misho-kr/Intermediate Python for Finance.md

## Intermediate Python for Finance.md

      
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              Intermediate Python for Finance.md
            
          
    Intermediate Python for Finance

Learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. Work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. Calculate risk based on stock price data, and display this data in easy to read plots.
By Kennedy Behrman, Data Engineer, Author, Founder
Datetimes and Dictionaries

Create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. Store and efficiently look up items using Python dictionaries.

Datetimes

Datetime from string
String from datetime
Datetime attributes
Comparing datetimes

Subtraction returns a timedelta object
Creating relative datetimes


Dictionaries

Creating, adding to and deleting from


from datetime import datetime

black_monday = datetime(1987, 10, 19)
datetime.now()

black_monday_str = "Monday, October 19, 1987. 9:30 am"
format_str = "%A, %B %d, %Y. %I:%M %p"

datetime.datetime.strptime(black_monday_str, format_str)
dt.strftime(format_string)
from datetime import timedelta

offset = timedelta(weeks = 1)
cur_week = last_week + timedelta(weeks=1)
Control Flow and Logic

Use boolean logic to determine truth and use comparison and equality operators to control execution in if-statements and loops.

Comparison operators

Comparing datetimes
Comparing dictionaries
Comparing different types


Boolean operators

Object evaluation
AND and OR operators
Short circuit
NOT operator
Returning objects


IF statements
for and while loops

Skipping with continue
Stopping with break


Pandas Dataframe

Create and access DataFrames with pandas using financial data from other data structures, including Dicts, lists, and CSV files. Aggregate data across rows or columns, calculate averages, and extend your data using functions.

Creating Pandas DataFrame

From dictionary, list of dictionaries, or list of lists


Reading data
Accessing Data

Access column using brackets
Access column using dot-syntax
Access multiple columns
Access rows using brackets
loc() and iloc()
Columns with loc()


Setting a single value, multiple values or multiple columns
Aggregating and summarizing

DataFrame methods

.count(), min(), max(), first(), last(), sum(), prid(), max()
.mean(), media(), std(), var()


Axis: default axis=0, axis=rows, or axis=1, axis=columns


Extending and manipulating data

Adding and removing columns
Operations on DataFrames
apply()


import pandas as pd

pd.DataFrame()
data = {'Bank Code': ['BA', 'AAD', 'BA'],
        'Account#': ['ajfdk2', '1234nmk', 'mm3d90'],
        'Balance':[1222.00, 390789.11, 13.02]}
df = pd.DataFrame(data=data)

data = [['BA', 'ajfdk2', 1222.00],
        ['AAD', '1234nmk', 390789.11],
        ['BA', 'mm3d90', 13.02]]
columns = ['Bank Code', 'Account#', 'Balance']
df = pd.DataFrame(data=data, columns=columns)
df = pd.read_csv('/data/daily/transactions.csv', sep='|')

accounts['Balance']
accounts.Balance
accounts[['Bank Code', 'Account#']]
accounts[0:2]
accounts[[True, False, True]]

accounts.loc['b']
accounts.loc[['a','c']]
df.loc[[True, False, True]]

accounts.loc['a':'c', ['Balance','Account#']]
accounts.loc['a':'c',[True,False,True]]
accounts.loc['a':'c','Bank Code':'Balance']

accounts.iloc[0:2, [0,2]]

accounts.loc['a', 'Balance'] = 0
accounts.iloc[:2, 1:] = 'NA'
df.count()
df.sum(axis=1)

df.loc[:,'AAD'].max()
df.iloc[0].min()
pce['PCE'] = pce['PCDG'] + pce['PCND'] + pce['PCESV']
pce.drop(columns=['PCDG', 'PCND', 'PCESV'], axis=1, inplace=True)

pce.append(new_row)

all_rows = [row1, row2, row3, pce]
pd.concat(all_rows)

gdp['GDP'] = gdp.apply(np.sum, axis=1)
Working with NASDAQ Stock Data

Working with real-world NASDAQ stock data to interpret new data, create masks to filter data, and visualize your findings with plots.

Peeking at data with head(), tail(), and describe()
Filtering data

Column comparison
Masking by symbol
Pandas boolean operators
Combining conditions


Plotting data

Plot types: bar, barh, line, scatter, box, histogram, kde


import pandas as pd

aapl.head(3)

aapl.describe()
aapl.describe(include='object')
aapl.describe(include=['float', 'object'])
aapl.describe(include='all')

aapl.describe(percentiles=[.1, .5, .9])
aapl.describe(exclude='float')
prices.High > 2160

mask_symbol = prices.Symbol == 'AAPL'
aapl = prices.loc[mask_symbol]

mask_prices = prices['Symbol'] != 'AMZN'
mask_date = historical_highs['Date'] > datetime(2020, 4, 1)
mask_amzn = mask_prices & mask_date
prices.loc[mask_amzn]
my_dataframe.plot()
exxon.set_index('Date', inplace=True)
exxon.plot(x='Date', y='High', rot=90, title='Exxon Stock Price')

exxon.plot(y='High',kind='hist')