Data visualization using pandas in python

Data visualization using pandas:

We get more insight and details when we see a pictorial or graphical information than the numerical data in a table. Data visualization is important in most of the fields to compare and analyze large data sets. Pandas provides this option to view the data using various libraries. let us explore those in this section.

Introduction

Pandas is a popular data manipulation and analysis library in Python, and it provides various functions for data visualization. While Pandas itself does not have built-in plotting capabilities, it is tightly integrated with the Matplotlib library, which is a powerful and widely used plotting library in Python. Additionally, Pandas also provides some basic plotting functions that make it easy to create simple visualizations directly from DataFrames.

Here are some common techniques for data visualization in Pandas:

Basic Pandas Plots:

1. Pandas has a few built-in functions for basic plotting that are built on top of Matplotlib. You can use the plot() method on a Pandas DataFrame or Series to create simple line plots, bar plots, histograms, scatter plots, etc. For example:

   import pandas as pd
   import matplotlib.pyplot as plt
   
   # Create a DataFrame
   data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
   df = pd.DataFrame(data)
   
   # Line plot
   df.plot(x='A', y='B', kind='line')
   plt.show()

   The output of the above code is shown: Line plot

   

   Matplotlib Integration:

You can use Matplotlib directly with Pandas data structures to create more customized and complex visualizations. Pandas DataFrames and Series can be used as input to Matplotlib functions. For example:

import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)

# Scatter plot using Matplotlib with Pandas DataFrame
plt.scatter(df['A'], df['B'])
plt.xlabel('A')
plt.ylabel('B')
plt.title('Scatter Plot')
plt.show()

The output of the above code is shown: scatter plot

Seaborn Integration:

Seaborn is another data visualization library in Python that works well with Pandas. It provides a high-level interface for drawing attractive and informative statistical graphics. You can use Seaborn functions directly with Pandas DataFrames. For example

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create a DataFrame
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)

# Box plot using Seaborn with Pandas DataFrame
sns.boxplot(x='A', y='B', data=df)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame with categorical data
data = {'Category': ['A', 'B', 'A', 'C', 'B', 'A', 'C']}
df = pd.DataFrame(data)

# Bar plot of categorical data
df['Category'].value_counts().plot(kind='bar')
plt.show()