Dataframe - Table In Table From Nested Dictionary

I use python 3. This is my data structure: dictionary = { 'HexaPlex x50': { 'Vendor': 'Dell Inc.', 'BIOS Version': '12.72.9', 'Newest BIOS': '12.73.9',

Solution 1:

As already emphasized in the comments, pandas does not support "sub-dataframes". For the sake of KISS, I would recommend duplicating those rows (or to manage two separate tables... if really necessary).

The answers in the question you referred to (parsing a dictionary in a pandas dataframe cell into new row cells (new columns)) result in new (frame-wide) columns for each (row-local) "computer name". I doubt that this is what you aim for, considering your domain model.

---

The abbreviation of pandas can be circumvented by using another output engine, e.g. tabulate (Pretty Printing a pandas dataframe):

# standard pandas outputVendorBIOSVersionNewestBIOSAgainstM&SW10SupportComputerLocation...Category4Category5Category6Category7Category8Category9Category00DellInc.12.72.912.73.9YesYessomeName00112.72.9...SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory1DellInc.12.72.912.73.9YesYessomeName00212.73.9...SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory2DellInc.12.73.912.73.9YesYessomeName00312.73.9...SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory

[3rowsx17columns]

# tabulate psql (with headers)+----+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+||Vendor|BIOSVersion|NewestBIOS|AgainstM&S|W10Support|Computer|Location|Category1|Category2|Category3|Category4|Category5|Category6|Category7|Category8|Category9|Category0||----+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------||0|DellInc.|12.72.9|12.73.9|Yes|Yes|someName001|12.72.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory||1|DellInc.|12.72.9|12.73.9|Yes|Yes|someName002|12.73.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory||2|DellInc.|12.73.9|12.73.9|Yes|Yes|someName003|12.73.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|+----+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+# tabulate psql+---+------------+---------+---------+-----+-----+-------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+|0|DellInc.|12.72.9|12.73.9|Yes|Yes|someName001|12.72.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory||1|DellInc.|12.72.9|12.73.9|Yes|Yes|someName002|12.73.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory||2|DellInc.|12.73.9|12.73.9|Yes|Yes|someName003|12.73.9|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|SomeCategory|+---+------------+---------+---------+-----+-----+-------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+# tabulate plainVendorBIOSVersionNewestBIOSAgainstM&SW10SupportComputerLocationCategory1Category2Category3Category4Category5Category6Category7Category8Category9Category00DellInc.12.72.912.73.9YesYessomeName00112.72.9SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory1DellInc.12.72.912.73.9YesYessomeName00212.73.9SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory2DellInc.12.73.912.73.9YesYessomeName00312.73.9SomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategorySomeCategory

You could also use some groupBy(..).apply(..) + string magic to produce a string representation which simply hides the duplicates:

# tabulate +merge manually
+----+--------------+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------+|| Type         | Vendor     | BIOS Version   | Newest BIOS   | Against M & S   | W10 Support   | Computer    | Location   | Category1    | Category2    ||----+--------------+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------||0| HexaPlex x50 | Dell  Inc. |12.72.9|12.73.9| Yes             | Yes           | someName001 |12.72.9| SomeCategory | SomeCategory |||||12.72.9|||| someName002 |12.73.9|||||||12.73.9|||| someName003 |12.73.9|||+----+--------------+------------+----------------+---------------+-----------------+---------------+-------------+------------+--------------+--------------+

---

Styled output can be generated via the new Styling API which is still provisional and under development:

Again, you can use some logic to 'merge' consecutively redundant values in a column (quick example, I assume some more effort could result in much nicer output):

---

Code for the above examples

import pandas as pd
from tabulate import tabulate
import functools

defpprint(df, headers=True, fmt='psql'):
    # https://stackoverflow.com/questions/18528533/pretty-printing-a-pandas-dataframeprint(tabulate(df, headers='keys'if headers else'', tablefmt=fmt))

df = pd.DataFrame({
        'Type': ['HexaPlex x50'] * 3,
        'Vendor': ['Dell  Inc.'] * 3,
        'BIOS Version': ['12.72.9', '12.72.9', '12.73.9'],
        'Newest BIOS': ['12.73.9'] * 3,
        'Against M & S': ['Yes'] * 3,
        'W10 Support': ['Yes'] * 3,
        'Computer': ['someName001', 'someName002', 'someName003'],
        'Location': ['12.72.9', '12.73.9', '12.73.9'],
        'Category1': ['SomeCategory'] * 3,
        'Category2': ['SomeCategory'] * 3,
        'Category3': ['SomeCategory'] * 3,
        'Category4': ['SomeCategory'] * 3,
        'Category5': ['SomeCategory'] * 3,
        'Category6': ['SomeCategory'] * 3,
        'Category7': ['SomeCategory'] * 3,
        'Category8': ['SomeCategory'] * 3,
        'Category9': ['SomeCategory'] * 3,
        'Category0': ['SomeCategory'] * 3,
    })

print("# standard pandas print")
print(df)

print("\n# tabulate tablefmt=psql (with headers)")
pprint(df)
print("\n# tabulate tablefmt=psql")
pprint(df, headers=False)
print("\n# tabulate tablefmt=plain")
pprint(df, fmt='plain')

defmerge_cells_for_print(rows, ls='\n'):
    result = pd.DataFrame()
    for col in rows.columns:
        vals = rows[col].values
        ifall([val == vals[0] for val in vals]):
            result[col] = [vals[0]]
        else:
            result[col] = [ls.join(vals)]
    return result

print("\n# tabulate + merge manually")
pprint(df.groupby('Type').apply(merge_cells_for_print).reset_index(drop=True))

# https://pandas.pydata.org/pandas-docs/stable/style.html# https://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.io.formats.style.Styler.apply.html#pandas.io.formats.style.Styler.applydefhighlight_lower(ref, col):
    return [f'color: {"red"if hgl else""}'for hgl in col < ref]

defmerge_duplicates(col):
    vals = col.values
    return [''] + ['color: transparent'if curr == pred else''for pred, curr inzip(vals[1:], vals)]

withopen('only_red.html', 'w+') as f:
    style = df.style
    style = style.apply(functools.partial(highlight_lower, df['Newest BIOS']),
                        subset=['BIOS Version'])
    f.write(style.render())

withopen('red_and_merged.html', 'w+') as f:
    style = df.style
    style = style.apply(functools.partial(highlight_lower, df['Newest BIOS']),
                        subset=['BIOS Version'])
    style = style.apply(merge_duplicates)
    f.write(style.render())