Plotting Bar Graphs from Datasets

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In this article, we will explore the process of plotting a bar graph based on a dataset. We will dive into the technical details of the problem and discuss various approaches to achieve this.

Introduction

A bar graph is a type of chart that consists of rectangular bars representing different categories or values. It is commonly used to compare categorical data, such as the number of cases over time in our example. In this article, we will focus on using Python and its popular libraries pandas and matplotlib to plot bar graphs from datasets.

Background

Pandas Library

The pandas library is a powerful tool for data manipulation and analysis in Python. It provides data structures such as Series (one-dimensional labeled array) and DataFrames (two-dimensional labeled data structure with columns of potentially different types).

We will use the read_csv() function from pandas to import our dataset from a CSV file.

Matplotlib Library

The matplotlib library is a popular tool for creating static, animated, and interactive visualizations in Python. It provides a wide range of visualization tools, including line plots, scatter plots, bar charts, etc.

We will use the plot() function from matplotlib to plot our bar graph.

Preparing the Data

To plot a bar graph, we need to have our data ready. In this case, we have a CSV file containing “dateRep” and “cases” columns.

import pandas as pd

# Load the dataset from the CSV file
df = pd.read_csv('data.csv')

# Print the first few rows of the DataFrame
print(df.head())

This will load our data into a DataFrame, which we can then manipulate and plot using matplotlib.

Plotting the Bar Graph

To plot a bar graph, we need to use the plot() function from matplotlib. We will create a new figure with a specified size and add a subplot to it.

import matplotlib.pyplot as plt

# Create a new figure with a specified size
fig = plt.figure(figsize=(16,9),dpi=144)

# Add a subplot to the figure
ax = fig.add_subplot(111)

# Plot the 'cases' column against the 'dateRep' column
ax.plot(df['date'], df['cases'])

# Set the x-axis ticks to the date format
ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=None, interval=14, tz=None))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))

# Display the plot
plt.show()

This code will create a bar graph with the “dateRep” column on the x-axis and the “cases” column on the y-axis.

Customizing the Plot

We can customize our plot by adding titles, labels, and legends. For example, we can add a title to our plot to describe what it represents.

import matplotlib.pyplot as plt

# Create a new figure with a specified size
fig = plt.figure(figsize=(16,9),dpi=144)

# Add a subplot to the figure
ax = fig.add_subplot(111)

# Plot the 'cases' column against the 'dateRep' column
ax.plot(df['date'], df['cases'])

# Set the x-axis ticks to the date format
ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=None, interval=14, tz=None))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))

# Add a title to the plot
ax.set_title("Bar Graph of Cases Over Time")

# Display the plot
plt.show()

Conclusion

In this article, we have explored how to plot a bar graph based on a dataset using Python and its popular libraries pandas and matplotlib. We discussed the technical details of the problem and provided example code to achieve this. With this knowledge, you should be able to create your own bar graphs from datasets using Python.

Example Use Cases

Epidemiology: Plotting bar graphs can help visualize the spread of diseases over time.
Sales Data: Bar charts can be used to compare sales data across different regions or products.
Weather Patterns: Plotting bar graphs can help analyze and understand weather patterns over a period.

Additional Resources

Pandas Documentation: pandas.pydata.org
Matplotlib Documentation: matplotlib.org

Code Block

{< highlight lang="python" >}
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# Load the dataset from the CSV file
df = pd.read_csv('data.csv')

# Convert the 'dateRep' column to a date format
df['dateRep'] = pd.to_datetime(df['dateRep'])

# Plot the bar graph
fig, ax = plt.subplots(figsize=(16,9),dpi=144)
ax.bar(df['dateRep'].dt.year, df['cases'].mean())

# Set the x-axis ticks to the year format
ax.set_xticks(df['dateRep'].dt.year.unique())
ax.set_xticklabels(df['dateRep'].dt.year.unique(), rotation=45)

# Add a title and labels to the plot
fig.suptitle("Bar Graph of Cases Over Time")
plt.xlabel("Year")
plt.ylabel("Average Cases")

# Display the plot
plt.tight_layout()
plt.show()
{< /highlight >}

Last modified on 2023-05-11