Tips and tricks when working with datasets

Financial Forecasting in Python

Victoria Clark

CGMA Financial Analyst

Common challenges when working with financial data

Raw data is in different formats
Date format can be different!
- US format is Month-Day-Year, EU is Day-Month-Year
- 09-08-2018 in US is the 8th of September, EU is 9th of August
Can cause challenges in:
- Interpreting
- Combining

Using a dictionary

Dictionary: associative array
Keys are mapped to values
un-ordered key-value-pairs
For example:
- The value 01 has a key of Jan

dictionary = {01: 'Jan'}

dictionary

Remember to use the datetime library

Directive	Meaning	Example
%d	Day of the month as a zero-padded decimal number	01, 02, …, 31
%b	Month as locale’s abbreviated name	Jan, Feb, …, Dec
%B	Month as locale’s full name	January, …,
%m	Month as a zero-padded decimal number	01, 02, …, 12
%y	Year without century as a zero-padded decimal number	00, 01, …, 99
%Y	Year with century as a decimal number	1988, 2001, 2013

# Example: 19-02-2018 will be written:
('19-02-2018', '%d-%m-%Y')

Iterate over items

iteritems() function

# Create dictionary with strings as keys
# and ints as values
wordFrequency = {
    "Hello" : 7,
    "hi" : 10,
    "there" : 45,
    "at" : 23,
    "this" : 77
    }

# Iterate over dictionary using for loop
for key in wordFrequency:
    value = wordFrequency[key]
    print(key, " :: ", value)

Hello  ::  7
there  ::  45
at  ::  23
this  ::  77
hi  ::  10

Let's practice!

Financial Forecasting in Python