Data Visualization with Pandas
Last Updated : 17 Jan, 2025
Pandas allows to create various graphs directly from your data using built-in functions. This tutorial covers Pandas capabilities for visualizing data with line plots, area charts, bar plots, and more.
Introducing Pandas for Data Visualization
Pandas is a powerful open-source data analysis and manipulation library for Python. The library is particularly well-suited for handling labeled data such as tables with rows and columns.
Key Features for Data Visualization with Pandas:
Pandas offers several features that make it a great choice for data visualization:
- Variety of Plot Types: Pandas supports various plot types including line plots, bar plots, histograms, box plots, and scatter plots.
- Customization: Users can customize plots by adding titles, labels, and styling enhancing the readability of the visualizations.
- Handling of Missing Data: Pandas efficiently handles missing data ensuring that visualizations accurately represent the dataset without errors.
- Integration with Matplotlib: Pandas integrates with Matplotlib that allow users to create a wide range of static, animated, and interactive plots.
Installation of Pandas
To get started you need to install Pandas using pip:
pip install pandas
Importing necessary libraries and data files
Once Pandas is installed, import the required libraries and load your data Sample CSV files df1 and df2 used in this tutorial can be downloaded from here.
Python import numpy as np import pandas as pd df1 = pd.read_csv('df1', index_col=0) df2 = pd.read_csv('df2') Pandas DataFrame Plots
Pandas provides several built-in plotting functions to create various types of charts mainly focused on statistical data. These plots help visualize trends, distributions, and relationships within the data. Let's go through them one by one:
1. Line Plots using Pandas DataFrame
A Line plot is a graph that shows the frequency of data along a number line. It is best to use a line plot when the data is time series. It can be created using Dataframe.plot() function.
Python
We have got the well-versed line plot for df without specifying any type of features in the .plot() function.
2. Area Plots using Pandas DataFrame
Area plot shows data with a line and fills the space below the line with color. It helps see how things change over time. we can plot it using DataFrame.plot.area() function.
Python
3. Bar Plots using Pandas DataFrame
A bar chart presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally with DataFrame.plot.bar() function.
Python
4. Histogram Plot using Pandas DataFrame
Histograms help visualize the distribution of data by grouping values into bins. Pandas use DataFrame.plot.hist() function to plot histogram.
Python df1['A'].plot.hist(bins=50)
5. Scatter Plot using Pandas DataFrame
Scatter plots are used when you want to show the relationship between two variables. They are also called correlation and can be created using DataFrame.plot.scatter() function.
Python df1.plot.scatter(x ='A', y ='B')
6. Box Plots using Pandas DataFrame
A box plot displays the distribution of data, showing the median, quartiles, and outliers. we can use DataFrame.plot.box() function or DataFrame.boxplot() to create it.
Python
7. Hexagonal Bin Plots using Pandas DataFrame
Hexagonal binning helps manage dense datasets by using hexagons instead of individual points. It’s useful for visualizing large datasets where points may overlap. Let's create the hexagonal bin plot.
Python df.plot.hexbin(x ='a', y ='b', gridsize = 25, cmap ='Oranges')
8. Kernel Density Estimation plot (KDE) using Pandas DataFrame
KDE (Kernel Density Estimation) creates a smooth curve to show the shape of data by using the df.plot.kde() function. It's useful for visualizing data patterns and simulating new data based on real examples.
Python
Customizing Plots
Pandas allows you to customize your plots in many ways. You can change things like colors, titles, labels, and more. Here are some common customizations.
1. Adding a Title, Axis Labels, and Gridlines
You can customize the plot by adding a title and labels for the x and y axes. You can also enable gridlines to make the plot easier to read:
Python df.plot(title='Customized Line Plot', xlabel='Index', ylabel='Values', grid=True)
2. Line Plot with Different Line Styles
If you want to differentiate between the two lines visually you can change the line style (e.g., solid line, dashed line) with the help of pandas.
Python df.plot(style=['-', '--', '-.', ':'], title='Line Plot with Different Styles', xlabel='Index', ylabel='Values', grid=True)
3. Adjusting the Plot Size
Change the size of the plot to better fit the presentation or analysis context You can change it by using the figsize parameter:
Python df.plot(figsize=(12, 6), title='Line Plot with Adjusted Size', xlabel='Index', ylabel='Values', grid=True)
4. Stacked Bar Plot
A stacked bar plot can be created by setting stacked=True. It helps you visualize the cumulative value for each index.
Python df.plot.bar(stacked=True, figsize=(10, 6), title='Stacked Bar Plot', xlabel='Index', ylabel='Values', grid=True)
In this tutorial we explored how to visualize data using Pandas and customization without needing any additional visualization libraries. With Pandas' built-in plotting functions you can easily generate a variety of charts and graphs to gain insights into your data.
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