{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Software Analytics Mini Tutorial Part II: pandas and matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "This series of notebooks are a simple mini tutorial to introduce you to the basic functionality of Jupyter, Python, pandas and matplotlib. The comprehensive explanations should guide you to be able to analyze software data on your own. Therefore, the examples is chosen in such a way that we come across the typical methods in a data analysis. Have fun!\n", "\n", "*This is part II: The basic of the data analysis framework pandas and the visualization library matplotlib. For Jupyter Notebook and Python basics, go to [the other tutorial](00%20Jupyter%20Notebook%20and%20Python%20basics.ipynb).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I. Stating Question\n", "In this notebook, we want to take a closer look at the development history of the open source project \"Linux\" based on the history of the corresponding GitHub mirror repository.\n", "\n", "**We want to find out which people are the TOP 10 contributors.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II. Exploratory Data Analysis\n", "\n", "## The dataset\n", "\n", "A local clone of the GitHub repository https://github.com/torvalds/linux/ was created by using the command \n", "\n", "```\n", "git clone https://github.com/torvalds/linux.git\n", "```\n", "\n", "The relevant parts of the history for this analysis were produced by using\n", "\n", "```\n", "git log --pretty=\"%ad,%aN\" --no-merges > git_log_linux_authors_timestamps.csv\n", "```\n", "\n", "This command returned the commit timestamp (`%ad`) and the author name (`%aN`) for each commit of the Git repository. The corresponding values are separated by commas. We also indicated that we do not want to receive merge commits (via `--no-merges`). The result of the output was saved in the file `git_log_linux_authors_timestamps.csv` and compressed for a optimized file size with `gzip` to the file `git_log_linux_authors_timestamps.gz`.\n", "\n", "_Note: For an optimized demo, headers and the separator has been changed manually in the provided dataset to get through this analysis more easily. The differences can be seen at https://www.feststelltaste.de/developers-habits-linux-edition/, which was done with the original dataset._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting to know pandas\n", "Pandas is a data analysis tool written in Python (and C), which is perfect for the analysis of tabular data due to the use of effective data structures and built-in statistics functions. \n", "\n", "#### Load a module\n", "1. import the module `pandas` with `import as ` as abbreviated `pd`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check loading of the right module\n", "\n", "1. write `pd` and attach a `?` behind it\n", "1. make the documentation disappear with the `Esc` key." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [], "source": [ "pd?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import dataset\n", "1. use the `read_csv` method from pandas to read in the dataset `../datasets/git_log_linux_authors_timestamps.gz`.\n", "1. write the result into the variable `log`.\n", "1. display the first five entries of `log` with the `head()` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " timestamp author\n", "0 2017-12-31 14:47:43 Linus Torvalds\n", "1 2017-12-31 13:13:56 Linus Torvalds\n", "2 2017-12-31 13:03:05 Linus Torvalds\n", "3 2017-12-31 12:30:34 Linus Torvalds\n", "4 2017-12-31 12:29:02 Linus Torvalds" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log = pd.read_csv(\"../datasets/git_log_linux_authors_timestamps.gz\")\n", "log.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Getting to know the data\n", "1. call `info()` on `log`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 723214 entries, 0 to 723213\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 timestamp 723214 non-null object\n", " 1 author 723213 non-null object\n", "dtypes: object(2)\n", "memory usage: 11.0+ MB\n" ] } ], "source": [ "log.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A glimpse at the result\n", "\n", "We see that `log` is \n", "* a **DataFrame** that consists of\n", " * 723214 **entries** (also called **rows**)\n", " * 2 **columns** (so-called **Series**): `timestamp` and `author`\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Access a Series\n", "1. access the Series `author` by either\n", " * using the `['']` notation or\n", " * by directly using the name of the `Series`*.\n", "1. display the last five entries by using the `tail()` method\n", "\n", " \n", "**A word of caution: In this tutorial, we access a Series directly with the `.` notation (e. g. `log.author`). This works only if the names of the Series are different from the provided functions and properties of a Series. E. g. it doesn't work, when you try to access a Series named `count`, because `count()` is a function of a Series. Here, you have to use the `['']` notation (e.g. `log['count']`. The benefit of the direct access is that you are able to use auto completion (which is good at the beginning). The drawback is creating a source of potential future problems when pandas evolves and gets new functions and properties that are in conflict with your column names.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "text/plain": [ "723209 akpm@osdl.org\n", "723210 akpm@osdl.org\n", "723211 Neil Brown\n", "723212 Christoph Lameter\n", "723213 Linus Torvalds\n", "Name: author, dtype: object" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log.author.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Discussion: Think a little bit about possible data quality issues in this dataset." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "delete" ] }, "source": [ "*Possible answers:*\n", "\n", "* Some authors have an email address as name\n", "* There could be multiple names for one person" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III. Formal Modeling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Top 10 authors\n", "#### Execute a method of a Series\n", "1. count the number of the occuring author names with the method `value_counts()`.\n", "1. save the result in the variable `top10`*.\n", "1. list the first ten results of `top10`.\n", "\n", "**Note: Normally we would choose a more expressive name like `top10_contributors` to make sure we and others now what's in the variable. But in a tutorial, we don't want keep things short.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "text/plain": [ "Linus Torvalds 24259\n", "David S. Miller 9563\n", "Mark Brown 6917\n", "Takashi Iwai 6293\n", "Al Viro 6064\n", "H Hartley Sweeten 5942\n", "Ingo Molnar 5462\n", "Mauro Carvalho Chehab 5384\n", "Arnd Bergmann 5305\n", "Greg Kroah-Hartman 4687\n", "Name: author, dtype: int64" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top10 = log.author.value_counts().head(10)\n", "top10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IV. Interpretation\n", "\n", "##### Discussion\n", "\n", "Discuss the result:\n", "* Which insights does this list provided?\n", "* Which fallacies chould exist?\n", "* Do you find anything awkward?" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "delete" ] }, "source": [ "*Possible answers:*\n", "\n", "* Linux Torvalds has too many commits (Reason: In 2005, he copied the original repository to GitHub without keeping the existing history).\n", "* We ignored different commit habits of developers. Some may commit just once a week. Others maybe several times a day.\n", "* It's unclear what \"actionable insights\" are that impact our daily work (Patience, please, we're just getting started. :-) )." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## V. Communication\n", "\n", "Next, we want to visualize or plot the result. To display the plotting result of the internally used plotting library `matplotlib` directly in the notebook. This needs to be configured.\n", "\n", "#### Plot diagram\n", "1. tell Jupyter with `%matplotlib inline` to display generated graphics directly in the notebook.\n", "1. create a diagram of the Series `top10` with `plot()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "top10.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Discussion: What does this visualization display? Is it appropriate? Why and why not?" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "delete" ] }, "source": [ "*Possible answers:*\n", "\n", "* A line chart is the wrong representation for this kind of data.\n", "* There is no information what the axes mean.\n", "* The x-axis is missing values.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a bar chart\n", "1. call the `bar()` sub-method of `plot` for the data in `top10`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top10.plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Improve the output\n", "1. add a `;` to the call above and re-execute it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top10.plot.bar();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Question\n", "\n", "* What has changed?" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "delete" ] }, "source": [ "*Possible answer:*\n", "\n", "* The cryptic output text `SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top10.plot.pie();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Discussion: Why are pie charts considered harmful?" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "delete" ] }, "source": [ "*Possible answers:*\n", "\n", "* Comparing between entries within a pie chart is not easy\n", "* Comparing the values between different pie charts is very hard (especially when colors changed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a more beautiful pie chart (nevertheless)\n", "\n", "1. create another pie chart, this time directly with `plot()` of the Series `top10` and with the following parameters: \n", " * `kind=\"pie\"`\n", " * `figsize=[5,5]`\n", " * `title=\"Top 10 Contributors\"`\n", " * `label=\"\"` \n", "\n", "Tip: Use auto completion." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "delete" ] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "###### SOLUTION (Click the arrow on the left side if a hint is needed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [], "deletable": false, "editable": false, "hidden": true, "run_control": { "frozen": true } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top10.plot.pie(\n", " figsize=[5,5],\n", " title=\"Top 10 Contributors\",\n", " label=\"\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "This was a complete walkthrough through the basics of Jupyter Notebook, Python, pandas and matplotlib.\n", "\n", "The next sessions introduce you a little more advanced techniques where you get to know the power of this stack!\n", "\n", "If you want to dive deeper into this topic, take a look at my [blog posts on that topic](http://www.feststelltaste.de/category/software-analytics/). I'm looking forward to your comments and feedback on [GitHub](https://github.com/feststelltaste/software-analytics-workshop/issues) or on [Twitter](https://www.twitter.com/feststelltaste)!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 2 }