DataCamp.Project_001_The GitHub History of the Scala Language.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: The GitHub History of the Scala Language\n",
    "\n",
    "### 1. Scala's real-world project repository data\n",
    "\n",
    "With almost 30k commits and a history spanning over ten years, Scala is a mature programming language. It is a general-purpose programming language that has recently become another prominent language for data scientists.\n",
    "\n",
    "Scala is also an open source project. Open source projects have the advantage that their entire development histories -- who made changes, what was changed, code reviews, etc. -- are publicly available.\n",
    "\n",
    "We're going to read in, clean up, and visualize the real world project repository of Scala that spans data from a version control system (Git) as well as a project hosting site (GitHub). We will find out who has had the most influence on its development and who are the experts.\n",
    "\n",
    "The dataset we will use, which has been previously mined and extracted from GitHub, is comprised of three files:\n",
    "\n",
    "1. `pulls_2011-2013.csv` contains the basic information about the pull requests, and spans from the end of 2011 up to (but not including) 2014.\n",
    "2. `pulls_2014-2018.csv` contains identical information, and spans from 2014 up to 2018.\n",
    "3. `pull_files.csv` contains the files that were modified by each pull request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Loading in the data\n",
    "pulls_one = pd.read_csv('datasets/pulls_2011-2013.csv')\n",
    "pulls_two = pd.read_csv('datasets/pulls_2014-2018.csv')\n",
    "pull_files = pd.read_csv('datasets/pull_files.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        pid         user                  date\n",
      "0  11166973  VladimirNik  2013-12-31T23:10:55Z\n",
      "1  11161892      Ichoran  2013-12-31T16:55:47Z\n",
      "2  11153894      Ichoran  2013-12-31T02:41:13Z\n",
      "3  11151917      rklaehn  2013-12-30T23:45:47Z\n",
      "4  11131244        qerub  2013-12-29T17:21:01Z\n",
      "         pid       user                  date\n",
      "0  163314316     hrhino  2018-01-16T23:29:16Z\n",
      "1  163061502   joroKr21  2018-01-15T23:44:52Z\n",
      "2  163057333  mkeskells  2018-01-15T23:05:06Z\n",
      "3  162985594      lrytz  2018-01-15T15:52:39Z\n",
      "4  162838837  zuvizudar  2018-01-14T19:16:16Z\n",
      "         pid                                   file\n",
      "0  163314316        test/files/pos/t5638/Among.java\n",
      "1  163314316       test/files/pos/t5638/Usage.scala\n",
      "2  163314316             test/files/pos/t9291.scala\n",
      "3  163314316             test/files/run/t8348.check\n",
      "4  163314316  test/files/run/t8348/TableColumn.java\n"
     ]
    }
   ],
   "source": [
    "print(pulls_one.head(5))\n",
    "\n",
    "print(pulls_two.head(5))\n",
    "\n",
    "print(pull_files.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Preparing and cleaning the data\n",
    "\n",
    "First, we will need to combine the data from the two separate pull DataFrames.\n",
    "\n",
    "Next, the raw data extracted from GitHub contains dates in the ISO8601 format. However, `pandas` imports them as regular strings. To make our analysis easier, we need to convert the strings into Python's `DateTime` objects. `DateTime` objects have the important property that they can be compared and sorted.\n",
    "\n",
    "The pull request times are all in UTC (also known as Coordinated Universal Time). The commit times, however, are in the local time of the author with time zone information (number of hours difference from UTC). To make comparisons easy, we should convert all times to UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append pulls_one to pulls_two\n",
    "pulls = pd.concat([pulls_one,pulls_two], sort=True)\n",
    "\n",
    "# Convert the date for the pulls object\n",
    "pulls['date'] = pd.to_datetime(pulls['date'], format='%Y-%m-%dT%H:%M:%SZ', utc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       date       pid         user\n",
      "0 2013-12-31 23:10:55+00:00  11166973  VladimirNik\n",
      "1 2013-12-31 16:55:47+00:00  11161892      Ichoran\n",
      "2 2013-12-31 02:41:13+00:00  11153894      Ichoran\n",
      "3 2013-12-30 23:45:47+00:00  11151917      rklaehn\n",
      "4 2013-12-29 17:21:01+00:00  11131244        qerub\n"
     ]
    }
   ],
   "source": [
    "print(pulls.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Merging the DataFrames\n",
    "\n",
    "The data extracted comes in two separate files. Merging the two DataFrames will make it easier for us to analyze the data in the future tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the two DataFrames\n",
    "data = pd.merge(pulls, pull_files, on='pid', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Is the project still actively maintained?\n",
    "\n",
    "The activity in an open source project is not very consistent. Some projects might be active for many years after the initial release, while others can slowly taper out into oblivion. Before committing to contributing to a project, it is important to understand the state of the project. Is development going steadily, or is there a drop? Has the project been abandoned altogether?\n",
    "\n",
    "The data used in this project was collected in January of 2018. We are interested in the evolution of the number of contributions up to that date.\n",
    "\n",
    "For Scala, we will do this by plotting a chart of the project's activity. We will calculate the number of pull requests submitted each (calendar) month during the project's lifetime. We will then plot these numbers to see the trend of contributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Num pull request')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Create a column that will store the month\n",
    "data['month'] = data['date'].dt.month\n",
    "\n",
    "# Create a column that will store the year\n",
    "data['year'] = data['date'].dt.year\n",
    "\n",
    "data['month_year'] = data.apply(lambda x: str(x['date'].year) + '-' + '%02d'% x['date'].month, axis=1)\n",
    "\n",
    "# Group by the month and year and count the pull requests\n",
    "counts = data.groupby(['month_year'])['pid'].count()\n",
    "# Plot the results\n",
    "ax = counts.plot(kind='bar', figsize = (12,4))\n",
    "ax.set_title('Num of pull request by month')\n",
    "ax.set_xlabel('Year-Month')\n",
    "ax.set_ylabel('Num pull request')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Is there camaraderie in the project?\n",
    "\n",
    "The organizational structure varies from one project to another, and it can influence your success as a contributor. A project that has a very small community might not be the best one to start working on. The small community might indicate a high barrier of entry. This can be caused by several factors, including a community that is reluctant to accept pull requests from \"outsiders,\" that the code base is hard to work with, etc. However, a large community can serve as an indicator that the project is regularly accepting pull requests from new contributors. Such a project would be a good place to start.\n",
    "\n",
    "In order to evaluate the dynamics of the community, we will plot a histogram of the number of pull requests submitted by each user. A distribution that shows that there are few people that only contribute a small number of pull requests can be used as in indicator that the project is not welcoming of new contributors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Num pull requests')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Required for matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "# Group by the submitter\n",
    "by_user = data.groupby('user')['pid'].count()\n",
    "\n",
    "# Plot the histogram\n",
    "ax = by_user.hist(bins=50)\n",
    "ax.set_title('Num pull requests by user')\n",
    "ax.set_xlabel('User')\n",
    "ax.set_ylabel('Num pull requests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. What files were changed in the last ten pull requests?\n",
    "\n",
    "Choosing the right place to make a contribution is as important as choosing the project to contribute to. Some parts of the code might be stable, some might be dead. Contributing there might not have the most impact. Therefore it is important to understand the parts of the system that have been recently changed. This allows us to pinpoint the \"hot\" areas of the code where most of the activity is happening. Focusing on those parts might not the most effective use of our times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LICENSE',\n",
       " 'doc/LICENSE.md',\n",
       " 'doc/License.rtf',\n",
       " 'project/VersionUtil.scala',\n",
       " 'src/compiler/scala/reflect/reify/phases/Calculate.scala',\n",
       " 'src/compiler/scala/tools/nsc/backend/jvm/BCodeHelpers.scala',\n",
       " 'src/compiler/scala/tools/nsc/backend/jvm/PostProcessor.scala',\n",
       " 'src/compiler/scala/tools/nsc/backend/jvm/analysis/BackendUtils.scala',\n",
       " 'src/compiler/scala/tools/nsc/profile/AsyncHelper.scala',\n",
       " 'src/compiler/scala/tools/nsc/profile/Profiler.scala',\n",
       " 'src/compiler/scala/tools/nsc/symtab/classfile/ClassfileParser.scala',\n",
       " 'src/compiler/scala/tools/nsc/typechecker/Contexts.scala',\n",
       " 'src/library/scala/Predef.scala',\n",
       " 'src/library/scala/concurrent/Lock.scala',\n",
       " 'src/library/scala/util/Properties.scala',\n",
       " 'src/reflect/scala/reflect/internal/pickling/ByteCodecs.scala',\n",
       " 'src/reflect/scala/reflect/internal/tpe/GlbLubs.scala',\n",
       " 'src/scaladoc/scala/tools/nsc/doc/html/page/Entity.scala',\n",
       " 'src/scalap/decoder.properties',\n",
       " 'test/files/neg/leibniz-liskov.check',\n",
       " 'test/files/neg/leibniz-liskov.scala',\n",
       " 'test/files/pos/leibniz-liskov.scala',\n",
       " 'test/files/pos/leibniz_liskov.scala',\n",
       " 'test/files/pos/parallel-classloader.scala',\n",
       " 'test/files/pos/t10568/Converter.java',\n",
       " 'test/files/pos/t10568/Impl.scala',\n",
       " 'test/files/pos/t10686.scala',\n",
       " 'test/files/pos/t5638/Among.java',\n",
       " 'test/files/pos/t5638/Usage.scala',\n",
       " 'test/files/pos/t9291.scala',\n",
       " 'test/files/run/t8348.check',\n",
       " 'test/files/run/t8348/TableColumn.java',\n",
       " 'test/files/run/t8348/TableColumnImpl.java',\n",
       " 'test/files/run/t8348/Test.scala'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the last 10 pull requests\n",
    "last_10 = pulls.nlargest(10, 'date')\n",
    "\n",
    "# Join the two data sets\n",
    "joined_pr = pd.merge(last_10, pull_files, on='pid', how='inner')\n",
    "\n",
    "# Identify the unique files\n",
    "files = set(joined_pr['file'])\n",
    "\n",
    "# Print the results\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Who made the most pull requests to a given file?\n",
    "\n",
    "When contributing to a project, we might need some guidance. We might find ourselves needing some information regarding the codebase. It is important direct any questions to the right person. Contributors to open source projects generally have other day jobs, so their time is limited. It is important to address our questions to the right people. One way to identify the right target for our inquiries is by using their contribution history.\n",
    "\n",
    "We identified `src/compiler/scala/reflect/reify/phases/Calculate.scala` as being recently changed. We are interested in the top 3 developers who changed that file. Those developers are the ones most likely to have the best understanding of the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user\n",
      "xeno-by     11\n",
      "retronym     5\n",
      "soc          4\n",
      "Name: pid, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# This is the file we are interested in:\n",
    "file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala'\n",
    "\n",
    "# Identify the commits that changed the file\n",
    "file_pr = data[data['file'] == 'src/compiler/scala/reflect/reify/phases/Calculate.scala']\n",
    "\n",
    "# Count the number of changes made by each developer\n",
    "author_counts = file_pr.groupby('user')['pid'].count()\n",
    "\n",
    "print(author_counts.nlargest(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Who made the last ten pull requests on a given file?\n",
    "\n",
    "Open source projects suffer from fluctuating membership. This makes the problem of finding the right person more challenging: the person has to be knowledgeable and still be involved in the project. A person that contributed a lot in the past might no longer be available (or willing) to help. To get a better understanding, we need to investigate the more recent history of that particular part of the system.\n",
    "\n",
    "Like in the previous task, we will look at the history of `src/compiler/scala/reflect/reify/phases/Calculate.scala`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bjornregnell', 'retronym', 'soc', 'starblood', 'xeno-by', 'zuvizudar'}"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala'\n",
    "\n",
    "# Select the pull requests that changed the target file\n",
    "file_pr = data[data['file'] == file]\n",
    "\n",
    "# Merge the obtained results with the pulls DataFrame\n",
    "joined_pr = pd.merge(file_pr, pulls, on='pid', how='inner')\n",
    "\n",
    "# Find the users of the last 10 most recent pull requests\n",
    "users_last_10 = set(joined_pr.nlargest(10, 'date_x')['user_x'])\n",
    "\n",
    "# Printing the results\n",
    "users_last_10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. The pull requests of two special developers\n",
    "\n",
    "Now that we have identified two potential contacts in the projects, we need to find the person who was most involved in the project in recent times. That person is most likely to answer our questions. For each calendar year, we are interested in understanding the number of pull requests the authors submitted. This will give us a high-level image of their contribution trend to the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pull requests')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# The developers we are interested in\n",
    "authors = ['xeno-by', 'soc']\n",
    "\n",
    "# Get all the developers' pull requests\n",
    "by_author = pulls[pulls['user'].isin(authors)]\n",
    "\n",
    "# Count the number of pull requests submitted each year\n",
    "counts = by_author.groupby(['user', by_author['date'].dt.year]).agg({'pid': 'count'}).reset_index()\n",
    "\n",
    "# Convert the table to a wide format\n",
    "counts_wide = counts.pivot_table(index='date', columns='user', values='pid', fill_value=0)\n",
    "\n",
    "# Plot the results\n",
    "ax = counts_wide.plot(kind='bar')\n",
    "ax.set_title('Num of pull requests by user')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Pull requests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Visualizing the contributions of each developer\n",
    "\n",
    "As mentioned before, it is important to make a distinction between the global expertise and contribution levels and the contribution levels at a more granular level (file, submodule, etc.) In our case, we want to see which of our two developers of interest have the most experience with the code in a given file. We will measure experience by the number of pull requests submitted that affect that file and how recent those pull requests were submitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pull requests')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "authors = ['xeno-by', 'soc']\n",
    "file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala'\n",
    "\n",
    "# Select the pull requests submitted by the authors, from the `data` DataFrame\n",
    "by_author = data[data['user'].isin(authors)]\n",
    "\n",
    "# Select the pull requests that affect the file\n",
    "by_file = by_author[by_author['file'] == file]\n",
    "\n",
    "# Group and count the number of PRs done by each user each year\n",
    "grouped = by_file.groupby(['user', by_file['date'].dt.year]).count()['pid'].reset_index()\n",
    "\n",
    "# Transform the data into a wide format\n",
    "by_file_wide = grouped.pivot_table(index='date', columns='user', values='pid', fill_value=0)\n",
    "\n",
    "# Plot the results\n",
    "ax = by_file_wide.plot(kind='bar')\n",
    "ax.set_title('Pulls by year and user on specific file')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Pull requests')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "903016fd19c4268e3b50e09463256708479942c06a3978064b878120a2143b2b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}