6. Cheat Sheet SEABORN - Antonio Dagnino Mendez.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. cheat Sheet SEABORN - Antonio Dagnino Mendez"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lineplot\n",
    "- barplot\n",
    "- countplot\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FacetGrid', 'JointGrid', 'PairGrid', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_orig_rc_params', 'algorithms', 'axes_style', 'axisgrid', 'barplot', 'blend_palette', 'boxenplot', 'boxplot', 'categorical', 'catplot', 'choose_colorbrewer_palette', 'choose_cubehelix_palette', 'choose_dark_palette', 'choose_diverging_palette', 'choose_light_palette', 'clustermap', 'cm', 'color_palette', 'colors', 'countplot', 'crayon_palette', 'crayons', 'cubehelix_palette', 'dark_palette', 'desaturate', 'despine', 'distplot', 'distributions', 'diverging_palette', 'dogplot', 'external', 'factorplot', 'get_dataset_names', 'heatmap', 'hls_palette', 'husl_palette', 'jointplot', 'kdeplot', 'light_palette', 'lineplot', 'lmplot', 'load_dataset', 'lvplot', 'matrix', 'miscplot', 'mpl', 'mpl_palette', 'pairplot', 'palettes', 'palplot', 'plotting_context', 'pointplot', 'rcmod', 'regplot', 'regression', 'relational', 'relplot', 'reset_defaults', 'reset_orig', 'residplot', 'rugplot', 'saturate', 'scatterplot', 'set', 'set_color_codes', 'set_context', 'set_hls_values', 'set_palette', 'set_style', 'stripplot', 'swarmplot', 'timeseries', 'tsplot', 'utils', 'violinplot', 'widgets', 'xkcd_palette', 'xkcd_rgb']"
     ]
    }
   ],
   "source": [
    "print(dir(sns),end='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Creating DF -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating rows\n",
    "\n",
    "Expenses = []\n",
    "Year = []\n",
    "Month = []\n",
    "Exp_cat = []\n",
    "Month_library = ['January','February','March','April','May','June','July',\n",
    "                 'August','September','October','November','December']\n",
    "Exp_cat_library = ['Fixed Expenses','Personal Expense','Work Expense','Emergencies']\n",
    "for number in range(20):\n",
    "    Expenses.append(random.randint(10,500))\n",
    "    Year.append(random.randint(2018,2020))\n",
    "    Month.append(Month_library[random.randint(0,11)])\n",
    "    Exp_cat.append(Exp_cat_library[random.randint(0,3)])\n",
    "\n",
    "# Creating column names and assigning data \n",
    "data = {'Month':Month,'Year':Year,'Exp_cat':Exp_cat,'Expenses':Expenses}\n",
    "\n",
    "df['Month'] = pd.Categorical(df['Month'], ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August',\n",
    "                                           'September', 'October','November', 'December'])\n",
    "\n",
    "# Create dataframe\n",
    "df = pd.DataFrame(data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f40fe92710>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "sns.pairplot(df, palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1f410291978>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,3),dpi=100)\n",
    "axes = fig.add_axes([1,1,1,1])\n",
    "axes.set_ylim([0,500])\n",
    "sns.axes_style({axes.grid:True})\n",
    "sns.barplot(data=df[(df['Year'] == 2018)].groupby(['Year','Month']).sum().reset_index(),x='Month',y='Expenses',palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4102df0b8>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f41034b160>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.pivot_table(values='Expenses',index='Month',columns='Year'),cmap='coolwarm',linewidth=1,linecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4103b8cc0>"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['Year'] == 2019),'Expenses'] = np.nan\n",
    "\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.matrix.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)>"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.regression.lmplot(x, y, data, hue=None, col=None, row=None, palette=None, col_wrap=None, height=5, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None, size=None)>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.lmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4103ec1d0>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['Year'] == 2020)].groupby('Month')['Expenses'].sum().plot.line(x='Month',y='Expenses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4105eb908>"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Expenses'] = df['Expenses'].interpolate()\n",
    "df.loc[0,'Expenses'] = 300\n",
    "df['Expenses']\n",
    "sns.distplot(df['Expenses'],kde=True,bins=10,color='Green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "# month_map = {\n",
    "#     'January':1,\n",
    "#     'February':2,\n",
    "#     'March':3,\n",
    "#     'April':4,\n",
    "#     'May':5,\n",
    "#     'June':5,\n",
    "#     'July':7,\n",
    "#     'August':8,\n",
    "#     'September':9,\n",
    "#     'October':10,\n",
    "#     'November':11,\n",
    "#     'December':12   \n",
    "# }\n",
    "# df['Month_number'] = df['Month'].replace(month_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class datetime in module datetime:\n",
      "\n",
      "class datetime(date)\n",
      " |  datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])\n",
      " |  \n",
      " |  The year, month and day arguments are required. tzinfo may be None, or an\n",
      " |  instance of a tzinfo subclass. The remaining arguments may be ints.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      datetime\n",
      " |      date\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __hash__(self, /)\n",
      " |      Return hash(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __radd__(self, value, /)\n",
      " |      Return value+self.\n",
      " |  \n",
      " |  __reduce__(...)\n",
      " |      __reduce__() -> (cls, state)\n",
      " |  \n",
      " |  __reduce_ex__(...)\n",
      " |      __reduce_ex__(proto) -> (cls, state)\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __rsub__(self, value, /)\n",
      " |      Return value-self.\n",
      " |  \n",
      " |  __str__(self, /)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  __sub__(self, value, /)\n",
      " |      Return self-value.\n",
      " |  \n",
      " |  astimezone(...)\n",
      " |      tz -> convert to local time in new timezone tz\n",
      " |  \n",
      " |  ctime(...)\n",
      " |      Return ctime() style string.\n",
      " |  \n",
      " |  date(...)\n",
      " |      Return date object with same year, month and day.\n",
      " |  \n",
      " |  dst(...)\n",
      " |      Return self.tzinfo.dst(self).\n",
      " |  \n",
      " |  isoformat(...)\n",
      " |      [sep] -> string in ISO 8601 format, YYYY-MM-DDT[HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM].\n",
      " |      sep is used to separate the year from the time, and defaults to 'T'.\n",
      " |      timespec specifies what components of the time to include (allowed values are 'auto', 'hours', 'minutes', 'seconds', 'milliseconds', and 'microseconds').\n",
      " |  \n",
      " |  replace(...)\n",
      " |      Return datetime with new specified fields.\n",
      " |  \n",
      " |  time(...)\n",
      " |      Return time object with same time but with tzinfo=None.\n",
      " |  \n",
      " |  timestamp(...)\n",
      " |      Return POSIX timestamp as float.\n",
      " |  \n",
      " |  timetuple(...)\n",
      " |      Return time tuple, compatible with time.localtime().\n",
      " |  \n",
      " |  timetz(...)\n",
      " |      Return time object with same time and tzinfo.\n",
      " |  \n",
      " |  tzname(...)\n",
      " |      Return self.tzinfo.tzname(self).\n",
      " |  \n",
      " |  utcoffset(...)\n",
      " |      Return self.tzinfo.utcoffset(self).\n",
      " |  \n",
      " |  utctimetuple(...)\n",
      " |      Return UTC time tuple, compatible with time.localtime().\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  combine(...) from builtins.type\n",
      " |      date, time -> datetime with same date and time fields\n",
      " |  \n",
      " |  fromisoformat(...) from builtins.type\n",
      " |      string -> datetime from datetime.isoformat() output\n",
      " |  \n",
      " |  fromtimestamp(...) from builtins.type\n",
      " |      timestamp[, tz] -> tz's local time from POSIX timestamp.\n",
      " |  \n",
      " |  now(tz=None) from builtins.type\n",
      " |      Returns new datetime object representing current time local to tz.\n",
      " |      \n",
      " |        tz\n",
      " |          Timezone object.\n",
      " |      \n",
      " |      If no tz is specified, uses local timezone.\n",
      " |  \n",
      " |  strptime(...) from builtins.type\n",
      " |      string, format -> new datetime parsed from a string (like time.strptime()).\n",
      " |  \n",
      " |  utcfromtimestamp(...) from builtins.type\n",
      " |      Construct a naive UTC datetime from a POSIX timestamp.\n",
      " |  \n",
      " |  utcnow(...) from builtins.type\n",
      " |      Return a new datetime representing UTC day and time.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  fold\n",
      " |  \n",
      " |  hour\n",
      " |  \n",
      " |  microsecond\n",
      " |  \n",
      " |  minute\n",
      " |  \n",
      " |  second\n",
      " |  \n",
      " |  tzinfo\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  max = datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)\n",
      " |  \n",
      " |  min = datetime.datetime(1, 1, 1, 0, 0)\n",
      " |  \n",
      " |  resolution = datetime.timedelta(microseconds=1)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from date:\n",
      " |  \n",
      " |  __format__(...)\n",
      " |      Formats self with strftime.\n",
      " |  \n",
      " |  isocalendar(...)\n",
      " |      Return a 3-tuple containing ISO year, week number, and weekday.\n",
      " |  \n",
      " |  isoweekday(...)\n",
      " |      Return the day of the week represented by the date.\n",
      " |      Monday == 1 ... Sunday == 7\n",
      " |  \n",
      " |  strftime(...)\n",
      " |      format -> strftime() style string.\n",
      " |  \n",
      " |  toordinal(...)\n",
      " |      Return proleptic Gregorian ordinal.  January 1 of year 1 is day 1.\n",
      " |  \n",
      " |  weekday(...)\n",
      " |      Return the day of the week represented by the date.\n",
      " |      Monday == 0 ... Sunday == 6\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from date:\n",
      " |  \n",
      " |  fromordinal(...) from builtins.type\n",
      " |      int -> date corresponding to a proleptic Gregorian ordinal.\n",
      " |  \n",
      " |  today(...) from builtins.type\n",
      " |      Current date or datetime:  same as self.__class__.fromtimestamp(time.time()).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from date:\n",
      " |  \n",
      " |  day\n",
      " |  \n",
      " |  month\n",
      " |  \n",
      " |  year\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(datetime.datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df.loc[10,'Month']\n",
    "time_object = datetime.datetime.strptime(test,'%B')\n",
    "time_object.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "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>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Exp_cat</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Month_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>October</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>169.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  Year           Exp_cat  Expenses  Month_number\n",
       "0  February  2019       Emergencies     300.0             2\n",
       "1  December  2020  Personal Expense      50.0            12\n",
       "2   October  2019  Personal Expense     169.0            10"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month_number'] = df['Month'].apply(lambda x: datetime.datetime.strptime(x,'%B').month)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1f41062dc50>"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Month_number',y='Expenses',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}