How to dynamically access an attribute of an object in Python?

Question

I have a data that looks like this

data = [(u'Del', datetime.datetime(2019, 11, 1, 0, 0), 59L), (u'Bom', datetime.datetime(2019, 11, 1, 0, 0), 449L), (u'Del', datetime.datetime(2019, 12, 1, 0, 0), 0L), (u'Bom', datetime.datetime(2019, 12, 1, 0, 0), 45L)]

I am using pandas to do some data wrangling. Now I need to get a list of dates from the data in certain formats like hour, month, year etc. So to extract dates from the data in month, this is what I do

import pandas as pd
import datetime

df = pd.DataFrame(data)
dates = df[1].dt.month.values.tolist()

So I get a list of months. But in the method df[1].dt.year.values.tolist(), I want to assign the granularity dynamically ie it could either be dt.month or dt.year based on the variable value.

Say I have a variable called granularity_val

So if granularity_val = 'year', I should do

dates = df[1].dt.year.values.tolist()

Similarly if granularity_val = 'month', I should do

dates = df[1].dt.month.values.tolist()

So I tried doing something like this

granularity_val = 'year'
dates = df[1].dt.granularity_val.values.tolist()

But it throws an error like below

AttributeError: 'DatetimeProperties' object has no attribute 'granularity_val'

I understand it is trying to treat the variable as an attribute of dt object of pandas. So how do I make python understand that it needs to treat granularity_val not as an attribute but as a variable value?

Iain Shelvington · Accepted Answer · 2020-01-05 08:02:09Z

3

You can use getattr to reference any attribute by string

getattr(df[1].dt, granularity_val).values.tolist()

answered Jan 5, 2020 at 8:02

Iain Shelvington

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jezrael · Accepted Answer · 2020-01-05 08:02:32Z

2

Use getattr for get attribute and for pandas 0.24+ instead values use Series.to_numpy:

df = pd.DataFrame({1: pd.date_range('2010-01-02', periods=5)})

granularity_val = 'month'
dates = getattr(df[1].dt, granularity_val).to_numpy().tolist()
print (dates)
[1, 1, 1, 1, 1]

granularity_val = 'year'
dates = getattr(df[1].dt, granularity_val).to_numpy().tolist()
print (dates)
[2010, 2010, 2010, 2010, 2010]

answered Jan 5, 2020 at 8:02

jezrael

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1 Comment

jezrael Over a year ago

@SouvikRay so your pandas version is under 0.24+

YOLO · Accepted Answer · 2020-01-05 08:08:41Z

1

Here's one way to do using strftime:

granularity_val = 'year'

match = {'year': '%Y', 'month': '%m', 'day': '%d'}

date = df.date.dt.strftime(match[granularity_val])

print(date)
['01', '01', '01', '01', '01']

Data

df = pd.DataFrame({'date': pd.date_range(start='2019-01-01', freq='D', periods=5))

answered Jan 5, 2020 at 8:08

YOLO

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Comments

user14638282 · Accepted Answer · 2020-12-12 15:23:06Z

0

For an example, let's use python's turtle.

import turtle 
t = turtle.Turtle();
t.color('red','blue');

So this sets our turtle to the color red, and it's border to blue. Now, let's say later on in the code we would want turtle's color. We can use this code.

print({t.color()});

In turtle, this will give us a very odd set: set([('red','blue')]) But, in most other cases, this will give us better feedback.

answered Dec 12, 2020 at 15:23

user14638282

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