4. Cheat Sheet PANDAS - Antonio Dagnino Mendez.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PANDAS Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create DF\n",
    "    - Create Dummy DF\n",
    "    - Copy DF to another variable\n",
    "    - add / drop columns\n",
    "    - Re-order / Re-name columns\n",
    "    - add / drop rows\n",
    "    - delete df\n",
    "- Visualize df\n",
    "    - Header and Tail\n",
    "    - Columns\n",
    "    - info\n",
    "    - describe\n",
    "    - Correlation\n",
    "    - unique values\n",
    "    - Sort data / Specific order of elements in columns\n",
    "    - Slice Columns\n",
    "    - Show data based on conditions\n",
    "    - Group By\n",
    "- Modify Data\n",
    "    - Replace\n",
    "    - Map\n",
    "    - get dummies\n",
    "    - Missing Data\n",
    "    - astype\n",
    "    - interpolate\n",
    "    - lambda expression with datetime\n",
    "- Plotting with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BooleanDtype', 'Categorical', 'CategoricalDtype', 'CategoricalIndex', 'DataFrame', 'DateOffset', 'DatetimeIndex', 'DatetimeTZDtype', 'ExcelFile', 'ExcelWriter', 'Float64Index', 'Grouper', 'HDFStore', 'Index', 'IndexSlice', 'Int16Dtype', 'Int32Dtype', 'Int64Dtype', 'Int64Index', 'Int8Dtype', 'Interval', 'IntervalDtype', 'IntervalIndex', 'MultiIndex', 'NA', 'NaT', 'NamedAgg', 'Period', 'PeriodDtype', 'PeriodIndex', 'RangeIndex', 'Series', 'SparseDtype', 'StringDtype', 'Timedelta', 'TimedeltaIndex', 'Timestamp', 'UInt16Dtype', 'UInt32Dtype', 'UInt64Dtype', 'UInt64Index', 'UInt8Dtype', '__builtins__', '__cached__', '__doc__', '__docformat__', '__file__', '__getattr__', '__git_version__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_config', '_hashtable', '_is_numpy_dev', '_lib', '_libs', '_np_version_under1p16', '_np_version_under1p17', '_np_version_under1p18', '_testing', '_tslib', '_typing', '_version', 'api', 'array', 'arrays', 'bdate_range', 'compat', 'concat', 'core', 'crosstab', 'cut', 'date_range', 'describe_option', 'errors', 'eval', 'factorize', 'get_dummies', 'get_option', 'infer_freq', 'interval_range', 'io', 'isna', 'isnull', 'json_normalize', 'lreshape', 'melt', 'merge', 'merge_asof', 'merge_ordered', 'notna', 'notnull', 'offsets', 'option_context', 'options', 'pandas', 'period_range', 'pivot', 'pivot_table', 'plotting', 'qcut', 'read_clipboard', 'read_csv', 'read_excel', 'read_feather', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_orc', 'read_parquet', 'read_pickle', 'read_sas', 'read_spss', 'read_sql', 'read_sql_query', 'read_sql_table', 'read_stata', 'read_table', 'reset_option', 'set_eng_float_format', 'set_option', 'show_versions', 'test', 'testing', 'timedelta_range', 'to_datetime', 'to_numeric', 'to_pickle', 'to_timedelta', 'tseries', 'unique', 'util', 'value_counts', 'wide_to_long'] "
     ]
    }
   ],
   "source": [
    "print(dir(pd),end=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ----- Create Rows, Colums and Dataframe -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "\n",
    "# Create dataframe\n",
    "df = pd.DataFrame(data=data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Copy DF to another variable -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy df with slice notation\n",
    "df2 = df[:] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Add / Drop columns -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "# DROP column\n",
    "df2.drop('Month',axis=1,inplace=True)  \n",
    "\n",
    "# Warning will show up indicating the the value modified is from a sliced (copied) df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Month', 'Year', 'Exp_cat', 'Expenses'], dtype='object') \n",
      "\n",
      " Index(['Year', 'Exp_cat', 'Expenses'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns,'\\n\\n',df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ADD column\n",
    "df2['Month'] = data['Month'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Re-order and Re-name Columns ------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Year', 'Exp_cat', 'Expenses'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Re-order and Re-name columns\n",
    "df2 = df[['Month','Year','Exp_cat','Expenses']] #re-order\n",
    "\n",
    "#Rename option 1\n",
    "df2.columns = df2.columns.str.replace('Month','SeveralMonths')\n",
    "#Rename option 2\n",
    "df2.rename(columns={'SeveralMonths':'Month'},inplace=True)\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Add / drop rows  -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>February</td>\n",
       "      <td>2020</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Year       Exp_cat  Expenses\n",
       "19  November  2018  Work Expense       280\n",
       "20  February  2020   Emergencies       500"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add rows using loc ---IMPORTANT: Avoid growing dataframes. Instead, grow lists and then turn them into dataframes.\n",
    "\n",
    "df2.loc[df.index.max() + 1] = ['February',2020,'Emergencies',500]\n",
    "df2.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop(df2.index.max(),axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Delete DF -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'deleted df'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = 'deleted df'\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Data Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Head and tail -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header:\n",
      "       Month  Year           Exp_cat  Expenses\n",
      "0  December  2018       Emergencies       362\n",
      "1  December  2019  Personal Expense       456\n",
      "2      June  2019    Fixed Expenses        38 \n",
      "\n",
      "\n",
      " Tail:\n",
      "        Month  Year         Exp_cat  Expenses\n",
      "18     April  2020  Fixed Expenses       263\n",
      "19  November  2018    Work Expense       280\n"
     ]
    }
   ],
   "source": [
    "#head and tail\n",
    "print('Header:\\n',df.head(3),'\\n\\n\\n','Tail:\\n',df.tail(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Year', 'Exp_cat', 'Expenses'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Month     20 non-null     object\n",
      " 1   Year      20 non-null     int64 \n",
      " 2   Exp_cat   20 non-null     object\n",
      " 3   Expenses  20 non-null     int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 768.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2018.95</td>\n",
       "      <td>0.759155</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.25</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expenses</th>\n",
       "      <td>20.0</td>\n",
       "      <td>261.60</td>\n",
       "      <td>144.770744</td>\n",
       "      <td>23.0</td>\n",
       "      <td>166.25</td>\n",
       "      <td>281.5</td>\n",
       "      <td>362.25</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count     mean         std     min      25%     50%      75%     max\n",
       "Year       20.0  2018.95    0.759155  2018.0  2018.00  2019.0  2019.25  2020.0\n",
       "Expenses   20.0   261.60  144.770744    23.0   166.25   281.5   362.25   473.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Year</th>\n",
       "      <th>Expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expenses</th>\n",
       "      <td>0.111869</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Expenses\n",
       "Year      1.000000  0.111869\n",
       "Expenses  0.111869  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- unique values -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['Emergencies', 'Personal Expense', 'Fixed Expenses',\n",
       "        'Work Expense'], dtype=object),\n",
       " 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exp_cat'].unique(), df['Exp_cat'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Sort Data / Sort by specific order -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>July</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>September</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>April</td>\n",
       "      <td>2020</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>June</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>September</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Year           Exp_cat  Expenses\n",
       "8       March  2018  Personal Expense       283\n",
       "15      March  2018    Fixed Expenses       451\n",
       "10   November  2018      Work Expense        23\n",
       "19   November  2018      Work Expense       280\n",
       "0    December  2018       Emergencies       362\n",
       "7    December  2018    Fixed Expenses        30\n",
       "16   February  2019  Personal Expense       140\n",
       "6       March  2019    Fixed Expenses        89\n",
       "14      March  2019      Work Expense       179\n",
       "5         May  2019    Fixed Expenses       363\n",
       "2        June  2019    Fixed Expenses        38\n",
       "11       June  2019  Personal Expense       315\n",
       "9        July  2019  Personal Expense       473\n",
       "12  September  2019    Fixed Expenses       349\n",
       "1    December  2019  Personal Expense       456\n",
       "18      April  2020    Fixed Expenses       263\n",
       "3        June  2020  Personal Expense       303\n",
       "17  September  2020  Personal Expense       175\n",
       "4    December  2020  Personal Expense       424\n",
       "13   December  2020      Work Expense       236"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'] = pd.Categorical(df['Month'], ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August',\n",
    "                                           'September', 'October','November', 'December'])\n",
    "\n",
    "df.sort_values(by=['Year','Month'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Show specific columns -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  Expenses\n",
       "0  December       362\n",
       "1  December       456"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Slice DF show the columns specified only\n",
    "df[['Month','Expenses']].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Show data based on conditions -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Month, Year, Exp_cat, Expenses]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Month']=='January')&(df['Expenses']>50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expenses</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Year           Exp_cat  Expenses\n",
       "16  February  2019  Personal Expense       140\n",
       "6      March  2019    Fixed Expenses        89\n",
       "8      March  2018  Personal Expense       283\n",
       "14     March  2019      Work Expense       179\n",
       "15     March  2018    Fixed Expenses       451"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Month'].isin('January February March'.split()))].sort_values(by='Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Group by -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></th>\n",
       "      <th>Expenses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018</th>\n",
       "      <th>March</th>\n",
       "      <td>734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2019</th>\n",
       "      <th>February</th>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2020</th>\n",
       "      <th>April</th>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Expenses\n",
       "Year Month              \n",
       "2018 March         734.0\n",
       "     November      303.0\n",
       "     December      392.0\n",
       "2019 February      140.0\n",
       "     March         268.0\n",
       "     May           363.0\n",
       "     June          353.0\n",
       "     July          473.0\n",
       "     September     349.0\n",
       "     December      456.0\n",
       "2020 April         263.0\n",
       "     June          303.0\n",
       "     September     175.0\n",
       "     December      660.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Year','Month']).sum().dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modify Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Replace -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['December', 'June', 'May', 'March', 'July', 'November', 'September', 'February', 'April']\n",
       "Categories (9, object): ['December', 'June', 'May', 'March', ..., 'November', 'September', 'February', 'April']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace({'Fixed Expenses':'Fixed Expense'})\n",
    "df['Month'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Map -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Exp_Categories'] = df['Exp_cat'].map({'Personal Expense':1,'Fixed Expense':2,'Work Expense':np.nan,'Emergencies':4})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Get dummies -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Exp_Categories</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>362</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>June</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>424</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>363</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>89</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>283</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>July</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>473</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>September</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>349</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>451</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>September</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>175</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>April</td>\n",
       "      <td>2020</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>263</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Year           Exp_cat  Expenses  Exp_Categories  D1  D2  D3\n",
       "0    December  2018       Emergencies       362             4.0   0   0   0\n",
       "1    December  2019  Personal Expense       456             1.0   0   1   0\n",
       "2        June  2019     Fixed Expense        38             2.0   1   0   0\n",
       "3        June  2020  Personal Expense       303             1.0   0   1   0\n",
       "4    December  2020  Personal Expense       424             1.0   0   1   0\n",
       "5         May  2019     Fixed Expense       363             2.0   1   0   0\n",
       "6       March  2019     Fixed Expense        89             2.0   1   0   0\n",
       "7    December  2018     Fixed Expense        30             2.0   1   0   0\n",
       "8       March  2018  Personal Expense       283             1.0   0   1   0\n",
       "9        July  2019  Personal Expense       473             1.0   0   1   0\n",
       "10   November  2018      Work Expense        23             NaN   0   0   1\n",
       "11       June  2019  Personal Expense       315             1.0   0   1   0\n",
       "12  September  2019     Fixed Expense       349             2.0   1   0   0\n",
       "13   December  2020      Work Expense       236             NaN   0   0   1\n",
       "14      March  2019      Work Expense       179             NaN   0   0   1\n",
       "15      March  2018     Fixed Expense       451             2.0   1   0   0\n",
       "16   February  2019  Personal Expense       140             1.0   0   1   0\n",
       "17  September  2020  Personal Expense       175             1.0   0   1   0\n",
       "18      April  2020     Fixed Expense       263             2.0   1   0   0\n",
       "19   November  2018      Work Expense       280             NaN   0   0   1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['D1','D2','D3']] = pd.get_dummies(df['Exp_cat'],drop_first=True,)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Missing Data -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month             0\n",
       "Year              0\n",
       "Exp_cat           0\n",
       "Expenses          0\n",
       "Exp_Categories    4\n",
       "D1                0\n",
       "D2                0\n",
       "D3                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 1., 2.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exp_Categories'].dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Exp_Categories'] = df['Exp_Categories'].fillna(value=3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- astype -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['Expenses'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Expenses'] = df['Expenses'].astype('float64')\n",
    "type(df['Expenses'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Interpolate -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "473.0\n",
      "175.0\n"
     ]
    },
    {
     "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>Exp_Categories</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>362.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>June</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>303.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>424.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>363.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>283.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>July</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>September</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>September</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>April</td>\n",
       "      <td>2020</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>263.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>280.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Year           Exp_cat  Expenses  Exp_Categories  D1  D2  D3\n",
       "0    December  2018       Emergencies     362.0             4.0   0   0   0\n",
       "1    December  2019  Personal Expense     456.0             1.0   0   1   0\n",
       "2        June  2019     Fixed Expense      38.0             2.0   1   0   0\n",
       "3        June  2020  Personal Expense     303.0             1.0   0   1   0\n",
       "4    December  2020  Personal Expense     424.0             1.0   0   1   0\n",
       "5         May  2019     Fixed Expense     363.0             2.0   1   0   0\n",
       "6       March  2019     Fixed Expense      89.0             2.0   1   0   0\n",
       "7    December  2018     Fixed Expense      30.0             2.0   1   0   0\n",
       "8       March  2018  Personal Expense     283.0             1.0   0   1   0\n",
       "9        July  2019  Personal Expense     473.0             1.0   0   1   0\n",
       "10   November  2018      Work Expense       NaN             3.0   0   0   1\n",
       "11       June  2019  Personal Expense       NaN             1.0   0   1   0\n",
       "12  September  2019     Fixed Expense       NaN             2.0   1   0   0\n",
       "13   December  2020      Work Expense       NaN             3.0   0   0   1\n",
       "14      March  2019      Work Expense       NaN             3.0   0   0   1\n",
       "15      March  2018     Fixed Expense       NaN             2.0   1   0   0\n",
       "16   February  2019  Personal Expense       NaN             1.0   0   1   0\n",
       "17  September  2020  Personal Expense     175.0             1.0   0   1   0\n",
       "18      April  2020     Fixed Expense     263.0             2.0   1   0   0\n",
       "19   November  2018      Work Expense     280.0             3.0   0   0   1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set last 10 rows as null so interpolation could be explained\n",
    "print(df.loc[9]['Expenses'])\n",
    "print(df.loc[17]['Expenses'])\n",
    "df.loc[10:16,'Expenses'] = np.nan\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Exp_Categories</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>July</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>473.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>2018</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>435.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>398.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>September</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>361.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>December</td>\n",
       "      <td>2020</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>324.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>March</td>\n",
       "      <td>2019</td>\n",
       "      <td>Work Expense</td>\n",
       "      <td>286.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>March</td>\n",
       "      <td>2018</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>249.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>February</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>212.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>September</td>\n",
       "      <td>2020</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>175.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Year           Exp_cat  Expenses  Exp_Categories  D1  D2  D3\n",
       "9        July  2019  Personal Expense    473.00             1.0   0   1   0\n",
       "10   November  2018      Work Expense    435.75             3.0   0   0   1\n",
       "11       June  2019  Personal Expense    398.50             1.0   0   1   0\n",
       "12  September  2019     Fixed Expense    361.25             2.0   1   0   0\n",
       "13   December  2020      Work Expense    324.00             3.0   0   0   1\n",
       "14      March  2019      Work Expense    286.75             3.0   0   0   1\n",
       "15      March  2018     Fixed Expense    249.50             2.0   1   0   0\n",
       "16   February  2019  Personal Expense    212.25             1.0   0   1   0\n",
       "17  September  2020  Personal Expense    175.00             1.0   0   1   0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Expenses'] = df['Expenses'].interpolate()\n",
    "df[9:18][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**----- Lambda Expression with datetime -----**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Exp_Categories</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>Month_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>2018</td>\n",
       "      <td>Emergencies</td>\n",
       "      <td>362.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December</td>\n",
       "      <td>2019</td>\n",
       "      <td>Personal Expense</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>June</td>\n",
       "      <td>2019</td>\n",
       "      <td>Fixed Expense</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  Year           Exp_cat  Expenses  Exp_Categories  D1  D2  D3  \\\n",
       "0  December  2018       Emergencies     362.0             4.0   0   0   0   \n",
       "1  December  2019  Personal Expense     456.0             1.0   0   1   0   \n",
       "2      June  2019     Fixed Expense      38.0             2.0   1   0   0   \n",
       "\n",
       "  Month_number  \n",
       "0           12  \n",
       "1           12  \n",
       "2            6  "
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2018-06-29\n",
      "Time: 08:15:27\n",
      "Date-time: 2018-06-29 08:15:27\n"
     ]
    }
   ],
   "source": [
    "date_time_str = '2018-06-29 08:15:27'\n",
    "date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "print('Date:', date_time_obj.date())\n",
    "print('Time:', date_time_obj.time())\n",
    "print('Date-time:', date_time_obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Formats\n",
    "\n",
    "%a ---\tWeekday, short version\tWed\t\n",
    "%A ---\tWeekday, full version\tWednesday\t\n",
    "%w ---\tWeekday as a number 0-6, 0 is Sunday\t3\t\n",
    "%d ---\tDay of month 01-31\t31\t\n",
    "%b ---\tMonth name, short version\tDec\t\n",
    "%B ---\tMonth name, full version\tDecember\t\n",
    "%m ---\tMonth as a number 01-12\t12\t\n",
    "%y ---\tYear, short version, without century\t18\t\n",
    "%Y ---\tYear, full version\t2018\t\n",
    "%H ---\tHour 00-23\t17\t\n",
    "%I ---\tHour 00-12\t05\t\n",
    "%p ---\tAM/PM\tPM\t\n",
    "%M ---\tMinute 00-59\t41\t\n",
    "%S ---\tSecond 00-59\t08\t\n",
    "%f ---\tMicrosecond 000000-999999\t548513\t\n",
    "%z ---\tUTC offset\t+0100\t\n",
    "%Z ---\tTimezone\tCST\t\n",
    "%j ---\tDay number of year 001-366\t365\t\n",
    "%U ---\tWeek number of year, Sunday as the first day of week, 00-53\t52\t\n",
    "%W ---\tWeek number of year, Monday as the first day of week, 00-53\t52\t\n",
    "%c ---\tLocal version of date and time\tMon Dec 31 17:41:00 2018\t\n",
    "%C ---\tCentury\t20\t\n",
    "%x ---\tLocal version of date\t12/31/18\t\n",
    "%X ---\tLocal version of time\t17:41:00\t\n",
    "%% ---\tA % character\t%\t\n",
    "%G ---\tISO 8601 year\t2018\t\n",
    "%u ---\tISO 8601 weekday (1-7)\t1\t\n",
    "%V ---\tISO 8601 weeknumber (01-53)\t01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x2dd391b3d00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,2),dpi=80)\n",
    "axis = fig.add_axes([1,1,1,1])\n",
    "df[(df['Year'] == 2019)].groupby('Month')['Expenses'].sum().plot.bar(cmap='Set1',ylim=(0,500),title='Expenses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Corr BarPlot -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "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": [
    "df.corr()['Exp_Categories'].sort_values().plot(kind='bar',ylim=(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Line plot -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Expenses'}, xlabel='Month'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "df[(df['Year'] == 2019)].groupby('Month')['Expenses'].sum().plot.line(cmap='viridis',ylim=(0,500),title='Expenses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----- Scatter plot -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looks like pandas scatter plot has a bug when trying to plot from datetime values. Thus, this will be done the following way:\n",
    "df.drop('Month_number',inplace=True,axis=1)\n",
    "month_map = {\n",
    "'January':1,\n",
    "'February':2,\n",
    "'March':3,\n",
    "'April':4,\n",
    "'May':5,\n",
    "'June':6,\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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month_number', ylabel='Expenses'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='Month_number',y='Expenses',figsize=(12,4),color='brown',s=50)"
   ]
  }
 ],
 "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
}