3_Pandas_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents \n",
    "\n",
    "- **Series**\n",
    "- **DataFrame**\n",
    "    - Accessing a DataFrame\n",
    "    - Boolean Indexing\n",
    "    - Adding columns\n",
    "    - Deleting columns\n",
    "    - Importing Excel files as DataFrames\n",
    "    - Missing Data\n",
    "    - Writing and reading CSV files\n",
    "\n",
    "- **DataFrame Operations**\n",
    "    - Matrix operations\n",
    "    - Column operations\n",
    "\n",
    "- **Data Splitting**\n",
    "    - Grouping\n",
    "    \n",
    "- **Plotting with Pandas and Matplotlib: examples**\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data Mining Process \n",
    "\n",
    "Image from: \n",
    "\n",
    "    Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy. \n",
    "    Advances in Knowledge Discovery and Data Mining.\n",
    "    MIT Press, Menlo Park, CA, 1996\n",
    "\n",
    "![datamining](images/DataMiningProcess.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas** is desgined to make **data pre-processing and data analysis fast and easy in Python**. Pandas adopts many coding idioms from NumPy, such as avoiding the `for` loops, but it is designed for working with heterogenous data represented in tabular format.\n",
    "\n",
    "To use Pandas, you need to import the `pandas` module, using for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "ExecuteTime": {
     "end_time": "2020-06-24T12:57:09.909716Z",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np # we will also need numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This import style is quite standard; all objects and functions the `pandas` package will now be invoked with the `pd.` prefix.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy (**Num**erical **Py**thon) is the fundamental package for scientific computing with Python. It contains, among other things:\n",
    "\n",
    "- a powerful N-dimensional array object\n",
    "- sophisticated functions that support broadcasting (i.e. it allows to perform arithmetic operations between arrays with different shape)\n",
    "- tools for integrating C/C++ and Fortran code\n",
    "- useful linear algebra, Fourier transform, and random number capabilities\n",
    "\n",
    "The core object of numpy is **ndarray**: N-dimensional Array. It represents a multidimensional, homogeneous array of fixed-size items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of 1-dimensional array\n",
    "np.arange(0,1,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of 2-dimensional array\n",
    "my_ndarray = np.ones((3,5))\n",
    "my_ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.asarray([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(my_ndarray)\n",
    "print(my_ndarray.shape)\n",
    "print(my_ndarray.ndim)\n",
    "print(my_ndarray.size)\n",
    "print(my_ndarray.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<font color='blue'>Pandas has two main data structures, **Series** and **DataFrame**.</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series\n",
    "\n",
    "Series are the Pandas version of 1-D Numpy arrays. \n",
    "\n",
    "An instance of Series is a single dimension array-like object containing:\n",
    "- a *sequence of values*,\n",
    "- an array of *data labels*, called its **index**.\n",
    "\n",
    "A Series can be created easily from a Python list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:06:29.883282Z",
     "start_time": "2020-06-24T14:06:29.873731Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series([4, 8, 1, 3])\n",
    "print(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string representation of a Series display two columns: the first column represents the index array, the second column represents the values array. Since no index was specified, the default indexing consists of increasing integers starting from 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The underlying structure can be recovered with the `values` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:06:30.975916Z",
     "start_time": "2020-06-24T14:06:30.966676Z"
    }
   },
   "outputs": [],
   "source": [
    "print(ts.values)\n",
    "print(type(ts.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a Series with its own index, you can write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:10:32.009532Z",
     "start_time": "2020-06-24T13:10:31.999642Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series([4, 8, 1, 3], index=['first', 'second', 'third', 'fourth'])\n",
    "print(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The labels in the index can be used to select values in the Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:13:05.940761Z",
     "start_time": "2020-06-24T13:13:05.922266Z"
    }
   },
   "outputs": [],
   "source": [
    "print(ts['first'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ts[['second', 'fourth']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can think about a Series as a kind of fixed-length, ordered Python's `dict`, mapping index values to data values. In fact, it is possible to create a Series directlty from a Python's `dict`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:18:31.486140Z",
     "start_time": "2020-06-24T13:18:31.465527Z"
    }
   },
   "outputs": [],
   "source": [
    "my_dict = {'Pisa': 80, 'London': 300, 'Paris': 1}\n",
    "ts = pd.Series(my_dict)\n",
    "print(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Arithmetic operations** on Series are automatically aligned on the index labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1 = pd.Series([4, 8, 1, 3], index=['first', 'second', 'third', 'fourth'])\n",
    "ts2 = pd.Series([4, 8, 1], index=['first', 'second', 'pisa'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:29:55.852232Z",
     "start_time": "2020-06-24T13:29:55.833699Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ts_sum = ts1 + ts2\n",
    "print(ts_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here two index values are correctly computed (corresponding to the label `first` and `second`). The two other index labels `third` and `fourth` in `ts1` are missing in `ts2`, as well as the `pisa` index label in `ts2`. Hence, for each of these index label, a `NaN` value (*not a number*) appears, which Pandas considers as a **missing value**.\n",
    "\n",
    "The `pd.isnull` (or `pd.isna`) and `pd.notnull` (or `pd.notna`) functions detects missing data. There are also corresponding **instance methods**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:31:20.930884Z",
     "start_time": "2020-06-24T13:31:20.911850Z"
    }
   },
   "outputs": [],
   "source": [
    "print(pd.isnull(ts_sum))\n",
    "print()\n",
    "print(ts_sum.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd.notnull(ts_sum))\n",
    "print()\n",
    "print(ts_sum.notnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame\n",
    "\n",
    "A DataFrame is a **rectangular table of data**. It contains an ordered list of columns. Every column can be of a different type. \n",
    "\n",
    "A DataFrame has both a *row index* and a *column index*. It can be thought as a *dictionary of Series* (one per column) all sharing the same index labels.\n",
    "\n",
    "There are many way to construct a DataFrame, but the most common is using a dictionary of Python's lists (or NumPy's arrays):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:46:58.794613Z",
     "start_time": "2020-06-24T13:46:58.779817Z"
    }
   },
   "outputs": [],
   "source": [
    "cars = {'Brand': ['Honda Civic', 'Toyota Corolla', 'Ford Focus', 'Audi A4'],\n",
    "        'Price': [22000, 25000, 27000, 35000],\n",
    "        'Wheels': 4} # broadcast if possible\n",
    "\n",
    "df = pd.DataFrame(cars)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting DataFrame will receive its index automatically as with Series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To pretty-print a DataFrame in a Jupyter notebooks, it is enough to write its name (or using the `head()` instance method for very long DataFrames):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T13:48:19.586597Z",
     "start_time": "2020-06-24T13:48:19.564159Z"
    }
   },
   "outputs": [],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail(2)\n",
    "# df.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A summary of the *numerical* data is provided by `describe`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If working with a large table, it might be useful to sometimes have a list of all the columns' names. This is given by the `keys()` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:19:07.147448Z",
     "start_time": "2020-06-24T14:19:07.137275Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(df.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many feature from the NumPy package can be directly used with Pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:48:02.257248Z",
     "start_time": "2020-06-25T08:48:02.236802Z"
    }
   },
   "outputs": [],
   "source": [
    "print(df.values)\n",
    "print()\n",
    "print(type(df.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:48:03.529820Z",
     "start_time": "2020-06-25T08:48:03.499697Z"
    }
   },
   "outputs": [],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common way to create a DataFrame is to use a *nested dict of dicts*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:27:29.650782Z",
     "start_time": "2020-06-24T14:27:29.643762Z"
    }
   },
   "outputs": [],
   "source": [
    "population = {'Nevada': {2001: 2.4, 2002: 2.9},\n",
    "       'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this nested dict is passed to the DataFrame, the **outer dict keys are interpreted as column labels**, and the **inner keys are interpreted as row labels**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:28:37.554310Z",
     "start_time": "2020-06-24T14:28:37.535827Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(population)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a brand new DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:10:32.187393Z",
     "start_time": "2020-06-24T14:10:32.162017Z"
    }
   },
   "outputs": [],
   "source": [
    "dict_of_list = {'birth': [1860, 1770, 1858, 1906], \n",
    "                'death':[1911, 1827, 1924, 1975], \n",
    "                'city':['Kaliste', 'Bonn', 'Lucques', 'Saint-Petersburg']}\n",
    "composers_df = pd.DataFrame(dict_of_list, index=['Mahler', 'Beethoven', 'Puccini', 'Shostakovich'])\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple ways of accessing values or series of values in a Dataframe. Unlike in Series, a simple bracket gives access to a column and not an index, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:11:08.259631Z",
     "start_time": "2020-06-24T14:11:08.241496Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df['city']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "returns a Series. Alternatively one can also use the attributes syntax and access columns by using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:11:37.321286Z",
     "start_time": "2020-06-24T14:11:37.309806Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df.city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attributes syntax has some limitations, so in case something does not work as expected, revert to the brackets notation.\n",
    "\n",
    "When specifiying multiple columns, a DataFrame is returned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:12:06.558313Z",
     "start_time": "2020-06-24T14:12:06.540600Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df[['city', 'birth']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the [docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html):\n",
    ">The Python and NumPy indexing operators [$\\cdot$] and attribute operator  `.` provide quick and easy access to pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the **optimized pandas data access methods**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard indexing operators (just slices the rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas optimized data access methods:  `iloc` and `loc`.\n",
    "\n",
    "**Remember that `loc` and `iloc` are attributes, not methods, hence they use brackets `[]` and not parenthesis `()`.**\n",
    "\n",
    "The `loc` attribute allows to recover elements by using the index labels, while the `iloc` attribute can be used to recover the regular indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:16:36.990555Z",
     "start_time": "2020-06-24T14:16:36.979449Z"
    }
   },
   "outputs": [],
   "source": [
    "print(composers_df.iloc[0:2,:])\n",
    "print()\n",
    "print(composers_df.loc[['Mahler','Beethoven'], 'death'])\n",
    "print()\n",
    "print(composers_df.loc['Beethoven', 'death'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing\n",
    "\n",
    "Just like with Numpy, it is possible to subselect parts of a Dataframe using boolean indexing.\n",
    "A logical Series can be used as an index to select elements in the Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mask = composers_df['death'] > 1859\n",
    "print(mask)\n",
    "composers_df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More compact:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df[composers_df['birth'] > 1900]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is very simple to add a column to a Dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:25:29.267510Z",
     "start_time": "2020-06-24T14:25:29.253143Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df['country'] = '???'\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, an existing list can be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:25:30.513553Z",
     "start_time": "2020-06-24T14:25:30.500562Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df['country2'] = ['Austria','Germany','Italy','Russia']\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting columns and rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df.drop(columns = ['country2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df.drop('Puccini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df.drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df #note that, by default, drop does not operate in-place"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Excel files as DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another very common way of \"creating\" a Pandas Dataframe is by importing a table from another format like CSV or Excel. You may need to also install `xlrd` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# !pip install xlrd\n",
    "# !conda install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An Excel table is provided in the [composers.xlsx](data/composers.xlsx) file and can be read with the `pd.read_excel` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:46:22.696917Z",
     "start_time": "2020-06-24T14:46:22.651062Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx')\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reader automatically recognized the heaers of the file. However it created a new index. If needed we can specify which column to use as header:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_excel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:47:02.153798Z",
     "start_time": "2020-06-24T14:47:02.086758Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx', index_col = 'composer')\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we open the file in Excel, we see that it is composed of more than one sheet. Clearly, when not specifying anything, the reader only reads the first sheet. However we can specify a sheet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:51:26.183305Z",
     "start_time": "2020-06-24T14:51:26.141838Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx', index_col = 'composer', sheet_name='Sheet2')\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, some information is missing. Some missing values are marked as \"`unknown`\" while other are `NaN`. `NaN` is the standard symbol for unknown/missing values and is understood by Pandas while \"`unknown`\" is just seen as text. \n",
    "This is impractical as now we have columns with a mix of numbers and text which will make later computations difficult. What we would like to do is to replace all \"irrelevant\" values with the standard `NaN` symbol that says \"*no information*\".\n",
    "For this we can use the `na_values` argument to specify what should be a `NaN`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T14:58:59.168497Z",
     "start_time": "2020-06-24T14:58:59.139749Z"
    }
   },
   "outputs": [],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx', index_col = 'composer', sheet_name='Sheet2', \n",
    "                             na_values=['unknown'])\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing data\n",
    "pandas primarily uses the value `np.nan` to represent missing data. It is by default not included in computations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = composers_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a boolean mask where values are `np.nan` (as for Series)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.isna(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.isna().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To drop missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_new.dropna(how='any') # delete any row and column with at least one np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.dropna(how = 'all') # the whole row (or column) must be np.nan "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing and reading - CSV files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.to_csv('data/foo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_read = pd.read_csv('data/foo.csv')\n",
    "df_read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_read = pd.read_csv('data/foo.csv',index_col = 'composer')\n",
    "df_read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame Operations\n",
    "\n",
    "One of the great advantages of using Pandas to handle tabular data is how simple it is to extract valuable information from them. Here we are going to see various types of operations that are available for this.\n",
    "\n",
    "## Matrix operations\n",
    "\n",
    "The strength of Numpy is its natural way of handling matrix operations, and Pandas reuses a lot of these features. For example one can use simple mathematical operations to opereate at the cell level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:09:22.426144Z",
     "start_time": "2020-06-24T15:09:22.385407Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('data/composers.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:09:39.792603Z",
     "start_time": "2020-06-24T15:09:39.775993Z"
    }
   },
   "outputs": [],
   "source": [
    "df['birth'] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:10:00.912276Z",
     "start_time": "2020-06-24T15:10:00.899426Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.log(df['birth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can directly use an operation's output to create a new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:19:14.832808Z",
     "start_time": "2020-06-24T15:19:14.807838Z"
    }
   },
   "outputs": [],
   "source": [
    "df['age'] = df['death'] - df['birth']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we applied functions only to series. Indeed, since our Dataframe contains e.g. strings, no operation can be done on it. If however we have a homogenous Dataframe, this is possible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:10:52.552872Z",
     "start_time": "2020-06-24T15:10:52.534293Z"
    }
   },
   "outputs": [],
   "source": [
    "df[['birth', 'death']] * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column operations\n",
    "\n",
    "There are other types of functions whose purpose is to summarize the data. For example the mean or standard deviation. Pandas by default applies such functions column-wise and returns a series containing e.g. the mean of each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:11:31.318490Z",
     "start_time": "2020-06-24T15:11:31.299730Z"
    }
   },
   "outputs": [],
   "source": [
    "np.mean(df[['birth','death','age']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes one needs to apply to a column a very specific function that is not provided by default. In that case we can use one of the different `apply` methods of Pandas.\n",
    "\n",
    "The simplest case is to apply a function to a column, or Series of a DataFrame. Let's say for example that we want to define the age >60 as 'old' and <60 as 'young'. We can define the following general function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:16:11.585908Z",
     "start_time": "2020-06-24T15:16:11.579767Z"
    }
   },
   "outputs": [],
   "source": [
    "define_age = lambda x: 'old' if x > 60 else 'young'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now apply this function on an entire Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:19:19.576840Z",
     "start_time": "2020-06-24T15:19:19.558825Z"
    }
   },
   "outputs": [],
   "source": [
    "df['categorical age'] = df.age.apply(define_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['categorical age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Splitting\n",
    "\n",
    "Often Pandas tables mix regular variables (e.g. the size of cells in microscopy images) with categorical variables (e.g. the type of cell to which they belong). In that case, it is quite usual to split the data using the category to do computations. Pandas allows to do this very easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx', index_col = 'composer', sheet_name='Sheet5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:56:13.601372Z",
     "start_time": "2020-06-25T08:56:13.518195Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "composers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want now to count how many composers we have in each category? \n",
    "\n",
    "Pandas simplifies this with the `groupby()` function, which actually groups elements by a certain criteria, e.g. a categorical variable like the period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:57:05.723610Z",
     "start_time": "2020-06-25T08:57:05.693209Z"
    }
   },
   "outputs": [],
   "source": [
    "composer_grouped = composers_df.groupby('period')\n",
    "composer_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is a bit cryptic. What we actually have is a new object called *group* which has a lot of handy properties. First let's see what the groups actually are. As for the Dataframe, let's look at a summary of the object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:57:44.681494Z",
     "start_time": "2020-06-25T08:57:44.486278Z"
    }
   },
   "outputs": [],
   "source": [
    "composer_grouped.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have a dataframe with a statistical summary of the the contents. The \"names\" of the groups are here the indices of the Dataframe. These names are simply all the different categories that were present in the column we used for grouping. Now we can recover a single group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:58:36.933456Z",
     "start_time": "2020-06-25T08:58:36.907796Z"
    }
   },
   "outputs": [],
   "source": [
    "composer_grouped.get_group('baroque')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If one has multiple categorical variables, one can also do a grouping on several levels. For example here we want to classify composers both by period and country. For this we just give two column names to the `groupby()` function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-25T08:59:41.133661Z",
     "start_time": "2020-06-25T08:59:41.051149Z"
    }
   },
   "outputs": [],
   "source": [
    "composer_grouped = composers_df.groupby(['period','country'])\n",
    "composer_grouped.get_group(('baroque','Germany'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main advantage of this Group object is that it allows us to do very quickly both computations and plotting without having to loop through different categories. Indeed Pandas makes all the work for us: it applies functions on each group and then reassembles the results into a Dataframe (or Series depending on output).\n",
    "For example we can apply most functions we used for Dataframes (mean, sum etc.) on groups as well and Pandas seamlessly does the work for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting DataFrames\n",
    "\n",
    "Pandas builds on top of Matplotlib but exploits the knowledge included in Dataframes to improve the default output. \n",
    "\n",
    "We can pass Series to Matplotlib which manages to understand them. Here's a default scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verdi</th>\n",
       "      <td>1813</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dvorak</th>\n",
       "      <td>1841</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Czechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schumann</th>\n",
       "      <td>1810</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stravinsky</th>\n",
       "      <td>1882</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>1865</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>1732</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>classic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mozart</th>\n",
       "      <td>1756</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>classic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Messiaen</th>\n",
       "      <td>1908</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prokofiev</th>\n",
       "      <td>1891</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>RUssia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monteverdi</th>\n",
       "      <td>1567</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haendel</th>\n",
       "      <td>1685</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brahms</th>\n",
       "      <td>1833</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purcell</th>\n",
       "      <td>1659</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charpentier</th>\n",
       "      <td>1643</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruckner</th>\n",
       "      <td>1824</td>\n",
       "      <td>1896.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berg</th>\n",
       "      <td>1885</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Couperin</th>\n",
       "      <td>1626</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rameau</th>\n",
       "      <td>1683</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlioz</th>\n",
       "      <td>1803</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gounod</th>\n",
       "      <td>1818</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massenet</th>\n",
       "      <td>1842</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boulez</th>\n",
       "      <td>1925</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palestrina</th>\n",
       "      <td>1525</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gesualdo</th>\n",
       "      <td>1566</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caldara</th>\n",
       "      <td>1670</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pergolesi</th>\n",
       "      <td>1710</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarlatti</th>\n",
       "      <td>1685</td>\n",
       "      <td>1757.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caccini</th>\n",
       "      <td>1587</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cimarosa</th>\n",
       "      <td>1749</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>classic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donizetti</th>\n",
       "      <td>1797</td>\n",
       "      <td>1848.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leoncavallo</th>\n",
       "      <td>1858</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellini</th>\n",
       "      <td>1801</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dufay</th>\n",
       "      <td>1397</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lassus</th>\n",
       "      <td>1532</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borodin</th>\n",
       "      <td>1833</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mussorsgsky</th>\n",
       "      <td>1839</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soler</th>\n",
       "      <td>1754</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>classic</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albeniz</th>\n",
       "      <td>1860</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granados</th>\n",
       "      <td>1867</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dowland</th>\n",
       "      <td>1563</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Byrd</th>\n",
       "      <td>1540</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>renaissance</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walton</th>\n",
       "      <td>1902</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adès</th>\n",
       "      <td>1971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>modern</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bach</th>\n",
       "      <td>1685</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wagner</th>\n",
       "      <td>1813</td>\n",
       "      <td>1883.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strauss</th>\n",
       "      <td>1864</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orff</th>\n",
       "      <td>1895</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dusek</th>\n",
       "      <td>1731</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>classic</td>\n",
       "      <td>Czechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smetana</th>\n",
       "      <td>1824</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Czechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Janacek</th>\n",
       "      <td>1854</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Czechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copland</th>\n",
       "      <td>1900</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernstein</th>\n",
       "      <td>1918</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glass</th>\n",
       "      <td>1937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>modern</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth   death         period  country\n",
       "composer                                           \n",
       "Mahler         1860  1911.0  post-romantic  Austria\n",
       "Beethoven      1770  1827.0       romantic  Germany\n",
       "Puccini        1858  1924.0  post-romantic    Italy\n",
       "Shostakovich   1906  1975.0         modern   Russia\n",
       "Verdi          1813  1901.0       romantic    Italy\n",
       "Dvorak         1841  1904.0       romantic  Czechia\n",
       "Schumann       1810  1856.0       romantic  Germany\n",
       "Stravinsky     1882  1971.0         modern   Russia\n",
       "Sibelius       1865  1957.0  post-romantic  Finland\n",
       "Haydn          1732  1809.0        classic  Austria\n",
       "Mozart         1756  1791.0        classic  Austria\n",
       "Messiaen       1908  1992.0         modern   France\n",
       "Prokofiev      1891  1953.0         modern   RUssia\n",
       "Monteverdi     1567  1643.0    renaissance    Italy\n",
       "Haendel        1685  1759.0        baroque  Germany\n",
       "Brahms         1833  1897.0       romantic  Germany\n",
       "Purcell        1659  1695.0        baroque  England\n",
       "Charpentier    1643  1704.0        baroque   France\n",
       "Bruckner       1824  1896.0  post-romantic  Austria\n",
       "Berg           1885  1935.0         modern  Austria\n",
       "Couperin       1626  1661.0        baroque   France\n",
       "Rameau         1683  1764.0        baroque   France\n",
       "Berlioz        1803  1869.0       romantic   France\n",
       "Gounod         1818  1893.0       romantic   France\n",
       "Massenet       1842  1912.0       romantic   France\n",
       "Boulez         1925  2016.0         modern   France\n",
       "Palestrina     1525  1594.0    renaissance    Italy\n",
       "Gesualdo       1566  1613.0    renaissance    Italy\n",
       "Caldara        1670  1736.0        baroque    Italy\n",
       "Pergolesi      1710  1736.0        baroque    Italy\n",
       "Scarlatti      1685  1757.0        baroque    Italy\n",
       "Caccini        1587  1640.0        baroque    Italy\n",
       "Cimarosa       1749  1801.0        classic    Italy\n",
       "Donizetti      1797  1848.0       romantic    Italy\n",
       "Leoncavallo    1858  1919.0       romantic    Italy\n",
       "Bellini        1801  1835.0       romantic    Italy\n",
       "Dufay          1397  1474.0    renaissance  Belgium\n",
       "Lassus         1532  1594.0    renaissance  Belgium\n",
       "Borodin        1833  1887.0       romantic   Russia\n",
       "Mussorsgsky    1839  1881.0       romantic   Russia\n",
       "Soler          1754  1806.0        classic    Spain\n",
       "Albeniz        1860  1909.0       romantic    Spain\n",
       "Granados       1867  1916.0       romantic    Spain\n",
       "Dowland        1563  1626.0    renaissance  England\n",
       "Byrd           1540  1623.0    renaissance  England\n",
       "Walton         1902  1983.0         modern  England\n",
       "Adès           1971     NaN         modern  England\n",
       "Bach           1685  1750.0        baroque  Germany\n",
       "Wagner         1813  1883.0       romantic  Germany\n",
       "Strauss        1864  1949.0  post-romantic  Germany\n",
       "Orff           1895  1982.0         modern  Germany\n",
       "Dusek          1731  1799.0        classic  Czechia\n",
       "Smetana        1824  1884.0       romantic  Czechia\n",
       "Janacek        1854  1928.0         modern  Czechia\n",
       "Copland        1900  1990.0         modern      USA\n",
       "Bernstein      1918  1990.0         modern      USA\n",
       "Glass          1937     NaN         modern      USA"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "composers_df = pd.read_excel('data/composers.xlsx', index_col = 'composer', sheet_name='Sheet5')\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(composers_df.birth, composers_df.death, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different types of plots are accessible when using the `plot` function of DataFrame instances via the `kind` option. The variables to plot are column names passed as keywords instead of whole series like in Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers_df.plot(x = 'birth', y = 'death', kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers_df.plot(x = 'birth', y = 'death', kind = 'scatter',\n",
    "                  title = 'Composer birth and death', grid = True, fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some additional plotting options are available in the plot() module. For example histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers_df.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you see again the gain from using Pandas: without specifying anything, Pandas made a histogram of the two columns containing numbers, labelled the axis and even added a legend to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers_df['birth'].plot.hist(bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers_df.boxplot(column = 'birth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "recommended reading: \n",
    "- [Sample plots in Matplotlib](https://matplotlib.org/stable/gallery/index.html)\n",
    "\n",
    "Matplotlib is an excellent 2D and 3D graphics library for generating scientific figures. Some of the many advantages of this library include:\n",
    "* Easy to get started\n",
    "* Support for $\\LaTeX$ formatted labels and texts\n",
    "* Great control of every element in a figure, including figure size and DPI.\n",
    "* High-quality output in many formats, including PNG, PDF, SVG, EPS, and PGF.\n",
    "* The figure can be controlled programmatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(0,10,0.1)\n",
    "sin_t = np.sin(2*np.pi*t/5)\n",
    "cos_t = np.cos(2*np.pi*t/5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fa9422a940>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,cos_t)\n",
    "plt.plot(t,sin_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,cos_t,'.r', label = r\"$cos(\\theta)$\")\n",
    "plt.plot(t,sin_t,'--g', label = r\"$sin(\\theta)$\")\n",
    "plt.title('two functions')\n",
    "plt.xlabel(r\"$\\theta$\")\n",
    "plt.ylabel('values')\n",
    "plt.ylim([-1.1,1.1])\n",
    "plt.legend()\n",
    "plt.grid(axis = 'y')\n",
    "\n",
    "# plt.legend(loc=0) # let matplotlib decide the optimal location\n",
    "# plt.legend(loc=1) # upper right corner\n",
    "# plt.legend(loc=2) # upper left corner\n",
    "# plt.legend(loc=3) # lower left corner\n",
    "# plt.legend(loc=4) # lower right corner\n",
    "# # .. many more options are available\n",
    "\n",
    "plt.savefig('img/example_figure.png', format = 'png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Two subplots, the axes array is 1-d\n",
    "f, axarr = plt.subplots(2, sharex=True)\n",
    "axarr[0].plot(t,cos_t,'.r')\n",
    "axarr[1].plot(t,sin_t,'.b')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\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",
       "        fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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width=\"1066.2\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(x, x**2, lw=2, color=\"blue\")\n",
    "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n",
    "for label in ax1.get_yticklabels():\n",
    "    label.set_color(\"blue\")\n",
    "    \n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(x, x**3, lw=2, color=\"red\")\n",
    "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n",
    "for label in ax2.get_yticklabels():\n",
    "    label.set_color(\"red\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allow to use matplotlib interactively and set the desired backend\n",
    "%matplotlib inline\n",
    "# %matplotlib notebook\n",
    "# %matplotlib --list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magic functions\n",
    "\n",
    "[IPython's 'magic' functions](https://ipython.readthedocs.io/en/stable/interactive/magics.html)\n",
    "\n",
    "- The magic function system provides a series of **functions which allow you to\n",
    "control the behavior of IPython itself**, plus a lot of system-type\n",
    "features. There are two kinds of magics, **line-oriented** and **cell-oriented**:\n",
    "    - **Line magics are prefixed with the % character** and work much like OS\n",
    "command-line calls. They get as an argument the rest of the line, where\n",
    "arguments are passed without parentheses or quotes.\n",
    "    - **Cell magics are prefixed with %% (a double % character)**, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument. These magics are called with two arguments: the rest of the call line and the body of the cell, consisting of the lines below the first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "cmd": "Other",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "cls": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "copy": "Other",
        "ddir": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "echo": "Other",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "ren": "Other",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %conda  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type           Data/Info\n",
      "---------------------------------------\n",
      "ax1            AxesSubplot    AxesSubplot(0.125,0.11;0.775x0.77)\n",
      "ax2            AxesSubplot    AxesSubplot(0.125,0.11;0.775x0.77)\n",
      "axarr          ndarray        2: 2 elems, type `object`, 16 bytes\n",
      "composers_df   DataFrame                    birth   dea<...>N         modern      USA\n",
      "cos_t          ndarray        100: 100 elems, type `float64`, 800 bytes\n",
      "f              Figure         Figure(432x288)\n",
      "fig            Figure         Figure(1776.67x1333.33)\n",
      "label          Text           Text(1, 140.0, '140')\n",
      "np             module         <module 'numpy' from 'C:\\<...>ges\\\\numpy\\\\__init__.py'>\n",
      "pd             module         <module 'pandas' from 'C:<...>es\\\\pandas\\\\__init__.py'>\n",
      "plt            module         <module 'matplotlib.pyplo<...>\\\\matplotlib\\\\pyplot.py'>\n",
      "sin_t          ndarray        100: 100 elems, type `float64`, 800 bytes\n",
      "t              ndarray        100: 100 elems, type `float64`, 800 bytes\n",
      "x              ndarray        11: 11 elems, type `float64`, 88 bytes\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable   Type       Data/Info\n",
      "-------------------------------\n",
      "axarr      ndarray    2: 2 elems, type `object`, 16 bytes\n",
      "cos_t      ndarray    100: 100 elems, type `float64`, 800 bytes\n",
      "sin_t      ndarray    100: 100 elems, type `float64`, 800 bytes\n",
      "t          ndarray    100: 100 elems, type `float64`, 800 bytes\n",
      "x          ndarray    11: 11 elems, type `float64`, 88 bytes\n"
     ]
    }
   ],
   "source": [
    "%whos ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.7 µs ± 91.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.zeros((100,100))+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.3 µs ± 131 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit \n",
    "\n",
    "np.zeros((100,100))+5\n",
    "np.zeros((100,100))+5"
   ]
  }
 ],
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