How to create a Pandas DataFrame from row-based list of dictionaries

Question

I have a data structure like this:

data = [{
  "name": "leopard",
  "character": "mean",
  "skills": ["sprinting", "hiding"],
  "pattern": "striped",
 },
 {
  "name": "antilope",
  "character": "good",
  "skills": ["running"],
 },
 .
 .
 .
]

Each key in the dictionaries has values of type integer, string or list of strings (not all keys are in all dicts present), each dictionary represents a row in a table; all rows are given as the list of dictionaries.

How can I easily import this into Pandas? I tried

df = pd.DataFrame.from_records(data)

but here I get an "ValueError: arrays must all be same length" error.

See this question: [Creating dataframe from a dictionary where entries have different lengths](https://stackoverflow.com/questions/19736080/creating-dataframe-from-a-dictionary-where-entries-have-different-lengths) — Nathaniel, Mar 19 '19 at 07:34
Take a look into https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html — Nihal Sangeeth, Mar 19 '19 at 07:41
@Nihal Sorry, my problem. I had confused two variables I used. Will edit question to clarify. — halloleo, Mar 20 '19 at 04:36

score 0 · Accepted Answer · edited Mar 20 '19 at 04:35

The DataFrame constructor takes row-based arrays (amoungst other structures) as data input. Therefore the following works:

data = [{
  "name": "leopard",
  "character": "mean",
  "skills": ["sprinting", "hiding"],
  "pattern": "striped",
 },
 {
  "name": "antilope",
  "character": "good",
  "skills": ["running"],
 }]
df = pd.DataFrame(data)
print(df)

Output:

  character      name  pattern               skills
0      mean   leopard  striped  [sprinting, hiding]
1      good  antilope      NaN            [running]

How to create a Pandas DataFrame from row-based list of dictionaries

1 Answers1