Derive a Dataframe from existing one-CodePudding

I need some help here. In this DataFrame I need to perform a series of calculations by grouping column'cnpj'

What I have as input:

    cnpj    valor_cota     ln_ativo    ln_IBX        CDI    tracking
0   cat1    1114.7521   0.027152    -0.003659   23.893879   0.030811
1   cat1    1135.3557   0.018314    0.003691    23.904737   0.014623
2   cat1    1149.3381   0.012240    0.007960    23.915600   0.004281
3   cat2    500.0000    0.002002    -0.003659   23.893879   0.005661
4   cat2    501.0000    0.001998    0.003691    23.904737   -0.001693
5   cat2    510.0000    0.017805    0.007960    23.915600   0.009845
6   cat3    10000.0000  -0.006976   -0.003659   23.893879   -0.003317
7   cat3    10050.0000  0.004988    0.003691    23.904737   0.001297
8   cat3    10100.0000  0.004963    0.007960    23.915600   -0.002997

What I expect as an output:

    cnpj    ret_5_CDI   t_5_Asset     anual_5         vol       alfa        beta       sharpe   tracking_err          R^2
0   cat1     0,000909    0,031026    0,006130    0,119035    0,022631   -1,274627    0,253007       0,013372     0,997950 
1   cat2     0,000909    0,020000    0,003968    0,144852    0,004041    1,211459    0,131796       0,005841     0,608783 
2   cat3     0,000909    0,010000    0,001992    0,109531    0,003323    1,093127    0,082999       0,002576     0,866884

Here is what I got so far:

import numpy as np
import pandas as pd
from statistics import stdev

#Retorno CDI no periodo 5 anos (Dataframe)
def v1(g):
    first, last = g['CDI'].values[[0,-1]]
    return last/first-1
#Retorno Ativo no periodo 5 anos (Dataframe)
def v2(g):
    first, last = g['valor_cota'].values[[0,-1]]
    return last/first-1
#Retorno do ativo anualizado 5 anos
def v3(g):
    first, last = g['valor_cota'].values[[0,-1]]
    result = last/first-1
    return ((1 result)**(1/5))-1
#Volatilidade do ativo 5 anos
def v4(g):
    return stdev(g['ln_ativo'])*(252**0.5)   
#Alfa
def v5(g):
    slope, intercept = np.polyfit(g['ln_IBX'], g['ln_ativo'], 1)
    return intercept
#Beta
def v6(g):
    slope, intercept = np.polyfit(g['ln_IBX'], g['ln_ativo'], 1)
    return slope
#Sharpe
def v7(g):
    return (v2(g)-v1(g))/v4(g)
#Tracking Error
def v8(g):
    return stdev(g['tracking'])         
# R²
def v9(g):
    slope, intercept = np.polyfit(g['ln_ativo'], g['ln_IBX'], 1)
    r_squared = 1 - (sum((g['ln_IBX'] - (slope * g['ln_ativo']   intercept))**2) / ((len(g['ln_IBX']) - 1) * np.var(g['ln_IBX'], ddof=1)))
    return r_squared

data = {'cnpj': ['cat1', 'cat1', 'cat1', 'cat2', 'cat2', 'cat2', 'cat3', 'cat3', 'cat3'],
        'valor_cota': [1114.7521, 1135.3557, 1149.3381, 500, 501, 510, 10000, 10050, 10100 ],
        'ln_ativo': [0.02715207, 0.01831395, 0.01224022, 0.00200200, 0.00199800, 0.01780462, -0.00697561, 0.00498754, 0.00496279 ],
        'ln_IBX': [-0.00365884, 0.00369080, 0.00795953, -0.00365884, 0.00369080, 0.00795953, -0.00365884, 0.00369080, 0.00795953 ],
        'CDI' : [23.89387945, 23.90473731, 23.9156001, 23.89387945, 23.90473731, 23.9156001, 23.89387945, 23.90473731, 23.9156001 ],
        'tracking' : [0.030810905, 0.014623144, 0.004280688, 0.005660841, -0.0016928, 0.009845095, -0.00331678, 0.001296739, -0.00299674]
    }
df =pd.DataFrame(data)

df.groupby('cnpj').apply(v1).reset_index()
df.groupby('cnpj').apply(v2).reset_index()
df.groupby('cnpj').apply(v3).reset_index()
df.groupby('cnpj').apply(v4).reset_index()
df.groupby('cnpj').apply(v5).reset_index()
df.groupby('cnpj').apply(v6).reset_index()
df.groupby('cnpj').apply(v7).reset_index()
df.groupby('cnpj').apply(v8).reset_index()
df.groupby('cnpj').apply(v9).reset_index()

I would like each call of these functions as a column in the result DataFrame as noted as expected result above. Hope to have achieved best practice questioning.

CodePudding user response：

You can pass multiple functions to apply; then construct a DataFrame with the result:

grouped = df.groupby('cnpj').apply(lambda g: [v1(g),v2(g),v3(g),v4(g),v5(g),v6(g),v7(g),v8(g),v9(g)])
out = pd.DataFrame(grouped.tolist(), index=grouped.index, 
                   columns=['ret_5_CDI','t_5_Asset','anual_5','vol','alfa',
                            'beta','sharpe','tracking_err','R^2']).reset_index()

Output:

   cnpj  ret_5_CDI  t_5_Asset   anual_5       vol      alfa      beta    sharpe  tracking_err       R^2
0  cat1   0.000909   0.031026  0.006130  0.119035  0.022631 -1.274627  0.253007      0.013372  0.997950
1  cat2   0.000909   0.020000  0.003968  0.144852  0.004041  1.211459  0.131797      0.005841  0.608784
2  cat3   0.000909   0.010000  0.001992  0.109531 -0.001920  1.093126  0.082999      0.002576  0.866884