DATA VISUALISATION: While Pandas itself does not provide visualization capabilities, it integrates seamlessly with libraries like Matplotlib and Seaborn for data visualization.

Pandas itself doesn’t provide data visualization capabilities, but it integrates well with other Python libraries such as Matplotlib and Seaborn, which are commonly used for data visualization. Here’s how you can visualize data using pandas along with Matplotlib and Seaborn:

a. Matplotlib:
Matplotlib is a widely used plotting library in Python. You can use it directly with pandas to create various types of plots.

“`python
import pandas as pd
import matplotlib.pyplot as plt

# Assume ‘df’ is your DataFrame
df.plot(x=’x_column’, y=’y_column’, kind=’line’)
plt.show()
“`

b. Seaborn:
Seaborn is another popular visualization library that works well with pandas DataFrames and provides a high-level interface for drawing attractive statistical graphics.

“`python
import pandas as pd
import seaborn as sns

# Assume ‘df’ is your DataFrame
sns.lineplot(data=df, x=’x_column’, y=’y_column’)
“`

c. Plotly:
Plotly is a powerful library for interactive data visualization. While it doesn’t have built-in integration with pandas, you can convert pandas DataFrames to Plotly objects and then create interactive plots.

“`python
import pandas as pd
import plotly.express as px

# Assume ‘df’ is your DataFrame
fig = px.line(df, x=’x_column’, y=’y_column’)
fig.show()
“`

These are just a few examples of how you can visualize data using pandas in combination with other libraries. Depending on your specific requirements and the type of visualization you want to create, you can choose the appropriate library and plot types to effectively communicate insights from your data.