # prediction on loaded csv file157                158                    #st.title("make prediction on  your own file")159                    #uploaded_file = st.file_uploader("Choose your file")160                    #if uploaded_file is not None:161                        #df_loaded = pd.read_csv(uploaded_file)162        #uploaded_file= pd.DataFrame(uploaded_file)163                        #if st.button('prediction'):164                            #df_loaded = pd.DataFrame(scaler_TZ_AC_MLGL_correl_tx.transform(df_loaded), columns =df_loaded.columns)165                            #prediction = load_model_TZ_AC_MLGL_correl_tx.predict(df_loaded)166                            #st.success('value of TZ_AC_MLGL_air_max is {}'.format(prediction))167        #convert result to csv file168                           # prediction_MLGL_loaded_file = pd.to_numeric(prediction)169                           # colonnes = ["TZ_AC_MLGL_air_max"]170                            171                            #prediction_MLGL_loaded_file = pd.DataFrame(data = [[prediction_MLGL_loaded_file]], columns = colonnes )172                           173                            #def convert_df_to_csv(prediction_MLGL_loaded_file):174        # IMPORTANT: Cache the conversion to prevent computation on every rerun175                                #return prediction_MLGL_loaded_file.to_csv().encode('utf-8')    176                            #st.download_button(label="Download data as CSV",data = convert_df_to_csv(prediction_MLGL_loaded_file),177                            #file_name='prediction_MLGL_loaded_file.csv',mime='text/csv')         178                        179    180        181    #prediction for second model182                if task2: 183                    st.title("Right vertical force on the ground air")184                    scaler_TZ_AC_MLGR_air_max= joblib.load(open("./scalers/scaler_TZ_AC_MLGR_air_max.save",'rb'))185                    df = pd.DataFrame(scaler_TZ_AC_MLGR_air_max.transform(input_df), columns = input_df.columns)186            ## load the model file187                    load_model_TZ_AC_MLGR_air_max = pickle.load(open('./models/model_TZ_AC_MLGR_air_max.pkl', 'rb'))188                    189            # use the model to predict target190                    if st.button('predict Right vertical force on the ground "air" '):191                        prediction = load_model_TZ_AC_MLGR_air_max.predict(df)192                        st.success('Vertical ground force "air" of main lainding gear right{}'.format(prediction))193                        194            # Download result to csv file195                        prediction_MLGR = float(prediction)196                        colonnes = ["TZ_AC_MLGR_air_max"]197                        prediction_MLGR = pd.DataFrame(data = [[prediction_MLGR]], columns = colonnes )198                        199                        # concat choosen features and prediction to create dataframe200                        result_mlgr = pd.concat([input_df,prediction_MLGR], axis = 1)201                        # convert result to csv and save it202                        def convert_df_to_csv(result_mlgr):203                            # IMPORTANT: Cache the conversion to prevent computation on every rerun204                            return result_mlgr.to_csv().encode('utf-8')    205                        st.download_button(label="Download data as CSV",data = convert_df_to_csv(result_mlgr),206                                                file_name='prediction_TZ_MLGR.csv',mime='text/csv')207                    208                   209                # show user profile                                210                elif task3:# == "Users Profile":211                    #st.subheader("Users Profile")212                    user_result = view_all_users()213                    clean_db = pd.DataFrame(user_result,columns=['User','Password'])214                    st.dataframe(clean_db)                                   215            else:216                st.warning("Incorrect Username and Password")217                st.info('Retry or SignUp')218        #sign up session219    elif choice == "SignUp":...

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