How to add a percentage computation in pandas result

Question

I have the following working code. I need to add a percentage column to monitor changes. I dont know much on how to do it in pandas. I need ideas on what part needs to be modified.

import pandas as pd
dl = []
with open('sampledata.txt') as f:
    for line in f:
        parts = line.split()
        # Cleaning data here.. Conversions to int/float etc,
        if not parts[3][:2].startswith('($'):
            parts.insert(3,'0')
        if len(parts) > 5:
            temp = ' '.join(parts[4:])
            parts = parts[:4] + [temp]
        parts[1] = int(parts[1])
        parts[2] = float(parts[2].replace(',', ''))
        parts[3] = float(parts[3].strip('($)'))
        dl.append(parts)
headers = ['col1', 'col2', 'col3', 'col4', 'col5']
df = pd.DataFrame(dl,columns=headers)
df = df.groupby(['col1','col5']).sum().reset_index()
df = df.sort_values('col2',ascending=False)
df['col4'] =  '($' + df['col4'].astype(str) + ')'
df = df[headers]
print(df)

sampledata.txt #-- Sample Data Source file

alpha   1   54,00.01                    ABC DSW2S
bravo   3   500,000.00                  ACDEF
charlie 1   27,722.29 ($250.45)         DGAS-CAS
delta   2   11 ($10)                    SWSDSASS-CCSSW
echo    5   143,299.00 ($101)           ACS34S1
lima    6   45.00181 ($38.9)            FGF5GGD-DDD
falcon  3   0.1234                      DSS2SFS3
echo    8   145,300 ($125.01)           ACS34S1
charlie 10  252,336,733.383 ($492.06)   DGAS-CAS
romeo   12  980                         ASDS SSSS SDSD
falcon  5   9.19                        DSS2SFS3

Current Output: #-- working result

      col1  col2          col3       col4            col5
4     echo    13  2.885990e+05  ($226.01)         ACS34S1
7    romeo    12  9.800000e+02     ($0.0)  ASDS SSSS SDSD
2  charlie    11  2.523645e+08  ($742.51)        DGAS-CAS
5   falcon     8  9.313400e+00     ($0.0)        DSS2SFS3
6     lima     6  4.500181e+01    ($38.9)     FGF5GGD-DDD
1    bravo     3  5.000000e+05     ($0.0)           ACDEF
3    delta     2  1.100000e+01    ($10.0)  SWSDSASS-CCSSW
0    alpha     1  5.400010e+03     ($0.0)       ABC DSW2S

Improved Output: #-- with Additional Column for %

      col1  col2          col3       col4            col5   col6
4     echo    13  2.885990e+05  ($226.01)         ACS34S1   60%     #-- (5 + 8) = 13
7    romeo    12  9.800000e+02     ($0.0)  ASDS SSSS SDSD   0%
2  charlie    11  2.523645e+08  ($742.51)        DGAS-CAS   900%  #-- (1 + 10) = 11
5   falcon     8  9.313400e+00     ($0.0)        DSS2SFS3   66.67%  #-- (3 + 5) = 8
6     lima     6  4.500181e+01    ($38.9)     FGF5GGD-DDD   0%
1    bravo     3  5.000000e+05     ($0.0)           ACDEF   0%
3    delta     2  1.100000e+01    ($10.0)  SWSDSASS-CCSSW   0%
0    alpha     1  5.400010e+03     ($0.0)       ABC DSW2S   0%

Answer 1

Setup:

#sampledata.txt
df = pd.DataFrame(data={'col1': ['alpha', 'bravo', 'charlie', 'delta', 'echo','lima', 'falcon', 'echo', 'charlie', 'romeo', 'falcon'],
                        'col2': [1, 3, 1, 2, 5, 6, 3, 8, 10, 12, 5],
                        'col3': ['54,00.01', '500,000.00', '27,722.29 ($250.45)', '11 ($10)', '143,299.00 ($101)', '45.00181 ($38.9)', '0.1234', '145,300 ($125.01)', '252,336,733.383 ($492.06)', '980', '9.19'],
                        'col4': ['ABC DSW2S', 'ACDEF', 'DGAS-CAS', 'SWSDSASS-CCSSW', 'ACS34S1', 'FGF5GGD-DDD', 'DSS2SFS3', 'ACS34S1', 'DGAS-CAS', 'ASDS SSSS SDSD', 'DSS2SFS3']})

Code:

df['within_brackets'] = df['col3'].str.extract('.*\((.*)\).*') #Extract whats inside the brackets.
df['within_brackets'].replace('\$', '', regex=True, inplace=True)
df['col3'] = df['col3'].str.replace(r"(\s*\(.*\))|,", "", regex=True) #Extract whats outside the brackets
df.rename(columns={'col4': 'col5', 'within_brackets': 'col4'}, inplace=True)
df[['col3', 'col4']] = df[['col3', 'col4']].astype(float)

df = df.groupby(['col1', 'col5']).agg(col2 = pd.NamedAgg(column="col2", aggfunc="sum"),
                                      col3 = pd.NamedAgg(column="col3", aggfunc="sum"),
                                      col4 = pd.NamedAgg(column="col4", aggfunc="sum"),
                                      col6 = pd.NamedAgg(column="col2", aggfunc=pd.Series.pct_change)).reset_index()
df['col6'].fillna(0, inplace=True)
#print df here and you will get to know what output looks like till now.
df['col6'].fillna(0, inplace=True)
df['col6'] = df['col6'].apply(lambda x: f"{str(round(x[-1], 4) * 100)}%" if isinstance(x, np.ndarray) else f"{round(x, 4) * 100}%")
df = df[['col1', 'col2', 'col3', 'col4', 'col5', 'col6']]
df.sort_values(by=['col2'], ascending=False, inplace=True)
print(df)

Output:

      col1  col2          col3    col4            col5    col6
4     echo    13  2.885990e+05  226.01         ACS34S1   60.0%
7    romeo    12  9.800000e+02    0.00  ASDS SSSS SDSD      0%
2  charlie    11  2.523645e+08  742.51        DGAS-CAS  900.0%
5   falcon     8  9.313400e+00    0.00        DSS2SFS3  66.67%
6     lima     6  4.500181e+01   38.90     FGF5GGD-DDD      0%
1    bravo     3  5.000000e+05    0.00           ACDEF      0%
3    delta     2  1.100000e+01   10.00  SWSDSASS-CCSSW      0%
0    alpha     1  5.400010e+03    0.00       ABC DSW2S      0%

Update Use this with your existing code: (Use the following code after df = pd.DataFrame(dl,columns=headers) this line in your code.)

df = df.groupby(['col1', 'col5']).agg(col2 = pd.NamedAgg(column="col2", aggfunc="sum"),
                                      col3 = pd.NamedAgg(column="col3", aggfunc="sum"),
                                      col4 = pd.NamedAgg(column="col4", aggfunc="sum"),
                                      col6 = pd.NamedAgg(column="col2", aggfunc=pd.Series.pct_change)).reset_index()
df['col6'].fillna(0, inplace=True)
#print df here and you will get to know what output looks like till now.
df['col6'].fillna(0, inplace=True)
df['col6'] = df['col6'].apply(lambda x: f"{str(round(x[-1], 4) * 100)}%" if isinstance(x, np.ndarray) else f"{round(x, 4) * 100}%")
df['col4'] =  '($' + df['col4'].astype(str) + ')'
df = df[['col1', 'col2', 'col3', 'col4', 'col5', 'col6']]

Answer 2

You can add the following lines just after your code: The function compute_percentage() is using the list variable dl.

def compute_percentage(row):
    vl = [float(parts[1]) for parts in dl if parts[0] == row['col1']]
    i = round(100. * (vl[-1]-vl[0])/vl[0] if vl[0] != 0 else 0, 2)
    if float(int(i)) == i:
        i = int(i)
    return str(i) + '%'

df['col6'] = df.apply(compute_percentage, axis=1)

Output:

      col1  col2          col3       col4            col5    col6
4     echo    13  2.885990e+05  ($226.01)         ACS34S1     60%
7    romeo    12  9.800000e+02     ($0.0)  ASDS SSSS SDSD      0%
2  charlie    11  2.523645e+08  ($742.51)        DGAS-CAS    900%
5   falcon     8  9.313400e+00     ($0.0)        DSS2SFS3  66.67%
6     lima     6  4.500181e+01    ($38.9)     FGF5GGD-DDD      0%
1    bravo     3  5.000000e+05     ($0.0)           ACDEF      0%
3    delta     2  1.100000e+01    ($10.0)  SWSDSASS-CCSSW      0%
0    alpha     1  5.400010e+03     ($0.0)       ABC DSW2S      0%