How to transform pandas columns into a column and formatted as json accepted

Question

I need help on how to properly transform my df from this:

df_installation = pd.DataFrame({'InstallationID': ["Item 1", "Item 2", "Item 3","Item 1", "Item 2", "Item 3"],
                                      'Type': ["Metric", "Metric","Metric", "Imperial","Imperial","Imperial"],
                                 'Measure 1': [11998,   11076,12025,129145,119221,129],
                                 'Measure 2': [12000,12001,12002,129168,129178.764,129189.528],
                              'Measure Type': ["Morning","Afternoon","Evening","Morning","Afternoon","Evening"],
                            })

to this: –> (i attached an image sample too because the sample final df, I’m not sure how to create a json sample so i made it look like text so it will be accepted in a column)

df_installation_new = pd.DataFrame({'InstallationID': ['ID no1', "ID no2", "ID no3"],
               'Metric': ["""{"text 1": {"label": "Measure Time","value": "Morning"},"text 2": {"label": "Measure 1","value": 11998},"text 3": {"label": "Measure 2","value": 12000}}""",
                          """{"text 1": {"label": "Measure Time","value": "Afternoon"},"text 2": {"label": "Measure 1","value": 11076},"text 3": {"label": "Measure 2","value": 12001}}""",
                          """{"text 1": {"label": "Measure Time","value": "Evening"},"text 2": {"label": "Measure 1","value": 12025},"text 3": {"label": "Measure 2","value": 12002}}"""
                          ],
               'Imperial': ["""{"text 1": {"label": "Measure Time","value": "Morning"},"text 2": {"label": "Measure 1","value": 129145},"text 3": {"label": "Measure 2","value": 129168}}""",
                            """{"text 1": {"label": "Measure Time","value": "Afternoon"},"text 2": {"label": "Measure 1","value": 119221},"text 3": {"label": "Measure 2","value": 129178.764}}""",
                            """{"text 1": {"label": "Measure Time","value": "Evening"},"text 2": {"label": "Measure 1","value": 129},"text 3": {"label": "Measure 2","value": 129189.528}}"""
                          ],
                          })

Asked By: D P

||

Source

Answer 1

Given your input dataframe:

df = pd.DataFrame({'InstallationID': ["Item 1", "Item 2", "Item 3","Item 1", "Item 2", "Item 3"],
                                      'Type': ["Metric", "Metric","Metric", "Imperial","Imperial","Imperial"],
                                 'Measure 1': [11998,   11076,12025,129145,119221,129],
                                 'Measure 2': [12000,12001,12002,129168,129178.764,129189.528],
                              'Measure Type': ["Morning","Afternoon","Evening","Morning","Afternoon","Evening"],
                            })

You could do the following:

1- Create a function to take the measure type, measure 1, and measure 2 to convert them into the needed format:

def to_my_format(mt, m1, m2):
    return(f"""{{"text 1": {{"label": "Measure Time","value": "{mt}"}},"text 2": {{"label": "Measure 1","value": {m1}}},"text 3": {{"label": "Measure 2","value": {m2}}}}}""")

2- Apply this function to all rows of your dataframe:

df['json'] = df.apply(lambda x: to_my_format(x['Measure Type'], x['Measure 1'], x['Measure 2']), axis=1)

3- Now, pivot your dataframe on the first occurrence with ID as the index and Type as the columns:

df.pivot_table(index="InstallationID", columns="Type", values="json", aggfunc="first")

Output:


Type                Imperial                                            Metric
InstallationID      
Item 1             {"text 1": {"label": "Measure Time","value": "...    {"text 1": {"label": "Measure Time","value": "...
Item 2             {"text 1": {"label": "Measure Time","value": "...    {"text 1": {"label": "Measure Time","value": "...
Item 3             {"text 1": {"label": "Measure Time","value": "...    {"text 1": {"label": "Measure Time","value": "...

Please, for the future, provide some attempt on solving your problem.

Answered By: D.Manasreh

How to transform pandas columns into a column and formatted as json accepted

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