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

# # LFC Data Analysis: The Transfers

# See Terry's blog [Inspiring Transfers](http://terrydolan.blogspot.co.uk/2015/09/inspiring-transfers.html) for a discussion of the data analysis.

# This notebook analyses Liverpool FC's transfers over the last 5 seasons, from 2011-2012 to 2014-15. It also compares Liverpool's average net transfer spend, revenue and wage bill to the other teams in the top 6.
# 
# The analysis uses [IPython Notebook](http://ipython.org/notebook.html), [python](https://www.python.org/), [pandas](http://pandas.pydata.org/) and [matplotlib](http://matplotlib.org/) to explore the data. 

# ## Set-up

# Import the modules needed for the analysis.

# In[1]:


import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
import collections
from datetime import datetime
from __future__ import division

# enable inline plotting
get_ipython().run_line_magic('matplotlib', 'inline')


# Print version numbers.

# In[2]:


print 'python version: {}'.format(sys.version)
print 'pandas version: {}'.format(pd.__version__)
print 'matplotlib version: {}'.format(mpl.__version__)
print 'numpy version: {}'.format(np.__version__)


# ## Load the data into a dataframes and munge

# #### Create dataframe of LFC transfers
# Data source: lfchistory.net
# 
# Note that all transfers are shown for FSG's ownership, from Oct 2010 to September 2015. 

# In[3]:


LFC_TRANSFERS_CSV_FILE = 'data\lfc_transfers_2010-2011_2015-2016.csv'
dflfc_transfers = pd.read_csv(LFC_TRANSFERS_CSV_FILE, parse_dates=['Date'])

# convert Fee to millions of pounds
dflfc_transfers.Fee = dflfc_transfers.Fee/1000000

# show shape
dflfc_transfers.shape


# In[4]:


dflfc_transfers.head()


# In[5]:


dflfc_transfers.tail()


# In[6]:


# check Date (of transfer) column is datetime data type
dflfc_transfers.dtypes


# #### Create dataframe of LFC players giving birthdate of each player
# Data source: lfchistory.net

# In[7]:


LFC_PLAYERS_CSV_FILE = 'data\lfc_players_september2015_upd.csv' 
dflfc_players = pd.read_csv(LFC_PLAYERS_CSV_FILE, parse_dates=['birthdate'])
dflfc_players.rename(columns={'player': 'Player', 'birthdate': 'Birthdate', 'country': 'Country'}, inplace=True)
dflfc_players.shape


# In[8]:


dflfc_players.dtypes


# In[9]:


dflfc_players.head(10)


# #### Create dataframe of premier league transfers over last 5 seasons
# Data source: transferleague.co.uk

# In[10]:


PREM_TRANSFERS_CSV_FILE = 'data\prem_transfers_2011-2012_2015-2016.csv'
dfprem_transfers = pd.read_csv(PREM_TRANSFERS_CSV_FILE, skiprows=2, header=True)

# convert money to millions of pounds
dfprem_transfers.Purchased = np.round(dfprem_transfers.Purchased/1000000, 1)
dfprem_transfers.Sold = np.round(dfprem_transfers.Sold/1000000, 1)
dfprem_transfers.NetSpend = np.round(dfprem_transfers.NetSpend/1000000, 1)
dfprem_transfers.PerSeasonSpend = np.round(dfprem_transfers.PerSeasonSpend/1000000, 1)

# show shape
dfprem_transfers.shape


# In[12]:


dfprem_transfers.head()


# In[13]:


dfprem_transfers.tail()


# #### Create dataframe of premier league wage bill over last 5 seasons
# Data source: guardian.co.uk etc

# In[14]:


PREM_WAGES_CSV_FILE = 'data\prem_wages_2012-2013_2014-2015.csv'
dfprem_wages = pd.read_csv(PREM_WAGES_CSV_FILE, skiprows=4, header=True)

# show shape
dfprem_wages.shape


# In[15]:


dfprem_wages


# #### Create dataframe of league table for 2014-2015
# Data source: http://www.sportsmole.co.uk/football/premier-league/2014-15/table.html

# In[16]:


PREM_TABLE_CSV_FILE = 'data\prem_table_2014-2015.csv'
dfprem_table_2014_2015 = pd.read_csv(PREM_TABLE_CSV_FILE, skiprows=2, header=True)

# show shape
dfprem_table_2014_2015.shape


# In[17]:


dfprem_table_2014_2015.head(6)


# In[18]:


dfprem_table_2014_2015.tail()


# #### Create dataframe of Deloiite Football Money League
# Data source: [Deloitte Football Money League](http://www2.deloitte.com/uk/en/pages/sports-business-group/articles/deloitte-football-money-league.html)
# 
# The revenue (in pounds) is in the detailed report.

# In[19]:


# note that the revenue figures are for 2014
deloitte_review_2015 = {'Team': ['Manchester United', 'Manchester City', 'Chelsea', 'Arsenal', 'Liverpool', 'Tottenham Hotspur'],
                   'Revenue': [433.2, 346.5, 324.4, 300.5, 255.8, 180.5]}
df_revenue = pd.DataFrame(data=deloitte_review_2015, columns=['Team', 'Revenue'])
df_revenue = df_revenue.set_index('Team')


# In[20]:


df_revenue


# ## Analyse the data
# 
# Ask a question and find the answer!

# Show ins and outs for 2015-2016

# In[21]:


dflfc_transfers[dflfc_transfers.Season == '2015-2016']


# What was biggest fee paid for a Liverpool player over the data period?

# In[22]:


dflfc_transfers_in = dflfc_transfers[dflfc_transfers.Direction == 'In']
dflfc_transfers_in[dflfc_transfers_in.Fee == dflfc_transfers_in.Fee.max()]


# What was biggest fee received for a player?

# In[23]:


dflfc_transfers_out = dflfc_transfers[dflfc_transfers.Direction == 'Out']
dflfc_transfers_out[dflfc_transfers_out.Fee == dflfc_transfers_out.Fee.max()]


# Summarise Fees In and Out

# In[24]:


dflfc_transfers.groupby(['Season', 'Direction']).sum()


# Show as 'unstacked' dataframe

# In[25]:


df_fsg = dflfc_transfers.groupby(['Season', 'Direction']).sum().unstack()
df_fsg.columns = df_fsg.columns.droplevel()
del df_fsg.columns.name
df_fsg

Calculate the net spend for the data period, Oct 2011 to Aug 2015
# In[27]:


df_fsg['NetSpend'] = df_fsg.In - df_fsg.Out
df_fsg


# Calculate total Fees and NetSpend, over the 6 seasons

# In[28]:


df_fsg.sum()


# Calculate average (mean) Fees and NetSpend, over the 6 seasons

# In[29]:


df_fsg.mean()


# What is the the average Net Spend over last 5 seasons?

# In[30]:


df_fsg['2011-2012':'2015-2016'].NetSpend.mean()


# Where do most players come from?

# In[31]:


dflfc_transfers_in.Club.value_counts().head()


# Where do most players go to?

# In[32]:


dflfc_transfers_out.Club.value_counts().head()


# Which players were bought for more than £15M?

# In[33]:


dflfc_transfers_in[dflfc_transfers_in.Fee >= 15]


# Which players were sold for more than £15M?

# In[34]:


dflfc_transfers_out[dflfc_transfers_out.Fee >= 15]


# Plot FSG's LFC Transfers per Season for last 5 seasons (2011-12 to 2015-16)
# 
# Note that the analysis is carried out in September 2015. Therefore the 2015-2016 NetSpend does not include the January 2016 tranfer window.

# In[35]:


df_fsg.ix['2011-2012':]


# In[36]:


avg_netspend = df_fsg.ix['2011-2012':].NetSpend.mean()
print 'Average Transfer Net Spend per Season is £{}M'.format(round(avg_netspend, 1))
ax = df_fsg.ix['2011-2012':].plot(kind='bar', figsize=(12, 9), color=('r', 'y', 'b'), legend=False)
plt.axhline(avg_netspend, color='b', linestyle='--')
ax.set_ylabel('Transfers in Pounds (Millions)')
ax.set_title("FSG's LFC Transfers per Season for the Last 5 Seasons\n(2011-2012 to 2015-2016)")
ax.text(-.4, 97, 'prepared by: @lfcsorted', fontsize=9)

# create legend
l1 = plt.Line2D([], [], linewidth=7, color='r')
l2 = plt.Line2D([], [], linewidth=7, color='y')
l3 = plt.Line2D([], [], linewidth=7, color='b')
l4 = plt.Line2D([], [], linewidth=1, color='b', linestyle='--')
labels = ['Transfers In', 'Transfers Out', 'Transfer Net Spend', 'Average Transfer Net Spend per Season']
ax.legend([l1, l2, l3, l4], labels, fancybox=True, shadow=True, framealpha=0.8, loc='upper left')

# plot and save current figure
fig = plt.gcf() 
plt.show()
fig.savefig('FSGTransfersPerSeason2011-2015.png', bbox_inches='tight')


# Now let's compare Liverpool's transfers to the other top 6 teams.

# What is net spend per season for top 5 teams over last 5 seasons?

# In[37]:


dfprem_transfers


# In[39]:


top6_2014_15 = [ 'Chelsea', 'Manchester City', 'Manchester United', 'Arsenal', 'Tottenham Hotspur', 'Liverpool']
df_prem_transfers_top6 = dfprem_transfers[['Team', 'PerSeasonSpend']][dfprem_transfers.Team.isin(top6_2014_15)]
df_prem_transfers_top6


# Plot Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons

# In[40]:


ax = df_prem_transfers_top6.plot(x='Team', kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'r', 'b', 'b'), legend=False)
ax.set_ylabel('Net Spend per Season in Pounds (Millions)')
ax.set_title("Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons\n(2011-2012 to 2015-2016)")
plt.axhline(0, color='grey') # x axis at net spend = 0
ax.text(-0.45, -9, 'prepared by: @lfcsorted', fontsize=9)

# plot and save current figure
fig = plt.gcf() 
plt.show()
fig.savefig('PremTransferSpendPerSeason2011-2015.png', bbox_inches='tight')


# What is the revenue of top 6 premier league clubs?

# In[42]:


df_revenue.head(6)


# Plot Top 6 English Premier League Teams by Revenue

# In[43]:


ax = df_revenue.plot(kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'b', 'r', 'b'), legend=False)
ax.set_ylabel('Revenue in Pounds (Millions)')
ax.set_title('Top 6 English Premier League Teams by Revenue (2014)')
ax.text(4.45, 440, 'prepared by: @lfcsorted', fontsize=9)

# plot and save current figure
fig = plt.gcf() 
plt.show()
fig.savefig('PremRevenue2014.png', bbox_inches='tight')


# Let's now examine the age of the LIverpool transfers

# First create new transfer dataframe with birthdate of player

# In[44]:


dflfc_transfers.head()


# In[45]:


dflfc_transfers.shape


# In[46]:


dflfc_players.head()


# In[47]:


dflfc_transfers_with_dob = pd.DataFrame.merge(dflfc_transfers, dflfc_players, how='left')
dflfc_transfers_with_dob.shape


# In[48]:


dflfc_transfers_with_dob.head()


# In[49]:


dflfc_transfers_with_dob.dtypes


# In[50]:


# check to see if any Birthdates are missing
dflfc_transfers_with_dob.Birthdate.isnull().any()


# In[51]:


# show missing entries (these have been reported to lfchistory.net)
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Birthdate.isnull()]


# In[52]:


# fill in mising data
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Country'] = 'France'
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Birthdate'] = pd.Timestamp('19910526')
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Country'] = 'Sweden'
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Birthdate'] = pd.Timestamp('19941128')
dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Player == 'Chris Mavinga') | 
                         (dflfc_transfers_with_dob.Player == 'Kristoffer Peterson')]


# Add age at transfer date to the dataframe

# In[53]:


def age_at(dob, this_date):
    """Return age in years at this_date for given date of birth.
    
    Note that both dob and this_date are of type datetime."""
    
    return round((this_date - dob).days/365.0, 1)


# In[54]:


dflfc_transfers_with_dob['AgeAtTransfer'] = dflfc_transfers_with_dob.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_transfers_with_dob.tail()


# What is average age of incoming players by season?

# Average age of all transfers in, including free

# In[55]:


dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & 
                         (dflfc_transfers_with_dob.Fee >= 0)]\
                        ['AgeAtTransfer'].mean()


# Average age of all transfers in, excluding free

# In[56]:


dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & 
                         (dflfc_transfers_with_dob.Fee > 0)]\
                        ['AgeAtTransfer'].mean()


# Average age of all transfers in, excluding free, by season

# In[57]:


dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & 
                         (dflfc_transfers_with_dob.Fee > 0)]\
                        [['Season', 'AgeAtTransfer']].groupby('Season').mean()


# Average age of all transfers in, including free, by season (rounded)

# In[58]:


dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\
[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1))


# Plot age at transfer in

# In[59]:


dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\
[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', legend=False, 
                                                                                      title='Age at Transfer In')


# What is average age of incoming and outgoing players by season

# In[60]:


dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\
    .agg(lambda x: round(x.mean(), 1)).unstack()\
    .plot(kind='bar', title='Age at Transfer', ylim=(0,40), yticks=range(0,35,5))


# Excluding free

# In[61]:


dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\
    .agg(lambda x: round(x.mean(), 1)).unstack()\
    .plot(kind='bar', title='Age at Transfer (non-zero)', ylim=(0,40), yticks=range(0,35,5))


# In[62]:


dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))


# In[63]:


dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))


# #### Note that Outs do not include players leaving at end of contract e.g. Gerrard or those retiring e.g. Carragher

# Analyse age of starting line-ups over last 3 seasons

# In[64]:


LFC_GAME1_CSV_FILE = 'data\lfc_pl_opening_games_aug2015.csv'
dflfc_openers = pd.read_csv(LFC_GAME1_CSV_FILE, parse_dates=['Date'])

# show shape
dflfc_openers.shape


# In[65]:


dflfc_openers.tail()


# Add age at opening game

# In[66]:


dflfc_openers = pd.DataFrame.merge(dflfc_openers, dflfc_players[['Player', 'Birthdate']], how='left')
dflfc_openers.shape


# In[67]:


dflfc_openers.head()


# In[68]:


dflfc_openers['AgeAtOpener'] = dflfc_openers.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_openers.tail()


# Calculate average age of team

# In[69]:


dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1))


# In[70]:


dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', ylim=(24,28))


# In[71]:


dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In'][['Season', 'Player', 'Fee', 'AgeAtTransfer']]


# What is average age of team that *finished* againt Arsenal?

# In[71]:


LFC_ARSENAL_CSV_FILE = 'data\lfc_pl_vs_Arsenal_aug2015.csv'
dflfc_arsenal = pd.read_csv(LFC_ARSENAL_CSV_FILE, parse_dates=['Date'], skiprows=2)

# show shape
dflfc_arsenal.shape


# In[72]:


dflfc_arsenal = pd.merge(dflfc_arsenal, dflfc_players[['Player', 'Birthdate']])
dflfc_arsenal


# In[73]:


dflfc_arsenal['AgeAtGame'] = dflfc_arsenal.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_arsenal


# In[74]:


dflfc_arsenal.mean()


# Compare team wage bills for 2014-15

# In[75]:


dfprem_wages[['Team', '2014-2015']].head()


# In[76]:


dfprem_table_2014_2015[['Team', 'PTS']].head()


# In[77]:


dfprem_table_2014_2015_wages = pd.merge(dfprem_table_2014_2015[['Team', 'PTS']], dfprem_wages[['Team', '2014-2015']])


# In[78]:


dfprem_table_2014_2015_wages
dfprem_table_2014_2015_wages.rename(columns={'2014-2015': 'WageBill','PTS': 'Points'}, inplace=True)


# In[79]:


dfprem_table_2014_2015_wages.head()


# In[80]:


dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == 'Chelsea'].values[0]


# In[81]:


dfprem_table_2014_2015_wages.plot(kind='scatter', x='WageBill', y='Points')


# In[82]:


df = dfprem_table_2014_2015_wages.set_index('Team')


# In[83]:


(a,b) = df.ix['Liverpool']
print a, b


# In[84]:


# Ref: http://stackoverflow.com/questions/739241/date-ordinal-output
def n_plus_suffix(n):
    """Return n plus the suffix e.g. 1 becomes 1st, 2 becomes 2nd."""
    assert isinstance(n, (int, long)), '{} is not an integer'.format(n)
    
    if 10 <= n % 100 < 20:
        return str(n) + 'th'
    else:
       return  str(n) + {1 : 'st', 2 : 'nd', 3 : 'rd'}.get(n % 10, "th")


# Plot Wage Bill vs Points, with Top 6 highlighted (2014-2015)

# In[85]:


# calculate points total for 6th place
sixth_place_points = df.iloc[5].Points

# plot top 6 as blue circles
ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')

# plot others as black circles
df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')

# calculate Liverpool's points and plot as red circles
lfcpoints, lfcwage = df.ix['Liverpool']
ax.plot(lfcwage, lfcpoints, 'ro')

# add x and y labels etc
ax.set_xlabel('Wage Bill in Pounds (millions)')
ax.set_ylabel('Points')
ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')
ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)

# add text showing team and position for top 6
for team, (points, wagebill) in df.iterrows():
    pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])
    team_pos = '{} ({})'.format(team, n_plus_suffix(pos))
    if points >= sixth_place_points:
        ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))


# Prettify the plot - label all teams and highlight the top 4 and top 6 areas. 

# In[86]:


# list all of the teams
df.index.values


# In[87]:


# set position of text, default is centred on top of the circle
# note that posotionis decided by trial and error, to give clearest plot
TEAM_CL = ['Sunderland', 'West Ham United']
TEAM_CR = ['Stoke City', 'Aston Villa', 'Everton']
TEAM_CT = []
for team in df.index.values:
    if team not in TEAM_CL + TEAM_CR:
        TEAM_CT.append(team)

    
# calculate points total for 6th place
sixth_place_points = df.iloc[5].Points

# plot top 6 as blue circles
ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')

# plot others as black circles
df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')

# calculate Liverpool's points and plot as red circles
lfcpoints, lfcwage = df.ix['Liverpool']
ax.plot(lfcwage, lfcpoints, 'ro')

# add x and y labels etc
ax.set_xlabel('Wage Bill in Pounds (millions)')
ax.set_ylabel('Points')
ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')
ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)

# add text showing team and position
for team, (points, wagebill) in df.iterrows():
    pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])
    team_pos = '{} ({})'.format(team, n_plus_suffix(pos))
    if team in TEAM_CT:
        ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))
    elif team in  TEAM_CR:
        #print 'team cr: {}'.format(team)
        ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill+2, points-0.5))
    elif team in  TEAM_CL:
        #print 'team cl: {}'.format(team)
        ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-3*len(team)-6, points-0.5))
    else:
        raise('unexpected error')

# add key areas to the graph and label
from matplotlib.patches import Rectangle
XTEXT_OFFSET = 1
YTEXT_OFFSET = -2

# add top 4 area, with annotation in top left hand corner of rectangle
top4rect_bl_x = df[0:4].WageBill.min() # bottom left x co-ord of rectangle
top4rect_bl_y = df[0:4].Points.min() # bottom left y co-ord of rectangle
top4rect_width = df[0:4].WageBill.max() - top4rect_bl_x # width of rectangle
top4rect_height = df[0:4].Points.max() - top4rect_bl_y # height of rectangle
top4rect_tl_x = df[0:4].WageBill.min() # top left x co-ord for annotation
top4rect_tl_y = df[0:4].Points.max() # top left y co-ord for annotation
top4rect_xtext = top4rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation
top4rect_ytext = top4rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation
ax.add_patch(Rectangle((top4rect_bl_x, top4rect_bl_y), top4rect_width, top4rect_height, facecolor="blue", alpha=0.2))
ax.annotate(s='Top 4 area', xy=(top4rect_tl_x, top4rect_tl_y), xytext=(top4rect_xtext, top4rect_ytext), color='blue')

# add top 6 area, with annotation in top left hand corner of rectangle
top6rect_bl_x = df[0:6].WageBill.min() # bottom left x co-ord of rectangle
top6rect_bl_y = df[0:6].Points.min() # bottom left y co-ord of rectangle
top6rect_width = df[0:6].WageBill.max() - top6rect_bl_x # width of rectangle
top6rect_height = df[0:6].Points.max() - top6rect_bl_y # height of rectangle
top6rect_tl_x = df[0:6].WageBill.min() # top left x co-ord for annotation
top6rect_tl_y = df[0:6].Points.max() # top left y co-ord for annotation
top6rect_xtext = top6rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation
top6rect_ytext = top6rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation
ax.add_patch(Rectangle((top6rect_bl_x, top6rect_bl_y), top6rect_width, top6rect_height, facecolor="lightblue", alpha=0.2))
ax.annotate(s='Top 6 area', xy=(top6rect_tl_x, top6rect_tl_y), xytext=(top6rect_xtext, top6rect_ytext), color='blue', alpha=0.7)

# plot and save current figure
fig = plt.gcf() 
plt.show()
fig.savefig('PremWageBillvsPoints2014-2015.png', bbox_inches='tight')


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