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Data Manipulation and Visualization with Pandas and Seaborn — A Practical Introduction
{
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"%matplotlib notebook\n",
"\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"sns.set_context(\"paper\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"* [Intro](#Intro)\n",
"* [Data Loading](#Data-Loading)\n",
"* [Function Application](#Function-Application)\n",
"* [Group-By and Aggregation](#Group-By-and-Aggregation)\n",
"\t* [Plotting](#Plotting)\n",
"\t* [Multi-Index](#Multi-Index)\n",
"* [Pivoting, Stacking and Melting](#Pivoting,-Stacking-and-Melting)\n",
"\t* [Facet Grid](#Facet-Grid)\n",
"* [Filling time ranges](#Filling-time-ranges)\n",
"* [Notes](#Notes)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, I'm going to demonstrate with practical examples various concepts and methods related to Pandas and Seaborn. I will rely on the data format I used for my [Facebook Conversation Analyzer project](https://github.com/5agado/conversation-analyzer). For seemingly obvious reasons I didn't use a personal conversation but automatically generated a fake and nonsensical one. Projecting or imagining some conversation relevant to you will most likely help you to better understand and memorize the content of this notebook, even greater if you can play around with your actual data.\n",
"\n",
"The main topic is data manipulation with Pandas, for example function application, groupby, aggregation and multi-indexes. All along I'll mention handy tricks that you can use for various tasks and demonstrate how we can plot results in different ways using [Seaborn](http://seaborn.pydata.org/index.html) (based on matplotlib). Given the data format, special focus is put on time-series data manipulation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Loading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming you already have your data in a valid csv format, loading it in a Pandas dataframe is as easy as calling [*pd.read_csv*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html).\n",
"\n",
"We additionally care about the **automatic datetime parsing** for certain columns. With the *parse_dates* argument, we tell Pandas which date should be forced to datetime format. You can also pass a custom parser, something like: *date_parser = lambda x : pd.datetime.strptime(x, '%H:%M:%S'))*"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>datetime</th>\n",
" <th>sender</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016-01-11 00:16:51</td>\n",
" <td>Donnie</td>\n",
" <td>I... remember.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016-01-11 19:06:48</td>\n",
" <td>Donnie</td>\n",
" <td>I'm sure you would.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016-01-12 18:10:15</td>\n",
" <td>Donnie</td>\n",
" <td>Where is my mother! How oddly thou repliest! Y...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016-01-13 21:45:27</td>\n",
" <td>Donnie</td>\n",
" <td>I'm at Space Station Five, darling. How are you?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016-01-14 15:44:06</td>\n",
" <td>Frank</td>\n",
" <td>You were never invited to my house.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime sender \\\n",
"0 2016-01-11 00:16:51 Donnie \n",
"1 2016-01-11 19:06:48 Donnie \n",
"2 2016-01-12 18:10:15 Donnie \n",
"3 2016-01-13 21:45:27 Donnie \n",
"4 2016-01-14 15:44:06 Frank \n",
"\n",
" text \n",
"0 I... remember. \n",
"1 I'm sure you would. \n",
"2 Where is my mother! How oddly thou repliest! Y... \n",
"3 I'm at Space Station Five, darling. How are you? \n",
"4 You were never invited to my house. "
]
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"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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"source": [
"# Load conversation messages\n",
"msgs = pd.read_csv('test_conv.csv', parse_dates=['datetime'])\n",
"# Display first 5 entries of our dataframe\n",
"msgs.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 3 columns):\n",
"datetime 1000 non-null datetime64[ns]\n",
"sender 1000 non-null object\n",
"text 1000 non-null object\n",
"dtypes: datetime64[ns](1), object(2)\n",
"memory usage: 23.5+ KB\n"
]
}
],
"source": [
"# Double check of types\n",
"msgs.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Function Application"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function application allows you to map a function to elements (cells, rows, columns) of your dataframe (or series) in a computationally efficient way. \n",
"\n",
"We will try out function application in the preprocessing step for the textual component of our messages.\n",
"We are going to use a dummy method for tokenization (transform text to list of words) and then compute some stats on the spot (e.g. text length, word count).\n",
"\n",
"When using [*apply*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html) on a dataframe, always double check on the *axis* parameter, it's easy to forget or confuse it, causing annoying errors. 0 (default) is for applying the function to each column, 1 to each row."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"# Most simple and dummy method possible for tokenizing your text\n",
"def get_words(text):\n",
" return text.strip().split()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
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{
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" <td>2016-01-12 18:10:15</td>\n",
" <td>Donnie</td>\n",
" <td>Where is my mother! How oddly thou repliest! Y...</td>\n",
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" <th>3</th>\n",
" <td>2016-01-13 21:45:27</td>\n",
" <td>Donnie</td>\n",
" <td>I'm at Space Station Five, darling. How are you?</td>\n",
" <td>48</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
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" <th>4</th>\n",
" <td>2016-01-14 15:44:06</td>\n",
" <td>Frank</td>\n",
" <td>You were never invited to my house.</td>\n",
" <td>35</td>\n",
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" datetime sender \\\n",
"0 2016-01-11 00:16:51 Donnie \n",
"1 2016-01-11 19:06:48 Donnie \n",
"2 2016-01-12 18:10:15 Donnie \n",
"3 2016-01-13 21:45:27 Donnie \n",
"4 2016-01-14 15:44:06 Frank \n",
"\n",
" text text_len num_tokens \\\n",
"0 I... remember. 14 2 \n",
"1 I'm sure you would. 19 4 \n",
"2 Where is my mother! How oddly thou repliest! Y... 100 18 \n",
"3 I'm at Space Station Five, darling. How are you? 48 9 \n",
"4 You were never invited to my house. 35 7 \n",
"\n",
" num_types \n",
"0 2 \n",
"1 4 \n",
"2 16 \n",
"3 9 \n",
"4 7 "
]
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"execution_count": 6,
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"source": [
"# Our function to apply. When applied to a dataframe this will get a row as input\n",
"def extract_text_basic_stats(row):\n",
" # tokenize our message text\n",
" words = get_words(row['text'])\n",
" # Compute message stats and add entries to the row\n",
" # For demonstration purposes, but otherwise clearly inefficient way to do it\n",
" row['text_len'] = len(row['text'])\n",
" row['num_tokens'] = len(words)\n",
" row['num_types'] = len(set(words))\n",
" return row\n",
"\n",
"# We apply row wise, so axis = 1\n",
"msgs_stats = msgs.apply(extract_text_basic_stats, axis=1)\n",
"msgs_stats.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With previous approach (i.e. applying function on dataframe) we can rely on the entire information present in a row. For this specific case is a bit of an overhead, mostly cause we are using only the *text* field. \n",
"We can then instead simply **apply our function directly to our target column**, and pack the result values in a new dataframe that will be joined with our original one."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
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"data": {
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" <thead>\n",
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" <tr>\n",
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" <td>100</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>2016-01-13 21:45:27</td>\n",
" <td>Donnie</td>\n",
" <td>I'm at Space Station Five, darling. How are you?</td>\n",
" <td>48</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016-01-14 15:44:06</td>\n",
" <td>Frank</td>\n",
" <td>You were never invited to my house.</td>\n",
" <td>35</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" datetime sender \\\n",
"0 2016-01-11 00:16:51 Donnie \n",
"1 2016-01-11 19:06:48 Donnie \n",
"2 2016-01-12 18:10:15 Donnie \n",
"3 2016-01-13 21:45:27 Donnie \n",
"4 2016-01-14 15:44:06 Frank \n",
"\n",
" text text_len num_tokens \\\n",
"0 I... remember. 14 2 \n",
"1 I'm sure you would. 19 4 \n",
"2 Where is my mother! How oddly thou repliest! Y... 100 18 \n",
"3 I'm at Space Station Five, darling. How are you? 48 9 \n",
"4 You were never invited to my house. 35 7 \n",
"\n",
" num_types \n",
"0 2 \n",
"1 4 \n",
"2 16 \n",
"3 9 \n",
"4 7 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Our function to apply. When applied to a series/column this will get a cell value as input\n",
"def extract_text_basic_stats(text):\n",
" words = get_words(text)\n",
" # Return results as tuples. You can also return a dictionary for automatic insertion in a dataframe\n",
" return len(text), len(words), len(set(words))\n",
"\n",
"# Apply function to text column\n",
"stats = msgs['text'].apply(extract_text_basic_stats).values\n",
"# Pack results in a new dataframe and join it with our original one\n",
"msgs_stats = pd.DataFrame(list(stats), columns=['text_len', 'num_tokens', 'num_types'])\n",
"msgs_stats = msgs.join(msgs_stats)\n",
"msgs_stats.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Group-By and Aggregation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stats by day can be too granular for our interests, while seeing the trend per months can provide a better and clearer understanding on what's going on. Seems the right time to rely on [group-by and aggregation](http://pandas.pydata.org/pandas-docs/stable/groupby.html).\n",
"\n",
"*groupby* groups your entries by common values, where the values come from field (or set of) you specify. For example, I might group my messages by sender and month, and for each combination of them will obtain a list of associated and recorded values. **Aggregation** is about telling what do do with such list: sum, average, min, max, standard deviation are just some common examples. In the case of more complex types like list or string, you might instead perform concatenation or set operations.\n",
"\n",
"When dealing with time-series data is good to consider also the [resample](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html) method."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"format": "row"
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"outputs": [
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>text_len</th>\n",
" <th>num_tokens</th>\n",
" <th>num_types</th>\n",
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" <tr>\n",
" <th>sender</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">Donnie</th>\n",
" <th>1</th>\n",
" <td>1311</td>\n",
" <td>251</td>\n",
" <td>239</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1487</td>\n",
" <td>280</td>\n",
" <td>265</td>\n",
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" <th>3</th>\n",
" <td>2236</td>\n",
" <td>436</td>\n",
" <td>417</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>2379</td>\n",
" <td>449</td>\n",
" <td>430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1369</td>\n",
" <td>260</td>\n",
" <td>248</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" text_len num_tokens num_types\n",
"sender month \n",
"Donnie 1 1311 251 239\n",
" 2 1487 280 265\n",
" 3 2236 436 417\n",
" 4 2379 449 430\n",
" 5 1369 260 248"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create new feature by extracting the month from the date\n",
"msgs_stats['month'] = msgs_stats['datetime'].dt.month\n",
"# Group by month and sender and aggregate by sum\n",
"grouped_msgs = msgs_stats.groupby(['sender','month']).sum()\n",
"grouped_msgs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*.dt* is a quick and nice way to access just the part of your datetime object you interested in, something like *.dt.month*, *.dt.year*, *.dt.hour*. Notice you could directly group by month by passing *msgs_stats['datetime'].dt.month* to the groupby list, thus avoiding the need of creating a new month column beforehand.\n",
"\n",
"While the previous is a simple example of aggregation, we can also get further and **derive several stats in one go**, by specifying which function to apply for each new column. Again, you could also rely on lambda or previously defines functions, and pass them to the *agg* method."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>avg_tokens</th>\n",
" <th>num_types</th>\n",
" <th>max_tokens</th>\n",
" <th>num_tokens</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sender</th>\n",
" <th>month</th>\n",
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" <th rowspan=\"5\" valign=\"top\">Donnie</th>\n",
" <th>1</th>\n",
" <td>7.606061</td>\n",
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" <td>20</td>\n",
" <td>251</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>8.750000</td>\n",
" <td>265</td>\n",
" <td>20</td>\n",
" <td>280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9.478261</td>\n",
" <td>417</td>\n",
" <td>24</td>\n",
" <td>436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.803922</td>\n",
" <td>430</td>\n",
" <td>20</td>\n",
" <td>449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7.222222</td>\n",
" <td>248</td>\n",
" <td>19</td>\n",
" <td>260</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_tokens num_types max_tokens num_tokens\n",
"sender month \n",
"Donnie 1 7.606061 239 20 251\n",
" 2 8.750000 265 20 280\n",
" 3 9.478261 417 24 436\n",
" 4 8.803922 430 20 449\n",
" 5 7.222222 248 19 260"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create destination column for new stats\n",
"msgs_stats['max_tokens'] = msgs_stats['num_tokens']\n",
"msgs_stats['avg_tokens'] = msgs_stats['num_tokens']\n",
"# Groupby and apply specific aggregation to each column we are interested in\n",
"grouped_msgs = msgs_stats.groupby(['sender','month']).agg({'max_tokens' : np.max, 'avg_tokens' : np.mean,\n",
" 'num_tokens': sum, 'num_types' : sum})\n",
"grouped_msgs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look into some basic plotting with the current data. A pointplot seems a reasonable representation, and we just have to pass our data to the corresponding method. We can pass directly the x and y values, or pass the entire dataframe as *data* and specify column names associated to x and y. The *hue* parameter allows to specify the column which values will be plotted separately, with different colors. As an example see the representation of sender-specific stats in the following plot.\n",
"\n",
"Notice that I often call *reset_index()* before passing the data to the plot function, cause in such a way I can access also index columns by names. I'm currently still not aware of any way to use index names directly.\n",
"\n",
"Is good to also consider **alternative options for plotting**, like calling the *plot* function directly provided by Pandas. Or accessing Matplotlibs plot methods via *sns.plt.*"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
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" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.pointplot(x='month', y='num_tokens', data=grouped_msgs.reset_index(), hue='sender')\n",
"sns.plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-Index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using *groupby* the *as_index* parameter specifies if the columns used for grouping should be used as indexes of the resulting dataframe, if set to *False* such columns will otherwise end up to be part of the other set of columns. So if you group by multiple values, and keep them as index, you will end up with a multi-index dataframe, like the one in our previous example (sender and month). \n",
"Understanding and being able to manage [multi-indexes](http://pandas.pydata.org/pandas-docs/stable/advanced.html) is a fundamental requirement when working with more complex dataset, so I will now quickly play with them for a multiplot scenario.\n",
"\n",
"Now that you have your resampled data, what do you want to plot?\n",
"We can reuse this dataframe for different possible plots/queries. We might be interested in the statistics for a specific sender, specific month, combination of the two, or just the overall values.\n",
"\n",
"With single index we can access my sender with *.loc['Donnie']*, now with a multi-index we use the *.xs* method instead."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create figure with two plots\n",
"fig, axes = sns.plt.subplots(2, 2, sharey=True)\n",
"\n",
"# Get messages for a specific sender and plot values\n",
"msgs_sender = grouped_msgs.xs('Donnie', level='sender')\n",
"axes[0,0].set_title('Word Count - Donnie')\n",
"sns.barplot(x='month', y='num_tokens', data=msgs_sender.reset_index(), ax=axes[0,0], color=\"steelblue\")\n",
"\n",
"#or we can plot the total, reusing groupby, on the index this time, and summing the results\n",
"msgs_total = grouped_msgs.groupby(level='month').sum()\n",
"axes[0,1].set_title('Word Count - Total')\n",
"sns.barplot(x='month', y='num_tokens', data=msgs_total.reset_index(), ax=axes[0,1], color=\"steelblue\")\n",
"\n",
"#or check for a single month\n",
"msgs_month = grouped_msgs.xs(3, level='month')\n",
"axes[1,0].set_title('Word Count - March')\n",
"sns.barplot(x='sender', y='num_tokens', data=msgs_month.reset_index(), ax=axes[1,0])\n",
"\n",
"#or finally plot all in one, withouth additional selection\n",
"axes[1,1].set_title('Word Count')\n",
"sns.barplot(x='month', y='num_tokens', hue='sender', data=grouped_msgs.reset_index(), ax=axes[1,1])\n",
"\n",
"sns.plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pivoting, Stacking and Melting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is all this about? It's about swirling your dataframe data around in an ordered way, for friends: [reshaping](http://pandas.pydata.org/pandas-docs/stable/reshaping.html). \n",
"\n",
"With **pivot** you directly control which columns end up to form your index, which your columns, and which your values.\n",
"\n",
"**Stacking** operates a pivoting of columns (different columns become a single new attribute, duplicating the number of samples for each column label). \n",
"In the following lines we stack our messages statistics into a single *stats* column, that specifies which statistics we are considering, and has the associated value in the *val* column."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>num_tokens</th>\n",
" <th>num_types</th>\n",
" <th>max_tokens</th>\n",
" <th>avg_tokens</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sender</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">Donnie</th>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>2016</th>\n",
" <td>124</td>\n",
" <td>117</td>\n",
" <td>124</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>127</td>\n",
" <td>122</td>\n",
" <td>127</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>2016</th>\n",
" <td>150</td>\n",
" <td>140</td>\n",
" <td>150</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>130</td>\n",
" <td>125</td>\n",
" <td>130</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>2016</th>\n",
" <td>196</td>\n",
" <td>187</td>\n",
" <td>196</td>\n",
" <td>196</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" num_tokens num_types max_tokens avg_tokens\n",
"sender month year \n",
"Donnie 1 2016 124 117 124 124\n",
" 2017 127 122 127 127\n",
" 2 2016 150 140 150 150\n",
" 2017 130 125 130 130\n",
" 3 2016 196 187 196 196"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First prepare data by extracting year info and group-by sum\n",
"msgs_stats['year'] = msgs_stats['datetime'].dt.year\n",
"yearly_msgs = msgs_stats.groupby(['sender','month','year']).sum()\n",
"yearly_msgs = yearly_msgs.drop('text_len', axis=1)\n",
"yearly_msgs.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sender</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>stat</th>\n",
" <th>val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Donnie</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>num_tokens</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Donnie</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>num_types</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Donnie</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>max_tokens</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Donnie</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>avg_tokens</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Donnie</td>\n",
" <td>1</td>\n",
" <td>2017</td>\n",
" <td>num_tokens</td>\n",
" <td>127</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sender month year stat val\n",
"0 Donnie 1 2016 num_tokens 124\n",
"1 Donnie 1 2016 num_types 117\n",
"2 Donnie 1 2016 max_tokens 124\n",
"3 Donnie 1 2016 avg_tokens 124\n",
"4 Donnie 1 2017 num_tokens 127"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Stack our data and rename columns\n",
"yearly_msgs = yearly_msgs.stack().reset_index()\n",
"yearly_msgs.columns.values[3] = 'stat'\n",
"yearly_msgs.columns.values[4] = 'val'\n",
"yearly_msgs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Facet Grid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This format doesn't follow the [tidy data guidelines](http://vita.had.co.nz/papers/tidy-data.pdf), but can be useful for quick playing around and plotting, especially when using Seaborn [FacetGrid](http://seaborn.pydata.org/generated/seaborn.FacetGrid.html). We compose our plot specifying how subplots are built in terms of rows and columns, for then applying to each a basic plotting function.\n",
"\n",
"In the following example each column is a year, and different rows are different statistics about our messages. Each plot will then show the values along months, for each sender. Even if this explanation sounds convoluted, the actual plot is really intuitive and helpful when visualizing such kind of data. \n",
"\n",
"The **sharing of axis** (keep the same scale for the plots) is also an important part of the grid, that can help visualize how your values vary among different plots. At the same time some sharing might just be counterproductive, for example when comparing values with a big scale or range difference. We have one of such cases when comparing *text_len* with the other stats."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
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