{"cells":[{"metadata":{},"cell_type":"markdown","source":"* *
Cheatsheet - 70+ Plotly Charts
"},{"metadata":{},"cell_type":"markdown","source":"# Objective\n\nThe aim of this kernel is provide all essential and most commonly used ggplot charts in a single page.This kernel will hold almost all charts with different attributes used for them.It could be a great time saver for you.Just utilize it anytime when you are working on data visualizations. \n\n**Note**:\nI made this kernel with different types of plots(70+ in count) which required multiple tabular datasets to be used from kaggle platform to showcase interesting insights from them."},{"metadata":{},"cell_type":"markdown","source":"\n\n
\n

Table of content

\n\n* [Starters](#0)\n\nScatter\n* [1. Basic Scatter plot](#1)\n* [2. Scatter - Numerical](#2)\n* [3. Scatter - Categorical ](#3)\n* [4. Scatter - Color and size](#4)\n* [5. Line - Scatter](#5)\n* [6. Random sample scatter Plot](#6) \n \n \nBar\n* [7. Basic Bar chart](#7)\n* [8. stacked Bar chart](#8)\n* [9. Bar Chart - Grouped](#9)\n* [10. Graph Objects - Bar Chart](#10)\n* [11. Graph Objects - Grouped Chart](#11) \n* [12. Graph Objects - Stacked Chart](#12) \n* [13. Graph Object Customization of Colors](#13) \n* [14. Graphh Objects - Relative Bar mode](#14) \n* [15. Category Ascending order or descending Order](#15) \n* [16. Horizontal Stacked bar chart](#16)\n* [17. Bar Chart - Horizontal](#17)\n \n\nPie\n \n* [20. Basic Pie Plot](#20)\n* [21. Repeated labels Pie](#21)\n* [22. Customized Pie- Gradient & Text](#22)\n* [23. Donut Plot](#23)\n* [24. Ring Plot](#24)"},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install chart_studio","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd \nimport seaborn as sns\nimport numpy as np\nimport plotly.express as px \nimport plotly.graph_objects as go \nimport plotly.figure_factory as ff\nimport chart_studio.plotly as py\nimport plotly as ky","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"../input/100-sales-record/100-Sales-Records/100 Sales Records.csv\",parse_dates=[\"Order Date\"]) \ndf1 = pd.read_csv(\"../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv\") \ndf1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[\"Order year\"] =df[\"Order Date\"].dt.year","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n1. Basic Simple Scatter Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n2. Scatter - Numerical plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.scatter(df,x=\"Units Sold\",y=\"Unit Price\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n3.Scatter - Category plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.scatter(df,x=\"Units Sold\",y=\"Unit Price\",color=\"Country\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n4.Scatter Color and size plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.scatter(df,x=\"Units Sold\",y=\"Unit Price\",color=\"Region\",size=\"Unit Cost\",hover_data=[\"Sales Channel\"])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n5.Scatter - Line plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"df1 = px.data.gapminder().query(\"continent == 'Oceania'\")\nfig = px.line(df1, x='year', y='lifeExp', color='country')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"N = 1000\nt = np.linspace(0, 10, 100)\ny = np.sin(t)\n\nfig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"N = 100\nrandom_x = np.linspace(0, 1, N)\nrandom_y0 = np.random.randn(N) + 5\nrandom_y1 = np.random.randn(N)\nrandom_y2 = np.random.randn(N) - 5\n\nfig = go.Figure()\n\n# Add traces\nfig.add_trace(go.Scatter(x=random_x, y=random_y0,\n mode='markers',\n name='markers'))\nfig.add_trace(go.Scatter(x=random_x, y=random_y1,\n mode='lines+markers',\n name='lines+markers'))\nfig.add_trace(go.Scatter(x=random_x, y=random_y2,\n mode='lines',\n name='lines'))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(data=go.Scatter(\n x=[1, 2, 3, 4],\n y=[10, 11, 12, 13],\n mode='markers',\n marker=dict(size=[40, 60, 80, 100],\n color=[0, 1, 2, 3])\n))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(data = go.Scatter(x=df[\"Country\"],\n y=df[\"Total Profit\"],\n mode='markers',\n marker_color=df[\"Total Profit\"])) \nfig.update_layout(title=\"Total Cost over an country wise\")\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n6.Random Sample Scatter plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"N = 100000\nfig = go.Figure(data=go.Scattergl(\n x = np.random.randn(N),\n y = np.random.randn(N),\n mode='markers',\n marker=dict(\n color=np.random.randn(N),\n colorscale='plasma',\n line_width=1\n )\n))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n7.Basic Bar Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x=\"Country\",y=\"Total Cost\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df2 = pd.pivot_table(df,values=[\"Total Cost\"],index=['Country','Sales Channel'],aggfunc={'Total Cost':np.sum}) ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n8.Stacked bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x='Country',y=\"Total Cost\",color=\"Sales Channel\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.bar(df,x=\"Country\",y=\"Total Cost\",hover_data=[\"Region\",\"Sales Channel\"],height=500,color=\"Total Cost\") \nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x=\"Sales Channel\",y=\"Total Cost\",color=\"Region\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n9.Grouped Bar Chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x=\"Sales Channel\",y=\"Total Cost\",color=\"Region\",barmode=\"group\",height=500)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[\"Region\"].unique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x=\"Sales Channel\",y=\"Total Cost\",color=\"Region\",barmode=\"group\",facet_row=\"Sales Channel\",facet_col=\"Region\",height=500,\n category_orders={\"Region\":['Australia and Oceania', 'Central America and the Caribbean',\n 'Europe', 'Sub-Saharan Africa', 'Asia',\n 'Middle East and North Africa', 'North America'],\n \"Sales Channel\":[\"Offline\",\"Online\"]})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"names = [\"Richad\",\"Teja\",\"kumar\"] \nMarks = [90,100,80]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n10.Graph Objects bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"go.Figure([go.Bar(x=names,y=Marks)])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n11. Graph Objects grouped bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"animals=['giraffes', 'orangutans', 'monkeys']\n\nfig = go.Figure(data=[\n go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),\n go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])\n])\n# Change the bar mode\nfig.update_layout(barmode='group')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n12.Graph objects stacked bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"animals=['giraffes', 'orangutans', 'monkeys']\n\nfig = go.Figure(data=[\n go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),\n go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])\n])\n# Change the bar mode\nfig.update_layout(barmode='stack')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = ['Product A', 'Product B', 'Product C']\ny = [20, 14, 23]\n\n# Use textposition='auto' for direct text\nfig = go.Figure(data=[go.Bar(\n x=x, y=y,\n text=y,\n textposition='auto',\n )]) \nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.bar(df,x=\"Country\",y=\"Total Profit\",text=df[\"Total Profit\"]) \nfig.update_traces(texttemplate='%{text:.2s}',textposition=\"outside\") \n#fig.update_layout(uniformtext_minsize=180, uniformtext_mode='hide') \nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=months,\n y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],\n name='Primary Product',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=months,\n y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],\n name='Secondary Product',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n13.Graph Objects customization of colors\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"colors = ['mediumorchid',] * 5\ncolors[1] = 'crimson'\n\nfig = go.Figure(data=[go.Bar(\n x=['Feature A', 'Feature B', 'Feature C',\n 'Feature D', 'Feature E'],\n y=[20, 14, 23, 25, 22],\n marker_color=colors # marker color can be a single color value or an iterable\n)])\nfig.update_layout(title_text='Customize a each and every color')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(data=[go.Bar(\n x=[1, 2, 3, 5.5, 10],\n y=[10, 8, 6, 4, 2],\n width=[0.8, 0.8, 0.8, 3.5, 4],\n marker_color=\"mediumseagreen\"# customize width here\n)])\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n14.Relative mode Bar Chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"x = [1, 2, 3, 4]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16]))\nfig.add_trace(go.Bar(x=x, y=[6, -8, -4.5, 8]))\nfig.add_trace(go.Bar(x=x, y=[-15, -3, 4.5, -8]))\nfig.add_trace(go.Bar(x=x, y=[-1, 3, -3, -4]))\n\nfig.update_layout(barmode='relative', title_text='Relative Barmode')\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n15.Category Ascending order or descending order\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"x=['b', 'a', 'c', 'd']\nfig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal',marker_color=\"goldenrod\"))\nfig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))\nfig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))\n\nfig.update_layout(barmode='stack', xaxis={'categoryorder':'category ascending'})\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x=['b', 'a', 'c', 'd']\nfig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal',marker_color=\"goldenrod\"))\nfig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))\nfig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))\n\nfig.update_layout(barmode='stack', xaxis={'categoryorder':'total descending'})\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = [\n [\"BB+\", \"BB+\", \"BB+\", \"BB\", \"BB\", \"BB\"],\n [16, 17, 18, 16, 17, 18,]\n]\nfig = go.Figure()\nfig.add_bar(x=df[\"Country\"],y=df[\"Sales Channel\"])\nfig.add_bar(x=df[\"Country\"],y=df[\"Sales Channel\"])\nfig.update_layout(barmode=\"stack\")\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n16.Horizontal Stacked bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(df,x=\"Total Profit\",y=\"Region\",orientation='h',color=\"Country\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n17.Horizontal bar chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(go.Bar(\n x=[20, 14, 23],\n y=['giraffes', 'orangutans', 'monkeys'],\n orientation='h'))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n18.Basic Pie chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.pie(df,values=\"Total Profit\",names=df[\"Country\"])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n19.Pie chart - Setting in bulit colors\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.pie(df,names=\"Country\",values=\"Total Profit\",color_discrete_sequence=px.colors.sequential.Blugrn_r)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[\"Region\"].unique()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n20.Pie chart - Repeated Labels\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.pie(df,names=df[\"Region\"],values=df[\"Total Cost\"],color_discrete_map={'Sub-Sarahan':'lightcyan',\n 'Europe':'cyan',\n 'Asia':'royalblue',\n 'North America':'darkblue',\n 'Middle East and North Africa':'green',\n 'Central America and the Caribbean':\"yellow\",\n 'Australia and Oceania':'violet'}) \nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n21.Customization of pie Chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.pie(df,names=df[\"Region\"],values=df[\"Total Cost\"],color_discrete_map={'Sub-Sarahan':'lightcyan',\n 'Europe':'cyan',\n 'Asia':'royalblue',\n 'North America':'darkblue',\n 'Middle East and North Africa':'green',\n 'Central America and the Caribbean':\"yellow\",\n 'Australia and Oceania':'violet'},labels={'Region':'Region'}) \nfig.update_traces(textposition=\"inside\",textinfo='percent+label') \nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']\nvalues = [4500, 2500, 1053, 500]\n\nfig = go.Figure(data=[go.Pie(labels=labels, values=values)])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n22. Donut Chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.pie(df,names=\"Country\",values=\"Total Profit\",color_discrete_sequence=px.colors.sequential.Blugrn_r,hole=0.3)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n23.Pulling Sectors from The Center\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']\nvalues = [4500, 2500, 1053, 500]\n\nfig = go.Figure(data=[go.Pie(labels=labels, values=values,pull=[0,0,0.3,0.1])])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Tables "},{"metadata":{},"cell_type":"markdown","source":"\n24.Creation of table\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(data=[go.Table(header=dict(values=['A Scores', 'B Scores']),\n cells=dict(values=[[100, 90, 80, 90], [95, 85, 75, 95]]))\n ])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns[0:2]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n25.Setting colors for table \n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure(data=[go.Table(header=dict(values=df.columns[0:2],\n fill_color='lightskyblue',),\n cells=dict(values=[df.Region,df.Country], \n fill_color='lavender',\n align='left'))\n ])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n26.Figure Factory table\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"table = ff.create_table(df.iloc[:,0:4],colorscale='sunsetdark')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"table","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n27.Tree map\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.treemap(df,path=[\"Region\",\"Country\",\"Sales Channel\"])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n28.Suburst chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"df5 = px.data.tips()\nfig = px.sunburst(df5, path=['sex','day','time'], values='total_bill',color=\"time\",color_discrete_map={'(?)':'black', 'Lunch':'gold', 'Dinner':'darkblue'})\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n29.Scatter webgl plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"N = 100000\n\ndf2 = pd.DataFrame(dict(x=np.random.randn(N),\n y=np.random.randn(N)))\n\nfig = px.scatter(df2, x=\"x\", y=\"y\", render_mode='webgl')\n\nfig.update_traces(marker_line=dict(width=1, color='DarkSlateGray'))\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n30.Box Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.box(df,y=\"Total Cost\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n31.Box Plot - Specified Colors\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.box(df,x=\"Sales Channel\",y=\"Total Cost\",color_discrete_sequence=px.colors.sequential.Blues_r)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n32.Displaying data under box plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.box(df,x=\"Sales Channel\",y=\"Total Cost\",points=\"all\",color_discrete_sequence=px.colors.sequential.Blackbody)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n33.Styled Box Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.box(df,x=\"Sales Channel\",y=\"Total Cost\",points=\"all\",notched=True,color_discrete_sequence=px.colors.sequential.Agsunset)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n34.Strip Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.strip(df,x=\"Sales Channel\",y=\"Total Cost\",)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df3 = pd.read_csv(\"../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df3.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n35.Histogram\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,x=\"avg_glucose_level\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n36.Histogram - Specified Bins\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,x=\"avg_glucose_level\",nbins=20)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n37.Several histograms for the different values of one column\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,x=\"avg_glucose_level\",color=\"gender\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n38.Histogram - Rug Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,x=\"avg_glucose_level\",color=\"gender\",marginal='rug')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n39.Histogram - Horizontal\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,y=\"avg_glucose_level\",color=\"gender\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure()\nfig.add_trace(go.Histogram(x=df3[\"avg_glucose_level\"]))\nfig.add_trace(go.Histogram(x=df3[\"bmi\"]))\n\n# The two histograms are drawn on top of another\nfig.update_layout(barmode='stack')\nfig.show()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n40.Cummulative Histogram\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.histogram(df3,x=df3[\"avg_glucose_level\"],cumulative=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df2 = sns.load_dataset(\"tips\") \ndf2.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n41.Violin Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.violin(df2,y=\"tip\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n42.Violin with box Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.violin(df2,y=\"tip\",points=\"all\",box=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n43.Displaying data under Violin Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.violin(df2, y = \"tip\", x=\"time\",box=True,points=\"all\",hover_data=df2.columns)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n44.Overlay Violin Plot\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.violin(df2,y=\"tip\",points=\"all\",violinmode=\"overlay\",color=\"sex\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n45.2d density heat map\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.density_heatmap(df,x=\"Total Cost\",y=\"Total Profit\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = df.groupby(\"Region\")[\"Total Cost\"].sum()[0:].values\ny","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n46.Funnel Chart\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.funnel(df,y=df[\"Region\"].unique(),x=y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df5 = pd.read_csv(\"../input/istanbul-stock-exchange/istanbul_stock_exchange.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df5.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n47.Time Series data\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.line(df5,x=\"date\",y=\"EM\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n48.Time Series - Range Slider\n\nGo to TOC"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.line(df5,x=\"date\",y=\"EM\") \nfig.update_xaxes(rangeslider_visible=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"* * * ![](http://)![](http://)![](http://)![](http://)![](http://)![](http://)![](http://)![](http://)![](http://)![](http://)"},{"metadata":{},"cell_type":"markdown","source":"> > > > "},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}