Ok I need some help as regards to the following task python code using pandas Expand multiple days and repeat the whole calculation steps from the beginning with 2 different dates used in df0 and df1 this is my solution is this the proper way of writing the code and could you explain what is highlighted in bold without changing anything. #explain what this line does d0dtlist=['2023-04-09',2023-04-12] d1dtlist=['2023-04-09',2023-04-12] #creating an empty dataframe multidf = pd.DataFrame() for d1date,d2date in zip(d1dtlist,d0dtlist): #reading from the sql table df0=pd.read_sql(sql.format(d0=d1date),con) df1=pd.read_sql(sql.format(d1=d1date),con) #merge the dataframe dfa=df0.merge(df1,on=['CurrencyCurve','rateofmaturity'],how='outer',suffixes=('0','1') #sort the values based on the following dfa.sort_values(['CurrencyCurve','rateofmaturity']) dfb=dfa.copy() # Iterate over each row of the dataframe allcurve=dfb.CurrencyCurve.unique() for curve in allcurve: # Check if rate_0 is NaN and perform interpolation if pd.isna(df.loc[dfb['CurrencyCurve'] == curve, 'rate_0']).any(): tmp0 = df.loc[(dfb['CurrencyCurve'] == curve) & (~pd.isna(dfb['rate_0'])), ['daystomature', 'rate_0']] #create the interpolation function interpolate_rate_0 = interp1d(tmp0['daystomature'], tmp0['rate_0'], kind='linear', fill_value='extrapolate') #use the interpolation function to fill in the missing values dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_0'] = interpolate_rate_0(dfb.loc[dfb['CurrencyCurve'] == curve, 'daystomature']) # Check if rate_1 is NaN and perform interpolation if pd.isna(dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_1']).any(): tmp1 = dfb.loc[(dfb['CurrencyCurve'] == curve) & (~pd.isna(dfb['rate_1'])), ['daystomature', 'rate_1']] #create the interpolatio function interpolate_rate_1 = interp1d(tmp1['daystomature'], tmp1['rate_1'], kind='linear', fill_value='extrapolate') #use the interpolation function to fill in the missing values dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_1'] = interpolate_rate_1(dfb.loc[dfb['CurrencyCurve'] == curve, 'daystomature']) dfb['rate_change_final']=(dfb['rate_1']-dfb['rate_0')*100 #add this to distinguish beteen dates - is this the right way to put it and is there anything else missing ? dfb['date']=d1date #joining multi=pd.concat(['multidf',dfb]) thanks
Ok I need some help as regards to the following task python code using pandas
- Expand multiple days and repeat the whole calculation steps from the beginning with 2 different dates used in df0 and df1
this is my solution is this the proper way of writing the code and could you explain what is highlighted in bold without changing anything.
#explain what this line does
d0dtlist=['2023-04-09',2023-04-12]
d1dtlist=['2023-04-09',2023-04-12]
#creating an empty dataframe
multidf = pd.DataFrame()
for d1date,d2date in zip(d1dtlist,d0dtlist):
#reading from the sql table
df0=pd.read_sql(sql.format(d0=d1date),con)
df1=pd.read_sql(sql.format(d1=d1date),con)
#merge the dataframe
dfa=df0.merge(df1,on=['CurrencyCurve','rateofmaturity'],how='outer',suffixes=('0','1')
#sort the values based on the following
dfa.sort_values(['CurrencyCurve','rateofmaturity'])
dfb=dfa.copy()
# Iterate over each row of the dataframe
allcurve=dfb.CurrencyCurve.unique()
for curve in allcurve:
# Check if rate_0 is NaN and perform interpolation
if pd.isna(df.loc[dfb['CurrencyCurve'] == curve, 'rate_0']).any():
tmp0 = df.loc[(dfb['CurrencyCurve'] == curve) & (~pd.isna(dfb['rate_0'])), ['daystomature', 'rate_0']]
#create the interpolation function
interpolate_rate_0 = interp1d(tmp0['daystomature'], tmp0['rate_0'], kind='linear', fill_value='extrapolate')
#use the interpolation function to fill in the missing values
dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_0'] = interpolate_rate_0(dfb.loc[dfb['CurrencyCurve'] == curve, 'daystomature'])
# Check if rate_1 is NaN and perform interpolation
if pd.isna(dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_1']).any():
tmp1 = dfb.loc[(dfb['CurrencyCurve'] == curve) & (~pd.isna(dfb['rate_1'])), ['daystomature', 'rate_1']]
#create the interpolatio function
interpolate_rate_1 = interp1d(tmp1['daystomature'], tmp1['rate_1'], kind='linear', fill_value='extrapolate')
#use the interpolation function to fill in the missing values
dfb.loc[dfb['CurrencyCurve'] == curve, 'rate_1'] = interpolate_rate_1(dfb.loc[dfb['CurrencyCurve'] == curve, 'daystomature'])
dfb['rate_change_final']=(dfb['rate_1']-dfb['rate_0')*100
#add this to distinguish beteen dates - is this the right way to put it and is there anything else missing ?
dfb['date']=d1date
#joining
multi=pd.concat(['multidf',dfb])
thanks
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