Hello developers,
Now Codersarts launch the new python study package series, in which here, we will try to covers all python related topics which is related to python and data- science. This series is designed as per his official document and make it easy to understand for both programmers and non-programers.
Let's will start topic "Pandas", and next part we will covers other topic like "Numpy", "SciPy", "Matplotlib", etc.
Table of content
Pandas - Introduction
Pandas - Installation Guide
Pandas - Data Structure
Pandas - Series
Pandas - Data Frames
Pandas - Frequencies
Pandas - Panel
Pandas - DateTimeIndex
Pandas - Indexing and Selecting Data
Pandas -Window
Pandas - Aggregations
Pandas - Missing Data
Pandas - GroupBy
Pandas - Merging/Joining
Pandas - Sorting
Pandas - Concatenation
Pandas - Function
Pandas - Introduction
Pandas is an open source open-source python Library providing high-performance data manipulation and analysis tool using its powerful data structures in Python programming language.
Pandas - Installation Guide
We can install it using below commands
Via PyPI: Open cmd and install it
And then import
Test it By Running on Jupyter Notebook
Pandas - Data Structure
Pandas divided into three types of Data Structure
Series (Dimensions - 1)
DataFrame (Dimensions - 2)
Panel (Dimensions - 2)
In this higher dimensions is container of lower dimensions, means Panel is container of DataFrame, and DataFrame is container of Series.
We will discuss all three types of data structure separately in this blog so continue blog to read all about Data Structures.
Pandas - Series
It is a one-dimensional labeled array.
Holding any types of data like -
integer
string
float, etc.
pandas.Series : It can be created using the following constructor
How to create series: Pandas series can be created using various methods which is given below
Array
Dict
Scalar value or constant
Example
Create Empty series
Run on Jupyter notebook
Creating Series using array: You can create ndarray using with the help of below example
Run on Jupyter notebook
Creating using by default: You can also create it by default using own indexing values.
Run on Jupyter notebook
Creating using dict: It directly passed a input when no index is supplied, here we can understand it with the help of below example
Run on Jupyter notebook
We can also run it using assign default indexing values as per our requirements
p = pd.Series(data,index=['b','c','d','a'])
Then output look like that
b 6.0
c 7.0
d NaN
a 5.0
Pandas - Data Frames
It is the two-dimensional, and tabular data structure with labeled axes (rows and columns), which is used to managing data.
Syntax:
pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
Example1:
Run on Jupyter notebook
Example2:
Run on Jupyter notebook
Creating Using ndarray
Run on Jupyter notebook
Dataframes use with different types of attributes(like .loc, .iloc, etc) and methods(like, add, append, aggregate, etc) to manage data if you want to read complete all about attribute and methods the click here
Pandas - Panel
Python Pandas Panel is an important container for data which is 3-dimensional.
Panel Parameters
data
items
major-axis
minor-axis
copy
dtype
Create an Empty Panel: Follow the given below code to create empty panel
Creating Empty panel:
Example1:
Run on Jupyter notebook
Example2:
Run on Jupyter notebook
How to selecting data from panel
Select the data from the panel using −
Items
Major_axis
Minor_axis
Example
Using items -
Run on Jupyter notebook
Thanks for reading codersarts blog, we will continue remaining topic in next part - 2 of this series.
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