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What is Pandas in Python

Last Updated on March 21, 2023 by Prepbytes

Pandas is a library in Python that has been headlining for the past few years for reasons such as its support and data manipulation powers that have been driving factors for machine learning and data science. In this article, the library will be broadly covered through different sections.

Why is Pandas Important?

Pandas is an essential Python library created in 2008 by Wes Kinney and released as an open-source project in the year afterwards, in 2009, that can be used for cleaning, processing, manipulation, and visualization of data. It consists of handy data structures like data including Series and Dataframes that revolve around the purpose of storing and manipulating.

A series is a one-dimensional labelled array that is able to hold any type of Python object whether it belongs to integers, floats, strings, or even boolean values. On the other hand, a DataFrame is a two-dimensional labelled data structure with columns of various types, or in simpler words, it can be assumed as a table consisting of rows and columns where each column can be assumed as a Series. Be it any profession that deals with data, a good knowledge of Pandas becomes a necessity to excel in the field.

The source of files for data application can be CSV files, Excel spreadsheets, SQL databases, etc as all these can be imported or created in pandas using the DataFrame with valid operations performed on them.

Getting Started with Pandas

To get started with Pandas, one must ensure that Python is already installed on the user’s machine, in case this step is already fulfilled, you can head over to the terminal and run the command pip install pandas.

Once the installation is completed, open Python in your terminal and write import pandas as pd to ensure that python is successfully installed. Keep in mind that Python does not give errors on interpretation, so if you don’t get any errors, pandas are installed.

It is recommended to install a notebook, preferably Jupyter Notebook to work on data science and data analysis projects. However, there are options available to do on the go like Google Colab with E-Mail Authentication.

Operations in Pandas in Python

Now we have covered some stepping stones to answer what is pandas in python, let us move forward to see how we can be able to perform various operations using pandas.

We can create a DataFrame as easily as by just importing the pandas library, creating a dictionary in python, and then turning it into DataFrame with a pandas-enabled function. Given below is an example.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'Paris', 'London', 'Tokyo']}


       Name    Age      City
   0  Alice      25     New York
   1  Bob        30     Paris
   2  Charlie   35     London
   3  Dave      40    Tokyo

But there’s much more to that as pandas is also capable of operations such as loading and manipulation of data, cleaning, preprocessing and analysis of data.


# the date column to a datetime object
df['Date'] = pd.to_datetime(df['Date'])
import pandas as pd

# load the CSV file into a DataFrame
df = pd.read_csv('sales_data.csv')

# view the first few rows of the DataFrame

# check for missing values

# fill in missing values with 0
df.fillna(0, inplace=True)

# convert
# extract the year and month from the date column
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month

# group the data by year and month and calculate the total sales
monthly_sales = df.groupby(['Year', 'Month'])['Sale Amount'].sum()

# plot the monthly sales data as a line chart
monthly_sales.plot(kind='line', xlabel='Month', ylabel='Total Sales', title='Monthly Sales')

We first load the CSV file into a DataFrame using pd.read_csv(). We then use df.head() to view the first few rows of the DataFrame and df.isnull().sum() to check for missing values. Since there are missing values, we fill them in with 0 using df.fillna(). We then convert the date column to a datetime object using pd.to_datetime() and extract the year and month from the date column using df[‘Date’].dt.year and df[‘Date’].dt.month, respectively. We group the data by year and month and calculate the total sales using df.groupby() and .sum(). Finally, we plot the monthly sales data as a line chart using monthly_sales.plot().

Pandas vs SQL

One of the questions that have left many data enthusiasts curious is if SQL is already used for a such cause then what pandas offer better or their strikingly similar manipulation techniques to obtain data insights.

Pandas is a data manipulation library while SQL is used for managing or querying relational databases. Overall, it is proven that Pandas are suitable for medium-level datasets that require less memory while SQL is preferred for larger datasets stored in databases. Hence the choice of technology depends on what is your need such data manipulation makes pandas the choice but performing aggregate functions on a huge set of data makes SQL the better option.

Applications of Pandas

As we have covered in this article on what is pandas in python to some extent. Let us look at the applications of pandas with the most common use cases being:-

  1. Machine Learning
    Pandas is extensively used in machine-learning algorithms by cleaning and pre-processing and cleaning as well as used for the analysis of the machine-learning models.

  2. Financial Analysis
    Pandas has a major contribution to Python emerging as the programming language used in the finance sector. Frequent operations like calculating the volatility, returns and other indicators are done using Pandas.

  3. Time Series Analysis
    Pandas has important time series analysis functions for techniques like resampling, time zone handling and rolling windows.

  4. Cleaning and Analysis of Data
    Data consisting of missing values, duplicate values, and denormalized data can be handled using the Pandas library in Python. Inferential, Descriptive and Correlation Analysis can be performed using Pandas.

In this article, we studied what is pandas in python and moved forward looking at the origin of pandas, where and how they can be used along with some hands-on examples that can give you a better idea on how operations are executed. Also, we saw how to set Pandas up on your machine and what makes it different from SQL

Frequently Asked Questions

1. What is pandas and what are its key features?
Ans. Pandas is a Python library used for data manipulation and analysis. Its key features include powerful data structures for handling tabular and time series data, functions for data cleaning and preparation, and tools for data exploration and visualization.

2. How do I install pandas?
Ans. You can install pandas using pip, a package manager for Python. Simply open your command prompt or terminal and run the command "pip install pandas". Alternatively, you can install pandas as part of the Anaconda distribution, which includes many popular Python libraries for data science.

3. How do I load data into a pandas DataFrame?
Ans. You can load data into a pandas DataFrame using several functions, including pd.read_csv() for loading CSV files, pd.read_excel() for loading Excel files, and pd.read_sql() for loading data from a SQL database. You can also create a DataFrame from a Python dictionary or a NumPy array using the pd.DataFrame() function.

4. How do I clean and prepare data in pandas?
Ans. Pandas provides several functions for cleaning and preparing data, such as df.dropna() for removing rows with missing values, df.fillna() for filling in missing values, df.duplicated() for finding and removing duplicate rows, and df.replace() for replacing values in the DataFrame.

5.How do I perform data analysis and visualization in pandas?
Ans. Pandas provides several functions for data analysis and visualization, such as df.describe() for generating descriptive statistics, df.groupby() for grouping and aggregating data, df.corr() for calculating correlations between columns, and df.plot() for creating various types of plots, such as line plots, scatter plots, and histograms.

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