Mapping pandas DataFrame rows to a pandas Series

Question

I need to create a pandas series whose elements are each a function of a row from a DataFrame. Specifically the is a 'metadata' column which is a json string and I want a Series of dicts that are the json plus the rest of the columns. Ideally I would want something equivalent to a map method for a dataframe:

df.map(lambda row: json.loads(row.metadata).update({'timestamp':row.timestamp}))

(update is destructive and does not return a new dict but you get the point)

EDIT: You can copy this

metadata    timestamp
"{'a':1,'b':2}" 000000001
"{'a':1,'c':2}" 000000002
"{'a':1,'c':2}" 000000003

And load it with

In [8]: import pandas as pd

In [9]: pd.read_clipboard()
Out[9]:
        metadata  timestamp
0  {'a':1,'b':2}          1
1  {'a':1,'c':2}          2
2  {'a':1,'c':2}          3

The desired result should be a pandas.Series with the contents of this list:

[{"a":1,"b":2,"timestamp":000000001}
{"a":1,"c":2,"timestamp":000000002}
{"a":1,"c":2,"timestamp":000000003}]

Can you post a small example dataset? Ideally this would be something we can copy and then load immediately with pandas.read_clipboard() (test it yourself to check) — Marius
– Marius, Commented Nov 5, 2014 at 0:53

Daniele · Accepted Answer · 2014-11-05 14:07:38Z

1

What about to modify the strings? Something like:

new_metadata = df.apply(lambda x: '{}\b,"timestamp":{}}}'.format(x.metadata,x.timestamp),axis=1)

Which produces:

In [1]: new_metadata
Out[2]: 
0    {'a':1,'b':2,"timestamp":1}
1    {'a':1,'c':2,"timestamp":2}
2    {'a':1,'c':2,"timestamp":3}

answered Nov 5, 2014 at 14:07

Daniele

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2 Comments

Daniele Over a year ago

You can of course play with strings to produce the zeros of timestamp, I don't know which rules they should follow

fakedrake Over a year ago

I would rather a key-value map is saved in the series but this works for my specific case. Thank you

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