Pandas Review…Again;)
Python Review Free Exercises — #PySeries#Episode 18
Hi, answer these ten questions:
Pandas Exercises:)
Source of the Data:
The dataset will be valuable to those who seek to understand the dynamics of financial stability within the African context.ContentThe dataset specifically focuses on the Banking, Debt, Financial, Inflation and Systemic Crises that occurred, from 1860 to 2014, in 13 African countries, including: Algeria, Angola, Central African Republic, Ivory Coast, Egypt, Kenya, Mauritius, Morocco, Nigeria, South Africa, Tunisia, Zambia and Zimbabwe.* caseA number which denotes a specific country;* cc3A three letter country code;* country The name of the country;* year The year of the observation;* systemic_crisis “0” means that no systemic crisis occurred in the year and “1” means that a systemic crisis occurred in the year;* exch_usd The exchange rate of the country vis-a-vis the USD;* domestic_debt_in_default “0” means that no sovereign domestic debt default occurred in the year and “1” means that a sovereign domestic debt default occurred in the year;* sovereign_external_debt_default “0” means that no sovereign external debt default occurred in the year and “1” means that a sovereign external debt default occurred in the year;* gdp_weighted_default The total debt in default vis-a-vis the GDP;* inflation_annual_cpi The annual CPI Inflation rate;* independence “0” means “no independence” and “1” means “independence”;* currency_crises “0” means that no currency crisis occurred in the year and “1” means that a currency crisis occurred in the year;* inflation_crises “0” means that no inflation crisis occurred in the year and “1” means that an inflation crisis occurred in the year;* banking_crisis “no_crisis” means that no banking crisis occurred in the year and “crisis” means that a banking crisis occurred in the year;
Complete the ten Tasks Below:
01#PyEx — Python — Pandas— Importing:
Import pandas:
02#PyEx — Python — Pandas — IO:
Read in the african_econ_crises.csv:output expected:
# case cc3 country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis
#0 1 DZA Algeria 1870 1 0.052264 0 0 0.0 3.441456 0 0 0 crisis
#1 1 DZA Algeria 1871 0 0.052798 0 0 0.0 14.149140 0 0 0 no_crisis
#2 1 DZA Algeria 1872 0 0.052274 0 0 0.0 -3.718593 0 0 0 no_crisis
# …Source of the Data:
03#PyEx — Python — Pandas — Data Set:
Display the first 5 rows of the data set:#output expected:
# case cc3 country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis
#0 1 DZA Algeria 1870 1 0.052264 0 0 0.0 3.441456 0 0 0 crisis
#1 1 DZA Algeria 1871 0 0.052798 0 0 0.0 14.149140 0 0 0 no_crisis
#2 1 DZA Algeria 1872 0 0.052274 0 0 0.0 -3.718593 0 0 0 no_crisis
#3 1 DZA Algeria 1873 0 0.051680 0 0 0.0 11.203897 0 0 0 no_crisis
#4 1 DZA Algeria 1874 0 0.051308 0 0 0.0 -3.848561 0 0 0 no_crisis
04#PyEx — Python — Pandas —Counting:
How many countries are present in this data set?#output expected: 13
05#PyEx — Python — Pandas —Data Set:
Which countries are present in this data set?#output expected:
array([‘Algeria’, ‘Angola’, ‘Central African Republic’, ‘Ivory Coast’,‘Egypt’, ‘Kenya’, ‘Mauritius’, ‘Morocco’, ‘Nigeria’,‘South Africa’, ‘Tunisia’, ‘Zambia’, ‘Zimbabwe’], dtype=object)
06#PyEx — Python — Pandas — Grouping:
Which country had the highest annual CPI Inflation rate? What was the inflation rate?#output expected:
# case cc3 country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis
#1053 70 ZWE Zimbabwe 2008 1 0.002 1 1 0.0 21989695.22 1 1 1 crisis
07#PyEx — Python — Pandas — Indexing & Slicing:
In what year did Kenya have its first sistemic crisis?#ouput expected:
# case cc3 country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis
#475 35 KEN Kenya 1985 1 16.2843 0 0 0.0 11.398 1 0 0 crisis
08#PyEx — Python — Pandas —Grouping:
How many yearly systemic crisises have occurred per country?#output expected
# country
# Algeria 4
# Angola 0
# Central African Republic 19
# Egypt 6
# Ivory Coast 4
# Kenya 13
# Mauritius 0
# Morocco 2
# Nigeria 10
# South Africa 0
# Tunisia 5
# Zambia 4
# Zimbabwe 15
09#PyEx — Python — Pandas —Grouuping:
How many years did a sovereign external debt default occur in Zimbabwe?#output expected:
#africa_data[africa_data.iloc[3]==’ZWE’]
#output expected: 30
10#PyEx — Python — Pandas — Indexing & Slicing:
In which year did Algeria have its highest exchange rate?#output expected:
#case cc3 country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis
#84 1 DZA Algeria 2014 0 87.970698 0 0 0.0 2.917 1 0 0 no_crisis
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Cheers!
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Credits & References
INTRODUÇÃO A MACHINE LEARNING PARA CERTIFICAÇÃO HCIA-AI by crateus.ufc.br
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