Mastering Pandas in Python

Updated on May 1, 2024

Article Outline

Importance of Panda in Data Analysis Key Features of Python Pandas Primary Data Structures in Pandas: Series and DataFrame Python Pandas Sorting Python Pandas GroupBy Python Pandas: Merging 2: Tips and Tricks for Efficient Pandas Usage Conclusion FAQs

Pandas in Python refer to an open-source library that delivers high-performance, easy-to-use data structures and different data analysis tools. Python Panda is employed in a wide range of fields, including finance, academics, statistics, and more. It is well-suited for different types of data, including statistical data sets, unlabeled data, ordered and unordered time series data, and more. Dive into this article to learn more about Pandas in Python.

Table of Content:

Importance of Panda in Data Analysis
Key Features of Python Pandas
Primary Data Structures in Pandas: Series and DataFrame
Python Pandas Sorting
Python Pandas: GroupBy
Python Pandas: Merging
Tips and Tricks for Efficient Pandas Usage
FAQs

Importance of Panda in Data Analysis

Python Panda is a core library for data analysis. It is a base package with additional functionality from various other packages. Pandas in Python provide the ability to organize structured data into an array so that it can be managed easily.

Python Panda is useful for performing the following tasks:

Data wrangling
Reading and writing
Simple plotting
Logical processes
Updating data
SQL join
Instance counting

Therefore, the importance of Python Panda in data analysis stems from its potential to make data sets more accessible and comprehensible.

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Key Features of Python Pandas

A few key features of Pandas in Python are as follows:

Comes with a fast and efficient DataFrame object using default and customized indexing
Useful for reshaping data sets
Valuable for the alignment and integration of missing data
Grouping data for transformations and aggregations
Delivers the functionality of the time series
Can process data in different formats like time series, tabular heterogenous, matrix data, and more
Ability to handle different operations like slicing, subsetting, filtering, and re-shaping data sets
Supports integration with different libraries like SciPy
Deliver fast performance

Learn: What Is a List in Python: Functions with Examples

Primary Data Structures in Pandas: Series and DataFrame

The two primary data structures in Python Panda include series and DataFrame

Python Pandas Series

It is a one-dimensional array that contains different types of data. The row labels in a series are referred to as the index. A series cannot have multiple columns and comes with only one parameter.

Creating a Series in Panda Python

To create a series, you will have to import the numpy module and use the array() function. The code is as follows:

import pandas as pd  
import numpy as np  
info = np.array(['P','a','n','d','a','s'])  
a = pd.Series(info)  
print(a)  
Output:
0 P
1 a
2 n
3 d
4 a
5 s
dtype: object

Python Pandas DataFrame

It is suitable for a two-dimensional array with labeled rows and columns. Python Pandas DataFrame is widely used and comes with the row index and column index. The Pandas DataFrame in Python comes with the following features:

The columns can be heterogeneous with int, bool, and others

It can be defined as a dictionary of Series structures with indexed rows and columns.

Creating a DataFrame

You can easily make a DataFrame in Python with the help of a list. The code for creating a DataFrame is as follows:

import pandas as pd  
# a list of strings  
x = ['Python', 'Pandas']  
  
# Calling DataFrame constructor on list  
df = pd.DataFrame(x)  
print(df)  
Output:
0
0 Python
1 Pandas

Explore: Tuple in Python: Function with Example

Python Pandas Sorting

You will come across two types of sorting techniques in Python Panda. They are as follows:

By Label

The DataFrame can be sorted with the help of the sort_index() method. The code is as follows:

import pandas as pd
import numpy as np
unsorted_df = pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
   mns = ['col2','col1'])
sorted_df=unsorted_df.sort_index()
print sorted_df
The output is as follows:
           col2 col1
0 0.208464 0.627037
1 0.641004 0.331352
2 -0.038067 -0.464730
3 -0.638456 -0.021466
4 0.014646 -0.737438
5 -0.290761 -1.669827
6 -0.797303 -0.018737
7 0.525753 1.628921
8 -0.567031 0.775951
9 0.060724 -0.322425

By default, sorting on row labels takes place in ascending order.

By Actual Value

You can use the sort_values() method to perform sorting according to values. The code is as follows:

id="GroupBy">import pandas as pd
import numpy as np
unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
   sorted_df = unsorted_df.sort_values(by='col1')
print sorted_df
The output is as follows:
    col1 col2
1 1 3
2 1 2
3 1 4
0 2 1

Find out: Top 10 Python Libraries You Must Know In 2024

Python Pandas GroupBy

The groupby function in Python Panda can perform one of the following functions on original data:

Splitting the object
Combining the result
Applying a function

The syntax for dataframe.groupby() is as follows:

DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)

Python Pandas: Merging

If you are using Pandas in Python, you will be able to merge two DataFrames in the following way:

id="Tips">import pandas as pd
data1 = {
  "name": ["Sally", "Mary", "John"],
  "age": [50, 40, 30]
}
data2 = {
  "name": ["Sally", "Peter", "Micky"],
  "age": [77, 44, 22]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
newdf = df1.merge(df2, how='right')

2: Tips and Tricks for Efficient Pandas Usage

A few tips and tricks for using Pandas in Python are as follows:

Configure Settings and Options at Interpreter Startup

It is a major productivity saver that sets customized pandas options at interpreter startup, particularly when you are working in a scripting environment. For the purpose of Pandas in Python configuration, you will be able to use the pd.set_option().

H3:Create Toy Data Structures Using the Testing Module

Pandas in Python comes with a testing module that offers various convenient functions. Pandas in Python lets you use these toy data structures for the purpose of testing assertions, benchmarking, experimenting, and more.

Conclusion

Built on the Numpy library, Python Panda is valuable for data analysis, machine learning, and more. It comes with two types of data structures, including the series and DataFrames. Moreover, Pandas in Python can be used with a wide variety of libraries applicable to data science.

FAQs

What are Pandas used for in Python?

Pandas in Python offer fast, expressive, and flexible data structures to make working with labeled or relational data easy and intuitive. It is usually the basic high-level building block for performing real-world, practical data analysis with the help of Python.

When should you start using Python Pandas?

You need to start using Pandas in Python when you have to perform different data analysis tasks. Pandas in Python can also be used for various machine learning tasks. Python is built on top of another package called Numpy that offers support for multi-dimensional arrays.

How do I handle missing data in a DataFrame using Python Pandas?

The steps for handling missing data in a DataFrame using Python Pandas are as follows:

Import the required packages.
Use the red-CSV () function for going through the dataset.
The dataset gets printed, and you need to check when the record has missing data or NaN functions.
Next, the dropna() function is applied to the dataset.
The dataset gets printed.

What are some common techniques for cleaning and preprocessing data with Pandas?

A few common techniques for cleaning and preprocessing data with Pandas in Python are as follows:

Changing the DataFrame index
Dropping the DataFrame columns
Combining NumPy and str Methods to clean columns

Updated on May 1, 2024

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