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natashawatkins/pandas-panel-sol.ipynb

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pandas-panel-sol.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Solutions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas_datareader.data as web\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"pd.set_option('display.max_columns', 6) # display 6 columns for viewing purposes\n",
"pd.options.display.float_format = '{:,.2f}'.format # reduce decimal points to 2"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>UNIT</th>\n",
" <th colspan=\"3\" halign=\"left\">Percentage of total population</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">Thousand persons</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE</th>\n",
" <th colspan=\"3\" halign=\"left\">From 15 to 24 years</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">From 55 to 64 years</th>\n",
" </tr>\n",
" <tr>\n",
" <th>SEX</th>\n",
" <th colspan=\"3\" halign=\"left\">Females</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>INDIC_EM</th>\n",
" <th colspan=\"3\" halign=\"left\">Active population</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">Total employment (resident population concept - LFS)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>GEO</th>\n",
" <th>Austria</th>\n",
" <th>Belgium</th>\n",
" <th>Bulgaria</th>\n",
" <th>...</th>\n",
" <th>Turkey</th>\n",
" <th>United Kingdom</th>\n",
" <th>United States</th>\n",
" </tr>\n",
" <tr>\n",
" <th>FREQ</th>\n",
" <th>Annual</th>\n",
" <th>Annual</th>\n",
" <th>Annual</th>\n",
" <th>...</th>\n",
" <th>Annual</th>\n",
" <th>Annual</th>\n",
" <th>Annual</th>\n",
" </tr>\n",
" <tr>\n",
" <th>TIME_PERIOD</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>54.00</td>\n",
" <td>29.80</td>\n",
" <td>26.60</td>\n",
" <td>...</td>\n",
" <td>1,583.00</td>\n",
" <td>4,186.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>54.80</td>\n",
" <td>29.80</td>\n",
" <td>24.80</td>\n",
" <td>...</td>\n",
" <td>1,760.00</td>\n",
" <td>4,164.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>55.40</td>\n",
" <td>27.90</td>\n",
" <td>25.30</td>\n",
" <td>...</td>\n",
" <td>1,876.00</td>\n",
" <td>4,220.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>55.30</td>\n",
" <td>28.20</td>\n",
" <td>24.70</td>\n",
" <td>...</td>\n",
" <td>1,932.00</td>\n",
" <td>4,337.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>55.40</td>\n",
" <td>28.10</td>\n",
" <td>22.70</td>\n",
" <td>...</td>\n",
" <td>2,006.00</td>\n",
" <td>4,457.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>54.10</td>\n",
" <td>27.10</td>\n",
" <td>21.20</td>\n",
" <td>...</td>\n",
" <td>2,107.00</td>\n",
" <td>4,607.00</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 2520 columns</p>\n",
"</div>"
],
"text/plain": [
"UNIT Percentage of total population ... \\\n",
"AGE From 15 to 24 years ... \n",
"SEX Females ... \n",
"INDIC_EM Active population ... \n",
"GEO Austria Belgium Bulgaria ... \n",
"FREQ Annual Annual Annual ... \n",
"TIME_PERIOD ... \n",
"2010-01-01 54.00 29.80 26.60 ... \n",
"2011-01-01 54.80 29.80 24.80 ... \n",
"2012-01-01 55.40 27.90 25.30 ... \n",
"2013-01-01 55.30 28.20 24.70 ... \n",
"2014-01-01 55.40 28.10 22.70 ... \n",
"2015-01-01 54.10 27.10 21.20 ... \n",
"\n",
"UNIT Thousand persons \\\n",
"AGE From 55 to 64 years \n",
"SEX Total \n",
"INDIC_EM Total employment (resident population concept - LFS) \n",
"GEO Turkey \n",
"FREQ Annual \n",
"TIME_PERIOD \n",
"2010-01-01 1,583.00 \n",
"2011-01-01 1,760.00 \n",
"2012-01-01 1,876.00 \n",
"2013-01-01 1,932.00 \n",
"2014-01-01 2,006.00 \n",
"2015-01-01 2,107.00 \n",
"\n",
"UNIT \n",
"AGE \n",
"SEX \n",
"INDIC_EM \n",
"GEO United Kingdom United States \n",
"FREQ Annual Annual \n",
"TIME_PERIOD \n",
"2010-01-01 4,186.00 nan \n",
"2011-01-01 4,164.00 nan \n",
"2012-01-01 4,220.00 nan \n",
"2013-01-01 4,337.00 nan \n",
"2014-01-01 4,457.00 nan \n",
"2015-01-01 4,607.00 nan \n",
"\n",
"[6 rows x 2520 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"employ = web.DataReader('lfsi_emp_a', 'eurostat')\n",
"employ"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a large dataset so it is useful to explore the levels and variables available"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FrozenList(['UNIT', 'AGE', 'SEX', 'INDIC_EM', 'GEO', 'FREQ'])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"employ.columns.names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables within levels can be quickly retrieved with a loop"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"UNIT Index(['Percentage of total population', 'Thousand persons'], dtype='object', name='UNIT')\n",
"AGE Index(['From 15 to 24 years', 'From 15 to 64 years', 'From 20 to 64 years',\n",
" 'From 25 to 54 years', 'From 55 to 64 years'],\n",
" dtype='object', name='AGE')\n",
"SEX Index(['Females', 'Males', 'Total'], dtype='object', name='SEX')\n",
"INDIC_EM Index(['Active population', 'Total employment (resident population concept - LFS)'], dtype='object', name='INDIC_EM')\n",
"GEO Index(['Austria', 'Belgium', 'Bulgaria', 'Switzerland', 'Cyprus',\n",
" 'Czech Republic', 'Germany (until 1990 former territory of the FRG)',\n",
" 'Denmark', 'Euro area (17 countries)', 'Euro area (18 countries)',\n",
" 'Euro area (19 countries)', 'Estonia', 'Greece', 'Spain',\n",
" 'European Union (15 countries)', 'European Union (27 countries)',\n",
" 'European Union (28 countries)', 'Finland', 'France',\n",
" 'France (metropolitan)', 'Croatia', 'Hungary', 'Ireland', 'Iceland',\n",
" 'Italy', 'Japan', 'Lithuania', 'Luxembourg', 'Latvia',\n",
" 'Former Yugoslav Republic of Macedonia, the', 'Malta', 'Netherlands',\n",
" 'Norway', 'Poland', 'Portugal', 'Romania', 'Sweden', 'Slovenia',\n",
" 'Slovakia', 'Turkey', 'United Kingdom', 'United States'],\n",
" dtype='object', name='GEO')\n",
"FREQ Index(['Annual'], dtype='object', name='FREQ')\n"
]
}
],
"source": [
"for name in employ.columns.names:\n",
" print(name, employ.columns.get_level_values(name).unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``FREQ`` does not really provide any information, so we will drop it"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"employ.columns = employ.columns.droplevel(['FREQ'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To easily filter by country, swap ``GEO`` to the top level and sort the ``MultiIndex``"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"employ.columns = employ.columns.swaplevel(0,-1)\n",
"employ = employ.sortlevel(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to get rid of a few items in ``GEO`` which are not countries or in Europe\n",
"\n",
"A fast way to get rid of the EU areas is to use a list comprehension to find the level values in ``GEO`` that begin with 'Euro'\n",
"\n",
"We will also delete 'France (metropolitan)' and 'United States' from our dataframe"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic',\n",
" 'Denmark', 'Estonia', 'Finland',\n",
" 'Former Yugoslav Republic of Macedonia, the', 'France',\n",
" 'Germany (until 1990 former territory of the FRG)', 'Greece', 'Hungary',\n",
" 'Iceland', 'Ireland', 'Italy', 'Japan', 'Latvia', 'Lithuania',\n",
" 'Luxembourg', 'Malta', 'Netherlands', 'Norway', 'Poland', 'Portugal',\n",
" 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland',\n",
" 'Turkey', 'United Kingdom'],\n",
" dtype='object', name='GEO')"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_list = employ.columns.get_level_values('GEO').unique().tolist()\n",
"\n",
"countries = [x for x in geo_list if not x.startswith('Euro')]\n",
"\n",
"employ = employ[countries]\n",
"del employ['France (metropolitan)']\n",
"del employ['United States']\n",
"\n",
"employ.columns.get_level_values('GEO').unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select only percentage employed in the active population from the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>GEO</th>\n",
" <th colspan=\"3\" halign=\"left\">Austria</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">United Kingdom</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE</th>\n",
" <th colspan=\"3\" halign=\"left\">From 15 to 24 years</th>\n",
" <th>...</th>\n",
" <th colspan=\"3\" halign=\"left\">From 55 to 64 years</th>\n",
" </tr>\n",
" <tr>\n",
" <th>SEX</th>\n",
" <th>Females</th>\n",
" <th>Males</th>\n",
" <th>Total</th>\n",
" <th>...</th>\n",
" <th>Females</th>\n",
" <th>Males</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>TIME_PERIOD</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>54.00</td>\n",
" <td>62.60</td>\n",
" <td>58.30</td>\n",
" <td>...</td>\n",
" <td>51.10</td>\n",
" <td>69.20</td>\n",
" <td>60.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>54.80</td>\n",
" <td>63.60</td>\n",
" <td>59.20</td>\n",
" <td>...</td>\n",
" <td>51.30</td>\n",
" <td>68.40</td>\n",
" <td>59.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>55.40</td>\n",
" <td>63.10</td>\n",
" <td>59.20</td>\n",
" <td>...</td>\n",
" <td>53.00</td>\n",
" <td>69.50</td>\n",
" <td>61.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>55.30</td>\n",
" <td>62.30</td>\n",
" <td>58.80</td>\n",
" <td>...</td>\n",
" <td>55.30</td>\n",
" <td>70.60</td>\n",
" <td>62.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>55.40</td>\n",
" <td>60.70</td>\n",
" <td>58.00</td>\n",
" <td>...</td>\n",
" <td>56.40</td>\n",
" <td>70.90</td>\n",
" <td>63.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-01</th>\n",
" <td>54.10</td>\n",
" <td>60.70</td>\n",
" <td>57.40</td>\n",
" <td>...</td>\n",
" <td>57.70</td>\n",
" <td>71.40</td>\n",
" <td>64.40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 510 columns</p>\n",
"</div>"
],
"text/plain": [
"GEO Austria ... United Kingdom \\\n",
"AGE From 15 to 24 years ... From 55 to 64 years \n",
"SEX Females Males Total ... Females Males \n",
"TIME_PERIOD ... \n",
"2010-01-01 54.00 62.60 58.30 ... 51.10 69.20 \n",
"2011-01-01 54.80 63.60 59.20 ... 51.30 68.40 \n",
"2012-01-01 55.40 63.10 59.20 ... 53.00 69.50 \n",
"2013-01-01 55.30 62.30 58.80 ... 55.30 70.60 \n",
"2014-01-01 55.40 60.70 58.00 ... 56.40 70.90 \n",
"2015-01-01 54.10 60.70 57.40 ... 57.70 71.40 \n",
"\n",
"GEO \n",
"AGE \n",
"SEX Total \n",
"TIME_PERIOD \n",
"2010-01-01 60.00 \n",
"2011-01-01 59.70 \n",
"2012-01-01 61.10 \n",
"2013-01-01 62.80 \n",
"2014-01-01 63.50 \n",
"2015-01-01 64.40 \n",
"\n",
"[6 rows x 510 columns]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"employ_f = employ.xs(('Percentage of total population', 'Active population'),\n",
" level=('UNIT', 'INDIC_EM'),\n",
" axis=1)\n",
"employ_f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop the 'Total' value and the overlapping age groups before creating the grouped boxplot"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"employ_f = employ_f.drop('Total', level='SEX', axis=1)\n",
"employ_f = employ_f.drop(['From 15 to 64 years', 'From 20 to 64 years'], level='AGE', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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BaGgoM2fOxM/Pr6KvR0RERKTM3AahV199tdIaS09PZ8GCBbzzzjvk5OQwf/58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\ns/7aJD4CnQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7940ba4080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"box = employ_f['2015'].unstack().reset_index()\n",
"sns.boxplot(x=\"AGE\", y=0, hue=\"SEX\", data=box, palette=(\"husl\"), showfliers=False)\n",
"plt.xlabel('')\n",
"plt.xticks(rotation=35)\n",
"plt.ylabel('Percentage of population (%)')\n",
"plt.title('Employment in Europe (2015)')\n",
"plt.legend(bbox_to_anchor=(1,0.5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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