Importing and Managing Financial Data in Python
Stefan Jansen
Instructor
pd.DataFrame
spd.concat([amex, nasdaq, nyse])
pd.DataFrame
spd.concat([amex, nasdaq, nyse])
pd.DataFrame
spd.concat([amex, nasdaq, nyse])
amex = pd.read_excel('listings.xlsx', sheet_name='amex', na_values=['n/a'])
nyse = pd.read_excel('listings.xlsx', sheet_name='nyse', na_values=['n/a'])
pd.concat([amex, nyse]).info()
Int64Index: 3507 entries, 0 to 3146
Data columns (total 7 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 Stock Symbol 3507 non-null object
...
amex['Exchange'] = 'AMEX' # Add column to reference source nyse['Exchange'] = 'NYSE'
listings = pd.concat([amex, nyse])
listings.head(2)
Stock Symbol ... Exchange
0 XXII ... AMEX
1 FAX ... AMEX
xls = pd.ExcelFile('listings.xlsx')
exchanges = xls.sheet_names
# Create empty list to collect DataFrames listings = []
for exchange in exchanges: listing = pd.read_excel(xls, sheet_name=exchange) # Add reference col listing['Exchange'] = exchange # Add DataFrame to list listings.append(listing)
# List of DataFrames combined_listings = pd.concat(listings)
combined_listings.info()
Int64Index: 6674 entries, 0 to 3146
Data columns (total 8 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 Stock Symbol 6674 non-null object
1 Company Name 6674 non-null object
2 Last Sale 6590 non-null float64
3 Market Capitalization 6674 non-null float64
4 IPO Year 2852 non-null float64
5 Sector 5182 non-null object
6 Industry 5182 non-null object
7 Exchange 6674 non-null object
dtypes: float64(3), object(5)
Importing and Managing Financial Data in Python