/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
color: black;
}
.ansired {
color: darkred;
}
.ansigreen {
color: darkgreen;
}
.ansiyellow {
color: #c4a000;
}
.ansiblue {
color: darkblue;
}
.ansipurple {
color: darkviolet;
}
.ansicyan {
color: steelblue;
}
.ansigray {
color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
background-color: black;
}
.ansibgred {
background-color: red;
}
.ansibggreen {
background-color: green;
}
.ansibgyellow {
background-color: yellow;
}
.ansibgblue {
background-color: blue;
}
.ansibgpurple {
background-color: magenta;
}
.ansibgcyan {
background-color: cyan;
}
.ansibggray {
background-color: gray;
}
div.cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
border-radius: 2px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
border-width: 1px;
border-style: solid;
border-color: transparent;
width: 100%;
padding: 5px;
/* This acts as a spacer between cells, that is outside the border */
margin: 0px;
outline: none;
border-left-width: 1px;
padding-left: 5px;
background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
border-left-color: #90CAF9;
border-left-color: #E3F2FD;
border-left-width: 1px;
padding-left: 5px;
border-right-color: #E3F2FD;
border-right-width: 1px;
background: #E3F2FD;
}
@media print {
div.cell.jupyter-soft-selected {
border-color: transparent;
}
}
div.cell.selected {
border-color: #ababab;
border-left-width: 0px;
padding-left: 6px;
background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
div.cell.selected {
border-color: transparent;
}
}
div.cell.selected.jupyter-soft-selected {
border-left-width: 0;
padding-left: 6px;
background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
border-color: #66BB6A;
border-left-width: 0px;
padding-left: 6px;
background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
.edit_mode div.cell.selected {
border-color: transparent;
}
}
.prompt {
/* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
min-width: 14ex;
/* This padding is tuned to match the padding on the CodeMirror editor. */
padding: 0.4em;
margin: 0px;
font-family: monospace;
text-align: right;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
/* Don't highlight prompt number selection */
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
/* Use default cursor */
cursor: default;
}
@media (max-width: 540px) {
.prompt {
text-align: left;
}
}
div.inner_cell {
min-width: 0;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
border: 1px solid #cfcfcf;
border-radius: 2px;
background: #f7f7f7;
line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
is no content in the output_subarea and the prompt. The main purpose of this is
to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
padding-top: 0;
padding-bottom: 0;
}
div.unrecognized_cell {
padding: 5px 5px 5px 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.unrecognized_cell .inner_cell {
border-radius: 2px;
padding: 5px;
font-weight: bold;
color: red;
border: 1px solid #cfcfcf;
background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
color: inherit;
text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
color: inherit;
text-decoration: none;
}
@media (max-width: 540px) {
div.unrecognized_cell > div.prompt {
display: none;
}
}
div.code_cell {
/* avoid page breaking on code cells when printing */
}
@media print {
div.code_cell {
page-break-inside: avoid;
}
}
/* any special styling for code cells that are currently running goes here */
div.input {
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.input {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
color: #303F9F;
border-top: 1px solid transparent;
}
div.input_area > div.highlight {
margin: 0.4em;
border: none;
padding: 0px;
background-color: transparent;
}
div.input_area > div.highlight > pre {
margin: 0px;
border: none;
padding: 0px;
background-color: transparent;
}
/* The following gets added to the if it is detected that the user has a
* monospace font with inconsistent normal/bold/italic height. See
* notebookmain.js. Such fonts will have keywords vertically offset with
* respect to the rest of the text. The user should select a better font.
* See: https://github.com/ipython/ipython/issues/1503
*
* .CodeMirror span {
* vertical-align: bottom;
* }
*/
.CodeMirror {
line-height: 1.21429em;
/* Changed from 1em to our global default */
font-size: 14px;
height: auto;
/* Changed to auto to autogrow */
background: none;
/* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
/* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
/* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
overflow-y: hidden;
overflow-x: auto;
}
.CodeMirror-lines {
/* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
/* we have set a different line-height and want this to scale with that. */
padding: 0.4em;
}
.CodeMirror-linenumber {
padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
border-bottom-left-radius: 2px;
border-top-left-radius: 2px;
}
.CodeMirror pre {
/* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
/* .CodeMirror-lines */
padding: 0;
border: 0;
border-radius: 0;
}
/*
Original style from softwaremaniacs.org (c) Ivan Sagalaev
Adapted from GitHub theme
*/
.highlight-base {
color: #000;
}
.highlight-variable {
color: #000;
}
.highlight-variable-2 {
color: #1a1a1a;
}
.highlight-variable-3 {
color: #333333;
}
.highlight-string {
color: #BA2121;
}
.highlight-comment {
color: #408080;
font-style: italic;
}
.highlight-number {
color: #080;
}
.highlight-atom {
color: #88F;
}
.highlight-keyword {
color: #008000;
font-weight: bold;
}
.highlight-builtin {
color: #008000;
}
.highlight-error {
color: #f00;
}
.highlight-operator {
color: #AA22FF;
font-weight: bold;
}
.highlight-meta {
color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
color: #00f;
}
.highlight-string-2 {
color: #f50;
}
.highlight-qualifier {
color: #555;
}
.highlight-bracket {
color: #997;
}
.highlight-tag {
color: #170;
}
.highlight-attribute {
color: #00c;
}
.highlight-header {
color: blue;
}
.highlight-quote {
color: #090;
}
.highlight-link {
color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
color: #008000;
font-weight: bold;
}
.cm-s-ipython span.cm-atom {
color: #88F;
}
.cm-s-ipython span.cm-number {
color: #080;
}
.cm-s-ipython span.cm-def {
color: #00f;
}
.cm-s-ipython span.cm-variable {
color: #000;
}
.cm-s-ipython span.cm-operator {
color: #AA22FF;
font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
color: #333333;
}
.cm-s-ipython span.cm-comment {
color: #408080;
font-style: italic;
}
.cm-s-ipython span.cm-string {
color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
color: #f50;
}
.cm-s-ipython span.cm-meta {
color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
color: #555;
}
.cm-s-ipython span.cm-builtin {
color: #008000;
}
.cm-s-ipython span.cm-bracket {
color: #997;
}
.cm-s-ipython span.cm-tag {
color: #170;
}
.cm-s-ipython span.cm-attribute {
color: #00c;
}
.cm-s-ipython span.cm-header {
color: blue;
}
.cm-s-ipython span.cm-quote {
color: #090;
}
.cm-s-ipython span.cm-link {
color: #00c;
}
.cm-s-ipython span.cm-error {
color: #f00;
}
.cm-s-ipython span.cm-tab {
background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
background-position: right;
background-repeat: no-repeat;
}
div.output_wrapper {
/* this position must be relative to enable descendents to be absolute within it */
position: relative;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
/* ideally, this would be max-height, but FF barfs all over that */
height: 24em;
/* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
width: 100%;
overflow: auto;
border-radius: 2px;
-webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
margin: 0px;
padding: 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
div.out_prompt_overlay {
height: 100%;
padding: 0px 0.4em;
position: absolute;
border-radius: 2px;
}
div.out_prompt_overlay:hover {
/* use inner shadow to get border that is computed the same on WebKit/FF */
-webkit-box-shadow: inset 0 0 1px #000;
box-shadow: inset 0 0 1px #000;
background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
padding: 0px;
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.output_area .MathJax_Display {
text-align: left !important;
}
div.output_area
div.output_area
div.output_area img,
div.output_area svg {
max-width: 100%;
height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
max-width: none;
}
/* This is needed to protect the pre formating from global settings such
as that of bootstrap */
.output {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
@media (max-width: 540px) {
div.output_area {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
div.output_area pre {
margin: 0;
padding: 0;
border: 0;
vertical-align: baseline;
color: black;
background-color: transparent;
border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
the prompt div. */
div.output_subarea {
overflow-x: auto;
padding: 0.4em;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
output types */
/* all text output has this class: */
div.output_text {
text-align: left;
color: #000;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
background: #fdd;
/* very light red background for stderr */
}
div.output_latex {
text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
padding: 0;
}
.js-error {
color: darkred;
}
/* raw_input styles */
div.raw_input_container {
line-height: 1.21429em;
padding-top: 5px;
}
pre.raw_input_prompt {
/* nothing needed here. */
}
input.raw_input {
font-family: monospace;
font-size: inherit;
color: inherit;
width: auto;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0em 0.25em;
margin: 0em 0.25em;
}
input.raw_input:focus {
box-shadow: none;
}
p.p-space {
margin-bottom: 10px;
}
div.output_unrecognized {
padding: 5px;
font-weight: bold;
color: red;
}
div.output_unrecognized a {
color: inherit;
text-decoration: none;
}
div.output_unrecognized a:hover {
color: inherit;
text-decoration: none;
}
.rendered_html {
color: #000;
/* any extras will just be numbers: */
}
.rendered_html :link {
text-decoration: underline;
}
.rendered_html :visited {
text-decoration: underline;
}
.rendered_html h1:first-child {
margin-top: 0.538em;
}
.rendered_html h2:first-child {
margin-top: 0.636em;
}
.rendered_html h3:first-child {
margin-top: 0.777em;
}
.rendered_html h4:first-child {
margin-top: 1em;
}
.rendered_html h5:first-child {
margin-top: 1em;
}
.rendered_html h6:first-child {
margin-top: 1em;
}
.rendered_html * + ul {
margin-top: 1em;
}
.rendered_html * + ol {
margin-top: 1em;
}
.rendered_html pre,
.rendered_html tr,
.rendered_html th,
.rendered_html td,
.rendered_html * + table {
margin-top: 1em;
}
.rendered_html * + p {
margin-top: 1em;
}
.rendered_html * + img {
margin-top: 1em;
}
.rendered_html img,
.rendered_html img.unconfined,
div.text_cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.text_cell > div.prompt {
display: none;
}
}
div.text_cell_render {
/*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
outline: none;
resize: none;
width: inherit;
border-style: none;
padding: 0.5em 0.5em 0.5em 0.4em;
color: #000;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
a.anchor-link:link {
text-decoration: none;
padding: 0px 20px;
visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
visibility: visible;
}
.text_cell.rendered .input_area {
display: none;
}
.text_cell.rendered
.text_cell.unrendered .text_cell_render {
display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
font-weight: bold;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
font-size: 185.7%;
}
.cm-header-2 {
font-size: 157.1%;
}
.cm-header-3 {
font-size: 128.6%;
}
.cm-header-4 {
font-size: 110%;
}
.cm-header-5 {
font-size: 100%;
font-style: italic;
}
.cm-header-6 {
font-size: 100%;
font-style: italic;
}
.highlight .hll { background-color: #ffffcc }
.highlight { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
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Pybites Community Branch Activity¶
I wanted to play around with a dataset and see what I could find out about it. I decided on analyzing the little bit of data that I could collect from Github without having to use an OAuth key, which limits it to just 300 events.
To Run All of The Cells¶
You have the option of running each of the cells one at a time or you can just run them all in sequential order. Selecting a cell and either clicking on the Run button on the menu or using the key combination Shift+Enter will run the code in that cell if its code.
To run them all you will have to use the menu: Cell > Run All
In [1]:
importjsonfromcollectionsimportCounterfrompathlibimportPathimportmatplotlib.patchesasmpatchesimportmatplotlib.pyplotaspltimportnumpyasnpimportpandasaspdimportrequestsimportseabornassnsfromdateutil.parserimportparsefrommatplotlibimportrcfrommatplotlib.pyplotimportfigureIn [2]:
data_location=Path.cwd().joinpath("data")Retrieving and Importing the Data¶
The following code will load the three event json files in the data directory if the data directory exists. If the direcotry is not found it will be created and the files will be pulled down from Github and then loaded into memory.
In [3]:
defretrieve_data():ifnotdata_location.exists():data_location.mkdir()url="https://api.github.com/repos/pybites/challenges/events?page={}&per_page=1000"forpageinrange(1,4):response=requests.get(url.format(page))ifresponse.ok:file_name=data_location.joinpath(f"events{page}.json")try:file_name.write_text(json.dumps(response.json()))print(f" Created: {file_name.name}")exceptExceptionase:print(e)else:print(f"Something went wrong: [response.status_code]: {response.reason}")defload_data():ifdata_location.exists():forpageinrange(1,4):file_name=data_location.joinpath(f"events{page}.json")events.extend(json.loads(file_name.read_text()))print(f" Loaded: {file_name.name}")else:print("Data directory was not found:")retrieve_data()load_data()NOTE: If you want to work with the latest data, just remove the data directory and all its contents to have it pulled down once again.
In [4]:
events=[]load_data()print(f"Total Events Loaded: {len(events)}")Parsing the Data¶
From what I hear, we should just get used to cleaning data up before we can use it and its no exception here. I'm interested in exploring a few key points from the data. Mostly I'm interested in the following:
- Pull Request Events
- Data that they were created
- The username of the developer
- The amount of time spent on the challenge
- How difficult they found the challenge to be
In [5]:
# helper functiondefparse_data(line):if'['inline:data=line.split(': [')[1].replace(']','').strip()else:data=line.split(': ')[1].strip()returndata# list to store the datacreated=[]devs=[]diff_levels=[]time_spent=[]foreventinevents:# only insterested in pull request eventsifevent['type']=='PullRequestEvent':# developer usernamedev=event['actor']['login']# ignore pybites ;)ifdev!='pybites':# store developer usernamedevs.append(dev)# store the datecreated.append(event['created_at'].split('T')[0])# parse comment from user for datacomment=event['payload']['pull_request']['body']forlineincomment.split('\n'):# get difficulty level and time spentif'Difficulty level (1-10):'inline:diff=parse_data(line)elif'Estimated time spent (hours):'inline:spent=parse_data(line)# pandas DataFrames require that all columns are the same length# so if we have a missing value, None is used in its placeifdiff:diff_levels.append(int(diff))else:diff_levels.append(None)ifspent:time_spent.append(int(spent))else:time_spent.append(None)Creating The DataFrame¶
Now that we have the lists with the data that we parsed, a DataFrame can be created with them.
In [6]:
df=pd.DataFrame({'Developers':devs,'Difficulty_Levels':diff_levels,'Time_Spent':time_spent,'Date':created,})Data Exploration¶
Here, we can start exploring the data. To take a quick peek at how it's looking, there is no better choice then to use head().
In [7]:
df.head()Out[7]:
To get some quick statistacaly metrics on the dataset, describe() can be used.
In [8]:
df.describe()Out[8]:
Based on what I could see above, I Wanted to get a feel for the following portions. I can see the average difficulty level above, next to the 50%, but I also wanted to show you how to pull that out individually.
In [9]:
print(f'Developers: {len(df["Developers"])}')print(f'Average Difficulty: {df["Difficulty_Levels"].median()}')print(f'Time Spent: {df["Time_Spent"].sum()}')The following Counters are just me exploring the data even further.
In [10]:
developers=Counter(df['Developers']).most_common(6)developersOut[10]:
In [11]:
bite_difficulty=Counter(df['Difficulty_Levels'].dropna()).most_common()bite_difficultyOut[11]:
In [12]:
bite_duration=Counter(df['Time_Spent'].dropna()).most_common()bite_durationOut[12]:
In [13]:
created_at=sorted(Counter(df['Date'].dropna()).most_common())created_atOut[13]:
Hmm, how many days are we looking at?
In [14]:
len(created_at)Out[14]:
Time To Get Down To Business¶
Now that we've loaded our data and cleaned it up, lets see what it can tell us.
Number of Pull Request per Day¶
Pretty amazing that Pybites Blog Challenges had over 300 distinct github interactions in such a short time!
In [15]:
# resize graphfigure(num=None,figsize=(6,6),dpi=80,facecolor='w',edgecolor='k')# gather data into a custom DataFramedates=[day[0]fordayincreated_at]prs=[pr[1]forprincreated_at]df_prs=pd.DataFrame({'xvalues':dates,'yvalues':prs})# plotplt.plot('xvalues','yvalues',data=df_prs)# labelsplt.xticks(rotation='vertical',fontweight='bold')# titleplt.title('Number of Pull Request per Day')# show the graphicplt.show()
Top Blog Challenge Ninjas¶
Although there are many more contributors, I had to limit the count so that the data would be easier to visualize.
In [16]:
# resize graphfigure(num=None,figsize=(6,6),dpi=80,facecolor='w',edgecolor='k')# create labelslabels=[dev[0]fordevindevelopers]# get a count of the pull requestsprs=[dev[1]fordevindevelopers]# pull out top ninja sliceexplode=[0]*len(developers)explode[0]=0.1# create the pie chartplt.pie(prs,explode=explode,labels=labels,shadow=True,startangle=90)# add title and centerplt.axis('equal')plt.title('Top Blog Challenge Ninjas')# show the graphicplt.show()
Time Spent vs Difficulty Level per Pull Request¶
Finally I wanted to explore what the relation between time spent per PR vs how difficult the develop found the challenge to be.
In [17]:
# resize graphfigure(num=None,figsize=(15,6),dpi=80,facecolor='w',edgecolor='k')# drop null valuesdf_clean=df.dropna()# add legenddiff=mpatches.Patch(color='#557f2d',label='Difficulty Level')time=mpatches.Patch(color='#2d7f5e',label='Time Spent')plt.legend(handles=[time,diff])# y-axis in boldrc('font',weight='bold')# values of each groupbars1=df_clean['Difficulty_Levels']bars2=df_clean['Time_Spent']# heights of bars1 + bars2bars=df_clean['Difficulty_Levels']+df_clean['Time_Spent']# position of the bars on the x-axisr=range(len(df_clean))# names of group and bar widthnames=df_clean['Developers']barWidth=1# create green bars (bottom)plt.bar(r,bars1,color='#557f2d',edgecolor='white',width=barWidth)# create green bars (top), on top of the firs onesplt.bar(r,bars2,bottom=bars1,color='#2d7f5e',edgecolor='white',width=barWidth)# custom X axisplt.xticks(r,names,rotation='vertical',fontweight='bold')plt.xlabel("Developers",fontweight='bold')# titleplt.title('Time Spent vs Difficulty Level per Pull Request')# show graphicplt.show()
Conclusions¶
As you can see, the Pybites Ninjas are an active bunch. With such a small limited dataset its plain to see that some good information can be extracted from it. Would be interesting to see which challenges are getting the most action though, but I'll leave that as an exercise for you to explore!