CODEWITHSUNDEEP
pythonjsonpandas
gtomer
gtomer

Reputation: 6574

Python: Expand JSON structure in a column into columns in the same dataframe

I have a column consists of JSON structured data. My df looks like this:

ClientToken                      Data
7a9ee887-8a09-ff9592e08245       [{"summaryId":"4814223456","duration":952,"startTime":1587442919}]
bac49563-2cf0-cb08e69daa48       [{"summaryId":"4814239586","duration":132,"startTime":1587443876}]

I want to expand it to:

ClientToken                      summaryId         duration           startTime
7a9ee887-8a09-ff9592e08245       4814223456             952           1587442919
bac49563-2cf0-cb08e69daa48       4814239586             132           1587443876`

Any ideas?

Upvotes: 2

Views: 2394

Answers (2)

Alexandre B.
Alexandre B.

Reputation: 5500

You can try:

df[["ClientToken"]].join(df.Data.apply(lambda x: pd.Series(json.loads(x[1:-1]))))

Explanations:

  1. Select the Data column and apply the following steps:
    1. Because the "Data" content is wrapped in a list and this is a string, we can remove [] manually using x[1:-1] (remove first and last character).
    2. Since the "Data" column is a string and we actually want a JSON, we need to convert it. One solution is to use the json.loads() function from the json module. The code becomes json.loads(x[1:-1])
    3. Then, convert the dictto a pd.Series using pd.Series(json.loads(x[1:-1]))
  2. Add these new columns to the existing dataframe using join. Also, you will notice I used double [] to select the "ClientToken" column as a dataframe.

Code + illustration:

import pandas as pd
import json

# step 1.1
print(df.Data.apply(lambda x: x[1:-1]))
# 0    {"summaryId":"4814223456","duration":952,"star...
# 1    {"summaryId":"4814239586","duration":132,"star...
# Name: Data, dtype: object

# step 1.2
print(df.Data.apply(lambda x: json.loads(x[1:-1])))
# 0    {'summaryId': '4814223456', 'duration': 952, '...
# 1    {'summaryId': '4814239586', 'duration': 132, '...
# Name: Data, dtype: object

# step 1.3
print(df.Data.apply(lambda x: pd.Series(json.loads(x[1:-1]))))
#     summaryId  duration   startTime
# 0  4814223456       952  1587442919
# 1  4814239586       132  1587443876

# step 2
print(df[["ClientToken"]].join(df.Data.apply(lambda x: pd.Series(json.loads(x[1:-1])))))
#                   ClientToken   summaryId  duration   startTime
# 0  7a9ee887-8a09-ff9592e08245  4814223456       952  1587442919
# 1  bac49563-2cf0-cb08e69daa48  4814239586       132  1587443876

Edit 1:

As it seems that there are some rows where the list in Data has multiple dicts, you can try:

df[["ClientToken"]].join(df.Data.apply(lambda x: [pd.Series(y)
                                                  for y in json.loads(x)]) \
                    .explode() \
                    .apply(pd.Series))

Upvotes: 2

sammywemmy
sammywemmy

Reputation: 28729

An alternative, using defaultdict and ast literal eval:

from collections import defaultdict
import ast
d = defaultdict(list)
#iterate through the Data column and append to dictionary for each key
for ent in df.Data:
    for entry in ast.literal_eval(ent):
        for k, v in entry.items():
            d[k].append(v)

#concat to ClientToken column
pd.concat([df.ClientToken,pd.DataFrame(d)],axis=1)

    ClientToken summaryId   duration    startTime
0   7a9ee887-8a09-ff9592e08245  4814223456  952 1587442919
1   bac49563-2cf0-cb08e69daa48  4814239586  132 1587443876

Upvotes: 0

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