How to format the dataframe into a 2D table

I have following issue with formatting a pandas dataframe into a 2D format. My data is:

+----+------+-----------+---------+
|    | Jobs | Measure   |   Value |
|----+------+-----------+---------|
|  0 | Job1 | Temp      |    43   |
|  1 | Job1 | Humidity  |    65   |
|  2 | Job2 | Temp      |    48   |
|  3 | Job2 | TempS     |    97.4 |
|  4 | Job2 | Humidity  |   nan   |
|  5 | Job3 | Humidity  |    55   |
|  6 | Job1 | Temp      |    41   |
|  7 | Job1 | Duration  |    23   |
|  8 | Job3 | Temp      |    39   |
|  9 | Job1 | Temp      |   nan   |
| 10 | Job1 | Humidity  |    55   |
| 11 | Job2 | Temp      |    48   |
| 12 | Job2 | TempS     |    97.4 |
| 13 | Job2 | Humidity  |   nan   |
| 14 | Job3 | Humidity  |    55   |
| 15 | Job1 | Temp      |   nan   |
| 16 | Job1 | Duration  |    25   |
| 17 | Job3 | Temp      |   nan   |
| 18 | Job2 | Humidity  |    61   |
+----+------+-----------+---------+

and my code for now is:

from tabulate import tabulate
import pandas as pd

df = pd.read_csv('logs.csv')

#print(df)

print(tabulate(df, headers='keys', tablefmt='psql'))

grouped = df.groupby(['Jobs','Measure'], dropna=True)
average_temp = grouped.mean()

errors = df.groupby(['Jobs','Measure']).agg(lambda x: x.isna().sum())

frames = [average_temp, errors]

df_merged = pd.concat(frames, axis=1).set_axis(['Avg', 'Error'], axis='columns')
print(df_merged)

and the output of the print is:

Table-1
                               Avg  Error
    Jobs            Measure              
    Job1            Duration  24.0      0
                    Humidity  60.0      0
                    Temp      42.0      2
    Job3            Humidity  55.0      0
                    Temp      39.0      1
    Job2            Humidity  61.0      2
                    TempS     97.4      0
                    Temp      48.0      0

How can I format this table into something like this:

Table-2
    Jobs    Avg.Temp    Err.Temp    Avg.Humidity    Err.Humidity  Avg.Duration  ...
    Job1        42.0        2           60.0             0            24.0
    Job2        48.0        0           61.0             0            -
    Job3        39.0        1           55.0             1            -

So, what we see is that for example, Avg.Temp for Job1 in Table-2 is the Avg. value of Job1->Temp in Table-1. Another thing is that not all Jobs need to have the same measure fields and can also differ like for Job2 we have 'TempS'.

Update: using the answer from user24714682 the table looks like this.

+--------------+----------------+----------------+--------------+------------+------------------+------------------+----------------+--------------+
| Jobs         |   Avg.Duration |   Avg.Humidity |   Avg.S.Temp |   Avg.Temp |   Error.Duration |   Error.Humidity |   Error.S.Temp |   Error.Temp |
|--------------+----------------+----------------+--------------+------------+------------------+------------------+----------------+--------------|
| Job1         |             24 |             60 |        nan   |         42 |                0 |                0 |            nan |            2 |
| Job3         |            nan |             55 |        nan   |         39 |              nan |                0 |            nan |            1 |
| Job2         |            nan |             61 |         97.4 |         48 |                1 |                2 |              0 |            0 |
+--------------+----------------+----------------+--------------+------------+------------------+------------------+----------------+--------------+

How can I now sort the columns in that way to first show the Measure that has the highest total Error count first and the descending to the rest of total Error counts. example:

+--------------+------------+--------------+----------------+------------------+...
| Jobs         |   Avg.Temp |   Error.Temp |   Avg.Humidity |   Error.Humidity |
|--------------+------------+--------------|----------------+------------------+...
| Job1         |         42 |            2 |             60 |                0 |
| Job3         |         39 |            1 |             55 |                0 |
| Job2         |         48 |            0 |             61 |                2 |
+--------------+------------+--------------+----------------+------------------+...

In the above table the columns are sorted 1st Avg.Temp bacause it is the sensor with highest total error count of 3 and then it shows Avg.Humidity because it has the 2nd highest total error count and so on.

You can use unstack() and join():

import pandas as pd
from tabulate import tabulate

data = {
    'Jobs': ['Job1', 'Job1', 'Job2', 'Job2', 'Job2', 'Job3', 'Job1', 'Job1', 'Job3', 'Job1', 'Job1', 'Job2', 'Job2', 'Job2', 'Job3', 'Job1', 'Job1', 'Job3', 'Job2'],
    'Measure': ['Temp', 'Humidity', 'Temp', 'TempS', 'Humidity', 'Humidity', 'Temp', 'Duration', 'Temp', 'Temp', 'Humidity', 'Temp', 'TempS', 'Humidity', 'Humidity', 'Temp', 'Duration', 'Temp', 'Humidity'],
    'Value': [43, 65, 48, 97.4, None, 55, 41, 23, 39, None, 55, 48, 97.4, None, 55, None, 25, None, 61]
}

df = pd.DataFrame(data)

grouped = df.groupby(['Jobs', 'Measure'])
average_temp = grouped.mean()
errors = df.groupby(['Jobs', 'Measure']).agg(lambda x: x.isna().sum())

frames = (average_temp, errors)
df_merged = pd.concat(frames, axis=1).set_axis(['Avg', 'Error'], axis='columns')

df_avg, df_err = df_merged['Avg'].unstack(), df_merged['Error'].unstack()

res = pd.concat((df_avg, df_err), axis=1, keys=('Avg', 'Error'))
res.columns = ['.'.join(col).strip() for col in res.columns.values]

print(tabulate(res, headers='keys', tablefmt='psql'))

Prints

+--------+----------------+----------------+------------+-------------+------------------+------------------+--------------+---------------+
| Jobs   |   Avg.Duration |   Avg.Humidity |   Avg.Temp |   Avg.TempS |   Error.Duration |   Error.Humidity |   Error.Temp |   Error.TempS |
|--------+----------------+----------------+------------+-------------+------------------+------------------+--------------+---------------|
| Job1   |             24 |             60 |         42 |       nan   |                0 |                0 |            2 |           nan |
| Job2   |            nan |             61 |         48 |        97.4 |              nan |                2 |            0 |             0 |
| Job3   |            nan |             55 |         39 |       nan   |              nan |                0 |            1 |           nan |
+--------+----------------+----------------+------------+-------------+------------------+------------------+--------------+---------------+

You can reset_index to get columns from the index and use them to pivot the dataframe.

The resulting columns will be a MultiIndex, which you can replace with a listcomp.

Assuming this as input:

data = {
    "Jobs": ["Job1", "Job1", "Job2", "Job2", "Job2", "Job3", "Job1", "Job1", "Job3", "Job1", "Job1", "Job2", "Job2", "Job2", "Job3", "Job1", "Job1", "Job3", "Job2"],
    "Measure": ["Temp", "Humidity", "Temp", "TempS", "Humidity", "Humidity", "Temp", "Duration", "Temp", "Temp", "Humidity", "Temp", "TempS", "Humidity", "Humidity", "Temp", "Duration", "Temp", "Humidity"],
    "Value": [43, 65, 48, 97.4, np.nan, 55, 41, 23, 39, np.nan, 55, 48, 97.4, np.nan, 55, np.nan, 25, np.nan, 61],
}
df = pd.DataFrame(data)

# from OP
grouped = df.groupby(["Jobs", "Measure"], dropna=True)
average_temp = grouped.mean()
errors = df.groupby(["Jobs", "Measure"]).agg(lambda x: x.isna().sum())
frames = [average_temp, errors]
df_merged = pd.concat(frames, axis=1).set_axis(["Avg", "Error"], axis="columns")

                Avg  Error
Jobs Measure              
Job1 Duration  24.0      0
     Humidity  60.0      0
     Temp      42.0      2
Job2 Humidity  61.0      2
     Temp      48.0      0
     TempS     97.4      0
Job3 Humidity  55.0      0
     Temp      39.0      1

Use this solution:

df_merged = (
    df_merged.reset_index()
    .pivot(index="Jobs", columns="Measure", values=["Avg", "Error"])
    .reset_index()
)
df_merged.columns = [f"{agg}. {metric}" for agg, metric in df_merged.columns]

  Jobs.   Avg. Duration  Avg. Humidity  Avg. Temp  Avg. TempS  Error. Duration  Error. Humidity  Error. Temp  Error. TempS
0   Job1           24.0           60.0       42.0         NaN              0.0              0.0          2.0           NaN
1   Job2            NaN           61.0       48.0        97.4              NaN              2.0          0.0           0.0
2   Job3            NaN           55.0       39.0         NaN              NaN              0.0          1.0           NaN