How can I load data from mongodb collection into pandas' DataFrame?

Question

I am new to pandas (well, to all things \"programming\"...), but have been encouraged to give it a try. I have a mongodb database - \"test\" - with a collection called \"tw

Answer 1

If you have data in MongoDb like this:

[
    {
        "name": "Adam", 
        "age": 27, 
        "address":{
            "number": 4, 
            "street": "Main Road", 
            "city": "Oxford"
        }
     },
     {
        "name": "Steve", 
        "age": 32, 
        "address":{
            "number": 78, 
            "street": "High Street", 
            "city": "Cambridge"
        }
     }
]

You can put the data straight into a dataframe like this:

from pandas import DataFrame

df = DataFrame(list(db.collection_name.find({}))

And you will get this output:

df.head()

|    | name    | age  | address                                                   |
|----|---------|------|-----------------------------------------------------------|
| 1  | "Steve" | 27   | {"number": 4, "street": "Main Road", "city": "Oxford"}    | 
| 2  | "Adam"  | 32   | {"number": 78, "street": "High St", "city": "Cambridge"}  |

However the subdocuments will just appear as JSON inside the subdocument cell. If you want to flatten objects so that subdocument properties are shown as individual cells you can use json_normalize without any parameters.

from pandas.io.json import json_normalize

datapoints = list(db.collection_name.find({})

df = json_normalize(datapoints)

df.head()

This will give the dataframe in this format:

|    | name   | age  | address.number | address.street | address.city |
|----|--------|------|----------------|----------------|--------------|
| 1  | Thomas | 27   |     4          | "Main Road"    | "Oxford"     |
| 2  | Mary   | 32   |     78         | "High St"      | "Cambridge"  |