Python Pandas Series

What is pandas?

pandas is a data manipulation package in Python for tabular data. That is, data in the form of rows and columns, also known as DataFrames. Intuitively, you can think of a DataFrame as an Excel sheet.

pandas’ functionality includes data transformations, like sorting rows and taking subsets, to calculating summary statistics such as the mean, reshaping DataFrames, and joining DataFrames together. pandas works well with other popular Python data science packages, often called the PyData ecosystem, including

Where is the Pandas Codebase?

The source code for Pandas is located at this github repository https://github.com/pandas-dev/pandas

How to install pandas?

Installing pandas is straightforward; just use the pip install command in your terminal.

pip install pandas

Checking Pandas Version

import pandas as pd

print(pd.__version__) 

Python Pandas — Series

A pandas Series can be created using the following constructor −

pandas.Series( data, index, dtype, copy)

data  : data takes various forms like ndarray, list, constants
index : Index values must be unique and hashable, same length as data. Default np.arrange(n) if no index is passed.
dtype : dtype is for data type. If None, data type will be inferred
copy  : Copy data. Default False

Create an Empty Series

#import the pandas library and aliasing as pd
import pandas as pd
s = pd.Series()
print s

Its output is as follows −

Series([], dtype: float64)

Create a Series from ndarray

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
print s

Its output is as follows −

0   a
1   b
2   c
3   d
dtype: object

Create a Series from dict

Example 1

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data)
print s

Its output is as follows −

a 0.0
b 1.0
c 2.0
dtype: float64

Example 2

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data,index=['b','c','d','a'])
print s

Its output is as follows −

b 1.0
c 2.0
d NaN
a 0.0
dtype: float64

Observe − Index order is persisted and the missing element is filled with NaN (Not a Number).

Create a Series from Scalar

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
s = pd.Series(5, index=[0, 1, 2, 3])
print s

Its output is as follows −

0  5
1  5
2  5
3  5
dtype: int64

Accessing Data from Series with Position

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve the first element
print s[0]

Its output is as follows −

1

Retrieve the first three elements in the Series. If a : is inserted in front of it, all items from that index onwards will be extracted. If two parameters (with : between them) is used, items between the two indexes (not including the stop index)

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
#retrieve the first three element
print s[:3]

Its output is as follows −

a  1
b  2
c  3
dtype: int64

Retrieve Data Using Label (Index)

A Series is like a fixed-size dict in that you can get and set values by index label.

Retrieve a single element using index label value.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
#retrieve a single element
print s['a']

Its output is as follows −

1

Retrieve multiple elements using a list of index label values.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve multiple elements
print s[['a','c','d']]

Its output is as follows −

a  1
c  3
d  4
dtype: int64

If a label is not contained, an exception is raised.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve multiple elements
print s['f']

Its output is as follows −

…
KeyError: 'f'

Retrieving Index array and data array of a series object

import numpy as np   
import pandas as pd   
x=pd.Series(data=[2,4,6,8])   
y=pd.Series(data=[11.2,18.6,22.5], index=['a','b','c'])   
print(x.index)   
print(x.values)   
print(y.index)   
print(y.values)  

RangeIndex(start=0, stop=4, step=1)
[2 4 6 8]
Index(['a', 'b', 'c'], dtype='object')
[11.2 18.6 22.5]

Retrieving Types (dtype) and Size of Type (itemsize)

import numpy as np   
import pandas as pd   
a=pd.Series(data=[1,2,3,4])   
b=pd.Series(data=[4.9,8.2,5.6],   
index=['x','y','z'])   
print(a.dtype)   
print(a.itemsize)    
print(b.dtype)   
print(b.itemsize)  

int64
8
float64
8