嵌套字典中表中的数据帧表

Question

我使用python3。在

这是我的数据结构：

^{1}$

我已经成功地创建了一个表，显示从第一个嵌套字典（以'Vendor'开头）的键创建的列。行名是'HexaPlex x50'。其中一列包含带有数字的计算机，即嵌套字典：

^{pr2}$

我希望能够在列'Computers'下的单元格中的表中拥有键值对，实际上是一个嵌套表。在

ATM看起来像这样：

桌子应该看起来像这样

我怎样才能做到这一点？在

此外，我想给数字或者比最新版本的BIOS版本更低的单元上色。在

我还面临这样一个问题：在一个例子中，包含计算机的字典非常大，以至于即使我设置了pd.set_option('display.max_colwidth', -1)，它也会被缩写。看起来是这样的：

Answer 1

正如评论中已经强调的，pandas不支持“子数据帧”。为了KISS，我建议复制这些行（或者管理两个单独的表。。。如有必要）。在

您提到的问题（parsing a dictionary in a pandas dataframe cell into new row cells (new columns)）中的答案将为每个计算机名生成新的（帧宽）列。考虑到您的域模型，我怀疑这是否是您的目标。在

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pandas的缩写可以通过使用另一个输出引擎来避免，例如tabulate（Pretty Printing a pandas dataframe）：

# standard pandas output
       Vendor BIOS Version Newest BIOS Against M & S W10 Support     Computer Location      ...          Category4     Category5     Category6     Category7     Category8     Category9     Category0
0  Dell  Inc.      12.72.9     12.73.9           Yes         Yes  someName001  12.72.9      ...       SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory
1  Dell  Inc.      12.72.9     12.73.9           Yes         Yes  someName002  12.73.9      ...       SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory
2  Dell  Inc.      12.73.9     12.73.9           Yes         Yes  someName003  12.73.9      ...       SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory

[3 rows x 17 columns]

# tabulate psql (with headers)
+  +      +        +       -+        -+       -+      -+      +       +       +       +       +       +       +       +       +       +       +
|    | Vendor     | BIOS Version   | Newest BIOS   | Against M & S   | W10 Support   | Computer    | Location   | Category1    | Category2    | Category3    | Category4    | Category5    | Category6    | Category7    | Category8    | Category9    | Category0    |
|  +      +        +       -+        -+       -+      -+      +       +       +       +       +       +       +       +       +       +       |
|  0 | Dell  Inc. | 12.72.9        | 12.73.9       | Yes             | Yes           | someName001 | 12.72.9    | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory |
|  1 | Dell  Inc. | 12.72.9        | 12.73.9       | Yes             | Yes           | someName002 | 12.73.9    | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory |
|  2 | Dell  Inc. | 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 | Dell  Inc. | 12.72.9 | 12.73.9 | Yes | Yes | someName001 | 12.72.9 | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory |
| 1 | Dell  Inc. | 12.72.9 | 12.73.9 | Yes | Yes | someName002 | 12.73.9 | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory |
| 2 | Dell  Inc. | 12.73.9 | 12.73.9 | Yes | Yes | someName003 | 12.73.9 | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory | SomeCategory |
+ -+      +    -+    -+  -+  -+      -+    -+       +       +       +       +       +       +       +       +       +       +

# tabulate plain
    Vendor      BIOS Version    Newest BIOS    Against M & S    W10 Support    Computer     Location    Category1     Category2     Category3     Category4     Category5     Category6     Category7     Category8     Category9     Category0
 0  Dell  Inc.  12.72.9         12.73.9        Yes              Yes            someName001  12.72.9     SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory
 1  Dell  Inc.  12.72.9         12.73.9        Yes              Yes            someName002  12.73.9     SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory
 2  Dell  Inc.  12.73.9         12.73.9        Yes              Yes            someName003  12.73.9     SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory  SomeCategory

您还可以使用一些groupBy(..).apply(..)+string magic来生成一个字符串表示，它只隐藏重复项：

^{pr2}$

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样式化的输出可以通过新的Styling API生成，它仍然是临时的并且正在开发中：

同样，您可以使用一些逻辑来“合并”列中连续的冗余值（简单的例子，我假设更多的努力可以得到更好的输出）：

---

以上示例的代码

import pandas as pd
from tabulate import tabulate
import functools

def pprint(df, headers=True, fmt='psql'):
    # https://stackoverflow.com/questions/18528533/pretty-printing-a-pandas-dataframe
    print(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')

def merge_cells_for_print(rows, ls='\n'):
    result = pd.DataFrame()
    for col in rows.columns:
        vals = rows[col].values
        if all([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.apply

def highlight_lower(ref, col):
    return [f'color: {"red" if hgl else ""}' for hgl in col < ref]

def merge_duplicates(col):
    vals = col.values
    return [''] + ['color: transparent' if curr == pred else ''  for pred, curr in zip(vals[1:], vals)]

with open('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())

with open('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())