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Table of Contents

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Plotting with Plotly + Cufflinks in Python\n", "\n", "In this notebook, we will see how to use [plotly](https://plot.ly/python/) and [cufflinks](https://github.com/santosjorge/cufflinks) to create stunning, interactive figures in a single line of Python. This combination of libraries is simple to use, makes excellent charts, and, in my opinion, much more efficient than other methods of plotting in Python.\n", "\n", "This introduction will show us the basics of using plotly + cufflinks, focusing on what we can do in one line of code (for the most part). We'll see a wide variety of different charts, but won't get into the full capabilities of the library (such as updatable figures and using IPython widgets). \n", "\n", "Before you get started, you'll want to head to [Plotly](https://help.plot.ly/how-to-sign-up-to-plotly/#step-1-create-your-free-account) and create an account to get a username and api key. Then run the code below (this isn't actually necessary if you stick to `offline` plotting). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " import plotly \n", " # Replace with your information\n", " plotly.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotly + Cufflinks Overview\n", "\n", "The Plotly Python library is an open-source version of the [Plotly](https://plot.ly/) visualization software made by Plotly. You can create a free account and upload your graphs to share with others (this requires making the graphs and data public). \n", "\n", "We will run plotly completely in offline mode which means that we won't be publishing any of our graphs online. However, we can still open up any of the plots in the Plotly [chart studio](https://plot.ly/create/#/) and edit them for a presentation. \n", "\n", "[Cufflinks is a wrapper ](https://github.com/santosjorge/cufflinks) around the plotly library specifically for plotting with Pandas dataframes. With cufflinks, we don't have to dig into the details of plotly, instead building our charts with minimal code. Basically, you can make charts directly in plotly for more control, or you can use cufflinks to rapidly prototype plots and explore the data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:46.221896Z", "start_time": "2019-01-09T03:09:45.418794Z" } }, "outputs": [], "source": [ "# plotly standard imports\n", "import plotly.graph_objs as go\n", "import chart_studio.plotly as py\n", "\n", "# Cufflinks wrapper on plotly\n", "import cufflinks\n", "\n", "# Data science imports\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Options for pandas\n", "pd.options.display.max_columns = 30\n", "\n", "# Display all cell outputs\n", "from IPython.core.interactiveshell import InteractiveShell\n", "\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll be using plotly + cufflinks in offline mode. We will also set the global `cufflinks` theme to `pearl`. Later we'll take a look at the other theme options." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:46.699508Z", "start_time": "2019-01-09T03:09:46.639809Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plotly.offline import iplot\n", "\n", "cufflinks.go_offline()\n", "\n", "# Set global theme\n", "cufflinks.set_config_file(world_readable=True, theme=\"pearl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After importing cufflinks, plotly plots can be made using `df.iplot()` and then specifying parameters. This is a great replacement for matplotlib! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data\n", "\n", "We are using my medium article statistics data. See [this article](https://towardsdatascience.com/analyzing-medium-story-stats-with-python-24c6491a8ff0) for how to use your own Medium data! [Here](https://github.com/WillKoehrsen/Data-Analysis/tree/master/medium) is the toolkit used to gather the stats. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:48.764569Z", "start_time": "2019-01-09T03:09:48.700373Z" } }, "outputs": [ { "data": { "text/html": [ "
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clapsdays_since_publicationfanslinknum_responsespublicationpublished_dateread_ratioread_timereadsstarted_datetagstexttitletitle_word_counttypeviewsword_countclaps_per_wordediting_days<tag>Education<tag>Data Science<tag>Towards Data Science<tag>Machine Learning<tag>Python
1192574.8585942https://medium.com/p/screw-the-environment-but...0None2017-06-10 14:25:0041.987682017-06-10 14:24:00[Climate Change, Economics]Screw the Environment, but Consider Your Walle...Screw the Environment, but Consider Your Wallet8published16218590.001076000000
11818567.5406393https://medium.com/p/the-vanquishing-of-war-pl...0None2017-06-17 22:02:0032.9314542017-06-17 22:02:00[Climate Change, Humanity, Optimism, History]The Vanquishing of War, Plague and Famine Part...The Vanquishing of War, Plague and Famine8published16438910.004626000000
12150554.92076219https://medium.com/p/capstone-project-mercedes...0None2017-06-30 12:55:0020.19422152017-06-30 12:00:00[Machine Learning, Python, Udacity, Kaggle]Capstone Project: Mercedes-Benz Greener Manufa...Capstone Project: Mercedes-Benz Greener Manufa...7published1065120250.004158000011
1220554.0781600https://medium.com/p/home-of-the-scared-5af0fe...0None2017-07-01 09:08:0035.859192017-06-30 18:21:00[Politics, Books, News, Media Criticism]Home of the Scared A review of A Culture of Fe...Home of the Scared4published5325330.000000000000
1140550.0905070https://medium.com/p/the-triumph-of-peace-f485...0None2017-07-05 08:51:008.771452017-07-03 20:18:00[Books, Psychology, History, Humanism]The Triumph of Peace A review of The Better An...The Triumph of Peace4published5738920.000000100000
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" ], "text/plain": [ " claps days_since_publication fans \\\n", "119 2 574.858594 2 \n", "118 18 567.540639 3 \n", "121 50 554.920762 19 \n", "122 0 554.078160 0 \n", "114 0 550.090507 0 \n", "\n", " link num_responses \\\n", "119 https://medium.com/p/screw-the-environment-but... 0 \n", "118 https://medium.com/p/the-vanquishing-of-war-pl... 0 \n", "121 https://medium.com/p/capstone-project-mercedes... 0 \n", "122 https://medium.com/p/home-of-the-scared-5af0fe... 0 \n", "114 https://medium.com/p/the-triumph-of-peace-f485... 0 \n", "\n", " publication published_date read_ratio read_time reads \\\n", "119 None 2017-06-10 14:25:00 41.98 7 68 \n", "118 None 2017-06-17 22:02:00 32.93 14 54 \n", "121 None 2017-06-30 12:55:00 20.19 42 215 \n", "122 None 2017-07-01 09:08:00 35.85 9 19 \n", "114 None 2017-07-05 08:51:00 8.77 14 5 \n", "\n", " started_date tags \\\n", "119 2017-06-10 14:24:00 [Climate Change, Economics] \n", "118 2017-06-17 22:02:00 [Climate Change, Humanity, Optimism, History] \n", "121 2017-06-30 12:00:00 [Machine Learning, Python, Udacity, Kaggle] \n", "122 2017-06-30 18:21:00 [Politics, Books, News, Media Criticism] \n", "114 2017-07-03 20:18:00 [Books, Psychology, History, Humanism] \n", "\n", " text \\\n", "119 Screw the Environment, but Consider Your Walle... \n", "118 The Vanquishing of War, Plague and Famine Part... \n", "121 Capstone Project: Mercedes-Benz Greener Manufa... \n", "122 Home of the Scared A review of A Culture of Fe... \n", "114 The Triumph of Peace A review of The Better An... \n", "\n", " title title_word_count \\\n", "119 Screw the Environment, but Consider Your Wallet 8 \n", "118 The Vanquishing of War, Plague and Famine 8 \n", "121 Capstone Project: Mercedes-Benz Greener Manufa... 7 \n", "122 Home of the Scared 4 \n", "114 The Triumph of Peace 4 \n", "\n", " type views word_count claps_per_word editing_days \\\n", "119 published 162 1859 0.001076 0 \n", "118 published 164 3891 0.004626 0 \n", "121 published 1065 12025 0.004158 0 \n", "122 published 53 2533 0.000000 0 \n", "114 published 57 3892 0.000000 1 \n", "\n", " Education Data Science Towards Data Science \\\n", "119 0 0 0 \n", "118 0 0 0 \n", "121 0 0 0 \n", "122 0 0 0 \n", "114 0 0 0 \n", "\n", " Machine Learning Python \n", "119 0 0 \n", "118 0 0 \n", "121 1 1 \n", "122 0 0 \n", "114 0 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_parquet(\n", " \"https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/data/medium_data_2019_01_06?raw=true\"\n", ")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kinds of Plots\n", "\n", "We can make any of the following plots using cufflinks:\n", "\n", " scatter, bar, box, spread\n", " ratio, heatmap, surface\n", " histogram, bubble, bubble3d\n", " scatter3d, scattergeo, ohlc\n", " candle, pie, choropleth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Univariate (Single Variable) Distributions\n", "\n", "For single variables, I generally start out with histograms or boxplots. Plotly has these basic charts well-covered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histograms" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:52.276558Z", "start_time": "2019-01-09T03:09:51.925421Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "histfunc": "count", "histnorm": "", "marker": { "color": "rgba(255, 153, 51, 1.0)", "line": { "color": "#000000", "width": 1.3 } }, "name": "claps", "nbinsx": 30, "opacity": 0.8, "orientation": "v", "type": "histogram", "x": [ 2, 18, 50, 0, 0, 0, 73, 234, 2, 0, 682, 58, 5, 7, 87, 23, 4, 113, 8, 17, 8, 4800, 2700, 72, 123, 857, 186, 11, 119, 2000, 4100, 59, 2700, 77, 14, 7000, 1200, 275, 1600, 1600, 1000, 4300, 4980, 12900, 223, 4900, 101, 5900, 548, 895, 6700, 6500, 2700, 1100, 2900, 2600, 2800, 5300, 1300, 1000, 7900, 364, 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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#4D5663" }, "text": "Claps Distribution" }, "xaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "claps" }, "zerolinecolor": "#E1E5ED" }, "yaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "count" }, "zerolinecolor": "#E1E5ED" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"claps\"].iplot(\n", " kind=\"hist\",\n", " bins=30,\n", " xTitle=\"claps\",\n", " linecolor=\"black\",\n", " yTitle=\"count\",\n", " title=\"Claps Distribution\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that we can hover over any of the bars to get the exact numbers. You can also format the `text` to display different information on hovering." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Percentage Histogram\n", "\n", "To get the same chart but instead showing the percentage, we simple pass in `percent` as the histnorm parameter." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:54.035862Z", "start_time": "2019-01-09T03:09:53.952689Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "histfunc": "count", "histnorm": "percent", "marker": { "color": "rgba(255, 153, 51, 1.0)", "line": { "color": "#000000", "width": 1.3 } }, "name": "claps", "nbinsx": 30, "opacity": 0.8, "orientation": "v", "type": "histogram", "x": [ 2, 18, 50, 0, 0, 0, 73, 234, 2, 0, 682, 58, 5, 7, 87, 23, 4, 113, 8, 17, 8, 4800, 2700, 72, 123, 857, 186, 11, 119, 2000, 4100, 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"showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#4D5663" }, "text": "Reads Distribution in Percent" }, "xaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "reads" }, "zerolinecolor": "#E1E5ED" }, "yaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "percentage (%)" }, "zerolinecolor": "#E1E5ED" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"claps\"].iplot(\n", " kind=\"hist\",\n", " bins=30,\n", " xTitle=\"reads\",\n", " linecolor=\"black\",\n", " histnorm=\"percent\",\n", " yTitle=\"percentage (%)\",\n", " title=\"Reads Distribution in Percent\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grouped Histogram\n", "\n", "When we want to display two different distributions on the same plot, we can group together the data to show it side-by-side. This means setting `barmode` to `group` with two distributions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:55.109463Z", "start_time": "2019-01-09T03:09:55.106823Z" } }, "outputs": [], "source": [ "def to_time(dt):\n", " return dt.hour + dt.minute / 60" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:55.213009Z", "start_time": "2019-01-09T03:09:55.113582Z" }, "code_folding": [] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "histfunc": "count", "histnorm": "percent", "marker": { "color": "rgba(255, 153, 51, 1.0)", "line": { "color": "#000000", "width": 1.3 } }, "name": "time_started", "nbinsx": 24, "opacity": 0.8, "orientation": "v", "type": "histogram", "x": [ 14.4, 22.033333333333335, 12, 18.35, 20.3, 19.116666666666667, 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"#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#4D5663" }, "text": "Time Started and Time Published" }, "xaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "Time of Day" }, "zerolinecolor": "#E1E5ED" }, "yaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "(%) of Articles" }, "zerolinecolor": "#E1E5ED" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"time_started\"] = df[\"started_date\"].apply(to_time)\n", "df[\"time_published\"] = df[\"published_date\"].apply(to_time)\n", "\n", "df[[\"time_started\", \"time_published\"]].iplot(\n", " kind=\"hist\",\n", " linecolor=\"black\",\n", " bins=24,\n", " histnorm=\"percent\",\n", " bargap=0.1,\n", " opacity=0.8,\n", " barmode=\"group\",\n", " xTitle=\"Time of Day\",\n", " yTitle=\"(%) of Articles\",\n", " title=\"Time Started and Time Published\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overlaid Histogram\n", "\n", "If we prefer the bars to be laid over one another, we specify the `barmode` to be `overlay`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:56.183116Z", "start_time": "2019-01-09T03:09:56.090442Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[[\"time_published\", \"time_started\"]].iplot(\n", " kind=\"hist\",\n", " bins=24,\n", " linecolor=\"black\",\n", " opacity=0.8,\n", " histnorm=\"percent\",\n", " barmode=\"overlay\",\n", " xTitle=\"Time of day\",\n", " yTitle=\"(%) of articles\",\n", " title=\"Time Started and Time Published Overlaid\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a bar plot, we need to apply some sort of aggregation function and then plot. For example, we can show the `count` of articles in each publication with the following." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:58.198343Z", "start_time": "2019-01-09T03:09:58.073942Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "fans", "orientation": "v", "text": "", "type": "bar", "x": [ "Engineering @ Feature Labs", "None", "Noteworthy - The Journal Blog", "The Reality Project", "Towards Data Science" ], "y": [ 1, 41, 1, 3, 80 ] } ], "layout": { "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": 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"#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "Tag" }, "zerolinecolor": "#E1E5ED" }, "yaxis": { "gridcolor": "#E1E5ED", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "Number of Articles with Tag" }, "zerolinecolor": "#E1E5ED" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[[c for c in df if \"\" in c]].sum().iplot(\n", " kind=\"bar\",\n", " xTitle=\"Tag\",\n", " yTitle=\"Number of Articles with Tag\",\n", " title=\"Frequency of Tags\",\n", " linecolor=\"black\",\n", " sortbars=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Plot with Two Categories\n", "\n", "Here we'll show two distributions side-by-side. First, we'll set the index to be the date, then resample to month frequency, then take the mean and plot." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:09:59.050212Z", "start_time": "2019-01-09T03:09:59.036542Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Plot with Second Y-Axis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to put two very different ranges on the same graph, we can just use a secondary y-axis." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:02.398171Z", "start_time": "2019-01-09T03:10:02.328579Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "views", "orientation": "v", "text": "", "type": "bar", "x": [ "2017-06-30", "2017-07-31", "2017-08-31", "2017-09-30", "2017-10-31", "2017-11-30", "2017-12-31", "2018-01-31", "2018-02-28", "2018-03-31", "2018-04-30", "2018-05-31", "2018-06-30", "2018-07-31", "2018-08-31", 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}, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#4D5663" }, "text": "Monthly Averages" }, "xaxis": { "gridcolor": "#E1E5ED", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "Date" }, "zerolinecolor": "#E1E5ED" }, "yaxis": { "gridcolor": "#E1E5ED", "showgrid": true, "tickfont": { "color": "#4D5663" }, "title": { "font": { "color": "#4D5663" }, "text": "Average Views" }, "zerolinecolor": "#E1E5ED" }, "yaxis2": { "anchor": "x", "gridcolor": "#E1E5ED", "overlaying": "y", "showgrid": true, "side": "right", "tickfont": { "color": "#4D5663" }, "title": { "text": "Average Read Time" }, "zerolinecolor": "#E1E5ED" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df2 = (\n", " df[[\"views\", \"read_time\", \"published_date\"]]\n", " .set_index(\"published_date\")\n", " .resample(\"M\")\n", " .mean()\n", ")\n", "\n", "df2.iplot(\n", " kind=\"bar\",\n", " xTitle=\"Date\",\n", " secondary_y=\"read_time\",\n", " secondary_y_title=\"Average Read Time\",\n", " yTitle=\"Average Views\",\n", " title=\"Monthly Averages\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boxplot\n", "\n", "The boxplot is another good method for showing distributions." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:03.365976Z", "start_time": "2019-01-09T03:10:03.280610Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "boxpoints": false, "line": { "width": 1.3 }, "marker": { "color": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[[\"claps\", \"fans\"]].iplot(\n", " secondary_y=\"fans\",\n", " secondary_y_title=\"Fans\",\n", " kind=\"box\",\n", " yTitle=\"Claps\",\n", " title=\"Box Plot of Claps and Fans\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hover over the graphs to see the actual statistics! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boxplot with Different Categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we have different categories that we want to plot, we need to use a `pivot`. This transforms each unique value in a column into a separate column." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:06.033732Z", "start_time": "2019-01-09T03:10:06.022964Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "publication Engineering @ Feature Labs None Noteworthy - The Journal Blog \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN 34.0 NaN \n", "4 NaN 47.0 NaN \n", "\n", "publication The Reality Project Towards Data Science \n", "0 34.0 NaN \n", "1 29.0 NaN \n", "2 13.0 NaN \n", "3 NaN NaN \n", "4 NaN NaN " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.pivot(columns=\"publication\", values=\"fans\")\n", "df2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we are also explicitly specifying the layout as a dictionary. This gives us more control over different aspects of the plot such as the margin (like the border of the image)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:07.038885Z", "start_time": "2019-01-09T03:10:06.943906Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "boxpoints": false, "line": { "width": 1.3 }, "marker": { "color": "rgba(255, 153, 51, 1.0)" }, "name": "Engineering @ Feature Labs", "orientation": "v", "type": "box", "y": [ null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 52, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df[\"read_time\"] <= 10].pivot(columns=\"read_time\", values=\"reads\").iplot(\n", " kind=\"box\",\n", " colorscale=\"set2\",\n", " xTitle=\"Read Time\",\n", " yTitle=\"Number of Reads\",\n", " title=\"Box Plot of Reads by Reading Time\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Scatter Plots\n", "\n", "The scatter plot is a simple yet effective method for visualizing the relationship between two variables. There are many ways we can make scatterplots in plotly + cufflinks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time-Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's very simple to make time-series plots if we set the index to be the datetime. Then we can simply pass in a column as y and plotly will know to use the index to make a date xaxis. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:09.684308Z", "start_time": "2019-01-09T03:10:09.662521Z" } }, "outputs": [ { "data": { "text/html": [ "
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clapsdays_since_publicationfanslinknum_responsespublicationread_ratioread_timereadsstarted_datetagstexttitletitle_word_counttypeviewsword_countclaps_per_wordediting_days<tag>Education<tag>Data Science<tag>Towards Data Science<tag>Machine Learning<tag>Pythontime_startedtime_published
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2017-12-27 11:20:004800374.986885861https://towardsdatascience.com/random-forest-i...27Towards Data Science17.6821285662017-12-26 15:11:00[Machine Learning, Data Science, Random Forest...Random Forest in Python A Practical End-to-End...Random Forest in Python4published16159644941.06809100101115.18333311.333333
2018-01-06 20:15:00857364.615092112https://towardsdatascience.com/improving-rando...6Towards Data Science22.761772072018-01-03 21:38:00[Machine Learning, Python, Data Science, Learn...Improving the Random Forest in Python Part 1 G...Improving the Random Forest in Python Part 18published3165935040.24457820101121.63333320.250000
2018-01-07 20:37:00186363.59997945https://towardsdatascience.com/data-science-a-...1Towards Data Science28.64157752018-01-07 13:18:00[Data Science, Data, Weight Loss, Programming]Data Science: A Personal Application Charting ...Data Science: A Personal Application5published270635690.05211500100013.30000020.616667
2018-01-08 16:58:00119362.75202943https://towardsdatascience.com/a-theory-of-pre...2Towards Data Science31.53117402018-01-02 17:23:00[Statistics, Data Science, Book Review, Books]A Theory of Prediction Review of The Signal an...A Theory of Prediction4published234728170.04224450100017.38333316.966667
2018-01-09 21:49:002000361.550093392https://towardsdatascience.com/hyperparameter-...12Towards Data Science23.9912255052018-01-09 12:26:00[Machine Learning, Python, Data Science, Data]Hyperparameter Tuning the Random Forest in Pyt...Hyperparameter Tuning the Random Forest in Python7published10631124560.81433200101112.43333321.816667
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words: {w:.0f}'\n", " for m, w in zip(\n", " tds_monthly_totals.index.month_name(), tds_monthly_totals[\"word_count\"]\n", " )\n", "]\n", "\n", "tds_monthly_totals.iplot(\n", " mode=\"lines+markers+text\",\n", " text=\"text\",\n", " y=\"word_count\",\n", " opacity=0.8,\n", " xTitle=\"Date\",\n", " yTitle=\"Word Count\",\n", " title=\"Total Word Count by Month\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two Numeric Variables Scatterplot" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "marker": { "size": 12, "symbol": "circle" }, "mode": "markers", "name": "read_ratio", "text": [ "Screw the Environment, but Consider Your Wallet", "The Vanquishing of War, Plague and Famine", "Capstone Project: Mercedes-Benz Greener Manufacturing Competition", "Home of the Scared", "The Triumph of Peace", "NASA Internship Report", "Deep Neural Network Classifier", "Object Recognition with Google’s Convolutional Neural Networks", "Make an Effort, Not an Excuse", "The Ascent of Humanity", "Facial Recognition Using Google’s Convolutional Neural Network", "Exploratory Data Analysis with R", "Exploratory Data Analysis with R (No Code)", "The Technology Frontier", "Data Analysis with Python", "Data Wrangling with Python and SQLite", "Controlling your Location in Google Chrome", "Machine Learning with Python on the Enron Dataset", "The Worst They Can Say is No", "Artificial Intelligence Part 1: Search", "The Case for Criticism", "Random Forest in Python", "Random Forest Simple Explanation", "Complete Books of 2017", "Top Books of 2017", "Improving the Random Forest in Python Part 1", "Data Science: A Personal Application", "The Simple Science of Global Warming", "A Theory of Prediction", "Hyperparameter Tuning the Random Forest in Python", "Time Series Analysis in Python: An Introduction", "The Failures of Common Sense", "Stock Analysis in Python", "A Review of the Coursera Machine Learning Specialization", "The Perils of Rare Events", "Stock Prediction in Python", "Correlation vs. Causation: An Example", "Real Life Superpowers", "Learn By Sharing", "Overfitting vs. Underfitting: A Conceptual Explanation", "Overfitting vs. Underfitting: A Complete Example", "How to Master New Skills", "Statistical Significance Explained", "Python is the Perfect Tool for any Problem", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "Markov Chain Monte Carlo in Python", "Slow Tech: Take Back Your Mind", "Bayes’ Rule Applied", "Data Visualization Hackathon Style", "Unintended Consequences and Goodhart’s Law", "Beyond Accuracy: Precision and Recall", "Controlling the Web with Python", "Data Visualization with Bokeh in Python, Part I: Getting Started", "Data Visualization with Bokeh in Python, Part II: Interactions", "Histograms and Density Plots in Python", "Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard", "Visualizing Data with Pairs Plots in Python", "Introduction to Bayesian Linear Regression", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "If your files are saved only on your laptop they might as well not exist!", "A Complete Machine Learning Project Walk-Through in Python: Part One", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Complete Machine Learning Walk-Through in Python: Part Three", "Automated Machine Learning on the Cloud in Python", "Machine Learning Kaggle Competition Part One: Getting Started", "Automated Feature Engineering in Python", "Machine Learning Kaggle Competition Part Two: Improving", "A Feature Selection Tool for Machine Learning in Python", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "Automated Machine Learning Hyperparameter Tuning in Python", "Machine Learning Kaggle Competition: Part Three Optimization", "How to get the right data? Trying asking for it.", "Why Automated Feature Engineering Will Change the Way You Do Machine Learning", "The most important part of a data science project is writing a blog post", "Parallelizing Feature Engineering with Dask", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One", "A “Data Science for Good” Machine Learning Project Walk-Through in Python: Part Two", "How to Put Fully Interactive, Runnable Code in a Medium Post", "An Implementation and Explanation of the Random Forest in Python", "Practical Advice for Data Science Writing", "Another Machine Learning Walk-Through and a Challenge", "Five Minutes to Your Own Website", "Converting Medium Posts to Markdown for Your Blog", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "Featuretools on Spark", "Neural Network Embeddings Explained", "Building a Recommendation System Using Neural Network Embeddings", "Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data", "The Power of I Don’t Know", "My Weaknesses as a Data Scientist", "Overcome Your Biases with Data", "Recurrent Neural Networks by Example in Python", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "How to Create Value with Machine Learning", "Feature Engineering: What Powers Machine Learning", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Deploying a Python Web App on AWS", "Transfer Learning with Convolutional Neural Networks in PyTorch", "Estimating Probabilities with Bayesian Modeling in Python", "Python and Slack: A Natural Match", "Jupyter Notebook Extensions", "How to Write a Jupyter Notebook Extension", "Please Steal My Articles", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Stop Regretting the Present", "Docker for Data Science Without the Hassle", "On Blame", "What I learned in 2018", "Data Science for Virtual Energy Audits", "Virtual Building Energy Audits — Preliminary Report", "Building Energy Data Analysis Part Four", "Building Energy Data Analysis Part Two", "Building Energy Data Analysis Part Three", "Building Energy Data Analysis Part One", "Books of 2018 Notes", "Books of 2018", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "Data Science with Medium Story Stats in Python", "Announcing the Reality Project", "The Disappearing Poor", "Has Global Violence Declined? 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Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "If your files are saved only on your laptop they might as well not exist!", "A Complete Machine Learning Project Walk-Through in Python: Part One", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Complete Machine Learning Walk-Through in Python: Part Three", "Automated Machine Learning on the Cloud in Python", "Machine Learning Kaggle Competition Part One: Getting Started", "Automated Feature Engineering in Python", "Machine Learning Kaggle Competition Part Two: Improving", "A Feature Selection Tool for Machine Learning in Python", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "Automated 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"hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#F4F4F4" }, "text": "henanigans Time Started and Time Published Overlaid" }, "xaxis": { "gridcolor": "#343434", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#A4A4A4" }, "title": { "font": { "color": "#A4A4A4" }, "text": "Time of day" }, "zerolinecolor": "#444444" }, "yaxis": { "gridcolor": "#343434", "linecolor": "black", "showgrid": true, "tickfont": { "color": "#A4A4A4" }, "title": { "font": { "color": "#A4A4A4" }, "text": "(%) of articles" }, "zerolinecolor": "#444444" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for t in themes:\n", " df[[\"time_published\", \"time_started\"]].iplot(\n", " kind=\"hist\",\n", " bins=48,\n", " linecolor=\"black\",\n", " opacity=0.8,\n", " histnorm=\"percent\",\n", " barmode=\"overlay\",\n", " xTitle=\"Time of day\",\n", " yTitle=\"(%) of articles\",\n", " theme=t,\n", " title=f\"{t} Time Started and Time Published Overlaid\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Colorscales in cufflinks" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:27.730110Z", "start_time": "2019-01-09T03:10:27.722727Z" } }, "outputs": [ { "data": { "text/html": [ "
accent
blues
brbg
bugn
bupu
dark2
dflt
ggplot
gnbu
greens
greys
oranges
original
orrd
paired
pastel1
pastel2
piyg
plotly
polar
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pubugn
puor
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purples
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spectral
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cufflinks.colors.scales()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Colorscales in Plotly" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:28.407650Z", "start_time": "2019-01-09T03:10:28.404817Z" } }, "outputs": [], "source": [ "colorscales = [\n", " \"Greys\",\n", " \"YlGnBu\",\n", " \"Greens\",\n", " \"YlOrRd\",\n", " \"Bluered\",\n", " \"RdBu\",\n", " \"Reds\",\n", " \"Blues\",\n", " \"Picnic\",\n", " \"Rainbow\",\n", " \"Portland\",\n", " \"Jet\",\n", " \"Hot\",\n", " \"Blackbody\",\n", " \"Earth\",\n", " \"Electric\",\n", " \"Viridis\",\n", " \"Cividis\",\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pie Chart" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:28.826407Z", "start_time": "2019-01-09T03:10:28.796779Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "labels": [ "Engineering @ Feature Labs", "None", "Noteworthy - The Journal Blog", "The Reality Project", "Towards Data Science" ], "marker": { "colors": [ "rgba(255, 153, 51, 1.0)", "rgba(55, 128, 191, 1.0)", "rgba(50, 171, 96, 1.0)", "rgba(128, 0, 128, 1.0)", "rgba(219, 64, 82, 1.0)" ] }, "name": "", "type": "pie", "values": [ 1, 41, 1, 3, 80 ] } ], "layout": { "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "title": "Percentage of Reads by Publication", "titlefont": { "color": "#4D5663" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = df.groupby(\"publication\", as_index=False)[\"reads\"].count().iplot(\n", " kind=\"pie\",\n", " labels=\"publication\",\n", " values=\"reads\",\n", " title=\"Percentage of Reads by Publication\",\n", ")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:28.840537Z", "start_time": "2019-01-09T03:10:28.827996Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "labels": [ "Engineering @ Feature Labs", "None", "Noteworthy - The Journal Blog", "The Reality Project", "Towards Data Science" ], "marker": { "colors": [ "rgba(255, 153, 51, 1.0)", "rgba(55, 128, 191, 1.0)", "rgba(50, 171, 96, 1.0)", "rgba(128, 0, 128, 1.0)", "rgba(219, 64, 82, 1.0)" ] }, "name": "", "type": "pie", "values": [ 2087, 186788, 2172, 7641, 185562 ] } ], "layout": { "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" } }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "title": "Percentage of Words by Publication", "titlefont": { "color": "#4D5663" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.groupby(\"publication\", as_index=False)[\"word_count\"].sum().iplot(\n", " kind=\"pie\",\n", " labels=\"publication\",\n", " values=\"word_count\",\n", " title=\"Percentage of Words by Publication\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D Surface Plot" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:29.293864Z", "start_time": "2019-01-09T03:10:29.186579Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "colorscale": [ [ 0, "rgb(255, 153, 51)" ], [ 0.1111111111111111, "rgb(55, 128, 191)" ], [ 0.2222222222222222, "rgb(50, 171, 96)" ], [ 0.3333333333333333, "rgb(128, 0, 128)" ], [ 0.4444444444444444, "rgb(219, 64, 82)" ], [ 0.5555555555555556, "rgb(0, 128, 128)" ], [ 0.6666666666666666, 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"#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "color": "#4D5663" }, "text": "3D Bubble Plot Sized by Read Ratio and Colored by Type" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.iplot(\n", " x=\"word_count\",\n", " y=\"views\",\n", " z=\"fans\",\n", " kind=\"bubble3d\",\n", " xTitle=\"Word Count\",\n", " yTitle=\"Views\",\n", " zTitle=\"Fans\",\n", " theme=\"pearl\",\n", " size=\"read_ratio\",\n", " categories=\"type\",\n", " title=\"3D Bubble Plot Sized by Read Ratio and Colored by Type\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Editing in Plot Studio\n", "\n", "You can take any of the plots here and edit them in the plot studio. This allows us to finish up plots for showing to others or sharing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../medium/images/reads_vs_word_count.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../medium/images/annotations.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../medium/images/word_count_and_views.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions\n", "\n", "Hopefully you now have a little idea of the capabilties of plotly + cufflinks. We have only scrachted the surface of this library, so check out the [cufflinks documentation](https://github.com/santosjorge/cufflinks) and the [plotly documentation](https://plot.ly/python/) for plently of more examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../medium/images/plotting_enjoyment.png)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2019-01-09T03:10:34.441679Z", "start_time": "2019-01-09T03:10:32.484114Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "marker": { "size": 12, "symbol": "circle" }, "mode": "markers", "name": "claps", "text": "", "type": "scatter", "x": [ 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import plotly.graph_objs as go\n", "import cufflinks\n", "\n", "cufflinks.go_offline()\n", "\n", "layout = go.Layout(xaxis=dict(type=\"date\"))\n", "df = pd.read_parquet(\n", " \"https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/data/medium_data_2019_01_06?raw=true\"\n", ")\n", "\n", "df.iplot(\n", " kind=\"scatter\",\n", " y=\"claps\",\n", " x=\"published_date\",\n", " mode=\"markers\",\n", " title=\"Article Claps over Time\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }