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Data_eda.py
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Data_eda.py
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import pandas as pd
import numpy as np
def load_constiuents():
''' Loads S&P 500 members as of 2015-12-09'''
df=pd.read_csv("data/S&P_comp_20151209")
return df
def load_database():
''' Loads sharadar sf1 core us fundamentals from csv'''
df=pd.read_csv("data/SF1_20151209.csv")
df.columns= ['ticker_metric','date','value']
return df
def process_database(df):
'''Process the shardar sf1 database to a usable form and pivots'''
df['ticker'] = df['ticker_metric'].apply(lambda x: x.split('_',1)[0])
df['metric'] = df['ticker_metric'].apply(lambda x: x.split('_',1)[1])
df=df.drop('ticker_metric', axis=1)
df=pd.pivot_table(df,index=['date','ticker'], columns='metric',values='value')
return df
def save_pivot(df):
'''saves the pivot of the dataframe'''
df.to_csv("data/pivot.csv")
def read_pivot():
'''returns the dataframe object of the pivoted data frame'''
df = pd.read_csv("data/pivot.csv")
return df
def generate_quarterly(df):
'''Resamples the data frame to quarterly frequency'''
df = read_pivot()
df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)
columns = ['date', 'ticker', 'ACCOCI_ARQ',
'ASSETSAVG_ART', 'ASSETSC_ARQ','ASSETSNC_ARQ', 'ASSETS_ARQ',
'ASSETTURNOVER_ART', 'BVPS_ARQ','CAPEX_ARQ',
'CASHNEQ_ARQ', 'COR_ARQ', 'CURRENTRATIO_ARQ','DEBT_ARQ',
'DEPAMOR_ARQ','DE_ARQ', 'DILUTIONRATIO_ARQ',
'DIVYIELD', 'DPS_ARQ','EBITDAMARGIN_ART', 'EBITDA_ARQ',
'EBIT_ARQ','EBT_ARQ','EPSDILGROWTH1YR_ART', 'EPSDIL_ARQ',
'EPSGROWTH1YR_ART', 'EPS_ARQ', 'EQUITYAVG_ART', 'EQUITY_ARQ', 'EV',
'EVEBITDA_ART', 'EVEBIT_ART', 'EVENT','FCFPS_ARQ', 'FCF_ARQ',
'FILINGDATE', 'FILINGTYPE', 'GP_ARQ','GROSSMARGIN_ART',
'INTANGIBLES_ARQ', 'INTERESTBURDEN_ART','INTEXP_ARQ',
'INVCAPAVG_ART','INVCAP_ARQ', 'INVENTORY_ARQ','LEVERAGERATIO_ART',
'LIABILITIESC_ARQ', 'LIABILITIESNC_ARQ', 'LIABILITIES_ARQ',
'MARKETCAP', 'NCFCOMMON_ARQ', 'NCFDEBT_ARQ','NCFDIV_ARQ',
'NCFF_ARQ','NCFI_ARQ','NCFOGROWTH1YR_ART', 'NCFO_ARQ', 'NCFX_ARQ',
'NCF_ARQ','NETINCCMN_ARQ','NETINCDIS_ARQ','NETINCGROWTH1YR_ART',
'NETINC_ARQ','NETMARGIN_ART', 'PAYABLES_ARQ','PAYOUTRATIO_ART', 'PB_ARQ',
'PE1_ART','PE_ART', 'PREFDIVIS_ARQ',
'PRICE', 'PS1_ART', 'PS_ART', 'RECEIVABLES_ARQ', 'RETEARN_ARQ',
'REVENUEGROWTH1YR_ART', 'REVENUE_ARQ','RND_ARQ','ROA_ART', 'ROE_ART',
'ROIC_ART', 'ROS_ART', 'SGNA_ARQ',
'SHAREFACTOR', 'SHARESBAS', 'SHARESWADIL_ARQ', 'SHARESWAGROWTH1YR_ART',
'SHARESWA_ARQ', 'SPS_ART','TANGIBLES_ARQ', 'TAXEFFICIENCY_ART',
'TAXEXP_ARQ','TBVPS_ARQ', 'WORKINGCAPITAL_ARQ' ]
df = df[columns]
df = df.set_index('date')
df['quarter'] = df.index.to_period('Q')
df = df.groupby(['quarter', 'ticker']).mean()
df.to_csv("data/quarterly.csv")
def load_quarterly():
''' loads the quartley data frame'''
df = pd.read_csv("data/quarterly.csv")
df = df.reset_index(drop=True)
df['quarter'] = pd.to_datetime(df['quarter'], infer_datetime_format=True)
df = df.set_index('quarter')
df['quarter'] = df.index.to_period('Q')
# df['quarter'] = [row.index.ordinal for i,row in df.iterrows()]
return df
def load_changes():
''' loads the changes to the S&P 500 '''
df = pd.read_csv("data/S_P_500_changes.csv")
df['Remove_Date'] = pd.to_datetime(df['Remove Date'], infer_datetime_format=True)
df = df.set_index('Remove_Date')
df['Quarter_removed'] = df.index.to_period('Q')
df['Announcement Date'] = pd.to_datetime(df['Announcement Date'], infer_datetime_format=True)
df = df.set_index('Announcement Date')
df['Quarter_Annouced'] = df.index.to_period('Q')
df['Add Date'] = pd.to_datetime(df['Add Date'], infer_datetime_format=True)
df = df.set_index('Add Date')
df['Quarter_Added'] = df.index.to_period('Q')
df = df.reset_index()
return df
def generate_sp_membership_list():
''' generates the list of S&P 500 membership by quarter'''
membership_list = pd.read_csv('data/S&P_comp_20151209', header=None).values.flatten().tolist()
df = load_changes()
df = df.set_index('Quarter_Added')
# create a list of quarter and all stocks in sp&500 set to fourth quarter 2015
quarter_membership_lists = [membership_list[:] for i,row in enumerate(df.index.unique())]
quarter_order = df.index.unique().values.flatten().tolist()
for i, q in enumerate(df.index.unique()):
for k, row in enumerate(df.values):
#we want to add and remove tickers from membership in the s&p 500 if the period is greater then or equal to the current period
for x in xrange(i , quarter_order.index(row[-1].ordinal)):
try:
quarter_membership_lists[x].remove(row[2])
break
except ValueError:
pass
try:
if row[1] not in quarter_membership_lists[x]:
quarter_membership_lists[x].append(row[1])
break
except ValueError:
pass
pass
return quarter_order, quarter_membership_lists
def create_SP_500_member_df():
'''Creates the data frame that will be used for modeling with the column SP_500_member ==1 to when the stock is a member'''
quarter_order, quarter_membership_lists = generate_sp_membership_list()
df = load_quarterly()
SP_500_member = np.zeros((df.shape[0],1))
df = df.reset_index(drop=True)
for i, row in df.iterrows():
if row['quarter'].ordinal in quarter_order:
if row['ticker'] in quarter_membership_lists[quarter_order.index(row['quarter'].ordinal)]:
SP_500_member[i] = 1
df['SP_500_member'] = SP_500_member
items_added=0
rows_added_dict = {'quarter':{},'ticker':{},'SP_500_member':{}}
for quarter in df.quarter.unique():
for ticker in df.ticker.unique():
if ticker not in df[df.quarter == quarter].ticker.values:
rows_added_dict['quarter'].update({items_added:quarter.ordinal})
rows_added_dict['ticker'].update({items_added:ticker})
rows_added_dict['SP_500_member'].update({items_added:0})
items_added+=1
df = pd.concat([df,pd.from_dict(rows_added_dict)])
return df
if __name__ == '__main__':
# df = load_database()
# df = process_database(df)
# save_pivot(df)
# generate_quarterly(df)
# df = load_changes()
# df = load_quarterly()
# membership_list = generate_sp_membership_list()
# quarter_order, quarter_membership_lists = generate_sp_membership_list()
df = create_SP_500_member_df()
# print df.head()