#!/usr/bin/env python
# coding: utf-8

# # Guided Project: Storytelling Data Visualization on Exchange Rates
# 
# # Introducing the Dataset

# In[1]:


import pandas as pd

exchange_rates = pd.read_csv('euro-daily-hist_1999_2020.csv')
exchange_rates.head()


# In[2]:


exchange_rates.tail()


# In[3]:


exchange_rates.info()


# # Data Cleaning

# In[4]:


exchange_rates.rename(columns={'[US dollar ]': 'US_dollar',
                               'Period\\Unit:': 'Time'},
                      inplace=True)
exchange_rates['Time'] = pd.to_datetime(exchange_rates['Time'])
exchange_rates.sort_values('Time', inplace=True)
exchange_rates.reset_index(drop=True, inplace=True)
exchange_rates.head()


# In[5]:


euro_to_dollar = exchange_rates[['Time', 'US_dollar']].copy()
euro_to_dollar['US_dollar'].value_counts() # 62 '-' characters


# In[6]:


euro_to_dollar = euro_to_dollar[euro_to_dollar['US_dollar'] != '-']
euro_to_dollar['US_dollar'] = euro_to_dollar['US_dollar'].astype(float)
euro_to_dollar.info()


# # Rolling Mean

# In[7]:


import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])
plt.show()


# In[8]:


plt.figure(figsize=(9,6))

plt.subplot(3,2,1)
plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])
plt.title('Original values', weight='bold')

for i, rolling_mean in zip([2, 3, 4, 5, 6],
                           [7, 30, 50, 100, 365]):
    plt.subplot(3,2,i)
    plt.plot(euro_to_dollar['Time'],
             euro_to_dollar['US_dollar'].rolling(rolling_mean).mean())
    plt.title('Rolling Window:' + str(rolling_mean), weight='bold')
    
plt.tight_layout() # Auto-adjusts the padding between subplots
plt.show()


# In[13]:


euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()
euro_to_dollar


# # Coming Up With an Idea
# Here are a few story ideas for our data:
# 
# * We show how the euro-dollar rate has changed during the coronavirus pandemic. We can show the 2020 data and the 2016-2019 data as a baseline. We can use a line plot.
# * We show how the euro-dollar rate changed during the 2007-2008's financial crisis. We can also show the data for 2016 and 2009 for comparison. We can use a line plot.
# * We show comparatively how the euro-dollar rate changed under the last three US presidents (George W. Bush (2001-2009), Barack Obama (2009-2017), and Donald Trump (2017-2021)). We can use a line plot.
# 
# 
# # Storytelling Data Visualization Examples
# 
# # Financial Crisis Example

# In[14]:


financial_crisis = euro_to_dollar.copy(
                   )[(euro_to_dollar['Time'].dt.year >= 2006
                   ) & (euro_to_dollar['Time'].dt.year <= 2009)]
financial_crisis_7_8 = euro_to_dollar.copy(
                   )[(euro_to_dollar.Time.dt.year >= 2007
                   ) & (euro_to_dollar.Time.dt.year <= 2008)]


# In[15]:


### Adding the FiveThirtyEight style
import matplotlib.style as style
style.use('fivethirtyeight')

### Adding the plot
fig,ax = plt.subplots(figsize=(8,3))
ax.plot(financial_crisis['Time'],
        financial_crisis['rolling_mean'],
        linewidth=1, color='#A6D785')

### Highlighting the 2007-2008 period
ax.plot(financial_crisis_7_8['Time'],
        financial_crisis_7_8['rolling_mean'],
        linewidth=3, color='#e23d28')

### Highlihting the peak of the crisis
ax.axvspan(xmin=733112.0, xmax=733302.0, ymin=0.09,
           alpha=0.3, color='grey')
    
### Adding separate tick labels
ax.set_xticklabels([])
ax.set_yticklabels([])

x = 732272.0
for year in ['2006', '2007', '2008', '2009', '2010']:
    ax.text(x, 1.13, year, alpha=0.5, fontsize=11)
    x += 365

y = 1.193   
for rate in ['1.2', '1.3', '1.4', '1.5']:
    ax.text(732172.0, y, rate, alpha=0.5, fontsize=11)
    y += 0.1

## Adding a title and a subtitle    
ax.text(732172.0, 1.67, "Euro-USD rate peaked at 1.59 during 2007-2008's financial crisis",
        weight='bold')
ax.text(732172.0, 1.63, 'Euro-USD exchange rates between 2006 and 2010',
        size=12)

### Adding a signature
ax.text(732172.0, 1.07, '©DATAQUEST' + ' '*94 + 'Source: European Central Bank',
        color = '#f0f0f0', backgroundcolor = '#4d4d4d',
        size=10)

### Add some transparency to the grid
ax.grid(alpha=0.5)

plt.show()


# # The Three US Presidencies Example

# In[16]:


bush_obama_trump = euro_to_dollar.copy(
                   )[(euro_to_dollar['Time'].dt.year >= 2001) & (euro_to_dollar['Time'].dt.year < 2021)]
bush = bush_obama_trump.copy(
       )[bush_obama_trump['Time'].dt.year < 2009]
obama = bush_obama_trump.copy(
       )[(bush_obama_trump['Time'].dt.year >= 2009) & (bush_obama_trump['Time'].dt.year < 2017)]
trump = bush_obama_trump.copy(
       )[(bush_obama_trump['Time'].dt.year >= 2017) & (bush_obama_trump['Time'].dt.year < 2021)]


# Below, you'll notice we used matplotlib's functional approach to build the graphs. We use this approach because it offers more flexibility in arranging the subplots:
# 
# * We first build three of the graphs on a 2-by-3 grid (this grid should have six subplots, but we only build three; the bottom row remains empty).
# * We then build only the bottom graph of a 2-by-1 grid (this grid should have two subplots; the top row remains empty).
# * The two grids are merged, and we end up with three graphs on the top row and one graph on the bottom row.

# In[17]:


### Adding the FiveThirtyEight style
style.use('fivethirtyeight')

### Adding the subplots
plt.figure(figsize=(12, 6))
ax1 = plt.subplot(2,3,1)
ax2 = plt.subplot(2,3,2)
ax3 = plt.subplot(2,3,3)
ax4 = plt.subplot(2,1,2)
axes = [ax1, ax2, ax3, ax4]

### Changes to all the subplots
for ax in axes:
    ax.set_ylim(0.8, 1.7)
    ax.set_yticks([1.0, 1.2, 1.4, 1.6])
    ax.set_yticklabels(['1.0', '1.2','1.4', '1.6'],
                   alpha=0.3)
    ax.grid(alpha=0.5)    
    

### Ax1: Bush
ax1.plot(bush['Time'], bush['rolling_mean'],
        color='#BF5FFF')
ax1.set_xticklabels(['', '2001', '', '2003', '', '2005', '',
                     '2007', '', '2009'],
                   alpha=0.3)
ax1.text(731516.0, 1.92, 'BUSH', fontsize=18, weight='bold',
        color='#BF5FFF')
ax1.text(731216.0, 1.8, '(2001-2009)', weight='bold',
        alpha=0.3)


### Ax2: Obama
ax2.plot(obama['Time'], obama['rolling_mean'],
        color='#ffa500')
ax2.set_xticklabels(['', '2009', '', '2011', '', '2013', '',
                     '2015', '', '2017'],
                   alpha=0.3)
ax2.text(734288.0, 1.92, 'OBAMA', fontsize=18, weight='bold',
        color='#ffa500')
ax2.text(734138.0, 1.8, '(2009-2017)', weight='bold',
         alpha=0.3)


### Ax3: Trump
ax3.plot(trump['Time'], trump['rolling_mean'],
        color='#00B2EE')
ax3.set_xticklabels(['2017', '', '2018', '', '2019', '',
                     '2020', '', '2021'],
                   alpha=0.3)
ax3.text(736855.0, 1.92, 'TRUMP', fontsize=18, weight='bold',
        color='#00B2EE')
ax3.text(736745.0, 1.8, '(2017-2021)', weight='bold',
         alpha=0.3)

### Ax4: Bush-Obama-Trump
ax4.plot(bush['Time'], bush['rolling_mean'],
        color='#BF5FFF')
ax4.plot(obama['Time'], obama['rolling_mean'],
        color='#ffa500')
ax4.plot(trump['Time'], trump['rolling_mean'],
        color='#00B2EE')
ax4.grid(alpha=0.5)
ax4.set_xticks([])

### Adding a title and a subtitle
ax1.text(730016.0, 2.35, 'EURO-USD rate averaged 1.22 under the last three US presidents',
         fontsize=20, weight='bold')
ax1.text(730016.0, 2.14, '''EURO-USD exchange rates under George W. Bush (2001 - 2009), Barack Obama (2009-2017),
and Donald Trump (2017-2021)''',
        fontsize=16)

### Adding a signature
ax4.text(729916.0, 0.65, '©DATAQUEST' + ' '*103 + 'Source: European Central Bank',
        color = '#f0f0f0', backgroundcolor = '#4d4d4d',
        size=14)

plt.show()