Shantanu's Blog

Corporate Consultant

October 14, 2016

 

Using pandas series instead of dictionary

Pandas series object has several advantages over traditional dictionary object. For e.g.

import pandas as pd
myseries = pd.Series(index=['india', 'usa', 'india'], data=[10,20,30])

myseries.sum()
60

myseries.groupby(myseries.index).sum()
india    40
usa      20
_____

In order to get the total of all dic values, we need to use for loop because there is no such built-in method.

mydic=dict(zip(['india', 'usa'],[10,20]))

mytotal=0
for i in mydic:
    mytotal += mydic[i]

Apart from this level of complexity, there is a fundamental difference between the two. Dictionary does not support duplicate values.

mydic=dict(zip(['india', 'usa', 'india'],[10,20,30]))

The above code will generate a dictionary as shown below.

{'india': 30, 'usa': 20}

The first value india - 10 is overwritten by the next one with value 30. This can be avoided if we use pandas series.
 _____

I would suggest to convert the dictionary to pandas series and then use series methods on it. For e.g.

pd.Series(mydic).sum()
_____

Here is another advantage of using Series instead of Dict. Let's assume we need to sort a dictionary by it's values:

xs = {'a': 4777, 'b': 3, 'c': 2, 'd': 18}

There are 2 ways to do this. Using either lambda or itemgetter key from operator module.

sorted(xs.items(), key=lambda x: x[1])

import operator
sorted(xs.items(), key=operator.itemgetter(1))

Both the examples shown above use "sorted" function which returns list of tuples.

What if we convert the dictionary to pandas Series object and then sort on values?

import pandas as pd
x=pd.Series(xs)

x.sort_values()

Isn't this 3 lines of code short, readable and elegant? We can combine the 2 lines into one by chaining it together like this...

x=pd.Series(xs).sort_values(inplace=False)

_____

Here is how the series object can be used to group by key. In the following example, contents are saved as a list x and countries are listed as y.

x=['asia', 'asia','asia','africa','africa','europe', 'asia']

y=['india', 'nepal','china','namibia','tanzania','france', 'india']

Both the lists are linked to each other as per their position. For e.g. the first "Asia" is linked to first entry "india".

We can save this combination as Pandas Series and then use group by method. The unique countries can be listed as sets.

import pandas as pd
mys=pd.Series(y, index=x)

mys.groupby(mys.index).apply(set)

africa      {tanzania, namibia}
asia      {china, nepal, india}
europe                 {france}
dtype: object

The same is also possible by converting the series object into dataframe object using reset_index method.

mys.reset_index().groupby('index')[0].apply(set)



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