================ by Jawad Haider

01 - Series¶

Copyright Qalmaqihir For more information, visit us at www.github.com/qalmaqihir/

1 Series
1.1 Creating a Series
1.2 Using an Index
2 Great Job! Thats the end of this part.

Series¶

The first main data type we will learn about for pandas is the Series data type. Let’s import Pandas and explore the Series object.

A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn’t need to hold numeric data, it can hold any arbitrary Python Object.

Let’s explore this concept through some examples:

import numpy as np
import pandas as pd

Creating a Series¶

You can convert a list,numpy array, or dictionary to a Series:

labels = ['a','b','c']
my_list = [10,20,30]
arr = np.array([10,20,30])
d = {'a':10,'b':20,'c':30}

Using Lists¶

pd.Series(data=my_list)

0    10
1    20
2    30
dtype: int64

pd.Series(data=my_list,index=labels)

a    10
b    20
c    30
dtype: int64

pd.Series(my_list,labels)

a    10
b    20
c    30
dtype: int64

Using NumPy Arrays¶

pd.Series(arr)

0    10
1    20
2    30
dtype: int64

pd.Series(arr,labels)

a    10
b    20
c    30
dtype: int64

Using Dictionaries¶

pd.Series(d)

a    10
b    20
c    30
dtype: int64

Data in a Series¶

A pandas Series can hold a variety of object types:

pd.Series(data=labels)

0    a
1    b
2    c
dtype: object

# Even functions (although unlikely that you will use this)
pd.Series([sum,print,len])

0      <built-in function sum>
1    <built-in function print>
2      <built-in function len>
dtype: object

Using an Index¶

The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).

Let’s see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:

ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan'])

ser1

USA        1
Germany    2
USSR       3
Japan      4
dtype: int64

ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan'])

ser2

USA        1
Germany    2
Italy      5
Japan      4
dtype: int64

ser1['USA']

Operations are then also done based off of index:

ser1 + ser2

Germany    4.0
Italy      NaN
Japan      8.0
USA        2.0
USSR       NaN
dtype: float64

Great Job! Thats the end of this part.¶

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