pandas pivot-table python

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

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 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
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.


>>> df.pivot('stream_name', 'value_id', 'value')
value_id conductivity pressure salinity temperature
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?