Merge pull request #2 from Geometrein/master

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Geometrein · Jul 16, 2023 · 6544228 · 6544228
2 parents 85df849 + 18b7ea8
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diff --git a/01_population_pyramid.ipynb b/01_population_pyramid.ipynb
diff --git a/03_crime_rates.ipynb b/03_crime_rates.ipynb
@@ -92,7 +92,6 @@
  "def plot_crime_rates_by_year(crimes_df: pd.DataFrame, permits_df: pd.DataFrame) -> tuple:\n",
  " # Data\n",
  " grouped_crimes = crimes_df.groupby(['Year'])['Persons suspected of solved offences'].sum().reset_index()\n",
- "\n",
  " permits_df = permits_df.loc[(permits_df['Decision'] == 'Positive') & (permits_df['Year'] < 2023)]\n",
  " grouped_permits = permits_df.groupby(['Year'])['Number'].sum().reset_index()\n",
  " grouped_permits.sort_values(by='Number', inplace=True, ascending=False)\n",
@@ -104,16 +103,12 @@
  " # Labels\n",
  " plt.suptitle(f\"Total Number of Crimes Committed in Finland Between 2000-2022 \", y=0.93, fontsize=15)\n",
  " ax1.set(xlabel='Year', ylabel=f'Persons suspected of solved offences')\n",
- "\n",
  " ax1.set_xticks(range(2000, 2023, 1))\n",
  " ax1.set_xlim([2015, 2023])\n",
- "\n",
  " ax1.tick_params(axis='y', colors='#4489BD')\n",
  " ax2.tick_params(axis='y', colors='#2c8c1f')\n",
- "\n",
  " ax1.grid(axis='y')\n",
  " ax2.grid(False)\n",
- "\n",
  " ax1.yaxis.set_major_formatter(ticker.EngFormatter())\n",
  " ax2.yaxis.set_major_formatter(ticker.EngFormatter())\n",
  " # Author\n",
@@ -179,7 +174,6 @@
  " # Highest correlation\n",
  " ax.axhline(y=np.max(ccf), color='#4489BD', lw=.8, linestyle='--', label='highest correlation')\n",
  " ax.axhline(y=np.min(ccf), color='#4489BD', lw=.8, linestyle='--')\n",
- "\n",
  " ax.set(ylim = [-1, 1])\n",
  " ax.set_title('Cross Correlation Between Crime Rates and Issued Residence Permits', fontsize=15)\n",
  " ax.set_ylabel('Correlation Coefficients', weight='bold', fontsize=12)\n",

diff --git a/04_employment.ipynb b/04_employment.ipynb
@@ -62,7 +62,7 @@
  "entrepreneur_df = employment_df.loc[employment_df['Main type of activity']=='Total'].copy(deep=True)\n",
  "entrepreneur_df['type'] = entrepreneur_df['Occupational status'] + '/' + entrepreneur_df['Nationality']\n",
  "entrepreneur_df = entrepreneur_df.drop(columns=['Main type of activity', 'Sex', 'Nationality', 'Occupational status'])\n",
- "df_melt= pd.melt(entrepreneur_df, id_vars='type', var_name='year', value_name='number')\n",
+ "df_melt = pd.melt(entrepreneur_df, id_vars='type', var_name='year', value_name='number')\n",
  "df_melt[['type', 'Nationality']] = df_melt['type'].str.split(\"/\", expand = True)\n",
  "df_melt = df_melt[['Nationality', 'type', 'year', 'number']]\n",
  "df_melt = df_melt.loc[df_melt['type'] != 'Not employed']\n",

diff --git a/README.md b/README.md
@@ -2,15 +2,30 @@
 
 ---
 
-![](charts/population_pyramid.gif)
+![population Pyramid animation](charts/population_pyramid.gif)
 
+## About
 This repo contains a collection of jupyter notebooks and data extracts used to generate the following articles:
 
 [![Medium Badge](https://img.shields.io/badge/Medium-303030?style=for-the-badge&logo=medium&logoColor=white)]()
 [![Linkedin](https://img.shields.io/badge/Linkedin-0077B5?style=for-the-badge&logo=Linkedin&logoColor=white)]()
+
+## Data Sources
+
+---
+The data used in this project is extracted from the following sources:
+
+[![Statfin](https://img.shields.io/badge/Statfin_Database-0077B5?style=for-the-badge&logo=redis&logoColor=white)](https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/)
+
+
 ## Installation
 
 ---
+Launch the notebooks in binder:
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Geometrein/the-finnish-line/HEAD)
+
+#### Local install
 Move to the finnish line directory :) 
 ```
 cd the_finnish_line
@@ -26,11 +41,4 @@ source venv/bin/activate
 pip install -r requirements.txt
 ```
 
-## Data Sources
-
----
-The data used in this project is extracted from the following sources:
-
-[![Statfin](https://img.shields.io/badge/Statfin_Database-0077B5?style=for-the-badge&logo=redis&logoColor=white)](https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/)
-
 
diff --git a/charts/age_dependency_vs_cabinets.png b/charts/age_dependency_vs_cabinets.png
diff --git a/charts/population_pyramid_2000.png b/charts/population_pyramid_2000.png
diff --git a/charts/population_pyramid_2001.png b/charts/population_pyramid_2001.png
diff --git a/charts/population_pyramid_2002.png b/charts/population_pyramid_2002.png
diff --git a/charts/population_pyramid_2003.png b/charts/population_pyramid_2003.png
diff --git a/charts/population_pyramid_2004.png b/charts/population_pyramid_2004.png
diff --git a/charts/population_pyramid_2005.png b/charts/population_pyramid_2005.png
diff --git a/charts/population_pyramid_2006.png b/charts/population_pyramid_2006.png
diff --git a/charts/population_pyramid_2007.png b/charts/population_pyramid_2007.png
diff --git a/charts/population_pyramid_2008.png b/charts/population_pyramid_2008.png
diff --git a/charts/population_pyramid_2009.png b/charts/population_pyramid_2009.png
diff --git a/charts/population_pyramid_2010.png b/charts/population_pyramid_2010.png
diff --git a/charts/population_pyramid_2011.png b/charts/population_pyramid_2011.png
diff --git a/charts/population_pyramid_2012.png b/charts/population_pyramid_2012.png
diff --git a/charts/population_pyramid_2013.png b/charts/population_pyramid_2013.png
diff --git a/charts/population_pyramid_2014.png b/charts/population_pyramid_2014.png
diff --git a/charts/population_pyramid_2015.png b/charts/population_pyramid_2015.png
diff --git a/charts/population_pyramid_2016.png b/charts/population_pyramid_2016.png
diff --git a/charts/population_pyramid_2017.png b/charts/population_pyramid_2017.png
diff --git a/charts/population_pyramid_2018.png b/charts/population_pyramid_2018.png
diff --git a/charts/population_pyramid_2019.png b/charts/population_pyramid_2019.png
diff --git a/charts/population_pyramid_2020.png b/charts/population_pyramid_2020.png
diff --git a/charts/population_pyramid_2021.png b/charts/population_pyramid_2021.png
diff --git a/charts/population_pyramid_2022.png b/charts/population_pyramid_2022.png