{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
\n \n
","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-19T17:18:39.360987Z","iopub.execute_input":"2021-11-19T17:18:39.361524Z","iopub.status.idle":"2021-11-19T17:18:42.668197Z","shell.execute_reply.started":"2021-11-19T17:18:39.36149Z","shell.execute_reply":"2021-11-19T17:18:42.667325Z"}}},{"cell_type":"markdown","source":"# Importing","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \n\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nimport cufflinks as cf\ncf.go_offline()\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:44:06.499061Z","iopub.execute_input":"2021-11-19T17:44:06.499504Z","iopub.status.idle":"2021-11-19T17:44:06.519437Z","shell.execute_reply.started":"2021-11-19T17:44:06.499455Z","shell.execute_reply":"2021-11-19T17:44:06.517988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/g-research-crypto-forecasting/train.csv')\ndata.head(10).style.applymap(lambda x : \"background-color: #f5cb98\")","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:44:06.521578Z","iopub.execute_input":"2021-11-19T17:44:06.522204Z","iopub.status.idle":"2021-11-19T17:44:44.858771Z","shell.execute_reply.started":"2021-11-19T17:44:06.522161Z","shell.execute_reply":"2021-11-19T17:44:44.857998Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n \n
","metadata":{}},{"cell_type":"markdown","source":"# EDA\n
\n \n
","metadata":{}},{"cell_type":"code","source":"def EDA(df):\n \n print('\\033[1m' +'EXPLORATORY DATA ANALYSIS :'+ '\\033[0m\\n')\n print('\\033[1m' + 'Shape of the data :' + '\\033[0m')\n print(df.shape, \n '\\n------------------------------------------------------------------------------------\\n')\n \n print('\\033[1m' + 'All columns from the dataframe :' + '\\033[0m')\n print(df.columns, \n '\\n------------------------------------------------------------------------------------\\n')\n \n print('\\033[1m' + 'Datatpes and Missing values:' + '\\033[0m')\n print(df.info(), \n '\\n------------------------------------------------------------------------------------\\n')\n \n print('\\033[1m' + 'Summary statistics for the data :' + '\\033[0m')\n print(df.describe(include='all'), \n '\\n------------------------------------------------------------------------------------\\n')\n \n \n print('\\033[1m' + 'Memory used by the data :' + '\\033[0m')\n print(df.memory_usage(), \n '\\n------------------------------------------------------------------------------------\\n')\n \n print('\\033[1m' + 'Number of duplicate values :' + '\\033[0m')\n print(df.duplicated().sum())\n \nEDA(data)","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:44:44.860293Z","iopub.execute_input":"2021-11-19T17:44:44.860676Z","iopub.status.idle":"2021-11-19T17:46:09.245376Z","shell.execute_reply.started":"2021-11-19T17:44:44.860643Z","shell.execute_reply":"2021-11-19T17:46:09.24447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Preprocessing\n\n
\n \n
","metadata":{}},{"cell_type":"code","source":"# Naming every asset\n\nasset_name = ['Binance Coin', 'Bitcoin', 'Bitcoin Cash', 'Cardano', 'Dogecoin', 'EOS.IO', 'Ethereum', \n 'Ethereum Classic', 'IOTA', 'Litecoin', 'Maker', 'Monero', 'Stellar', 'TRON']\n\nfor index in range(0,14):\n data['Asset_ID'].replace({index : asset_name[index]}, inplace=True)\n \nnames = data.Asset_ID.unique()\nnames","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:46:09.246774Z","iopub.execute_input":"2021-11-19T17:46:09.247086Z","iopub.status.idle":"2021-11-19T17:47:04.659121Z","shell.execute_reply.started":"2021-11-19T17:46:09.247046Z","shell.execute_reply":"2021-11-19T17:47:04.658243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Making timestamp column the index \n\ndata['timestamp'] = pd.to_datetime(data['timestamp'], unit='s')\ndata.set_index('timestamp', inplace = True)\ndata.index.dtype","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:47:04.660904Z","iopub.execute_input":"2021-11-19T17:47:04.661119Z","iopub.status.idle":"2021-11-19T17:47:06.304265Z","shell.execute_reply.started":"2021-11-19T17:47:04.661093Z","shell.execute_reply":"2021-11-19T17:47:06.303219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Reducing memory taken by the data\n\ndef reduce_memory(df):\n \n before = df.memory_usage().sum() \n \n for col in df.columns: \n dtype = df[col].dtype\n if dtype == 'float64':\n c_min = df[col].min()\n c_max = df[col].max() \n if c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n else:\n df[col] = df[col].astype(np.float64) \n \n after = df.memory_usage().sum()\n \n print('Memory taken before transformation : ', before)\n print('Memory taken after transformation : ', after)\n print('Memory taken reduced by : ',( before - after) * 100/ before, '%')\n \n return df\n\ndata = reduce_memory(data)","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:47:06.305347Z","iopub.execute_input":"2021-11-19T17:47:06.305558Z","iopub.status.idle":"2021-11-19T17:47:10.08365Z","shell.execute_reply.started":"2021-11-19T17:47:06.305532Z","shell.execute_reply":"2021-11-19T17:47:10.082449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Making separate dataframes\n\nbitcoin_cash = data[data['Asset_ID'] == 'Bitcoin Cash']\nbinance_coin = data[data['Asset_ID'] == 'Binance Coin']\nbitcoin = data[data['Asset_ID'] == 'Bitcoin']\neos_io = data[data['Asset_ID'] == 'EOS.IO']\nethereum_classic = data[data['Asset_ID'] == 'Ethereum Classic']\nethereum = data[data['Asset_ID'] == 'Ethereum']\nlitecoin = data[data['Asset_ID'] == 'Litecoin']\nmonero = data[data['Asset_ID'] == 'Monero']\ntron = data[data['Asset_ID'] == 'TRON']\nstellar = data[data['Asset_ID'] == 'Stellar']\ncardano = data[data['Asset_ID'] == 'Cardano']\niota = data[data['Asset_ID'] == 'IOTA']\nmaker = data[data['Asset_ID'] == 'Maker']\ndogecoin = data[data['Asset_ID'] == 'Dogecoin']\n\n# Clearing memory\ndel data","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2021-11-19T17:47:10.084971Z","iopub.execute_input":"2021-11-19T17:47:10.085175Z","iopub.status.idle":"2021-11-19T17:48:02.083351Z","shell.execute_reply.started":"2021-11-19T17:47:10.085151Z","shell.execute_reply":"2021-11-19T17:48:02.082439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Resampling all datasets\n\nday = {\n 'Open': 'first',\n 'High': 'max',\n 'Low': 'min',\n 'Close': 'last',\n 'Volume': 'sum'\n}\n\nbitcoin_cash = bitcoin_cash.resample('D').apply(day)\nbinance_coin = binance_coin.resample('D').apply(day)\nbitcoin = bitcoin.resample('D').apply(day)\neos_io = eos_io.resample('D').apply(day)\nethereum_classic = ethereum_classic.resample('D').apply(day)\nethereum = ethereum.resample('D').apply(day)\nlitecoin = litecoin.resample('D').apply(day)\nmonero = monero.resample('D').apply(day)\ntron = tron.resample('D').apply(day)\nstellar = stellar.resample('D').apply(day)\ncardano = cardano.resample('D').apply(day)\niota = iota.resample('D').apply(day)\nmaker = maker.resample('D').apply(day)\ndogecoin = dogecoin.resample('D').apply(day)\n\ndf_list = [bitcoin_cash, binance_coin, bitcoin, eos_io, ethereum_classic, ethereum,\n litecoin, monero, tron, stellar, cardano, iota, maker, dogecoin]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-11-19T17:48:02.084731Z","iopub.execute_input":"2021-11-19T17:48:02.08494Z","iopub.status.idle":"2021-11-19T17:48:03.168113Z","shell.execute_reply.started":"2021-11-19T17:48:02.084915Z","shell.execute_reply":"2021-11-19T17:48:03.167075Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Crypto Dashboard\n\n
\n \n
","metadata":{}},{"cell_type":"code","source":"date_buttons = [\n{'count': 4, 'step': \"year\", 'stepmode': \"todate\", 'label': \"Year\"},\n{'count': 6, 'step': \"month\", 'stepmode': \"todate\", 'label': \"Month\"},\n{'count': 60, 'step': \"day\", 'stepmode': \"todate\", 'label': \"Week\"},\n{'count': 20, 'step': \"day\", 'stepmode': \"todate\", 'label': \"Day\"},\n ]\n\n\nbuttons = []\ni = 0\nvis = [False] * 14\n\nfor df in df_list:\n vis[i] = True\n buttons.append({ 'label' : names[i],\n 'method' : 'update',\n 'args' : [{'visible' : vis},\n {'title' : names[i] + ' Chart'}] })\n i+=1\n vis = [False] * 14\n\nfig = go.Figure()\n\nfor df in df_list:\n fig.add_trace(go.Candlestick(x = df.index,\n open = df['Open'],\n high = df['High'],\n low = df['Low'],\n close = df['Close'],\n increasing_line_color= '#42eff5',\n decreasing_line_color= '#8f4040'))\n\nfig.update_xaxes(\n tickfont = dict(size=15, family = 'monospace', color='#B8B8B8'),\n tickmode = 'array',\n ticklen = 6,\n showline = False,\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside')\n\nfig.update_yaxes(\n tickfont = dict(size=15, family = 'monospace', color='#B8B8B8'),\n tickmode = 'array',\n showline = False,\n ticksuffix = '$',\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside') \n \n \nfig.update_layout(height = 650,\n font_family = 'monospace',\n xaxis = dict(rangeselector = dict(buttons = date_buttons)),\n updatemenus = [dict(type = 'dropdown',\n x = 1,\n y = 1.15,\n showactive = True,\n active = 2,\n buttons = buttons)],\n title = dict(text = 'Cryptocurrency Candlestick
Dashboard',\n font = dict(color = '#FFFFFF'), \n x = 0.525),\n plot_bgcolor = '#1a253d',\n paper_bgcolor = '#1a253d',\n font = dict(color='#858585'), \n annotations = [\n dict( text = \"Choose
Crypto : \",\n font = dict(size = 12),\n showarrow=False,\n x = 0.96, y = 1.25,\n xref = 'paper', yref = \"paper\",\n align = \"left\")])\n \n \nfor i in range(0,14):\n fig.data[i].visible = False\nfig.data[2].visible = True\n\nfig.show()","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2021-11-19T17:48:03.16967Z","iopub.execute_input":"2021-11-19T17:48:03.17024Z","iopub.status.idle":"2021-11-19T17:48:04.113955Z","shell.execute_reply.started":"2021-11-19T17:48:03.170211Z","shell.execute_reply":"2021-11-19T17:48:04.112658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"date_buttons = [\n{'count': 4, 'step': \"year\", 'stepmode': \"backward\", 'label': \"Year\"},\n{'count': 6, 'step': \"month\", 'stepmode': \"todate\", 'label': \"Month\"},\n{'count': 60, 'step': \"day\", 'stepmode': \"todate\", 'label': \"Week\"},\n{'count': 20, 'step': \"day\", 'stepmode': \"todate\", 'label': \"Day\"},\n ]\n\nfig = make_subplots(rows=2, cols=1, shared_xaxes=True, \n vertical_spacing=0.3, subplot_titles=('', 'Volume'), row_width=[0.3, 0.8])\n\n\nbuttons = []\ni = 0\nvis = [False] * 14\n\nfor df in df_list:\n vis[i] = True\n buttons.append({ 'label' : names[i],\n 'method' : 'update',\n 'args' : [{'visible' : vis},\n {'title' : names[i] + ' Linechart'}] })\n i+=1\n vis = [False] * 14\n\n\nfor df in df_list:\n fig.add_trace(go.Scatter(x = df.index,\n y = df['Open'],\n mode = 'lines',\n marker = dict(color = '#44eff2')), row=1, col=1)\n \nfor df in df_list:\n fig.add_trace(go.Bar(x = df.index,\n y = df['Volume'],\n marker = dict(color = '#94afb0')), row=2, col=1)\n\nfig.update_xaxes(\n tickfont = dict(size=15, family = 'monospace', color='#B8B8B8'),\n tickmode = 'array',\n ticklen = 6,\n showline = False,\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside')\n\nfig.update_yaxes(\n tickfont = dict(size=15, family = 'monospace', color='#B8B8B8'),\n tickmode = 'array',\n showline = False,\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside') \n \n \nfig.update_layout(showlegend = False,\n height = 650,\n font_family = 'monospace',\n xaxis = dict(rangeselector = dict(buttons = date_buttons)),\n updatemenus = [dict(type = 'dropdown',\n x = 1,\n y = 1.15,\n showactive = True,\n active = 0,\n buttons = buttons)],\n title = dict(text = 'Cryptocurrency Linechart Dashboard
with Volumes',\n font = dict(color = '#FFFFFF'), \n x = 0.5),\n plot_bgcolor = '#1a253d',\n paper_bgcolor = '#1a253d',\n font = dict(color='#858585'),\n annotations = [dict(text = \"Choose Crypto :\",\n font = dict(size = 10, color ='#44eff2'),\n showarrow=False,\n x = 1, y = 1.25,\n xref = 'paper', yref = \"paper\",\n align = \"left\"), \n dict(text = \"<----DRAG HERE TO MOVE---->\",\n font = dict(size = 10, color ='#44eff2'),\n showarrow=False,\n x = 0.5, y = 0.45,\n xref = 'paper', yref = \"paper\",\n align = \"left\")])\n\nfor i in range(1,14):\n fig.data[i].visible = False\n\nfig.show()","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2021-11-19T17:48:04.115875Z","iopub.execute_input":"2021-11-19T17:48:04.116101Z","iopub.status.idle":"2021-11-19T17:48:05.589358Z","shell.execute_reply.started":"2021-11-19T17:48:04.116075Z","shell.execute_reply":"2021-11-19T17:48:05.58835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Candlestick Charts with Indicators\n\n
\n \n
","metadata":{}},{"cell_type":"code","source":"def indicators(df=None, title='Chart', symbol = 'Candlesticks'):\n \n layout1 = cf.Layout(height=1250,\n font_family = 'monospace',\n xaxis = dict( rangeselector = dict(buttons = date_buttons),\n tickfont = dict(size=12, color='#d4d4d4'),\n tickmode = 'array',\n ticklen = 6,\n showline = False,\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside'),\n\n yaxis = dict(\n tickfont = dict(size=15, color='#d4d4d4'),\n tickmode = 'array',\n showline = False,\n ticksuffix = '$',\n showgrid = True,\n gridcolor = '#595959',\n ticks = 'outside'),\n\n title = dict(text = '{} chart
with Indicators'.format(title),\n font = dict(color = '#FFFFFF'), \n x = 0.5),\n \n legend = dict(orientation=\"h\",\n yanchor=\"bottom\", y=0.92,\n xanchor=\"center\", x=0.5),\n \n plot_bgcolor = '#1a253d',\n paper_bgcolor = '#1a253d',\n font = dict(color='#858585'),\n annotations = [dict(text = \" \",\n font = dict(size = 1, color ='white'),\n showarrow=False,\n x = 1, y = 1.25,\n xref = 'paper', yref = \"paper\",\n align = \"left\"), \n dict(text = \"RSI\",\n font = dict(size = 15, color ='white'),\n showarrow=False,\n x = 0.5, y = 0.62,\n xref = 'paper', yref = \"paper\",\n align = \"left\"),\n dict(text = \"Volume\",\n font = dict(size = 15, color ='white'),\n showarrow=False,\n x = 0.5, y = 0.38,\n xref = 'paper', yref = \"paper\",\n align = \"left\"),\n dict(text = \"MACD\",\n font = dict(size = 15, color ='white'),\n showarrow=False,\n x = 0.5, y = 0.14,\n xref = 'paper', yref = \"paper\",\n align = \"left\"),\n dict(text = \"<-----SLIDER----->\",\n font = dict(size = 15, color ='white'),\n showarrow=False,\n x = 0.5, y = -0.06,\n xref = 'paper', yref = \"paper\",\n align = \"left\")]\n )\n \n qf = cf.QuantFig(df, name = symbol, down_color='red')\n\n qf.add_sma([100], width=2, color=['white'])\n\n qf.add_rsi(periods=14, color='#b876b3')\n\n qf.add_volume(color='#999999')\n\n qf.add_macd(color = ['yellow', 'blue'])\n\n qf.add_bollinger_bands(periods=20, boll_std=2 ,colors=['orange','grey'], fill=True)\n\n qf.iplot(layout = layout1)","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2021-11-19T17:48:05.592331Z","iopub.execute_input":"2021-11-19T17:48:05.592853Z","iopub.status.idle":"2021-11-19T17:48:05.610485Z","shell.execute_reply.started":"2021-11-19T17:48:05.592818Z","shell.execute_reply":"2021-11-19T17:48:05.60953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(bitcoin, title='Bitcoin', symbol='BTC')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:05.612011Z","iopub.execute_input":"2021-11-19T17:48:05.612343Z","iopub.status.idle":"2021-11-19T17:48:07.81865Z","shell.execute_reply.started":"2021-11-19T17:48:05.612298Z","shell.execute_reply":"2021-11-19T17:48:07.817829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(ethereum, title='Ethereum', symbol='ETH')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:07.819889Z","iopub.execute_input":"2021-11-19T17:48:07.820628Z","iopub.status.idle":"2021-11-19T17:48:09.188869Z","shell.execute_reply.started":"2021-11-19T17:48:07.820581Z","shell.execute_reply":"2021-11-19T17:48:09.188174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(litecoin, title='Litecoin', symbol='LTC')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:09.190012Z","iopub.execute_input":"2021-11-19T17:48:09.190811Z","iopub.status.idle":"2021-11-19T17:48:10.596628Z","shell.execute_reply.started":"2021-11-19T17:48:09.190759Z","shell.execute_reply":"2021-11-19T17:48:10.595751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(iota, title='IOTA', symbol='IOTA')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:10.597953Z","iopub.execute_input":"2021-11-19T17:48:10.598276Z","iopub.status.idle":"2021-11-19T17:48:11.871473Z","shell.execute_reply.started":"2021-11-19T17:48:10.598244Z","shell.execute_reply":"2021-11-19T17:48:11.870742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(bitcoin_cash, title='Bitcoin Cash', symbol='BCH')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:11.872759Z","iopub.execute_input":"2021-11-19T17:48:11.873326Z","iopub.status.idle":"2021-11-19T17:48:13.348623Z","shell.execute_reply.started":"2021-11-19T17:48:11.873282Z","shell.execute_reply":"2021-11-19T17:48:13.347757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(ethereum_classic, title='Ethereum Classic', symbol='Ξ')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:13.3498Z","iopub.execute_input":"2021-11-19T17:48:13.350058Z","iopub.status.idle":"2021-11-19T17:48:14.731491Z","shell.execute_reply.started":"2021-11-19T17:48:13.350028Z","shell.execute_reply":"2021-11-19T17:48:14.730649Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(monero, title='Monero', symbol='XMR')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:14.73275Z","iopub.execute_input":"2021-11-19T17:48:14.733011Z","iopub.status.idle":"2021-11-19T17:48:16.116472Z","shell.execute_reply.started":"2021-11-19T17:48:14.732981Z","shell.execute_reply":"2021-11-19T17:48:16.115741Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(dogecoin, title='Dogecoin', symbol='Ð')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:16.117722Z","iopub.execute_input":"2021-11-19T17:48:16.118021Z","iopub.status.idle":"2021-11-19T17:48:17.110314Z","shell.execute_reply.started":"2021-11-19T17:48:16.117991Z","shell.execute_reply":"2021-11-19T17:48:17.109374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"indicators(tron, title='TRON', symbol='TRON')","metadata":{"execution":{"iopub.status.busy":"2021-11-19T17:48:17.111484Z","iopub.execute_input":"2021-11-19T17:48:17.111719Z","iopub.status.idle":"2021-11-19T17:48:18.466495Z","shell.execute_reply.started":"2021-11-19T17:48:17.111678Z","shell.execute_reply":"2021-11-19T17:48:18.465869Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# If you want to plot other cryptocurrencies, just use the function above ^^","metadata":{}},{"cell_type":"markdown","source":"# Thank you\n* Thanks for sitting through my notebook.\n* Hope you liked it.\n","metadata":{}}]}