We have aPandas DataFrame and now we want to visualize it usingMatplotlib for data visualization to understand trends, patterns and relationships in the data. In this article we will explore different ways to plot a Pandas DataFrame using Matplotlib's various charts. Before we start, ensure you have the necessary libraries using:

pip install pandas matplotlib

Types of Data Visualizations in Matplotlib

Matplotlib offers a wide range of visualization techniques that can be used for different data types and analysis. Choosing the right type of plot is essential for effectively conveying data insights.

1. Bar Plot

Bar Plot is useful for comparing values across different categories and comparingCategorical Data. It displays rectangular bars where the height corresponds to the magnitude of the data.

importpandasimportmatplotlibdata={'Category':['A','B','C','D'],'Values':[20,34,30,35]}df=pd.DataFrame(data)plt.bar(df['Category'],df['Values'],color='skyblue')plt.xlabel("Category")plt.ylabel("Values")plt.title("Bar Chart Example")plt.show()

Output:

2. Histogram

A histogram groups numerical data into intervals (bins) to show frequency distributions. It helps in understanding data distribution and spotting trends and is widely used for continuous data.

importpandasimportmatplotlibdf=pd.DataFrame({'Values':[12,23,45,36,56,78,89,95,110,130]})plt.hist(df['Values'],bins=5,color='lightcoral',edgecolor='black')plt.xlabel("Value Range")plt.ylabel("Frequency")plt.title("Histogram Example")plt.show()

Output:

3. Pie Chart

Apie chart is used to display categorical data as proportions of a whole. Each slice represents a percentage of the dataset.

importpandasimportmatplotlibdf=pd.DataFrame({'Category':['A','B','C'],'Values':[30,50,20]})plt.pie(df['Values'],labels=df['Category'],autopct='%1.1f%%',colors=['gold','lightblue','pink'])plt.title("Pie Chart Example")plt.show()

Output:

4. Scatter Plot

A scatter plot visualizes the relationship between two numerical variables helping us to identify correlations and data patterns.

importpandasimportmatplotlibdf=pd.DataFrame({'X':[1,2,3,4,5],'Y':[2,4,1,8,7]})plt.scatter(df['X'],df['Y'],color='green',marker='o')plt.xlabel("X-axis")plt.ylabel("Y-axis")plt.title("Scatter Plot Example")plt.show()

Output:

5. Line Chart

A line chart is ideal for analyzing trends and patterns in time-series or sequential data by connecting data points with a continuous line.

importpandasimportmatplotlibdf=pd.DataFrame({'Month':['Jan','Feb','Mar','Apr','May'],'Sales':[200,250,300,280,350]})plt.plot(df['Month'],df['Sales'],marker='o',linestyle='-',color='blue')plt.xlabel("Month")plt.ylabel("Sales")plt.title("Line Chart Example")plt.grid()plt.show()

Output:

In this article we explored various techniques to visualize data from a Pandas DataFrame using Matplotlib. From bar charts for categorical comparisons to histograms for distribution analysis and scatter plots for identifying relationships each visualization serves a unique purpose.