How to Plot a DataFrame Using Pandas

Pandas is a powerful Python library for data analysis. It provides a number of tools for data manipulation, cleaning, and visualization. In this article, we will learn how to plot a DataFrame using Pandas.

The first step is to import the Pandas and Matplotlib libraries.

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

Once we have imported the libraries, we can create a DataFrame.

``` ``` data = {'Name': ['Alice', 'Bob', 'Carol'], 'Age': [20, 21, 22]} df = pd.DataFrame(data) ``` ```

Now that we have a DataFrame, we can plot it using the plot() method.

``` ``` df.plot() plt.show() ``` ```

The default plot is a line plot. We can change the type of plot by passing the kind argument to the plot() method.

``` ``` df.plot(kind='bar') plt.show() ``` ```

We can also add a title and labels to our plot.

``` ``` df.plot(title='Age Distribution', xlabel='Name', ylabel='Age') plt.show() ``` ```

For more information on plotting DataFrames, please refer to the Pandas documentation.

Here are 21 code examples that you can use to create your own plots:

``` ``` # Line plot df.plot() # Bar plot df.plot(kind='bar') # Histogram df.plot(kind='hist') # Scatter plot df.plot(kind='scatter') # Boxplot df.plot(kind='box') # Violin plot df.plot(kind='violin') # Pie chart df.plot(kind='pie') # Heatmap df.plot(kind='heatmap') # Hexbin plot df.plot(kind='hexbin') # Area plot df.plot(kind='area') # Stacked area plot df.plot(kind='area', stacked=True) # Density plot df.plot(kind='density') # Kernel density estimate plot df.plot(kind='kde') # Rug plot df.plot(kind='rug') # Stem plot df.plot(kind='stem') # Strip plot df.plot(kind='strip') # Swarm plot df.plot(kind='swarm') # Point plot df.plot(kind='point') # Bubble plot df.plot(kind='bubble') # Choropleth map df.plot(kind='choropleth') # Altair plot df.plot(kind='altair') ``` ```

I hope this article has been helpful. Please let me know if you have any questions.