In [1]:
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import sys # system module, used to get Python version
import os # operating system tools (check files)
import datetime as dt # date tools, used to note current date
import openpyxl as op
import numpy as np
# these are new
import requests, io # internet and input tools
from bs4 import BeautifulSoup # website parsing
%matplotlib inline
print('\nPython version: ', sys.version)
print('Pandas version: ', pd.__version__)
print('Requests version: ', requests.__version__)
print("Today's date:", dt.date.today())
In [41]:
path = '/Users/zoeseward/Desktop/DoingBusiness.csv'
df = pd.read_csv(path, encoding='latin1')
In [42]:
col_list = ['Economy',
'Year','Starting a business-DTF','Dealing with Construction Permits-DTF', 'Getting Electricity-DTF','Registering Property-DTF','Getting Credit-DTF','Protecting Minority Investors-DTF','Paying Taxes-DTF', 'Trading Across Borders-DTF', 'Enforcing Contracts-DTF','Resolving Insolvency-DTF']
df = df[col_list]
In [43]:
df=df.rename(columns = {'Economy':'Country',
'Starting a business-DTF':'Starting a Business',
'Dealing with Construction Permits-DTF':'Dealing with Construction Permits',
'Getting Electricity-DTF':'Getting Electricity',
'Registering Property-DTF':'Registering Property',
'Getting Credit-DTF':'Getting Credit',
'Protecting Minority Investors-DTF':'Protecting Minority Investors',
'Paying Taxes-DTF':'Paying Taxes',
'Trading Across Borders-DTF':'Trading Across Borders',
'Enforcing Contracts-DTF':'Enforcing Contracts',
'Resolving Insolvency-DTF':'Resolving Insolvency'})
In [44]:
df['Year'] = df['Year'].map(lambda x: x.lstrip('DB'))
In [45]:
df=df.replace(' ',np.nan,regex=True)
In [46]:
df['Dealing with Construction Permits']=df['Dealing with Construction Permits'].astype(float)
In [47]:
df['Year']=df['Year'].astype(int)
In [48]:
df['Getting Electricity']=df['Getting Electricity'].astype(float)
In [49]:
df['Registering Property']=df['Registering Property'].astype(float)
In [50]:
df['Getting Credit']=df['Getting Credit'].astype(float)
In [51]:
df['Protecting Minority Investors']=df['Protecting Minority Investors'].astype(float)
In [52]:
df['Paying Taxes']=df['Paying Taxes'].astype(float)
In [53]:
df['Trading Across Borders']=df['Trading Across Borders'].astype(float)
In [54]:
df['Country']=df['Country'].astype(str)
In [55]:
df=df.sort_values(['Country','Year'], axis=0, ascending=True, inplace=False)
In [56]:
df=df[df['Year'].map(int) >= 2006]
In [65]:
df.head()
Out[65]:
In [57]:
d2=df.drop('Country',axis=1)
In [58]:
d2=d2.pct_change(periods=1, fill_method='pad', limit=None)
In [59]:
d2=d2.rename(columns = {'Starting a Business':'Starting a Business Annual Change',
'Dealing with Construction Permits':'Dealing with Construction Permits Annual Change',
'Getting Electricity':'Getting Electricity Annual Change',
'Registering Property':'Registering Property Annual Change',
'Getting Credit':'Getting Credit Annual Change',
'Protecting Minority Investors':'Protecting Minority Investors Annual Change',
'Paying Taxes':'Paying Taxes Annual Change',
'Trading Across Borders':'Trading Across Borders Annual Change',
'Enforcing Contracts':'Enforcing Contracts Annual Change',
'Resolving Insolvency':'Resolving Insolvency Annual Change'})
In [60]:
d2=d2.drop('Year',axis=1)
In [61]:
df=df.join(d2, on=None, how='left', lsuffix='', rsuffix='', sort=False)
In [62]:
df=df[df['Year'].map(int) >= 2008]
In [63]:
to_drop = ['Switzerland','Singapore','United States','Netherlands','Germany','Sweden','United Kingdom',
'Japan','Hong Kong','Finland','Norway','Denmark','New Zealand','China',
'Canada','United Arab Emirates','Belgium','Qatar','Austria','Luxembourg','France','Australia',
'Ireland','Israel','Malaysia','South Korea','Iceland','China','Saudi Arabia','Estonia',
'Czech Republic','Spain','Chile','Thailand','Lithuania','Poland','Azerbaijan','Kuwait',
'India','Malta','Indonesia','Panama','Russian Federation','Italy','Mauritius','Portugal',
'South Africa','Bahrain','Latvia','Bulgaria','Mexico','nan']
df=df[~df['Country'].isin(to_drop)]
In [64]:
df.head()
Out[64]:
In [66]:
Afghanistan=df.iloc[1:10]
Afghanistan
Out[66]:
In [67]:
import matplotlib
import seaborn as sns
sns.set_style("whitegrid")
Afghanistan.plot(subplots=True,figsize=(10,40))
Out[67]:
In [68]:
sum( Afghanistan [['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1))
Out[68]:
In [3]:
indicator={'Afghanistan':0.6935612350429966, 'Albania': 0,
'Algeria':-0.00092285897216760893,
'Angola':0.051252762326626698,
'Argentina':0.13999459671544395,
'Armenia':0.22340618756288891,
'Bangladesh':-0.10258298519888757,
'Barbados':-0.082963181174153383,
'Belarus':0,
'Belize':-0.041873978192264114,
'Benin':0.29309240576723328,
'Bhutan':0.39239735187582425,
'Bolivia':0.044927585329123235,
'Botswana':0.12663815444048271,
'Brazil':0.30200697013716771,
'Burundi':0.61853823057908197,
'Cambodia':0,
'Cameroon':0.28145364654716665,
'Chad':0,
'Colombia':0.38316161542610783,
'Comoros':0.19010799668247047,
'Croatia':0.51083023755235524,
'Cyprus':0.032935620306650572,
'Djibouti':0.11214833993512356,
'Dominica':0.064306962823139691,
'Ecuador':0.054546536908037811,
'Eritrea':0.039881142340442953,
'Ethiopia':0.15395635132719246,
'Fiji':-0.093388065648656537,
'Gabon':0.10596344297956363,
'Georgia':0.37494234427171097,
'Ghana':-0.0121790517138873,
'Greece':0.20115392743882576,
'Grenada':-0.070204543700137331,
'Guatemala':0.14424010491983738,
'Guinea':0.30065456555582376,
'GuineaBissau':1.630926949702374,
'Guyana':0.27797696529340038,
'Haiti':0.000898055511520582,
'Honduras':0.13018414293694475,
'Hungary':0.16138908300778687,
'Iraq':0.051368161730860742,
'Jamaica':0.15720621624357661,
'Jordan':0.013530513624706961,
'Kazakhstan':1.1455818282536971,
'Kenya':0.15301380979631918,
'Kiribati':-0.19039544166321937,
'Kosovo':0.28578409543571409,
'Lebanon':0.12567787165093053,
'Lesotho':0.29054498222818875,
'Liberia':0.11795282945358923,
'Libya':-0.16075049037368921,
'Madagascar':0.17793089030403286,
'Malawi':0.39168081858245352,
'Maldives':0,
'Mali':0.42149662621258693,
'Mauritania':0.26897761811259924,
'Moldova':0.29701158769329844,
'Mongolia':0.35889593864424535,
'Montenegro':0.3986930267883963,
'Morocco':0.3986930267883963,
'Mozambique':0.069518020937512109,
'Myanmar':0.21032828511171764,
'Namibia':0.0010021146834223564,
'Nepal':0.080120587230437176,
'Nicaragua':0.025213023786210868,
'Niger':0.53936434586122162,
'Nigeria':0.054534436842349099,
'Oman':0.092548558399797184,
'Pakistan':0.023381946585084759,
'Palau':0.65470778622467829,
'Paraguay':0.10167008758621501,
'Peru':0.13014770102015447,
'Philippines':1.0155657127960913,
'Romania':0.19251405360538873,
'Rwanda':1.9884817095446534,
'Samoa':0.087029830625160939,
'Senegal':0.42629275328282418,
'Serbia':0.46217511182356619,
'Seychelles':0.21227409994393964,
'Slovenia':0.26434836296441855,
'Somalia':0,
'Sudan':0.018524375952776069,
'Suriname':0.38178310419342054,
'Swaziland':0.13563518541660419,
'Tajikistan':1.6747776656577369,
'Tanzania':0.24374248099292628,
'TimorLeste':0.46848345489738807,
'Togo':0.39553729140457866,
'Tonga':0.079063223401074981,
'Tunisia':0.084388988143127022,
'Turkey':0.097560775878298825,
'Uganda':0.22113258272942699,
'Ukraine':1.1270876983356628,
'Uruguay':0.16132441679449119,
'Uzbekistan':0,
'Vanuatu':0.1574509878456361,
'Vietnam':0.19305937045735175,
'Zambia':0.19305937045735175,
'Zimbabwe':47.997916485473141}
df1=pd.DataFrame.from_dict([indicator])
In [4]:
import heapq
from operator import itemgetter
top = dict(heapq.nlargest(30,indicator.items(), key=itemgetter(1)))
top
Out[4]:
In [5]:
df2=pd.DataFrame.from_dict([top])
df2
Out[5]:
In [6]:
del df2['Zimbabwe']
In [14]:
del df2['Afghanistan']
In [15]:
del df2['Benin']
In [16]:
del df2['Burundi']
In [17]:
del df2['Lesotho']
In [18]:
del df2['Malawi']
In [19]:
del df2['Mali']
In [20]:
del df2['Niger']
In [21]:
del df2['Palau']
In [22]:
del df2['Suriname']
In [23]:
del df2['TimorLeste']
In [24]:
del df2['Togo']
In [27]:
del df2['GuineaBissau']
In [69]:
del df2 ['Montenegro']
In [70]:
df2
Out[70]:
In [220]:
import seaborn as sns
sns.set_style('whitegrid')
fig, ax = plt.subplots()
df2.plot(ax=ax,kind='bar',colormap='Spectral')
ax.set_title('Countries with the Fastest Indicator Growth in the Past 10 Years', fontsize=14)
ax.set_xlabel('Country', fontsize=12)
ax.set_ylabel('Average Annual % Change', fontsize=12)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
Out[220]:
In [113]:
list(df2.columns)
Out[113]:
In [151]:
Bhutan=df.iloc[106:115]
Bhutan
Out[151]:
In [112]:
Bhutandf=Bhutan[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
Bhutandf
Out[112]:
In [74]:
Brazil=df.iloc[136:145]
In [95]:
Brazildf=Brazil[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [75]:
Colombia=df.iloc[186:195]
In [96]:
Colombiadf=Colombia[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [76]:
Croatia=df.iloc[206:215]
In [97]:
Croatiadf=Croatia[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [83]:
Georgia=df.iloc[296:305]
In [98]:
Georgiadf=Georgia[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [77]:
Guinea=df.iloc[346:355]
In [99]:
Guineadf=Guinea[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [78]:
Kazakhstan=df.iloc[436:445]
In [100]:
Kazakhstandf=Kazakhstan[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [79]:
Moldova=df.iloc[559:568]
In [101]:
Moldovadf=Moldova[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [80]:
Mongolia=df.iloc[569:578]
In [102]:
Mongoliadf=Mongolia[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [81]:
Morocco=df.iloc[589:598]
In [103]:
Moroccodf=Morocco[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [82]:
Philippines=df.iloc[714:723]
In [127]:
Philippinesdf=Philippines[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [84]:
Rwanda=df.iloc[734:743]
In [104]:
Rwandadf=Rwanda[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [85]:
Senegal=df.iloc[754:763]
In [105]:
Senegaldf=Senegal[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [86]:
Serbia=df.iloc[764:773]
In [106]:
Serbiadf=Serbia[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [87]:
Tajikistan=df.iloc[825:835]
In [107]:
Tajikistandf=Tajikistan[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [88]:
Ukraine=df.iloc[906:915]
In [108]:
Ukrainedf=Ukraine[['Starting a Business Annual Change',
'Dealing with Construction Permits Annual Change',
'Getting Electricity Annual Change',
'Registering Property Annual Change',
'Getting Credit Annual Change',
'Protecting Minority Investors Annual Change',
'Paying Taxes Annual Change',
'Trading Across Borders Annual Change',
'Enforcing Contracts Annual Change',
'Resolving Insolvency Annual Change']].mean(axis=1)
In [122]:
top15=['Bhutan',
'Brazil',
'Colombia',
'Croatia',
'Georgia',
'Guinea',
'Kazakhstan',
'Moldova',
'Mongolia',
'Morocco',
'Philippines',
'Rwanda',
'Senegal',
'Serbia',
'Tajikistan',
'Ukraine']
for country in top15:
print(country,':',country,'df', sep='')
In [128]:
print ('Bhutan',Bhutandf,
'Brazil',Brazildf,
'Colombia',Colombiadf,
'Croatia',Croatiadf,
'Georgia',Georgiadf,
'Guinea',Guineadf,
'Kazakhstan',Kazakhstandf,
'Moldova',Moldovadf,
'Mongolia',Mongoliadf,
'Morocco',Moroccodf,
'Philippines',Philippinesdf,
'Rwanda',Rwandadf,
'Senega',Senegaldf,
'Serbia',Serbiadf,
'Tajikistan',Tajikistandf,
'Ukraine',Ukrainedf)
In [246]:
Bhutandict= {2008:0.021447,
2009:0.004977,
2010:0.133820,
2011:-0.016136,
2012:0.081561,
2013:0.009858,
2014:0.016726,
2015:0.132115,
2016:0.008030}
Brazildict={2008:0.011155,
2009:0.026930,
2010:0.069811,
2011:-0.005724,
2012:0.003110,
2013:-0.009425,
2014:0.189966,
2015:0.038911,
2016:-0.022728}
Colombiadict={2008:0.113970,
2009:0.114683,
2010:0.071555,
2011:0.020238,
2012:0.038293,
2013:-0.000256,
2014:0.014536,
2015:0.008793,
2016:0.001350}
Croatiadict={2008:0.081999,
2009:0.042520,
2010:0.112977,
2011:0.013989,
2012:0.020562,
2013:-0.008862,
2014:0.089583,
2015:0.150819,
2016:0.007243}
Georgiadict={2008:0.066593,
2009:0.196219,
2010:0.052521,
2011:0.037497,
2012:0.020825,
2013:0.011953,
2014:-0.053442,
2015:0.010392,
2016:0.032384}
Guineadict={2008:0.008304,
2009:-0.044315,
2010:-0.041689,
2011:0.119286,
2012:0.020825,
2013:0.203825,
2014:-0.048962,
2015:0.036384,
2016:0.001278}
Kazakhstandict={2008:0.048729,
2009:0.131127,
2010:0.146269,
2011:0.080524,
2012:-0.029872,
2013:0.007446,
2014:0.654782,
2015:0.035615,
2016:0.070961}
Moldovadict={2008:0.002311,
2009:0.010242,
2010:0.011407,
2011:0.025669,
2012:0.018171,
2013:0.079894,
2014:0.126810,
2015:0.007189,
2016:0.015318}
Mongoliadict={2008:0.026734,
2009:0.014034,
2010:-0.003917,
2011:0.030982,
2012:0.030594,
2013:0.140158,
2014:0.097233,
2015:0.012136,
2016:0.010942}
Moroccodict={2008:0.004986,
2009:0.244239,
2010:-0.009822,
2011:0.081876,
2012:-0.017827,
2013:0.049230,
2014:-0.014185,
2015:0.024579,
2016:0.035618}
Philippinesdict={2008:0.026734,
2009:0.014034,
2010:-0.003917,
2011:0.030982,
2012:0.030594,
2013:0.140158,
2014:0.097233,
2015:0.012136,
2016:0.010942}
Rwandadict={2008:0.162963,
2009:0.508082,
2010:0.034986,
2011:0.006684,
2012:0.093672,
2013:1.153108,
2014:-0.015600,
2015:0.025979,
2016:0.018607}
Senegaldict={2008:0.131993,
2009:-0.052066,
2010:0.004936,
2011:0.099471,
2012:0.007243,
2013:0.069603,
2014:0.023683,
2015:0.121395,
2016:0.020035}
Serbiadict={2008:-0.004626,
2009:0.092919,
2010:0.020482,
2011:0.009547,
2012:0.027372,
2013:0.084411,
2014:0.100629,
2015:0.086797,
2016:0.044644}
Tajikistandict={2008:0.062022,
2009:0.099213,
2010:0.066529,
2011:0.089293,
2012:-0.021178,
2013:0.005779,
2014:-0.005409,
2015:0.062124,
2016:0.034833}
Ukraineandict={2008:-0.061095,
2009:0.007183,
2010:0.325917,
2011:0.008716,
2012:0.183498,
2013:0.578730,
2014:0.051998,
2015:0.011322,
2016:0.020818}
In [247]:
df4=pd.DataFrame.from_dict([Bhutandict,Brazildict,Colombiadict,Croatiadict,Georgiadict,Guineadict,
Kazakhstandict,Moldovadict,Mongoliadict,Moroccodict,Philippinesdict,
Rwandadict,Senegaldict,Serbiadict,Tajikistandict,Ukraineandict])
df4
Out[247]:
In [248]:
df4_new=df4.transpose()
df4_new
Out[248]:
In [249]:
df5=df4_new.rename(columns = {0:'Bhutan',1:'Brazil',2:'Colombia',
3:'Croatia',4:'Georgia',5:'Guinea',6:'Kazakhstan',
7:'Moldova',8:'Mongolia',9:'Morocco',10:'Philippines',11:'Rwanda',
12:'Senegal',13:'Serbia', 14:'Tajikistan', 15:'Ukraine'})
df5
Out[249]:
In [250]:
import seaborn as sns
sns.set_style('whitegrid')
fig, ax = plt.subplots()
df5.plot(ax=ax,kind='bar',stacked=True, colormap='Spectral')
ax.set_title('Countries with the Fastest Indicator Growth in the Past 10 Years', fontsize=14)
ax.set_xlabel('Year', fontsize=12)
ax.set_ylabel('Average Annual % Change', fontsize=12)
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
Out[250]:
In [251]:
df2
Out[251]:
In [252]:
del df5['Brazil']
In [253]:
del df5 ['Guinea']
In [254]:
del df5 ['Moldova']
In [255]:
del df5 ['Georgia']
In [256]:
del df5 ['Bhutan']
In [257]:
del df5 ['Morocco']
In [258]:
del df5 ['Mongolia']
In [259]:
del df5 ['Colombia']
In [260]:
del df5 ['Ukraine']
In [261]:
del df5 ['Philippines']
In [263]:
del df5 ['Serbia']
In [265]:
import seaborn as sns
sns.set_style('whitegrid')
fig, ax = plt.subplots()
df5.plot(ax=ax,kind='bar',colormap='Spectral')
ax.set_title('Top 5 Countries with the Fastest Indicator Growth in the Past 10 Years', fontsize=14)
ax.set_xlabel('Year', fontsize=12)
ax.set_ylabel('Average Annual % Change', fontsize=12)
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
Out[265]:
In [ ]: