Python Pandas: pivot only certain columns in the DataFrame while keeping others

Question

I am trying to re-arrange a DataFrame that I automatically read in from a json using Pandas. I've searched but have had no success.

I have the following json (saved as a string for copy/paste convenience) with a bunch of json objects/dictionarys under the tag 'value'

json_str = '''{"preferred_timestamp": "internal_timestamp",
    "internal_timestamp": 3606765503.684,
    "stream_name": "ctdpf_j_cspp_instrument",
    "values": [{
        "value_id": "temperature",
        "value": 9.8319
    }, {
        "value_id": "conductivity",
        "value": 3.58847
    }, {
        "value_id": "pressure",
        "value": 22.963
    }]
}'''

I use the function 'json_normalize' in order to load the json into a flattened Pandas dataframe.

>>> from pandas.io.json import json_normalize
>>> import simplejson as json
>>> df = json_normalize(json.loads(json_str), 'values', ['preferred_timestamp', 'stream_name', 'internal_timestamp'])
>>> df
      value      value_id preferred_timestamp  internal_timestamp  \
0   9.83190   temperature  internal_timestamp        3.606766e+09   
1   3.58847  conductivity  internal_timestamp        3.606766e+09   
2  22.96300      pressure  internal_timestamp        3.606766e+09   
3  32.89470      salinity  internal_timestamp        3.606766e+09   

               stream_name  
0  ctdpf_j_cspp_instrument  
1  ctdpf_j_cspp_instrument  
2  ctdpf_j_cspp_instrument  
3  ctdpf_j_cspp_instrument  

Here is where I am stuck. I want to take the value and value_id columns and pivot these into new columns based off of value_id.

I want the dataframe to look like the following:

stream_name              preferred_timestamp  internal_timestamp  conductivity  pressure  salinity  temperature    
ctdpf_j_cspp_instrument  internal_timestamp   3.606766e+09        3.58847       22.96300  32.89470  9.83190

I've tried both the pivot and pivot_table Pandas functions and even tried to manually pivot the tables by using 'set_index' and 'stack' but it's not quite how I want it.

>>> df.pivot_table(values='value', index=['stream_name', 'preferred_timestamp', 'internal_timestamp', 'value_id'])
stream_name              preferred_timestamp  internal_timestamp  value_id    
ctdpf_j_cspp_instrument  internal_timestamp   3.606766e+09        conductivity     3.58847
                                                                  pressure        22.96300
                                                                  salinity        32.89470
                                                                  temperature      9.83190
Name: value, dtype: float64

This is close, but it didn't seem to pivot the values in 'value_id' into separate columns.

and

>>> df.pivot('stream_name', 'value_id', 'value')
value_id                 conductivity  pressure  salinity  temperature
stream_name                                                           
ctdpf_j_cspp_instrument       3.58847    22.963   32.8947       9.8319

Close again, but it lacks the other columns that I want to be associated with this line.

I'm stuck here. Is there an elegant way of doing this or should I split the DataFrames and re-merge them to how I want?

Answer 1

Your first attempt was nearly correct, just use columns='value_id' instead of including it in the index.

# Perform the pivot.
df = df.pivot_table(
    values='value',
    index=['stream_name', 'preferred_timestamp', 'internal_timestamp'],
    columns='value_id'
    )

# Formatting.
df.reset_index(inplace=True)
df.columns.name = None

This isn't an issue in your example data, but keep in mind that pivot_table will aggregate values if multiple values are pivoted to the same position (taking the mean by default).