Introduction to JSON

Streamlined Data Ingestion with pandas

Amany Mahfouz

Instructor

Javascript Object Notation (JSON)

Common web data format
Not tabular
- Records don't have to all have the same set of attributes
Data organized into collections of objects
Objects are collections of attribute-value pairs
Nested JSON: objects within objects

Reading JSON Data

read_json()
- Takes a string path to JSON _or_ JSON data as a string
- Specify data types with dtype keyword argument
- orient keyword argument to flag uncommon JSON data layouts
  - possible values in pandas documentation

Data Orientation

JSON data isn't tabular
- pandas guesses how to arrange it in a table
pandas can automatically handle common orientations

Record Orientation

Most common JSON arrangement

[
  {
      "age_adjusted_death_rate": "7.6",
      "death_rate": "6.2",
      "deaths": "32",
      "leading_cause": "Accidents Except Drug Posioning (V01-X39, X43, X45-X59, Y85-Y86)",
      "race_ethnicity": "Asian and Pacific Islander",
      "sex": "F",
      "year": "2007"
  },
  {
      "age_adjusted_death_rate": "8.1",
      "death_rate": "8.3",
      "deaths": "87",
      ...

Column Orientation

More space-efficient than record-oriented JSON

{
  "age_adjusted_death_rate": {
      "0": "7.6",
      "1": "8.1",
      "2": "7.1",
      "3": ".",
      "4": ".",
      "5": "7.3",
      "6": "13",
      "7": "20.6",
      "8": "17.4",
      "9": ".",
      "10": ".",
      "11": "19.8",
      ...

Specifying Orientation

Split oriented data - nyc_death_causes.json

{
  "columns": [
      "age_adjusted_death_rate",
      "death_rate",
      "deaths",
      "leading_cause",
      "race_ethnicity",
      "sex",
      "year"
  ],
  "index": [...],
  "data": [
      [
          "7.6",

Specifying Orientation

import pandas as pd

death_causes = pd.read_json("nyc_death_causes.json",
                            orient="split")

print(death_causes.head())

  age_adjusted_death_rate death_rate deaths             leading_cause              race_ethnicity sex  year
0                     7.6        6.2     32  Accidents Except Drug...  Asian and Pacific Islander   F  2007
1                     8.1        8.3     87  Accidents Except Drug...          Black Non-Hispanic   F  2007
2                     7.1        6.1     71  Accidents Except Drug...                    Hispanic   F  2007
3                       .          .      .  Accidents Except Drug...          Not Stated/Unknown   F  2007
4                       .          .      .  Accidents Except Drug...       Other Race/ Ethnicity   F  2007

[5 rows x 7 columns]

Let's practice!

Streamlined Data Ingestion with pandas