npl part 1

data_prep.ipynb

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"## An analysis of non-performing loan (NPL) rates globally ",
" ",
"A recent analysis of the Southeast Asian credit markets in an assignment at work brought to light some nuances regarding credit culture in this particular region. The data got me thinking about global credit health and non-performing loan rates globally. Given my recent foray into data analysis, I thought a notebook was in order. ",
" ",
"Through various data techniques, I looked into answering the following questions: ",
" 1. What are recent trends in non-performing loans globally? ",
" 2. How do core economic variables (income level, GDP etc.) impact NPL rates? ",
" 3. How important are other variables such as a nation's credit culture, market volatility, and real estate market? ",
" ",
"### But first, what is the credit market and what are non-performing loans? ",
" ",
"By examining the credit market, we are essentially looking at the market in which individuals, corporations, and governments seek to borrow money from investors. This form of borrowing can take on many different forms, from your daily credit card expenses to mortgages on your next beach home. ",
" ",
"Non-performing loans (NPL) refer to loans in which the borrower has not been able to make scheduled repayments for some time (usually over 90 days). ",
" ",
"When NPL levels rise, it is often an indicator of deteriorating credit market health. In short, more people are not paying back what they owe. ",
" ",
"We'll examine NPL levels across different countries from the World Bank databank."
]
},
{
cell_type: "markdown",
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source: [
"First, we start by importing the necessary packages..."
]
},
{
cell_type: "code",
execution_count: 727,
metadata: {
collapsed: true
},
outputs: [ ],
source: [
"import pandas as pd ",
"import seaborn as sb ",
"import numpy as np ",
"import matplotlib ",
"import matplotlib.pyplot as plt"
]
},
{
cell_type: "code",
execution_count: 728,
metadata: { },
outputs: [ ],
source: [
"import missingno as msno"
]
},
{
cell_type: "code",
execution_count: 729,
metadata: {
collapsed: true
},
outputs: [ ],
source: [
"from itertools import islice ",
"import csv"
]
},
{
cell_type: "markdown",
metadata: { },
source: [
"As there are some rows with filler column information, I skip the first 4 rows with islice."
]
},
{
cell_type: "code",
execution_count: 730,
metadata: { },
outputs: [
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" <td>Country Code</td> ",
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" <td>Indicator Code</td> ",
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" <td>Aruba</td> ",
" <td>ABW</td> ",
" <td>Bank nonperforming loans to total gross loans (%)</td> ",
" <td>FB.AST.NPER.ZS</td> ",
" <td></td> ",
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" <th>2</th> ",
" <td>Afghanistan</td> ",
" <td>AFG</td> ",
" <td>Bank nonperforming loans to total gross loans (%)</td> ",
" <td>FB.AST.NPER.ZS</td> ",
" <td></td> ",
" <td></td> ",
" <td></td> ",
" <td></td> ",
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" <td>...</td> ",
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" <td>49.9013229069412</td> ",
" <td>4.6968178252385</td> ",
" <td>4.95890140377645</td> ",
" <td>4.85228704838131</td> ",
" <td>7.7810908959605</td> ",
" <td>12.0536179767696</td> ",
" <td>11.0743789647307</td> ",
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" <th>3</th> ",
" <td>Angola</td> ",
" <td>AGO</td> ",
" <td>Bank nonperforming loans to total gross loans (%)</td> ",
" <td>FB.AST.NPER.ZS</td> ",
" <td></td> ",
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" <td>3.17305056414187</td> ",
" <td>2.01496821504011</td> ",
" <td>4.35011884491608</td> ",
" <td>7.97676918024597</td> ",
" <td>10.1905320738466</td> ",
" <td>10.6112339715733</td> ",
" <td></td> ",
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" <tr> ",
" <th>4</th> ",
" <td>Albania</td> ",
" <td>ALB</td> ",
" <td>Bank nonperforming loans to total gross loans (%)</td> ",
" <td>FB.AST.NPER.ZS</td> ",
" <td></td> ",
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" ",
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"1 Bank nonperforming loans to total gross loans (%) FB.AST.NPER.ZS ",
"2 Bank nonperforming loans to total gross loans (%) FB.AST.NPER.ZS ",
"3 Bank nonperforming loans to total gross loans (%) FB.AST.NPER.ZS ",
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"[5 rows x 63 columns]"
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execution_count: 730,
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source: [
"all_data = [] ",
"with open('NPL.csv') as data: ",
" for row in islice(csv.reader(data), 4, None): ",
" all_data.append(np.array(row)) ",
" ",
"df = pd.DataFrame(all_data) ",
"df.head()"
]
},
{
cell_type: "code",
execution_count: 731,
metadata: { },
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"dtypes: object(63) ",
"memory usage: 130.5+ KB "
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source: [
"df.info()"
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