Data Visualization with matplotlib: Build Bar Chart in 6 easy steps

Data visualization is the graphical representation of information and data. It helps in identifying trends, patterns, and outliers that might go unnoticed in a plain data table. Bar charts, line graphs, histograms, and pie charts are some popular ways to visualize data.

In Python, we use powerful libraries like Pandas and Matplotlib to:

• Manipulate and process data easily.
• Generate high-quality, visually appealing charts.

In the code we are analyzing, we will focus on using Pandas to aggregate sales and tips data and Matplotlib for data visualization with a bar chart.

Data Visualization with matplotlib: Build Bar Chart in 7 easy steps

Understanding the code step by step:

Step 1: Importing Necessary Libraries

import pandas as pd                    # for dataset manipulation
import matplotlib.pyplot as plt        # for data visualization
import numpy as np                     # for statistical data

The code begins by importing three essential libraries:

• Pandas: A data manipulation library used to handle structured data, like reading and aggregating the CSV file.
• Matplotlib: A plotting library used for generating graphs and visualizations.
• Numpy: A numerical computing library. It provides tools arange() to generate ranges of values (useful for bar positioning).

Step 2: Reading the Dataset

df = pd.read_csv('tips.csv')

Here, we load the dataset using pd.read_csv(). This function reads the CSV file and stores the data in a DataFrame, which is a table-like data structure in Pandas.

Data Visualization with matplotlib: Build Bar Chart in 7 easy steps

Step 3: Aggregating Data using Pandas

ndf = df.groupby('day').agg(
    total_sale=('total_bill', 'sum'),
    total_tip=('tip', 'sum')
).reset_index()
print(ndf)

In this step, we group the data by the day column using the groupby() function. Then, we use the agg() function to:

• Sum the total bills (total_bill) for each day.
• Sum the tips (tip) for each day.

Step 4: Setting up the Bar Chart with Matplotlib

x_pos = np.arange(len(ndf))
plt.title('Sale vs Tips on Days', color='red')

1. Creating positions for bars:
   We use np.arange() to generate an array representing the positions of the bars on the x-axis. It is important to space out the bars correctly.
2. Adding a title:
   plt.title() sets the title of the chart. The color='red' argument makes the title appear in red.

Data Visualization with matplotlib: Build Bar Chart in 7 easy steps

Step 5: Customizing the Chart for Better Insights

plt.bar(x_pos, ndf['total_sale'], width=0.5, label='sale', color='Green')
plt.bar(x_pos + 0.2, ndf['total_tip'], width=0.3, label='tips', color='orange')

1. Creating two bar plots:
   We generate two sets of bars: one for sales and another for tips.
   • The first plt.bar() plots the sales data.
   • The second plt.bar() plots the tips data but shifts the bars slightly to the right (x_pos + 0.2) to prevent overlap.
2. Customizing colors and widths:
   • The color parameter sets the bar colors.
   • The width parameter controls the width of the bars.
   • The label parameter provides a name for the legend.

These customizations improve the readability of the chart by clearly distinguishing between sales and tips.

Step 6: Displaying the Final Chart

plt.xlabel('Days', color='green')
plt.ylabel('Sale', color='green')
plt.xticks(x_pos, ndf['day'])
plt.legend()
plt.grid()
plt.show()

Adding axis labels:
plt.xlabel() and plt.ylabel() define the labels for the x and y axes, respectively. These labels help viewers understand what the chart represents.

Setting x-axis ticks:
plt.xticks() positions the day names directly under each bar. We use x_pos as the positions and ndf['day'] as the labels.

Adding a legend and grid:

• plt.legend() displays a legend to explain what each bar color represents.
• plt.grid() adds grid lines, making it easier to compare values visually.

Displaying the chart:
Finally, plt.show() renders the chart and displays it to the user.

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Output and Insights

The plot generated by this code compares monthwise retail sales for 2023 and 2024. Some key takeaways include:

• Identifying Trends: You can quickly see if sales are increasing or decreasing on specific days.
• Comparing Performance: It’s easy to compare performance between the sales and tips on a particular day.

Data Visualization with matplotlib: Build Bar Chart in 7 easy steps

The Code:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df=pd.read_csv('tips.csv')

ndf=df.groupby('day').agg(
    total_sale=('total_bill','sum'),
    total_tip=('tip','sum')
).reset_index()
print(ndf)


x_pos=np.arange(len(ndf))
plt.title('Sale vs Tips on Days',color='red')
plt.bar(x_pos,ndf['total_sale'],width=0.5,label='sale',color='Green')
plt.bar(x_pos+0.2,ndf['total_tip'],width=0.3,label='tips',color='orange')

plt.xlabel('Days',color='green')
plt.ylabel('Sale',color='green')

plt.xticks(x_pos,ndf['day'])

plt.legend()
plt.grid()
plt.show()

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Data Visualization with matplotlib: Build Bar Chart in 7 easy steps

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Data Visualization with matplotlib: Build Bar Chart in 7 easy steps