Pandas is an open-source Python Library useful for performing various data manipulation and data analysis operations in the field of Data Science. It was released in 2009 and has become a popular tool for performing data analysis operations. It is widely considered as the most powerful tool for data science because performs operations on data like cleaning, organizing, analyzing, visualizing, building a model, and deploying. The Pandas library consists of methods and functions which help speed up the data analysis tasks.

A programmer should be familiar with Python programming language to work with Pandas, as Pandas is a Python library. Pandas is like Excel for Python because it works with rows and columns, i.e., it helps a programmer to work with tabular data, such as the data contained in Databases and Spreadsheets. This library is flexible, fast, and expressive. It helps a programmer to gain insights from the given data, in the stage of Data pre-processing which saves most of the time of a data science programmer. In Pandas, a data table is termed as Data Frame. If you are really interested in learning data science with programming, you could start by checking out Data Science course eligibility and understand what the prerequisites are to take up a Data Science course.

Pandas can accept various types of inputs like CSV files, Excel files, and webpages, and help a programmer to gain valuable insights from data. It has many applications in many industries like manufacturing, healthcare, business, software, automobile etc., We can work with Pandas on some of the platforms like Python IDLE, Google Colab, Jupyter Notebook etc., by importing it and by using different Python packages, methods and functions present in the Python library. You can explore our course Practical Data Science with Python to know about Data Science with real-world applications.

Why Pandas?

Pandas Library helps to find a solution to a Data Science Problems quickly with the help of many functions and methods present in it. It helps in making data-driven decisions, by bringing up patterns in the data. It plays a key role in developing solutions for problems related to Data Science and Data Analytics.

The Pandas library is built on top of the Numpy Library and helps in better scientific computation of the given data. It provides additional functionalities by incorporating other python libraries such as Numpy (used for Mathematical Operations), Matplotlib (used for Visualization of data), and Scikit-Learn (used for machine learning problems).

Key Features of Pandas:

It is fast and efficient to perform a multitude of operations on data

It accepts many types of file formats to load the data and work on it

It performs slicing, segregating, grouping operations on the data

It can perform joining and merging operations on the data

It can work with any type of data either it is Homogeneous or Heterogeneous

Pandas Library Has Many Functionalities Like:

Collecting data

Cleaning the data to remove Null values, missing values, Noise etc.

Helping the developer to gain insights from data using Exploratory Data Analysis mechanism.

Building a model using machine learning and some other libraries like scikit-learn, etc.,

Helping in the model deployment

Pandas Library Has Many Uses as Follows:

When a particular data file is imported we can make the changes to the data present in it using Pandas library i.e., manipulation of data is possible

When we complete the Exploratory Data Analysis, the data is said to be crystal clear without any improper data. Pandas helps to visualize this data in the form of charts, plots, histograms etc., with the help of a library called Matplotlib.

Pandas help to develop many powerful machine learning projects with the combination of Scikit-learn.

How to Install Pandas?

Before installing Pandas, we must ensure Python environment is present in our local system to carry out the installation process smoothly. The installation commands vary based on the type of platform we are working on, shown as follows: -

Windows users – pip install pandas

Ubuntu users – sudo apt-get install python-pandas

Fedora Users – sudo yum install python-pandas

But if you install Anaconda in your local system, Pandas library would be installed by default during the configuration. We will also look at installation of Pandas on some online platforms as follows:

Jupyter Notebook - !pip install pandas

Google Colab – !pip install pandas or !apt-get install pandas

Kaggle - !pip install pandas

** After installing the pandas library in the respective platforms, it should me imported into the working space to obtain it’s functionality. The syntax for importing the pandas library is as follows

Syntax for Importing pandas: - import pandas as pd

Note: The installation and the importing statements are “case sensitive”, so use only small letters when performing those actions.

Pandas Data Structures

All the programming languages including Python have data structures as a part of it to perform complex tasks like Lists, tuples, sets etc., Likewise, Pandas for data science also has two data structures namely “DataFrame” & “Series”. Pandas performs operations on the data in the tabular format i.e., Excel formats, table etc., and the data is in the form of Rows and Columns.

Mutability is a characteristic of performing manipulations on the data, i.e., we can change the data if required. All the Pandas data structures are Mutable except Pandas Series data structure as it is size immutable. Out of all the data structures present in Pandas, DataFrame is widely used when working with data science applications.

An overview of Pandas Data Structures is illustrated as follows:

Series

Pandas Series is a one-dimensional labeled array, which can work with any data type like integers, float values, strings, objects in python etc., It is similar to working with Numpy array, but it has the capability of labeling the data items given in the dataset. The axis labels are called as the index. We can apply the Pandas Series Data Structure on different things like Python Dictionary, N-Dimensional array (ndarray), or on any of the scalars (i.e., constant values like 3,4,5 etc.,). As Pandas Series is termed as a 1-D array, it is also referred as a column of an Excel Spreadsheet. Series data structure in pandas is size immutable. It deals with homogeneous data only.

Syntax of Pandas Series:-

import pandas as pd
pd.Series(data, index, dtype, name, copy)

where the parameters are described as follows:-

data – can take any data as input like arrays, lists, dictionaries or scalars.

index – It must be unique and hashable, should have same length as data.

dtype – It is the datatype of the input we are giving like str, integer, float. (optional)

name – It is the name given to the pandas series in the form of str (optional).

copy – copying the input data, of the Boolean form, and it is false by default.

Some Operations Using Series Data Structure in Pandas: -

We will work in an online environment named Google Colab, for experimenting the data with pandas Series Data Structure.

We can install any libraries simultaneously with a space between the names of the library as shown below

Importing the libraries as follows

Converting a dictionary variable into a series object

Generating the scalar objects using Series Data structure

Knowing the insights of data, by generating random values using series object.

Performing some slicing operations on the random Series Variable (s) considered in the above case

Working with the naming attribute in pandas library

Performing Arithematic operations on the data

Data Frame

A Data Frame is a two-dimensional (2D) array, and it represents an Excel-like structure with rows and columns. It is termed as the Primary Data Structure for pandas. It can deal with both the homogeneous data and heterogeneous data, and it is mostly used in pandas object to deal with data science problems. In the scenario of Data Frame, rows are termed as index and the columns are termed as columns only. It can take inputs of various types of data like a Series object, numpy arrays, another Data Frame object, lists, dictionaries, series etc.,

The DataFrame is helpful to solve a particular data science problem, from the starting process of collecting and cleaning the given dataset to the end process of model development in dealing with a data science problem. The size of an DataFrame is Mutable (varying size), and we can perform different types of operations on the data present in the form of rows and columns.

The syntax of DataFrame is as follows:

import pandas as pd
pd.DataFrame(data, index, columns, dtype, copy)

where the parameters are described as follows: -

data – It can be an array, dictionary, DataFrame or a series object.

index – It is a symbolic representation of Index like structure of an array.

columns – It represents the column labels, it is also like an array structure.

dtype – Corresponds to Data Type like integer, float, string etc.,

copy – It is a Boolean whose default value is False.

Some Operations Using DataFrame Data Structure in Pandas:

For this example, we are working in an online environment named Google Colab, for experimenting the data with pandas DataFrame Data Structure.

We can install any libraries simultaneously with a space between the names of the library as shown below

Importing the libraries as follows:

Generating a DataFrame using dictionaries

Assigning a data frame to a variable and performing some operations

Changing the row and column labels of a data frame

Adding extra columns to the existing Data Frame and finding out the data types of the values

Generating Data Frames using list of Dictionaries

Extracting particular columns, and creating a data frame using numpy array

Operations like Selection, Deletion of columns on a DataFrame

The above discussed operations were a sample operations that are operated on the pandas Data Structures like Series and DataFrame. In the real world scenarios, we will be mostly using the Data Structure named DataFrame to carry out tasks related to solve a data science problem. DataFrame is widely used because it is a replica of Excel Spreadsheet which is in the form of MxN matrix representing rows(M) and columns(N).

Learn more about Python concepts with our free Python Tutorial.

Important Pandas Functions for Data Manipulation and Analysis

Pandas is an important library in the Data Science or Data Analysis applications, which is widely used to carry out various analysis tasks. It is also called python data analysis library. This pandas library consist of various methods and functions present in it to carry out the data analysis process. While dealing with a problem related to data either in Data Science or Data Analysis, pandas helps us carry out the Exploratory Data Analysis, to gain insights on the given dataset.

Let us look at some of the methods used in pandas, by the help of small IRIS dataset which can consist of max 150 entries with some varying features. This analysis process is carried out in the Google Colab, by importing the dataset into the working file location. The actual dataset location is as follows:

We can read the required file in pandas using the following Python syntax.

Syntax: - pd.read_csv(“filename” or “location of the file”)

We can read various types of formats of data using pandas to solve a data science problem. Some of them are pd.read_html(), pd.read_json, pd.read_excel() etc., Initially the file is read using a dataframe, and all the operations are performed on the dataframe without affecting the original dataset.

3. head()

Initially by applying the head() funciton on the dataframe, the top 5 rows of the dataframe will be fetched. We can fetch some more rows of the given dataframe by adding a parameter in the head() function like [df.head(20) – fetches the top 20 rows of the given dataframe]

4. tail()

Initially by applying the tail() function on the dataframe, the last 5 rows of the dataframe will be fetched. We can fetch some more rows of the given dataframe by adding a parameter in the tail() function like [df.tail(10) – fetches the last 10 rows of the given dataframe]

5. Shape

Applying this shape function on the dataframe, a tuple is generated as a result containing 2 parameters. One parameter corresponds to total no of rows, and the other parameter corresponds to total no of columns of the given dataframe.

6. info()

This function is used to tell us the names of columns, count of non-null values, and the datatype of the columns present in the dataframe. It also gives information about the memory usage, Range index etc.,

7. isnull()

It results a Boolean value among the values present in the given dataframe. It results “False” if there is no null value present in a row or a column, and results “True” if any null value is present in a row or a column of the given dataframe.

Handling these missing values is an important step in the data analysis process. It is considered as a top priority because it may affect the accuracy of the analysis process at the end.

8. isnull().sum()

This method is used to count the number of null values present in the particular column of the given dataframe.

9. describe()

This function is used to describe more about the dataframe to gain better insights for a programmer. It brings out various information related to data like count of rows, mean value, standard deviation, minimum value, median value (50%), maximum value, 25% and 75% values of each columns present in the given dataframe.

10. sample()

This function helps a user to select random values from the dataframe and takes a parameter of no of rows. Based on the parameter we have passed in the function, we get the output of that particular no of rows.

11. Columns

This function results with the names of the columns present in the given dataframe.

12. value_counts()

This function is used to identify the different categories in a particular feature and it returns the count of the particular category respectively from a dataframe.

13. dtypes

This function tells about the data type stored in the particular column of the given dataset. Some of the data type formats like int, float, str etc.,

14. size

This function results with the total data present in the given dataframe. It simply replicates the count of data cells present in the data frame. It is the result obtained by multiplying no of rows and no of columns, when the shape function is applied on the dataframe.

15. isna

This function results a Boolean value for the columns present in the given dataframe, which consists of any null values. It returns False if there are no null values, result True if there are any null values.

16. index

This function results the index range of the rows present in the given dataframe with the result indicating as start, stop and step value. The step value represents the difference of any two consecutive rows.

17. memory_usage()

This function returns the memory utilized by each column in the given dataframe. Memory usage is represented in bytes. The concept of memory_usage is useful when we work with bigger datasets.

18. slicing

This function helps us to fetch range of rows or columns with the help of index values or by using the labels of the given dataframe.

19. loc()

This function is a part of slicing process to fetch some amount of data from the given dataset based on the labels. These labels are called the column names of the dataframe. This function is used to access the group of data present in rows and columns using a label (Column Name), this can be used when we have a large number of columns in the given dataset.

20. iloc()

This function is a part of the slicing process to fetch some amount of data from the given dataframe based on the positions. Positions are based on the range index starting from 0 to the count of rows or columns present in the given dataframe. This function slices the data with the specified rows and columns. This function is used when there are minimal number of columns in the given dataset, and it position-based functionality to get data from required rows and columns.

21. dropna()

This function is used to remove the rows or columns of the dataframe, where the values are null or NaN.

22. nunique()

This function results with the no of unique entries present in rows or columns of the given dataframe. This is especially useful when we don’t know how many categories of data present in the given dataframe.

We have discussed the importance of the pandas library, and it is mostly used as the data analysis library to perform operations on the given dataset. We have seen the installation process of pandas library, and how to import it when working with data on different platforms like Jupyter Notebook or Google Colab or kaggle platforms etc., You can learn more about Python tools, development platforms, and applications with KnowledgeHut’s Practical Data Science with Python. Different Pandas Data Structures like Series and DataFrames were introduced and some operations also performed on it. We have also seen various types of functions present in the pandas library, that can be used to perform various Data Manipulation and Analysis tasks when working with data. It is concluded that Pandas is an crucial library in the data science platform.

Frequently Asked Questions (FAQs)

1. Is pandas good for data science?

Yes, Pandas is good for data science as it performs many data manipulation and analysis techniques to draw better insights on the given dataset.

2. What is pandas used for in data science?

Pandas is a data analysis library used to perform operations on the data for better understanding. Pandas in Data Science helps to carry out the Exploratory Data Analysis step, which is the most crucial step to solve any data science problem.

3. Is pandas used for Data Manipulation?

Yes, pandas is used for data manipulation. It has some functions like head, tail, info, describe etc., which makes a programmer to better understand about the given data and find the appropriate solution of the given data science problem.

Harsha Vardhan Garlapati

Blog Writer at KnowledgeHut

Harsha Vardhan Garlapati is a Data Science Enthusiast and loves working with data to draw meaningful insights from it and further convert those results and implement them in business growth. He is a final year undergraduate student and passionate about Data Science. He is a smart worker, passionate learner, an Ice-Breaker and loves to participate in Hackathons to work on real time projects. He is a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator and problem solver.

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