Displaying None and Values with pandas dictionaries Python-CodePudding

There are None values indicating there is no value for the Last Month row within the dictionary a below. How would I be able to modify the pandas style format so that it could print table a and still place dollar signs and in front of the set columns?

import numpy as np
import pandas as pd

a =   {'Timeframes': ['Entirety:',
      'Last Month:',
      'Three Months:',
      'Six Months:',
      'Last Year:',
      'Last Two Years:'],
     'Compounding With Lev':  np.array([2398012.89, None, 90.07, 85.29,
            620.39, 30611.48], dtype=object),
     'Compounding With Seperate Levs': np.array([21165662669.71, None, 91.18, 107.54,
            3004.87, 13287947.75], dtype=object),
     'Adjusted Long Compounding Lev': np.array([3.25, None, 1.0, 1.0, 3.5, 4.75], dtype=object),
     'Adjusted Short Compounding Lev': np.array([3.75, None, 1.0, 3.0, 1.0, 2.0], dtype=object),
     'Non Compounding With Lev': np.array([3626.41, None, 89.95, 95.73,
            1577.75, 1380.80], dtype=object),
     'Non Compounding With Seperate Levs': np.array([5679.53, None, 91.15, 408.40,
            1953.53, 2530.58], dtype=object),
     'Adjusted Long NonCompounding Lev': np.array([4.25, None, 1.0, 1.0, 10.5, 4.25], dtype=object),
     'Adjusted Short NonCompounding Lev': np.array([7.75, None, 1.0, 33.25, 1.0, 7.75], dtype=object)}

display(pd.DataFrame(a).style.format(formatter={'Compounding With Lev': '${:,.2f}',
                       'Compounding With Seperate Levs': '${:,.2f}',
                       'Non Compounding With Lev': '${:,.2f}',
                       'Non Compounding With Seperate Levs': '${:,.2f}'}))

Expected Output:

CodePudding user response：

import numpy as np
import pandas as pd

a =   {'Timeframes': ['Entirety:',
      'Last Month:',
      'Three Months:',
      'Six Months:',
      'Last Year:',
      'Last Two Years:'],
     'Compounding With Lev':  np.array([2398012.89, None, 90.07, 85.29,
            620.39, 30611.48], dtype=float),
     'Compounding With Seperate Levs': np.array([21165662669.71, None, 91.18, 107.54,
            3004.87, 13287947.75], dtype=float),
     'Adjusted Long Compounding Lev': np.array([3.25, None, 1.0, 1.0, 3.5, 4.75], dtype=float),
     'Adjusted Short Compounding Lev': np.array([3.75, None, 1.0, 3.0, 1.0, 2.0], dtype=float),
     'Non Compounding With Lev': np.array([3626.41, None, 89.95, 95.73,
            1577.75, 1380.80], dtype=float),
     'Non Compounding With Seperate Levs': np.array([5679.53, None, 91.15, 408.40,
            1953.53, 2530.58], dtype=float),
     'Adjusted Long NonCompounding Lev': np.array([4.25, None, 1.0, 1.0, 10.5, 4.25], dtype=float),
     'Adjusted Short NonCompounding Lev': np.array([7.75, None, 1.0, 33.25, 1.0, 7.75], dtype=float)}

display(pd.DataFrame(a).style.format(formatter={'Compounding With Lev': '${:,.2f}',
                       'Compounding With Seperate Levs': '${:,.2f}',
                       'Non Compounding With Lev': '${:,.2f}',
                       'Non Compounding With Seperate Levs': '${:,.2f}'}, na_rep='None'))

By changing dtype=float we allow numpy to place nan values in the array. na_rep parameter is what the formatter will put when the formatting is not applicable.

To convert dictionary a in case your dictionary is read like that from a file and the arrays are already dtype=object:

for k, v in a.items():
    if isinstance(v, np.ndarray):
        a[k] = v.astype(float)